Graphical Program Node Suggestion via Terminal Analysis
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Solution Overview
Problem
Graphical programming environments, such as LabVIEW, require users to manually select and connect nodes for complex programs, which can be time-consuming and inefficient, especially when users need to create programs for industrial automation, process control, or test and measurement functions.
Innovation Solution
A system and method that automatically suggests graphical program elements based on user input and terminal IDs, analyzing connections between nodes to predict and display the most likely nodes to connect, allowing users to select and integrate suggested nodes directly into the program.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually select and connect nodes in graphical programming environments, then users have full control over program creation, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary analysis of terminal IDs and connection patterns before the user needs to make selections. By pre-processing the graphical program data to identify common connections and user-specific tendencies, the system prepares suggestion candidates in advance, significantly reducing the time required for node selection and connection operations.
Solution Approach 2:
The system enables self-service by automatically analyzing the graphical program structure and connection patterns, then generating node connection suggestions without requiring manual intervention. The system serves itself by using its own program data to generate intelligent suggestions, freeing the user from tedious manual node selection while maintaining full control when needed.
2Productivity
If the system automatically suggests nodes based on analysis, then node selection time is reduced, but the system complexity increases
Solution Approach 1:
The system segments the complex task of node suggestion into distinct functional modules: terminal ID extraction, connection pattern analysis, user tendency detection, and suggestion generation. Each module handles a specific aspect of the problem, making the overall system more manageable and maintainable while delivering sophisticated functionality.
Solution Approach 2:
The system introduces an intermediary suggestion layer between the user and the node connection process. Rather than directly connecting nodes or requiring full manual selection, the suggestion system acts as a mediator that provides intelligent recommendations based on analyzed patterns, simplifying the user interaction while managing system complexity through abstraction.
3Measurement precision
If the system analyzes terminal IDs and connection patterns, then suggestion accuracy improves, but the processing overhead increases
Solution Approach 1:
The system applies partial analysis by focusing on the most relevant features (terminal IDs and connection patterns) rather than analyzing all possible attributes of nodes. This selective approach achieves sufficient suggestion accuracy without the excessive processing overhead that would result from comprehensive analysis of all node properties and connection possibilities.
Data Source
AI summary
System and method for editing a graphical program. One or more nodes are included in a graphical program in response to user input selecting the one or more nodes, where each node has a respective one or more terminals for receiving input to or providing output from the node. User input is received indicating a terminal on a first node of the one or more nodes to which a node is to be connected. One or more suggested graphical program elements to include in the graphical program are automatically presented based on the indicated terminal, where the one or more suggested graphical program elements are selectable by the user for inclusion in the graphical program.