Graphical Model Processing Framework Grouping
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Solution Overview
Problem
Current systems face inefficiencies in processing graphical models that contain parts operating within different analysis frameworks, as they lack an effective mechanism to identify and group model parts for dedicated processing, leading to suboptimal executable representations.
Innovation Solution
A method and system that identify model parts operating within the same analysis framework, group them together, and transform parts operating in a different framework to align with the same analysis framework, allowing for dedicated algorithm processing and conversion into a single model portion, thereby achieving efficient intermediate processing results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graphical models contain parts operating within different analysis frameworks, then the model can represent diverse system characteristics, but the processing efficiency deteriorates due to lack of effective grouping mechanism
Solution Approach 1:
The patent segments the graphical model into multiple model portions based on analysis framework boundaries. Each model portion contains model parts operating within the same analysis framework, enabling dedicated algorithm processing for each segment while preserving the overall model's diversity and versatility.
Solution Approach 2:
The patent applies different processing strategies to different parts of the model based on their local characteristics. Model parts operating within the same analysis framework are grouped together and processed using dedicated algorithms optimized for that specific framework, while other parts are processed differently, achieving local optimization that improves overall processing efficiency.
2Measurement precision
If model parts are processed using dedicated algorithms for their specific analysis frameworks, then processing precision improves, but system complexity increases due to multiple framework handling
Solution Approach 1:
By segmenting the model into portions with uniform analysis frameworks, the system can apply dedicated algorithms to each segment, improving processing precision while managing complexity through organized separation rather than monolithic handling of diverse frameworks.
Solution Approach 2:
The patent introduces model portions as intermediary structures that bridge different analysis frameworks. These portions act as boundaries that enable dedicated algorithm processing within each framework while providing a structured interface for integrating results across frameworks, thus managing system complexity.
3Measurement precision
If model parts operating in different analysis frameworks are kept separate, then dedicated algorithm processing is enabled, but the executable representation becomes suboptimal due to lack of integration
Solution Approach 1:
The patent merges model parts operating within the same analysis framework into unified model portions. This merging enables dedicated algorithm processing for each portion while maintaining proper integration at the model level, producing optimized executable representations that benefit from both specialized processing and coherent integration.
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.