The Survey on Multi-Source Data Fusion in Cyber-Physical-Social Systems:Foundational Infrastructure for Industrial Metaverses and Industries 5.0
arxiv(2024)
摘要
As the concept of Industries 5.0 develops, industrial metaverses are expected
to operate in parallel with the actual industrial processes to offer
“Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness
“6S" manufacturing solutions. Industrial metaverses not only visualize the
process of productivity in a dynamic and evolutional way, but also provide an
immersive laboratory experimental environment for optimizing and remodeling the
process. Besides, the customized user needs that are hidden in social media
data can be discovered by social computing technologies, which introduces an
input channel for building the whole social manufacturing process including
industrial metaverses. This makes the fusion of multi-source data cross
Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This
work firstly proposes a multi-source-data-fusion-driven operational
architecture for industrial metaverses on the basis of conducting a
comprehensive literature review on the state-of-the-art multi-source data
fusion methods. The advantages and disadvantages of each type of method are
analyzed by considering the fusion mechanisms and application scenarios.
Especially, we combine the strengths of deep learning and knowledge graphs in
scalability and parallel computation to enable our proposed framework the
ability of prescriptive optimization and evolution. This integration can
address the shortcomings of deep learning in terms of explainability and fact
fabrication, as well as overcoming the incompleteness and the challenges of
construction and maintenance inherent in knowledge graphs. The effectiveness of
the proposed architecture is validated through a parallel weaving case study.
In the end, we discuss the challenges and future directions of multi-source
data fusion cross CPSS for industrial metaverses and social manufacturing in
Industries 5.0.
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