Bridge structure monitoring and maintenance system based on multi-source perception

Abstract

The invention relates to a bridge structure monitoring and maintenance system based on multi-source sensing. The bridge structure monitoring and maintenance system based on multi-source sensing comprises a multi-source sensing unit, an edge computing node, a cloud server and a maintenance execution device. The multi-source sensing unit comprises optical fiber sensors which are arranged in bridge pier and main beam areas and used for obtaining strain and temperature distribution information of a bridge structure in real time, and a DIC camera which is installed at a bridge support and used for collecting deformation image data of the bridge structure. The edge computing node is arranged on a bridge site, is connected with the multi-source sensing unit through an optical cable, and is used for receiving the multi-source data collected by the multi-source sensing unit and carrying out filtering noise reduction and feature extraction localization processing. And the cloud server is used for receiving the multi-source data subjected to the localization processing of the edge computing node and carrying out fusion analysis on the multi-source data so as to identify the abnormal state of the bridge structure and generate a maintenance strategy. And the maintenance execution device is used for receiving the maintenance strategy generated by the cloud server to execute maintenance operation.

Description

Technical Field

[0001] This invention relates to the field of bridge monitoring technology, and more specifically to a bridge structure monitoring and maintenance system based on multi-source sensing. Background Technology

[0002] Current bridge structure monitoring relies on manual inspections combined with localized data collection using single-type sensors. This approach suffers from several drawbacks, including high dependence on personnel experience, significant safety risks associated with working at heights, long inspection cycles, and a high probability of misjudgments and missed detections. Furthermore, the lack of effective data fusion between different measurement sources makes it difficult to accurately assess the overall condition of the bridge. Additionally, the data collected is typically transmitted back to the system for centralized processing, resulting in a lengthy analysis process, untimely system response, and difficulty in providing timely support for maintenance and repair. Summary of the Invention

[0003] The purpose of this invention is to provide a bridge structure monitoring and maintenance system based on multi-source sensing, which can improve the accuracy of structural status identification through multi-source data fusion, shorten the data transmission and processing links through on-site localization to improve response speed, and reduce the reliance on high-risk manual operations and subjective judgment through maintenance execution devices, thereby effectively solving the problems of insufficient timeliness, insufficient fusion capability and high safety risks in the prior art.

[0004] To achieve the above objectives, the present invention provides a bridge structure monitoring and maintenance system based on multi-source sensing, characterized in that it includes: The multi-source sensing unit includes fiber optic sensors deployed in the pier and main beam areas for real-time acquisition of strain and temperature distribution information of the bridge structure, and DIC cameras installed on the bridge supports for acquiring image data of bridge structure deformation. An edge computing node is deployed at the bridge site and connected to the multi-source sensing unit via optical cable. It is used to receive multi-source data collected by the multi-source sensing unit and perform filtering, noise reduction, and feature extraction localization processing. A cloud server is used to receive multi-source data processed locally by the edge computing nodes and perform fusion analysis on the multi-source data to identify abnormal states of the bridge structure and generate maintenance strategies. A maintenance execution device is used to receive maintenance policies generated by the cloud server and execute maintenance operations.

[0005] Optionally, the bridge structure monitoring and maintenance system based on multi-source sensing further includes: A sensor placement optimization module, connected to the fiber optic sensor, is used to optimize the placement position of the fiber optic sensor.

[0006] Optionally, the cloud server includes: The analysis and evaluation module is used to identify surface defects on bridges and locate damaged areas based on multi-source data, while also assessing the health status of the bridge structure. The maintenance decision module is used to generate maintenance strategies by employing particle swarm optimization simulated annealing algorithm when the health status assessment of the bridge structure is in an abnormal state, and to optimize the maintenance strategies by combining historical stored data.

[0007] Optionally, the cloud server further includes: The feedback iteration module is used to verify the maintenance effect of the maintenance strategy in real time through the bridge structural mechanics model, and to evaluate the maintenance and repair quality so as to feed the verification and evaluation results back to the maintenance decision module for algorithm optimization.

[0008] Optionally, the cloud server further includes: An anti-interference processing module is used to suppress noise in the localized multi-source data; The dynamic data stream management module, connected to the anti-interference processing module, is used to synchronously collect and process multi-source data after noise suppression in real time, and transmit it to the analysis and evaluation module.

[0009] Optionally, the bridge structure monitoring and maintenance system based on multi-source sensing further includes: The monitoring data visualization module, connected to the analysis and evaluation module, is used to provide monitoring data to management personnel and, when the bridge structure health status is assessed as abnormal, to provide management personnel with a bridge structure health status visualization report and abnormal early warning information.

