Paper
27 March 2024 Fusion diagnostics based on blockchain technology
Jingzhe Zhu, Qiyue Huang, Pan Liu, Zilong Yuan, Zhangchun Tang, Gaoyang Liu, Ziqian Li
Author Affiliations +
Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131054C (2024) https://doi.org/10.1117/12.3026328
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China
Abstract
Nuclear fusion is a form of energy release that has received a lot of attention as it can free mankind from fossil fuels. However, the fusion process requires strict monitoring and measurement to ensure the reliability and safety of the reaction. Conventional fusion diagnostic devices suffer from problems such as low data credibility, data security and privacy protection, which may lead to inaccurate diagnostic results and thus threaten the stability and safety of the reaction. Therefore, how to solve these problems is an urgent issue at present. Blockchain technology is a distributed database technology, which has the characteristics of decentralisation, non-tampering and traceability. Blockchain technology has been widely used in finance, logistics, medical and other fields. In this paper, we will explore how to use blockchain technology to solve the problems of nuclear fusion diagnostic devices and improve the credibility and safety of nuclear fusion diagnosis. In this paper, a blockchain platform will be built to integrate mainly thermal imaging, mass spectrometer and neutron measurement data in fusion diagnostics. These data are stored on the blockchain using a consensus algorithm and establishing a multi-node mechanism. This prevents the data from being tampered with and can be traced at the same time. In terms of processing speed, due to the centralization and unification of the data and the nature of the consensus algorithm, the speed of processing data can be increased by several orders of magnitude through blockchain technology.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jingzhe Zhu, Qiyue Huang, Pan Liu, Zilong Yuan, Zhangchun Tang, Gaoyang Liu, and Ziqian Li "Fusion diagnostics based on blockchain technology", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131054C (27 March 2024); https://doi.org/10.1117/12.3026328
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KEYWORDS
Blockchain

Diagnostics

Data fusion

Plasma diagnostics

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