Intelligent diagnosis method for storage operation and maintenance based on experience learning and hierarchical synchronization

By generating standardized experience data packages and performing layered synchronization, the problems of experience silos and difficulties in knowledge flow in storage system operation and maintenance are solved. This enables continuous learning and self-evolution of operation and maintenance knowledge, improves operation and maintenance efficiency and diagnostic accuracy, and breaks down the knowledge flow barriers in network isolation environments.

CN122285355APending Publication Date: 2026-06-26CHINA ELECTRONICS CLOUD DIGITAL INTELLIGENCE TECH CO LTD
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Patent Information

Application Number
CN202610398582.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-30
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing storage system operations and maintenance suffer from problems such as siloed experience, repetitive problem localization, low levels of automation and intelligence, and difficulties in knowledge flow in network isolation environments, resulting in low operational efficiency and high diagnostic difficulty.

Method used

By generating standardized experience data packages, and selecting online direct connection or offline export methods to transmit them to the knowledge platform center, deduplication, evaluation and merging are performed. Experience-driven or root cause-driven modes are selected for diagnosis, and feature vectorization matching and verification feedback are carried out to form a closed-loop learning, realizing the accumulation of operation and maintenance knowledge, cross-environment knowledge circulation and continuous learning.

Benefits of technology

It has enabled the assetization and self-evolution of operational knowledge, enhanced the certainty and reliability of the diagnostic process, broken down knowledge flow barriers, significantly improved operational efficiency and response speed, achieved the standardization and equalization of team capabilities, and possesses excellent fault location capabilities.

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Abstract

The invention relates to the technical field of information technology operation and maintenance, and provides a storage operation and maintenance intelligent diagnosis method based on empirical learning and hierarchical synchronization, which comprises the following steps: intercepting a diagnosis command through an operation and maintenance terminal, associating system context information, and generating a standardized empirical data packet; transmitting the standardized empirical data packet to a knowledge platform center according to a network connection state of an environment where the operation and maintenance terminal is located; performing duplicate removal, evaluation and combination processing on the received standardized empirical data packet through the knowledge platform center to update the global knowledge base; when a diagnosis request is received, generating a full-amount health state snapshot; and performing feature vectorization matching on the current diagnosis information and the historical experience, outputting the historical experience with the highest matching degree and a solution thereof, and receiving verification feedback of operation and maintenance personnel to form closed-loop learning. According to the method, capitalization and a self-evolution mechanism of operation and maintenance knowledge can be realized, the certainty and reliability of a diagnosis process are enhanced, and the operation and maintenance efficiency and the response speed are remarkably improved.
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