Research On Data Correction And Compression Storage Based On Bridge Monitoring System

The bridge monitoring system can effectively guarantee the safety and integrity of the service bridge.On the one hand,due to the effects of device aging and environment,some sensors have data deviations such as zero drift and jitter.On the other hand,the large number of monitoring sensors on large span bridges leads to massive data.Therefore,efficient monitoring data correction and compression storage have important practical significance for the field of bridge monitoring.Based on the monitoring system project of Ganjiang Super Large Bridge,thesis analyzes the methods of data correction and storage.The main work includes:(1)Aiming at the phenomenon of zero drift in the monitoring data of some acceleration sensors,a joint algorithm of Polynomial Fitting and Morphological Filtering(PF-MF)is proposed.For the high frequency sampling characteristics of acceleration,the algorithm can effectively reduce the zero drift in a short time and improve the accuracy of signal acquisition.(2)In the traditional health monitoring technology,the sensors have difficulties in installation and wiring,and affect the bridge structure.The system in this paper collects track displacement data through image processing technology.Due to the influence of bridge structure vibration,high definition camera shake and other factors,a weighted wavelet threshold algorithm is proposed for noise reduction,which can effectively improve the accuracy of track displacement data collection.(3)In view of the problem of large capacity storage of monitoring signals in long span bridge monitoring systems,this paper uses compressed sensing theory for data compression storage,and proposes to use QR Decomposition combined with Singular Value Decomposition algorithm(QR-SVD)to optimize the measurement matrix.At the same time,Double Threshold Stagewise OMP(DTStOMP)is used for signal reconstruction,which effectively solves the problem of excessive storage space occupied by large amounts of data.The data correction and compression storage methods designed in this paper have been integrated into the bridge health monitoring system software platform based on the MVC architecture.The system runs well and provides effective support for the stable operation of the bridge.