[0010] Optionally, a wireless communication module for signal data transmission is provided between the edge computing node and the cloud server, and between the cloud server and the monitoring data visualization module.

[0011] Optionally, the maintenance execution device is a maintenance and repair robot.

[0012] Optionally, the maintenance operations performed by the maintenance execution device include crack grouting, bolt tightening, or surface cleaning.

[0013] The beneficial effects of this invention are as follows: By using a multi-source sensing unit to simultaneously acquire bridge structural strain and temperature distribution information from fiber optic sensors and bridge structural deformation image data from DIC cameras at key locations such as piers and main beams, complementary spatial resolution and measurement dimensions are achieved. This expands the bridge structural state characterization from a single parameter to a multi-dimensional parameter, thereby improving the detectability of subtle anomalies and adaptability to complex working conditions, and reducing missed detections and misjudgments caused by insufficient information from a single channel. Furthermore, edge computing nodes perform localized processing of multi-source data on-site, including filtering, noise reduction, and feature extraction. This transforms highly redundant raw data into structured features before uploading, significantly reducing data transmission volume and link occupancy, and shortening the time from data acquisition to distribution. The system analyzes the time path for judgment and maintains continuous output and stable response under conditions such as network fluctuations. After receiving multi-source data processed by edge computing nodes, the cloud server performs fusion analysis and forms a bridge structural health status assessment based on the consistency and correlation criteria of multiple measurement sources. Based on this, it generates a maintenance strategy, transforming the decision-making basis from manual experience judgment to a data-driven judgment mechanism, thereby improving the consistency and verifiability of the assessment results. The maintenance execution device performs maintenance operations according to the maintenance strategy generated by the cloud server, transferring high-altitude and hazardous environment operations from manual methods to equipment execution, reducing reliance on high-risk manual operations and subjective judgment, and improving the repeatability and controllability of maintenance actions.

[0014] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0015] Figure 1 This is a schematic structural diagram of a bridge structure monitoring and maintenance system based on multi-source sensing, according to an embodiment of the present invention. In the diagram: 1. Multi-source sensing unit; 11. Fiber optic sensor; 12. DIC camera; 2. Edge computing node; 3. Cloud server; 31. Anti-interference processing module; 32. Dynamic data stream management module; 33. Analysis and evaluation module; 34. Maintenance decision module; 35. Feedback iteration module; 4. Maintenance execution device; 5. Monitoring data visualization module; 6. Sensor layout optimization module. Detailed Implementation

[0016] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0017] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0018] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. Furthermore, the technical features involved in the different embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0019] Please see Figure 1 The bridge structure monitoring and maintenance system based on multi-source sensing, as shown in a preferred embodiment of this application, includes a multi-source sensing unit 1, an edge computing node 2, a cloud server 3, and a maintenance execution device 4. The multi-source sensing unit 1 includes fiber optic sensors 11 deployed in the piers and main beam areas for real-time acquisition of bridge structure strain and temperature distribution information, and a DIC camera 12 installed at the bridge supports for acquiring bridge structure deformation image data. The edge computing node 2 is deployed at the bridge site and connected to the multi-source sensing unit 1 via optical cable. It receives the multi-source data collected by the multi-source sensing unit 1 and performs filtering, noise reduction, and feature extraction localization processing. The cloud server 3 receives the localized multi-source data from the edge computing node 2 and performs fusion analysis on the multi-source data to identify abnormal bridge structure states and generate maintenance strategies. The maintenance execution device 4 receives the maintenance strategies generated by the cloud server 3 and executes maintenance operations.

[0020] According to the scheme of this embodiment of the invention, the multi-source sensing unit 1 simultaneously acquires bridge structural strain and temperature distribution information from the fiber optic sensor 11 and bridge structural deformation image data from the DIC camera 12 at key locations such as piers and main beams. This complements each other in terms of spatial resolution and measurement dimensions, expanding the bridge structural state representation from a single parameter to a multi-dimensional parameter, thereby improving the detectability of subtle anomalies and adaptability to complex working conditions, and reducing missed detections and misjudgments caused by insufficient information from a single channel. The edge computing node 2 performs localized processing of multi-source data on the bridge site, including filtering, noise reduction, and feature extraction. It transforms highly redundant raw data into structured features before uploading, significantly reducing data transmission volume and link occupancy, and shortening the time from data acquisition to transmission. The system analyzes and determines the time path, maintaining continuous output and stable response under conditions such as network fluctuations. After receiving multi-source data processed by edge computing node 2, cloud server 3 performs fusion analysis and forms a bridge structure health status assessment based on the consistency and correlation criteria of multiple measurement sources. Based on this, it generates a maintenance strategy, transforming the decision-making basis from manual experience judgment to a data-driven judgment mechanism, improving the consistency and verifiability of the assessment results. Maintenance execution device 4 performs maintenance operations according to the maintenance strategy generated by cloud server 3, transferring high-altitude and hazardous environment operations from manual methods to equipment execution, reducing reliance on high-risk manual operations and subjective judgment, and improving the repeatability and controllability of maintenance actions.

