A Federated Visual Intelligence Framework for Sustainable Safety Governance in Grain Warehouse Infrastructure

Grain warehouses are critical nodes in food storage, circulation, and reserve systems, and their safe operation is relevant to occupational protection, reserve stability, and food-system resilience. However, unsafe warehouse behaviors are still mainly identified through manual patrols and fragmented inspections, making continuous observation, cross-site coordination, and early intervention difficult. To address this problem, this study proposes a grain warehouse safety governance framework with federated visual intelligence. The framework combines a real-time visual perception module, data-local federated learning, and a city-level digital supervision platform. Drawing on the Wenzhou grain reserve application scenario, the study defines twelve visually observable risk categories, including personal protective equipment (PPE) violations, and links artificial intelligence (AI) recognition with multi-source inspection, spatial mapping, warning generation, collaborative verification, work approval, closed-loop rectification, and large-model-assisted analysis. A four-client technical pilot compares isolated local, centralized, Federated Averaging (FedAvg), and Federated Proximal Optimization (FedProx) training across three selected recognition tasks. The task-specific results provide quantitative evidence from held-out warehouse-level validation and an intermittent-client stress test. The best held-out weighted F1-score (F1) values were 0.7712 for crossing over a conveyor, 0.5891 for passing under a conveyor, and 0.8481 for missing five-point safety harness. In the one-small-client-absent stress test, FedProx achieved weighted F1 scores of 0.5701, 0.4324, and 0.8064 for the same tasks, respectively. The framework translates visual detections into warning outputs, management indicators, and accountable safety workflows under data-governance constraints. It positions computer vision (CV) as an automated observation mechanism for responsible AI governance and embeds visual intelligence into a human-confirmed, data-local, and auditable safety-management workflow for sustainable food-storage infrastructure.