An artificial intelligence early warning system for bridge deformation monitoring

Abstract

This invention discloses an artificial intelligence early warning system for bridge deformation monitoring, comprising: a service identification module, used to output analysis parameter feature vectors matching service types based on asset information accessed by users, output intent categories and intent consistency scores based on user search queries, and construct spatiotemporal feature vectors based on user login time, device type, and geographical location; a behavior sequence analysis module, connected to perform semantic and differentiated sequence anomaly identification, generating offset order feature signals, and generating dynamic baselines for time period perception and device type perception based on the spatiotemporal feature vectors; a behavior frequency analysis module, generating frequency mutation feature quantities; a sensitivity classification judgment module, calculating the asset's graded sensitivity weight; and a notification content push module, dynamically matching push objects and optimizing the structure and hierarchy of early warning information based on the graded sensitivity weight, generating and sending role-based early warning push information.

Description

Technical Field

[0001] This invention relates to the field of bridge monitoring technology, and in particular to an artificial intelligence early warning system for bridge deformation monitoring. Background Technology

[0002] Against the backdrop of accelerated urbanization and surging traffic flow, bridges, as critical infrastructure, bear immense transportation pressure. Traditional manual inspections and periodic monitoring methods suffer from inherent flaws such as inefficiency, lack of real-time capability, and subjective bias, making it difficult to meet the stringent requirements of modern bridge safety management for real-time performance, accuracy, and comprehensiveness. The rapid development of artificial intelligence (AI) technology offers a revolutionary solution to this predicament. By integrating advanced AI technologies such as high-precision sensor networks, machine vision recognition, and deep learning algorithms, a real-time, continuous, and automated monitoring system covering the entire bridge lifecycle can be constructed. This system enables precise capture and dynamic tracking of bridge structural deformations (such as displacement, cracks, and stress changes). Furthermore, intelligent analysis models process deformation data in real-time and predict trends, promptly identifying potential safety hazards (such as accumulated structural damage and load-bearing capacity degradation). Multi-level early warning mechanisms (such as threshold triggering, trend extrapolation, and anomaly identification) provide advance warnings to management departments, effectively preventing major safety accidents such as bridge collapses and fractures.

[0003] In existing technologies, the selection of monitoring points (PS / DS points) depends on the scattering characteristics of SAR images themselves, which is disconnected from the mechanical weak points of the bridge structure (high stress areas, connection parts), and may lead to missed detection of key parts. Therefore, an artificial intelligence early warning system for bridge deformation monitoring is proposed. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing an artificial intelligence early warning system for bridge deformation monitoring.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An artificial intelligence-based early warning system for bridge deformation monitoring includes: Multi-source heterogeneous data acquisition and preprocessing module: synchronously acquires time-series SAR satellite imagery, 3D laser point cloud, meteorological load data and bridge design data, performs standardized processing, and generates standardized data packages; Multi-source data fusion and high-precision deformation field calculation module: Receives the standardized data packet, obtains the initial deformation field through InSAR deformation detection and point cloud registration, and removes the deformation components caused by temperature and load based on the deformation decomposition method constrained by mechanism, and extracts the long-term structural deformation components that characterize foundation settlement and material degradation. Structural Sensitive Area Identification and Key Point Extraction Module: Integrates the theoretical sensitive areas obtained from the bridge finite element model analysis with the measured deformation field characterized by the long-term structural deformation components, and selects the key structural mechanical points for core monitoring through a multi-criteria decision model; Machine learning-driven deformation prediction and early warning module: Based on the long-term structural deformation history data of the key structural mechanics points, combined with meteorological load factors, deformation trend is predicted through a time series prediction model, and dynamic graded early warning is triggered according to prediction deviation or threshold. Component-based risk assessment and visualization module: Based on the component type of the key structural mechanics points, it differentiates and integrates current deformation data, predicted trends and historical conditions to conduct component-level risk assessment, and generates a risk heat map and evaluation report of the entire bridge based on the risk scores of all key points.

