Intelligent detection method for bridge structure damage

Abstract

The invention relates to the technical field of bridge structure detection, and discloses a bridge structure damage intelligent detection method comprising the following steps: S1, multi-modal data fusion and lightweight processing, including integration of heterogeneous sensor data (acceleration, displacement, visual images and the like), and reduction of calculation load through algorithm compression and feature extraction; and S2, high-precision displacement capture is realized by using a non-contact measurement (vision and unmanned aerial vehicle) and embedded sensor fused distributed monitoring architecture, and the influence of environmental noise is overcome. According to the invention, through multi-modal data fusion and lightweight processing, including integration of heterogeneous sensor data (acceleration, displacement, visual images and the like), algorithm compression and feature extraction, calculation load is reduced, and a non-contact measurement (visual and unmanned aerial vehicle) and embedded sensor fused distributed monitoring architecture is utilized to complete signal preprocessing and feature extraction, so that the system is more accurate and reliable. Therefore, the method has the advantages of suppressing environmental noise interference, realizing intelligent fusion of multi-source data and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge structure detection, in particular to an intelligent detection method for bridge structure damage. BACKGROUND

[0002] With the growth of transportation demand, the bridge structure is subjected to vehicle load, environmental erosion and material aging for a long time, and the performance degradation caused by damage accumulation has become a major safety hazard.

[0003] The traditional detection method has low efficiency, strong subjectivity and cannot realize real-time monitoring. The manual detection has low efficiency: it depends on visual inspection or local instrument measurement (such as hammering method, ultrasonic flaw detection), needs to close traffic, has high cost and is difficult to cover hidden damage; although the damage identification algorithm based on vibration response signal (such as frequency analysis method) is widely used, the environmental noise interference (such as wind load, temperature change) and strong vibration during train passing will significantly reduce the modal parameter identification accuracy; the data collected by the current sensor network is seriously discretized, and there is lack of effective feature extraction and fusion mechanism, so it is difficult to establish quantitative correlation between damage and response. SUMMARY

[0004] Technical problem to be solved In view of the defects of the prior art, the present application provides an intelligent detection method for bridge structure damage, which has the advantages of suppressing environmental noise interference and realizing intelligent fusion of multi-source data, and solves the above problems.

[0005] (II) Technical scheme In order to achieve the above purpose, the present application provides the following technical scheme: an intelligent detection method for bridge structure damage, comprising the following steps: S1, multi-modal data fusion and light processing, including integrating heterogeneous sensor data (acceleration, displacement, visual image, etc.), reducing calculation load through algorithm compression and feature extraction; S2, using a distributed monitoring architecture of non-contact measurement (vision, unmanned aerial vehicle) and embedded sensor fusion to realize high-precision displacement capture and overcome environmental noise influence; S3, deploying a light AI model in the detection terminal to complete signal preprocessing and feature extraction, realizing real-time response of damage positioning and degree evaluation, and designing a self-adaptive threshold light classifier; S4, integrating a cross-platform collaborative detection system of unmanned aerial vehicle, robot and fixed sensor network to construct a multi-scale detection closed loop; S5, scanning the suspicious area through the unmanned aerial vehicle, arranging the robot to approach for detailed inspection, ensuring that the data of multiple devices are tamper-proof in the blockchain, and using an interpretable AI damage evaluation engine to make decision diagnosis.

[0006] Preferably, the S1 integrates heterogeneous sensor data (accelerometer, displacement meter, visual image, etc.), correlates complementary information through algorithms, improves damage identification accuracy, and the heterogeneity of the data: the acceleration sensor captures high-frequency vibration details, the displacement sensor (such as laser / optical) monitors low-frequency deformation, and the visual image locates surface cracks. The CNN+bidirectional GRU model is used to fuse vibration and strain data, improve recognition accuracy, and reduce hardware deployment costs and energy consumption through low-power sensors, wireless transmission, and edge computing. The use of compression sensing and feature extraction technology reduces the computational load and improves the efficiency of damage identification.

