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Top1. Introduction
With the continuous improvement of living standards, people are paying more attention to their own health. At present, with the introduction of wearable medical devices by major companies, a large number of people will choose to use wearable medical devices in their daily life to detect their own health in the future, which will produce a huge amount of physiological data (Tan et al. 2022; Huarng et al. 2022). How to analyze and process these physiological data quickly has become an emergent issue in the medical field (Moge et al. 2022).
At present, the existing telemedicine monitoring and diagnosis systems are all independent and decentralized large hospitals. Various services are independently provided by each hospital, which isolates a medical diagnosis system from other medical diagnosis systems (Lohani & Thirunavukkarasan 2021). Thus, it results in low information sharing between different systems and high construction and maintenance costs. In addition, due to the current shortage of cloud technology related talents, once the system meets issues, it cannot obtain technical support and maintain regular system updates in time (Bommala et al. 2021; Cresswell et al. 2022). Thus, it is necessary to design an new system which can upgrade and improve its own medical smart information to meet the requirements of people for their own physiological health services. It is urgent to build an efficient medical diagnosis platform with high intelligence and strong analysis ability. The cloud computing mode can change the decentralized application mode of traditional medical service system to enhance the processing capacity of physiological data and conform to the development trend of medical informationize. The cloud computing based medical diagnosis has great advantages over the traditional medical diagnosis model (Cengiz 2021; Dang et al. 2019). The cloud medical service platform has strong data processing capability, which can quickly and timely analyze the physiological data uploaded by users. The medical platform gives full play to the powerful resource integration capability of cloud computing technology, which helps to accelerate the establishment of a comprehensive medical service standard and provide citizens with comprehensive health resource management. With the help of data mining technology, we can mine valuable information from these massive physiological data to provide citizens with conveniently online query function and let them know their health status in real time (Chen et al. 2018; Kumar et al. 2019). Thus, they can make corresponding adjustments in daily life according to whether their bodies are in a healthy state stably. At the same time, medical experts can pay attention to patients' physical conditions at any time through visiting cloud sources and quickly make immediate response in case of emergency medical events.
Although cloud computing based medical platform provides an efficient computing platform for medical data processing, the growth rate of network bandwidth is far behind the growth rate of data generated by various IoT based medical devices (Ali et al. 2018). The single computing resource based on cloud computing model can no longer meet the real-time, security, low energy consumption and massive data processing in medical care. In order to satisfy the requirements of the data transmission process in terms of fast connection, real-time business, data optimization, application intelligence, security and privacy protection, it is necessary to make full use of the edge network composed of sensor nodes, smart phones and other terminals, as well as cellular base stations and other infrastructure to process the content requested by IoT applications at the network edge near the data source, and provide edge intelligent services to optimize the connection, unload traffic and enhance the user’s experience (Greco et al. 2020).