[0006] The above technical solution further includes: Furthermore, the multi-source heterogeneous data acquisition and preprocessing module specifically includes: The unit used to acquire time-series synthetic aperture radar satellite imagery covering the bridge area has a resolution better than 3 meters and a revisit period of no more than 12 days. The unit used to acquire three-dimensional laser point clouds of bridges and surrounding terrain through vehicle-mounted or airborne laser scanning systems has a point spacing of no more than 5 mm. A unit used to collect real-time data on temperature, rainfall, and wind speed at the bridge site via an Internet of Things (IoT) sensor network. Units used to acquire traffic load data through video recognition or weighing systems; Units used to import structured design data containing bridge finite element models, material parameters, and historical inspection reports; A unit used to integrate and process the above-mentioned multi-source heterogeneous data into a standardized data packet with a unified spatiotemporal reference and output it.

[0007] Furthermore, the deformation decomposition method based on mechanism constraints in the multi-source data fusion and high-precision deformation field solution module includes: A temperature-deformation response model based on the linear thermal expansion coefficient or the equivalent thermal expansion coefficient is established to calculate and extract the quasi-periodic deformation component caused by temperature change from the original deformation time series. The theoretical deformation mode of a bridge under typical vehicle load conditions is simulated based on the input finite element model, and the elastic deformation component related to traffic load is separated from the measured deformation field by principal component analysis. After stripping the deformation components, the remaining deformation signal is processed using a joint algorithm of empirical mode decomposition and singular value decomposition to extract the long-term structural deformation components.

[0008] Furthermore, the multi-criteria decision model used for selecting key structural mechanical points in the structural sensitive area identification and key point extraction module specifically performs the following steps: Static and dynamic analysis and parameter sensitivity analysis were performed based on the bridge finite element model to calculate and identify the theoretical mechanical sensitive areas that have the greatest impact on the overall structural response. The spatial location information of the theoretically sensitive mechanical region is spatially overlaid with the measured significant deformation region characterized by the long-term structural deformation component, and then correlated with the historical disease record database. The Analytic Hierarchy Process (AHP) was applied to assign weights to multiple criteria, including theoretical sensitivity, measured deformation, historical defects, and point cloud geometric features. Through weighted scoring and cluster analysis, the set of key structural mechanics points and their corresponding component types were selected.

[0009] Furthermore, the machine learning-driven deformation prediction and early warning module specifically includes: For long-term structural deformation sequences with obvious trends and seasonal cycles, the Prophet time series forecasting model is used for modeling, and planned maintenance events are used as external regression factors. For nonlinear deformation sequences, a deep learning model with a long short-term memory network or Transformer encoder architecture is used to make predictions using the long-term structural deformation history sequence and synchronous meteorological load data as multivariate inputs. A dynamic early warning rule is established. When the real-time deformation monitoring value of the key structural mechanics point continuously deviates from the confidence interval of its predicted value, or when the predicted value itself exceeds the preset safety allowable threshold according to the component type, the dynamic early warning rule automatically triggers early warning signals of different levels.

[0010] Furthermore, the component-based risk assessment and visualization module is specifically implemented as follows: Based on the bridge component type to which the key structural mechanics points belong, differentiated core risk indicators are defined for each key structural mechanics point. The core risk indicators include the cumulative settlement and differential settlement rate of the piers, the deflection change rate and predicted deflection extreme value of the main beam, and the relative displacement and rotation of the supports. A multidimensional dynamic risk matrix is ​​constructed, and a fuzzy comprehensive evaluation method is used to integrate and calculate multiple dimensions such as the current deformation, deformation prediction trend, real-time deformation rate, component design importance coefficient, and historical health status of each structural mechanics key point to obtain a comprehensive risk score. The comprehensive risk scores of all key structural mechanics points are interpolated on the surface of the bridge's 3D model using an inverse distance weighted spatial interpolation algorithm. This generates a structural health risk heat map that visually displays the spatial distribution of risk using color gradients. An overall risk assessment report is then generated based on the heat map.

