Jin Hui
ABSTRACT
This system aims to meet the personalized needs of dormitory management by creating an efficient and standardized business processing process, while establishing a unified and standardized data storage architecture, laying a solid foundation for efficient management and utilization of data. At the level of data processing and analysis, the system introduces the FP-Growth association rule mining algorithm to conduct in-depth analysis of the data, identify potential association patterns and trends, and provide valuable decision support for the system. Universities face a series of complex issues in dormitory management, including room allocation, cumbersome check-in and check-out processes, inaccurate bill payment processing, slow response to maintenance requests, lax security and access control, poor communication, and chaotic inventory management. To address these issues, we plan to develop a dormitory management system with decision support. The system will cover multiple functional modules such as room allocation, check-in and check-out, bill payment, repair requests, security and access control, communication, and inventory management. In terms of room allocation, we will use preference and availability-based allocation algorithms, considering factors such as student preferences, room availability, gender, grade, and major, to achieve automatic room allocation. The check-in and check-out module uses automated process algorithms to automatically identify student identity information and room information, automatically review and confirm relevant procedures, ensuring smooth processes and accurate information. The bill payment module utilizes automated calculation and payment algorithms to automatically calculate fees based on factors such as students’ accommodation time and water and electricity usage and provides convenient payment methods. The maintenance request module adopts intelligent scheduling algorithms to automatically allocate maintenance tasks based on students’ maintenance requests and the availability of maintenance personnel, and track maintenance progress and status in real time. In addition, we will also manage the entry and exit of the dormitory building and monitor the implementation of security measures through access control systems, surveillance cameras, and other devices to ensure the safety of the dormitory. At the same time, it provides functions such as announcement release, event notification, and policy communication to ensure smooth information flow between residents and administrators. The inventory management module tracks the inventory status of dormitory furniture, equipment, and supplies in real time, providing functions such as procurement, warehousing, outbound, and inventory of materials. To improve the decision support capability of the system, we will also identify important predictive factors in various functional modules, such as student preferences, room utilization, gender ratio, grade distribution, etc., to more accurately predict and evaluate the performance and effectiveness of the system, providing strong support for system optimization and improvement.
Keywords: Big data, dormitory management system, decision support, data mining, FP growth algorithm