Azure Digital Twin: A Comprehensive Guide

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Azure Digital Twin is a powerful tool that enables you to create a digital replica of your physical assets, systems, and processes. This allows you to simulate, analyze, and optimize their performance in a virtual environment.

By doing so, you can improve efficiency, reduce costs, and enhance decision-making. Digital twins can be used in various industries, including manufacturing, healthcare, and transportation.

Digital twins can be created using various data sources, including IoT sensors, historical data, and simulation models. This data is then used to create a highly accurate and realistic digital representation of the physical system.

With Azure Digital Twin, you can also integrate with other Azure services, such as Azure IoT Hub and Azure Machine Learning, to create a comprehensive and connected solution.

What is Azure Digital Twin

Azure Digital Twin is an Azure-based cloud service that implements a digital twin model and hosting platform with persistent storage.

It offers a rich set of features for describing the contents of digital twins, including properties, components, inheritance, and more.

Credit: youtube.com, Azure Digital Twins demo | Creating replicas of real-world environments

The Azure Digital Twins Explorer GUI tool lets users view digital twin models and instances, as well as their relationships.

The service focuses on describing infrastructures with many components and their relationships.

However, processing messages from data sources falls under the domain of other Azure services, such as Azure Functions and the Azure Event Grid.

Creating and Managing Models

Creating and managing models is a crucial part of Azure Digital Twin. To create a model, you can use the Digital Twins Definition Language (DTDL) and define its attributes, such as properties, relationships, and components. Properties represent the state of an entity, relationships let you represent how a digital twin can be involved with other digital twins, and components allow you to build your model interface as an assembly of other interfaces.

You can also use the DTDL v3 Language Description to define your models, which is based on JSON-LD and is programming-language independent. To help you validate your models, a .NET client-side DTDL parsing library is provided on NuGet: DTDLParser.

To manage your digital twins, you can create, modify, and remove entities in your environment, represented by digital twins. You can use the CreateOrReplaceDigitalTwinAsync() method on the service client to create a twin, and provide the model and any desired initialization of twin data through the initData parameter.

Manage

Credit: youtube.com, Management Decision Making Models

Managing models is a crucial aspect of creating a comprehensive digital twin solution. To manage models, you can use the Digital Twins Definition Language (DTDL) to define the structure and properties of your models.

You can validate your models offline before uploading them to your Azure Digital Twins instance using the DTDLParser library. This ensures that your models are correct and can be used effectively in your digital twin solution.

To manage digital twins, you can use the CreateOrReplaceDigitalTwinAsync() method on the service client to create or update twins. This method allows you to specify the ID, model, and initial data for the twin.

You can also use JSON Patch to update properties of a digital twin. This involves crafting a JSON Patch document containing the update information and passing it into the UpdateDigitalTwin() method.

Here are the steps to update a digital twin using JSON Patch:

  • Replace the mass and radius property values of the digital twin it's applied to.
  • Use the Azure .NET SDK's JsonPatchDocument to create a JSON Patch document.
  • Pass the JSON Patch document into the UpdateDigitalTwin() method.

By following these steps, you can effectively manage your digital twins and ensure that your digital twin solution is accurate and up-to-date.

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Here's an example of a JSON Patch document that updates a digital twin's mass and radius property values:

```json

[

{

"op": "replace",

"path": "/properties/mass",

"value": 10

},

{

"op": "replace",

"path": "/properties/radius",

"value": 5

}

]

```

This JSON Patch document updates the mass and radius property values of the digital twin it's applied to.

Delete a Model

Deleting a model is a straightforward process that can be done from the Models page.

You can delete a model by clicking the "Delete" button next to the model name.

Model Definition and Attributes

DTDL, or Digital Twins Definition Language, is used to define models for Azure Digital Twins. It's based on JSON-LD and is programming-language independent.

You can view the full language description for DTDL v3 in GitHub, including reference details and examples to help you get started writing your own DTDL models.

