CN-122016800-A - Intelligent monitoring and evaluating system and method for unmanned aerial vehicle inspection bridge

CN122016800ACN 122016800 ACN122016800 ACN 122016800ACN-122016800-A

Abstract

The invention discloses an intelligent monitoring and evaluating system and method for an unmanned aerial vehicle inspection bridge, comprising a multi-source sensor data acquisition module, a data preprocessing and synchronous calibration module, a bridge structural feature extraction and defect identification module, a defect parameter quantification and risk evaluation module, an inspection path self-adaptive planning module, a real-time communication and data transmission module and a historical data fusion and trend prediction module; the invention integrates vision, infrared, laser and inertia measurement multisource sensors, not only captures the surface defects of the structure, but also can identify the internal hidden defects, effectively reduces the data deviation by combining the data preprocessing and synchronous calibration technology, solves the problems of insufficient detection dimension and low precision of the traditional single sensor, realizes the extraction of structural features, the identification and the classification of defects by adopting a deep learning algorithm, quantifies the severity of the defects by adopting a standardized formula, avoids subjectivity and false detection omission of manual interpretation, and improves the objectivity and reliability of an evaluation result.

Inventors

LIN CHENHUAN
LI JIANHUI
TANG SHENGLONG

Assignees

南京林业大学

Dates

Publication Date: 20260512
Application Date: 20260114

Claims (10)

1. The intelligent monitoring and evaluating system for the unmanned aerial vehicle inspection bridge is characterized by comprising a multi-source sensor data acquisition module, a data preprocessing and synchronous calibration module, a bridge structural feature extraction and defect identification module, a defect parameter quantification and risk evaluation module, an inspection path self-adaptive planning module, a real-time communication and data transmission module and a historical data fusion and trend prediction module; The multi-source sensor data acquisition module is used for acquiring the surface and internal characteristic data of the bridge structure and comprises a vision acquisition unit, an infrared thermal imaging unit, a laser ranging unit and an inertia measurement unit; The data preprocessing and synchronous calibration module is used for carrying out noise reduction, registration and synchronous processing on the original data acquired by the multi-source sensor and comprises a data noise reduction unit, a space-time synchronous calibration unit and a data format standardization unit; The bridge structural feature extraction and defect identification module is used for extracting key features of a bridge structure and identifying various structural defects and comprises a structural feature extraction unit, a defect candidate region detection unit and a defect type confirmation unit; The defect parameter quantification and risk assessment module is used for calculating defect key parameters and assessing the structural safety influence degree and comprises a defect parameter measurement unit, a defect severity assessment unit and a structural risk level judgment unit; The inspection path self-adaptive planning module is used for dynamically planning an optimal inspection path and comprises a bridge structure modeling unit, an initial path generating unit, a path dynamic adjusting unit and an obstacle avoidance path planning unit; The real-time communication and data transmission module is used for realizing data interaction and instruction transmission between the unmanned aerial vehicle and the ground control center and comprises a data uplink transmission unit, an instruction downlink transmission unit and a data cache unit; The historical data fusion and trend prediction module is used for integrating historical inspection data and analyzing the structural performance attenuation trend and comprises a historical data association unit, a structural performance attenuation modeling unit and a maintenance suggestion generation unit.
2. The intelligent monitoring and evaluating system for the unmanned aerial vehicle inspection bridge is characterized in that a vision acquisition unit of the multi-source sensor data acquisition module consists of a high-definition RGB camera and a long-focus macro lens, an infrared thermal imaging unit adopts a non-refrigeration infrared focal plane detector, a laser ranging unit is based on a pulse laser ranging principle, and an inertial measurement unit integrates a triaxial accelerometer, a triaxial gyroscope and a triaxial magnetometer.
3. The intelligent monitoring and evaluating system for the unmanned aerial vehicle inspection bridge is characterized in that a space-time synchronization calibration unit of the data preprocessing and synchronization calibration module is used for realizing time synchronization of multi-source data by taking a GPS time stamp of a unmanned aerial vehicle flight control system as a reference and a time stamp alignment algorithm, and a coordinate conversion model is established based on unmanned aerial vehicle gesture data and sensor installation parameters to realize space synchronization of the multi-source data.
4. The intelligent monitoring and evaluating system for the unmanned aerial vehicle inspection bridge is characterized in that a structural feature extraction unit of the bridge structural feature extraction and defect identification module adopts an improved U-Net semantic segmentation model to segment key structural parts of the bridge, a defect candidate region detection unit adopts YOLOv target detection algorithm and temperature threshold segmentation algorithm to locate defect candidate regions, and a defect type confirmation unit adopts MobileNetV classification network to achieve defect type accurate classification.
5. The intelligent monitoring and evaluating system for the unmanned aerial vehicle inspection bridge is characterized in that the defect severity evaluating unit of the defect parameter quantification and risk evaluation module calculates a defect severity coefficient S through a weighted summation formula, and the formula is as follows: Wherein ω 1 、ω 2 、ω 3 、ω 4 is a weight and the sum is 1, d is a defect actual key parameter, d 0 is a defect limit allowable parameter, s is a defect actual area, s 0 is a structural component reference area, l is a defect actual length, l 0 is a structural component reference length, and e is an environmental impact coefficient.
6. The intelligent monitoring and evaluating system for the unmanned aerial vehicle inspection bridge is characterized in that an initial path generating unit of the inspection path self-adaptive planning module adopts an improved A algorithm to plan an initial inspection path, a path dynamic adjusting unit corrects the inspection path according to a newly identified high-risk defect area or structural form deviation, and an obstacle avoidance path planning unit adopts an RRT algorithm to generate a local obstacle avoidance path.
7. The intelligent monitoring and evaluating system for the unmanned aerial vehicle inspection bridge is characterized in that a structural performance attenuation modeling unit of the historical data fusion and trend prediction module adopts an exponential attenuation model to establish a structural performance prediction formula, and the formula is as follows: P(t)=P 0 ·e -k·t +ε Wherein P (t) is the structural performance index at the predicted time t, P 0 is the initial service state performance index, k is the performance attenuation coefficient, t is the service time, and epsilon is the error correction term.
8. The intelligent monitoring and evaluating system for the unmanned aerial vehicle inspection bridge is characterized in that the real-time communication and data transmission module adopts a 5G and WiFi6 dual-mode communication mode, the data uplink transmission unit adopts a block compression transmission strategy for large-capacity image data, and the data caching unit temporarily stores data when communication is interrupted and supplements transmission after communication is recovered.
9. The intelligent monitoring and evaluating system for the unmanned aerial vehicle inspection bridge is characterized in that the structural risk level judging unit of the defect parameter quantification and risk evaluation module divides low, medium and high three-level risks according to the defect severity coefficient S, and determines the structural risk level of the whole bridge and each part by combining the importance weights of the structural parts.
10. The evaluation method of the intelligent monitoring and evaluation system for the unmanned aerial vehicle inspection bridge is characterized by comprising the following steps of: step 1, system initialization and task configuration, wherein a ground control center inputs inspection parameters, and an unmanned aerial vehicle completes module self-inspection and calibration; step 2, multi-source data acquisition and preprocessing, wherein the unmanned aerial vehicle flies according to an initial path, and the multi-source sensor acquires data and processes the data through a preprocessing module; Step 3, extracting structural features and identifying defects, extracting bridge structural features and accurately identifying defect types; Step 4, defect quantification and risk assessment, namely calculating defect parameters and severity coefficients, and judging risk levels; step 5, dynamically adjusting and avoiding the obstacle in the inspection path, and dynamically optimizing the inspection path according to the defect distribution and the obstacle; Step 6, data transmission and historical data fusion, wherein the data is transmitted to a ground control center and is associated with the historical data; and 7, generating maintenance advice and system reset, and generating maintenance decision report and return reset of the unmanned aerial vehicle.

