Research on the Design Scheme of the University State-Owned Asset Management Platform Based on the Internet of Things

An in-depth analysis of current state-owned asset management in higher education institutions is conducted, with its characteristics, current status, and informatization development being explored in this paper. It is revealed that university state-owned asset management is grounded in physical custodianship, emphasizes the alignment of accounts with physical assets, and adopts a management model of “centralized coordination and categorized oversight” . However, the level of informatization varies significantly, manifesting as a tendency to prioritize business operations over data management, emphasize localized solutions over systematic integration, favor software applications over hardware infrastructure, rely on human oversight over intelligent management, and focus on administrative control over service delivery. To address these challenges, a survey is conducted through questionnaires among universities under the Ministry of Education, analyzing the main challenges in current university state-owned asset management. Through a case analysis of an IoT-based asset management system deployed in an eastern Chinese university, a design scheme for a university state-owned asset management platform based on the Internet of Things is proposed, incorporating a hierarchical structure design, perception layer optimization, network layer expansion, data layer enrichment, and application layer interface design. The scheme emphasizes standardized signal protocols, deployment of integrated sensing devices, flexible phased implementation, network layer technological expansion, integration of data processing tools, diversified data output, and personalized interface design. Furthermore, management assistance is enhanced through system log data mining. This paper aims to improve university state-owned asset management and enhance asset utilization efficiency through IoT technology, thereby providing a practical reference and a direction for continuous optimization.