Article Details

Abstract

To address the problems of low efficiency and poor traceability in the manual management of traditional elderly care institutions, this paper develops a smart elderly care management system based on the Spring Boot and Vue frameworks. The system adopts a front-end and back-end separation architecture with distributed deployment, including modules such as bed management, meal management, care management, admission/discharge/leave registration, and data visualization and statistics. It introduces an intelligent care matching algorithm based on a rule engine and establishes personalized care recommendations and anomaly alerts. Through RESTful interfaces and WebSocket, the system builds a closed-loop business process and applies intelligent algorithms to analyze health data and perform rule matching. Simulation testing demonstrates that the system increases bed utilization by 17.7%, reduces the processing time of care documents from 28 minutes to 10 minutes, lowers the meal complaint rate by 35.4%, and achieves an accuracy of 92.3% in intelligent care matching. It significantly promotes response speed, resource allocation, and service intelligence, providing a lightweight technological solution for elderly care institutions.

Keywords

smart elderly care; elderly care center; Spring Boot; Vue; rule engine; intelligent nursing

References

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Funding

Henan Province Undergraduate Research Teaching Demonstration Course (Cloud Application Development Course) Project (Jiao Gao [2024] No. 403-59); Construction Project of Intelligent Curriculum (Cloud Application Development) for Undergraduate Universities in Henan Province (Jiao Ban Gao [2025] No. 200-51)

About the authors

Liang Bing (2004-), female, Han ethnicity, from Shangqiu, Henan Province, undergraduate studies, research direction: distributed system software development, intelligent algorithm scenario application; Corresponding author: Guo Lei (1976-), male, Han nationality, from Anyang, Henan, professor, department head, master's student, research direction: image processing, deep learning.