关键词: 数字孪生, 人员宏观行为, 智能检测, 深度学习算法

Abstract: As an important factor in manufacturing,the human working activities are highly autonomous and stochastic,which makes it a chore in modeling the digital twins for human working activities for the development of the digital twin workshops.Besides,for production safety,some defense vendors such as certain aerospace complex mechanical and electrical products usually strictly restrict the number of workers in some key working areas,where the density of people may cause severe accidents.To well handle the workers and improve the production efficiency,a fast approach for modeling global working activities was proposed,and a 3-Stage Cascade Convolutional Neural Network (3-Stage CCNN) was constructed for fast recognition of workers in workshops,and the position of workers were useful to build the digital twin of global working activities.Experiments on open datasets Caltech Pedestrian and the real-world production dataset from certain aerospace vender demonstrated that the approach was both effective and efficient,and was of great practical impacts.

Key words: digital twin, global human activities, intelligent detection, deep learning algorithm