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AWS IoT TwinMaker

AWS IoT TwinMaker Features

AWS IoT TwinMaker makes it faster and easier for you to create and use digital twins to optimize industrial operations, increase production output, and improve equipment performance. Digital twins are virtual representations of physical systems that are regularly updated with real-world data to mimic the structure, state, and behavior of the systems they represent to drive business outcomes. With AWS IoT TwinMaker, you can use built-in connectors or create your own connectors to easily access and use data from a variety of data sources, such as equipment sensors, video feeds, and business applications. Import your existing 3D visual models to quickly create digital twins of your facilities, processes, and equipment that update in real time with data from connected sensors and cameras, visualize insights and predictions based on the data, and raise alarms to identify when data or predictions deviate from expectations. Easily integrate these digital twins into web-based applications that allow your plant operators and maintenance engineers to monitor and improve your operations.

Features

Data connectors

AWS IoT TwinMaker provides built-in data connectors for the following AWS services: AWS IoT SiteWise for collecting, organizing, and storing equipment and time-series sensor data; and Amazon Kinesis Video Streams for capturing, processing, and storing video data. AWS IoT TwinMaker also provides a framework for you to easily create custom data connectors to use with other AWS or third-party data sources, such as Amazon Timestream, Snowflake, and Siemens Mindsphere. These data connectors allow your applications to only use the AWS IoT TwinMaker unified data access API to read from and write to the different data stores without needing to query each data source using their own individual API.

Model builder

To model your physical environment, you can create entities in AWS IoT TwinMaker that are virtual representations of your physical systems, such as a furnace or an assembly line. You can also specify custom relationships between these entities to accurately represent the real-world deployment of these systems. You then connect these entities to your various data stores to form a digital twin graph, which is a knowledge graph that structures and organizes information about the digital twin for easier access and understanding. As you build out this model of your physical environment, AWS IoT TwinMaker automatically creates and updates the digital twin graph by organizing the relationship information in a graph database.

Scene composer

With AWS IoT TwinMaker, you build a 3D digital twin by using your existing and previously built 3D visual models, such as CAD files, Building Information Modeling (BIM) files, or point cloud scans. Using the AWS IoT TwinMaker scene composer and simple 3D tools, you import these visual assets into a scene and position them to match your physical environment—for example a factory and its equipment. You can then add interactive video and sensor data overlays from the connected data sources, insights from connected machine learning (ML) and simulation services, and maintenance records and operational documents to provide you with a regularly updated, spatially aware visualization of your operations.

Applications

Once you’ve created the digital twin, AWS IoT TwinMaker provides a low-code experience for building a web application so your plant operators and maintenance engineers can access and interact with the digital twin. AWS IoT TwinMaker comes with a plug-in for Grafana, a popular open-source dashboard and visualization platform from Grafana Labs. The plug-in provides custom visualization panels, including a 3D scene viewer and dashboard templates, as well as a data-source component to connect to your digital twin data, allowing you to quickly create 3D-enabled applications for your specific needs. The plug-in can also be used to build applications with Amazon Managed Grafana, which is a fully managed service for open-source Grafana.