<p>In the context of intelligent manufacturing, the increasing diversity and personalization of product demands, together with higher requirements for product quality, impose greater demands on the monitoring and control of production lines. As a core enabling technology, digital twin technology enables near-real-time monitoring and control of production states by constructing digital twin models of production lines in virtual space and leveraging high-quality production data. However, equipment behavior in production processes is complex and dynamic, and production lines are frequently adjusted due to changes in product demands and equipment replacement. These impose higher requirements on the representational capability and scalability of models, thereby limiting the application of existing modeling methods in production lines. To address this gap, this paper proposes a digital twin modeling method of production lines for production state monitoring and control. The proposed method, which is based on equipment units and oriented towards production lines, first employs a four-model-based approach (i.e., digital model, computational model, rule model, and behavior model) to develop equipment unit digital twin models with both high fidelity and good scalability, enabling accurate representation of the production behaviors and operational states of physical equipment. Subsequently, based on the equipment unit digital twin models, the production line digital twin is created rapidly and flexibly, thereby effectively responding to production line adjustments caused by changes in production requirements. Finally, the effectiveness and feasibility of the proposed method are validated through a typical production line modeling case study.</p>