Fault diagnosis method and device and computer readable storage medium
By acquiring multi-layered data from smart terminals to construct fused feature information, and using neural networks and fault trees for fault diagnosis, the accuracy and efficiency issues of voice fault diagnosis for smart terminals have been resolved, achieving higher diagnostic accuracy and user self-repair capabilities.
CN120951243APending Publication Date: 2025-11-14SHENZHEN TCL NEW-TECH CO LTD
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Patent Information
- Application Number
- CN202511055620.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-29
- Publication Date
- 2025-11-14
AI Technical Summary
Technical Problem
Currently, voice fault diagnosis of smart terminals relies on manual analysis, and the accuracy and efficiency need to be improved.
Method used
By acquiring data from the hardware, system, and application layers, we construct fused feature information, utilize a pre-defined neural network model and fault tree to perform fault diagnosis, and generate fault handling strategies.
Benefits of technology
It improves the comprehensiveness and accuracy of fault diagnosis, reduces missed diagnoses and misdiagnoses, and makes it easier for users to solve faults independently.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a fault diagnosis method and device and a computer readable storage medium. The fault diagnosis method comprises the steps that hardware layer data, system layer data and application layer data are acquired; based on the hardware layer data, the system layer data and the application layer data, constructing fusion feature information; performing fault diagnosis based on the fused feature information to obtain fault diagnosis information; and generating a fault processing strategy based on the fault diagnosis information. According to the method, the multi-dimensional data of the hardware layer, the system layer and the application layer are acquired, the fusion feature information is constructed to perform fault diagnosis and generate the processing strategy, so that the performance of the fault at different levels can be comprehensively captured, the accuracy of positioning the fault reason is improved, missed diagnosis and misdiagnosis are reduced, the comprehensiveness and accuracy of fault diagnosis are improved, and the fault diagnosis efficiency is improved. Meanwhile, a user can conveniently and autonomously solve faults.
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