Pod failure prediction method, device, equipment and storage medium

By combining multi-scale convolution and self-attention feature extraction with cross-modal attention mechanism, we have achieved accurate prediction and root cause localization of Pod failures, generated automatic intervention strategies, solved the problems of prediction bias and passive response in existing technologies, and improved the efficiency and accuracy of failure prediction.

CN122431934APending Publication Date: 2026-07-21SHANGHAI DONGPU INFORMATION TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANGHAI DONGPU INFORMATION TECH CO LTD
Filing Date
2026-05-09
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing Pod failure prediction technologies mostly rely on single time-series indicators, ignoring log and topology dependency data. This results in highly biased predictions, making it difficult to balance long-term trends with short-term fluctuations. Furthermore, they lack accurate prediction of the root causes of failures and automatic generation of intervention strategies, leaving operations and maintenance personnel to respond to failures passively.

Method used

By collecting time-series metrics, log data, and topology dependency data of Pods in real time, multi-scale convolution and self-attention feature extraction are used, combined with cross-modal multi-head attention mechanism, to generate fused state vectors, perform fault analysis, and generate automatic intervention strategies.

Benefits of technology

It enables accurate prediction and root cause localization of Pod failures, automatically generates intervention strategies, improves the efficiency and accuracy of failure prediction, and can proactively avoid risks in a timely manner, ensuring the stability and reliability of Pod operation.

✦ Generated by Eureka AI based on patent content.

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Abstract

The present application relates to the field of container failure, and discloses a pod failure prediction method, device, equipment and storage medium, which is used for predicting pod failure and generating intervention strategy.The method comprises the following steps: collecting time sequence indexes, log data and topology dependency data of the pod in real time, and performing index prediction processing on the extracted multi-scale time sequence features to obtain a comprehensive prediction index sequence and a time sequence prediction feature vector; generating a log semantic feature vector and a topology feature vector, and combining the time sequence prediction feature vector to generate a fusion state vector; performing failure analysis processing on the fusion state vector to obtain a failure prediction result, a failure prediction score, a failure root cause prediction result and a root cause prediction score; calculating a comprehensive failure risk score based on the comprehensive prediction index sequence, the failure prediction score and the root cause prediction score; and if the comprehensive failure risk score exceeds a preset intervention threshold, generating an intervention strategy.
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