Data Fault Identification and Repair Method of Traffic Detector

The quality control and evaluation of traffic detector data are a prerequisite for subsequent applications. Considering that the PCA method is not ideal when detecting fault information with time-varying and multi-scale features, an improved MSPCA model is proposed in this paper. In combination with wavelet packet energy analysis and principal component analysis, data fault identification for traffic detectors is realized. On the basis of traditional multi-scale principal component analysis, detailed information is obtained by wavelet packet multi-scale decomposition, and a principal component analysis model is established in different scale matrices; fault data is separated by wavelet packet energy difference; according to the time characteristics and space of the detector data Correlation fixes fault data. Through case analysis, the feasibility of the method was verified.