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Azure Digital Twins for Smart Environment Modeling

Azure Digital Twins IoT Modeling Smart Buildings

Azure Digital Twins enables you to create comprehensive digital models of physical environments. Whether modeling buildings, factories, energy networks, or cities, Digital Twins provides the foundation for understanding and optimizing your physical world through digital representation.

Understanding Digital Twins

Digital Twins creates a knowledge graph of your environment:

  • Models: Define the types of entities (DTDL - Digital Twins Definition Language)
  • Twins: Instances of models representing physical entities
  • Relationships: Connections between twins
  • Properties: Current state of each twin

Setting Up Azure Digital Twins

# Create Digital Twins instance
az dt create \
    --dt-name mydigitaltwins \
    --resource-group myResourceGroup \
    --location eastus

# Assign role to yourself
az dt role-assignment create \
    --dt-name mydigitaltwins \
    --assignee "user@company.com" \
    --role "Azure Digital Twins Data Owner"

# Get endpoint
az dt show \
    --dt-name mydigitaltwins \
    --query hostName \
    --output tsv

Defining Models with DTDL

// Building model
{
    "@id": "dtmi:example:Building;1",
    "@type": "Interface",
    "displayName": "Building",
    "@context": "dtmi:dtdl:context;2",
    "contents": [
        {
            "@type": "Property",
            "name": "name",
            "schema": "string"
        },
        {
            "@type": "Property",
            "name": "address",
            "schema": "string"
        },
        {
            "@type": "Property",
            "name": "totalFloors",
            "schema": "integer"
        },
        {
            "@type": "Property",
            "name": "yearBuilt",
            "schema": "integer"
        },
        {
            "@type": "Relationship",
            "name": "hasFloor",
            "target": "dtmi:example:Floor;1"
        }
    ]
}

// Floor model
{
    "@id": "dtmi:example:Floor;1",
    "@type": "Interface",
    "displayName": "Floor",
    "@context": "dtmi:dtdl:context;2",
    "contents": [
        {
            "@type": "Property",
            "name": "floorNumber",
            "schema": "integer"
        },
        {
            "@type": "Property",
            "name": "totalRooms",
            "schema": "integer"
        },
        {
            "@type": "Relationship",
            "name": "hasRoom",
            "target": "dtmi:example:Room;1"
        }
    ]
}

// Room model with telemetry
{
    "@id": "dtmi:example:Room;1",
    "@type": "Interface",
    "displayName": "Room",
    "@context": "dtmi:dtdl:context;2",
    "contents": [
        {
            "@type": "Property",
            "name": "roomName",
            "schema": "string"
        },
        {
            "@type": "Property",
            "name": "roomType",
            "schema": {
                "@type": "Enum",
                "valueSchema": "string",
                "enumValues": [
                    { "name": "conference", "enumValue": "conference" },
                    { "name": "office", "enumValue": "office" },
                    { "name": "restroom", "enumValue": "restroom" },
                    { "name": "lobby", "enumValue": "lobby" }
                ]
            }
        },
        {
            "@type": "Property",
            "name": "capacity",
            "schema": "integer"
        },
        {
            "@type": ["Property", "Temperature"],
            "name": "temperature",
            "schema": "double",
            "unit": "degreeCelsius"
        },
        {
            "@type": "Property",
            "name": "humidity",
            "schema": "double"
        },
        {
            "@type": "Property",
            "name": "occupancy",
            "schema": "integer"
        },
        {
            "@type": "Relationship",
            "name": "hasSensor",
            "target": "dtmi:example:Sensor;1"
        }
    ]
}

// Sensor model
{
    "@id": "dtmi:example:Sensor;1",
    "@type": "Interface",
    "displayName": "Sensor",
    "@context": "dtmi:dtdl:context;2",
    "contents": [
        {
            "@type": "Property",
            "name": "sensorId",
            "schema": "string"
        },
        {
            "@type": "Property",
            "name": "sensorType",
            "schema": "string"
        },
        {
            "@type": "Property",
            "name": "lastReading",
            "schema": "double"
        },
        {
            "@type": "Property",
            "name": "lastReadingTime",
            "schema": "dateTime"
        },
        {
            "@type": "Property",
            "name": "batteryLevel",
            "schema": "double"
        }
    ]
}

