Based on the Publish/Subscribe and RMI for the Railway Power Network Integrated Large Data Set Synchronization
- https://doi.org/10.2991/eia-17.2017.51How to use a DOI?
- railway power network; primary load; network structure; publishing / subscription technology; RMI
Railway power network is an important part of modern rail transportation, especially in high-speed railway. There are three problems: (1) With the increasing number of high-speed rail mileage, along the line of signal equipment, lighting equipment and occlusion device increasing quickly; (2) As the high-speed rail traffic density continues to increase, the locomotive is constantly in the movement of the flow, resulting in the power grid, the number of primary load increases rapidly, in order to ensure the reliable power supply equipment, the need for real-time recording of these devices on-site operating data; (3) The railway power supply dispatching system can not be compatible, so the interaction is difficult that the information is difficult to share. In order to solve above problems, this paper proposes a new method of large data synchronization based on P / S technology and RMI, which can reduce the coupling between a series of cooperative classes on the basis of improving system interaction efficiency and system compatibility Degree, easy to expand the system function, efficient completion of massive data across the database synchronization.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhijian QU AU - Ruilin ZHOU AU - Shengao YUAN PY - 2017/07 DA - 2017/07 TI - Based on the Publish/Subscribe and RMI for the Railway Power Network Integrated Large Data Set Synchronization BT - Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017) PB - Atlantis Press SP - 238 EP - 241 SN - 1951-6851 UR - https://doi.org/10.2991/eia-17.2017.51 DO - https://doi.org/10.2991/eia-17.2017.51 ID - QU2017/07 ER -