基于OPC UA的数字孪生车间实时数据融合与建模研究

Research on Real-Time Data Fusion and Modeling of Digital Twin Workshop Based on OPC UA

数字孪生车间是智能制造背景下车间全息监控、精准分析、实时决策的有效手段。而实时高效的异构数据采集和融合,以及虚实车间的交互连接是驱动数字孪生车间运转的核心。为此,依托制造物联网和OPC UA通信技术,建立了数字孪生车间实时数据采集框架。采用分层融合的策略,去除原始数据中的信息冗余,匹配产生中间事件和生产过程数据,同时基于空间尺度构建实时数据空间对象模型。另外针对提高数据传输实时性的目的,按照信息分类—自适应压缩—节点合并的路线,缩减OPC UA信息模型节点数目和节点容量,设计了面向数字孪生车间的OPC UA信息建模方案。最后,以某航天结构件生产线为案例,为数字孪生车间开发了实时数据采集系统,并验证了此方案的可行性。

In intelligent manufacturing,digital twin workshop(DTW)is an effective approach for holographic monitoring,precise analysis and real-time decision-making.Multi-source heterogeneous data acquisition and fusion in real-time and the effective connection among physical and virtual workshop are the core of DTW.Therefore,a real-time data acquisition architecture is established based on the Internet of manufacturing things and OPC UA communication technology.Moreover,a hierarchical fusion strategy is employed to generate production process data and spatial object model.In addition,in order to improve the efficiency of data transmission,an OPC UA information modeling scheme for DTW is designed according to the mainline of’’information classification-adaptive compression-node merging’’,which reduces the number and capacity of OPC UA information model nodes.Finally,taking an aerospace structure product line as an application case,a real-time data acquisition system is developed for the DTW and the feasibility of the proposed method is verified.