科学技术与工程2024,Vol.24Issue(15) :6558-6566.DOI:10.12404/j.issn.1671-1815.2305450

基于LPWAN和AQI指数预测的空气质量监测系统

Air Quality Monitoring System Based on LPWAN and AQI Index Predictions

张天娇 海涛 王钧 黄孝平 招兴业
科学技术与工程2024,Vol.24Issue(15) :6558-6566.DOI:10.12404/j.issn.1671-1815.2305450

基于LPWAN和AQI指数预测的空气质量监测系统

Air Quality Monitoring System Based on LPWAN and AQI Index Predictions

张天娇 1海涛 1王钧 2黄孝平 3招兴业1
扫码查看

作者信息

  • 1. 广西大学电气工程学院,南宁 530004
  • 2. 华蓝设计(集团)有限公司,南宁 530011
  • 3. 南宁学院电气工程学院,南宁 530200
  • 折叠

摘要

针对传统空气质量监测系统耗电量大、抗干扰能力差、稳定性不高以及空气质量指数(air quality index,AQI)预测精度不足等问题,设计了一种集低功耗广域物联网LPWAN、One-NET云平台和循环神经网络(gated recurrent unit,GRU)于一体的空气质量监测系统.系统采用光伏为主、市电为辅的混合模式供电;利用远距离无线电LoRa技术采集环境参数,集合云平台技术、麻雀搜索算法-变分模态分解-循环神经网络(sparrow search algorithm-variational mode decomposition-GRU,SSA-VMD-GRU)耦合模型实现远程监控和预测AQI指数.通过通信测试,结果表明通信距离1 000 m内,通信率在96%以上,丢包率不超过4%.将采集到的特征参数用传统的GRU模型、VMD-GRU模型和本文提出的SSA-VMD-GRU模型进行训练、测试仿真和对比,结果表明SSA-VMD-GRU模型相较于传统的GRU模型和VMD-GRU模型对AQI指数有更好的预测效果,均方根误差分别减小了 22.434、0.833,平均绝对误差分别减小了 16.849、0.623,预测误差率在3%以内.该系统能够实现对空气质量的实时监控和AQI指数的精准预测,为准确发布空气质量预警提供借鉴.

Abstract

Aiming at the problems of large power consumption,poor anti-interference ability,low stability and insufficient prediction accuracy of AQI(air quality index)of traditional air quality monitoring system,an air quality monitoring system integrating low-power wide-area IoT LPWAN,One-NET cloud platform and gated recurrent unit was designed.The system adopted a hybrid mode power supply based on photovoltaics and supplemented by mains power.Long-distance radio LoRa technology was used to collect environmental parameters,and the cloud platform technology and SSA-VMD-GRU(sparrow search algorithm-variational mode decomposition-GRU)coupling model were integrated to realize remote monitoring and prediction of AQI index.Through the communication test,the results showed that within the communication distance of 1 000 meters,the communication rate was above 96%,and the packet loss rate was not more than 4%.The results showed that the SSA-VMD-GRU model had a better prediction effect on AQI index than the traditional GRU model and VMD-GRU model,the root mean square error decreased by 22.434 and 0.833,the mean absolute error decreased by 16.849 and 0.623,respectively,and the prediction error rate was within 3%.The system can realize real-time monitoring of air quality and accurate prediction of AQI index,providing reference for accurate issuance of air quality warnings.

关键词

低功耗广域物联网/AQI指数预测/SSA-VMD-GRU模型/监测系统/光伏供电

Key words

low-power wide-area internet of things/AQI index forecast/SSA-VMD-GRU model/monitoring systems/photovoltaic power supply

引用本文复制引用

基金项目

国家自然科学基金(52277138)

广西壮族自治区重点研发计划(22035037)

出版年

2024
科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子:0.338
ISSN:1671-1815
参考文献量18
段落导航相关论文