Hairong Dong

Title: Innovations of Hairong Dong in Train Operating Condition Identification

Introduction

Hairong Dong is a notable inventor based in Beijing, China. He has made significant contributions to the field of train operations through his innovative patent. His work focuses on enhancing the safety and reliability of train systems.

Latest Patents

Hairong Dong holds a patent for an "Adaptive control method and system based on train operating condition identification." This patent discloses a method that includes acquiring train operating state data under various traction conditions and calculating traction and braking performance data. An adhesion condition characteristic data set is formed, and train adhesion conditions are divided into N grades based on this data set. The current adhesion condition grade N is identified using the number of adhesion condition grades and current operating state data. This method establishes a mapping relationship between an adhesion coefficient and maximum deceleration for braking under the identified grade. By clustering and grading adhesion conditions through a clustering algorithm, the method achieves more accurate adhesion condition analysis. The adaptive generation of optimal train protection curves based on adhesion condition grades enhances the reliability and safety of train operations.

Career Highlights

Hairong Dong is associated with the CRSC Research & Design Institute Group Co., Ltd. His work at this organization has allowed him to focus on innovative solutions for train operations. His contributions are vital in improving the efficiency and safety of train systems.

Collaborations

Hairong Dong has collaborated with notable coworkers, including Ling Liu and Hui Yang. Their combined efforts contribute to advancements in train technology and operational safety.

Conclusion

Hairong Dong's innovative work in train operating condition identification showcases his commitment to enhancing train safety and reliability. His patent reflects a significant advancement in the field, demonstrating the importance of adaptive control methods in modern train operations.