Abnormal data detection method, device, storage medium and program product

Abstract

The invention provides an abnormal data detection method, an abnormal data detection device, a storage medium and a program product. The method comprises the steps of conducting at least two structural treatments on to-be-detected target data so as to obtain at least two structured data; carrying out feature extraction on each structured data, and acquiring the feature data of each structured data; performing feature fusion on each feature data so as to obtain target feature data; carrying out machine learning on the target feature data, and obtaining the recognition probability of the targetdata, wherein the recognition probability represents the probability that the target data is recognized as normal data. By means of the method, rich feature data can be extracted when no or few abnormal data are available. The accuracy of data anomaly detection is improved. The dependence on data distribution, variable independence or data volume balance is relieved. The technical problems that inthe prior art, abnormal detection is difficult to achieve and the accuracy is low are solved.

Description

technical field [0001] The present invention relates to the technical field of deep learning, in particular to an abnormal data detection method, device, storage medium and program product. Background technique [0002] Anomaly detection aims to detect data that does not meet expectations, and has a wide range of applications in fault detection, fraud detection, intrusion detection, and other fields, such as vehicle fault detection. [0003] Existing anomaly detection methods can be divided into supervised learning methods and unsupervised learning methods. However, when using unsupervised learning methods for anomaly detection, it is necessary to establish a hypothetical model of normal data, such as assuming that the variables are independent of each other, and the unsupervised learning method extracts fewer data features, which cannot fully represent the data pattern, resulting in abnormal detection. The accuracy is low; the supervised learning method requires a balanced...

AI Technical Summary

Problems solved by technology

[0005] Therefore, the first purpose of the present invention is to propose an abnormal data detection method to extract rich feature data in scenarios where there is a lack of abnormal data or a small amount of abnormal data, improve the accuracy of data abnormal detection, and relieve the problem of data distribution. , independence of variables or dependence on data volume balance, to solve the technical problems that anomaly detection is difficult to achieve and low accuracy in the existing technology

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