Industrial Automation Control Using Cross-System Pattern Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial automation systems lack the ability to efficiently adjust operations based on data from multiple sources, leading to suboptimal energy usage and production efficiency due to the lack of awareness and communication between components within the system.
Innovation Solution
Implementing an industrial automation system that allows components to connect to a network, recognize each other, and share data, enabling contextualization of received data across different scopes or hierarchical levels, and adjusting operations accordingly through a cloud-computing platform or local control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If industrial automation components operate independently without network connectivity, then device complexity is reduced, but energy usage and production efficiency deteriorate due to lack of coordination
Solution Approach 1:
The patent segments the automation system into independent components (machines, devices, controllers) that can operate autonomously while connecting to a network. Each component maintains its own control logic and can function independently, but when connected, they share data and coordinate operations to improve overall productivity without requiring complete system redesign.
Solution Approach 2:
The patent introduces an intermediary layer (network infrastructure, communication protocols, data exchange standards) that enables components to interact without direct complex point-to-point connections. This intermediary allows efficient coordination and data sharing while keeping individual component complexity low, as each component only needs standard interface capabilities.
2Use of energy by moving object
If industrial automation components share data across the network, then energy usage optimization improves, but data security and system reliability worsen due to increased vulnerability
Solution Approach 1:
The patent implements feedback mechanisms where components continuously monitor and exchange operational data, energy consumption metrics, and status information across the network. This real-time feedback enables dynamic energy optimization decisions while maintaining system reliability through continuous monitoring and anomaly detection, allowing the system to respond to changing conditions without compromising stability.
Solution Approach 2:
The patent applies preliminary action by implementing data validation, authentication, and security protocols before data exchange occurs. Components perform preliminary checks on incoming data, verify sender credentials, and validate operational commands before execution. This preliminary processing ensures energy optimization opportunities are captured while preventing malicious or erroneous data from compromising system reliability.
3Productivity
If real-time data analysis is performed across multiple sources, then production efficiency improves, but processing time and computational resources worsen
Solution Approach 1:
The patent applies local quality by enabling each automation component to perform local data analysis and filtering before sharing processed information across the network. Instead of centralizing all data processing, each component analyzes its own operational data locally, extracting relevant insights and sharing only essential information. This distributed approach improves production efficiency through localized optimization while reducing overall processing time and network bandwidth requirements.
Data Source
AI summary
An industrial automation component may receive a first set of data associated with the industrial automation component, such that the industrial automation component is associated with a first industrial automation system. The industrial automation component may then receive a second set of data associated with one or more other industrial automation components, such that the one or more other industrial automation components are associated with one or more other industrial automation systems. The industrial automation component may then identify one or more similar patterns in the first set of data and the second set of data and adjust one or more operations of the industrial automation component based on the similar patterns.


