Granular Control Network for Cross-Entity Action Sharing
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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 properly modeled, and existing methods lack efficient ways to share optimal control actions across disparate entities.
Innovation Solution
A control network comprising a control hub, data repository, and control agents that model entities as granular components, allowing for optimal control actions to be determined and communicated across entities, enabling consistent modeling and sharing of control actions through a hierarchical structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entities are modeled manually or automatically using traditional control system methods, then the control model can be established, but the modeling accuracy and optimal control performance deteriorate when entities are not properly modeled
Solution Approach 1:
The patent segments entities into granular components with standardized types, allowing detailed representation while maintaining modelability. Each entity is decomposed into fundamental components (e.g., heating elements, sensors, actuators) that can be systematically modeled and controlled, resolving the contradiction between detailed accuracy and controllable modeling.
Solution Approach 2:
The patent transforms performance data into standardized component-level parameters that can be processed by control algorithms. By changing the representation from raw entity-level data to structured component parameters, the system achieves both accurate modeling and reliable optimal control through standardized data transformation.
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 deteriorates
Solution Approach 1:
The patent creates universal control patterns that can be applied across multiple entity types through standardized component modeling. Control actions developed for one entity type can be transferred to others by mapping their standardized components, enabling both optimized local control and broad applicability across disparate entities.
Solution Approach 2:
The patent enables copying of optimal control actions between entities by representing them in terms of standardized granular components. Once a control pattern is established for a component type, it can be replicated across other entities with the same component type, facilitating knowledge transfer while maintaining local optimization.
3Device complexity
If entities are modeled at a high level without granular decomposition, then the modeling process is simpler, but the ability to implement precise control actions and share control knowledge across entities deteriorates
Solution Approach 1:
The patent segments entities into standardized granular components, creating a hierarchical model that balances complexity and precision. The segmentation provides a structured framework that simplifies modeling through repetition of standard components while enabling precise control at the component level, resolving the contradiction between model simplicity and control precision.
4Ease of manufacture
If traditional control loops are used without granular component modeling, then the control system is easier to implement, but the optimal control performance and timeliness deteriorate
Solution Approach 1:
The patent transforms performance data into standardized component parameters through automated data transformation processes. This parameter transformation enables efficient processing and timely control decisions while maintaining ease of implementation through standardized data formats and automated transformation routines, resolving the contradiction between implementation ease and control timeliness.
Data Source
AI summary
A control network, system and method is disclosed for control of a plurality of entities. The control network comprises a control hub, a data repository and a plurality of control agents. The data repository models each entity as a plurality of granular components, each granular component having a type selected from a set of granular component types. The control hub is configured to receive performance data on each entity, translate the performance data into data on the granular components and store the transformed data in the data repository in association with its respective granular component. The control hub is further configured to determine an optimal control action in respect of a granular component of one of the entities in dependence on the granular component's transformed data and on transformed data in the data repository for other granular components having the same type. Each of the plurality of control agents being linked to one of the entities and associated with one or more of the respective entity's granular components, the control hub being arranged to communicate the control action to the control agent associated with the granular component to trigger the control agent to effect the control action via the link.