Intelligent manufacturing industry parameter optimization method based on machine learning and industrial Internet of Things

Abstract

The invention discloses an intelligent manufacturing industry parameter optimization method based on machine learning and industrial Internet of Things, and the method comprises the following steps: S1, building an industrial Internet of Things system, bottom-layer equipment being connected into the industrial Internet of Things system, and extracting the operation data of the bottom-layer equipment; S2, analyzing process steps of the intelligent production line, building a digital production model of the intelligent production line, and the collected data corresponding to variables in the model and storing at different positions in a database; S3, building an association analysis model; S4, building a sequence model, and quantifying relevance rules; and S5, obtaining the relationship between different sections of the production line, and then building a gray model to calculate the change trend of the data between different sections. By importing the industrial Internet of Things system, centralized collection, centralized cleaning and centralized processing of production data are achieved, data support is provided for subsequent work, the optimal value of each part is calculated through a machine learning model, an engineer adjusts with the optimal value as the reference value, and a large amount of debugging workloads are reduced.

Description

Technical field [0001] The present invention relates to the technical field of industrial intelligent production lines, and more specifically to a method for optimizing parameters of the intelligent manufacturing industry based on machine learning and industrial Internet of Things, which automatically adjusts the parameters of each sub-part through a constructed digital and intelligent production line model. Background technique [0002] At present, each unit on the production line is an independent unit, and each unit is connected through three methods: timing triggering, sensor triggering, and PLC triggering in the unit. There is no correlation between each other, which results in the later import of model algorithms. Great difficulty. [0003] At present, when companies in the market encounter product changes on the production line, they first manually adjust the procedures or parameters of each part, and finally run the adjusted production line. In a dynamic environment, check ...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved in the present invention is to provide a parameter optimization method for the intelligent manufacturing industry based on machine learning and the Industrial Internet of Things, so as to solve the problem that the equipment on the production line is independent of each other and does not share data with each other. When a problem occurs in a certain link, Subsequent workshops cannot adjust the production mode in time, manual debugging is inefficient, and there are serious waste problems. To use the industrial Internet of Things system to connect the equipment on the production line to the network, realize data uploading and processing in a unified manner, and provide basic support for subsequent model import. To reduce a lot of workload, ensure the quality of products, and achieve the maximum efficiency of the entire production line

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