[0011] An artificial intelligence-based early warning method for bridge deformation monitoring includes the following steps: Synchronously collect and preprocess time-series synthetic aperture radar images, high-precision three-dimensional laser point clouds, real-time environmental meteorological and traffic load data, and bridge structural design data covering the target bridge to form a standardized data set; The standardized dataset is fused and processed. Initial deformation information is obtained through interferometry and point cloud matching technology. Based on the physical mechanism model, the deformation caused by temperature effect and load effect is separated, and the long-term structural deformation component reflecting the long-term evolution of the bridge structure is extracted. Combining the theoretically weak areas derived from the finite element analysis of the bridge with the spatial distribution of the long-term structural deformation components, multi-criteria decision analysis was used to screen out the key structural mechanical points for continuous monitoring. Based on the historical long-term structural deformation sequence of the key structural mechanics points, and by integrating external influencing factor data, a machine learning model is used to predict future deformation trends, and dynamic early warnings are implemented based on the comparison between the prediction results and real-time data. Based on the component category to which the key structural mechanics points belong, a differentiated index system is used to conduct risk assessment and quantitative scoring, and a visualized overall risk distribution map of the bridge and a detailed diagnostic report are generated based on the scoring results of all key points.

[0012] The present invention has the following beneficial effects: In this invention, the structural sensitive area identification and key point extraction module combines theoretical mechanics analysis with measured deformation fields. Through multi-criteria decision-making, it selects key structural mechanics points, ensuring that limited monitoring resources are accurately allocated to mechanically sensitive points and significant deformation points that are crucial to structural safety. This improves the efficiency and effectiveness of the monitoring network. The component-based risk assessment and visualization module defines differentiated risk indicators for different component types such as piers, main beams, and supports. It integrates current values, predicted trends, and other multi-dimensional information for comprehensive evaluation. The resulting risk heat map can intuitively and quantitatively display the distribution of risks in the bridge space and accurately locate the lesions. Attached Figure Description

[0013] Figure 1 This is a system block diagram of an artificial intelligence early warning system for bridge deformation monitoring proposed in this invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figure 1 As shown, the present invention is an artificial intelligence early warning system for bridge deformation monitoring, comprising: Multi-source heterogeneous data acquisition and preprocessing module: synchronously acquires time-series SAR satellite imagery, 3D laser point cloud, meteorological load data and bridge design data, performs standardized processing, and generates standardized data packages; Multi-source data fusion and high-precision deformation field calculation module: Receives the standardized data packet, obtains the initial deformation field through InSAR deformation detection and point cloud registration, and removes the deformation components caused by temperature and load based on the deformation decomposition method constrained by mechanism, and extracts the long-term structural deformation components that characterize foundation settlement and material degradation. It should be noted that the specific analysis process for multi-source data fusion and high-precision deformation field calculation is as follows: Receive standardized data packets from the data acquisition module, which contain time-series SAR satellite imagery, 3D laser point clouds, synchronous meteorological and load data, and bridge design information; InSAR deformation detection is performed on the time-series SAR satellite imagery to obtain candidate deformation points and their time-series phases covering the bridge area. At the same time, the three-dimensional laser point cloud and the InSAR deformation field are registered with high precision to obtain an initial deformation field with a unified spatial reference system. More specifically, InSAR deformation detection is performed on time-series SAR satellite images: the amplitude deviation index method and the FaSHP (Fast and Robust Scatterer and Hotspot Point) similarity test method are used to jointly extract permanent scattering points with stable scattering characteristics and distributed scattering points with high correlation characteristics from the time-series SAR satellite images as deformation candidate points. The relative elevation of the triangular network constructed from all deformation candidate points is calculated based on the periodic diagram method, and its absolute height error is corrected using external digital elevation model data to obtain high-precision candidate point elevation information. Geocoding is performed on the candidate deformation points after elevation correction, and they are transformed from radar coordinate system to geodetic coordinate system to generate an initial set of deformation observation points with precise spatial location information; High-precision point cloud registration between 3D laser point cloud and InSAR deformation field: KD tree spatial indexes are constructed for the 3D laser point cloud and InSAR deformation candidate points respectively to accelerate the subsequent nearest neighbor search and feature matching process. Calculate the fast point feature histogram descriptor for each point, and establish initial corresponding feature point pairs between the two point clouds based on the descriptor similarity; The RANSAC random sampling consensus algorithm is used to iteratively filter the initial corresponding feature point pairs, eliminate mismatched point pairs, and estimate the initial rigid body transformation matrix. Based on the initial rigid body transformation matrix, the iterative nearest point algorithm is used for fine registration. By minimizing the distance error between the two points, the final transformation matrix for coordinate system one is obtained. Based on the mechanism-constrained deformation decomposition method, the physical causes of the initial deformation field are separated. Specifically, this includes using meteorological data and structural parameters to establish a temperature-deformation response model to remove the deformation component caused by temperature, and using load data and a finite element model to separate the elastic deformation component caused by traffic load. The long-term structural deformation components characterizing bridge foundation settlement and time-varying material performance degradation are extracted from the separated deformation signals and used as input data for subsequent key point extraction and risk analysis.

