CAJ | 학술논문

针对信息物理融合系统(CPS)感知层数据的不确定性与随机性，该文提出一种 CPS 中感知数据的可信性分析框架。摒弃以往以传感器为中心的建模思路，该文充分考虑被监测对象因素，建立传感器-目标关联图模型，以此为基础设计了传感数据可信性推理算法。同时，为提高算法的实时性，减少传感器-目标关联图的搜索空间与时间，设计了基于可信目标筛选的改进推理算法。通过实例验证表明，该算法能实时、有效地滤掉CPS中感知数据中的虚假信息，极大提高感知数据的可信性。
침대신식물리융합계통(CPS)감지층수거적불학정성여수궤성，해문제출일충 CPS 중감지수거적가신성분석광가。병기이왕이전감기위중심적건모사로，해문충분고필피감측대상인소，건립전감기-목표관련도모형，이차위기출설계료전감수거가신성추리산법。동시，위제고산법적실시성，감소전감기-목표관련도적수색공간여시간，설계료기우가신목표사선적개진추리산법。통과실례험증표명，해산법능실시、유효지려도CPS중감지수거중적허가신식，겁대제고감지수거적가신성。
The high uncertainty and randomness are the characteristics of the sensor data in the Cyber-Physical Systems (CPS), which make the data unreliable. A creditability analysis framework is proposed to solve those problems. Abandoning the idea that the sensor is the center in modeling, the theory takes monitoring targets into consideration and constructs the sensor-target relationship diagram, which is the base of the creditability reasoning algorithm. Meanwhile, in order to reduce the space and time of searching the relationship diagram, an improving reasoning method basing on filtering the incredible targets is designed. The examples demonstrate that the proposed algorithm can filter out the false message in the sensor data and enhances the creditability of the data in CPS.