Service system anomaly detection method and device, computer equipment and storage medium

Abstract

The invention relates to artificial intelligence, and provides a service system anomaly detection method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a multi-scale signature matrix according to the multivariate time sequence data of each index generated by a service system; inputting the multi-scale signature matrix into a convolution layerto encode a spatial mode of the multi-scale signature matrix, and outputting a spatial feature map; inputting the spatial feature map into an attention-based convolutional long-short-term memory network layer, and updating the hidden state of the spatial feature map through the attention-based convolutional long-short-term memory network layer to obtain an updated spatial feature map; inputting the updated spatial feature map into a deconvolution layer to decode and reconstruct the updated spatial feature map to obtain a reconstructed signature matrix; comparing the reconstructed signature matrix with the multi-scale signature matrix, and determining an abnormal index of the service system. In addition, the invention also relates to a blockchain technology, and the multivariate time seriesdata can be stored in the blockchain. By adopting the method, the anomaly detection accuracy can be improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a service system anomaly detection method, device, computer equipment and storage medium. Background technique [0002] With the development of computer technology, various service systems have emerged. The service system is a socialized technical system, which is a networked configuration of a specific technology or organization. The provision of services through the service system can meet the needs and expectations of users. In order to ensure the normal operation of the service system, it is usually necessary to monitor the system to detect abnormalities. Accurate detection can control the loss to the greatest extent, and identifying the root cause will also help the system operator to perform system diagnosis and maintenance. [0003] At present, in order to monitor the system, it is usually realized by analyzing a large amount of multivariate tim...

AI Technical Summary

Problems solved by technology

[0004] However, in addition to time dependence, multivariate time series data usually contain certain noise

When the noise becomes relatively serious, it will have a certain impact on anomaly detection, especially it is very likely to increase the probability of false positive detection, thereby reducing the accuracy of anomaly detection

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