Abstract: Intelligent mining represents a critical path for the safe and efficient extraction of challenging coal resources, particularly those with steep and extreme inclinations, in China. Large-scale physical simulations leveraging digital twin technology are pivotal in addressing the intricate mechanical behaviors of coal and rock, as well as the intelligent control challenges posed by gravity and inclination effects. This study comprehensively delineates the design framework, structural features, and testing/detection methodologies of a large-scale coal mining face physical simulation system empowered by digital twin technology. This system facilitates data visualization, robust human-machine interaction, and process self-optimization during mining operations. Addressing the challenges traditional hydraulic supports encounter in areas such as real-time monitoring, predictive maintenance, design optimization, and physical modeling, a posture perception and simulation system was developed. This system, grounded in hydraulic support and digital twin technology, employs software like SolidWorks, Maya, and Unity3D to create digital twin models of hydraulic supports. Through the integration of various posture perception sensors, it gathers posture and load data from the physical hydraulic supports. This setup enables precise posture alignment and immediate feedback between the digital twins and their physical counterparts. The system's mapping between virtual and real domains is achieved through detailed analysis and processing of the gathered data. Ultimately, the feasibility and efficacy of this system are corroborated through a multi-functional, variable-angle large-scale "support-surrounding rock" system physical simulation platform. This platform conducts reliability and stability tests under various inclination conditions, validating the system's operational capabilities.