Cause-Aware Network Fault Analysis for Event Propagation
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Solution Overview
Problem
Existing network fault analysis methods suffer from low accuracy due to the assumption that abnormal events with the same identifier are caused by the same reason, when in fact they can be caused by different factors, leading to inaccurate fault determination.
Innovation Solution
The method considers not only the identifiers but also the underlying causes of abnormal events by using extraction templates to determine fault cause description information, and analyzes fault propagation relationships between events to refine the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If abnormal events with the same identifier are classified as the same type of abnormal events, then the complexity of fault analysis is reduced, but the accuracy of network fault analysis result deteriorates
Solution Approach 1:
The patent segments the fault analysis process into multiple dimensions: event identifier classification, event type classification, and cause description classification. By dividing the analysis into these hierarchical levels, the system reduces overall complexity while maintaining high accuracy through multi-level discrimination of abnormal events
Solution Approach 2:
The patent adds a new dimension of analysis by introducing cause description information beyond traditional event identifiers. This dimensional expansion allows the system to distinguish between events with identical identifiers but different causes, thereby improving accuracy without proportionally increasing complexity
2Productivity
If only the identifier of abnormal events is considered for fault analysis, then the processing speed is improved, but the accuracy of fault determination deteriorates
Solution Approach 1:
The patent performs preliminary extraction and classification of cause description information from alarm messages before detailed fault analysis. By pre-processing and organizing cause information in advance, the system enables faster subsequent processing while ensuring accurate fault determination through comprehensive cause consideration
Solution Approach 2:
The patent replaces traditional mechanical identifier-matching methods with intelligent text processing and pattern recognition systems. Natural language processing techniques extract cause descriptions from alarm messages, enabling both rapid processing and accurate determination without relying solely on rigid identifier comparisons
3Measurement precision
If the cause description information of abnormal events is extracted and analyzed, then the accuracy of fault analysis is improved, but the complexity of the analysis process increases
Solution Approach 1:
The patent segments cause description analysis into structured components: extraction, classification, and matching. By dividing the complex analysis process into these manageable segments, the system achieves high accuracy through systematic processing while keeping overall complexity controlled through modular architecture
Solution Approach 2:
The patent introduces an intermediary classification layer that bridges raw alarm messages and final fault determination. This intermediary stage organizes cause description information into standardized formats, simplifying subsequent analysis while maintaining accuracy through structured information representation
Data Source
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AI summary
This application discloses a network fault analysis method and apparatus, a device, and a storage medium, and pertains to the field of network technologies. The method includes: obtaining information about a first abnormal event and information about a second abnormal event; determining first fault cause description information and second fault cause description information respectively based on the information about the first abnormal event and the information about the second abnormal event, where each of the first and the second fault cause description information is used to describe a cause of occurrence of a corresponding abnormal event; and determining, based on event identifiers in the information about the first and the second abnormal events, and the first and the second fault cause description information, that the first abnormal event corresponding to the first fault cause description information is a cause event that causes occurrence of the second abnormal event corresponding to the second fault cause description information. In the method, when network fault analysis is performed, the cause event in the first and second abnormal events is determined based on the identifiers of the abnormal events and the corresponding fault cause description information, so that accuracy of a network fault analysis result can be improved.