Here are the main attributes available in DTDL that are supported in Azure Digital Twins:

  • Property - Properties are data fields that represent the state of an entity.
  • Relationship - Relationships let you represent how a digital twin can be involved with other digital twins.
  • Component - Components allow you to build your model interface as an assembly of other interfaces.

Components can also be used for organization, to group sets of related properties within a model interface.

Nomenclature

Credit: youtube.com, Nomenclature rules for attributes

In the context of this document, it's essential to understand the terminology used. Azure Digital Twins refers to Microsoft's cloud service.

The term Azure digital twin specifically refers to a digital twin running in the service. This distinction is crucial for clarity in our discussion.

An Azure digital twin instance is a deployed digital twin running in the Azure Digital Twins service, not an instance of the service itself.

Model Definition Language

The Digital Twins Definition Language (DTDL) is a crucial part of Azure Digital Twins. It's used to define models for Azure Digital Twins, and you can view the full language description for DTDL v3 in GitHub.

DTDL is based on JSON-LD and is programming-language independent, making it a versatile and widely applicable language. It's not exclusive to Azure Digital Twins, but also used to represent device data in other IoT services like IoT Plug and Play.

The main information about a model is given by its attributes, which are defined within the contents section of the model interface. A DTDL model interface used for Azure Digital Twins may contain zero, one, or many of each of the following fields: Property, Relationship, and Component.

Credit: youtube.com, 2. Hierarchical Model

Properties are data fields that represent the state of an entity, have backing storage, and can be read at any time. Relationships let you represent how a digital twin can be involved with other digital twins, and can represent different semantic meanings. Components allow you to build your model interface as an assembly of other interfaces.

Here are the supported attributes in DTDL for Azure Digital Twins:

  • Property
  • Relationship
  • Component

Note that writable DTDL attributes for properties are not currently supported in Azure Digital Twins.

Integration and Orchestration

Integration and Orchestration is crucial for Azure Digital Twins, allowing you to connect and process data from various sources in real-time. This is achieved through scalable cloud computing resources, such as Microsoft Azure, which provides a flexible, secure, and robust platform for real-time decision support.

One customer ingests 77 million events per day through their XMPRO Data Streams, demonstrating the vast amounts of data that can be processed in real-time. This is especially important for companies with thousands of assets across multiple plants.

Curious to learn more? Check out: Azure Data Studio vs Azure Data Explorer

Credit: youtube.com, Demo: Azure Digital Twins and Azure Synapse integration

The XMPro Agent integrates with data sources and updates the digital twin in near real-time, ensuring accurate and up-to-date information about assets and systems. This integration is crucial for companies to have confidence in their digital twins accurately reflecting the state of their devices and systems.

Here are some key features of the ScaleOut Azure Digital Twins Integration:

Real-Time Integration

Real-Time Integration is a crucial aspect of any data-driven system. It enables companies to have accurate and up-to-date information about their assets and systems. This is achieved through the XMPro Agent, which integrates with data sources and ensures that the digital twin is updated in near real-time.

Integration with data sources is a must for real-time decision support. By leveraging cloud computing resources, companies can ingest large volumes of data at high velocity, providing their team with real-time decision support. For instance, one of our customers ingest 77 million events per day through their XMPRO Data Streams.

Credit: youtube.com, The Latest in API Orchestration, Mediation, and Integration

The ScaleOut Azure Digital Twins Integration lets the application developer add a real-time component to an Azure digital twin model. This real-time component is implemented by a ScaleOut real-time digital twin, which defines state information and a message-processing method that hosts real-time analytics.

Here are some key features of real-time integration:

  • Store properties
  • Process messages from the data source
  • Access properties in other Azure digital twin instances

Message processing is a critical part of real-time integration. A ScaleOut real-time digital twin processes incoming messages from each data source using its digital twin instance’s properties. This is done using a message-processing method that runs in ScaleOut’s in-memory computing platform to accelerate performance.

Real-time integration also enables companies to manage assets and hierarchies in real-time. With XMPRO’s No Code Application Composition toolset, companies can integrate Azure Data Explorer (ADX) time series data and analytics with Azure Digital Twins for a complete web-based historian and industrial analytics solution.

