Guangdong University of Technology

Other

Guolei Ruan

Qiang Liu

Jiewu Leng

Man Zhou

Hu Zhang

Duxi Yan

Ding Zhang

Qianyi Su

Zisheng Lin

Alan Zhou

Shide Ye

Dewen Wang

Jianbo Jing

Weinan Sha

The disclosure discloses an inhomogeneous sample equalization method and system for a product assembly process. The method includes the following steps of: A: calculating a similarity among different samples; B: constructing a fuzzy compatibility matrix S for representing the similarity among all the samples, and constructing a fuzzy compatibility space X with different granule layers through the fuzzy compatibility matrix S; C: based on a granular calculating mode, screening out a granule layer with a maximum comprehensive value of an information increment and the similarity among the samples from the fuzzy compatible space X to serve as an optimal granule layer; and D: carrying out equalization processing on the sample of the optimal granule layer.

Inhomogeneous sample equalization method and system for product assembly process

A method for constructing and training a Decentralized Migration Diagram Neutral Network (DMDNN) model for a production process, including: determining a production task input management node, distributed management nodes and a granularity of each of the distributed management nodes, and constructing a production system network; constructing network calculation nodes on each of the distributed management nodes; constructing and training a DMDNN model; and applying the trained DMDNN model in management and control of the production process.

Method for constructing and training decentralized migration diagram neural network model for production process

Disclosed is a blockchain-enabled edge computing method for production scheduling. The method includes modeling a smart contract between a device and a manufacturing unit, and using the smart contract to perform production scheduling on the device inside the manufacturing unit; one of the manufacturing units includes multiple devices; mounting each device on the blockchain operating node, the MES issues production instructions to the nodes of each manufacturing unit, at the same time, the nodes acquire production data of the device through multiple data sources of the device, the operating state data and process parameter data of each device are acquired in real time, and the data is directly chained from the device level; according to the production instructions and device parameters obtained by the manufacturing unit, using edge computing to dynamically adjust the device load, efficiency, and utilization.

Blockchain-enabled edge computing method for production scheduling

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Guangzhou, China

Guolei Ruan