An Auditable Downstream Safety-Intelligence Framework for Manufacturing Workplaces: Reliability-Aware PPE Evidence, Regulation-Grounded Retrieval, and Role-Specific Decision Support

Vision-based industrial safety monitoring increasingly integrates heterogeneous accidents, personal protective equipment (PPE), and semantic evidence, yet preserving their reliability, uncertainty, and provenance throughout downstream decision support remains challenging. This study proposes an auditable downstream safety-intelligence framework that transforms frozen multimodal perception outputs into structured event evidence. Worker-centered temporal PPE observations are processed into Candidate and Confirmed operational evidence while accident-related uncertainty is preserved. In parallel, regulation-grounded retrieval maps the same evidence to candidate regulatory context from a frozen Korean industrial-safety corpus, and manager- and worker-oriented outputs are generated under distinct information boundaries and independently verified. On a fixed internal benchmark of 150 videos and 3000 frames, the Full-clean configuration achieved 98.23% frame-level accuracy (Macro-F1, 97.47%) and 98.67% video-level accuracy, although the video-level difference from the intermediate configuration was not statistically significant. PPE-violation detection based on the Raw temporal state achieved an F1-score of 99.31% on the ground-truth normal-scene stratum. Controlled Legal-RAG regression reproduced all 117 invariant cases and behaved as predefined across the 32 query-invariant evidence-change cases and the single query-changed case. All 234 notification–report links, 44 PPE semantic checks, and 82 verifier findings satisfied predefined validation criteria. These findings constitute an internal feasibility validation of provenance-preserving, human-review-oriented safety intelligence rather than independent external validation or evidence of field performance.