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Study of Mechanical Product Platform Module based on User Big Data

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DOI: 10.23977/cii2019.42

Author(s)

Xin Shang, Wenjie Gong and Yiquan Xu

Corresponding Author

Yiquan Xu

ABSTRACT

This paper studies how to extract the knowledge required of mechanical product platform module information from data redundancy and inconsistent data for the mechanical product platform. By analyzing the data obtained by users concerned about the attribute of mechanical product module, the fuzzy rough set is proposed. Knowledge discovery is performed to eliminate the redundancy of these attributes and turn them into consistency decision problems. To this end, the information system of the mechanical product platform module attributes based on user data is first extended, and then according to the characteristics of the user module attributes. It is determined that the distribution of decision table dt is used for reduction and decision table conversion, which realizes the reduction and inconsistency of module attribute data redundancy when the mechanical product platform is established, which provides help for the correct establishment of mechanical product modules. Finally, taking the film supply module of the packaging machine as an example, through the distribution reduction of the rough set decision table dt, the reduction and consistency conversion of the user demand attribute when the platform film supply module is established is realized, and the established module is more in line with the user group. The requirements indicate that the method is feasible.

KEYWORDS

Demand big data, mechanical product platform, rough set, attribute, knowledge discovery

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