Architecture and Collaborative Optimization of Smart Health Platform with AIGC and Multimodal Perception

To address the drawbacks of traditional health and wellness platforms, such as single-dimensional perception, insufficient personalized services, and poor human-computer interaction adaptability, this paper proposes a smart health and wellness platform architecture that integrates Artificial Intelligence Generated Content (AIGC) and Multimodal Perception Technologies (MPT). By constructing a five-layer architecture of “Perception Layer-Data Layer-Algorithm Layer-Service Layer-Application Layer”, designing a three-level collaborative mechanism of “Data-Algorithm-Application”, and introducing optimization strategies including attention mechanism and model lightweighting mechanism, the architecture achieves efficient fusion of multi-source heterogeneous health and wellness data and precise generation of intelligent services.