计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 299-307.doi: 10.11896/jsjkx.200600106

• 人工智能 • 上一篇    下一篇

基于趋势特征向量的火灾烟气流动与温度分布预测

尹云飞1, 林跃江1, 黄发良1,2, 白翔宇1   

  1. 1 重庆大学计算机学院 重庆400044
    2 南宁师范大学计算机与信息工程学院 南宁530001
  • 收稿日期:2020-06-17 修回日期:2020-08-31 出版日期:2021-07-15 发布日期:2021-07-02
  • 通讯作者: 尹云飞(yinyunfei@cqu.edu.cn)
  • 基金资助:
    国家自然科学基金(61962038);广西八桂学者创新团队基金(201979)

Prediction of Fire Smoke Flow and Temperature Distribution Based on Trend Feature Vector

YIN Yun-fei1, LIN Yue-jiang1, HUANG Fa-liang1,2, BAI Xiang-yu1   

  1. 1 College of Computer Science,Chongqing University,Chongqing 400044,China
    2 School of Computer and Information Engineering,Nanning Normal University,Nanning 530001,China
  • Received:2020-06-17 Revised:2020-08-31 Online:2021-07-15 Published:2021-07-02
  • About author:YIN Yun-fei,born in 1976,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include machine lear-ning and IoT engineering.
  • Supported by:
    National Natural Science Foundation of China(61962038) and Guangxi Bagui Teams for Innovation and Research(201979).

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。

关键词: 火灾预测, 趋势特征, 深度学习, 温度分布, 烟气流动

Abstract: The prediction of smoke movement and temperature distribution when a fire occurs is a popular technology in the field of construction and fire protection.At present,this prediction has not been combined with deep neural network technology.Aiming at the current situation that the prediction of fire smoke movement and temperature distribution is cumbersome and the prediction accuracy is low,a prediction model of fire smoke movement and temperature distribution based on trend feature vector is proposed.The deep learning methods are used to train and predict relevant data,which is of great significance to reveal the law of fire occurrence and development and can provide auxiliary information for fire-fighting and fire evacuation.The proposed model can extract the trend features in the fire time series data,and uses these features as a priori knowledge to accelerate and optimize the training process of the deep neural network.This paper designs LSTM-TFV (LSTM based on Trend Feature Vector) algorithm.Experimental results show that the proposed prediction model improves the accuracy of the prediction of fire smoke movement and temperature distribution,and realizes efficient and convenient fire time series data prediction.

Key words: Deep learning, Fire prediction, Smoke flow, Temperature distribution, Trend characteristics

中图分类号: 

  • TP391
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