Abnormal state detection method, system, storage medium, program, server
An abnormal state and detection method technology, applied in the fields of servers, storage media, abnormal state detection methods, programs, and systems, can solve problems such as the increase in the scale and complexity of the server, and the inability to issue early warning information, and achieve the effect of avoiding losses.
Active Publication Date: 2022-03-11
XIDIAN UNIV
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Problems solved by technology
[0004] Through the above analysis, the existing problems and defects of the existing technology are: the scale and complexity of the server continue to increase, resulting in the diversity of server software failures; the failure to issue early warning information for possible problems in the future
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[0049] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0050] Aiming at the problems existing in the prior art, the present invention provides an abnormal state detection method, system, storage medium, program, and server. The present invention will be described in detail below in conjunction with the accompanying drawings.
[0051] Such as figure 1 As shown, the abnormal state detection method provided by the present invention includes the following steps:
[0052] S101: Prepare the server's original log set, a type of log set under normal operation status, a type of log set with abnormal operation status, and corresponding abnormal events;
[0053] S102: Preprocessing the ...
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The invention belongs to the technical field of server fault processing, and discloses an abnormal state detection method, system, storage medium, program, and server, which perform unsupervised learning on LSTM1, supervised learning on LSTM2, and use log information to quickly solve server faults; The LSTM1 cyclic neural network is used to predict the log information with time series characteristics, and the alarm is issued by identifying the abnormal occurrence of the log information, and assists the operation and maintenance personnel to check the cause of the fault; LSTM2 uses the log information within a period of time before the fault occurs. Find out the cause of the failure that caused the current situation. The present invention adopts a machine learning method to automatically learn an applicable model through data; adopts deep learning to convert the data structure accepted by a neural network, and adopts a cyclic neural network to mine long-term and short-term dependencies among log relationships. In supervised learning, the potential relationship between log sequences and failure causes can be found.
Description
technical field [0001] The invention belongs to the technical field of server failure processing, and in particular relates to an abnormal state detection method, system, storage medium, program and server. Background technique [0002] The normal operation of the server is crucial to every enterprise or other organization. Once the server fails, the possible losses are immeasurable. Server failures are usually divided into hardware failures and software failures. About 70% of the failures are caused by software, which may cause server downtime in severe cases. For troubleshooting, there are many commercial software, such as: IBM Tivoli software allows customers to spend less time diagnosing problems and deploying solutions, and spend more time effectively managing their own business; splunk is a managed log file management tool that requires significant skill and domain knowledge to use. CA Spectrum is an industry-leading infrastructure and fault management solution tha...
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IPC IPC(8): G06F11/30G06K9/62G06N3/04G06N3/08
CPCG06F11/3072G06N3/049G06N3/084G06N3/047G06N3/045G06F18/2415G06F18/241
Inventor 沈玉龙任忠李腾张志为祝幸辉焦成义
Owner XIDIAN UNIV