Research on the Construction of a Classical Chinese Medicine Health Management System from the Perspective of Big Data and AI Integration
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Abstract
Existing Traditional Chinese Medicine (TCM) health management systems continue to face challenges including dispersed data, limited intelligent correlation of physiological signal acquisition, and insufficient objective quantification of health interventions. This paper proposes a TCM health management system centered on intelligent textile health monitoring based on the integration of Big Data and Artificial Intelligence (AI). As wearable electromagnetic sensing and wireless physiological monitoring technologies become increasingly important for precision healthcare, the proposed framework designs a textile sensing unit integrating temperature, pulse, and skin conductance sensors to enable continuous physiological data acquisition. Multi-source heterogeneous data fusion algorithms are employed to establish personalized health profiles, while Deep Neural Networks (DNNs) and TCM diagnostic knowledge graphs are combined to predict pathological trends. Reinforcement learning is further introduced to dynamically optimize health intervention strategies. The system is validated using 12,000 clinical samples, achieving a prediction accuracy of 93.1% and a personalized intervention satisfaction rate of 88.6%. The results demonstrate the effective integration of AI-driven intelligent textile perception with TCM health management, providing a feasible solution for wearable health monitoring, precise conditioning, and electromagnetic sensing-assisted intelligent healthcare applications.
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