Journal of Physics: Conference Series | 2021
Optimization Research on Server Detection Based on RGB Maximum Mean Features and Deep Belief Network Model
Abstract
The intelligent detection about the running state of server equipment in computer room is a very important research field. In general, the state recognition research of signal lamp mainly includes the color and shape characteristics of signal lamp, and machine learning models. At present, the research results in this respect are not ideal for the recognition of signal lamps, Due to the small size and high density of the detected server lamp source, its shape characteristics cannot be accurately obtained, and state information is difficult to be perceived. This paper proposes a RGBMM features algorithm and a DBN network model algorithm, which is different from the traditional signal state recognition algorithm. According to the characteristics of server signal lamp, the color state image data of signal lamp is extracted effectively by RGBMM features algorithm, then the DBN network model algorithm is used to evaluate and identify the sampled signals, so as to judge whether the server is working normally. The experimental results show that the state recognition accuracy of the signal lamp is up to 98%, while the recognition rate of the signal lamp color state image using HSV( ( Hue, Saturation, Value) ) transformation is 90.7%. The difference between the three is different the algorithms used. So the RGBMM features algorithm and the DBN network model algorithm have a wider use for the signal lamp color status recognition.