基于数字孪生的岸边集装箱起重机钢结构应力状态监测研究OA

Considering challenges associated with testing,the insufficient coverage of measurement points,and the high real-time requirements in stress monitoring of steel structures in quayside container cranes,a virtual sensing method for the global stress state based on digital twin technology is proposed.A digital twin framework for the steel structures of quayside container cranes was constructed,encompassing a high-precision twin model,a data acquisition and transmission system,and a three-dimensional visualization module.This framework enables real-time monitoring and dynamic analysis of the stress distribution in the steel structures of quayside container cranes.Additionally,a stress prediction model based on support vector machine was developed.This model was trained using simulation data and incorporates a mesh simplification method based on patch contraction,combined with a weighted K-nearest neighbor algorithm.The results demonstrate that the proposed digital twin system is capable of accurately mapping the global stress state of the quayside container crane under various working conditions,with the relative error of the test sample points remaining below 15%.This study validates the effectiveness of the proposed method and offers a novel approach for intelligent monitoring and operational state evaluation of the steel structure in quayside container cranes.