[0016] Structural Sensitive Area Identification and Key Point Extraction Module: Integrates the theoretical sensitive areas obtained from the bridge finite element model analysis with the measured deformation field characterized by the long-term structural deformation components, and selects the key structural mechanical points for core monitoring through a multi-criteria decision model; It should be noted that the specific analysis process for intelligent key point extraction is as follows: Theoretical sensitive area information and measured deformation field data are obtained. The theoretical sensitive area information is obtained by static and dynamic and sensitivity analysis of the finite element model of the target bridge. The measured deformation field data is the spatial distribution data of long-term structural deformation components extracted after multi-source data fusion and deformation decomposition. Spatial overlay and correlation analysis is performed to overlay the spatial location of the theoretically sensitive area with the spatial distribution field of the long-term structural deformation components, and to perform spatial correlation with the historical disease database to identify overlapping areas where both theoretical sensitivity and measured deformation are significant. A multi-criteria decision-making model is constructed, taking the candidate points in the identified area as the evaluation objects, and establishing a multi-dimensional evaluation criterion system that includes theoretical sensitivity weight, measured deformation rate, cumulative deformation, historical disease severity, and local geometric complexity. The structural mechanics key points are selected, and the candidate points are comprehensively scored and ranked based on the multi-criteria decision model. The spatial clustering algorithm is combined to ensure the spatial representative distribution of the key points. Finally, the set of structural mechanics key points and their attribute information are output for subsequent prediction and risk assessment. Machine learning-driven deformation prediction and early warning module: Based on the long-term structural deformation history data of the key structural mechanics points, combined with meteorological load factors, deformation trend is predicted through a time series prediction model, and dynamic graded early warning is triggered according to prediction deviation or threshold. Component-based risk assessment and visualization module: Based on the component type of the key structural mechanics points, it differentiates and integrates current deformation data, predicted trends and historical conditions to conduct component-level risk assessment, and generates a risk heat map and evaluation report of the entire bridge based on the risk scores of all key points.

[0017] In one embodiment, the multi-source heterogeneous data acquisition and preprocessing module specifically includes: The unit used to acquire time-series synthetic aperture radar satellite imagery covering the bridge area has a resolution better than 3 meters and a revisit period of no more than 12 days. The unit used to acquire three-dimensional laser point clouds of bridges and surrounding terrain through vehicle-mounted or airborne laser scanning systems has a point spacing of no more than 5 mm. A unit used to collect real-time data on temperature, rainfall, and wind speed at the bridge site via an Internet of Things (IoT) sensor network. Units used to acquire traffic load data through video recognition or weighing systems; Units used to import structured design data containing bridge finite element models, material parameters, and historical inspection reports; A unit used to integrate and process the above-mentioned multi-source heterogeneous data into a standardized data packet with a unified spatiotemporal reference and output it.

