The invention discloses a computer mainboard abnormity identification method and
system, and relates to the technical field of
computer hardware diagnosis and maintenance, and the method comprises the following steps: collecting
voltage, temperature and current
signal parameters on a mainboard in real time; preprocessing the
signal parameters to extract features; and inputting the preprocessed
feature data into a pre-trained
machine learning model, wherein the model is generated based on historical fault case training. According to the computer mainboard abnormity identification method and
system, the
voltage, temperature and current
signal parameters of the mainboard are collected in real time, the pre-trained
machine learning model is used for abnormal mode identification, the accuracy and the real-time performance of mainboard abnormity detection are remarkably improved, dominant faults can be identified, and the fault detection efficiency is improved. In addition, potential anomalies with high concealment, intermittency and relevance can be captured, the missing report rate and the false report rate are effectively reduced,
time sequence analysis and
spectrum analysis are combined, the capturing capacity of instantaneous anomalies is enhanced, and anomaly positioning is more accurate.