Network Element Health Detection Using KPI Fluctuation Scores
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Solution Overview
Problem
Current network operation and maintenance management systems inaccurately determine network element health status by considering only single-point moment performance data, failing to provide a comprehensive assessment.
Innovation Solution
A method and device that calculate a fluctuation score for key performance indicators (KPIs) over a time window, using steady state values and considering multiple metrics like standard deviation, average deviation, and variation coefficient, to accurately assess network element health status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-point moment performance data is used to determine network element health status, then the detection process is simple and fast, but the accuracy of health status determination is poor
Solution Approach 1:
The patent transitions from single-point moment detection to time-window-based detection, adding the time dimension to the detection process. By collecting performance data over a specified time window and analyzing trends, the system achieves more accurate health status determination while maintaining reasonable operational complexity.
Solution Approach 2:
The system pre-calculates baseline values and thresholds before actual detection. By establishing reference ranges and trends in advance, the detection process becomes more efficient and accurate when evaluating actual performance data against these pre-established criteria.
2Reliability
If threshold alarm system is used for network element monitoring, then the implementation is straightforward, but the fault detection accuracy is insufficient
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors performance data, compares it against baseline values and trends, and adjusts health status assessments accordingly. This feedback loop enables more reliable fault detection by considering both absolute threshold violations and relative changes from normal behavior patterns.
Solution Approach 2:
The system evaluates multiple parameters simultaneously including performance metrics, baseline deviations, and trend changes. By changing from single-parameter threshold checking to multi-parameter comprehensive assessment, the system achieves higher detection reliability while managing complexity through systematic parameter evaluation.
Data Source
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AI summary
Embodiments of the present invention relate to a network element health status detection method and device, and relate to the field of communications technologies. The method specifically includes: determining, by a detection device, sampled data of at least one key performance indicator KPI of a target network element in a first time window; obtaining, by the detection device, a fluctuation score of any KPI in the at least one KPI according to sampled data of the any KPI in the first time window and a steady state value of the any KPI; and determining, by the detection device, a health status of the target network element based on a fluctuation score of each of the at least one KPI. Therefore, a network element health status is determined by using single-point performance data of a network element and performance data in a network element time window. A problem of inaccurate judgment by considering only single-point moment performance data of the network element is resolved. Therefore, this solution can be used to identify the network element health status more accurately.