This article innovatively proposes a method for network element topology connection relationship discrimination based on PrefixSpan and LightGBM.The PrefixSpan algorithm is used to extract and mine alarm data,and the mining results are analyzed.The analysis results are then input into LightGBM for supervised learning to obtain the final network element topology connection relationship judgment model.The experimental results show that the f1 value of this method in inferring the topological connection relationship between base stations and related network elements reaches 0.89,effectively improving the accuracy of network element topological connection relationship discrimination,providing a powerful means for correcting network element topological connection relationships,and laying a solid foundation for the construction of digital twin networks.
关键词
数字孪生网络/频繁项集/时序/网元拓扑连接/机器学习
Key words
digital twin networks/frequent itemsets/time series/element topological connectivity/machine learning
维普
万方数据