In the internet of medical things, data primarily exhibits time-series and streaming characteristics, featuring typical attributes such as large-scale volume, high transmission rates, and significant heterogeneity. Given these data properties and the application requirements of medical scenarios, the development of specialized data platforms tailored to these needs holds considerable research significance and practical value. This study innovatively proposes the internet of medical things data platform solution based on a cloud-edge-end architecture, and elaborates on its architecture, functions, and implementation effects. The edge side is responsible for streaming data access, storage, and computation; the cloud side encompasses three layers of services: resources, data, and applications, constructing a data lake to provide data analysis services. This study has been implemented in PLA General Hospital for verification. From 2021 to 2024, 263 medical devices have been connected accumulatively, with a total data volume of 24.07 TB and stable operation within 4 years. In the performance stress test, the platform achieved the data access throughput of 23.91 MB/s and the data storage efficiency of 30.98 MB/s. These results demonstrate the feasibility of the architecture platform. This study has engineered and successfully applied the cloud-edge-end architecture in complex internet of medical things scenarios, addressing challenges such as heterogeneous protocol compatibility of medical devices, real-time response to clinical operations, and large-scale storage and application of the internet of things data. The established data platform provides a solid data foundation for smart medical applications and holds significant value for the research of medical artificial intelligence and the construction of future smart hospitals.
