[1]李湘文,周辅杰,崔 崴,等.基于物联网的地下矿井空气质量智能预测[J].计算机技术与发展,2020,30(08):115-119.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 019]
　LI Xiang-wen,ZHOU Fu-jie,CUI Wei,et al.Intelligent Prediction of Mine Air Quality Based on Internet of Things[J].,2020,30(08):115-119.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 019]
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基于物联网的地下矿井空气质量智能预测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年08期
页码:
115-119
栏目:
安全与防范
出版日期:
2020-08-10

文章信息/Info

Title:
Intelligent Prediction of Mine Air Quality Based on Internet of Things
文章编号:
1673-629X(2020)08-0115-05
作者:
李湘文; 周辅杰; 崔 崴; 邓琴秀; 张辉雨
成都理工大学 工程技术学院,四川 乐山 614000
Author(s):
LI Xiang-wen; ZHOU Fu-jie; CUI Wei; DENG Qin-xiu; ZHANG Hui-yu
Engineering & Technical College,Chengdu University of Technology,Leshan 614000,China
关键词:
物联网; 矿井环境指数; RNN; 人工神经网络; 机器学习; TensorFlow
Keywords:
IoT (Internet of Things); MEI; RNN; ANNs (Artificial Neural Networks); machine learning; TensorFlow
分类号:
TD712
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 08. 019
摘要:
为了提高安全性,全世界都在寻求实施无线传感器网络(WSNs)来监测复杂的、动态的和环境恶劣的地下煤矿。文中引入了一种可靠的物联网(IoT)空气质量监测系统, 该系统由传感器模块、通信协议和基站组成。 基于 STM32 的传感器模块具有八个不同的参数,安装在可操作的地下煤矿的不同位置。 基于感知数据,该系统用煤矿环境指数(MEI)对地下煤矿矿井空气质量进行评价。 采用主成分分析法确定了 CH4 、CO、SO2和 H2S 是影响矿井空气质量最主要的气体。 将主成分分析的结果输入到 RNN 神经网络模型中,实现了 MEI 的预测。结果表明,基于主成分分析的神经网络在 MEI 预测中具有较好的性能, 主成分分析+RNN 预测模型的性能指标 R2和 RMSE 值分别为 0.489 0 和 0.120 4, 提高了线性回归模型对矿井大气污染物的预测精度。 因此,提出的基于 STM32 和 Tensorflow 平台的人工神经网络可以快速评估和预测矿井空气质量,提高矿井环境安全性。
Abstract:
In order to improve security,the world is seeking to implement wireless sensor network (WSNs) to monitor complex,dynamic and harsh under-ground coal mines. We introduce a reliable Internet of Things (IoT) air quality monitoring system which is composed of sensor module, communi-cation prot-ocol and base station. The sensor module based on STM32 has eight different parameters and is installed in different positions of operable underg-round coal mine. This system evaluates the air quality of underground coal mines with the coal mine environmental index (MEI). The principal com-ponent analysis method is used to determine that CH4 ,CO,SO2 and H2S are the main gases affecting mine air quality. The system inputs the results of principal component analysis into the RNN neural network model and realizes the prediction of MEI. The results show that the neural network based on principal component analysis has better performance in MEI prediction. Principal component analysis combined with RNN prediction model method reduces the error. Its error indices R2 and RMSE are 0. 489 0 and 0.120 4 respectively,which improve the prediction accuracy of linear regression model for mine air pollutants. Therefore,the proposed neural network based on STM32 and Tensorflow platform can quickly evaluate and predict mine air quality and improve mine environmental safety.

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更新日期/Last Update: 2020-08-10