[0018] In one embodiment, the deformation decomposition method based on mechanism constraints in the multi-source data fusion and high-precision deformation field calculation module includes: A temperature-deformation response model based on the linear thermal expansion coefficient or the equivalent thermal expansion coefficient is established to calculate and extract the quasi-periodic deformation component caused by temperature change from the original deformation time series. The theoretical deformation mode of a bridge under typical vehicle load conditions is simulated based on the input finite element model, and the elastic deformation component related to traffic load is separated from the measured deformation field by principal component analysis. After stripping the deformation components, the remaining deformation signal is processed using a joint algorithm of empirical mode decomposition and singular value decomposition to extract the long-term structural deformation components.

[0019] In one embodiment, the multi-criteria decision model for selecting key structural mechanical points in the structural sensitive area identification and key point extraction module specifically performs the following steps: Static and dynamic analysis and parameter sensitivity analysis were performed based on the bridge finite element model to calculate and identify the theoretical mechanical sensitive areas that have the greatest impact on the overall structural response. The spatial location information of the theoretically sensitive mechanical region is spatially overlaid with the measured significant deformation region characterized by the long-term structural deformation component, and then correlated with the historical disease record database. The Analytic Hierarchy Process (AHP) was applied to assign weights to multiple criteria, including theoretical sensitivity, measured deformation, historical defects, and point cloud geometric features. Through weighted scoring and cluster analysis, the set of key structural mechanics points and their corresponding component types were selected.

[0020] In one embodiment, the machine learning-driven deformation prediction and early warning module specifically includes: For long-term structural deformation sequences with obvious trends and seasonal cycles, the Prophet time series forecasting model is used for modeling, and planned maintenance events are used as external regression factors. For nonlinear deformation sequences, a deep learning model with a long short-term memory network or Transformer encoder architecture is used to make predictions using the long-term structural deformation history sequence and synchronous meteorological load data as multivariate inputs. A dynamic early warning rule is established. When the real-time deformation monitoring value of the key structural mechanics point continuously deviates from the confidence interval of its predicted value, or when the predicted value itself exceeds the preset safety allowable threshold according to the component type, the dynamic early warning rule automatically triggers early warning signals of different levels.

[0021] In one embodiment, the component-based risk assessment and visualization module is specifically implemented as follows: Based on the bridge component type to which the key structural mechanics points belong, differentiated core risk indicators are defined for each key structural mechanics point. The core risk indicators include the cumulative settlement and differential settlement rate of the piers, the deflection change rate and predicted deflection extreme value of the main beam, and the relative displacement and rotation of the supports. A multidimensional dynamic risk matrix is ​​constructed, and a fuzzy comprehensive evaluation method is used to integrate and calculate multiple dimensions such as the current deformation, deformation prediction trend, real-time deformation rate, component design importance coefficient, and historical health status of each structural mechanics key point to obtain a comprehensive risk score. The comprehensive risk scores of all key structural mechanics points are interpolated on the surface of the bridge's 3D model using an inverse distance weighted spatial interpolation algorithm. This generates a structural health risk heat map that visually displays the spatial distribution of risk using color gradients. An overall risk assessment report is then generated based on the heat map.

