Hybrid Graphical-Textual Model Execution in Technical Computing
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Solution Overview
Problem
Conventional computing environments require users to develop code in statically-typed languages like C++, C, or Fortran, which are inefficient for tasks in disciplines such as mathematics, science, and engineering, and lack integration of time-based and event-based simulation capabilities.
Innovation Solution
A technical computing environment (TCE) that combines graphical and textual models, allowing users to create models with graphical and textual elements, where the graphical environment uses dynamically-typed languages like MATLAB, enabling time-based and event-based simulation by parsing and transforming elements to be understood by both graphical and textual engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional statically-typed languages (C++, C, Fortran) are used for code development, then type safety and compile-time checking are improved, but efficiency for mathematical/scientific/engineering tasks and integration with time-based simulation deteriorates
Solution Approach 1:
The patent merges the graphical modeling environment with textual code execution capabilities, allowing MATLAB-like dynamically-typed code to be embedded and executed within the graphical model. This integration enables users to leverage both the visual intuition of graphical models and the computational power of textual code for mathematical and engineering tasks, resolving the contradiction between type safety and computational efficiency.
Solution Approach 2:
The system provides a universal computing environment that supports multiple programming paradigms and languages (graphical models, MATLAB-style code, C/C++ code) within a single integrated platform. This multi-functionality allows users to switch between different coding approaches depending on the task requirements, achieving both reliability and productivity.
2Ease of operation
If purely graphical models are used, then visual intuition and ease of modeling are improved, but capability for complex computational tasks and runtime flexibility deteriorates
Solution Approach 1:
The patent combines graphical model elements with textual code execution capabilities, allowing users to embed MATLAB-style code within graphical model blocks. This merger enables the system to maintain visual intuition and ease of modeling while simultaneously providing the computational power and flexibility needed for complex mathematical and engineering tasks.
Solution Approach 2:
The system segments the computational model into graphical components (for structure and flow) and textual code components (for complex computations). This segmentation allows each part to excel at its intended function while working together as an integrated whole, preserving visual intuition while enhancing computational capability.
3Adaptability or versatility
If textual code is embedded in graphical models, then computational flexibility is improved, but resolution ambiguity of data and function symbols deteriorates
Solution Approach 1:
The patent introduces an intermediary resolution process that acts as a mediator between the graphical model elements and the embedded textual code. This intermediary systematically resolves data and function symbols by searching through the graphical hierarchy and code context, eliminating ambiguity while preserving the computational flexibility of embedded code.
Solution Approach 2:
The system performs preliminary resolution of symbols during the model compilation or initialization phase, before execution begins. This preliminary action identifies and resolves all data and function symbols in advance, creating a clear mapping that eliminates runtime ambiguity while maintaining the flexibility of the embedded code.
4Productivity
If hybrid graphical-textual models are used, then simulation capability is improved, but complexity of model execution and coordinate system management deteriorates
Solution Approach 1:
The patent introduces coordinate structure information as an intermediary layer that automatically manages the transformation between graphical model coordinates and code execution coordinates. This intermediary handles the complexity of coordinate system management, allowing the hybrid model to achieve enhanced simulation capability without requiring the user to manually manage execution complexity.
Solution Approach 2:
The system implements self-service mechanisms where the execution environment automatically manages the complexity of running hybrid models. The system autonomously handles coordinate transformations, code injection, result integration, and error handling, freeing users from managing execution complexity while benefiting from enhanced simulation capabilities.
Data Source
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AI summary
A device may receive a chart generated via a technical computing environment, where the chart includes a textual portion and a graphical portion, and the graphical portion includes state information. The device may parse the chart into the textual portion and the graphical portion, and may process the textual portion with a textual engine of the technical computing environment to generate textual results. The device may process the graphical portion with a graphical engine of the technical computing environment to generate graphical results, and may combine the textual results with the graphical results to generate chart results. The device may output or store the chart results.