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Z-FFR (Z-Pinch drive fusion fission hybrid reactor) is an important base-load energy route with high research significance for early global carbon neutrality. Z-FFR is a nuclear device and its reliability is a central and critical part. This paper attempts to use big data in collaboration with artificial intelligence and industrial software for Z-FFR reliability analysis. Reliability data analysis mining is carried out by artificial intelligence, which is divided into three main levels: foundation platform layer, data management layer and data application layer. The foundation platform is the server layer, including application server, database server, file server and integration interface. The data management layer is the database layer, which provides data management functions for the basic information of Z-FFR, fusion reliability data, blast chamber reliability data, sub-critical reactor reliability data, and reliability data of the whole Z-FFR machine and its direct components; the data application layer is the user layer for data query, reliability data analysis, two-dimensional graph display of reliability data, and three-dimensional graph display of reliability data, which is mainly supported by mainstream This part is mainly supported by mainstream industrial software. The work in this paper provides data support for quality prediction of Z-Pinch and hybrid stack devices, thus improving the quality stability and consistency of the devices, and laying the foundation for the improvement of Z-FFR's refinement management, precision manufacturing, quality assurance and information management.
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