Description

Intelligent monitoring and evaluating system and method for unmanned aerial vehicle inspection bridge Technical Field The invention relates to the technical field of bridge structure monitoring, in particular to an intelligent monitoring and evaluating system and method for an unmanned aerial vehicle inspection bridge. Background The bridge is used as a core component of the traffic infrastructure, and the structural health state of the bridge is directly related to traffic safety and transportation efficiency. Along with the increase of service life, the accumulation of load action and the influence of environmental erosion, structural defects such as cracks, rust, flaking, deformation and the like of the bridge are easy to occur, and if the structural defects are not detected and evaluated in time, the structural failure risk can be caused. The existing bridge inspection and monitoring technology has various bottlenecks that the traditional manual inspection relies on operators to climb or set up scaffolds at high altitude, labor intensity is high, operation efficiency is low, safety risks are extremely high when large-span bridges and high-altitude structural parts are operated, the existing unmanned aerial vehicle inspection is mostly provided with a single vision sensor, data dimension is single, internal defects of a structure are difficult to comprehensively capture, defect identification is mostly dependent on manual interpretation, subjectivity is high, false inspection rate is high, standardized quantitative analysis means are lacked, inspection paths are mostly preset fixed routes, inspection blind areas cannot be dynamically adjusted according to actual structural forms, defect distribution and environmental changes of the bridges, defect positioning and parameter measurement accuracy are affected due to existence time and space synchronization deviation of the multi-source sensors, and the existing system can only realize single defect detection, lack of fusion analysis and structure performance attenuation trend prediction of historical data and cannot provide forward decision support for bridge preventive maintenance. Disclosure of Invention The invention aims to provide an intelligent monitoring and evaluating system and method for an unmanned aerial vehicle inspection bridge, which are used for solving the problems in the prior art. In order to achieve the purpose, the intelligent monitoring and evaluating system for the unmanned aerial vehicle inspection bridge comprises a multi-source sensor data acquisition module, a data preprocessing and synchronous calibration module, a bridge structural feature extraction and defect identification module, a defect parameter quantification and risk evaluation module, an inspection path self-adaptive planning module, a real-time communication and data transmission module and a historical data fusion and trend prediction module; The multi-source sensor data acquisition module is used for acquiring the surface and internal characteristic data of the bridge structure and comprises a vision acquisition unit, an infrared thermal imaging unit, a laser ranging unit and an inertia measurement unit; The data preprocessing and synchronous calibration module is used for carrying out noise reduction, registration and synchronous processing on the original data acquired by the multi-source sensor and comprises a data noise reduction unit, a space-time synchronous calibration unit and a data format standardization unit; The bridge structural feature extraction and defect identification module is used for extracting key features of a bridge structure and identifying various structural defects and comprises a structural feature extraction unit, a defect candidate region detection unit and a defect type confirmation unit; The defect parameter quantification and risk assessment module is used for calculating defect key parameters and assessing the structural safety influence degree and comprises a defect parameter measurement unit, a defect severity assessment unit and a structural risk level judgment unit; The inspection path self-adaptive planning module is used for dynamically planning an optimal inspection path and comprises a bridge structure modeling unit, an initial path generating unit, a path dynamic adjusting unit and an obstacle avoidance path planning unit; The real-time communication and data transmission module is used for realizing data interaction and instruction transmission between the unmanned aerial vehicle and the ground control center and comprises a data uplink transmission unit, an instruction downlink transmission unit and a data cache unit; The historical data fusion and trend prediction module is used for integrating historical inspection data and analyzing the structural performance attenuation trend and comprises a historical data association unit, a structural performance attenuation modeling unit and a maintenance suggestion generation uni