Property Access APIs

Property Access APIs are a game-changer for integrating and orchestrating your digital twins. They allow you to read and update properties in Azure digital twin instances within message-processing methods, making it easier to manage complex data flows.

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With ScaleOut's real-time digital twin model, you can use C# and Java APIs to access properties in Azure digital twin instances. This eliminates the need for multiple serverless functions to perform property updates in Azure Digital Twins.

The APIs provide methods to retrieve or update selected properties stored within Azure digital twin instances. You can use these methods to:

  • Retrieve the list of Azure digital twin models
  • Retrieve the list of digital twin instances in an Azure digital twin model
  • Retrieve the JSON model definition for an Azure digital twin model
  • Retrieve a property from an Azure digital twin instance
  • Save a property to an Azure digital twin instance

For example, consider a thermostat digital twin that needs to update its parent digital twin that models the room in which it is located. The APIs make it possible to update the parent digital twin's properties directly from the thermostat's message-processing method.

Here's a summary of the APIs' capabilities:

These APIs are a powerful tool for integrating and orchestrating your digital twins, and can help you build more efficient and scalable data flows.

IoT and Business Systems

Azure Digital Twins is a powerful tool for connecting the physical and digital worlds, but it's not just a technical solution - it's also a business system. You define your business environment by creating custom twin types called models, which are essentially a specialized vocabulary to describe your business.

Credit: youtube.com, Azure Digital Twins: Powering the Next Generation of IoT | INT177B

These models are defined in a JSON-like language called Digital Twins Definition Language (DTDL), and they describe types of entities according to their state properties, commands, and relationships. You can design your own model sets from scratch or get started with pre-existing sets based on common vocabulary for your industry.

The DTDL models are used to create digital twins that represent each specific entity in your environment, and you can connect twins to each other using relationships in the model definitions. This forms a conceptual graph that you can view in Azure Digital Twins Explorer.

To supercharge your Azure Digital Twins solution, you can use XMPro iDTS, a sophisticated software platform that offers real-time insights and predictive analytics for proactive decision-making. This platform integrates seamlessly with existing systems and is tailored to enhance asset lifecycle management by facilitating predictive maintenance.

XMPro iDTS includes several key modules, such as the Datastream Designer, XMPro AI, App Designer, and Recommendation Manager. These modules provide a robust set of tools for optimizing operations, including real-time data integration, predictive analytics, custom application development, and actionable recommendations.

Here are some key features of the XMPro iDTS modules:

  • Datastream Designer: Configures and manages data flow from various sources into the digital twin environment.
  • XMPro AI: Leverages machine learning algorithms to analyze data and predict outcomes.
  • App Designer: Creates custom applications within the XMPro iDTS environment.
  • Recommendation Manager: Suggests actionable recommendations based on data-driven insights.

By integrating these modules, XMPro iDTS delivers a powerful solution for organizations looking to leverage digital twin technology for improved efficiency, reduced costs, and strategic advantage in their operations.

Use Cases and Topology

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Azure Digital Twins is a versatile platform that can be used in many different industries and applications. It can be used to create digital twin models of physical structures, cities, industrial environments, supply chain networks, and remote environments.

One of the top use cases for Azure Digital Twins is in smart buildings, where it can be used to optimize building performance, improve energy efficiency, and enhance the occupant experience. This can be achieved by analyzing and visualizing data from sensors and other devices in the building.

Azure Digital Twins can also be used to create digital twin models of entire cities, which can be used to optimize traffic flow, improve public safety, and enhance the overall livability of the city.

Top Use Cases

Azure Digital Twins is a versatile platform that can be used in many different industries and for many different applications. The top use cases for Azure Digital Twins include smart buildings, where it can be used to create digital twin models of buildings and other physical structures.

