摘要
为更加准确地掌握重要场所环境数据,提出一种基于传感器技术的环境监测系统。其中,以STM32作为环境采集的主控芯片,以风速传感器、光照强度传感器等作为采集单元,对重要场所的环境数据进行采集;然后以采集到的空气质量数据为基础,构建改进遗传算法的空气质量预测模型,以提高整个监测系统的预测功能。结果表明,与其他优化方法相比,本研究的PSO-GA优化算法能更快地达到最优的适应度;在预测模型性能方面,与改进前的预测模型和其他预测模型相比,设计的基于改进遗传算法的SVM空气质量预测模型具有更小的预测误差,MSE和R^(2)两个评价指标分别为4.5*10^(-5)和99.11%。以上结果表明,设计的重要场所环境数据监测系统能更好地实现环境监测,可用于实际场景。
In order to more accurately grasp the environmental data of important places,an environmental monitoring system based on sensor technology is proposed.Among them,STM32 is used as the main control chip for environmental collection,and wind speed sensors,light intensity sensors,etc.are used as collection units to collect environmental data of important places;Then,based on the collected air quality data,an improved genetic algorithm air quality prediction model is constructed to improve the prediction function of the entire monitoring system.The results show that compared with other optimization methods,the PSO-GA optimization algorithm in this study can achieve the optimal fitness faster;In terms of prediction model performance,compared with the pre improved prediction model and other prediction models,the designed SVM air quality prediction model based on the improved genetic algorithm has smaller prediction errors,with MSE and R^(2)evaluation indicators of 4.5*10^(-5) and 99.11%,respectively.The above results indicate that the designed environmental data monitoring system for important places can better achieve environmental monitoring and can be used in practical scenarios.
作者
刘军
何建庄
LIU Jun;HE Jianzhuang(Guangxi radio and television,Nanning 530022,China;Guangxi Radio&TV Network Big DATA Technology Co.,Ltd.,Nanning 530022,China)
出处
《自动化与仪器仪表》
2024年第4期158-162,共5页
Automation & Instrumentation
