Data fusion prediction method for damage detection of structural adhesives in hyperbolic glass curtain walls

Abstract

The present application provides a data fusion prediction method for damage detection of structural adhesives in hyperbolic glass curtain walls, the method comprising: collecting ultrasonic reflection signals corresponding to structural adhesives in the hyperbolic glass curtain wall through a preset ultrasonic sensor array; obtaining surface temperature distribution data of the hyperbolic glass curtain wall through an infrared thermal imager; inputting the ultrasonic reflection signals and the surface temperature distribution data into a preset hybrid neural network model, wherein the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signals and the surface temperature distribution data, and outputs a three-dimensional fusion feature vector; the three-dimensional fusion feature vector includes damage geometric features, energy dissipation features, and thermodynamic features; obtaining historical structural adhesive detection data corresponding to the hyperbolic glass curtain wall; performing spatiotemporal correlation analysis on the three-dimensional fusion feature vector and the historical structural adhesive detection data to obtain structural adhesive detection results corresponding to the hyperbolic glass curtain wall, including damage expansion trend prediction and remaining life assessment.

Description

Technical Field

[0001] The present application relates to the technical field of glass curtain walls, and in particular to a data fusion prediction method for damage detection of structural adhesives of hyperbolic glass curtain walls. Background Art

[0002] Hyperbolic curtain walls, as highly curved building facades, play a crucial role in both aesthetic design and structural function. However, the complex geometry of hyperbolic curtain walls makes damage detection challenging, and traditional damage detection methods struggle to meet the unique requirements of curved structures. Existing infrared thermal imaging and ultrasonic testing technologies have proven successful on flat curtain walls, but data collection and processing for hyperbolic curtain wall inspections present additional challenges. These challenges include: Existing technologies often require individual inspections of each test point when inspecting structural adhesives on glass curtain walls, a cumbersome and time-consuming process. This results in low detection efficiency and makes it difficult to meet the demands of large-scale inspections. Both ultrasonic signals and infrared thermal imaging data are complex, multidimensional data, requiring specialized data processing and analysis techniques to extract useful information. Furthermore, merging the two data types presents a technical challenge, requiring advanced algorithms and computing power. Existing technologies often rely on manual operation and judgment, lacking intelligent data processing and prediction capabilities. This makes the interpretation of test results and the decision-making process susceptible to human influence, reducing detection efficiency and accuracy.

[0003] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0004] This application provides a data fusion prediction method for damage detection of structural adhesives in hyperbolic glass curtain walls. This method aims to address the problem that existing technologies, which mostly rely on manual operation and judgment and lack intelligent data processing and prediction capabilities, make the interpretation of test results and the decision-making process susceptible to human factors, reducing detection efficiency and accuracy.

[0005] In a first aspect, the present application provides a data fusion prediction method for damage detection of structural adhesives for hyperbolic glass curtain walls, comprising:

[0006] In the hyperbolic glass curtain wall, the ultrasonic reflection signal corresponding to the structural adhesive is collected through a preset ultrasonic sensor array;

[0007] Acquiring surface temperature distribution data of the hyperbolic glass curtain wall by using an infrared thermal imager;

[0008] The ultrasonic reflection signal and the surface temperature distribution data are input into a preset hybrid neural network model, and the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data to output a three-dimensional fusion feature vector; the three-dimensional fusion feature vector includes damage geometry features, energy dissipation features, and thermodynamic features;

[0009] Obtain historical structural adhesive test data corresponding to the hyperbolic glass curtain wall;

[0010] A spatiotemporal correlation analysis is performed on the three-dimensional fusion feature vector and historical structural adhesive detection data to obtain a structural adhesive detection result corresponding to the hyperbolic glass curtain wall; the structural adhesive detection result includes a damage expansion trend prediction and a remaining life assessment.

[0011] In some embodiments, after obtaining the structural adhesive detection results corresponding to the hyperbolic glass curtain wall, the method further includes: generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall according to the structural adhesive detection results; and dynamically adjusting the ultrasonic emission frequency corresponding to the ultrasonic sensor array and the sampling interval corresponding to the infrared thermal imager based on a reinforcement learning algorithm to achieve a balanced optimization of the accuracy and efficiency of the structural adhesive damage detection.

[0012] Exemplarily, generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall based on the structural adhesive detection result includes: obtaining a damage probability and an attenuation coefficient of the damage probability corresponding to the structural adhesive detection result; generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall based on the structural adhesive detection result based on the damage probability and the attenuation coefficient; the dynamic volatility coefficient of the ant colony optimization algorithm is:

[0013] ;

[0014] in, is the dynamic volatility coefficient, is the initial volatility coefficient, is the attenuation coefficient, is the damage probability.

[0015] It should be noted that, in some embodiments, the reward function of the reinforcement learning algorithm includes a detection coverage factor, an energy consumption factor, and a prediction confidence factor corresponding to the hyperbolic glass curtain wall.

[0016] It should be noted that, in some embodiments, the weight coefficients corresponding to the detection coverage factor, energy consumption factor, and prediction confidence factor are dynamically adjusted according to the ambient temperature and humidity parameters corresponding to the hyperbolic glass curtain wall.

[0017] In some embodiments, the plurality of array units corresponding to the ultrasonic sensor array adopt a curved conformal array layout.

[0018] For example, the expression corresponding to the spacing of each array unit is:

[0019] ;

[0020] in is the spacing, is the propagation speed of ultrasonic waves in structural adhesives, is the reference transmission frequency corresponding to the array unit, is the local curvature radius of the hyperbolic glass curtain wall, is the curvature deviation allowable threshold corresponding to the hyperbolic glass curtain wall. When the actual curvature radius of the hyperbolic glass curtain wall changes beyond the curvature deviation allowable threshold, the spacing of the array units is automatically adjusted according to the expression.

[0021] In some embodiments, the hybrid neural network model includes a convolutional neural network model and a Transformer model; the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data, including: the Transformer model calculates the association weight matrix of the ultrasonic frequency domain features corresponding to the ultrasonic reflection signal and the infrared spatial features corresponding to the surface temperature distribution data according to the attention mechanism, and dynamically adjusts the contribution of the ultrasonic frequency domain features and infrared spatial features in the association weight matrix based on the feature gating mechanism to complete the cross-modal fusion of the ultrasonic reflection signal and the surface temperature distribution data.

[0022] In some embodiments, before inputting the ultrasonic reflection signal and the surface temperature distribution data into a preset hybrid neural network model, it also includes: preprocessing the ultrasonic reflection signal and the surface temperature distribution data based on a wavelet packet decomposition algorithm to complete denoising and filtering of the ultrasonic reflection signal and the surface temperature distribution data.

[0023] Exemplarily, the number of decomposition layers corresponding to the wavelet packet decomposition algorithm is:

[0024] ;

[0025] in, is the number of decomposition layers, is the sampling frequency corresponding to the ultrasonic reflection signal and the surface temperature distribution data, is the lower limit of the damage characteristic frequency band corresponding to the hyperbolic glass curtain wall.

