基于改进决策树的电力通信设备状态预测算法研究

电力通信网络的快速增长,传统被动响应的事后运维模式迫切需要向分析预测的事前运维模式转型。论文在深入研究典型决策树理论的基础上,针对电力通信设备运行状态的关联特性,提出了一种改进的决策树学习算法,通过借鉴粗糙集理论对决策表属性的约简、求核与泛化过程,最终构造出一种多变量的决策树。通过算法仿真,该方法构建的决策树结构更为简化和合理,大大降低了计算量,提高了预测分析效率,为电力通信的运维提供了一种快速、简捷的通信设备状态预测方法,克服了经典决策树算法的不足,具有一定的实用性。

Research on State Prediction Algorithms of Electric Power Communication Equipment Based on Improved Decision Tree

With the rapid growth of power communication network,the traditional passive response mode of ex post operation and maintenance urgently needs to be transformed to the analysis and prediction mode of ex ante operation and maintenance.Based on the in-depth study of the typical decision tree theory,an improved decision tree learning algorithm is proposed in this paper,aiming at the correlation characteristics of the operation status of power communication equipment.By referring to the reduction,nu?cleation and generalization process of decision table attributes based on rough set theory,a multi-variable decision tree is finally constructed.Through algorithm simulation,the structure of decision tree constructed by this method is simpler and more reason?able,which greatly reduces the amount of calculation and improves the efficiency of prediction and analysis.It provides a fast and simple method for state prediction of communication equipment for operation and maintenance of power communication,overcomes the shortcomings of classical decision tree algorithm,and has certain practicability.