An order production progress real-time prediction method based on double-layer transfer learning

Abstract

The invention discloses an order production progress real-time prediction method based on double-layer transfer learning, and the method specifically comprises the steps: dividing a data set added with real-time production state data into a historical order data set and a current order data set, and carrying out the normalization processing; Establishing a feature extraction model of the historical order data set by adopting a deep sparse autoencoder, and constructing a support vector regression model mh by taking the historical order data subjected to feature extraction as a secondary training set; Finely adjusting the deep sparse autoencoder based on the current order data set, and establishing a first layer of transfer learning model; And establishing a support vector regression model mc by using the current order data set after feature extraction, and establishing a second-layer transfer learning model as a production progress prediction model of the current order based on the similarity of mc and mh. According to the invention, the problems of low prediction precision and poor generalization performance caused by a small amount of current order data are solved.

Description

technical field [0001] The invention belongs to the field of production schedule prediction, in particular to a method for real-time forecasting of order production schedule based on double-layer transfer learning. Background technique [0002] In the face of fierce market competition and diverse customer needs, the current production mode of discrete manufacturing has changed from traditional stock-based production to order-based production. On the one hand, accurate real-time forecasting of order production progress can be used to monitor the implementation of production plans, Detect production abnormalities in advance, so as to trigger production logistics for scheduling management in time, and optimize remaining production tasks. On the other hand, it can identify the main factors that affect the on-time execution of production plans, which is conducive to precise control of the production process, and is conducive to improving production efficiency and ensuring products...

AI Technical Summary

Problems solved by technology

[0004] Purpose of the invention: In order to solve the above-mentioned problems of low prediction efficiency, low prediction accuracy and poor generalization performance, the present invention provides an order production schedule based on two-layer transfer learning real-time forecasting method

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