[0026] In a second aspect, the present application provides a data fusion prediction device for damage detection of structural adhesives for hyperbolic glass curtain walls, comprising:

[0027] A signal acquisition unit is used to collect ultrasonic reflection signals corresponding to the structural adhesive in the hyperbolic glass curtain wall through a preset ultrasonic sensor array;

[0028] a data acquisition unit, configured to acquire surface temperature distribution data of the hyperbolic glass curtain wall through an infrared thermal imager;

[0029] a data input unit, configured to input the ultrasonic reflection signal and the surface temperature distribution data into a preset hybrid neural network model, wherein the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data and outputs a three-dimensional fusion feature vector; the three-dimensional fusion feature vector includes damage geometric features, energy dissipation features, and thermodynamic features;

[0030] A history acquisition unit, used to acquire historical structural adhesive detection data corresponding to the hyperbolic glass curtain wall;

[0031] A result acquisition unit is used to perform spatiotemporal correlation analysis on the three-dimensional fusion feature vector and historical structural adhesive detection data to obtain the structural adhesive detection results corresponding to the hyperbolic glass curtain wall; the structural adhesive detection results include damage expansion trend prediction and remaining life assessment.

[0032] In a third aspect, the present application provides a computer device comprising a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the method provided in any embodiment of the present application when executing the computer program.

[0033] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer-readable instructions are executed by the processor, one or more processors execute the method provided in any embodiment of the present application.

[0034] This application example provides a data fusion prediction method for detecting damage in hyperbolic glass curtain wall structural adhesives. By combining ultrasonic reflection signals with infrared thermal imaging data and utilizing a hybrid neural network model for cross-modal information fusion, this method achieves efficient and accurate assessment of the structural adhesive condition. The specific steps are as follows:

[0035] Ultrasonic signal collection: An array of ultrasonic sensors, positioned at predetermined locations on the hyperbolic glass curtain wall, transmits ultrasonic waves toward the structural adhesive area and receives reflected echoes. These signals reveal the presence and approximate location of internal structural defects. Temperature distribution measurement: An infrared thermal imager scans the entire curtain wall surface, recording temperature values at various points. When the structural adhesive is damaged, its thermal conductivity changes, resulting in increased local temperature differences and creating recognizable thermal image signatures.

[0036] The two different types of data (ultrasound reflection signals and infrared thermal images) are fed into a pre-trained hybrid neural network model. This model utilizes a deep learning architecture, including multiple convolutional layers to automatically extract key features from the image. Long short-term memory (LSTM) units are also incorporated to capture correlations between time series data. After processing, the model outputs a 3D fused feature vector containing information about the geometry of the injury site, energy loss, and temperature variation patterns.

[0037] Collect and organize all structural adhesive inspection records for the target building over a period of time, including but not limited to repair history and changes in environmental conditions. Analyze this historical data using statistical methods or machine learning algorithms to establish a baseline reference system for subsequent comparison and analysis of current conditions against past trends.

[0038] The previously generated 3D fusion feature vectors are combined with relevant information in the historical database, and spatiotemporal correlation analysis techniques are used to predict the type and extent of future problems. The final report will cover the following aspects: the specific location and extent of the damage; the expected development trend (such as whether it will further deteriorate); the estimated remaining service life; and maintenance recommendations.

[0039] In order to implement the above process, the following hardware equipment and technical support are needed: Ultrasonic sensor: Select products with high sensitivity and a wide frequency response range to ensure that tiny cracks can be detected. Infrared thermal imager: It is required to have high resolution and fast response speed to accurately capture instantaneous temperature changes. Computing platform: A server cluster equipped with high-performance GPU accelerator cards is used to run complex deep learning model training tasks. Database management system: Used to store massive amounts of raw data and processing results, and support efficient query operations. Cloud computing service: Provides users with a remote access interface, so that the health of the curtain wall can be monitored in real time even if they are not on site.

[0040] The provided method has the following beneficial effects:

[0041] Improved detection accuracy: Compared with traditional manual visual inspection, this solution can detect more hidden and subtle signs of damage, avoiding missing important issues due to negligence.

[0042] Improved work efficiency: The application of automated tools significantly shortens the time from problem discovery to action, reducing maintenance costs.

[0043] Provides comprehensive information support: In addition to locating potential fault points, it also provides detailed diagnostic conclusions and development forecasts to help decision makers make more scientific and reasonable plans.

[0044] Promotes optimal resource allocation: By learning from historical data, the system can, to a certain extent, foresee failure modes that are more likely to occur under certain conditions, thereby guiding the direction of preventive maintenance work and extending the overall service life of the facility.

[0045] In summary, the hyperbolic glass curtain wall structural adhesive damage detection method based on multi-source data fusion proposed in the present invention not only solves the limitations of the existing technology, but also lays a solid foundation for further improving the level of building safety management.

[0046] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 This is a schematic flow chart of the steps of a data fusion prediction method for damage detection of structural adhesives for hyperbolic glass curtain walls provided by an embodiment of the present application;

[0049] Figure 2 This is a schematic diagram of the principle of a data fusion prediction method for damage detection of structural adhesives for hyperbolic glass curtain walls provided in one embodiment of the present application;

[0050] Figure 3 This is a schematic block diagram of the structure of a computer device provided in one embodiment of the present application.

[0051] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0053] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0054] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items having substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0055] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0056] It will also be understood that the term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0057] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0058] Hyperbolic curtain walls, as highly curved building facades, play a crucial role in both aesthetic design and structural function. However, the complex geometry of hyperbolic curtain walls makes damage detection challenging, and traditional damage detection methods struggle to meet the unique requirements of curved structures. Existing infrared thermal imaging and ultrasonic testing technologies have proven successful on flat curtain walls, but data collection and processing for hyperbolic curtain wall inspections present additional challenges. These challenges include: Existing technologies often require individual inspections of each test point when inspecting structural adhesives on glass curtain walls, a cumbersome and time-consuming process. This results in low detection efficiency and makes it difficult to meet the demands of large-scale inspections. Both ultrasonic signals and infrared thermal imaging data are complex, multidimensional data, requiring specialized data processing and analysis techniques to extract useful information. Furthermore, merging the two data types presents a technical challenge, requiring advanced algorithms and computing power. Existing technologies often rely on manual operation and judgment, lacking intelligent data processing and prediction capabilities. This makes the interpretation of test results and the decision-making process susceptible to human influence, reducing detection efficiency and accuracy.

[0059] Therefore, a method is urgently needed to solve at least one of the above problems.

[0060] To resolve the above issues, please refer to Figure 1 ,like Figure 1 As shown, the provided data fusion prediction method for detecting damage to structural adhesives in hyperbolic glass curtain walls includes steps S101 to S105. The data fusion prediction method for detecting damage to structural adhesives in hyperbolic glass curtain walls is executed by a computer device, which can be a single server or a server cluster, or can be a handheld terminal, a laptop computer, a wearable device, or a robot.

[0061] like Figure 1 As shown, steps S101-S105 are described in detail as follows:

[0062] Step S101: In a hyperbolic glass curtain wall, ultrasonic reflection signals corresponding to structural adhesive are collected by a preset ultrasonic sensor array.

[0063] Specifically, this step involves scanning the structural adhesive on the hyperbolic curtain wall using a pre-set array of ultrasonic sensors. These sensors are capable of emitting ultrasonic pulses of a specific frequency and receiving echo signals reflected back from different layers within the structure.