Credit: youtube.com, Lab Topology and Use Case to be Covered in Lab

In smart buildings, Azure Digital Twins can analyze and visualize data from sensors and other devices, optimizing building performance, improving energy efficiency, and enhancing the occupant experience. This is particularly useful for large commercial buildings or university campuses where energy efficiency is crucial.

Azure Digital Twins is also well-suited for smart cities, where it can create digital twin models of entire cities and analyze data from a wide range of sensors and devices. This can be used to optimize traffic flow, improve public safety, and enhance the overall livability of the city.

Industrial IoT is another key use case for Azure Digital Twins, where it can be used to create digital twin models of industrial environments and analyze data from sensors and other devices. This can be used to optimize production, improve equipment performance, and enhance the overall efficiency of the facility.

Supply chain optimization is also a key use case for Azure Digital Twins, where it can create digital twin models of supply chain networks and analyze data from sensors and other devices. This can be used to optimize logistics, improve inventory management, and enhance the overall efficiency of the supply chain.

Remote monitoring and control is another important use case for Azure Digital Twins, where it can be used to create digital twin models of remote environments, such as offshore platforms and remote mining sites. This can be used to monitor and control the environment, improve safety, and reduce downtime.

See what others are reading: Azure Devices

Sample Solution Architecture

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A sample solution architecture for Azure Digital Twins can be complex, but it's essential to understand the components that make it work. The Azure Digital Twins service instance is at the core, storing twin models and their state while orchestrating event processing.

This service instance can be driven by one or more client apps that configure models, create topology, and extract insights from the twin graph. These client apps are crucial for getting the most out of Azure Digital Twins.

External compute resources, such as Azure Functions, can be used to process events generated by Azure Digital Twins or connected data sources like devices. This helps to ensure that the system is scalable and can handle a large amount of data.

An IoT hub is also a necessary component, providing device management and IoT data stream capabilities. This is where devices can be connected and data can be streamed in.

Credit: youtube.com, Solution vs Enterprise Architecture POV

The following components can be part of a complete solution using Azure Digital Twins:

  • Azure Digital Twins service instance
  • Client apps
  • External compute resources (e.g. Azure Functions)
  • IoT hub
  • Downstream services (e.g. workflow integration, cold storage, analytics)

These components work together to provide a comprehensive solution for IoT and digital twin applications. By understanding the roles of each component, you can design a solution that meets your specific needs.

Worth a look: Solution Azure

More About Xpro

XMPro is a powerful tool that can supercharge Microsoft Azure Digital Twins in several ways. It allows companies to automate processes, such as controlling the physical environment and triggering alarms or notifications, based on data from the digital twin model.

By creating custom workflows and decision trees, companies can make decisions based on data from the digital twin model. This can help automate tasks and make the decision-making process more efficient.

XMPro provides a visual interface to create, manage, and monitor workflows, making it easier for non-technical users to participate in the management of the digital twin. This can help make better decisions based on the data from the digital twin.

Credit: youtube.com, Network Topologies (Star, Bus, Ring, Mesh, Ad hoc, Infrastructure, & Wireless Mesh Topology)

XMPro can integrate various data sources, such as IoT devices, ERP systems, CRM systems, and SCADA systems, to create a holistic view of the environment. This can help make more accurate decisions and gain a better understanding of the environment.

Here are the key benefits of using XMPro with Azure Digital Twins:

  • Automating processes
  • Enhancing decision-making
  • Creating a holistic view
  • Streamlining operations
  • Improving scalability

XMPro can automate repetitive tasks, such as data collection and analysis, which can save time and resources for companies and streamline their operations.

Project and Resource Management

Project and Resource Management is a crucial aspect of Azure Digital Twin, allowing you to manage and optimize your digital twin's performance and resources efficiently.

With Azure Digital Twin, you can create a digital representation of your physical assets and systems, and then use this digital twin to simulate and analyze different scenarios, making it easier to identify potential issues and optimize resource allocation.

You can also use Azure Digital Twin to automate tasks and workflows, freeing up your team to focus on higher-value tasks and improving overall productivity.

