Research on data collection and real-time management mechanisms for smart teaching campuses based on edge computing

With the continuous advancement of smart education, smart teaching campuses have placed higher demands on the real-time nature, accuracy, and security of data collection. Traditional cloud computing-based data processing models often struggle to meet the requirements for real-time response and local decision-making when faced with large-scale, high-frequency teaching environment data due to bandwidth limitations and latency issues. To address this, this paper proposes a data collection and real-time management mechanism for smart educational campuses based on an edge computing architecture, aiming to enhance system response speed, reduce data transmission pressure, and strengthen the system's autonomy and security capabilities. The paper first analyzes the types of data in educational campuses and the key issues in the data collection process, followed by the design of edge computing node deployment schemes, local preprocessing and decision-making mechanisms, as well as edge-cloud collaborative data synchronization and security management strategies. Based on this, a prototype system is constructed and functional verification and performance evaluation are conducted. The results show that this mechanism has significant advantages in terms of response latency control, processing efficiency, and system stability. This study provides new insights for optimizing the data infrastructure of smart campuses and lays the theoretical and technical foundation for the deepening application of edge computing in educational scenarios.