电力骨干通信网中告警信息的及时高效处理能够为故障处置提供先机。利用人工智能技术, 基于历史数据可以预测告警的发生, 完备的数据集是成功实现预测的基本条件。在现实网络中采集的数据往往有分布不均匀、数据缺失等问题, 如何构建完备的数据集是基于人工智能技术实现电力骨干通信网中告警预测的一个关键问题。文章提出了一种针对电力骨干通信网中告警预测的数据预处理方法, 采用数据增强技术完成了数据的类均衡, 使得最终的数据集符合模型输入的数据尺寸。仿真结果表明, 基于数据增强的告警预测数据预处理方法能够在较大程度上提高告警预测的准确率。

The timely and efficient processing of alarm information in the power backbone communication network can provide an opportunity for fault handling. By applying artificial intelligence technology based on historical data, the occurrence of alarms can be predicted. A complete data set is the basic condition for successful prediction. The data collected in the real network often has problems such as uneven distribution and missing data. How to construct a complete data set is a key issue based on artificial intelligence technology. This paper proposes a data preprocessing method for alarm prediction in power backbone communication network. Data enhancement technology is used to complete the class balance of data, so that the final data set conforms to the data size input by the model. The simulation results show that the data pre-processing method based on data enhancement can improve the accuracy of alarm prediction to a large extent.