Chengdu SIWI Power Electronic Technology Co., Ltd.

本文聚焦于锂电池火灾监测场景,深入探讨了一种融合无线传感器网络与权重自适配决策模型的锂电池火灾监测系统。该系统整合了ZigBee无线传感器网络、卷积神经网络和随机森林模型。其中,ZigBee模块凭借低成本、易部署的优势,负责采集锂电池环境中的温度、烟雾浓度、CO浓度等重要指标。然而,受终端节点有效温度工作范围限制,85℃以上的数据严重失真、缺失,故本算法关键在于部分关键数据缺失时实现火灾监测。CNN针对采集的数据既能实现特征提取,又可完成权重分比计算,并将适配权重实时反馈至随机森林模型。从实验结果来看,随机森林模型可依据接收的权重信息精准判定火灾状况。相较于传统采用红外热成像火灾探测器实现类似数据采集与火灾预测功能需承担高额成本,本文巧用ZigBee模块,不仅成功完成环境数据采集及基于模型的预测任务,还实现了成本有效节约。这一创新举措在锂电池箱火灾监测的环境数据监测与分析方面有重要意义,为相关应用提供了经济高效的可行方案。

This paper focuses on the lithium battery fire monitoring scenario and deeply discusses a lithiumbattery fire monitoring system that combines a wireless sensor network and a weight self -adaptive decisionmodel.This system integrates ZigBee wireless sensor network, convolutional neural network and random forestmodel.Among them, ZigBee module is responsible for collecting important indicators such as temperature,smoke concentration and CO concentration in the lithium battery environment by virtue of its advantages of lowcost and easy deployment.However, due to the limitation of the effective temperature working range of the terminal node,the data above 85℃ is seriously distorted and missing.Therefore, the key of this algorithm is torealize fire monitoring when some key data is missing.For the collected data, CNN can not only realize featureextraction, but also complete the weight ratio calculation, and feed back the adaptive weight to the random forest model in real time.Judging from the experimental results, the random forest model can accurately deter mine the fire situation according to the received weight information.Compared with the traditional use of infrared thermal imaging fire detectors to achieve similar data collection and fire prediction functions, which requires high costs, this paper cleverly uses the ZigBee module, which not only successfully completes the environmental data collection and model-based prediction tasks, but also realizes effective cost savings.This innovative measure is of great significance in the environmental data monitoring and analysis of lithium battery firemonitoring,and provides an economical and efficient feasible solution for related applications.