基于APRIORI-贝叶斯优化XGBoost的电力通信网根告警预测

Root Alarm Prediction of Power Communication Network Applying APRIORI-Bayesian Optimization XGBoost

电力通信网根源性告警的精准预测,能够辅助运维人员提前对通信网高风险点进行高效排查和快速定位,从根源上避免区域性通信故障和衍生告警,降低网络风险和运维成本。针对现有研究中电力通信网根告警预测源数据冗余、准确率不高的问题,面向电力通信网根告警提出基于APRIORI-贝叶斯优化XGBoost的预测模型,利用APRIORI算法优化预测模型输入,挖掘根告警影响因素间的关联规则,借助关联规则概率化方法确定关键影响因子,降低贝叶斯优化XGBoost模型训练数据冗余度,提高数据价值密度,进而提升模型效率和告警预测精度。实验结果表明,所提算法在预测准确率、召回率和F-值等性能上均取得良好的效果,并在最小支持度为15%时达到最优预测结果,能为电力通信网高效运维和故障排查提供技术支撑。

The accurate prediction of the root alarms in the electric power communication network can assist the operation and maintenance personnel to efficiently investigate and quickly locate the high-risk points of the communication network in advance, then avoid regional communication failures and derivative alarms from the root, and reduce network risks and operation and maintenance costs. Aiming at the redundancy of source data and low accuracy of root alarm prediction in the existing research, this paper proposes a prediction model based on APRIORI-Bayesian optimization XGBoost for root alarms of electric power communication network. The APRIORI algorithm is used to optimize the input of the prediction model and mine the association rules among the influencing factors of root alarms. With the aid of the probabilistic method of association rules, the key influence factors are determined to reduce the training data redundancy of the Bayesian optimized XGBoost model, increase the data value density, and then improve the model efficiency and warning prediction accuracy. Then the prediction model is constructed on the basis of the Bayesian optimized XGBoost algorithm with the key factors. Finally, the experimental results show that the proposed algorithm performs well in prediction accuracy, recall and F-value, and achieves the optimal prediction accuracy when the minimum support is 15%, which can provide technical support for efficient maintenance and troubleshooting of power communication network.