How to Build Efficient Low-code Operational Digital Twins
In today's digital revolution, the concept of the
Operational Digital Twin stands out as a beacon of innovation. But what does it entail, and how can we efficiently construct them using low-code platforms?
This blog post seeks to answer these pressing questions.
Table of Contents
Decoding the Operational Digital Twin
At its core, an Operational Digital Twin serves as a digital mirror, reflecting both the real-time and historical operations of tangible systems or assets. This could span vast infrastructures like oil rigs or be as compact as a single machine. Dr. Adam Rasheed insights from AWS shed light on its essence, emphasizing the real-time transparency it grants into the workings of physical systems.
Such transparency paves the way for:
Boosting Efficiency: Real-time data becomes a catalyst for system enhancement. For instance, predictive maintenance can curtail downtime.
Economizing Costs: Monitoring component lifespans allows for optimal replacements, translating to cost efficiency.
Analyzing Trends: Past data becomes a goldmine for discerning system performance and pinpointing optimization zones
Augmenting Performance: Real-time insights empower both IT and field teams, equipping them to optimize system operations.
Architectural Blueprint of an Operational Digital Twin
Visualize the Operational Digital Twin as a tri-layered structure:
Physical Data Reservoirs: The genesis of raw data, be it from sensors, machinery, controllers, or even multimedia sources. This data can be extracted directly from tangible equipment, databases, or software conduits.
Data Engine: This powerhouse processes raw data, distilling insights that find their home in a database or data repository.
Visualization Facade: Think of this as an interactive dashboard or a 3D canvas, painting a picture of trends, insights, and directives.
Traditional Implementation: The Roadblocks
While the digital twin paradigm is groundbreaking, its conventional blueprinting has been marred by sluggishness, exorbitant costs, and internal support challenges. The culprit? Over-reliance on intricate software programming techniques.
The Low-Code Digital Twin Revolution
Prescient's methodology is a game-changer, championing low-code platforms:
Node-RED: A visual-centric programming dialect rooted in blocks. Its hierarchical nature ensures simplicity and clarity, making data analytics a breeze.
InfluxDB: The go-to database for chronological data, renowned for its speed, efficiency, and versatile deployment options.
Grafana: A visualization maestro, offering a plethora of visualization genres and syncing flawlessly with InfluxDB.
Prescient has supercharged these tools, introducing distributed data processing in Node-RED, infusing Grafana with custom JavaScript for interactivity, and crafting APIs to bridge Grafana and Node-RED.
Conclusion
The low-code paradigm promises swift rollouts, cost-effectiveness, and in-house support. It's a catalyst for ceaseless innovation, sans the need for mammoth software brigades. In our fast-paced digital realm, the ability to double-speed solution implementation can be a game-changer.
For a comprehensive visual guide, do check out this detailed video. We value your insights, feel free to share your thoughts!