[0022] An artificial intelligence-based early warning method for bridge deformation monitoring includes the following steps: Synchronously collect and preprocess time-series synthetic aperture radar images, high-precision three-dimensional laser point clouds, real-time environmental meteorological and traffic load data, and bridge structural design data covering the target bridge to form a standardized data set; The standardized dataset is fused and processed. Initial deformation information is obtained through interferometry and point cloud matching technology. Based on the physical mechanism model, the deformation caused by temperature effect and load effect is separated, and the long-term structural deformation component reflecting the long-term evolution of the bridge structure is extracted. Combining the theoretically weak areas derived from the finite element analysis of the bridge with the spatial distribution of the long-term structural deformation components, multi-criteria decision analysis was used to screen out the key structural mechanical points for continuous monitoring. Based on the historical long-term structural deformation sequence of the key structural mechanics points, and by integrating external influencing factor data, a machine learning model is used to predict future deformation trends, and dynamic early warnings are implemented based on the comparison between the prediction results and real-time data. Based on the component category to which the key structural mechanics points belong, a differentiated index system is used to conduct risk assessment and quantitative scoring, and a visualized overall risk distribution map of the bridge and a detailed diagnostic report are generated based on the scoring results of all key points.

[0023] All data obtained in this invention has been authorized by the user.

[0024] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence early warning system for bridge deformation monitoring, characterized in that, include: Multi-source heterogeneous data acquisition and preprocessing module: synchronously acquires time-series SAR satellite imagery, 3D laser point cloud, meteorological load data and bridge design data, performs standardized processing, and generates standardized data packages; Multi-source data fusion and high-precision deformation field calculation module: Receives the standardized data packet, obtains the initial deformation field through InSAR deformation detection and point cloud registration, and extracts the long-term structural deformation components by stripping the deformation components caused by temperature and load based on the deformation decomposition method constrained by mechanism. Structural Sensitive Area Identification and Key Point Extraction Module: Integrates the theoretical sensitive areas obtained from the bridge finite element model analysis with the measured deformation field characterized by the long-term structural deformation components, and selects the key structural mechanical points for core monitoring through a multi-criteria decision model; Machine learning-driven deformation prediction and early warning module: Based on the long-term structural deformation history data of the key structural mechanics points, combined with meteorological load factors, deformation trend is predicted through a time series prediction model, and dynamic graded early warning is triggered according to prediction deviation or threshold. Component-based risk assessment and visualization module: Based on the component type of the key structural mechanics points, it differentiates and integrates current deformation data, predicted trends and historical conditions to conduct component-level risk assessment, and generates a risk heat map and evaluation report of the entire bridge based on the risk scores of all key points.

2. The artificial intelligence early warning system for bridge deformation monitoring according to claim 1, characterized in that, The multi-source heterogeneous data acquisition and preprocessing module specifically includes: Unit used to acquire time-series synthetic aperture radar satellite imagery covering bridge areas; A unit used to acquire three-dimensional laser point clouds of bridges and surrounding terrain through vehicle-mounted or airborne laser scanning systems; A unit used to collect real-time data on temperature, rainfall, and wind speed at the bridge site via an Internet of Things (IoT) sensor network. Units used to acquire traffic load data through video recognition or weighing systems; Units used to import structured design data containing bridge finite element models, material parameters, and historical inspection reports; A unit used to integrate and process the above-mentioned multi-source heterogeneous data into a standardized data packet with a unified spatiotemporal reference and output it.

3. The artificial intelligence early warning system for bridge deformation monitoring according to claim 1, characterized in that, The deformation decomposition method based on mechanism constraints in the multi-source data fusion and high-precision deformation field solution module includes: A temperature-deformation response model based on the linear thermal expansion coefficient or the equivalent thermal expansion coefficient is established to calculate and extract the quasi-periodic deformation component caused by temperature change from the original deformation time series. The theoretical deformation mode of a bridge under typical vehicle load conditions is simulated based on the input finite element model, and the elastic deformation component related to traffic load is separated from the measured deformation field by principal component analysis. After stripping the deformation components, the remaining deformation signal is processed using a joint algorithm of empirical mode decomposition and singular value decomposition to extract the long-term structural deformation components.

