Gangli Yao

Title: Gangli Yao and Coal-Mine Disaster Early Warning

Introduction

Gangli Yao is an inventor associated with Chongqing, China, and CCTEG Chongqing Research Institute. Her work includes a patented approach to combining multiple disaster indicators for early warning in coal mines.

Latest Patents

Yao is associated with 1 patent: “Multi-disaster fusion natural fission early warning method and system in coal mine.” The invention describes a method and system for analyzing coal-mine safety hazards by building and updating a relationship network among early-warning entities. It uses an LSTM artificial neural network to predict trends in monitored objects and applies natural-fission analysis rules. When the system detects an anomaly in one disaster index, it assesses related indexes using depth-first traversal and the specified rules to support targeted forecasting. The patent describes correlation analysis as a way to form predictive chains among early-warning indexes, enabling multidimensional analysis and earlier trend prediction.

Career Highlights

Yao’s listed patent focuses on integrating disaster indicators into a connected early-warning process. Its approach combines trend prediction, anomaly identification, and analysis of relationships among potential hazards.

Collaborations

Yao’s listed coworkers include Haitao Sun and Zhigang Zhang. The available information identifies her affiliation with CCTEG Chongqing Research Institute.

Conclusion

Gangli Yao’s patent presents a multi-indicator approach to coal-mine safety monitoring, linking anomaly detection and trend forecasting to support earlier, more targeted disaster warnings.