Industrial HMI Prompt Engineering for AI-Driven Screen and Tag Binding
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Solution Overview
Problem
The existing HMI development process is cumbersome and time-consuming, particularly due to the graphical and menu-driven workflow that requires manual selection and binding of graphical elements to data tags, which can be inefficient and labor-intensive.
Innovation Solution
An HMI development system utilizing generative artificial intelligence (AI) to assist in developing industrial HMI applications through natural language prompts, leveraging custom models trained with domain-specific industrial data to generate display screens, layouts, and data links, thereby automating the design process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a graphical and menu-driven development workflow is used for HMI applications, then the development process provides structured control and element selection capabilities, but the process becomes cumbersome and time-consuming due to manual selection and binding of graphical elements to data tags
Solution Approach 1:
The patent replaces the manual graphical drag-and-drop development workflow with an AI-based natural language processing system. The generative AI model translates natural language descriptions directly into HMI configuration code and data bindings, eliminating the mechanical process of manually selecting and binding graphical elements. This substitution of mechanical interaction with intelligent automation resolves the contradiction by maintaining ease of operation through natural language input while dramatically reducing development time.
2Manufacturing precision
If manual selection and binding of graphical elements to data tags is required, then precise control over HMI configuration is achieved, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The patent implements a self-service mechanism where the generative AI model automatically performs the configuration binding process. Instead of requiring developers to manually bind graphical elements to data tags, the system autonomously analyzes natural language input, generates appropriate configuration code, and establishes data bindings automatically. This self-service approach maintains configuration precision through AI-driven accuracy while eliminating labor-intensive manual operations, thereby resolving the contradiction between precision and productivity.
Solution Approach 2:
The generative AI model serves as an intermediary between natural language input and HMI configuration output. It translates high-level natural language descriptions into precise technical configuration code and data bindings, acting as a mediator that bridges the gap between user intent and system implementation. This intermediary layer maintains configuration precision through intelligent translation while dramatically improving productivity by eliminating manual binding operations.
3Adaptability or versatility
If traditional HMI development methods are used, then comprehensive control over interface elements is maintained, but the development process is inefficient and labor-intensive
Solution Approach 1:
The patent extracts the complex manual configuration tasks from the development process and replaces them with AI-generated code. By taking out the labor-intensive elements of manual selection and binding, the system maintains comprehensive control over HMI elements through programmatically generated configurations while simplifying the overall development process. This extraction resolves the contradiction by preserving adaptability through code-based control while reducing process complexity.
Data Source
AI summary
A human-machine interface (HMI) development system leverages a generative AI model to assist in development of HMI projects in accordance with specified functional requirements, which can be provided to the development system as intuitive natural language spoken or written text. The system can formulate and implement HMI project edits during design time based on analysis of this natural language design input. After the HMI project is deployed as a runtime application, the system can also receive and process natural language requests to modify the runtime HMI in accordance with described modification criteria.