Resource Limitations

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Resource Limitations are a crucial aspect of project and resource management, and Azure Digital Twins is no exception.

The Azure Digital Twins service imposes numerous limits on resource usage by digital twins deployed in the service.

You can store up to 32KB of property data in a real-time digital twin instance, which is a significant constraint.

Digital twins can be created at a rate of 50 per second by default, and updated at a rate of 1000 updates per second by default.

ScaleOut Digital Twins can be configured to throttle updates to the Azure Digital Twins service to meet these resource limits.

This means you can dramatically scale the effective message-processing throughput of Azure Digital Twins, making it a powerful tool for managing large-scale projects.

Advanced Features and Extensions

Azure Digital Twin offers a range of advanced features and extensions that enable you to create more sophisticated models. One of the key features is the ability to add non-core features to your models using DTDL v3 feature extensions.

Recommended read: Azure Features

Credit: youtube.com, #Azure Back To School - Developing with Azure Digital Twins

These extensions are identified by their context specifier, a unique Digital Twin Model Identifier (DTMI) value, and can be added to the model's @context field. You can add multiple feature extensions to the same model.

The Historization extension is used to designate a property in a DTDL v3 model as something that should be historized. Its context specifier is dtmi:dtdl:extension:historization;1.

The Historization extension includes the Historized adjunct type, which can be added as a co-type to a DTDL property element to indicate that the service should persist the element's historical values.

The QuantitativeTypes extension is used to enable semantic types, unit types, and units in a DTDL v3 model. Its context specifier is dtmi:dtdl:extension:quantitativeTypes;1.

With the QuantitativeTypes extension, you can add semantic types as adjunct types to a CommandRequest, a Field, a MapValue, or a property in DTDL v3.

Data and Model Validation

Before uploading your models to Azure Digital Twins, it's essential to validate them offline. This can be done using the DTDLParser library, which is available on NuGet.

Credit: youtube.com, Azure Digital Twins demo | Creating replicas of real-world environments

The DTDLParser library allows you to validate your models directly in your C# code. You can also view sample use of the parser in the DTDLParserResolveSample on GitHub.

To ensure a smooth migration to a new model, you can use the UpdateDigitalTwin() function. This function can also be used to migrate a digital twin to a different model.

For example, if you're migrating a digital twin from model foo_old to foo_new, you'll need to update both the model and the twin's temperature property. The patch for this situation should look like the following JSON Patch document:

:::code language="json" source="~/digital-twins-docs-samples/models/patch-model-2.json":::

Next Steps

Now that you've started building your Azure Digital Twin, it's time to take it to the next level. To create models based on industry-standard ontologies, you should learn about what an ontology is.

You can dive deeper into managing models with API operations by checking out the Manage DTDL models section. This will give you a better understanding of how to work with your models.

Credit: youtube.com, Azure Digital Twins demo | Creating replicas of real-world environments

To create digital twins, you need to learn about how models are used to create digital twins, which you can find in the Digital twins and the twin graph section.

If you want to create and manage relationships between your digital twins, you should definitely check out the next steps section.

Here are some specific next steps to keep in mind:

  • Learn about creating models based on industry-standard ontologies: What is an ontology?
  • Dive deeper into managing models with API operations: Manage DTDL models
  • Learn about how models are used to create digital twins: Digital twins and the twin graph
  • See how to create and manage relationships between your digital twins:

Frequently Asked Questions

What is the difference between Azure device twin and digital twin?

Digital twins model and predict complex system behavior, while Azure Device Twins simulate real devices for testing applications

Is Azure digital twins free?

Azure Digital Twins has no upfront cost or termination fee, and you only pay for what you use. The cost is based on consumption of Operations, Messages, and Query Units.

Margarita Champlin

Writer

Margarita Champlin is a seasoned writer with a passion for crafting informative and engaging content. With a keen eye for detail and a knack for simplifying complex topics, she has established herself as a go-to expert in the field of technology. Her writing has been featured in various publications, covering a range of topics, including Azure Monitoring.

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