基于数据融合的空气污染物监测技术研究

Air Pollutant Monitoring Technology Based on Data Fusion

由于当前已有方法未能对采集的空气污染数据进行归一化的加权平均处理,导致空气污染物监测执行率和监测结果准确性下降。为此,结合数据融合技术,提出一种基于数据融合的空气污染物监测方法。采集空气中不同污染物数据,将其进行融合。在此基础上,将归一化加权平均算法和模糊神经网络算法结合,对空气污染物质量进行监测,获取不同污染物的浓度变化情况。仿真实验结果表明,所提方法可以全面提升空气污染物监测执行率以及监测结果准确性,能够实现空气污染物准确监测。

As the current existing methods fail to perform normalized weighted average processing on the collected air pollution data,the implementation rate of air pollutant monitoring and the accuracy of the monitoring results decrease.Combined with data fusion technology,an air pollutant monitoring method based on data fusion is proposed to collect data of different pollutants in the air and merge them.On this basis,the normalized weighted average algorithm and the fuzzy neural network algorithm are combined to monitor the quality of air pollutants and obtain the concentration changes of different pollutants.The simulation experiment results show that the proposed method can comprehensively improve the implementation rate of air pollutant monitoring and the accuracy of the monitoring results,and can achieve accurate monitoring of air pollutants.