Abstract: A digital twin monitoring method for assembly workshops was proposed to address issues such as dispersed data sources, untimely perception of assembly information, and inaccurate data collection. Initially, key monitoring items were selected using a combination of the Analytic Hierarchy Process (AHP) and the Entropy Weight Method, and an information transmission network was established based on a client/server architecture. Secondly, a particle swarm optimization-Savitzky-Golay (PSO-SG) filtering data processing method is proposed, where the parameters of the Savitzky-Golay (SG) filter are optimized using the PSO algorithm to improve the data processing performance. Furthermore, a digital twin monitoring platform was constructed based on finite state machine theory, integrating two-dimensional data visualization dashboards with three-dimensional virtual models to establish a comprehensive monitoring system for the assembly workshop. Finally, the feasibility of this research was validated through an example involving a workstation within an automated assembly line for key components of a marine diesel engine.