摘要
设计了一种基于物联网的实时监测油烟浓度的系统,系统通过包含高精度的FIGROO系列等多个传感器实时采集餐馆排风口气体浓度,并在基站进行初步数据融合;随后将初步融合数据通过GSM/GPRS网络传输到服务器端,基于D-S证据方法以及神经网络等进行深度数据融合和实时显示、监控报警。利用多传感器以及数据融合技术,同时,解决了传统油烟浓度监测中的费时费力、高成本、精度低等缺点。
This paper designs a real-time cooking fume detection system based on IOT（Internet Of Thing）, the system gets mul-ti-sensors include FIGROO series to collect the air information of the restaurants and conduct the junior data fusion in the base sta-tion; then the data will be transmitted to the server by the GPRS. Based on the D-S evidence theory and NeuralNetworks, we will do the senior data fusion process and real-time presenting and alarming. Using the multi-sensors and data fusion technology, the prob-lem of high cost and low precision can be both solved in the traditional cooking fume detection way.
出处
《微型电脑应用》
2013年第9期18-20,共3页
Microcomputer Applications
关键词
物联网
油烟监测
多传感器
D—S证据
神经网络
数据融合
IOT
Cooking Fume Detection
Multi-sensor
D-S Evidence Theory
NeuralNetworks
Data Fusion
