计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 299-307.doi: 10.11896/jsjkx.200600106
尹云飞1, 林跃江1, 黄发良1,2, 白翔宇1
YIN Yun-fei1, LIN Yue-jiang1, HUANG Fa-liang1,2, BAI Xiang-yu1
摘要： 火灾发生时烟气流动与温度分布预测是建筑和消防领域中的热门技术。针对现有的火灾烟气流动与温度分布预测工作烦琐、预测准确度低的现状,提出基于趋势特征向量的火灾烟气流动与温度分布预测模型,用深度学习方法进行相关数据的训练与预测,对揭示火灾发生及其发展规律有重要意义,可为火灾扑救和人员疏散提供辅助信息。所提模型能够抽取火灾时间序列数据中的趋势特征,并将这些特征作为先验知识来加速和优化深度神经网络的训练过程。文中还设计了LSTM-TFV(LSTM based on Trend Feature Vector)算法。实验结果表明,所提预测模型提高了火灾烟气流动与温度分布预测的准确度,实现了高效且方便的火灾时间序列数据预测。
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