Working with Digital Twins SDK

from azure.digitaltwins.core import DigitalTwinsClient
from azure.identity import DefaultAzureCredential
import json

# Connect to Digital Twins
credential = DefaultAzureCredential()
client = DigitalTwinsClient(
    "https://mydigitaltwins.api.eus.digitaltwins.azure.net",
    credential
)

# Upload models
models = [building_model, floor_model, room_model, sensor_model]
created_models = client.create_models(models)

# Create Building twin
building_twin = {
    "$metadata": {
        "$model": "dtmi:example:Building;1"
    },
    "name": "Headquarters",
    "address": "123 Main St, Seattle, WA",
    "totalFloors": 5,
    "yearBuilt": 2015
}
client.upsert_digital_twin("building-hq", building_twin)

# Create Floor twins
for floor_num in range(1, 6):
    floor_twin = {
        "$metadata": {
            "$model": "dtmi:example:Floor;1"
        },
        "floorNumber": floor_num,
        "totalRooms": 10
    }
    client.upsert_digital_twin(f"floor-{floor_num}", floor_twin)

    # Create relationship to building
    relationship = {
        "$relationshipId": f"building-hq-floor-{floor_num}",
        "$sourceId": "building-hq",
        "$relationshipName": "hasFloor",
        "$targetId": f"floor-{floor_num}"
    }
    client.upsert_relationship(
        "building-hq",
        f"building-hq-floor-{floor_num}",
        relationship
    )

# Create Room twins
room_configs = [
    {"id": "room-101", "name": "Conference A", "type": "conference", "floor": 1, "capacity": 20},
    {"id": "room-102", "name": "Executive Office", "type": "office", "floor": 1, "capacity": 1},
    {"id": "room-201", "name": "Open Space", "type": "office", "floor": 2, "capacity": 50},
]

for room in room_configs:
    room_twin = {
        "$metadata": {
            "$model": "dtmi:example:Room;1"
        },
        "roomName": room["name"],
        "roomType": room["type"],
        "capacity": room["capacity"],
        "temperature": 22.0,
        "humidity": 45.0,
        "occupancy": 0
    }
    client.upsert_digital_twin(room["id"], room_twin)

    # Relationship to floor
    client.upsert_relationship(
        f"floor-{room['floor']}",
        f"floor-{room['floor']}-{room['id']}",
        {
            "$relationshipId": f"floor-{room['floor']}-{room['id']}",
            "$sourceId": f"floor-{room['floor']}",
            "$relationshipName": "hasRoom",
            "$targetId": room["id"]
        }
    )

Querying Digital Twins

# Query all buildings
query = "SELECT * FROM digitaltwins WHERE IS_OF_MODEL('dtmi:example:Building;1')"
result = client.query_twins(query)
for twin in result:
    print(f"Building: {twin['name']}")

# Query rooms with high temperature
query = """
    SELECT room
    FROM digitaltwins room
    WHERE IS_OF_MODEL(room, 'dtmi:example:Room;1')
    AND room.temperature > 25
"""
hot_rooms = list(client.query_twins(query))

# Query with relationships - find all rooms in a building
query = """
    SELECT room
    FROM digitaltwins building
    JOIN floor RELATED building.hasFloor
    JOIN room RELATED floor.hasRoom
    WHERE building.$dtId = 'building-hq'
"""
rooms_in_building = list(client.query_twins(query))

# Aggregate queries
query = """
    SELECT COUNT()
    FROM digitaltwins room
    WHERE IS_OF_MODEL(room, 'dtmi:example:Room;1')
    AND room.occupancy > 0
"""
occupied_count = list(client.query_twins(query))

# Complex query with multiple conditions
query = """
    SELECT room, floor
    FROM digitaltwins floor
    JOIN room RELATED floor.hasRoom
    WHERE floor.floorNumber = 2
    AND room.roomType = 'conference'
    AND room.occupancy < room.capacity
"""
available_conference_rooms = list(client.query_twins(query))

