基于BP神经网络的多传感器新风调控系统研究

Research on multi-sensor fresh air regulation system based on BP neural network

室内空气环境污染具有积累性、长期性和多样性三方面的特征。为了更加精确检测出室内的环境从而来对新风调控系统进行更加有效智能的控制,使得室内空气质量保持一个相对良好的状态并且能够预防各种突发情况的发生。结合现有的新风调控系统的研究,本文利用分布式多传感器检测的方法来将室内环境转成数据信息,采用BP神经网络算法建立调控系统模型将多源数据信息融合处理。实验仿真结果表明该模型可以准确地根据室内各种环境状况判断出需要调制的风速。

Indoor air pollution is characterized by accumulation,long - term and diversity. In order to detect the indoor environment more accurately and control the fresh air control system more effectively and intelligently,the indoor air quality is kept in a relatively good state and various emergencies can be prevented. In combination with current research on fresh air control system,this paper the method of using distributed multisensor detection to the indoor environment to data information,BP neural network algorithm is adopted to establish the system for regulating the multi-source data fusion processing model. The simulation results show that the model can accurately judge the wind speed that needs to be modulated according to various indoor environmental conditions.