To address the challenges of weak feature extraction capability and poor adaptability to multi-source data encountered by by intelligent inspection algorithms in urban rail transit equipment rooms under complex environments, this paper proposes an innovative inspection scheme based on large vision models. This scheme leverages the powerful deep semantic modeling capability of large vision models to perform multi-granularity feature extraction equipment-room monitoring images, while simultaneously introducing an adaptive multi-scale fusion module to optimize feature representation capability. To balance computational efficiency and detection accuracy, a cloud-edge collaborative architecture is designed. The cloud hosts the complete large vision model and is responsible for high-precision inference in complex scenarios and model iteration, while the edge side deploys a lightweight inference module for preprocessing, real-timedetection, and fault early warning of inspection images, ensuring the timeliness and safety of equipment room operation and maintenance. To validate the effectiveness of the proposed scheme, tests are conducted using real operational data from the equipment room of Dalian North Station on Dalian Metro Line 1, covering various equipment types. Experimental results show that the algorithm achieves an mAP@50 of 0.923 and an F1-score of 0.915 in equipment state detection tasks, outperforming mainstream inspection algorithms in overall detection performance. Economic benefit analysis indicates that the scheme incurs an average annual operating cost of approximately RMB 440,000 yuan, representing an annual saving of about RMB 460,000 yuan per year compared with traditional manual and periodic maintenance approaches, demonstrating sound economic feasibility. Furthermore, the scheme adopts a general-purpose industrial hardware acquisition architecture, standardized data access protocols, and a modular algorithm design, offering strong scenario adaptability. It is not limited to urban rail transit equipment rooms and can be extended to other rail transit scenarios such as subway tunnels and stations.