Network fault prediction method and system
By constructing network fluctuation-time data links, clustering analysis and sub-volatility impact factor calculation, the problems of low accuracy of network fault prediction and poor adaptability in the dynamic environment in the prior art are solved, and more accurate fault warning and more efficient network operation and maintenance are achieved.
CN119728397AActive Publication Date: 2025-03-28BEIJING GUOXIN LANDUN TECH CO LTD
4 Cites 2 Cited by
Patent Information
- Application Number
- CN202510238401.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-03
- Publication Date
- 2025-03-28
- Estimated Expiration
- 2045-03-03
AI Technical Summary
Technical Problem
The existing network fault prediction methods have low accuracy, poor adaptability to dynamic environments, and lack historical data analysis.
Method used
By intercepting network data within the preset period, it is possible to construct a fluctuation-time data link, split it into abnormal and normal fluctuation data links, extract network features for cluster analysis, calculate sub-volatility impact factors, and integrate it into the final impact factor to judge the fault warning.
Benefits of technology
It improves the accuracy of fault warning, adapts to complex and changeable network environments, monitors network conditions in real time, accurately predicts potential risks, and improves the efficiency and response speed of network operations and maintenance.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of data management, and discloses a network fault prediction method and system, and the method comprises the steps: intercepting network data in a preset time period, and constructing a network fluctuation-moment data link; comparing the network fluctuation value with the fluctuation range, and splitting the network fluctuation-moment data chain into an abnormal fluctuation data chain and a normal fluctuation data chain; extracting network features to construct a same-type network sequence, and obtaining sub-fluctuation influence factors of the same-type network sequence; obtaining a fluctuation influence factor set according to all the sub-fluctuation influence factors; and integrating the fluctuation influence factor set to obtain a final influence factor of the network data, and judging whether to carry out network fault early warning or not according to the final influence factor. Through deep analysis of historical data, refining of a fluctuation mode and multi-dimensional information fusion, the problems of poor accuracy, weak adaptability and the like in the prior art are solved, and an efficient and intelligent network fault prediction solution is provided.
Need to check novelty before this filing date? Find Prior Art