主管:中华人民共和国应急管理部
主办:应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU

消防科学与技术 ›› 2022, Vol. 41 ›› Issue (4): 526-529.

• • 上一篇    下一篇

基于神经网络算法的多参数火灾探测系统设计

贾荣田1,苑春苗1,蔡景治1,郑皓天2   

  1. (1.东北大学 资源与土木工程学院,辽宁 沈阳 110819; 2.安徽理工大学 安全科学与工程学院,安徽 淮南 232001)
  • 出版日期:2022-04-15 发布日期:2022-04-15
  • 作者简介:贾荣田(1999-),男,辽宁营口人,东北大学资源与土木工程学院硕士研究生,主要从事工业火灾爆炸理论及防治技术研究,辽宁省沈阳市和平区文化路3巷11号,110819。
  • 基金资助:
    国家自然科学基金面上项目(51874070,51974189);辽宁省自然科学基金项目(2020-KF-13-01)

Design of multi parameter fire detection system based on neural network algorithm

JIA Rong-tian1, YUAN Chun-miao1, CAI Jing-zhi1, ZHENG Hao-tian2   

  1. (1. College of Resources and Civil Engineering, Northeastern University, Liaoning Shenyang 110819, China; 2. College of Safety Science and Engineering, Anhui University of Science and Technology, Anhui Huainan 232001, China)
  • Online:2022-04-15 Published:2022-04-15

摘要: 为进一步拓宽神经网络算法在火灾探测领域的应用,利用以阴燃火、明火和无火发生概率作为输出结果的国家标准火实验数据,在MATLAB平台上训练了一个多层BP神经网络,此网络对于测试集样本拟合的决定性系数均达到了0.95以上。以AT89C52为主控芯片,在Proteus中设计出了一种能满足火灾探测目的的仿真电路图。通过将训练好的BP神经网络参数导入到神经网络的仿真公式中,设计了能够驱使电路动作的软件程序,并且最终实现了火灾探测系统的电路仿真测试。

Abstract: To further broaden the application of neural network algorithms in the field of fire detection, a national standard fire experimental data with a smoldering fire, open fire, and no fire occurrence probability as output results was used to train a multi-layer BP neural network on the MATLAB platform. The decisive coefficients of the network for the sample fitting of the test set are more than 0.95. Using AT89C52 as the main control chip, a simulation circuit diagram which can meet the purpose of fire detection is designed in Proteus. By introducing the trained BP neural network parameters into the simulation formula of the neural network, the software program that can drive the circuit action is designed, and the circuit simulation test of the fire detection system is realized.

Key words: fire detection, 51 single chip microcomputer, MATLAB, BP neural network