Research and Implementation of Elderly Care Service System Based on Cloud Computing Platform

The elderly population is rapidly increasing, accompanied by a demand for better nursing services. In recent years, cloud computing has become a powerful tool for improving healthcare services. This article discusses the research and implementation of a cloud computing platform based elderly care service system. The system aims to provide personalized care services for the elderly at home. It uses cloud computing technology to store and process data related to the health status of the elderly. The system collects data from various sources, such as wearable devices, medical sensors, and electronic health records. Then, machine learning algorithms are used to analyze the collected data to identify patterns and trends in the health status of the elderly. Based on this analysis, personalized care plans were developed to meet the specific needs of each individual. The implementation of this system has several benefits, such as improving the quality of care, reducing medical costs, and improving efficiency. It also provides a platform for remote monitoring and remote medical services. In summary, researching and implementing a cloud computing platform based elderly care service system is an important step towards improving medical services for the elderly population. It provides a scalable solution that can be customized to meet individual needs while reducing costs associated with traditional healthcare systems. This article studies the K-Means clustering algorithm and implements its parallelization. Finally, based on the data integration analysis requirements of the system, this article designs a data integration processing scheme based on Map/Reduce, analyzes and processes the data in the system, explores the impact of elderly family education level on elderly people's admission to elderly care institutions, and obtains clustering results that provide auxiliary decision-making for elderly care institutions.