Research on fault detection of power company computer room information management automation operation and maintenance platform based on big data

The number of enterprise application servers continues to grow rapidly, which brings trouble to the enterprise application server architecture. The complicated work makes the operation and maintenance of enterprise server inefficient. Throughout the Internet enterprises at home and abroad, the research of information automatic operation and maintenance has always been in an important position. This paper studies the fault detection of the automatic operation and maintenance platform of power company's computer room information management based on big data. Firstly, using the literature research method, this paper expounds the significance of automatic operation and maintenance of information management in the computer room of power companies and the problems existing in the platform, and introduces the related technologies of automatic operation and maintenance platform of information management. Then, by using big data mining technology, this paper studies the automatic operation and maintenance platform of power company's computer room information management, and mainly studies the accuracy of fault detection of power company's computer room information management automatic operation and maintenance platform. According to the survey results, the main types of failure problems are database failure (insufficient archiving space, cluster service exception, service exception, etc.) and virtualization platform failure (insufficient space, host performance, network exception, etc.), in which database failure accounts for about 46% of virtualization platform failure, and operating system failure (insufficient disk space, insufficient CPU memory performance, abnormal system shutdown, etc.) accounts for a low proportion of virtualization platform failure, but there are. The failure prediction of computer room information management automation operation and maintenance platform is mainly the prediction of operating system failure. Its prediction accuracy is high, but the fault prediction of database and virtualization platform is not high. It can be seen from this that the power company has a relatively large investment in power equipment, and the research on database and virtual platform technology is not mature.