Implementation of Core Algorithms for Automated Mine Ventilation Network Diagram Generation

Ventilation network diagrams play a vital role in mine safety, enabling simulation, airflow control, and emergency planning. Traditional manual drawing methods are time-consuming, error-prone, and difficult to synchronize with evolving mine structures. To address this, we propose an automated framework that generates schematic ventilation diagrams from spatial models while preserving topological fidelity. The framework integrates a hierarchical node layering algorithm based on Hasse diagrams, a trunk–branch decomposition strategy for structural abstraction, and a deformation-based curve layout model inspired by water droplet geometry to enhance visual clarity and structural alignment. A 3D elevation inference module ensures semantic consistency across 2D diagrams, 3D models, and graph representations. The system further supports reactive multi-view synchronized editing, enabling coherent modifications across different representations while preserving user-defined constraints. Although developed and validated in mine ventilation, the framework is generally applicable to directed-graph–based industrial systems. Its generality is further demonstrated on a schematic exhaust pipeline case, where a tree structure rather than a network diagram provides a more suitable abstraction, highlighting that the method supports domain-adaptive abstraction guided by topological characteristics. This approach has been integrated into real-world mining operations through the “Ventilation Brain System,” offering a scalable solution for intelligent layout generation, adaptive design iteration, and responsive decision-making. By bridging spatial realism with schematic abstraction, the proposed method streamlines diagram construction and reinforces intelligent mine ventilation management.