This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Ccteg Chongqing Research Institute. Active years: 2026.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:
Years Active: 2026
Title: Shan Si and Coal Mine Multi-Disaster Early Warning
Introduction
Shan Si is an inventor based in Chongqing, China, whose patent work addresses safety analysis and early warning in coal mines. His invention combines multiple disaster indicators to support earlier, more targeted risk forecasting.
Latest Patents
The provided information lists 1 patent for Shan Si: “Multi-disaster fusion natural fission early warning method and system in coal mine.” The invention builds and updates a relationship network among early-warning entities, then uses an LSTM neural network to predict trends in monitored objects. It also applies rules for natural-fission analysis and identifies anomalies in individual disaster indicators. When an anomaly is detected, a depth-first traversal algorithm and natural-fission rules help assess related indicators and guide targeted forecasts. The approach uses correlation analysis to form predictive chains among early-warning indicators, supporting multidimensional analysis and advanced trend prediction.
Career Highlights
Shan Si is associated with CCTEG Chongqing Research Institute. His listed patent reflects a focus on applying predictive models and interconnected disaster indicators to coal mine safety.
Collaborations
The provided information names Haitao Sun and Zhigang Zhang as Shan Si’s coworkers. Their specific roles in the patent or related research are not specified.
Conclusion
Shan Si’s listed invention brings together trend prediction, anomaly detection, and relationships among disaster indicators to support more comprehensive early warning in coal mines.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile