Jinfeng Cao

Title: Jinfeng Cao and Collaborative Multi-Physics Disaster Warning

Introduction

Jinfeng Cao is an inventor associated with Qingdao University of Technology in Qingdao, China. Her patent work focuses on using multiple types of monitoring data to assess potential hazards in project structures.

Latest Patents

Cao is associated with one patent, titled “Project Disaster Warning Method and System Based on Collaborative Fusion of Multi-Physics Monitoring Data.” The invention describes a method for collecting and preprocessing real-time data from multiple sensors monitoring potentially dangerous parts of a structure. It normalizes time-series data from different physical fields and uses statistical analysis to assess how monitoring indicators relate to structural safety.

The method uses those sensitivity analyses to guide the initial training of a long short-term memory (LSTM) network. It then combines the network’s outputs through an improved Dempster–Shafer evidence theory based on Chebyshev distance, producing probability assignments for different warning levels. A decision method based on those assignments determines the danger level of potentially hazardous structural areas.

Career Highlights

Cao’s listed patent reflects an approach that combines sensor monitoring, statistical analysis, machine learning, and evidence fusion for project disaster warning. The available information identifies her affiliation with Qingdao University of Technology and lists 1 patent.

Collaborations

Cao’s listed coworkers include Liming Zhang and Zhongyuan Liu. The available information does not specify their roles in the patent or the nature of their collaboration.

Conclusion

Jinfeng Cao’s patent presents a multi-stage framework for evaluating structural hazards by integrating monitoring data and computational methods to support disaster warnings.