[0007] Preferably, the S2 uses a monocular vision + time sequence optical flow algorithm, directly generates a deflection sequence through motion blur correction (avoids multi-exposure hardware dependence), uses a spatio-temporal graph convolution network (ST-GCN) to filter wind and rain vibration noise, and uses a laser Doppler vibration radar + distributed acoustic sensing (DAS) to achieve 0.01mm-level displacement monitoring at a distance of 10m. The unmanned aerial vehicle multi-modal perception system uses laser scanning (UAV-LiDAR) to obtain a millimeter-level precision bridge three-dimensional point cloud model, real-time monitor structure deformation and settlement; infrared thermal imaging: detects concrete cracks, internal cavities and other damages, and locates potential defects through temperature anomalies; high-definition visual sensing: combined with AI image recognition (such as YOLO model), automatically identifies surface cracks, corrosion and other damages, deploys high-speed cameras for multi-angle displacement tracking, uses digital image correlation method (DIC) to realize sub-pixel level displacement measurement, and the precision reaches 0.01mm. Embedded sensors include fiber Bragg grating sensors (FBG): embedded in key parts of the bridge, real-time monitor strain and temperature changes, and resist electromagnetic interference; piezoelectric film sensors: capture high-frequency vibration signals and identify micro-crack expansion; non-contact TEV sensors: detect insulation deterioration through electromagnetic coupling without direct contact with equipment; embedded edge computing unit performs Kalman filtering in real time to suppress environmental interference such as temperature and wind load.

[0008] Preferably, the adaptive threshold lightweight classifier in S3 includes: front-end: binary decision tree rapid screening of abnormalities (<1 ms response); back-end: incremental learning CNN dynamically updates damage threshold (avoids fixed 5% threshold), introduces reinforcement learning reward mechanism, automatically optimizes threshold according to historical data, deploys AI models with small volume and low calculation (such as MobileNet, TensorFlowLite version) in bridge detection terminals (such as sensor nodes, drones, lightweight monitoring equipment), realizes local data processing, avoids dependence on cloud transmission, traditional cloud processing needs to transmit a large amount of raw data (such as vibration signals, images), resulting in high delay and high energy consumption, edge deployment can move the data processing link forward, reducing transmission costs by more than 90%, simplifying the processing of raw sensor signals (such as vibration, strain, acoustic emission) at the terminal, extracting key features (rather than transmitting raw data), reducing the calculation burden, preferentially extracting statistical features (such as mean, variance, kurtosis) and simplified time-frequency features (such as the main frequency peak value of fast Fourier transform (FFT)), rather than complex time-frequency analysis, avoiding the use of complex signal decomposition algorithms (such as empirical mode decomposition (EEMD)), and instead using moving average, low-pass filtering and other lightweight methods, for image-type damage (such as cracks, corrosion), using ShuffleNet (90% lower power requirement than AlexNet) to process high-definition images taken by drones in real time, realizing crack positioning.

[0009] Preferably, the S4 deploys accelerometers, strain gauges and other sensors to collect bridge dynamic response signals under environmental excitation (wind, traffic load), uses the DATA-SSI algorithm (data-driven random subspace identification) to extract natural frequencies by reorganizing signals, drones carry high-definition / infrared cameras, LiDAR, realize non-contact detection of bridge surface damage, multi-sensor fusion: visible light identifies cracks (accuracy 0.1 mm) + infrared thermal imaging detects internal voids; magnetic / track-type robots carry ultrasonic probes, corrosion sensors to detect internal defects of piers and box girders; AI hub integrates multi-source data to realize "monitoring-analysis-repair verification" closed loop.

[0010] Preferably, the wide-area scanning of the unmanned aerial vehicle in S5 includes high-precision sensor fusion: laser radar (precision up to 2mm point cloud), multispectral camera (identify rust), infrared thermal imaging (detect internal hollowing), and 4K high-definition lens to realize multi-dimensional data acquisition; adaptive cruise algorithm: based on Beidou / GNSS positioning, automatically plan the scanning path of high-risk areas (such as bridge piers, stay cables); suspicious area positioning: AI pre-screening cracks (identification accuracy 0.1mm), rust and other diseases, generate a three-dimensional heat map to mark high-risk coordinates; magnetic wall-climbing robot: four-wheel drive permanent magnet design, can climb 80-300mm diameter stay cables, cross obstacles such as sheath damage; underwater detection unmanned aerial vehicle: equipped with sonar and waterproof camera, high-definition imaging of bridge pier foundation scouring and exposed steel bars, replacing high-risk frogman operations; micro-damage sampling module: mechanical arm cooperates with ultrasonic flaw detector to quantitatively detect crack depth; blockchain storage includes: multi-source data chaining: unmanned aerial vehicle / robot original image, point cloud data, AI analysis report real-time hash value, written into the alliance chain; cross-institutional collaboration: maintenance units and supervisory agencies share distributed ledgers, any data tampering will be rejected by the consensus mechanism, ensuring the legal effectiveness of the detection report, multi-modal fusion analysis generates a digital report containing risk levels (such as I-IV levels) and repair recommendations, and synchronously pushes the maintenance system.

