煤矿安全监测多传感器分层数据融合模型研究

Research of hierarchical multi-sensor data fusion model for coal mine safety monitoring

为提高多传感器检测系统预警的精度,降低多传感器监测过程中出现的状态不明或状态误判的发生率,提出了一种多传感器分层数据融合模型。该模型在数据层运用层次分析法确定隶属度和相应权数,在特征层运用模糊评价法进行数据融合,在决策层运用D-S证据理论进行数据融合,可根据对应输入融合数据类型的不同运用合适的算法进行计算处理。试验结果表明,与初始数据比较,该模型能够将目标的安全、轻微、危险3种状态的隶属度分别提高8.3%,6%,29.2%,验证了该模型的有效性。

A hierarchical multi-sensor data fusion model was proposed in order to improve precision of early warning of multi-sensor detection system and reduce rate of unknowing or false judgement of the state in monitoring process of the multi-sensor. The model uses hierarchical analysis method to determine membership degree and the corresponding weights in data level, uses fuzzy evaluation method to fuse data in feature level, and adopts D-S evidence theory to fuse data in decision level. It can calculate with different algorithms according to kind of the input fusion data. The experimental results show that the model can increase membership grade of security, slight, dangerous states 8.3%, 6%, 29.2% compared with initial data, and verify the effectiveness of the model.