4. The artificial intelligence early warning system for bridge deformation monitoring according to claim 1, characterized in that, The multi-criteria decision-making model used for selecting key structural mechanical points in the structural sensitive area identification and key point extraction module specifically performs the following steps: Static and dynamic analysis and parameter sensitivity analysis were performed based on the bridge finite element model to calculate and identify the theoretical mechanical sensitive areas that have the greatest impact on the overall structural response. The spatial location information of the theoretically sensitive mechanical region is spatially overlaid with the measured significant deformation region characterized by the long-term structural deformation component, and then correlated with the historical disease record database. The Analytic Hierarchy Process (AHP) is applied to assign weights, and the set of key structural mechanics points and their corresponding component types are selected through weighted scoring and cluster analysis.

5. An artificial intelligence early warning system for bridge deformation monitoring according to claim 1, characterized in that, The machine learning-driven deformation prediction and early warning module specifically includes: For long-term structural deformation sequences with obvious trends and seasonal cycles, the Prophet time series forecasting model is used for modeling, and planned maintenance events are used as external regression factors. For nonlinear deformation sequences, a deep learning model with a long short-term memory network or Transformer encoder architecture is used to make predictions using the long-term structural deformation history sequence and synchronous meteorological load data as multivariate inputs. A dynamic early warning rule is established. When the real-time deformation monitoring value of the key structural mechanics point continuously deviates from the confidence interval of its predicted value, or when the predicted value itself exceeds the preset safety allowable threshold according to the component type, the dynamic early warning rule automatically triggers early warning signals of different levels.

6. An artificial intelligence early warning system for bridge deformation monitoring according to claim 1, characterized in that, The component-based risk assessment and visualization module is specifically implemented as follows: Based on the bridge component type to which the key structural mechanics points belong, differentiated core risk indicators are defined for each key structural mechanics point. The core risk indicators include the cumulative settlement and differential settlement rate of the piers, the deflection change rate and predicted deflection extreme value of the main beam, and the relative displacement and rotation of the supports. A multidimensional dynamic risk matrix is ​​constructed, and a fuzzy comprehensive evaluation method is used to integrate and calculate multiple dimensions such as the current deformation, deformation prediction trend, real-time deformation rate, component design importance coefficient, and historical health status of each structural mechanics key point to obtain a comprehensive risk score. The comprehensive risk scores of all key structural mechanics points are interpolated on the surface of the bridge's 3D model using an inverse distance weighted spatial interpolation algorithm. This generates a structural health risk heat map that visually displays the spatial distribution of risk using color gradients. An overall risk assessment report is then generated based on the heat map.

7. An artificial intelligence-based early warning method for bridge deformation monitoring, characterized in that, An artificial intelligence early warning system for bridge deformation monitoring, as described in any one of claims 1, comprises the following steps: Synchronously collect and preprocess time-series synthetic aperture radar images, 3D laser point clouds, real-time environmental meteorological and traffic load data, and bridge structural design data covering the target bridge to form a standardized data set; The standardized dataset is fused and processed. Initial deformation information is obtained through interferometry and point cloud matching technology. Based on the physical mechanism model, the deformation caused by temperature effect and load effect is separated, and the long-term structural deformation component reflecting the long-term evolution of the bridge structure is extracted. Combining the theoretically weak areas derived from the finite element analysis of the bridge with the spatial distribution of the long-term structural deformation components, multi-criteria decision analysis was used to screen out the key structural mechanical points for continuous monitoring. Based on the historical long-term structural deformation sequence of the key structural mechanics points, and by integrating external influencing factor data, a machine learning model is used to predict future deformation trends, and dynamic early warnings are implemented based on the comparison between the prediction results and real-time data. Based on the component category to which the key structural mechanics points belong, a differentiated index system is used to conduct risk assessment and quantitative scoring, and a visualized overall risk distribution map of the bridge and a detailed diagnostic report are generated based on the scoring results of all key points.

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