月刊
ISSN 1000-7229
CN 11-2583/TM
电力建设 ›› 2022, Vol. 43 ›› Issue (1): 113-121.doi: 10.12204/j.issn.1000-7229.2022.01.013
程路明1, 楼平1, 诸骏豪1, 李凌雁1, 崔晓昱2(), 孙毅2
收稿日期:
2021-04-29
出版日期:
2022-01-01
发布日期:
2021-12-21
通讯作者:
崔晓昱
E-mail:524651088@qq.com
作者简介:
程路明(1984),男,硕士研究生,高级工程师,主要研究方向为电力通信系统运行;基金资助:
CHENG Luming1, LOU Ping1, ZHU Junhao1, LI Lingyan1, CUI Xiaoyu2(), SUN Yi2
Received:
2021-04-29
Online:
2022-01-01
Published:
2021-12-21
Contact:
CUI Xiaoyu
E-mail:524651088@qq.com
Supported by:
摘要:
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。
中图分类号:
程路明, 楼平, 诸骏豪, 李凌雁, 崔晓昱, 孙毅. 基于APRIORI-贝叶斯优化XGBoost的电力通信网根告警预测[J]. 电力建设, 2022, 43(1): 113-121.
CHENG Luming, LOU Ping, ZHU Junhao, LI Lingyan, CUI Xiaoyu, SUN Yi. Root Alarm Prediction of Power Communication Network Applying APRIORI-Bayesian Optimization XGBoost[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(1): 113-121.
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