Anomaly detection method and device and electronic equipment

Abstract

Embodiments of the invention provide an anomaly detection method and device and electronic equipment, and belong to the technical field of internet. The method comprises the following steps of: obtaining a current real value, at a current moment, of a to-be-monitored index; obtaining a current predicted value, at the current moment, of the to-be-monitored index according to a pre-established timerecurrent neural network model and the current moment, wherein the time recurrent neural network model is established according to a history real value of the to-be-monitored index; taking a difference value between the current real value and the current predicted value as a current predicted error, and judging whether the current predicted error is in a preset threshold value range or not; and ifthe current predicted error is not in the preset threshold value range, determining that the to-be-monitored index is abnormal at the current moment. According to the method, the to-be-monitored index is predicted through the time recurrent neural network model, and the obtained current predicted value at the current moment is compared with the current real value, so that the self-adaptability ofanomaly detection is improved and then the correctness of anomaly detection is improved.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to an anomaly detection method, device and electronic equipment. Background technique [0002] In system operation and maintenance, monitoring key performance indicators is a very important issue. By monitoring key performance indicators, abnormalities can be detected in time and alarm information can be sent to system maintenance personnel. In practice, anomaly detection in key performance indicator monitoring is mainly achieved by operation and maintenance personnel setting fixed alarm thresholds. Specifically, when the monitored key performance indicator exceeds the alarm threshold, the system marks it as abnormal and sends an alarm message. [0003] However, the inventor found in the process of realizing the present invention that at least the following problems exist in the prior art: the monitored key performance indicators are usually affected by multiple factors, and u...

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Problems solved by technology

[0003] However, the inventor found in the process of realizing the present invention that at least the following problems exist in the prior art: the monitored key performance indicators are usually affected by multiple factors, and under the influence of multiple factors, the monitored key performance indicators The value is unstable; if the alarm threshold is set to a fixed value, then it will lead to false positives of the alarm information

Therefore, the accuracy of existing anomaly detection methods is relatively low

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