Intelligent Robots for Mass Personalization: A Cloud-Edge-Terminal Collaborative Mechanism for Deep Learning Models

Mass personalization manufacturing demands intelligent robotic systems with real-time decision-making and dynamic adaptability, yet deep learning (DL) deployment faces critical challenges: terminal devices lack computational resources while centralized cloud processing incurs prohibitive latency. This paper proposes a Cloud-Edge-Terminal collaborative framework that strategically distributes DL intelligence across hierarchical tiers. We present vertical distributed DL with early-exit mechanisms and partitioned DL strategies, integrated with incremental learning and model compression techniques. Experimental validation demonstrates 84% response time reduction, 88% communication overhead decrease, and 16-fold faster model updates compared to baseline approaches. A real-world smartphone assembly case study confirms practical viability, achieving 31% cycle time reduction and 72% downtime decrease. This framework establishes a foundational architecture for intelligent manufacturing systems capable of efficiently producing highly personalized products at scale.