Industrial big data driven total completion time prediction method

Abstract

The invention discloses an industrial big data driven total completion time prediction method and relates to the field of engineering application. The method includes: establishing an industrial big data analysis platform; applying an association rule mining algorithm to analyze and mine total completion time influence factors; establishing a neural network model BP; dynamically improving a weight and a threshold of the neural network model BP to acquire a dynamical neural network model DBP; applying an AIGA (adaptive immune genetic algorithm) to optimize the dynamical neural network model DBP so as to acquire a prediction model AIGA-DBP, and computing a total completion time prediction value according to the prediction model AIGA-DBP; when an error of the total completion time prediction value and a total completion time expectation value meets preset conditions, outputting the total completion time prediction value. By the method, the total completion time can be predicated accurately, the work flow of enterprises is optimized, the production efficiency of the enterprises can be improved, and the method is adaptable to various changes of the enterprise due to time lapse.

Description

Technical field [0001] The invention relates to the field of engineering applications, in particular to a method for predicting total completion time driven by industrial big data. Background technique [0002] The so-called "total completion time" means that in the scheduling problem, the scheduled object is generally a collection of N jobs (Job), which is called an instance. Use I for instance, J j Represents the jth artifact among them. Each artifact J j Each has its own arrival time (Release time) R j And processing time P j (Processing time). In the scheduling plan S, the start time is S j (Starttime), the completion time is C j (Completion time), and the total completion time is ∑C j (Total completion time). The so-called "total completion time prediction" refers to the use of scientific mathematical models to predict the total completion time of tasks or projects. [0003] In engineering applications, there is a widespread demand for total completion time prediction. For...

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Problems solved by technology

[0006] There have been a lot of researches on the forecasting methods of total completion time in the field of engineering applications and manufacturing enterprises, but so far, there is no

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