[0011] Compared with the prior art, the intelligent detection method for bridge structure damage provided by the present application has the following beneficial effects: 1、The present application realizes high-precision displacement capture, overcomes environmental noise influence, and through the deployment of a lightweight AI model in the detection terminal, completes signal preprocessing and feature extraction, realizes real-time response of damage positioning and degree evaluation, so that the method has the advantages of suppressing environmental noise interference and realizing intelligent fusion of multi-source data.

[0012] 2、The present application detects internal voids by visible light recognition cracks + infrared thermal imaging, and detects internal defects of bridge piers and box girders by magnetic attraction / track-type robots carrying ultrasonic probes and rust sensors; the AI hub integrates multi-source data to realize a "monitoring-analysis-repair verification" closed loop.

[0013] 3、The present application realizes multi-source data chaining: unmanned aerial vehicle / robot original image, point cloud data, AI analysis report real-time hash value, written into the alliance chain; cross-institutional collaboration: maintenance units and supervisory agencies share distributed ledgers, any data tampering will be rejected by the consensus mechanism, multi-modal fusion analysis generates a digital report containing risk levels, repair recommendations, and synchronously pushes the maintenance system. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 A schematic diagram of the method steps of the present application. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0016] Please refer to Figure 1 An intelligent detection method for bridge structure damage, comprising the following steps: S1, multi-modal data fusion and lightweight processing, including integrating heterogeneous sensor data (accelerometer, displacement, visual image, etc.), reducing computational load through algorithm compression and feature extraction; S2, using a distributed monitoring architecture of non-contact measurement (vision, unmanned aerial vehicle) and embedded sensor fusion to achieve high-precision displacement capture and overcome environmental noise influence; S3, deploying a lightweight AI model on a detection terminal to complete signal preprocessing and feature extraction, realizing real-time response of damage positioning and degree evaluation, and designing an adaptive threshold lightweight classifier; S4, integrating a cross-platform collaborative detection system of unmanned aerial vehicle, robot, and fixed sensor network to build a multi-scale detection closed loop; S5, scanning suspicious areas through an unmanned aerial vehicle, arranging a robot to approach for detailed inspection, ensuring that multi-device data is tamper-proof in a blockchain, and using an interpretable AI damage evaluation engine to make decision diagnosis.

[0017] In S1, heterogeneous sensor data (accelerometer, displacement meter, visual image, etc.) are integrated, complementary information is associated through algorithms, damage recognition accuracy is improved, and the complementary nature of heterogeneous data is that an acceleration sensor captures high-frequency vibration details, a displacement sensor (such as laser / optical) monitors low-frequency deformation, and a visual image locates surface cracks. A CNN+bidirectional GRU model is used to fuse vibration and strain data to improve recognition accuracy. Lightweight data acquisition and transmission are achieved through low-power sensors, wireless transmission, and edge computing to reduce hardware deployment cost and energy consumption. Compression sensing and feature extraction technology are used to reduce computational load and improve damage recognition efficiency.

[0018] In S2, the monocular vision + time sequence optical flow algorithm is adopted, the deflection sequence is directly generated through motion blur correction (avoiding multi-exposure hardware dependence), the space-time graph convolution network (ST-GCN) is used to filter wind and rain vibration noise, the laser Doppler vibration radar + distributed acoustic sensing (DAS) is used to realize 0.01mm level displacement monitoring at a distance of 10m, the unmanned aerial vehicle multi-modal perception system obtains millimeter level precision bridge three-dimensional point cloud model through laser scanning (UAV-LiDAR): real-time monitoring of structure deformation and settlement; infrared thermal imaging: detects concrete cracks, internal cavities and other damages, and locates potential defects through temperature anomalies; high-definition visual sensing: combined with AI image recognition (such as YOLO model), automatically identifies surface cracks, corrosion and other damages, deploys high-speed cameras for multi-angle displacement tracking, uses digital image correlation method (DIC) to realize sub-pixel level displacement measurement, the precision reaches 0.01mm, embedded sensors include fiber Bragg grating sensors (FBG): embedded in key parts of the bridge, real-time monitoring of strain and temperature change, anti-electromagnetic interference; piezoelectric film sensor: captures high-frequency vibration signal, identifies micro-crack expansion; non-contact TEV sensor: detects insulation deterioration through electromagnetic coupling, without direct contact with the equipment, embedded edge computing unit performs Kalman filtering in real time, suppresses temperature, wind load and other environmental interference, and pixel displacement is obtained by integrating optical flow , combined with camera intrinsic K (focal length ƒ, pixel equivalent κ) and distance Z, converted into actual deflection D: .

