基于工业数字孪生仿真建模的虚拟工厂业务协同模型研究

着重改善物理实体智慧工厂在工程实践中日益凸显的环境变量动态变化实时感知失效、多维因素约束下,设备互联与数字集成失衡和较长周期内自主预测机制缺失等若干缺陷,构建了基于工业数字孪生仿真建模的虚拟工厂业务协同模型,并进行了典型环境下的仿真验证。首先,引入工业数字孪生仿真建模技术,构建了面向虚拟工厂业务协同模型的体系架构,给出了虚拟工厂数据流与控制流之间的耦合关系;然后,从数据感知及高效传输模型、融合事件驱动的虚实映射模型和基于深度学习的虚拟工厂业务协同模型等方面,详细给出了模型体系架构涉及的关键问题解决方案;最后,在 PyCharm 集成环境下构建虚拟工厂业务最优协同模型典型应用环境,并进行了多维仿真验证。以某有色金属冶炼行业重点企业为应用案例,对模型进行了工程应用实践验证,验证结果表明,模型较好改善了物理实体智慧工厂在工程实践中日益凸显的若干不足,具有环境变量动态变化实时感知全面、多维因素约束下设备互联与数字集成和较长周期内自主决策趋向明显等优势。

Research on Virtual Factory Business Collaboration Model Based on Industrial Digital Twin Simulation Modeling

Aiming at improving a number of defects such as the daily increasingly prominent real- time perception failures of dynamic changes of environmental variables in the engineering practice of the physical entity smart factories , the imbalance of equipment interconnection and digital integration under the constraints of multi-dimensional factors , and the lack of autonomous prediction mechanisms in a long period of time , etc. , the virtual factory business collaboration model based on industrial digital twin simulation modeling was constructed and simulated andverified in a typical environment.Firstly , the industrial digital twin simulation modeling technology is introduced , and the architecture of the virtual factory business collaboration model is constructed , and the coupling relationship between the data flow and control flow of the virtual factory is given , then , the key problem solutions involved in the model architecture are given in detail from the aspects of data perception and efficient transmission model , fusion event-driven virtual and real mapping model , and virtual factory business collaboration model based on deep learning.Finally , the typical application environment for the optimal?collaborative?model of virtual factory business was constructed under the PyCharm integrated environment and multi-dimensional?simulation?verification was carried out.Taking the key enterprise in the non-ferrous metal smelting industry as the application case , the model was verified by engineering application practice , the verification results show that the model has improved severa deficiencies that have become increasingly prominent in the engineering practice of the physical entity smart factory , it has the advantages of real-time perception of the dynamic changes of environmental variables , device interconnection and digital integration under the constraints of multi-dimensional factors , and obvious trends in autonomous decision-making in a long period.