This study constructs a series of models for water flow propagation in mine tunnel water inrushes and dynamic escape path planning for miners, aiming to provide reference escape strategies for mine water disasters. For single-point water outburst scenarios, the study abstracts the roadway network as a graph structure. Employing a breadth-first search algorithm, it calculates the time of first water arrival at endpoints and the time of roadway flooding, incorporating spatial parameters and initial outburst flow rates. For optimal miner escape route planning, the study introduces dynamic velocity constraints based on water depth: 4 m/s in dry conditions, 2 m/s downstream and 1 m/s upstream in low water, with high water levels prohibiting movement. The defined velocity constraints under different water depths provide essential ergonomic performance metrics for the development of miners'protective clothing in hydraulic environments. The model employs a time-extended Dijkstra algorithm to search for the shortest total escape time path. Building upon this foundation, the model is extended to predict flow superposition in delayed dual-source water inrushes. By applying time axis shifts, selecting minimum arrival times, and superimposing flow rates, it calculates flow states under dual-source interactions, enabling dynamic simulation of multi-source inrush systems. Finally, integrating dualsource flow results with the time-varying Dijkstra algorithm, the model plans optimal miner escape routes during delayed dual-source water inrushes. This study offers a theoretical framework for integrating wearable safety systems into intelligent mine disaster mitigation.

protective clothing performance, breadth-first search, time-extended dijkstra algorithm, water flow propagation model