[0064] The specific implementation method of this step:

[0065] Sensor placement: A number of ultrasonic sensors are placed at strategic locations on the curtain wall, forming a network that covers the entire detection area. The number and layout of sensors should be optimized based on the specific shape and size of the curtain wall to ensure comprehensive coverage of all critical areas.

[0066] Signal transmission and reception: Each sensor transmits ultrasonic pulses at predetermined intervals and records the time delay and intensity changes of the return signal. By adjusting the transmission frequency and power, it can better adapt to the characteristics of different materials.

[0067] Data storage and transmission: The collected data will be temporarily stored on the local device or directly transmitted to the central processing unit. To ensure the integrity and real-time nature of the data, wireless transmission technologies such as Wi-Fi or Bluetooth can be used to ensure fast and reliable data transmission.

[0068] Compared to traditional point-to-point inspection methods, the use of a sensor array can simultaneously acquire information from multiple locations, significantly increasing inspection speed. Furthermore, by analyzing the changing patterns of reflected signals, potential problems can be more accurately located. This multi-point, simultaneous inspection approach not only improves efficiency but also reduces operator error.

[0069] Step S102: Acquire surface temperature distribution data of the hyperbolic glass curtain wall using an infrared thermal imager.

[0070] Specifically, a high-resolution infrared camera is used to capture the surface of the curtain wall to obtain its temperature distribution. Due to differences in material properties (such as thermal conductivity), defective areas will show different temperature characteristics from normal areas.

[0071] Choose appropriate weather conditions (avoid strong sunlight) and time of day for measurement. Ideally, measurements should be conducted on cloudy days or at night to minimize the impact of external light. Ensure the infrared camera is in optimal working condition and adjust the focus to ensure clear images. Regularly calibrate the camera to maintain measurement accuracy. Repeatedly capture the same area from multiple angles to ensure comprehensive coverage. Use drones or mobile platforms to capture images from different heights and angles to ensure no blind spots. After capture, perform preliminary processing on the infrared images, such as noise reduction and contrast enhancement, to facilitate subsequent analysis.

[0072] Non-contact measurement avoids physical damage to the building itself; thermal images visually display areas of thermal anomalies, helping to quickly identify suspected fault points. This method can not only detect damage to structural adhesives, but also reveal other potential problems such as water leaks and insulation failure.

[0073] Step S103. Input the ultrasonic reflection signal and the surface temperature distribution data into a preset hybrid neural network model, and the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data, and outputs a three-dimensional fusion feature vector; the three-dimensional fusion feature vector includes damage geometry characteristics, energy dissipation characteristics and thermodynamic characteristics.

[0074] Specifically, the different types of data collected in the first two steps (i.e., ultrasonic signals and infrared images) are fed into a pre-trained hybrid neural network model. This model has a strong ability to integrate multi-source information, effectively extracting and combining the advantages of both.

[0075] Design and optimize a deep learning architecture suitable for this application scenario, such as combining a convolutional neural network (CNN) to process visual data and a recurrent neural network (RNN) to analyze time series signals. CNNs can effectively extract spatial features from infrared images, while RNNs can capture the temporal dynamics of ultrasonic signals.

[0076] Data preprocessing: The raw data is preprocessed, including normalization, filtering, feature extraction and other steps to improve the input quality of the model. For example, the ultrasonic signal is Fourier transformed to extract frequency domain features; the infrared image is edge detected to highlight the boundaries of temperature changes. A large number of labeled samples are used to train the model to ensure that it has good generalization performance for different types of damage. The training data should cover various common types and degrees of damage to improve the robustness of the model. The preprocessed raw data is fed into the model, and a comprehensive feature vector containing geometric shape, energy consumption and temperature characteristics is output. Through cross-modal fusion, the model can comprehensively utilize multiple information to improve the accuracy and reliability of diagnosis.

[0077] Intelligent algorithms enable automated feature extraction and matching, significantly simplifying manual analysis while improving diagnostic accuracy. This approach not only identifies existing damage but also predicts potential risks, providing strong support for maintenance decisions.

[0078] Step S104: Obtain historical structural adhesive detection data corresponding to the hyperbolic glass curtain wall.

[0079] Specifically, historical inspection reports stored in the database are called up to provide background support for the current assessment, including but not limited to details of previously discovered issues, remediation measures, and their effectiveness.

[0080] Establish a structured database to store historical inspection results, repair records, and other relevant information. The database should support efficient query and retrieval. Filter the most relevant cases for reference based on factors such as geographic location and construction date. Use natural language processing techniques to categorize and summarize text data for quick search. Perform statistical analysis of historical data to identify common damage patterns and repair strategies. By comparing current inspection results with historical data, you can better understand the reasons behind the current situation.

[0081] By drawing on past experience and lessons learned, engineers can better understand the reasons behind the current situation and develop more reasonable maintenance plans. This approach can not only improve maintenance efficiency, but also reduce maintenance costs and extend the service life of the building.

[0082] Step S105: Performing spatiotemporal correlation analysis on the three-dimensional fusion feature vector and historical structural adhesive detection data to obtain structural adhesive detection results corresponding to the hyperbolic glass curtain wall; the structural adhesive detection results include damage expansion trend prediction and remaining life assessment.

[0083] Specifically, based on all the above information, statistical principles and technical means are used to conduct a comprehensive evaluation of the target object, which is not limited to the description of the current situation, but also includes the prediction of future development trends.

[0084] Apply time series analysis techniques to study the temporal evolution of structural adhesive damage. Use models such as ARIMA and LSTM to predict future damage trends. Develop mathematical models that incorporate environmental factors (such as climate change) and usage conditions to simulate possible change paths under different scenarios. For example, use Monte Carlo simulations to predict the lifespan of structural adhesives under different climate conditions. Provide optimal repair recommendations based on a comprehensive consideration of cost-benefit ratios. Use economic models to calculate the costs and benefits of different repair options and select the most cost-effective option. Present analysis results in charts, reports, and other formats to facilitate management understanding and decision-making. Use data visualization tools such as Tableau or Power BI to generate intuitive visual reports.

[0085] This approach provides early warnings of potential major safety risks and guides managers to take preventative measures. It also saves companies unnecessary expenses and extends the lifespan of buildings. This approach not only improves safety but also enhances management efficiency, achieving both economic and social benefits.

[0086] In summary, this data fusion prediction method for damage detection of hyperbolic glass curtain wall structural adhesive introduces advanced sensing technology and data analysis tools, such as Figure 2As shown, the computer equipment, as the main executor of this solution, performs data collection and preprocessing through steps S101-S102. S103 performs data fusion processing and simultaneously builds and trains an intelligent prediction model (representing a hybrid neural network model). Finally, combined with S104-S105, it completes implementation effect evaluation and optimization. This overcomes the limitations of traditional methods and achieves a fundamental shift from passive response to proactive management. It not only improves the accuracy, efficiency, and intelligence of detection, but also provides a scientific basis for building maintenance, which is of great significance for ensuring public safety and promoting sustainable development.

