Abstract: A machine learning-based method for automatically determining abnormal points of a single indicator includes step 1: randomly selecting M sample points from training data as subsamples, and putting them into a root node of a tree; and step 2: randomly specifying a data dimension for projection, and randomly generating a cutting point p in data of a current node, where the cutting point is generated between a maximum value and a minimum value of the specified dimension in the data of the current node. The present disclosure optimizes conventional data analysis linear model functions and regression model functions, constructs a computer neural network in the algorithm, puts multiple perceptron parameters in a multi-layer network for learning and training, and adopts the principle of principal component analysis, to find out the abnormal data that violates the data correlation.

Abstract: A visual recognition system for wind turbines, including an intelligent analysis unit configured to analyze and determine data information, an intelligent service unit configured to transmit image data, a telecontrol platform, a graphic workstation, an intelligent processing unit configured to process video files, a router, a video storage unit configured to store video information, an intelligent binocular perception terminal, an intelligent perception terminal and an intelligent recognition terminal, where the intelligent binocular perception terminal, the intelligent perception terminal and the intelligent recognition terminal are connected to the video storage unit and the intelligent processing unit through optical fibers and the router.