基于NB-IoT技术的环境监测系统优化设计

Optimization design of environmental monitoring system based on NB-IoT technology

为进一步提升工业生产过程的安全系数,提出一种基于窄带物联网NB-IoT的环境监测系统。其中,以NB-IoT技术作为系统的主要通信,以传感器为主要的环境数据采集工具,以改进的BP神经网络作为预测方法进行环境风险预测。实验结果表明,与传统的BP神经网络相比,经过粒子群算法PSO优化的BP神经网络具有更高的预测精度,且稳定性较好,将其应用于环境风险的预测时误差始终保持在1%的误差范围内。设计的基于NB-IoT的环境监测系统能够进行准确的数据采集和风险预测,能够进一步保障生产安全,可行性较高。

In order to further improve the safety factor of industrial production process,an environmental monitoring system based on narrowband Internet of Things NB IoT was proposed.Among them,NB IoT technology was used as the main communication method of the system,sensors were used as the main environmental data collection tool,and BP neural network was used as the basic prediction method for environmental risk prediction.The experimental results showed that compared with traditional BP neural networks,the BP neural network optimized by particle swarm optimization algorithm PSO had higher prediction accuracy and better stability.When applied to environmental risk prediction,the error was always within the 1%error range.The designed NB IoT based environmental monitoring system can accurately collect data and predict risks,further ensuring production safety,and has high feasibility.