[0019] In S3, the adaptive threshold lightweight classifier includes: front end: binary decision tree for rapid screening of abnormalities (<1ms response); back end: incremental learning CNN dynamically updates damage threshold (avoids fixed 5% threshold), decision tree splitting based on information gain: Where S is the dataset, A is the feature, T is the split subset, H(S) is the entropy, and the reinforcement learning reward mechanism is introduced to automatically optimize the threshold value based on historical data. Small volume and low computational AI models such as MobileNet and TensorFlow Lite are deployed on bridge detection terminals such as sensor nodes, drones, and lightweight monitoring equipment to achieve local data processing and avoid relying on cloud transmission. Traditional cloud processing requires the transmission of large amounts of raw data such as vibration signals and images, resulting in high latency and high energy consumption. Edge deployment can move the data processing link forward and reduce transmission costs by more than 90%. The terminal simplifies the processing of raw sensor signals such as vibration, strain, and acoustic emission, extracts key features (rather than raw data), reduces computational burden, and prioritizes statistical features (such as mean, variance, and kurtosis) and simplified time-frequency features (such as the main frequency peak of the Fast Fourier Transform (FFT)) rather than complex time-frequency analysis. Avoid using complex signal decomposition algorithms such as Empirical Mode Decomposition (EEMD) and use lightweight methods such as moving average and low-pass filtering. For image-based damage such as cracks and corrosion, ShuffleNet (which requires 90% less computing power than AlexNet) is used to process high-definition images taken by drones in real time, enabling crack positioning.

[0020] S4 Deploy accelerometers, strain gauges, and other sensors to collect bridge dynamic response signals under environmental excitation (wind, traffic load). Use the DATA-SSI algorithm (Data-Driven Stochastic Subspace Identification) to extract natural frequencies by reorganizing signals. Drones equipped with high-definition / infrared cameras and LiDAR enable non-contact detection of bridge surface damage. Multi-sensor fusion: visible light identifies cracks (accuracy 0.1 mm) + infrared thermal imaging detects internal voids; Magnetic / track-type robots carry ultrasonic probes and corrosion sensors to detect internal defects in bridge piers and box girders; AI hub integrates multi-source data to achieve a "monitoring-analysis-repair verification" closed loop.

[0021] The wide-area scanning of the unmanned aerial vehicle in S5 includes high-precision sensor fusion: the unmanned aerial vehicle is equipped with a laser radar (with a precision of 2 mm point cloud), a multispectral camera (capable of identifying rust), an infrared thermal imager (capable of detecting internal hollowing), and a 4K high-definition lens, thereby achieving multi-dimensional data acquisition; an adaptive cruise algorithm: based on Beidou / GNSS positioning, the scanning path of high-risk areas (such as bridge piers and stay cables) is automatically planned; suspicious area positioning: AI pre-screens cracks (with an identification accuracy of 0.1 mm) and diseases such as rust, and generates a three-dimensional heat map to mark high-risk coordinates; a magnetic wall-climbing robot: a four-wheel drive permanent magnet design that can climb 80-300 mm diameter stay cables and cross obstacles such as damaged sheaths; an underwater detection unmanned aerial vehicle: equipped with a sonar and a waterproof camera, capable of high-definition imaging of bridge pier foundation scouring and exposed steel bars, thereby replacing high-risk frogman operations; a micro-damage sampling module: a mechanical arm cooperates with an ultrasonic flaw detector to quantitatively detect the depth of cracks; blockchain storage includes: multi-source data chaining: real-time generation of hash values for original images, point cloud data, and AI analysis reports of the unmanned aerial vehicle / robot, and writing into a consortium chain; cross-institutional collaboration: maintenance units and supervisory agencies share a distributed ledger, any data tampering will be rejected by the consensus mechanism, ensuring the legal effectiveness of the detection report, multi-modal fusion analysis generates a digital report containing risk levels (such as levels I-IV) and repair recommendations, and synchronously pushes the report to the maintenance system.

