基于贝叶斯网络的数据校正方法

精确的物料平衡模型是数据校正技术的基础，但实际上，频繁发生的调度事件动态地改变着物料的流向，目前的研究中往往容易被忽视，为此，从工程实践的角度出发提出一种新的处理方法.依据专家经验选择贝叶斯网络关键变量，利用大量的历史数据学习出贝叶斯网络，继而利用贝叶斯网络的诊断功能实现对调度事件的实时跟踪，最后建立精简模型, 增强了数据校正的可行性.仿真研究证实了该方法的有效性.

A method of data rectification based on Bayesian network

A precise mass balance model is the basis of data rectification. However, the streams of material in industrial processes change dynamically due to frequently occurring scheduling events. A new method was proposed to update the mass balance model in view of application. Bayesian network was used, its structure was selected according to experCs experiences and its variables were trained by historical data. Consequently, scheduling events were identified based on diagnostic function of Bayesian network, and an updated simplified model was finally established. In this way, the feasibility of data rectification was enhanced. And simulation results demonstrated the efficiency of the proposed method.