AI Log Analysis System for Fault Detection
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Solution Overview
Problem
Current operation and maintenance methods, including manual experience and log analysis tools, are inadequate for efficiently handling the complexity and volume of log data in large-scale systems, failing to meet requirements for timely and advanced fault detection and prediction.
Innovation Solution
An integrated operation and maintenance system comprising a data acquisition module, data storage module, exception and fault labeling module, automatic model training and assessment module, operation and maintenance management and task execution module, and result checking module, which uses AI to acquire, store, label, and analyze log data for automatic exception detection and fault prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual experience-based troubleshooting is used, then operation and maintenance personnel can quickly locate faults in mature stable systems, but it becomes inadequate for large-scale complex clusters with continuously superimposed new software due to huge amount of log data and variety of log types
Solution Approach 1:
The patent replaces manual mechanical troubleshooting processes with an automated intelligent system that uses machine learning models and algorithms to analyze log data, detect exceptions, and locate faults automatically, eliminating the need for manual intervention in complex scenarios
Solution Approach 2:
The system enables self-service operation and maintenance by automatically performing exception detection, fault location, and root cause analysis without requiring human operators to manually examine logs, allowing the system to serve itself in identifying and resolving issues
2Ease of operation
If traditional log analysis tools are used, then log retrieval and simple statistical analysis can be performed, but advanced operation and maintenance requirements such as automatic exception detection, rapid fault location, and early fault warning cannot be met
Solution Approach 1:
The patent transforms the operational parameters of log analysis from simple retrieval and statistical functions to advanced intelligent analysis by incorporating machine learning models that dynamically adjust analysis depth, exception detection thresholds, and fault prediction parameters based on system state
Solution Approach 2:
The system performs preliminary actions by continuously training and updating exception detection models and fault prediction algorithms in advance, preparing the intelligence infrastructure before actual faults occur, enabling rapid response when exceptions are detected
3Quantity of substance
If cloud computing and big data technologies are adopted to accumulate massive data, then more comprehensive operation and maintenance information is available, but manual stepwise troubleshooting and simple statistical analysis cannot satisfy current basic requirements for timeliness and functionality
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated intelligent systems that use machine learning algorithms to process massive log data, automatically detecting exceptions and locating faults without human intervention, thereby maintaining high productivity despite increased data volume
Solution Approach 2:
The system introduces an intermediary intelligent analysis layer between data accumulation and operation and maintenance decision-making, using trained machine learning models to transform raw massive data into actionable insights, bridging the gap between data quantity and operational efficiency
Data Source
AI summary
Embodiments of the present disclosure provide an operation and maintenance system and method. The operation and maintenance system comprises a plurality of interconnected modules including: a data acquisition module, a data storage module, an exception and fault labeling module, an automatic model training and assessment module, an operation and maintenance management and task execution module, and a result checking module.