Improving digital twin experience through big data, IoT and social analysis: An architecture and a case study

Area of Science:

• Digitalization and Data Management
• Computer Science
• Industrial Technology

Background:

• Industries are increasingly adopting digital technologies, leading to a surge in data from sensors and the Internet of Things (IoT).
• Digital twins are emerging as crucial tools for monitoring and decision support, but often struggle with integrating diverse data streams.

Purpose of the Study:

• To propose and demonstrate an architecture for enhancing digital twin experiences by integrating information from multiple sources.
• To validate the architecture's effectiveness and user acceptance in a real-world industrial setting.

Main Methods:

• Developed a novel architecture for multi-source data integration in digital twins.
• Implemented the architecture using the Matterport 3D media platform for a digital twin of an Italian company's office.
• Integrated various technologies and sensors to feed data into the digital twin.

Main Results:

• The implemented digital twin successfully integrated data from diverse sources.
• Evaluation demonstrated high user acceptance of the enhanced digital twin solution.
• The architecture proved effective in enriching the virtual model with additional data.

Conclusions:

• The proposed architecture significantly improves digital twin capabilities for managing and visualizing multi-source data.
• This approach offers a scalable solution for creating more comprehensive and interactive digital twins in various industries.
• The successful implementation highlights the potential for advanced data integration in digital twin applications.