Abstract:

China’s demand for mineral resources remains persistently high; however, factors such as fluctuations in domestic supply and the volatile international market environment have exacerbated supply chain risks. It is therefore urgent to enhance the collaborative management of the industrial chain and risk response capabilities through intelligent technologies.This paper systematically elaborates on the theoretical foundation and technical pathways for building the platform. It integrates multi-source heterogeneous data, combines spatial analysis, time series analysis, and spatiotemporal linkage methods, and establishes a full-process analysis model covering “data fusion-mechanism analysis-solution output”. The platform incorporates technologies including artificial intelligence, cloud computing, complex networks, and remote sensing, supports multi-scenario simulation and dynamic early warning, and is capable of responding to emergencies such as market price fluctuations.In the empirical section, by simulating the scenario of a sharp surge in iron ore prices, the effectiveness of the platform in impact assessment and strategy formulation is verified. Finally, the paper looks ahead to future research directions, which include expanding the application to the fields of non-ferrous metals and energy minerals, optimizing algorithm models, and responding to the impact of the new energy transition on the resource demand structure.This platform provides crucial technical support for enhancing national mineral resource security guarantees and the scientific nature of decision-making.

Key words: spatio-temporal big data, mineral resource industry chain, intelligent analysis platform, iron ore