Graphical Model Processing Grouping Analysis Frameworks
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Solution Overview
Problem
Current systems face inefficiencies in identifying and processing model parts that operate within the same analysis framework, leading to suboptimal execution of graphical models with multiple analysis frameworks.
Innovation Solution
A method and system that identify and group model parts operating within the same analysis framework, transforming them to operate under a single framework for efficient processing, using dedicated algorithms to achieve intermediate results and convert them into a single model portion with a single dedicated algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model parts operating within the same analysis framework are processed separately due to topological separation by different framework components, then the model can handle multiple diverse analysis frameworks, but the processing efficiency decreases and execution time increases
Solution Approach 1:
The patent segments the graphical model into distinct model portions based on analysis framework boundaries. Each model portion contains model parts that operate within the same analysis framework, allowing for targeted processing. The system identifies these segments by detecting topological separations caused by demarcating blocks and groups model parts accordingly, enabling efficient framework-specific processing while maintaining overall model integrity.
Solution Approach 2:
The patent merges model parts that operate within the same analysis framework into unified model portions, even when these parts are topologically separated by components from different frameworks. By combining these scattered model parts into single model portions, the system enables them to be processed together using the same dedicated algorithm, improving processing efficiency without losing the benefits of multi-framework support.
2Ease of operation
If demarcating blocks are used to separate different analysis frameworks, then the model structure becomes clearer and framework boundaries are defined, but the device complexity increases
Solution Approach 1:
The patent extracts and identifies demarcating blocks from the graphical model to detect framework boundaries. By specifically targeting these boundary-defining components, the system can clearly distinguish between different analysis frameworks without requiring extensive modification of the overall model structure. The extraction process focuses on identifying the essential separating elements rather than restructuring the entire model.
Solution Approach 2:
The patent creates a universal processing mechanism that handles multiple analysis frameworks through a single integrated system. The framework identification and model portion creation processes work across different framework types (continuous, discrete, algebraic, etc.) using the same underlying logic, reducing the need for framework-specific demarcating blocks and simplifying the overall device complexity.
3Device complexity
If all model parts are processed using a single unified algorithm, then the processing system becomes simpler, but the manufacturing precision and accuracy of framework-specific computations decrease
Solution Approach 1:
The patent applies the principle of local quality by assigning different dedicated algorithms to different model portions based on their specific analysis framework requirements. Each model portion receives processing tailored to its framework type (continuous, discrete, algebraic, etc.), ensuring optimal computational accuracy for each local region. This localized processing approach maintains high precision while avoiding the need for a single overly complex universal algorithm.
4Reliability
If model parts from the same analysis framework are kept topologically separated, then the model can maintain framework-specific boundaries, but the loss of time for processing increases
Solution Approach 1:
The patent performs preliminary grouping of model parts by analysis framework before the actual processing occurs. By pre-identifying and consolidating model parts that belong to the same framework into unified model portions, the system prepares the model for efficient processing. This preliminary action ensures that framework boundary integrity is maintained while eliminating the time penalty that would result from processing scattered model parts separately during execution.
Data Source
AI summary
In a graphical modeling environment supporting a model having at least two different analysis frameworks operating therein, a system and corresponding method of processing the graphical model modify the model to group model portions together for processing in the same analysis framework. Model parts are identified and associated with the analysis framework in which they operate. Model parts are then grouped based on their association with their analysis framework to form model portions that operate in one of the different analysis frameworks. In instances where topological separation of model portions operating in the same analysis framework occurs, the system and method reconfigure intervening model portions to be amenable with operation in the analysis framework of the surrounding model portions to improve processing efficiency.