[0087] This application example provides a data fusion prediction method for detecting damage in hyperbolic glass curtain wall structural adhesives. By combining ultrasonic reflection signals with infrared thermal imaging data and utilizing a hybrid neural network model for cross-modal information fusion, this method achieves efficient and accurate assessment of the structural adhesive condition. The specific steps are as follows:

[0088] Ultrasonic signal collection: An array of ultrasonic sensors, positioned at predetermined locations on the hyperbolic glass curtain wall, transmits ultrasonic waves toward the structural adhesive area and receives reflected echoes. These signals reveal the presence and approximate location of internal structural defects. Temperature distribution measurement: An infrared thermal imager scans the entire curtain wall surface, recording temperature values at various points. When the structural adhesive is damaged, its thermal conductivity changes, resulting in increased local temperature differences and creating recognizable thermal image signatures.

[0089] The two different types of data (ultrasound reflection signals and infrared thermal images) are fed into a pre-trained hybrid neural network model. This model utilizes a deep learning architecture, including multiple convolutional layers to automatically extract key features from the image. Long short-term memory (LSTM) units are also incorporated to capture correlations between time series data. After processing, the model outputs a 3D fused feature vector containing information about the geometry of the injury site, energy loss, and temperature variation patterns.

[0090] Collect and organize all structural adhesive inspection records for the target building over a period of time, including but not limited to repair history and changes in environmental conditions. Analyze this historical data using statistical methods or machine learning algorithms to establish a baseline reference system for subsequent comparative analysis of current conditions against past trends.

[0091] The previously generated 3D fusion feature vectors are combined with relevant information in the historical database, and spatiotemporal correlation analysis techniques are used to predict the type and extent of future problems. The final report will cover the following aspects: the specific location and extent of the damage; the expected development trend (such as whether it will further deteriorate); the estimated remaining service life; and maintenance recommendations.

[0092] In order to implement the above process, the following hardware equipment and technical support are needed: Ultrasonic sensor: Select products with high sensitivity and a wide frequency response range to ensure that tiny cracks can be detected. Infrared thermal imager: It is required to have high resolution and fast response speed to accurately capture instantaneous temperature changes. Computing platform: A server cluster equipped with high-performance GPU accelerator cards is used to run complex deep learning model training tasks. Database management system: Used to store massive amounts of raw data and processing results, and support efficient query operations. Cloud computing service: Provides users with a remote access interface, so that the health of the curtain wall can be monitored in real time even if they are not on site.

[0093] The provided method has the following beneficial effects:

[0094] Improved detection accuracy: Compared with traditional manual visual inspection, this solution can detect more hidden and subtle signs of damage, avoiding missing important issues due to negligence.

[0095] Improved work efficiency: The application of automated tools significantly shortens the time from problem discovery to action, reducing maintenance costs.

[0096] Provides comprehensive information support: In addition to locating potential fault points, it also provides detailed diagnostic conclusions and development forecasts to help decision makers make more scientific and reasonable plans.

[0097] Promotes optimal resource allocation: By learning from historical data, the system can, to a certain extent, foresee failure modes that are more likely to occur under certain conditions, thereby guiding the direction of preventive maintenance work and extending the overall service life of the facility.

[0098] In summary, the hyperbolic glass curtain wall structural adhesive damage detection method based on multi-source data fusion proposed in the present invention not only solves the limitations of the existing technology, but also lays a solid foundation for further improving the level of building safety management.

[0099] In some embodiments, after obtaining the structural adhesive detection results corresponding to the hyperbolic glass curtain wall, the method further includes: generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall according to the structural adhesive detection results; and dynamically adjusting the ultrasonic emission frequency corresponding to the ultrasonic sensor array and the sampling interval corresponding to the infrared thermal imager based on a reinforcement learning algorithm to achieve a balanced optimization of the accuracy and efficiency of the structural adhesive damage detection.

[0100] Based on the structural adhesive inspection results, the damage probability and attenuation coefficient of each inspection point are extracted. An adaptive inspection path for the hyperbolic glass curtain wall is generated based on the ant colony optimization algorithm. The ant colony optimization algorithm optimizes path selection by simulating the process of ants searching for food. A reinforcement learning algorithm is used to dynamically adjust the transmission frequency of the ultrasonic sensor array and the sampling interval of the infrared thermal imager. The reward function includes the following factors: Detection coverage factor: Ensures that the inspection path covers all critical areas. Energy consumption factor: Reduces unnecessary energy consumption and improves efficiency. Prediction confidence factor: Ensures the accuracy of the inspection results. Dynamic adjustment of weight coefficients: Dynamically adjusts the weight coefficients of these factors based on the temperature and humidity parameters of the environment in which the hyperbolic glass curtain wall is located. For example, in a high humidity environment, the weight of the energy consumption factor may need to be increased to ensure the normal operation of the equipment.

[0101] By dynamically adjusting the inspection path and parameters, structural adhesive damage can be more accurately located and assessed. Adaptive inspection paths reduce redundant inspections and improve overall inspection efficiency. Dynamic parameter adjustment reduces energy consumption while maintaining inspection quality. Dynamically adjusting weighting coefficients based on varying environmental conditions makes the system more adaptable and robust.

[0102] Exemplarily, generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall based on the structural adhesive detection result includes: obtaining a damage probability and an attenuation coefficient of the damage probability corresponding to the structural adhesive detection result; generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall based on the structural adhesive detection result based on the damage probability and the attenuation coefficient; the dynamic volatility coefficient of the ant colony optimization algorithm is: ;in, is the dynamic volatility coefficient, is the initial volatility coefficient, is the attenuation coefficient, is the damage probability.

[0103] The introduction of dynamic volatility coefficients makes path selection more flexible, better adapting to areas with varying damage probabilities. By optimizing the path, redundant inspections are reduced, improving inspection efficiency. This makes path selection more precise, helping to identify potential damage points.

[0104] It should be noted that, in some embodiments, the reward function of the reinforcement learning algorithm includes a detection coverage factor, an energy consumption factor, and a prediction confidence factor corresponding to the hyperbolic glass curtain wall.

[0105] The reinforcement learning algorithm's reward function includes the following factors: Detection coverage factor: Ensures that the detection path covers all critical areas. Energy consumption factor: Reduces unnecessary energy consumption and improves efficiency. Prediction confidence factor: Ensures the accuracy of detection results. Dynamic weight adjustment: Dynamically adjusts the weights of these factors based on the temperature and humidity parameters of the hyperbolic glass curtain wall's environment. For example, in high humidity environments, the weight of the energy consumption factor may need to be increased to ensure proper operation.

[0106] Through a multi-factor reward function, a comprehensive optimization of detection coverage, energy consumption, and prediction confidence is achieved. The weight coefficients are dynamically adjusted according to environmental parameters, making the system more adaptable and robust.

[0107] It should be noted that, in some embodiments, the weight coefficients corresponding to the detection coverage factor, energy consumption factor, and prediction confidence factor are dynamically adjusted according to the ambient temperature and humidity parameters corresponding to the hyperbolic glass curtain wall.

[0108] Real-time monitoring of the temperature and humidity parameters of the environment surrounding the hyperbolic glass curtain wall. Dynamically adjust the weighting coefficients of the detection coverage factor, energy consumption factor, and prediction confidence factor based on these parameters. For example, in high-humidity environments, the weight of the energy consumption factor is increased; in low-temperature environments, the weight of the prediction confidence factor is increased.

