Industrial Control Load Balancing for Production Bottlenecks
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Solution Overview
Problem
Industrial automation systems face challenges in efficiently balancing processing loads and optimizing energy usage across components, leading to inefficiencies and potential bottlenecks in production and energy consumption.
Innovation Solution
An industrial control system that connects and communicates with various components within the automation network, allowing for data analysis and load balancing between control systems, and adjusting operations to maintain optimal energy consumption levels by distributing processing loads and optimizing hierarchical levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If processing loads are concentrated in fewer control systems, then device complexity is reduced, but productivity decreases due to bottlenecks
Solution Approach 1:
The patent segments the control system into multiple distributed control systems, each handling specific processing loads. This segmentation allows parallel processing across multiple nodes, eliminating bottlenecks while maintaining manageable complexity through modular architecture. Each control system operates semi-autonomously, processing local data and making decisions without requiring centralized coordination for every operation.
Solution Approach 2:
The patent introduces a hierarchical dimension to the control architecture, organizing control systems into multiple levels (e.g., field level, cell level, shop level). This dimensional organization allows processing loads to be distributed vertically across hierarchy levels while maintaining horizontal scalability. Higher levels handle strategic decisions and lower levels handle operational tasks, optimizing both complexity management and productivity.
2Productivity
If more control systems are used to balance processing loads, then productivity improves, but device complexity increases
Solution Approach 1:
The patent implements universal control system modules that can perform multiple functions across different contexts. Each control system is designed with standardized interfaces and capabilities that allow them to handle various processing loads interchangeably. This multi-functionality reduces the need for specialized components, simplifying the overall system architecture while maintaining the benefits of distributed processing.
Solution Approach 2:
The patent incorporates feedback mechanisms where control systems continuously monitor their own processing loads and the loads of neighboring systems. This feedback enables automatic load balancing, where control systems dynamically adjust their operational parameters to optimize the distribution of processing loads. The feedback loop maintains system efficiency without requiring complex centralized management, as each system autonomously responds to system-wide conditions.
3Device complexity
If processing loads are not balanced, then device complexity is reduced, but energy consumption increases due to inefficiencies
Solution Approach 1:
The patent enables control systems to self-regulate their processing loads through autonomous decision-making. Each control system monitors its own energy consumption patterns and processing capacity, automatically adjusting its operational state to maintain optimal efficiency. This self-service capability allows the system to balance loads in response to energy conditions without requiring complex external management, reducing both complexity and energy waste.
4Productivity
If real-time data analysis is implemented across all control systems, then productivity improves, but energy consumption increases
Solution Approach 1:
The patent implements local quality by enabling each control system to perform data analysis primarily on local data relevant to its specific functions. Rather than every system processing all system-wide data, each control system focuses computational resources on analyzing data that directly impacts its operational decisions. This localized approach maintains high productivity through relevant real-time analysis while significantly reducing overall energy consumption by avoiding redundant processing across the entire system.
Data Source
AI summary
An industrial control system may receive processing information from at least two control systems associated with at least two components within an industrial automation system. The processing information may include a processing load value for each of the at least two control systems. The industrial control system may then distribute processing loads associated with the at least two control systems when a total processing load between the at least two control systems is unbalanced.