Digital twins ‘made easy’ with new AWS IoT service

The service,AWS IoT TwinMaker, is designed to make it easy for developers to integrate data from multiple sources like equipment sensors, video cameras, and business applications—and combines that data to create a knowledge graph that models the real-world environment. With AWS IoT TwinMaker, says the company, many more customers can use digital twins to build applications that mirror real-world systems that improve operational efficiency and reduce downtime.

“Industrial companies collect and process vast troves of data about their equipment and facilities from sources like equipment sensors, video cameras, and business applications (e.g., enterprise resource planning systems or project management systems),” says the company. “Many customers want to combine these data sources to create a virtual representation of their physical systems (called a digital twin) to help them simulate and optimize operational performance.”

However, says the company, building and managing digital twins is hard even for the most technically advanced organizations. To build digital twins, customers must manually connect different types of data from diverse sources (e.g., time-series sensor data from equipment, video feeds from cameras, maintenance records from business applications, etc.). Then customers have to create a knowledge graph that provides common access to all the connected data and maps the relationships between the data sources to the physical environment.

完成数字的双胞胎,客户部ild a 3D virtual representation of their physical systems (e.g., buildings, factories, equipment, production lines, etc.) and overlay the real-world data on to the 3D visualization—and then ensure the digital twin is kept up to date as conditions change. Once they have a virtual representation of their real-world systems with real-time data, customers can build applications for plant operators and maintenance engineers who can leverage machine learning and analytics to extract business insights about the real-time operational performance of their physical systems. Because the work required is complex, the vast majority of organizations are unable to use digital twins to improve their operations.

AWS IoT TwinMaker is offered as making it significantly faster and easier to create digital twins of real-world systems. Using AWS IoT TwinMaker, developers can get started quickly building digital twins of devices, equipment, and processes by connecting AWS IoT TwinMaker to data sources like equipment sensors, video feeds, and business applications. AWS IoT TwinMaker contains built-in connectors for Amazon Simple Storage Service (Amazon S3), AWS IoT SiteWise, and Amazon Kinesis Video Streams (or customers can add their own connectors for data sources like Amazon Timestream, Snowflake, and Siemens MindSphere) to make it easy to gather data from a variety of sources.

AWS IoT TwinMaker automatically creates a knowledge graph that combines and understands the relationships of the connected data sources, so it can update the digital twin with real-time information from the system being modeled. Customers can import existing 3D models (e.g., CAD and BIM files, point cloud scans, etc.), directly into AWS IoT TwinMaker to easily create 3D visualizations of the physical system and overlay the data from the knowledge graph on to the 3D visualizations to create the digital twin.