[0109] The method can automatically adjust the strategy according to different environmental conditions, improving the adaptability and robustness of the system. The weight coefficient is dynamically adjusted so that the system can maintain optimal performance under different environmental conditions.

[0110] In some embodiments, the plurality of array units corresponding to the ultrasonic sensor array adopt a curved conformal array layout.

[0111] The spacing between array elements automatically adjusts to the curvature of the curtain wall, ensuring uniform detection coverage. This adaptive layout allows the ultrasonic sensor array to better adapt to the complex geometry of the curtain wall, improving detection accuracy. Automatic spacing adjustment also makes the system more robust when dealing with curtain walls of varying curvatures.

[0112] For example, the expression corresponding to the spacing of each array unit is:

[0113] ;

[0114] in is the spacing, is the propagation speed of ultrasonic waves in structural adhesives (the sound speed of structural adhesives is generally in the range of 1000-2500 m / s), is the reference transmission frequency corresponding to the array unit (e.g., a value within 1-5 MHz), is the local curvature radius of the hyperbolic glass curtain wall (monitored in real time by the micro-inertial measurement unit built into the array unit), The curvature deviation threshold corresponding to the hyperbolic glass curtain wall is allowed (e.g., 0.05 ), when the actual curvature radius of the hyperbolic glass curtain wall exceeds the curvature deviation threshold, the spacing of the array elements is automatically adjusted according to the expression. This automatic spacing adjustment allows the array elements to better adapt to changes in the curtain wall's curvature, ensuring uniform detection coverage. This adaptive layout improves detection accuracy, especially for curtain walls with complex geometries. Automatic spacing adjustment makes the system more robust when dealing with curtain walls of varying curvatures.

[0115] In some embodiments, the hybrid neural network model includes a convolutional neural network model and a Transformer model; the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data, including: the Transformer model calculates the association weight matrix of the ultrasonic frequency domain features corresponding to the ultrasonic reflection signal and the infrared spatial features corresponding to the surface temperature distribution data according to the attention mechanism, and dynamically adjusts the contribution of the ultrasonic frequency domain features and infrared spatial features in the association weight matrix based on the feature gating mechanism to complete the cross-modal fusion of the ultrasonic reflection signal and the surface temperature distribution data.

[0116] The hybrid neural network model consists of a convolutional neural network (CNN) and a Transformer model. The Transformer model uses an attention mechanism to calculate the association weight matrix between the ultrasonic frequency-domain features corresponding to the ultrasonic reflection signal and the infrared spatial features corresponding to the surface temperature distribution data. A feature gating mechanism dynamically adjusts the contribution of the ultrasonic frequency-domain and infrared spatial features in the association weight matrix, completing the cross-modal fusion of the ultrasonic reflection signal and the surface temperature distribution data.

[0117] The Transformer model's attention mechanism effectively integrates multimodal information from ultrasonic and infrared images. Cross-modal fusion improves the accuracy and reliability of detection results. The feature gating mechanism enables the model to dynamically adjust the contribution of features from different modalities, enhancing the robustness of the system.

[0118] In some embodiments, before inputting the ultrasonic reflection signal and the surface temperature distribution data into a preset hybrid neural network model, it also includes: preprocessing the ultrasonic reflection signal and the surface temperature distribution data based on a wavelet packet decomposition algorithm to complete denoising and filtering of the ultrasonic reflection signal and the surface temperature distribution data.

[0119] Before inputting the ultrasonic reflection signal and surface temperature distribution data into the hybrid neural network model, the data is preprocessed using a wavelet packet decomposition algorithm to perform denoising and filtering. Wavelet packet decomposition effectively removes noise from the data, improving data quality. Feature extraction: By calculating the number of decomposition layers, frequency band information related to damage characteristics can be extracted. Improving detection accuracy: The preprocessed data is purer, helping to improve the accuracy and reliability of subsequent detection.

[0120] Exemplarily, the number of decomposition layers corresponding to the wavelet packet decomposition algorithm is:

[0121] ;

[0122] in, is the number of decomposition layers, is the sampling frequency corresponding to the ultrasonic reflection signal and the surface temperature distribution data, is the lower limit of the damage characteristic frequency band corresponding to the hyperbolic glass curtain wall.

[0123] Based on the calculated number of decomposition layers, wavelet packet decomposition is performed on the ultrasonic reflection signal and surface temperature distribution data to achieve denoising and filtering. Wavelet packet decomposition effectively removes noise from the data, improving data quality. By calculating the number of decomposition layers, frequency band information related to damage characteristics can be extracted. This preprocessed data is cleaner, helping to improve the accuracy and reliability of subsequent inspections.

[0124] In order to achieve efficient, accurate and intelligent damage detection of structural adhesives in hyperbolic glass curtain walls, we integrated the above steps and embodiments to design a complete detection system.

[0125] 1. System architecture:

[0126] Data acquisition module:

[0127] Ultrasonic sensor array: arranged at key locations on the curtain wall to collect ultrasonic reflection signals.

[0128] Infrared thermal imager: captures the temperature distribution on the curtain wall surface.

[0129] Environmental monitoring sensor: real-time monitoring of environmental temperature and humidity parameters.

[0130] Data preprocessing module:

[0131] Wavelet packet decomposition: Denoise and filter the ultrasonic reflection signal and surface temperature distribution data.

[0132] Data normalization: Normalize the preprocessed data to ensure the consistency of the input data.

[0133] Feature extraction module:

[0134] Ultrasonic signal processing: Perform Fourier transform on the ultrasonic reflection signal to extract frequency domain features.

[0135] Infrared image processing: perform edge detection on infrared images and extract spatial features.

[0136] Cross-modal fusion module:

[0137] Hybrid neural network model: Combines convolutional neural network (CNN) and Transformer model to perform cross-modal fusion and output three-dimensional fused feature vector.

[0138] Path planning and parameter adjustment module:

[0139] Ant Colony Optimization Algorithm: Generating Adaptive Detection Paths.

[0140] Reinforcement learning algorithm: Dynamically adjust the transmission frequency of the ultrasonic sensor array and the sampling interval of the infrared thermal imager.

[0141] Historical data analysis module:

[0142] Database management: store all previous inspection results and maintenance records.

[0143] Data Analysis: Perform statistical analysis on historical data to extract common damage patterns and repair strategies.

[0144] Spatiotemporal correlation analysis module:

[0145] Time series analysis: predicting future injury trends.

[0146] Environmental factor simulation: Combine environmental factors to simulate possible change paths under different scenarios.

[0147] Cost-benefit analysis: Provide optimal maintenance recommendations.

[0148] Result visualization module:

[0149] Data visualization: presenting analysis results in the form of charts, reports, etc.

[0150] User interface: Provides an intuitive user interface to facilitate managers' understanding and decision-making.

[0151] 2. Workflow

[0152] Data Collection: Ultrasonic sensor arrays and infrared thermal imagers are used to collect ultrasonic reflection signals and surface temperature distribution data from the curtain wall. Ambient temperature and humidity parameters are monitored in real time.

[0153] Data preprocessing: De-noise and filter the collected data through wavelet packet decomposition. Normalize the preprocessed data.

