基于LPWAN和AQI指数预测的空气质量监测系统
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X51,TP29

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国家自然科学基金(52277138);广西重点研发计划项目(22035037)


Air quality monitoring system based on LPWAN and AQI index predictions
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    摘要:

    针对传统空气质量监测系统抗干扰能力差、稳定性不高以及AQI指数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GRU于一体的空气质量监测系统。系统利用LoRa技术采集环境参数,集合云平台技术、SSA-VMD-GRU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1000米内,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RMSE和平均绝对误差MAE均有降低,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 poor anti-interference ability, low stability and insufficient prediction accuracy of AQI index of traditional air quality monitoring systems, an air quality monitoring system integrating low-power wide-area IoT LPWAN, One-NET cloud platform and recurrent neural network GRU is designed. The system uses LoRa technology to collect environmental parameters, and integrates cloud platform technology and SSA-VMD-GRU model to realize remote monitoring and prediction of AQI index. Through the communication test, the results show that within the communication distance of 1000 meters, the communication rate is above 96%, and the packet loss rate is not more than 4%. The results show that the SSA-VMD-GRU model has a better prediction effect on the AQI index than the traditional GRU model and VMD-GRU model, the RMSE and MAE are reduced, and the prediction error rate is 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.

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张天娇,海涛,王钧,等. 基于LPWAN和AQI指数预测的空气质量监测系统[J]. 科学技术与工程, 2024, 24(15): 6558-6566.
Zhang Tianjiao, Hai Tao, Wang Jun, et al. Air quality monitoring system based on LPWAN and AQI index predictions[J]. Science Technology and Engineering,2024,24(15):6558-6566.

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  • 收稿日期:2023-07-19
  • 最后修改日期:2024-02-28
  • 录用日期:2023-10-24
  • 在线发布日期: 2024-06-04
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