Under the Industry 4.0 paradigm, this paper addresses challenges in industrial robot virtual commissioning—including virtual-real data asynchrony, insufficient simulation accuracy in traditional offline programming, and low efficiency in inverse kinematics (IK) solving. A digital twin-based virtual commissioning and simulation method is proposed. Targeting unresolved issues in existing digital twin systems (e.g., unidirectional monitoring, simplistic IK analysis, and inadequate data transmission), this study first investigates D-H parameter modeling and an analytical IK solution optimized via least-squares. This establishes a real-time mapping between joint space and Cartesian space, significantly enhancing IK computation efficiency and accuracy through optimized joint configuration selection. Second, to resolve unidirectional monitoring, a geometrically and kinematically consistent digital twin model is constructed, enabling precise real-time state mapping of robots and PLC devices. Subsequently, to overcome data transmission limitations, an OPC UA-based hierarchical communication framework is designed. By classifying heterogeneous data, compressing nodes, and enabling efficient transmission, bidirectional real-time closed-loop control—encompassing command interaction and state synchronization between physical robots and twin models—is achieved, substantially improving virtual-physical mapping accuracy and real-time performance. Experimental validation demonstrates that compared to traditional iterative and analytical methods, the proposed IK solution drastically reduces single-point computation time while achieving end-effector positioning accuracy of ±0.07 mm. Virtual-physical synchronization latency is reduced to milliseconds, with real-time state synchronization between virtual and physical spaces. The method effectively resolves core issues of insufficient simulation accuracy and virtual-real asynchrony caused by information isolation in offline programming, significantly enhancing state synchronization real-time performance, data processing capability, simulation precision, and operational efficiency, thereby providing robust technical support for intelligent manufacturing upgrades.