LLM-driven natural language AI intelligent operation and maintenance automatic execution method and device

By using an LLM-driven natural language AI-powered intelligent operation and maintenance method, the problems of high skill requirements for operation and maintenance personnel and difficulty in cross-scenario adaptation in traditional operation and maintenance models have been solved. This has enabled automated operation and maintenance for various architectures, improving operation and maintenance efficiency and security.

CN122132201APending Publication Date: 2026-06-02GUANGZHOU HANZHISOFT CO LTD
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Patent Information

Application Number
CN202610001945.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-04
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Traditional operation and maintenance models require operation and maintenance personnel to master multi-language scripts and server interface protocols. Manual execution of processes leads to high delays in fault response, difficulty in cross-scenario adaptation, and inability for non-professionals to operate.

Method used

It adopts an LLM-driven natural language AI intelligent operation and maintenance method, which preprocesses and verifies fuzzy instructions to generate operation and maintenance scripts, and automatically executes them according to the target architecture by selecting the API protocol. It supports multiple architectures such as cloud servers, physical machines and containers.

Benefits of technology

It reduces the failure rate and response delay of operations and maintenance, improves the security of operations and maintenance and the ability to adapt to different scenarios, and reduces the difficulty of operations and maintenance.

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Abstract

The invention provides an LLM-driven natural language AI intelligent operation and maintenance automatic execution method and device. The method comprises the steps of obtaining a fuzzy instruction, preprocessing the fuzzy instruction, inputting the preprocessed instruction into an LLM model, and generating an operation and maintenance script. And automatically checking the operation and maintenance script to obtain an execution risk level. And if the execution risk level is a low risk level, selecting an API protocol according to the type of the target operation and maintenance architecture. And converting the operation and maintenance script into an operation and maintenance instruction according to the type of the API protocol, sending the operation and maintenance instruction to the target operation and maintenance architecture, and receiving an execution result returned by the target operation and maintenance architecture. A verification mechanism is provided on the basis of automatically generating the operation and maintenance script, and multiple verification of grammar, logic, authority and high-risk commands is carried out on the operation and maintenance script, so that the operation and maintenance fault rate and fault response delay are reduced. According to the method, on the premise that the operation and maintenance safety is improved and the operation and maintenance difficulty is reduced, operation and maintenance operations of various architectures are supported, and cross-scene adaptation is achieved.
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