DeepLynx Supervisory Control Adapter
A software adapter that enables DeepLynx to generate and transmit control requests to data acquisition systems or human machine interfaces (HMIs).
The Challenge
Most digital twin systems focus on data ingestion and analysis but lack direct pathways for issuing control actions back to the physical environment. Bridging the gap between digital predictions and physical responses remains a key limitation, especially for systems seeking autonomous or semi-autonomous control capabilities. Without such communication, valuable model predictions cannot influence real-time operations or process adjustments.
How It Works
• The Supervisory Control Adapter operates as a Flask-based Python application within the DeepLynx ecosystem.
• It listens for prediction events within DeepLynx and evaluates these predictions against predefined thresholds.
• Based on simple or advanced logic, it generates a control request (currently as a file) and makes it available to connected systems such as HMIs or data acquisition systems.
• This enables a digital twin system using DeepLynx to issue actionable control instructions, allowing physical assets to be updated automatically.
• The adapter can compare predictions from various sources (e.g., machine learning models or physics-based simulations) to determine appropriate actions.
• The system can evolve to include more complex decision-making logic, such as assessing the outcomes of previous control actions or weighting prediction sources.
Key Advantages
• Enables bidirectional communication between DeepLynx and physical control systems.
• Provides a method for digital twins to influence real-world processes autonomously.
• Supports multiple prediction sources and logic customization.
• Offers a foundation for more complex control decision algorithms as systems mature.
• Can be integrated without requiring changes to existing HMI or control infrastructure.
Market Applications
• Manufacturing Systems: Automate process adjustments based on real-time model forecasts.
• Industrial Monitoring: Enable feedback control from digital models to operational systems.
• Smart Facilities Management: Adjust environmental or mechanical systems based on predictive digital twin inputs.
• Digital Twin Research Projects: Implement closed-loop systems in academic or commercial R&D settings.
This software is open source and available at no cost. Download now by visiting the product's GitHub page.