Method and device for identifying data exception, server and medium

Abstract

The embodiment of the invention discloses a method and device for identifying data exception, a server and a medium. A specific embodiment of the method comprises: obtaining a target data sequence in a preset time period; determining a predicted value corresponding to the target data sequence; extracting a data feature index based on the target data sequence and the predicted value; and inputting the data feature indexes into a pre-trained autonomous learning model, and generating prompt information used for representing whether data exception exists or not. According to the embodiment, the workload of personnel is reduced, and the accuracy of data exception identification is improved.

Description

technical field [0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, server and medium for identifying data anomalies. Background technique [0002] With the development of Internet technology, the scale of data is increasing day by day. For large-scale data, how to identify outliers in a timely and effective manner is of great significance for the normal operation of business systems and other aspects. [0003] In the prior art, it is often judged whether the data is abnormal by manually monitoring data changes or setting a fluctuation threshold. However, manual monitoring requires a lot of manpower, and it is difficult to ensure that personnel can detect abnormalities in large-scale data in a timely manner. However, since the data in the actual scene usually fluctuates, the accuracy of judging whether the data is abnormal by setting the fluctuation threshold "one size fits all" is not good. ...

AI Technical Summary

Problems solved by technology

However, manual monitoring requires a lot of manpower, and it is difficult to ensure that personnel can detect abnormalities in large-scale data in a timely manner

However, since the data in the actual scene usually fluctuates, the accuracy of judging whether the data is abnormal is not good by setting the fluctuation threshold "one size fits all".

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