公开(公告)号：US20240288855A1

公开(公告)日：2024-08-29

申请号：US18215266

申请日：2023-06-28

Applicant: Beijing Institute of Technology

Inventor： Jian SUN ,  Runqing WANG ,  Gang WANG ,  Minggang GAN ,  Jie CHEN

IPC: G05B19/418

CPC classification number: G05B19/41865 ,  G05B19/4183

Abstract: The present disclosure provides a flexible job-shop scheduling method and system and an electronic device. The method according to the present disclosure includes: randomly generating a plurality of flexible job-shop environments in a production job-shop according to a preset production target; constructing a scheduling strategy model of the production job-shop based on a Markov decision process; optimizing a feature extraction network, an actor network and a critic network simultaneously by using the scheduling strategy model and a plurality of data sets, and determining a scheduling plan corresponding to a maximum completion time as an optimal scheduling plan after the completion of the optimization; and completing the preset production target based on the optimal scheduling plan. According to the present disclosure, feature extraction is performed on the plurality of job-shop environments to generate a scheduling scheme, so as to improve the efficiency and rationality of flexible job-shop scheduling.

公开(公告)号：US20240289610A1

公开(公告)日：2024-08-29

申请号：US18214881

申请日：2023-06-27

Applicant: Beijing Institute of Technology

Inventor： Gang WANG ,  Hongjie CAO ,  Jian SUN ,  Minggang GAN ,  Jie CHEN

IPC: G06N3/08 ,  G06F17/14 ,  G06F17/16 ,  G06N3/0442 ,  G06N3/0464 ,  H03H17/02

CPC classification number: G06N3/08 ,  G06F17/142 ,  G06F17/16 ,  G06N3/0442 ,  G06N3/0464 ,  H03H17/0257

Abstract: Disclosed is a hybrid data- and model-driven method for predicting remaining useful life of a mechanical component. The method of the present disclosure uses an extended Kalman filter to calibrate parameters of an exponential random model, automatically learns input embedded position information by means of an adaptive encoding layer of a hybrid driven prediction model, and then models a mapping relation between input data and the remaining useful life by means of a multi-head attention mechanism. The present disclosure retains both accuracy of a model-based method and a generalization capability of a data-driven method in combination with the calibrated exponential random model and a multi-head attention neural network structure, can improve accuracy of predicting the remaining useful life of the mechanical component, and has great significance for use of the hybrid data- and model-driven method in the field of intelligent manufacturing and health management of mechanical apparatuses.