This write-up will focus on newly launched AWS IoT Twinmaker service.
Digital Twins
- Digital twins are virtual representations of physical systems such as buildings, factories, production lines, and equipment that are regularly updated with real-world data to mimic the structure, state, and behavior of the systems they represent.
- To create and use digital twins of real-world systems to monitor and optimize operations.
- We can create digital twins of equipment, processes, and facilities by connecting data from different data sources like equipment sensors, video feeds, and business applications.
Digital Twin Graph
- AWS IoT TwinMaker forms a digital twin graph that combines and understands the relationships between virtual representations of your physical systems and connected data sources, so you can accurately model your real-world environment.
- Import existing 3D models (such as CAD files, and point cloud scans) to compose and arrange 3D scenes of a physical space and its contents (e.g. a factory and its equipment) using simple 3D tools.
- We can add data to these models/scenes for visualization as:
- Interactive video.
- sensor data overlays from the connected data sources.
- insights from connected machine learning (ML) and simulation services.
- equipment maintenance records and manuals.
- Using the digital twin graph, customers can now issue geospatial queries such as finding all cameras that are pointing to an equipment to help with root cause analysis.
Amazon Managed Grafana plugin
- Can be used to create a web-based application for end users.
- Use Grafana applications to observe and interact with the digital twin to help them optimize factory operations, increase production output, and improve equipment performance.
Model Builder
- Allows you to create workspaces that will hold the resources, such as entity models and visual assets needed to create a digital twin.
- In this workspace, create entities that represent digital replicas of your equipment.
- Specify custom relationships between these entities to create a digital twin graph of your real-world system.
- Using the digital twin graph, customers can now issue geospatial queries such as finding all cameras that are pointing to an equipment to help with root cause analysis.
Data Connector
- Associate entities with connectors (called as components in AWS IoT TwinMaker) to data stores such as AWS IoT SiteWise, to provide context to the data present in various data stores.
- Built-in Data Connectors for the following AWS services:
- AWS IoT SiteWise for equipment and time-series sensor data
- Amazon Kinesis Video Streams for video data
- Amazon Simple Storage Service (S3) for storage of visual resources (for example, CAD files) and data from business applications.
- Provides a framework for you to create your own Data Connectors to use with other data sources (such as Snowflake and Siemens MindSphere).
Scene Composer
- A tool to create visualizations in 3D.
- You can bring previously built 3D/CAD models into your resource library in Amazon S3.
- these visual assets can be brought into a scene, and position the 3D assets to match your real-world systems.
- visual annotations such as tags on top of the base scene.