[0022] In use, the heterogeneous sensor data (acceleration, displacement, visual images, etc.) is integrated, the computational load is reduced through algorithm compression and feature extraction, a distributed monitoring architecture is used that fuses non-contact measurements (vision, unmanned aerial vehicle) and embedded sensors to achieve high-precision displacement capture and overcome environmental noise, a lightweight AI model is deployed at the detection terminal to complete signal preprocessing and feature extraction, real-time response for damage localization and degree assessment is achieved, an adaptive threshold lightweight classifier is designed: front end: binary decision tree for rapid screening of abnormalities (<1 ms response); back end: incremental learning CNN dynamically updates damage thresholds, a cross-platform collaborative detection system is integrated that uses unmanned aerial vehicles, robots, and fixed sensor networks, a multi-scale detection closed loop is constructed, suspicious areas are scanned by the unmanned aerial vehicle, robots are arranged for close inspection, and the data of multiple devices is ensured to be tamper-proof in the blockchain, and an interpretable AI damage assessment engine is used to make a decision diagnosis.

[0023] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An intelligent detection method for bridge structural damage, characterized in that: Includes the following steps: S1. Multimodal data fusion and lightweight processing, including integrating heterogeneous sensor data (acceleration, displacement, visual images, etc.) and reducing computational load through algorithm compression and feature extraction; S2. A distributed monitoring architecture that integrates non-contact measurement (vision, drone) with embedded sensors is used to achieve high-precision displacement capture and overcome the influence of environmental noise. S3. By deploying a lightweight AI model at the detection terminal, signal preprocessing and feature extraction are completed, enabling real-time response for damage localization and severity assessment. An adaptive threshold lightweight classifier is designed. S4 is a cross-platform collaborative detection system that integrates drones, robots, and fixed sensor networks to build a multi-scale detection closed loop. S5. It uses drones to scan suspicious areas over a wide area, arranges robots to conduct close-up inspections, and ensures that data from multiple devices is immutable in the blockchain, integrating an interpretable AI-powered damage assessment engine for decision diagnosis.

2. The intelligent detection method for bridge structural damage according to claim 1, characterized in that: The S1 integrates heterogeneous sensor data (accelerometers, displacement gauges, visual images, etc.), and improves the accuracy of damage identification by associating complementary information through algorithms. The heterogeneous data complementarity is achieved by: accelerometers capturing high-frequency vibration details, displacement sensors (such as lasers / optics) monitoring low-frequency deformation, and visual images locating surface cracks. A CNN+bidirectional GRU model is used to fuse vibration and strain data to improve the recognition accuracy. Lightweight data acquisition and transmission are achieved through low-power sensors, wireless transmission, and edge computing to reduce hardware deployment costs and energy consumption. Compressed sensing and feature extraction technologies are used to reduce computational load and improve damage identification efficiency.

3. The intelligent detection method for bridge structural damage according to claim 1, characterized in that: The S2 scheme employs a monocular vision + temporal optical flow algorithm, directly generating deflection sequences through motion blur correction. It utilizes a spatiotemporal graph convolutional network (ST-GCN) to filter wind and rain vibration noise. Through a laser Doppler vibration radar and distributed fiber optic acoustic sensing (DAS), it achieves displacement monitoring at a distance of 10m down to the 0.01mm level. The UAV multimodal perception system uses laser scanning (UAV-LiDAR) to acquire millimeter-precision 3D point cloud models of the bridge for real-time monitoring of structural deformation and settlement; infrared thermal imaging to detect damage such as concrete cracks and internal voids, locating potential defects through temperature anomalies; and high-definition visual sensing combined with AI image processing. The system automatically identifies surface cracks, corrosion, and other damage using features such as YOLO models. High-speed cameras are deployed for multi-angle displacement tracking, and digital image correlation (DIC) is employed to achieve sub-pixel-level displacement measurement with an accuracy of 0.01 mm. Embedded sensors include fiber optic grating (FBG) sensors embedded in key bridge components to monitor strain and temperature changes in real time and resist electromagnetic interference; piezoelectric thin-film sensors to capture high-frequency vibration signals and identify micro-crack propagation; and non-contact TEV sensors that detect insulation degradation through electromagnetic coupling without direct contact with the equipment. An embedded edge computing unit performs real-time Kalman filtering to suppress environmental interference from temperature, wind load, and other factors.