[0154] Feature extraction: Perform Fourier transform on ultrasonic reflection signals to extract frequency domain features. Perform edge detection on infrared images to extract spatial features.

[0155] Cross-modal fusion: The extracted features are input into the hybrid neural network model for cross-modal fusion, and a three-dimensional fused feature vector is output.

[0156] Path planning and parameter adjustment: An ant colony optimization algorithm is used to generate an adaptive detection path. A reinforcement learning algorithm is used to dynamically adjust the transmission frequency of the ultrasonic sensor array and the sampling interval of the infrared thermal imager.

[0157] Historical data analysis: Retrieve historical inspection results and repair records from the database. Perform statistical analysis on historical data to extract common damage patterns and repair strategies.

[0158] Spatiotemporal Correlation Analysis: Use time series analysis techniques to predict future damage trends. Combined with environmental factors, simulate possible change paths under different scenarios. Conduct cost-benefit analysis and provide optimal repair recommendations.

[0159] Result visualization: Present analysis results in the form of charts, reports, etc. Provide an intuitive user interface to facilitate managers' understanding and decision-making.

[0160] The provided integrated system has the following beneficial effects:

[0161] Improve detection accuracy: Through multimodal data fusion and advanced algorithms, the accuracy and reliability of detection results are improved.

[0162] Optimize detection efficiency: Adaptive detection paths and dynamic parameter adjustments reduce redundant detection and improve overall detection efficiency.

[0163] Reduce energy consumption: By dynamically adjusting parameters, energy consumption can be reduced while ensuring detection quality.

[0164] Enhanced adaptability: Dynamically adjust strategies according to different environmental conditions to make the system more adaptable and robust.

[0165] Data-driven decision-making: Combining historical data and spatiotemporal correlation analysis provides a scientific basis to help managers make more reasonable maintenance decisions.

[0166] In summary, this solution combines ultrasonic reflection signals and infrared thermal imaging data, employs a hybrid neural network model for cross-modal fusion, and incorporates historical data and advanced optimization algorithms to provide an efficient, accurate, and intelligent method for detecting structural adhesive damage in hyperbolic glass curtain walls. The embodiments and examples further optimize detection paths and parameters, improving the system's adaptability and robustness, providing strong support for safeguarding public safety and promoting sustainable development.

[0167] The embodiments of the present application provide a data fusion prediction device for detecting damage to structural adhesives in hyperbolic glass curtain walls. The data fusion prediction device for detecting damage to structural adhesives in hyperbolic glass curtain walls is used to execute the steps of the data fusion prediction method for detecting damage to structural adhesives in hyperbolic glass curtain walls shown in the above embodiments. The data fusion prediction device for detecting damage to structural adhesives in hyperbolic glass curtain walls can be a single server or a server cluster, or the data fusion prediction device for detecting damage to structural adhesives in hyperbolic glass curtain walls can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0168] The data fusion prediction device for damage detection of structural adhesives of hyperbolic glass curtain walls includes:

[0169] A signal acquisition unit is used to collect ultrasonic reflection signals corresponding to the structural adhesive in the hyperbolic glass curtain wall through a preset ultrasonic sensor array;

[0170] a data acquisition unit, configured to acquire surface temperature distribution data of the hyperbolic glass curtain wall through an infrared thermal imager;

[0171] a data input unit, configured to input the ultrasonic reflection signal and the surface temperature distribution data into a preset hybrid neural network model, wherein the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data and outputs a three-dimensional fusion feature vector; the three-dimensional fusion feature vector includes damage geometric features, energy dissipation features, and thermodynamic features;

[0172] A history acquisition unit, used to acquire historical structural adhesive detection data corresponding to the hyperbolic glass curtain wall;

[0173] A result acquisition unit is used to perform spatiotemporal correlation analysis on the three-dimensional fusion feature vector and historical structural adhesive detection data to obtain the structural adhesive detection results corresponding to the hyperbolic glass curtain wall; the structural adhesive detection results include damage expansion trend prediction and remaining life assessment.

[0174] In some embodiments, after obtaining the structural adhesive detection results corresponding to the hyperbolic glass curtain wall, the method further includes: generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall according to the structural adhesive detection results; and dynamically adjusting the ultrasonic emission frequency corresponding to the ultrasonic sensor array and the sampling interval corresponding to the infrared thermal imager based on a reinforcement learning algorithm to achieve a balanced optimization of the accuracy and efficiency of the structural adhesive damage detection.

[0175] Exemplarily, generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall based on the structural adhesive detection result includes: obtaining a damage probability and an attenuation coefficient of the damage probability corresponding to the structural adhesive detection result; generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall based on the structural adhesive detection result based on the damage probability and the attenuation coefficient; the dynamic volatility coefficient of the ant colony optimization algorithm is:

[0176] ;

[0177] in, is the dynamic volatility coefficient, is the initial volatility coefficient, is the attenuation coefficient, is the damage probability.

[0178] It should be noted that, in some embodiments, the reward function of the reinforcement learning algorithm includes a detection coverage factor, an energy consumption factor, and a prediction confidence factor corresponding to the hyperbolic glass curtain wall.

[0179] It should be noted that, in some embodiments, the weight coefficients corresponding to the detection coverage factor, energy consumption factor, and prediction confidence factor are dynamically adjusted according to the ambient temperature and humidity parameters corresponding to the hyperbolic glass curtain wall.

[0180] In some embodiments, the plurality of array units corresponding to the ultrasonic sensor array adopt a curved conformal array layout.

[0181] For example, the expression corresponding to the spacing of each array unit is:

[0182] ;

[0183] in is the spacing, is the propagation speed of ultrasonic waves in structural adhesives, is the reference transmission frequency corresponding to the array unit, is the local curvature radius of the hyperbolic glass curtain wall, is the curvature deviation allowable threshold corresponding to the hyperbolic glass curtain wall. When the actual curvature radius of the hyperbolic glass curtain wall changes beyond the curvature deviation allowable threshold, the spacing of the array units is automatically adjusted according to the expression.

[0184] In some embodiments, the hybrid neural network model includes a convolutional neural network model and a Transformer model; the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data, including: the Transformer model calculates the association weight matrix of the ultrasonic frequency domain features corresponding to the ultrasonic reflection signal and the infrared spatial features corresponding to the surface temperature distribution data according to the attention mechanism, and dynamically adjusts the contribution of the ultrasonic frequency domain features and infrared spatial features in the association weight matrix based on the feature gating mechanism to complete the cross-modal fusion of the ultrasonic reflection signal and the surface temperature distribution data.

[0185] In some embodiments, before inputting the ultrasonic reflection signal and the surface temperature distribution data into a preset hybrid neural network model, it also includes: preprocessing the ultrasonic reflection signal and the surface temperature distribution data based on a wavelet packet decomposition algorithm to complete denoising and filtering of the ultrasonic reflection signal and the surface temperature distribution data.

[0186] Exemplarily, the number of decomposition layers corresponding to the wavelet packet decomposition algorithm is:

[0187] ;

[0188] in, is the number of decomposition layers, is the sampling frequency corresponding to the ultrasonic reflection signal and the surface temperature distribution data, is the lower limit of the damage characteristic frequency band corresponding to the hyperbolic glass curtain wall.