[0021] The following detailed description uses specific examples: Specifically, in this embodiment, the system continuously samples the strain and temperature of the bridge structure at a frequency of 1 kHz, and the DIC camera 12 acquires deformation images at a frequency of 5 frames per second.

[0022] Specifically, please see Figure 1 The cloud server 3 includes an anti-interference processing module 31, a dynamic data stream management module 32, an analysis and evaluation module 33, a maintenance decision module 34, and a feedback iteration module 35. The anti-interference processing module 31 suppresses noise in the locally processed multi-source data based on filtering algorithms and redundancy design. The dynamic data stream management module 32, connected to the anti-interference processing module 31, synchronously collects and processes the noise-suppressed multi-source data in real time, and transmits it to the analysis and evaluation module 33. The analysis and evaluation module 33 identifies bridge surface defects and locates damaged areas based on the multi-source data, while simultaneously assessing the health status of the bridge structure. The maintenance decision module 34, when the bridge structure's health status is assessed as abnormal, uses a particle swarm optimization simulated annealing algorithm to generate a maintenance strategy and optimizes the strategy using historical stored data. The feedback iteration module 35 verifies the maintenance effect of the maintenance strategy in real time using a bridge structural mechanical model and evaluates the maintenance repair quality, feeding the verification and evaluation results back to the maintenance decision module 34 for algorithm optimization.

[0023] The aforementioned modules are integrated and work collaboratively within the cloud server 3. First, the anti-interference processing module 31 reduces the impact of environmental disturbances on multi-source data at the source, reducing false alarms and missed alarms from the input side, thereby improving data accuracy and system stability. The dynamic data flow management module 32 ensures the alignment and continuity of multi-source data on the time axis, providing the analysis and evaluation module 33 with a stable data flow that can be directly used for fusion judgment, ensuring the timeliness of system response, thereby improving the reliability and verifiability of anomaly identification. The analysis and evaluation module 33 and the maintenance decision module 34 are connected, so that the diagnostic results and strategy generation run under a unified data standard and criteria. The maintenance decision module 34 generates maintenance strategies that meet resource constraints and on-site conditions through particle swarm optimization simulated annealing algorithm and combined with historical stored data, reducing the probability of over-maintenance and under-maintenance. The feedback iteration module 35 verifies and evaluates the maintenance effect using a bridge structural mechanics model and sends the results back to the maintenance decision module 34, achieving continuous adaptive optimization, so that the maintenance strategy tends to stabilize in long-term operation and is consistent with the actual structural response.

[0024] Please see Figure 1 The bridge structure monitoring and maintenance system based on multi-source sensing also includes a monitoring data visualization module 5. This module 5 is connected to the analysis and evaluation module 33 and is used to provide monitoring data to management personnel. When the bridge structure health status is assessed as abnormal, it provides management personnel with a visualized report on the bridge structure health status and anomaly warning information. Specifically, the monitoring data visualization module 5 imports the bridge BIM model through the IFC standard format and establishes a mapping relationship between sensor numbers and spatial coordinates of the BIM model. The monitoring data is transmitted to the visualization engine via the OPC UA protocol and presented in the corresponding position of the BIM model in the form of color gradients or numerical labels. For example, stress monitoring data can be represented by the color depth of the component to indicate the stress level, and displacement data can be displayed through dynamic deformation animation to show the structural response.

[0025] Specifically, in this embodiment, a wireless communication module for signal data transmission is provided between the edge computing node 2 and the cloud server 3, and between the cloud server 3 and the monitoring data visualization module 5. The maintenance execution device 4 is a maintenance and repair robot. By setting a monitoring threshold, when the data from the fiber optic sensor 11 exceeds the threshold, the maintenance decision module generates a maintenance command. After receiving the command, the maintenance execution device 4 moves along a preset path to the target location to perform the maintenance operation. Further, the maintenance operations performed by the maintenance execution device 4 include crack grouting, bolt tightening, or surface cleaning. For example, when the width of a concrete crack exceeds the limit, the maintenance execution device 4 locates the crack and performs grouting repair work.

