一种具有自学习能力的用户感知人工智能测量方法

An AI measurement method of user perception with self-learning ability

用户感知分析体系对运营商网络运维各环节均有重要的支撑作用,是提升网络竞争力的重要保障,但网络及业务的变化,对感知分析带来了新的挑战。本文分析了现网主流的感知分析体系原理,指出其在效果、成本等方面的劣势,进而提出一种两阶段的用户感知测量和分析框架,第一阶段通过对大量业务模型的学习建立一套通用的单项业务感知质量的量化评估模型;第二阶段构造出一种准无监督的机器学习模型,使得该满意度评估方法具备自学习的能力能够适应网络的动态变化,同时可以将网络短板定位到具体小区和具体业务,大大提高了方法的实用性和可用性。现网分析表明,该方法的查全率、查准率远高于传统方法,实践中基于本方法的满意度修复准确度、修复资源投入双优于传统方法。最后对该体系在能力、效能、运营等方面的演进前景进行了展望。

User Perception Analyzation System(UPAS)plays important supportive role in maintaining every step of network operation and maintenance for communication carrier.However,the consistently changing of network and its relevant businesses bring forth great challenge to the perception analyzation.Based on the UPAS theory,this research first investigated its possible drawbacks on benefit and cost,and proposes a two-stage user perception measurement and analysis framework.In the first stage,a general quantitative evaluation model is constructed for evaluating single service quality.In the second stage,a quasi-unsupervised machine learning model is constructed,so that the satisfaction evaluation method has the ability of self-learning to adapt to the dynamic changes of the network.It can reduce the shortcomings of the network to specific cells and specific services that can greatly improves the practicability and usability of the method.The analysis from the existing network shows that the recall rate and precision rate of this method are much higher than those of the traditional method.Finally,the future evolution of the system in capacity,efficiency and operation are viewed.