A 5G network service quality abnormity monitoring and prediction method and system

Abstract

The invention provides a 5G network service quality abnormity monitoring and prediction method and system, and belongs to the technical field of communication networks. The system comprises a data acquisition module which is used for acquiring 5G network service quality data and network KPI performance monitoring data; The data processing module is used for preprocessing and marking the network service quality data; The Qos data storage module is used for storing the marked network service quality data; The model training module is used for establishing a supervised machine learning model andtraining to obtain a QoS abnormity monitor and a QoS abnormity predictor; The QoS abnormity monitor is used for monitoring current 5G network service quality data; The QoS anomaly predictor is used for predicting future 5G network service quality data anomaly; And the QoS strategy decision module is used for marking and storing abnormal data and reporting an abnormal result. The service quality of5G network users can be guaranteed, and the service quality is improved.

Description

Technical field [0001] The invention belongs to the technical field of communication networks, and in particular relates to a method and system for monitoring and predicting abnormal 5G network service quality based on a decision tree. Background technique [0002] Network quality of service (QoS, Quality of Service) is a necessary support to guarantee network performance. Traditional network service quality guarantee adopts differentiated service Diffserv model or integrated service Interserv model. Diffserv cannot guarantee global optimization. The Interserv model involves complex signaling control. As a result, existing networks can only provide best-effort service quality and cannot guarantee service quality. [0003] In the context of massive device connections, ultra-high traffic density, ultra-high connection density, and ultra-high mobility, there are huge challenges in how 5G networks meet user services. The traditional network service quality architecture cannot adapt to ...

AI Technical Summary

Problems solved by technology

Diffserv cannot guarantee global optimization, and the Interserv model involves complex signaling control, so the existing network can only provide best-effort service quality but cannot provide service quality guarantee

[0003] In the context of massive device connections, ultra-high traffic density, ultra-high connection density, and ultra-high mobility, there are huge challenges in how 5G networks can meet user services. The traditional network quality of service architecture cannot adapt to complex and dynamic 5G network application scenarios.

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