The Digital Twin

Digital Twin 3D models for any asset

The Digital Twin is a virtual representation of a real world asset and acts as a collection of digital data representing the asset, allowing industry to use advanced 3D models connected to AI-driven analytics to better measure, predict and mitigate the assets data.

The Digital Twin utilizes the Internet of Things (IoT) via enabled sensors that feed into machine learning models.

Snaploader constructs a grey-scale, furnished 3D model of any asset – building, stand-alone floor – and embeds the 3D model with relevant sensor and zone meshes to link to any interface via our custom built API.

3D models are displayed in our web viewer which is designed to be embedded, and provides an API for two-way interactions with the embedding software.

This arrangement ensures that the embedding software is able to use our viewer in a way that makes sense for their vertical, without us needing to make assumptions about data models etc.

Essentially you can think of our web viewer as an advanced user interface component.

In addition to the basics (i.e. the ability to control which content is displayed) we allow embedding software to inject custom user interface as overlays tied to 3D points in the scene. We also allow embedding software to listen for events e.g. when a 3D object in the scene is clicked on, thus the embedding software can, for example, open relevant information in its own UI.

Digital twins have 4 significant value propositions within the built environment:

Building maintenance and operations

Environmental impact and sustainability

Health and wellness

People improvement and real estate interface

What we need you to email us to quote on your project?

For the Digital Twin:

An Architectural set of plans in pdf format including the furnished 2D floor plan and if available the elevations. Later we require sensor and zone information to record required data points.

T +61 1300 364 550

E eric.fink@snaploader.com

W snaploader.com

Snaploader
Level 6/11 York StSydney NSW 2016
Australia