Integrating with IoT Hub

# Azure Function to process IoT Hub telemetry and update Digital Twins
import azure.functions as func
from azure.digitaltwins.core import DigitalTwinsClient
from azure.identity import ManagedIdentityCredential
import json

def main(event: func.EventHubEvent):
    # Parse IoT Hub message
    body = json.loads(event.get_body().decode('utf-8'))
    device_id = event.iothub_metadata['connection-device-id']

    # Connect to Digital Twins
    credential = ManagedIdentityCredential()
    client = DigitalTwinsClient(
        "https://mydigitaltwins.api.eus.digitaltwins.azure.net",
        credential
    )

    # Find the room twin associated with this sensor
    query = f"""
        SELECT room
        FROM digitaltwins sensor
        JOIN room RELATED sensor.installedIn
        WHERE sensor.sensorId = '{device_id}'
    """
    results = list(client.query_twins(query))

    if results:
        room_id = results[0]['room']['$dtId']

        # Update room properties based on sensor data
        update_patch = []

        if 'temperature' in body:
            update_patch.append({
                "op": "replace",
                "path": "/temperature",
                "value": body['temperature']
            })

        if 'humidity' in body:
            update_patch.append({
                "op": "replace",
                "path": "/humidity",
                "value": body['humidity']
            })

        if 'occupancy' in body:
            update_patch.append({
                "op": "replace",
                "path": "/occupancy",
                "value": body['occupancy']
            })

        client.update_digital_twin(room_id, update_patch)

        # Also update sensor's last reading
        sensor_patch = [
            {"op": "replace", "path": "/lastReading", "value": body.get('temperature', 0)},
            {"op": "replace", "path": "/lastReadingTime", "value": event.enqueued_time.isoformat()}
        ]
        client.update_digital_twin(device_id, sensor_patch)

Event Routing

# Create Event Grid endpoint
az dt endpoint create eventgrid \
    --dt-name mydigitaltwins \
    --endpoint-name adt-eventgrid \
    --eventgrid-resource-group myResourceGroup \
    --eventgrid-topic myeventgridtopic

# Create event route
az dt route create \
    --dt-name mydigitaltwins \
    --endpoint-name adt-eventgrid \
    --route-name adt-route \
    --filter "type = 'Microsoft.DigitalTwins.Twin.Update'"

Visualization with 3D Scenes

// 3D Scene Studio configuration (simplified example)
const sceneConfig = {
    scenes: [
        {
            id: "building-view",
            displayName: "Building Overview",
            twins: [
                {
                    twinId: "building-hq",
                    primaryTwinIdExpression: "$dtId",
                    behaviors: [
                        {
                            id: "temperature-color",
                            type: "ColorChange",
                            valueExpression: "temperature",
                            colorRanges: [
                                { min: 0, max: 18, color: "#0000FF" },  // Cold - Blue
                                { min: 18, max: 24, color: "#00FF00" }, // Normal - Green
                                { min: 24, max: 35, color: "#FF0000" }  // Hot - Red
                            ]
                        }
                    ]
                }
            ]
        }
    ]
};

Best Practices

  1. Model Design: Start with clear entity relationships
  2. Naming Conventions: Use consistent twin and relationship IDs
  3. Query Optimization: Index frequently queried properties
  4. Event Handling: Use routes for downstream processing
  5. Security: Apply least privilege with RBAC

Conclusion

Azure Digital Twins enables sophisticated modeling of physical environments:

  • Knowledge Graph: Rich representation of entities and relationships
  • Real-Time Updates: Live state from IoT devices
  • Powerful Queries: SQL-like querying across the twin graph
  • Event Integration: React to changes in real-time
  • Visualization: 3D scene rendering for intuitive understanding

It’s the foundation for smart building, manufacturing, and infrastructure solutions.

Michael John Pena

Michael John Pena

Senior Data Engineer based in Sydney. Writing about data, cloud, and technology.