基于CPS的建筑环境数据级检测融合方法

CPS-based fusion method for detection of data level in built environment

针对建筑环境CPS数据级融合可靠性的问题,采用线性最小方差估计的方法对传统自适应加权融合算法进行改进,不仅减小了均方误差,而且使得融合结果可靠性更高。在数据融合前进行限幅处理,判断数据是否异常,对异常数据的加权因子进行配置处理,既提高了数据的筛选性,又保证了系统数据融合结果的可靠性。仿真实验表明了该方法的有效性。

For problem of CPS data level fusion reliability in built environment,the way of linear minimum variance estimation is used to improve the tradition adaptive weighted fusion algorithm,which not only reduces the mean square error,but also makes the fusion result more reliable. The amplitude limiting process should be carried out before data fusion,so as to determine whether data is abnormal and allocate weighting factor of the abnormal data,which can improve the filtration ability of the data and ensure the reliability of the data fusion result. The simulation experiment indicated the effectiveness of the way.