[0026] Furthermore, the bridge structure monitoring and maintenance system also includes a sensor layout optimization module 6, which is connected to the fiber optic sensor 11 and used to optimize the layout position of the fiber optic sensor 11. Specifically, the generalized genetic algorithm of the sensor layout optimization module 6 uses the modal confidence criterion as the fitness function, represents the sensor layout scheme through binary encoding, randomly generates the initial population, and iteratively searches for the optimal sensor position through roulette wheel selection, single-point crossover, and bit-flip mutation operations. The fitness function can adopt strain energy density or damage detectability index to optimize for different monitoring targets.

[0027] When the system is in use, the multi-source sensing unit 1 continuously collects strain, temperature and deformation image data of the bridge structure at key parts such as piers and main beams. The edge computing node 2 filters and denoises the collected data and extracts features before sending the results to the cloud server 3. The cloud server 3 performs fusion analysis on the multi-source data through the analysis and evaluation module 33 and forms a bridge structure health status assessment. When the assessment indicates an abnormal state, the maintenance decision module 34 uses the particle swarm optimization simulated annealing algorithm and combines it with historical stored data to generate a maintenance strategy and sends it to the maintenance execution device 4 to carry out the maintenance operation. After the maintenance is completed, the feedback iteration module 35 verifies the maintenance effect based on the bridge structure mechanical model and uses the assessment results for subsequent strategy optimization. At the same time, the monitoring data visualization module 5 displays a visualization report and abnormal warning information to the management personnel, thereby completing a closed-loop process from data collection, status assessment, strategy generation, maintenance execution to effect verification and information feedback.

[0028] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0029] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A bridge structure monitoring and maintenance system based on multi-source sensing, characterized in that, include: The multi-source sensing unit includes fiber optic sensors deployed in the pier and main beam areas for real-time acquisition of strain and temperature distribution information of the bridge structure, and DIC cameras installed on the bridge supports for acquiring image data of bridge structure deformation. An edge computing node is deployed at the bridge site and connected to the multi-source sensing unit via optical cable. It is used to receive multi-source data collected by the multi-source sensing unit and perform filtering, noise reduction, and feature extraction localization processing. A cloud server is used to receive multi-source data processed locally by the edge computing nodes and perform fusion analysis on the multi-source data to identify abnormal states of the bridge structure and generate maintenance strategies. A maintenance execution device is used to receive maintenance policies generated by the cloud server and execute maintenance operations.

2. The bridge structure monitoring and maintenance system based on multi-source sensing according to claim 1, characterized in that, Also includes: A sensor placement optimization module, connected to the fiber optic sensor, is used to optimize the placement position of the fiber optic sensor.

3. The bridge structure monitoring and maintenance system based on multi-source sensing according to claim 1, characterized in that, The cloud server includes: The analysis and evaluation module is used to identify surface defects on bridges and locate damaged areas based on multi-source data, while also assessing the health status of the bridge structure. The maintenance decision module is used to generate maintenance strategies by employing particle swarm optimization simulated annealing algorithm when the health status assessment of the bridge structure is in an abnormal state, and to optimize the maintenance strategies by combining historical stored data.

4. The bridge structure monitoring and maintenance system based on multi-source sensing according to claim 3, characterized in that, The cloud server also includes: The feedback iteration module is used to verify the maintenance effect of the maintenance strategy in real time through the bridge structural mechanics model, and to evaluate the maintenance and repair quality so as to feed the verification and evaluation results back to the maintenance decision module for algorithm optimization.

5. The bridge structure monitoring and maintenance system based on multi-source sensing according to claim 3, characterized in that, The cloud server also includes: An anti-interference processing module is used to suppress noise in the localized multi-source data; The dynamic data stream management module, connected to the anti-interference processing module, is used to synchronously collect and process multi-source data after noise suppression in real time, and transmit it to the analysis and evaluation module.

6. The bridge structure monitoring and maintenance system based on multi-source sensing according to claim 3, characterized in that, Also includes: The monitoring data visualization module, connected to the analysis and evaluation module, is used to provide monitoring data to management personnel and, when the bridge structure health status is assessed as abnormal, to provide management personnel with a bridge structure health status visualization report and abnormal early warning information.

7. The bridge structure monitoring and maintenance system based on multi-source sensing according to claim 6, characterized in that, A wireless communication module for signal data transmission is provided between the edge computing node and the cloud server, and between the cloud server and the monitoring data visualization module.

8. The bridge structure monitoring and maintenance system based on multi-source sensing according to claim 1, characterized in that, The maintenance execution device is a maintenance and repair robot.

9. The bridge structure monitoring and maintenance system based on multi-source sensing according to claim 1, characterized in that, The maintenance operations performed by the maintenance execution device include crack grouting, bolt tightening, or surface cleaning.

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