Research on Multimodal Large-Model-Driven Mining Safety Early Warning Mechanism

Abstract

Mining safety production is a high-risk sector within the industrial system, facing severe challenges such as complex geological conditions, dynamic environmental changes, and intertwined human operation risks. Traditional single-modal monitoring technologies exhibit significant limitations in real-time perception and intelligent decision-making, failing to meet the demands of modern mining safety management. To address these issues, this paper proposes a multimodal large-model-driven mining safety early warning mechanism. By leveraging multimodal data integration and cross-modal learning, the mechanism achieves comprehensive perception and accurate early warnings for mining operations. The research encompasses multimodal data collection and processing, large-model design and implementation, as well as the construction and application of the safety warning mechanism. This approach significantly enhances the intelligence level of mining safety management and accident prevention, providing essential technical support for the development of smart mining.

Keywords

Mining Safety / Multimodal Technology / Large Model / Safety Early Warning / Smart Mining

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