首页|基于KNIME工作流机器学习预测室分天线外打故障

基于KNIME工作流机器学习预测室分天线外打故障

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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.
Indoor Distribution Antenna Outward Emitting Fault Prediction Based on Machine Learning of KNIME Workflow
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.

KNIME workflowMachine learningPrecise positioning

李国博

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中国移动通信集团广东有限公司中山分公司,广东中山 528400

KNIME工作流 机器学习 精准定位

2024

邮电设计技术
中讯邮电咨询设计院有限公司

邮电设计技术

影响因子:0.647
ISSN:1007-3043
年,卷(期):2024.(7)
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