Abstract: When testing and verifying the man-machine interface of substation monitoring system, it is common to assess whether the monitoring software is up to standard by comparing the monitoring picture observed by the human eye with the information sent by the test command, but the accuracy and efficiency of the human eye in observing the complex and variable monitoring information is not guaranteed. In this paper, we design a method to automatically identify information on substation monitoring pictures using image processing and machine learning techniques. A template matching method based on the best primitive is proposed to solve the problem of automatic positioning of electrical primitive in the picture.The FHOG operator is proposed to describe the topological features of the picture and speed up the recognition of the monitoring pictures and primitives. For problems such as the separation of the left and right body structure of Chinese characters and the sticking of characters in the warning message picture, an algorithm for segmentation and recognition of synergies is proposed to locate characters and deep convolutional neural networks are used for recognition. The effectiveness of the method is verified in the actual substation monitoring pictures. We also design an online verification system, obtaining the recognition accuracy of 96.04%.

Key words: substation monitoring picture recognition, FHOG, primitive localization, primitive state recognition, character segmentation and recognition