Automated Signal Grouping in Model-Based Design
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Solution Overview
Problem
The manual process of grouping signals in a model into a semantically viable aggregation is time-consuming, reduces productivity, and wastes computing resources, as it involves multiple steps such as selecting blocks, editing properties, and connecting signals.
Innovation Solution
Automating the creation of a new block to group signals by analyzing compatibility and orientation, allowing for automatic selection and connection of signals into a semantically viable aggregation, reducing the need for manual intervention and context switches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual process is used to group signals into semantically viable aggregation, then signals can be grouped with semantic correctness, but time consumption increases and productivity decreases
Solution Approach 1:
The system performs self-service by automatically analyzing signal compatibility and orientation, then autonomously creating blocks and connecting signals to form semantically viable aggregations without requiring manual intervention. The algorithm inspects the model, identifies compatible signals, and executes the grouping operation independently.
Solution Approach 2:
The system performs preliminary actions by pre-analyzing signal compatibility and orientation attributes before creating the aggregation block. This preliminary inspection allows the system to prepare the grouping configuration in advance, reducing the overall time required for signal aggregation while maintaining semantic correctness.
2Ease of operation
If manual process is used to group signals, then user control over grouping is maintained, but computing resources are wasted due to repetitive manual operations
Solution Approach 1:
The system performs self-service by automatically analyzing signal compatibility and orientation, then autonomously creating blocks and connecting signals to form semantically viable aggregations without requiring manual intervention. The algorithm inspects the model, identifies compatible signals, and executes the grouping operation independently.
Solution Approach 2:
The patent replaces the mechanical manual operation system with an automated computational system. Instead of manual drag-and-drop operations and property editing, the system uses algorithms to inspect signals, determine compatibility, and automatically create and connect aggregation blocks, thereby eliminating wasted computing resources from repetitive manual tasks.
3Productivity
If automated block creation is implemented, then time and productivity are improved, but device complexity increases
Solution Approach 1:
The automation process is segmented into distinct functional modules: signal inspection to identify candidates, compatibility analysis to verify semantic viability, orientation determination to establish connection direction, and block creation to generate the aggregation structure. This segmentation manages complexity by breaking down the automated process into manageable, independent steps.
Solution Approach 2:
The patent introduces an intermediary algorithmic layer that mediates between the model elements and the automated block creation process. This intermediary inspects signals, determines compatibility and orientation, and orchestrates the creation of aggregation blocks, thereby managing the complexity of automation while maintaining productivity improvements.
Data Source
AI summary
A device receives a selection of signals associated with a model, and analyzes the selected signals to identify signals, of the selected signals, that can be grouped into a semantically viable aggregation. The device receives an instruction to create a block for the identified signals. The device provides a block to group the identified signals into a semantically viable aggregation with a particular number of inputs, a particular number of outputs, a particular size, a particular position, and a particular orientation, in relation to the model, based on the identified signals, and provides the block in the model.