融合LSTM和改进A^(*)算法的火灾逃生路径规划方法

Fire escape path planning method based on LSTM and improved A^(∗) algorithm

本文针对高温环境下传感器节点存在误报、漏报、工作状态异常等问题,提出了融合长短时记忆网络模型(long short term memory,LSTM)和改进A^(*)算法的火灾逃生路径规划研究方法。根据LSTM自适应学习火灾实时态势信息,建立异常节点数据预测模型,实现异常节点的温度、一氧化碳浓度等威胁态势的预测;基于室内火灾实时态势信息,搭建火势威胁态势蔓延模型,利用改进的A^(*)算法动态规划逃生路径,获取异常情况下火灾最佳安全逃生路径。结果表明,该方法在不同火灾时期均能规划出最佳安全逃生路径,为人员的撤退争取宝贵的时间,具有实际应用价值。

Aiming at the problems of false alarms,missing alarms and abnormal working status of sensor nodes in high temperature environment,this paper proposes a fire escape path planning research method combining LSTM and improved A^(∗)algorithm.According to the LSTM,the real-time fire situation information was adaptive learned,and the abnormal node data prediction model was established to predict the threat situation of abnormal nodes,such as temperature and carbon monoxide concentration.Based on the real-time situation information of indoor fire,the fire threat situation spread model was built,and the improved A^(∗)algorithm was used to dynamically plan the escape path to obtain the best safe escape path under abnormal conditions.The results show that this method can plan the best escape path in different fire periods,and gain valuable time for the evacuation of personnel,which has practical application value.