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

A technology for transfer learning and production progress, applied in neural learning methods, prediction, biological neural network models, etc., can solve problems such as low prediction efficiency, poor generalization performance, and low prediction accuracy, to improve prediction accuracy and meet online analysis. The effect of forecasting and improving budget efficiency

Active Publication Date: 2019-06-18
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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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 gener

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

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[0045] The drawings constituting a part of the present invention are used to provide a further understanding of the present invention, and the schematic embodiments and descriptions of the present invention are used to explain the present invention, and do not constitute an improper limitation of the present invention.

[0046] This embodiment provides a method for real-time forecasting of order production progress based on two-layer transfer learning. The specific implementation method is as follows: figure 1 As shown, it includes the following steps:

[0047] Step (1): Using IoT sensing equipment deployed in the workshop to collect massive production data (manufacturing data) of multiple continuous production processes in time series, the massive production data includes order task data OT, task data CT completed at the current moment, and real-time production data. State data RC and forecast time ΔT; and form a data set characterized by data OT, CT, RC and ΔT, and divide th...

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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...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/04G06N3/04G06N3/08
CPCY02P90/30
Inventor 黄少华郭宇刘道元杨能俊张蓉杨辰
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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