Dynamic Risk Inference Method for Chemical Industrial Inspection Based on Spatio-Temporal Scene Graphs

To address the challenge of high false alarm rates caused by dynamic viewpoint noise in mobile chemical inspections, this study established a highly robust adaptive dynamic risk inference model. This research proposes an inference framework integrating spatio-temporal semantic constraints. Spatially, this study constructed a heterogeneous dynamic scene graph and introduced a kinematic-aware anisotropic dynamic field. This field transforms geometric hard boundaries into continuous risk gradients that deform dynamically with target intentions to suppress observation ambiguity. Temporally, the work designed an uncertainty-aware adaptive hysteresis filter, whose state machine thresholds adjust dynamically according to real-time sensor noise levels. Comparative tests on a real-world chemical dataset show that the model achieves a peak F1-Score of 93.1%, reduces the false alarm rate to 1.3 times/h, and requires a single-frame processing time of only 24.8 ms. The method theoretically achieves spatio-temporal dynamic noise reduction, significantly mitigates topological mutations and alarm chattering under complex visual noise conditions, meets edge computing deployment requirements, and provides a high-confidence sensing decision hub for industrial process safety monitoring.