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案件記録

COMPUTER IMPLEMENTED METHODS, SYSTEMS AND PROGRAM INSTRUCTIONS FOR DETECTING ANOMALIES IN A CORE NETWORK OF A TELECOMMUNICATIONS NETWORK

発明審査中
1閲覧数
21請求項 · 4 独立
§ Ⅰ

案件概要

発明者

Ahmed HANY; Yosr MOHAMED

IPC分類

G6F 11/7

CPC分類

G6F11/79G6F11/709

The computer implemented methods, systems, and program instructions detect anomalies in a core network of a telecommunications network. The method comprises: receiving data representative of streams of time series data of a plurality of Key Performance Indicators (KPIs) of the performance of nodes of the core network; comparing the received time series data for each of the KPIs to predicted time series values for each KPI generated by one or more time series analysis algorithms trained with historical data for each KPI to predict the KPI over time; determining any KPIs having deviations between the received time series data and the predicted time series data during a specific time period, wherein each deviation is an anomaly; grouping the streams of time series data for each KPI determined to be deviated to generate anomaly data; using an artificially intelligent clustering algorithm to generate a plurality of clusters, wherein each cluster comprises a subset of the KPIs determined to be deviated that have been assigned to said cluster by the artificially intelligent clustering algorithm; wherein each of the clusters is identified as having an associated root cause.

原文(中国語)

The computer implemented methods, systems, and program instructions detect anomalies in a core network of a telecommunications network. The method comprises: receiving data representative of streams of time series data of a plurality of Key Performance Indicators (KPIs) of the performance of nodes of the core network; comparing the received time series data for each of the KPIs to predicted time series values for each KPI generated by one or more time series analysis algorithms trained with historical data for each KPI to predict the KPI over time; determining any KPIs having deviations between the received time series data and the predicted time series data during a specific time period, wherein each deviation is an anomaly; grouping the streams of time series data for each KPI determined to be deviated to generate anomaly data; using an artificially intelligent clustering algorithm to generate a plurality of clusters, wherein each cluster comprises a subset of the KPIs determined to be deviated that have been assigned to said cluster by the artificially intelligent clustering algorithm; wherein each of the clusters is identified as having an associated root cause.

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