Granular Component Control Networks for Cross-Entity Optimization
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Solution Overview
Problem
Control systems face challenges in accurately modeling and managing entities, leading to suboptimal control loops when entities are not modeled correctly, and existing solutions fail to efficiently share optimal control actions across disparate entities.
Innovation Solution
A control network and method that model entities as granular components, allowing for hierarchical control actions and sharing of control strategies across entities, using a control hub to communicate and optimize control actions based on data from similar components, while maintaining confidentiality through anonymization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entities are modeled manually or automatically using traditional methods, then the control model can be established, but the modeling accuracy and consistency across disparate entities remain insufficient
Solution Approach 1:
The patent segments entities into granular components with standardized types (e.g., heat exchanger, pump, valve) that can be independently modeled and controlled. This segmentation enables consistent modeling across disparate entities by breaking them down into comparable elemental units, thereby improving modeling accuracy without proportionally increasing overall system complexity.
Solution Approach 2:
The patent creates a universal control model framework that can accommodate multiple entity types through standardized component classifications. This universal approach allows the same modeling methodology to be applied across different entities (manufacturing equipment, HVAC systems, transportation), improving consistency and accuracy while avoiding the need to develop separate complex models for each entity type.
2Productivity
If control actions are optimized for individual entities, then local control performance improves, but the ability to share and transfer optimal control actions across disparate entities is limited
Solution Approach 1:
The patent enables control actions to be copied and transferred across entities by modeling them in terms of standardized granular components. When an optimal control action is identified for one entity, it can be replicated for other entities with similar component types, improving adaptability and knowledge sharing while maintaining the productivity gains from optimized control.
Solution Approach 2:
The patent uses parameter-based component modeling where control actions are defined in terms of standardized parameters (flow rates, temperatures, pressures) rather than entity-specific characteristics. This allows control actions to be adapted to different entities by changing parameters while maintaining the same control logic, thereby improving both control performance and shareability.
3Adaptability or versatility
If control actions are shared across entities from different organizations or markets, then the versatility and applicability of control strategies improve, but proprietary or confidential data may be exposed
Solution Approach 1:
The patent extracts only the essential control-relevant parameters and component relationships from entity models, separating this information from proprietary operational data. By sharing only the standardized component types and control logic rather than complete entity models, the system improves control strategy applicability across organizations while protecting confidential information.
Solution Approach 2:
The patent introduces a standardized component model framework as an intermediary layer between different entities and organizations. This intermediary enables control actions to be shared and transferred across organizational boundaries by translating entity-specific control problems into standardized component terms, thereby improving versatility while maintaining data confidentiality through abstraction.
Data Source
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AI summary
A control network comprises a control hub, a data repository and a plurality of control agents. The data repository models entities as a plurality of typed granular components. The control hub receives performance data on each entity, translates the performance data into data on the components and stores the transformed data in the repository in association with its respective component. The control hub further determines an optimal control action in respect of a component of one of the entities in dependence on the component's transformed data and on transformed data in the data repository for other components having the same type. Each control agent is linked to one of the entities and associated with one or more of the respective entity's components. The control hub communicates the control action to the agent associated with the component to trigger the agent to effect the action via said link.