摘要
随着网络结构、业务形态、用户模型的不断变化以及大数据、云计算等技术的不断演进,网络运维成本压力逐年上升,依赖于专业测试软件进行道路测试的传统优化模式已经难以满足未来发展的需要,网络优化亟待智能转型。基于定轨道路用户的真实海量数据,通过聚类算法模型对上海全路段用户感知问题进行定位分析研究。研究结果表明,大数据分析和AI算法的定位精度和优化效率远大于传统路测。
With the continuous change of network structure,business form,user model and the continuous evolution of big data,cloud computing and other technologies,the pressure of network operation and maintenance cost is increasing year by year.The traditional optimization mode relying on professional test software for road test is difficult to meet the needs of future development,and network optimization needs intelligent transformation.Based on the real mass data of rail users,it analyzes and studies the user perception problem location of the whole road section in Shanghai by clustering algorithm model.The results show that the positioning accuracy and optimization efficiency of big data analysis and AI algorithm are much higher than those of traditional road test.
作者
潘晖
齐咏嘉
杭旭峰
姚赛彬
黄久成
Pan Hui;Qi Yongjia;Hang Xufeng;Yao Saibin;Huang Jiucheng(China Unicom Shanghai Branch,Shanghai 200080,China)
出处
《邮电设计技术》
2021年第3期77-83,共7页
Designing Techniques of Posts and Telecommunications
关键词
智能转型
聚类算法
大数据分析
用户感知
Intelligent transformation
Clustering algorithm
Big data analysis
User perception
