Traffic detector anomaly diagnosis method and device based on random forest classifier

The invention relates to the technical field of intelligent traffic control, in particular to a traffic detector anomaly diagnosis method and device based on a random forest classifier. The inventionprovides a traffic detector anomaly diagnosis method based on a random forest classifier. The method comprises the following steps: performing fault analysis calibration on original data from a traffic detector to obtain an original calibration data set; extracting data with a preset percentage value as a training set, and constructing a first-level decision tree according to different types of feature indexes of the data in the training set; based on the first-level decision tree, calculating through a first-level random forest classifier to obtain suspected fault judgment of the traffic detector and a result data set of the suspected fault judgment; constructing a secondary decision tree based on the result data set, and performing calculation through a secondary random forest classifierto obtain an intersection