基于AI预测通信基站铅酸蓄电池后备供电保障能力的研究应用

Research and application of AI based prediction of backup power supply guarantee capability forlead-acid batteries in communication base stations

当市电中断或电源故障发生时,蓄电池为通信基站内网络设备的稳定运行提供了最后的供电保障。而铅酸蓄电池后备供电保障的优劣直接决定了设备运行的维持时长。现有技术在预测铅酸蓄电池后备供电保障方面,主要依靠放电测试或经验判断。这些方法不仅费时耗力、效率低下,而且受电池使用环境、充放电频率等因素影响,导致预测结果存在不准确和时效性差等诸多问题。本文通过采用一种基于神经网络Stacking堆叠模型,结合人工智能算法,可以实现对铅酸蓄电池后备供电保障能力的预测。这种方法能够提前发现电池性能劣化的迹象,从而提高运维效率,降低运维成本,提升蓄电池运行的安全性。

When the mains power is interrupted or power failure occurs,the battery provides the final power supply guarantee for the stable operation of network equipment in the communication base station.The quality of backup power supply guarantee for lead-acid batteries directly determines the maintenance duration of equipment operation.The existing technology mainly relies on discharge testing or empirical judgment in predicting the backup power supply guarantee of lead-acid batteries.These methods are not only time-consuming and inefficient,but also affected by factors such as battery usage environment and charging and discharging frequency,resulting in inaccurate prediction results and poor timeliness.This article uses a neural network-based Stacking model combined with artificial intelligence algorithms to predict the backup power supply guarantee capability of lead-acid batteries.This method can detect signs of battery performance degradation in advance,thereby improving operation and maintenance efficiency,reducing operation and maintenance costs,and enhancing the safety of battery operation.