公开(公告)号：US20250036111A1

公开(公告)日：2025-01-30

申请号：US18916505

申请日：2024-10-15

Applicant: GUANGDONG UNIVERSITY OF TECHNOLOGY

Inventor： Jiewu LENG ,  Rongjie LI ,  Caiyu XU ,  Yuanwei ZHONG ,  Junxing XIE ,  Keyou ZHENG ,  Qiang LIU

IPC: G05B19/418 ,  H04L9/00

Abstract: In a method for predicting product assembly quality based on longitudinal unified learning, sample alignment is performed on a data sample of each participant to resolve problems of decentralization, nonuniformity, and scarcity of data; data partitioning is performed by a multi-player parallel structure on product assembly data by using a customized data partitioning policy, layer normalization is firstly performed by an encoder on partitioned data, and feature extraction is performed by a multi-thread attention layer to mine a correlation between each assembly production line inside the participant and assembly data of each device, so that a model feature extraction capability is enhanced; gradient security aggregation is performed on the local model of each participant by using a homomorphic encipherment method of secure multi-player computation, to obtain a global model, so that data of a plurality of sub-factories is merged to co-train a high-precision assembly quality prediction model.