Abstract

According to multisource quality safety data of defective automobile products, key quality safety factors of defective automobile products are extracted, a defect information indicator system for automobile products is systematically constructed and a correlated graph is established between quality safety factors. Based on the optimization and correlation of the quality safety factor indicator system, Big Data technology is used to design a data structure for multisource quality safety information cluster, develop a data platform for the defect information analysis of automobile products and achieve information clustering and correlation analysis based on multisource quality safety data, providing technical support for the recall management of defective automobile products.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.