[0189] It should be noted that, those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the data fusion prediction device and each module for detecting damage to hyperbolic glass curtain wall structural adhesives described above can refer to the corresponding processes in the data fusion prediction method embodiments for detecting damage to hyperbolic glass curtain wall structural adhesives described in the above embodiments, and will not be repeated here.

[0190] The above-mentioned data fusion prediction method for damage detection of structural adhesive of hyperbolic glass curtain wall can be implemented in the form of a computer program, and the computer program can be run on the provided device.

[0191] See also Figure 3 , Figure 31 is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0192] The storage medium can store an operating device and a computer program. The computer program includes program instructions, which, when executed, can enable the processor to execute any data fusion prediction method for damage detection of structural adhesives for hyperbolic glass curtain walls.

[0193] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0194] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any data fusion prediction method for damage detection of hyperbolic glass curtain wall structural adhesive.

[0195] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0196] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0197] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0198] The pre-set self-propelled robot is equipped with Lamb wave detection equipment, SLAM module and adaptive surface fitting mechanism;

[0199] In the hyperbolic glass curtain wall, the ultrasonic reflection signal corresponding to the structural adhesive is collected through a preset ultrasonic sensor array;

[0200] Acquiring surface temperature distribution data of the hyperbolic glass curtain wall by using an infrared thermal imager;

[0201] The ultrasonic reflection signal and the surface temperature distribution data are input into a preset hybrid neural network model, and the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data to output a three-dimensional fusion feature vector; the three-dimensional fusion feature vector includes damage geometry features, energy dissipation features, and thermodynamic features;

[0202] Obtain historical structural adhesive test data corresponding to the hyperbolic glass curtain wall;

[0203] A spatiotemporal correlation analysis is performed on the three-dimensional fusion feature vector and historical structural adhesive detection data to obtain a structural adhesive detection result corresponding to the hyperbolic glass curtain wall; the structural adhesive detection result includes a damage expansion trend prediction and a remaining life assessment.

[0204] In some embodiments, after obtaining the structural adhesive detection results corresponding to the hyperbolic glass curtain wall, the method further includes: generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall according to the structural adhesive detection results; and dynamically adjusting the ultrasonic emission frequency corresponding to the ultrasonic sensor array and the sampling interval corresponding to the infrared thermal imager based on a reinforcement learning algorithm to achieve a balanced optimization of the accuracy and efficiency of the structural adhesive damage detection.

[0205] Exemplarily, generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall based on the structural adhesive detection result includes: obtaining a damage probability and an attenuation coefficient of the damage probability corresponding to the structural adhesive detection result; generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall based on the structural adhesive detection result based on the damage probability and the attenuation coefficient; the dynamic volatility coefficient of the ant colony optimization algorithm is:

[0206] ;

[0207] in, is the dynamic volatility coefficient, is the initial volatility coefficient, is the attenuation coefficient, is the damage probability.

[0208] It should be noted that, in some embodiments, the reward function of the reinforcement learning algorithm includes a detection coverage factor, an energy consumption factor, and a prediction confidence factor corresponding to the hyperbolic glass curtain wall.

[0209] It should be noted that, in some embodiments, the weight coefficients corresponding to the detection coverage factor, energy consumption factor, and prediction confidence factor are dynamically adjusted according to the ambient temperature and humidity parameters corresponding to the hyperbolic glass curtain wall.

[0210] In some embodiments, the plurality of array units corresponding to the ultrasonic sensor array adopt a curved conformal array layout.

[0211] For example, the expression corresponding to the spacing of each array unit is:

[0212] ;

[0213] in is the spacing, is the propagation speed of ultrasonic waves in structural adhesives, is the reference transmission frequency corresponding to the array unit, is the local curvature radius of the hyperbolic glass curtain wall, is the curvature deviation allowable threshold corresponding to the hyperbolic glass curtain wall. When the actual curvature radius of the hyperbolic glass curtain wall changes beyond the curvature deviation allowable threshold, the spacing of the array units is automatically adjusted according to the expression.

[0214] In some embodiments, the hybrid neural network model includes a convolutional neural network model and a Transformer model; the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data, including: the Transformer model calculates the association weight matrix of the ultrasonic frequency domain features corresponding to the ultrasonic reflection signal and the infrared spatial features corresponding to the surface temperature distribution data according to the attention mechanism, and dynamically adjusts the contribution of the ultrasonic frequency domain features and infrared spatial features in the association weight matrix based on the feature gating mechanism to complete the cross-modal fusion of the ultrasonic reflection signal and the surface temperature distribution data.

[0215] In some embodiments, before inputting the ultrasonic reflection signal and the surface temperature distribution data into a preset hybrid neural network model, it also includes: preprocessing the ultrasonic reflection signal and the surface temperature distribution data based on a wavelet packet decomposition algorithm to complete denoising and filtering of the ultrasonic reflection signal and the surface temperature distribution data.

[0216] Exemplarily, the number of decomposition layers corresponding to the wavelet packet decomposition algorithm is:

[0217] ;

[0218] in, is the number of decomposition layers, is the sampling frequency corresponding to the ultrasonic reflection signal and the surface temperature distribution data, is the lower limit of the damage characteristic frequency band corresponding to the hyperbolic glass curtain wall.

[0219] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working processes of the computer equipment and each module described above can refer to the corresponding processes in the data fusion prediction method embodiments for damage detection of hyperbolic glass curtain wall structural adhesives described in the above embodiments, and will not be repeated here.

[0220] The present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the steps of the data fusion prediction method for damage detection of hyperbolic glass curtain wall structural adhesives as provided in any embodiment of the present application.

[0221] The computer-readable storage medium may be an internal storage unit of the computer device described in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, etc., equipped on the computer device.

[0222] Exemplarily, the medium is used to implement the following steps:

[0223] In the hyperbolic glass curtain wall, the ultrasonic reflection signal corresponding to the structural adhesive is collected through a preset ultrasonic sensor array;

[0224] Acquiring surface temperature distribution data of the hyperbolic glass curtain wall by using an infrared thermal imager;

[0225] The ultrasonic reflection signal and the surface temperature distribution data are input into a preset hybrid neural network model, and the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data to output a three-dimensional fusion feature vector; the three-dimensional fusion feature vector includes damage geometry features, energy dissipation features, and thermodynamic features;

[0226] Obtain historical structural adhesive test data corresponding to the hyperbolic glass curtain wall;

[0227] A spatiotemporal correlation analysis is performed on the three-dimensional fusion feature vector and historical structural adhesive detection data to obtain a structural adhesive detection result corresponding to the hyperbolic glass curtain wall; the structural adhesive detection result includes a damage expansion trend prediction and a remaining life assessment.

[0228] In some embodiments, after obtaining the structural adhesive detection results corresponding to the hyperbolic glass curtain wall, the method further includes: generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall according to the structural adhesive detection results; and dynamically adjusting the ultrasonic emission frequency corresponding to the ultrasonic sensor array and the sampling interval corresponding to the infrared thermal imager based on a reinforcement learning algorithm to achieve a balanced optimization of the accuracy and efficiency of the structural adhesive damage detection.

