Digital Twin Information Retrieval System

Building a Digital Twin Information Retrieval System involves creating a system that can store, retrieve, and process data related to a digital twin—a virtual representation of a physical object or system. Below is a high-level Python implementation using FastAPI for the API backend, Elasticsearch for search, and SQLite for structured metadata storage.

System Components

1. FastAPI: API framework for handling requests.

2. Elasticsearch: Search engine for efficient retrieval.

3. SQLite: Stores metadata for the digital twin.

4. Pydantic: Ensures data validation.

Step 1: Install Dependencies

pip install fastapi uvicorn elasticsearch sqlite3 pydantic

Step 2: Set Up Elasticsearch Connection

from elasticsearch import Elasticsearch

# Initialize Elasticsearch client

es = Elasticsearch("http://localhost:9200")  # Ensure Elasticsearch is running

# Define index mapping for digital twin documents

INDEX_NAME = "digital_twin"

mapping = {

    "mappings": {

        "properties": {

            "id": {"type": "keyword"},

            "name": {"type": "text"},

            "description": {"type": "text"},

            "metadata": {"type": "nested"},

        }

    }

}

# Create index if it doesn't exist

if not es.indices.exists(index=INDEX_NAME):

    es.indices.create(index=INDEX_NAME, body=mapping)

Step 3: Set Up SQLite Database for Metadata

import sqlite3

conn = sqlite3.connect("digital_twin.db")

cursor = conn.cursor()

cursor.execute("""

CREATE TABLE IF NOT EXISTS twins (

    id TEXT PRIMARY KEY,

    name TEXT,

    description TEXT,

    metadata TEXT

)

""")

conn.commit()

Step 4: FastAPI API Implementation

from fastapi import FastAPI, HTTPException

from pydantic import BaseModel

import json

import uuid

app = FastAPI()

class DigitalTwin(BaseModel):

    name: str

    description: str

    metadata: dict

@app.post("/add_twin/")

def add_twin(twin: DigitalTwin):

    twin_id = str(uuid.uuid4())  # Generate unique ID

    twin_dict = twin.dict()

    

    # Store in Elasticsearch

    es.index(index=INDEX_NAME, id=twin_id, body=twin_dict)

    

    # Store in SQLite

    cursor.execute("INSERT INTO twins (id, name, description, metadata) VALUES (?, ?, ?, ?)", 

                   (twin_id, twin.name, twin.description, json.dumps(twin.metadata)))

    conn.commit()

    return {"id": twin_id, "message": "Digital twin added successfully"}

@app.get("/search_twin/")

def search_twin(query: str):

    search_body = {"query": {"multi_match": {"query": query, "fields": ["name", "description", "metadata"]}}}

    results = es.search(index=INDEX_NAME, body=search_body)

    return {"results": results["hits"]["hits"]}

@app.get("/get_twin/{twin_id}")

def get_twin(twin_id: str):

    cursor.execute("SELECT * FROM twins WHERE id=?", (twin_id,))

    twin_data = cursor.fetchone()

    

    if not twin_data:

        raise HTTPException(status_code=404, detail="Digital twin not found")

    

    return {

        "id": twin_data[0],

        "name": twin_data[1],

        "description": twin_data[2],

        "metadata": json.loads(twin_data[3])

    }

Step 5: Running the System

Save the script and run it:

uvicorn script_name:app --reload

Access endpoints:

• POST /add_twin/ → Add a digital twin

• GET /search_twin/?query=xyz → Search twins by text

• GET /get_twin/{twin_id} → Retrieve twin by ID

Enhancements

• Integrate Kafka for real-time updates.

• Use GraphQL for flexible querying.

• Implement AI-driven analytics on the retrieved data.

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