基于KNIME工作流机器学习预测室分天线外打故障
Indoor Distribution Antenna Outward Emitting Fault Prediction Based on Machine Learning of KNIME Workflow
李国博1
作者信息
- 1. 中国移动通信集团广东有限公司中山分公司,广东中山 528400
- 折叠
摘要
室内分布系统天线外打故障因难以快速准确定位,对用户体验和网络运维带来了严重挑战.基于KNIME工作流的机器学习算法预测模型,提出了一种新方法.这种方法能够及时发现并提前解决室内分布系统天线外打故障,实现了从"事后发现"到"事先预测"以及从"全面排查"到"精准定位"的转变,确保室外分布系统天线故障的全面精准定位.
Abstract
Due to the difficulty in quickly and accurately locating indoor distribution antenna outward emitting fault,it poses serious chal-lenges to user experience and network operation and maintenance.Based on machine learning prediction model of KNIME workflow,a new method is proposed,which can timely detect and solve indoor distribution antenna outward emitting fault in advance,achieving a transition from"post discovery"to"pre prediction"and from"comprehensive investigation"to"precise po-sitioning",ensuring the comprehensive and accurate positioning of indoor distribution antenna outward emitting fault.
关键词
KNIME工作流/机器学习/精准定位Key words
KNIME workflow/Machine learning/Precise positioning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据