[0229] Exemplarily, generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall based on the structural adhesive detection result includes: obtaining a damage probability and an attenuation coefficient of the damage probability corresponding to the structural adhesive detection result; generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall based on the structural adhesive detection result based on the damage probability and the attenuation coefficient; the dynamic volatility coefficient of the ant colony optimization algorithm is:

[0230] ;

[0231] in, is the dynamic volatility coefficient, is the initial volatility coefficient, is the attenuation coefficient, is the damage probability.

[0232] It should be noted that, in some embodiments, the reward function of the reinforcement learning algorithm includes a detection coverage factor, an energy consumption factor, and a prediction confidence factor corresponding to the hyperbolic glass curtain wall.

[0233] It should be noted that, in some embodiments, the weight coefficients corresponding to the detection coverage factor, energy consumption factor, and prediction confidence factor are dynamically adjusted according to the ambient temperature and humidity parameters corresponding to the hyperbolic glass curtain wall.

[0234] In some embodiments, the plurality of array units corresponding to the ultrasonic sensor array adopt a curved conformal array layout.

[0235] For example, the expression corresponding to the spacing of each array unit is:

[0236] ;

[0237] in is the spacing, is the propagation speed of ultrasonic waves in structural adhesives, is the reference transmission frequency corresponding to the array unit, is the local curvature radius of the hyperbolic glass curtain wall, is the curvature deviation allowable threshold corresponding to the hyperbolic glass curtain wall. When the actual curvature radius of the hyperbolic glass curtain wall changes beyond the curvature deviation allowable threshold, the spacing of the array units is automatically adjusted according to the expression.

[0238] In some embodiments, the hybrid neural network model includes a convolutional neural network model and a Transformer model; the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data, including: the Transformer model calculates the association weight matrix of the ultrasonic frequency domain features corresponding to the ultrasonic reflection signal and the infrared spatial features corresponding to the surface temperature distribution data according to the attention mechanism, and dynamically adjusts the contribution of the ultrasonic frequency domain features and infrared spatial features in the association weight matrix based on the feature gating mechanism to complete the cross-modal fusion of the ultrasonic reflection signal and the surface temperature distribution data.

[0239] In some embodiments, before inputting the ultrasonic reflection signal and the surface temperature distribution data into a preset hybrid neural network model, it also includes: preprocessing the ultrasonic reflection signal and the surface temperature distribution data based on a wavelet packet decomposition algorithm to complete denoising and filtering of the ultrasonic reflection signal and the surface temperature distribution data.

[0240] Exemplarily, the number of decomposition layers corresponding to the wavelet packet decomposition algorithm is:

[0241] ;

[0242] in, is the number of decomposition layers, is the sampling frequency corresponding to the ultrasonic reflection signal and the surface temperature distribution data, is the lower limit of the damage characteristic frequency band corresponding to the hyperbolic glass curtain wall.

[0243] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working processes of the storage medium and each module described above can refer to the corresponding processes in the data fusion prediction method embodiments for damage detection of hyperbolic glass curtain wall structural adhesives described in the above embodiments, and will not be repeated here.

[0244] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A data fusion prediction method for damage detection of structural adhesives in hyperbolic glass curtain walls, characterized in that: include: In the hyperbolic glass curtain wall, the ultrasonic reflection signal corresponding to the structural adhesive is collected through a preset ultrasonic sensor array; Acquiring surface temperature distribution data of the hyperbolic glass curtain wall by using an infrared thermal imager; Inputting the ultrasonic reflection signal and the surface temperature distribution data into a preset hybrid neural network model, the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data, and outputs a three-dimensional fusion feature vector; the three-dimensional fusion feature vector includes damage geometry characteristics, energy dissipation characteristics, and thermodynamic characteristics; Obtain historical structural adhesive test data corresponding to the hyperbolic glass curtain wall; Performing spatiotemporal correlation analysis on the three-dimensional fusion feature vector and historical structural adhesive test data to obtain structural adhesive test results corresponding to the hyperbolic glass curtain wall; the structural adhesive test results include damage expansion trend prediction and remaining life assessment; After obtaining the structural adhesive test result corresponding to the hyperbolic glass curtain wall, the method further includes: generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall according to the structural adhesive test result, including: obtaining a damage probability and an attenuation coefficient of the damage probability corresponding to the structural adhesive test result; generating a curved surface adaptive detection path corresponding to the hyperbolic glass curtain wall according to the structural adhesive test result based on an ant colony optimization algorithm according to the damage probability and the attenuation coefficient; the dynamic volatility coefficient of the ant colony optimization algorithm is: ； in, is the dynamic volatility coefficient, is the initial volatility coefficient, is the attenuation coefficient, is the damage probability; based on the reinforcement learning algorithm, the ultrasonic emission frequency corresponding to the ultrasonic sensor array and the sampling interval corresponding to the infrared thermal imager are dynamically adjusted to achieve a balanced optimization of the structural adhesive damage detection accuracy and efficiency; the multiple array units corresponding to the ultrasonic sensor array adopt a curved surface conformal array layout; the expression corresponding to the spacing of each array unit is: ； in is the spacing, is the propagation speed of ultrasonic waves in structural adhesives, is the reference transmission frequency corresponding to the array unit, is the local curvature radius of the hyperbolic glass curtain wall, is the curvature deviation threshold corresponding to the hyperbolic glass curtain wall, 0.05 When the actual curvature radius of the hyperbolic glass curtain wall changes beyond the curvature deviation allowable threshold, the spacing of the array units is automatically adjusted according to the expression.

2. The method according to claim 1, characterized in that The reward function of the reinforcement learning algorithm includes a detection coverage factor, an energy consumption factor, and a prediction confidence factor corresponding to the hyperbolic glass curtain wall.

3. The method according to claim 2, characterized in that The weight coefficients corresponding to the detection coverage factor, energy consumption factor and prediction confidence factor are dynamically adjusted according to the ambient temperature and humidity parameters corresponding to the hyperbolic glass curtain wall.

4. The method according to claim 1, wherein The hybrid neural network model includes a convolutional neural network model and a Transformer model; the hybrid neural network model performs cross-modal fusion on the ultrasonic reflection signal and the surface temperature distribution data, including: The Transformer model calculates the association weight matrix of the ultrasonic frequency domain features corresponding to the ultrasonic reflection signal and the infrared spatial features corresponding to the surface temperature distribution data based on the attention mechanism, and completes the cross-modal fusion of the ultrasonic reflection signal and the surface temperature distribution data by dynamically adjusting the contribution of the ultrasonic frequency domain features and infrared spatial features in the association weight matrix based on the feature gating mechanism.

5. The method according to claim 1, wherein Before inputting the ultrasonic reflection signal and the surface temperature distribution data into a preset hybrid neural network model, the method further includes: The ultrasonic reflection signal and the surface temperature distribution data are preprocessed based on a wavelet packet decomposition algorithm to complete denoising and filtering of the ultrasonic reflection signal and the surface temperature distribution data.

6. The method according to claim 5, characterized in that The number of decomposition layers corresponding to the wavelet packet decomposition algorithm is: ； in, is the number of decomposition layers, is the sampling frequency corresponding to the ultrasonic reflection signal and the surface temperature distribution data, is the lower limit of the damage characteristic frequency band corresponding to the hyperbolic glass curtain wall.

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