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기록

MACHINE LEARNING ASSISTANCE FOR INDUSTRIAL AUTOMATION PROGRAMMING AND DATA ENVIRONMENTS

발명심사 중
20청구항 · 3 독립항
§ Ⅰ

개요

발명자

Jordan C. Reynolds; Troy W. Mahr; Thomas K. Jacobsen; Giancarlo Scaturchio; John J. Hagerbaumer

IPC 분류

G5B 19/42

CPC 분류

G5B19/426G5B2219/23077

Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments of the present technology include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a user interface component configured to display a programming environment for editing control logic, wherein operational data from the industrial automation environment is accessible from within the programming environment through a data pipeline. A machine learning-based data science engine is configured to process the operational data from the industrial automation environment to generate processed data and identify a portion of the processed data relevant to a component of the control logic. The user interface component is further configured to surface the portion of the processed data in the programming environment.

원문 (중국어)

Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments of the present technology include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a user interface component configured to display a programming environment for editing control logic, wherein operational data from the industrial automation environment is accessible from within the programming environment through a data pipeline. A machine learning-based data science engine is configured to process the operational data from the industrial automation environment to generate processed data and identify a portion of the processed data relevant to a component of the control logic. The user interface component is further configured to surface the portion of the processed data in the programming environment.