Data Collaboration Model for Nuclear Power Business
- DOI
- 10.2991/978-94-6463-064-0_20How to use a DOI?
- Keywords
- deep learning; data collaboration; blockchain; distributed storage
- Abstract
To unify different departments, business systems and terminal protocols in nuclear power business, data collaboration technology and distributed data storage are introduced to realize intelligent collaboration between cloud servers and edge nodes with the help of edge-cloud collaboration, which can break through the bottleneck of multi-source heterogeneous data access. We propose a secure and efficient federated learning scheme for sharing private energy data in smart grids through edge-cloud collaboration to cope with unpredictable communication delays and potential security and efficiency issues. This model sinks the cloud computing technology to the edge side, realizes the cloud-side data collaboration model, promotes the transformation of nuclear power business to a unified and intelligent advanced model. This can effectively improve the working efficiency of nuclear power business, and ensure the security of data in the collaborative mode.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Hui Zhang AU - Siqi Jin AU - Wei Dong AU - Menghao Han AU - Zhenyan Ji PY - 2022 DA - 2022/12/27 TI - Data Collaboration Model for Nuclear Power Business BT - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) PB - Atlantis Press SP - 175 EP - 180 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-064-0_20 DO - 10.2991/978-94-6463-064-0_20 ID - Zhang2022 ER -