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

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1000 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。

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 1000 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.