Industrial Automation Agent Generation Using Clustered State Data
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Solution Overview
Problem
Creating agents in industrial automation systems is a costly and labor-intensive process requiring significant human interaction, especially for data collection, programming, and deployment, which hinders efficiency and scalability.
Innovation Solution
A method utilizing cluster analysis to process data from automation systems, forming clusters that can be automatically or manually selected to generate agents, leveraging supervised machine learning algorithms and context data to create mathematical models for autonomous behavior, thereby reducing human intervention and increasing automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual methods are used for agent creation, then agents can be created with high precision and reliability, but the process becomes extremely labor-intensive and costly
Solution Approach 1:
The system enables automated self-service for agent creation through cluster analysis and machine learning algorithms. The agent suggestion component automatically processes data from the data sink, forms clusters, and generates agent models without requiring manual human interaction for data collection and programming, thus dramatically improving productivity while maintaining reliability through systematic automated processes.
Solution Approach 2:
The patent replaces the mechanical manual process of agent creation with an automated information processing system. Instead of manual data collection, analysis, and programming, the system uses cluster analysis algorithms and machine learning to automatically process data, form clusters, and generate agent models, substituting human labor with computational processes.
2Productivity
If automated cluster analysis is used to create agents, then productivity and efficiency are significantly improved, but the complexity of the system increases
Solution Approach 1:
The system segments the agent creation process into distinct modular components: a data sink for data storage, an agent suggestion component with cluster analysis functionality, and an agent generation component. This segmentation allows each component to perform its specific function independently, managing system complexity through functional decomposition while enabling high productivity through automated processing.
Solution Approach 2:
The agent suggestion component acts as an intermediary between the data sink and the agent generation component. It processes raw data through cluster analysis, forms meaningful clusters, and presents them to the agent generation component, thereby mediating the complex data processing tasks and simplifying the overall system architecture.
3Manufacturing precision
If more human interaction is required for agent creation, then the quality and contextual understanding of agents improve, but the time and cost requirements increase significantly
Solution Approach 1:
The system performs preliminary action by automatically collecting data in the data sink and pre-processing it through cluster analysis before agent creation is actually needed. When an agent needs to be created, the clustered data and patterns are already prepared, allowing rapid agent generation without requiring time-consuming manual data collection and analysis, thus reducing creation time while maintaining quality.
Solution Approach 2:
The patent substitutes manual human analysis and programming activities with automated machine learning algorithms and cluster analysis. The system automatically processes data, identifies patterns, forms clusters, and generates agent models, replacing the mechanical process of manual agent creation with computational automation that achieves both high quality and rapid deployment.
Data Source
AI summary
The invention relates to a method and a device for proposing and/or creating at least one agent (A) in an industrial automation system (AS), wherein the industrial automation system (AS) comprises: • automation devices (DEV) that at least partially include a data source (SRC), • wherein the data sources (SRC) collect and/or process data (DATA) from the automation devices (DEV), • a data sink (SINK) in which data (DATA), in particular state data, from the data sources (SRC) are stored. The invention further relates to a device for carrying out such a method, an automation system (AS) with such a device, and an automation device (DEV) comprising a framework configured for executing an agent (A).To specify a method for modern automation systems that makes it possible to propose and/or create the aforementioned agents more easily and efficiently, an agent proposal component (ASUG) is proposed that processes data (DATA) from the data sink (SINK) into clusters (C) using cluster analysis (CA), and wherein the agent proposal component (ASUG) makes the clusters (C) available at an interface (INT) such that an agent generation component (AGEN) can create a model (MOD) for the agent (A) based on at least a selection (SEL) of the clusters (C).