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.

Intelligent monitoring in grain-reserve warehouses increasingly combines fixed sensors, UAVs, quadruped robots, human review, and site information systems, yet the resulting operational records are typically stored separately. This fragmentation makes it difficult to reconstruct how a detected problem evolves from observation to execution, evidence, and review, and limits traceable data-driven decision support. To address this issue, this study proposes a Geospatial Operational Event Knowledge Graph (GOEKG) that uses a stable managed-event identity to organize geographic anchors, source provenance, air–ground tasks, evidence, review, and lifecycle states within a property graph. The framework combines source-record normalization and graph mapping with competency queries and operational-semantic constraints for temporal order, spatial continuity, review reachability, lifecycle transitions, and provenance-aware reconciliation. A Panqiao warehouse case is used to instantiate and evaluate the graph. The case instantiated 11 reference-case nodes and 15 typed relations; six competency queries recovered the corresponding paths with temporal and spatial continuity across both robot branches. Controlled mutations of a source-derived record fixture returned UNKNOWN for missing temporal anchors, resolved spatial conflicts by explicit rules, and rejected temporal-order, spatial-linkage, and unauthorized-closure violations. A Neo4j benchmark on synthetic fixtures scaled from 1000 to 50,000 events. Secondary evaluations measured chronological alert-type relation completion and strict temporal fixed-snapshot follow-up candidate ranking. Under the latter protocol, Context-only achieved Macro-F1 0.783 and balanced accuracy 0.836, while Full context achieved average precision 0.952 and prioritized Pending Verification records in the limited-budget queue. The fixed-snapshot analysis supports context-assisted review prioritization under a retrospective event-time protocol. GOEKG therefore provides a traceable event-centered integration layer for intelligent monitoring and review-oriented operational decision support.