Improvement and analysis of a monitoring system for garment cleaning processes based on Cloud technologies

Abstract

This dissertation investigates the development of a cloud-based monitoring system for garment cleaning processes, integrating modern digital technologies such as cloud computing, the Internet of Things (IoT), and data analytics. The increasing demand for efficient resource utilization, quality assurance, and automation in service industries has necessitated the implementation of intelligent monitoring solutions. The proposed system enables real-time data acquisition from sensors installed in laundry equipment, transmission of data to a cloud platform, and intelligent analysis for performance optimization. The study develops a mathematical model describing the cleaning process, proposes an algorithmic framework for system operation, and introduces a scalable cloud architecture. Experimental results demonstrate that the system significantly improves operational efficiency, reduces energy and water consumption, and enhances process reliability. The findings confirm the feasibility and effectiveness of cloud-based monitoring in intelligent service systems.