Graph-Based Control Modeling for Dynamic System Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack effective models for optimizing operation across complex systems with dynamic structures and environments, leading to challenges in achieving optimal performance under varying conditions.
Innovation Solution
The use of graph systems as models to represent complex systems, allowing for the construction of optimization models that can adapt to changes and optimize operation across multiple systems and objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional control systems are used for complex systems with dynamic structures, then the system structure remains simple and manageable, but the system cannot achieve optimal performance under varying conditions
Solution Approach 1:
The patent applies dynamics by transforming static system models into dynamic graph representations that automatically adapt to changing conditions. The graph system dynamically reconfigures itself based on real-time data, allowing the model to evolve with the system it represents without requiring manual intervention or complex reprogramming.
Solution Approach 2:
The patent implements universality through a unified graph-based modeling framework that can represent diverse system types (electrical, mechanical, thermal, etc.) using the same data structures and optimization algorithms. This multi-functional approach eliminates the need for separate modeling tools for different system domains, reducing overall complexity while enhancing adaptability.
2Productivity
If optimized operation is pursued for systems with dynamic structure, then system performance is enhanced, but the difficulty of control increases significantly
Solution Approach 1:
The patent applies self-service by enabling the system to automatically generate and solve its own optimization problems without external intervention. The graph system continuously monitors itself, identifies optimization opportunities, and executes control actions autonomously, reducing the burden on external controllers while maintaining high performance.
Solution Approach 2:
The patent implements feedback mechanisms where the graph system continuously monitors system state, compares actual performance against optimal targets, and automatically adjusts control parameters. This closed-loop feedback enables sustained optimized operation while the system adapts to changing conditions, reducing control difficulty through automated adjustment.
3Speed
If real-time responsive modeling is implemented, then system operation remains optimal under changing conditions, but computational requirements and system complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the overall system model into modular graph components that can be independently updated and processed. This allows real-time responsiveness by updating only the affected portions of the system when changes occur, rather than reprocessing the entire system model, thus reducing computational complexity while maintaining speed.
Data Source
AI summary
The present disclosure is directed to systems and methods for enhancing control of a system using a graph system. A graph system is constructed comprising one or more vertices, one or more edges, and one or more graph layers. Each vertex represents an entity in the system that includes one or more attributes. Each edge represents a flow of an attribute with respect to at least one vertex (entity in the system). Each graph layer comprising one or more vertices and one or more intra-layer edges. An optimization model is constructed from the graph system. The optimization model can be optimized utilizing an optimization algorithm to identify the set of control values that optimize the optimization model. A dispatch set of control values for a set of controllable output attributes of the system is provided to the system to control operation of the system.