Research on Techno-Economic Restructuring of Digital Twin and Big Data in Intelligent Manufacturing

To address three critical challenges in traditional digital twin applications for smart manufacturing—static mirroring, localized optimization, and economic decoupling—we propose and validate a novel paradigm: the Twin-Data Mid-End (TDME) system driven by dual technological-economic mechanisms. By integrating real-time big data from production lines, equipment, supply chains, and market terminals through unified semantic frameworks, microservices-based technical modules, and deep reinforcement learning decision engines, this system generates instant reward signals based on multi-dimensional economic metrics including marginal profits, delivery cycles, and inventory turnover. This enables seamless “hot-swappable” reconfiguration of process algorithms, equipment controls, scheduling strategies, and organizational structures without production interruption. The system concurrently facilitates technological iteration and economic restructuring while dynamically optimizing efficiency-profit Pareto frontiers. Objective validation across 12 months of closed-loop industrial trials demonstrates reduced line changeover time by 37%, decreased unit comprehensive costs by 14.6%, shortened market response cycles by 42%, and increased return on investment by 11%, highlighting the paradigm’s practical applicability and broad adaptability.