4. The intelligent detection method for bridge structural damage according to claim 1, characterized in that: The adaptive threshold lightweight classifier in S3 includes: a front-end: a binary decision tree for rapid anomaly screening (<1ms response); and a back-end: an incremental learning CNN for dynamically updating the damage threshold (avoiding a fixed 5% threshold), introducing a reinforcement learning reward mechanism to automatically optimize the threshold based on historical data, and utilizing small-sized, low-computation AI models (such as MobileNet and TensorFlow). The Lite version is deployed on bridge inspection terminals (such as sensor nodes, drones, and lightweight monitoring equipment) to achieve local data processing, avoiding reliance on cloud transmission. Traditional cloud processing requires the transmission of large amounts of raw data (such as vibration signals and images), resulting in high latency and high energy consumption. Edge deployment can move the data processing step forward, reducing transmission costs by more than 90%. The raw sensor signals (such as vibration, strain, and acoustic emission) are simplified at the terminal to extract key features (instead of transmitting raw data), reducing the computational burden. Statistical features (such as mean, variance, and kurtosis) and simplified time-frequency features (such as the main frequency peaks of the Fast Fourier Transform (FFT)) are extracted first, rather than complex time-frequency analysis. Complex signal decomposition algorithms (such as Empirical Mode Decomposition (EEMD)) are avoided, and lightweight methods such as moving average and low-pass filtering are used instead. For image-based damage (such as cracks and corrosion), ShuffleNet (which requires 90% less computing power than AlexNet) is used to process high-definition images captured by drones in real time to achieve crack localization.

5. The intelligent detection method for bridge structural damage according to claim 1, characterized in that: The S4 system deploys sensors such as accelerometers and strain gauges to collect bridge dynamic response signals under environmental excitations (wind, traffic loads). It employs the DATA-SSI algorithm (data-driven random subspace recognition) to extract natural frequencies by recombining signals. The UAV, equipped with a high-definition / infrared camera and LiDAR, enables non-contact detection of bridge surface damage. Multi-sensor fusion is used: visible light is used to identify cracks (accuracy 0.1mm) + infrared thermal imaging is used to detect internal voids; magnetic / tracked robots carry ultrasonic probes and corrosion sensors to detect internal defects in piers and box girders; and the AI ​​hub integrates multi-source data to achieve a closed loop of "monitoring-analysis-repair verification".

6. The intelligent detection method for bridge structural damage according to claim 1, characterized in that: The S5's wide-area scanning of unmanned aerial vehicles includes high-precision sensor fusion: equipped with lidar (accuracy up to 2mm point cloud), multispectral camera (for identifying corrosion), infrared thermal imaging (for detecting internal hollow areas), and 4K high-definition lens, to achieve multi-dimensional data acquisition; adaptive cruise algorithm: based on BeiDou / GNSS positioning, automatically plans scanning paths for high-risk areas (such as bridge piers and cable stays); suspicious area positioning: AI pre-screens cracks (identification accuracy 0.1mm), corrosion and other defects, generating a 3D heat map to mark high-risk coordinates; magnetic adsorption wall-climbing robot: four-wheel drive permanent magnet design, capable of climbing cable stays with a diameter of 80-300mm and crossing obstacles such as damaged sheaths; Underwater inspection drones: Equipped with sonar and waterproof cameras, they provide high-definition imaging of bridge pier foundation erosion and exposed steel bars, replacing high-risk frogman operations; Minimally damaged sampling module: A robotic arm, in conjunction with an ultrasonic flaw detector, quantitatively detects the depth of cracks; Blockchain evidence storage includes: Multi-source data on-chain: Real-time generation of hash values ​​from original drone / robot images, point cloud data, and AI analysis reports, which are then written to the consortium blockchain; Cross-agency collaboration: Maintenance units and regulatory agencies share a distributed ledger, and any data tampering will be rejected by the consensus mechanism to ensure the legal validity of the inspection report. Multimodal fusion analysis generates a digital report that includes risk level (such as I-IV) and maintenance recommendations, which is then pushed to the maintenance system simultaneously.

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