This invention relates to a multimodal sensing-based industrial process anomaly
safety control method, belonging to the field of intelligent industrial manufacturing. This method collects data on equipment, environment, materials, and personnel behavior through a multimodal heterogeneous
sensor array, and generates a standardized safety dataset through spatiotemporal alignment and fusion. It employs a differentiable industrial field
coupling propagation model to estimate production environment
field data, achieving dynamic compensation and enhanced
perception. A digital twin evolutionary network embedded with
production cycle time, energy efficiency, and safety
coupling constraints is constructed to output equipment health trends and residual safety capability assessments. Based on an interpretable
causal reasoning engine and aligned with a
safety procedure knowledge graph,
root cause diagnosis and compensation calculation for
process deviations are achieved. Temporal dependency constraints are introduced into the attention mechanism to achieve hierarchical early warning and proactive process reconfiguration. This invention improves the safety level and production continuity assurance capabilities of industrial production.