Research on the Large Data Intelligent Classification Method for Long-Term Health Monitoring of Bridge

Abstract

In order to improve the intelligent management and information scheduling ability of bridge long-term health monitoring, the real-time data monitoring and automatic collection design of bridge long-term health monitoring are carried out with big data analysis method. A classification method of bridge long-term health monitoring data based on fuzzy correlation feature detection and grid area clustering is proposed. The information fusion and fuzzy chromatography analysis method are used to realize the information fusion of the real-time data of bridge long-term health monitoring, and the adaptive feature extraction of related data is carried out. Excavate the positive correlation characteristic quantity of bridge long-term health monitoring real-time monitoring data flow, carry on the fuzzy clustering and information prediction of bridge long-term health monitoring data flow, and improve the accuracy of bridge long-term health monitoring real-time data monitoring. The simulation results show that the intelligent classification of bridge long-term health monitoring based on this method has high accuracy and low error rate, which improves the real-time performance of bridge monitoring.