Discrete manufacturing workshop digital twin model adaptive dynamic updating method

Abstract

The invention discloses an adaptive dynamic updating method for a digital twinborn model of a discrete manufacturing workshop, which comprises the following steps of: firstly, acquiring data of a virtual workshop and an actual workshop, selecting a characteristic data set, respectively calculating a precision error of a true value of a performance index and a theoretical value predicted by the model, and then, carrying out adaptive dynamic updating on the digital twinborn model of the discrete manufacturing workshop based on a Mann-Kendall precision error trend analysis method. Taking the result as a triggering condition for dynamic correction of the twin model; then, DNN and LSTM are selected as base learners, continuous iterative training is carried out by adopting an integrated learning method, a weight updating mode of the base learners is improved from two perspectives of data importance and performance importance, a twinborn model dynamic correction algorithm based on Adaboost-DNN-LSTM is formed, and dynamic correction of the twinborn model is completed by adopting a difference base learner optimization method. The digital twinning model adaptive dynamic updating method provided by the invention provides a model updating method for accurate application of digital twinning of a discrete manufacturing system, and has an important value for improving the workshop production management and control intelligence level.

Description

technical field [0001] The invention relates to the technical field of workshop digitalization, and mainly relates to an adaptive dynamic update method for a digital twin model of a discrete manufacturing workshop. Background technique [0002] With the rise of "Industry 4.0" and intelligent manufacturing, new technical means and tools such as the Internet of Things, big data, and artificial intelligence continue to emerge. These new technologies and tools help the transformation and upgrading of traditional manufacturing industries to promote the development of manufacturing industries. High-quality development. The application of intelligent technology also puts forward new requirements for the transformation and upgrading of traditional discrete mechanical product production workshops. Digital twin is an emerging technology of intelligent manufacturing in recent years, and it is also one of the means for the application of intelligent manufacturing. As a new mode of wor...

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Problems solved by technology

Finally, after slicing the workshop production data, as the basis for correction, DNN and LSTM are used as the base learner for model training, and the Adaboost integrated learning method is used to form a strong learner, and the strong learner is used to replace the inaccurate twin model. The performance model is enough, and the adaptive dynamic correction of the twin model is completed, which solves the technical problem that the twin model cannot be dynamically corrected in the application of the digital twin technology in the existing discrete manufacturing workshop

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