Abnormal data detection method and device, storage medium and computer equipment

Abstract

The invention discloses an abnormal data detection method and device, a storage medium and computer equipment, relates to the technical field of information, and mainly aims to improve the detection efficiency and detection precision of abnormal data. The method comprises the following steps: acquiring attribute features corresponding to a to-be-detected data set; inputting the attribute features into a preset anomaly detection algorithm prediction model for algorithm prediction to obtain a target anomaly detection algorithm suitable for the to-be-detected data set; and detecting abnormal data in the to-be-detected data set by using the target anomaly detection algorithm to obtain an abnormal data detection result corresponding to the to-be-detected data set. The method and the device are suitable for detecting abnormal data.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to a detection method, device, storage medium and computer equipment for abnormal data. Background technique [0002] In the Internet era, big data applications are ubiquitous. Faced with a wide variety of massive data from all walks of life, in order to ensure the availability of data and facilitate the analysis of later data, it is necessary to detect abnormal data in the data set. [0003] Currently, abnormal data in datasets are usually detected by manually selecting an anomaly detection algorithm. However, this method needs to manually analyze the data characteristics in advance, and select the anomaly detection algorithm based on the experience of the staff, and then perform anomaly detection on the data according to the selected anomaly detection algorithm, which will lead to low detection efficiency of abnormal data. At the same time, this method of artificiall...

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Problems solved by technology

However, this method needs to manually analyze the data characteristics in advance, and select the anomaly detection algorithm based on the experience of the staff, and then perform anomaly detection on the data according to the selected anomaly detection algorithm, which will lead to low detection efficiency of abnormal data. At the same time, this method of artificially selecting anomaly detection algorithms is greatly affected by the subjective factors of the staff, and there is no guarantee that the selected anomaly detection algorithm is suitable for this type of data set. If the selected anomaly detection algorithm is not suitable for this type of data set, It will lead to low detection accuracy for abnormal data

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