Method and system for monitoring computing power risk based on cross-source fusion computing power data

Abstract

This invention discloses a method and system for monitoring computing power risks based on cross-source fusion computing power data. The method includes: acquiring computing power operation data from multiple monitoring dimensions sent by multiple computing power systems; identifying abnormal event data in the computing power operation data based on an identification algorithm; training an anomaly prediction model based on event parameters corresponding to the abnormal event data and system data of the computing power systems; and inputting real-time computing power operation data from any of the computing power systems into the anomaly prediction model to obtain output risk monitoring information. Therefore, this invention can achieve accurate anomaly prediction model construction and risk monitoring based on multi-dimensional data anomaly identification, improving the accuracy and real-time performance of computing power system fault warnings, and reducing the risk of system crashes due to untimely identification of abnormal events.

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