Deep-Learning HMI Screen Arrangement for Adaptive Displays
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Solution Overview
Problem
Existing HMI screen design and arrangement methods are inefficient, tedious, and require manual customization to adapt to different display sizes and capabilities, limiting the applicability of master templates and increasing the time and effort required for customization.
Innovation Solution
A computing system utilizing a generative adversarial network (GAN) or autoencoder to learn from historical HMI screen designs and automatically arrange HMI elements based on requirements, including determining element properties, spatial constraints, and generating layouts that maintain the look and feel of the original design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual arrangement of HMI elements is used, then design flexibility and control are improved, but time consumption and labor effort increase
Solution Approach 1:
The system performs automatic arrangement of HMI elements without requiring manual intervention. The computing system autonomously determines element positions, sizes, and layouts based on historical design data and current screen requirements, eliminating the need for engineers to manually arrange each element while maintaining design quality
Solution Approach 2:
The patent replaces the manual mechanical process of dragging and dropping HMI elements with an automated computational system. The computing system uses algorithms to calculate and determine element arrangements, substituting human manual operations with automated digital processing
2Stability of the object's composition
If master templates are used for different display sizes, then design consistency is improved, but adaptability to specific display capabilities deteriorates
Solution Approach 1:
The system applies different arrangement strategies to different regions and elements based on their specific requirements. Each HMI element can have customized positioning and sizing parameters determined by the computing system, allowing local optimization for different display capabilities while maintaining overall design consistency
Solution Approach 2:
The arrangement of HMI elements becomes dynamic and adaptive rather than static. The computing system adjusts element positions, sizes, and layouts based on the specific display dimensions and capabilities, allowing the design to dynamically adapt to different display environments while maintaining consistency through learned design patterns
3Manufacturing precision
If manual customization is performed for each display, then design precision is improved, but productivity and efficiency decrease
Solution Approach 1:
The computing system autonomously performs the customization process without requiring manual intervention. It automatically determines optimal element arrangements, sizes, and positions based on display specifications and historical design data, achieving both precision and efficiency simultaneously
Solution Approach 2:
The system pre-processes and learns from historical HMI design data to establish optimal arrangement patterns before actual design execution. This preliminary learning phase enables the system to quickly generate precise designs for new displays without requiring time-consuming manual customization
Data Source
AI summary
HMI screens are typically manually engineered or arranged. For example, HMI screens can include various elements, and an engineer often arranges the HMI elements on a given HMI screen, for instance one by one, until the final design of the HMI screen is complete. It is recognized herein that existing approaches to designing and arranging HMI screens lack efficiency, and can be tedious and redundant. Method, systems, and apparatuses described herein can automatically arrange elements of human-machine interface (HMI) screens. In an example aspect, a computing system includes a deep learning model, for instance an autoencoder or a generative adversarial network (GAN), configured to learn and design HMI screen arrangements.