AIGC-Driven Intelligent Asset Management in Universities: Technical Paths and Strategy Optimization

In response to issues such as low efficiency and insufficient decision-making support in university asset management, this paper investigates the application strategies of artificial intelligence generated content (AIGC) technology in intelligent asset management. By analyzing core AIGC technologies such as generative adversarial networks (GANs), natural language processing (NLP), and multimodal learning, and considering the characteristics of university asset management, it proposes tailored solutions for scenarios such as intelligent asset inventory, predictive maintenance, and resource allocation optimization. The paper examines implementation challenges, including unstructured data processing, model generalization ability, and data privacy, and puts forward countermeasures such as constructing a “human-AI collaboration” management model and formulating data standards and sharing mechanisms. It also presents two major innovations: first, technological integration innovation, proposing a three-dimensional asset management model for universities that combines AIGC with digital twin technology; second, management model innovation, designing a transformation path from a “passive response” paradigm to an “active prediction” paradigm. The findings demonstrate that AIGC technology enables intelligent asset inventory and accurate asset demand prediction. It holds great significance in reducing labor costs, optimizing resource allocation, and providing a scientific basis for decision-making. It can effectively advance the intelligent and modern transformation of university asset management, significantly improving the management level.