The national material reserve system is the core of the national reserve system and an important component of safeguarding national security. Its efficient, intelligent and secure management is directly related to social stability and national security. Against the dual backdrop of escalating global supply chain risks and the upgrading of national strategic security needs, as the most cutting-edge and advanced technological representative in the field of artificial intelligence, large model technology provides a key driving force for building an intelligent and resilient reserve system by virtue of its powerful capabilities in multi-modal data fusion, complex decision-making optimization and adaptive learning. This paper is committed to systematically constructing an overall research framework for empowering the management of national material reserves with large models. Firstly, it elaborates on the technological evolution and core advantages of large model, and deeply analyzes the inevitability of application in material reserves. Secondly, combining the technical characteristics of large models and the demands of material reserves, it focuses on looking ahead to the enabling paths in core scenarios such as intelligent prediction, dynamic optimization, emergency response, intelligent scheduling, smart management, risk simulation, knowledge sharing, and presents the key technical points involved in the enabling process. Finally, it analyzes the challenges faced in the enabling process from technical, management and other dimensions. The purpose of this paper is to provide directional guidance for theoretical research, technological breakthroughs and practical exploration, and promote the in-depth integration of large model technology with the needs of national material reserves.