Dynamic State Management in Physical System Networks
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Solution Overview
Problem
Current systems for modeling and simulating physical systems lack effective methods to dynamically manage and visualize the state of complex networks, particularly in representing geometric configurations and constraints, which limits their ability to accurately simulate and predict system behavior.
Innovation Solution
A technical computing environment (TCE) is developed that includes tools for building and executing networks representing systems, allowing for dynamic state management, geometric visualization, and simulation, using a combination of graphical and textual interfaces to specify targets, constraints, and scaffolds, and automatically generating state objects to establish values of states based on these inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current systems are used for modeling and simulating physical systems, then basic simulation functionality is provided, but the ability to dynamically manage and visualize the state of complex networks is insufficient
Solution Approach 1:
The system segments the complex network state into discrete state objects, each representing specific geometric configurations and constraints. This segmentation allows the system to manage complex networks by breaking them down into manageable state representations that can be individually visualized and manipulated, resolving the contradiction between visualization accuracy and system complexity.
Solution Approach 2:
The patent introduces state objects as intermediary elements between the physical system and the visualization interface. These state objects serve as mediators that capture and represent the dynamic state of the system, enabling accurate state visualization without requiring direct complex interactions between the physical system and the visualization components.
2Adaptability or versatility
If tools are added for dynamic state management and geometric visualization, then simulation capability is improved, but device complexity increases
Solution Approach 1:
The state objects introduced in the patent serve multiple functions: they represent geometric configurations, enforce constraints, enable visualization, and support simulation. This multi-functionality allows the system to improve simulation capability and adaptability without proportionally increasing complexity, as the same state objects fulfill multiple roles throughout the system.
3Reliability
If current modeling tools are used, then basic system representation is achieved, but accurate simulation and prediction of system behavior is limited
Solution Approach 1:
The system performs preliminary actions by establishing state objects that pre-define geometric configurations and constraints before simulation begins. This preliminary structuring of system states enables more accurate prediction and simulation of behavior, as the foundational geometric relationships and constraints are already captured and organized in the state representations.
Data Source
AI summary
In an embodiment, a network may represent a physical system. The network may have an element that represents an entity of the physical system. A value of a state associated with the network may be identified (e.g., generated) using various techniques. The state may be a low-level state associated with the network. The techniques may include, but are not limited to, for example, generating the value based on a scaffold defined for the network, generating the value based on a target value for the state, and/or generating the value based on applying an operation to various values of the state. The identified value may be associated with an identifier. The identifier may distinguish the value, for example, from other values of other states in the network and/or other values of states in other networks.