Multi-core Processor Energy Management via Organizational Model
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Solution Overview
Problem
Industrial automation systems lack effective methods to analyze and optimize energy data across scalable parts, leading to inefficient energy usage and lack of context for energy information within the system as a whole.
Innovation Solution
A multi-core processor-based energy management system that leverages an organizational model to categorize and manage energy data, enabling integrated energy management across the industrial automation system by structuring energy data, controlling assets, managing security, and ensuring safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If energy data is collected from individual assets, then energy information is available for specific devices, but the data lacks context regarding its relation to the system as a whole
Solution Approach 1:
The patent segments energy data collection and processing by implementing separate functional cores: an energy data core for collecting energy information from assets, an energy inference core for determining unmeasured energy data, and a control core for executing control actions. This segmentation allows each core to specialize in specific tasks while working together to provide comprehensive system-wide energy context.
Solution Approach 2:
The patent introduces an energy inference engine as an intermediary component that bridges the gap between individual asset energy data and system-wide energy context. This inference engine uses organizational models and inference algorithms to determine unmeasured energy data, thereby connecting isolated data points into a coherent system-level view without requiring direct measurement from every asset.
2Productivity
If individual asset energy data is monitored, then device-level energy usage is known, but system-wide energy optimization is difficult to achieve
Solution Approach 1:
The patent implements dynamic energy management by continuously collecting real-time energy data from assets, dynamically inferring unmeasured energy consumption using organizational models, and continuously optimizing control actions. This dynamic approach enables the system to adapt to changing conditions and achieve system-wide energy optimization rather than static, device-level monitoring.
Solution Approach 2:
The patent establishes a feedback loop where energy data is collected from assets, processed through inference algorithms to determine system-wide energy context, and used to generate control actions that are applied back to the assets. This closed-loop feedback mechanism enables continuous optimization of energy usage across the entire system based on both individual asset performance and system-level objectives.
3Loss of information
If comprehensive energy monitoring is implemented across all assets, then complete energy visibility is achieved, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies partial action by implementing energy measurement on only those assets that are most critical or feasible to monitor, while using the energy inference core to determine unmeasured energy data for other assets. This approach achieves comprehensive energy visibility without requiring physical measurement devices on every single asset, thereby reducing system complexity while maintaining data completeness.
Data Source
AI summary
A system may include a multi-core processor that may include a first core configured to determine structured energy data associated with one or more assets in an automation system, wherein the structured energy data comprises a logical grouping of assets in the automation system, a second core configured to control the one or more assets based on the structured energy data, a third core configured to manage security operations in the automation system, and a fourth core configured to manage safety operations in the automation system.