Skip to content

Streaming

System Overview

  1. Data Source: Your PLC/SCADA system acts as the data source, providing real-time and historical process data.
  2. OPC-UA Server: An OPC-UA server acts as an intermediary, exposing your PLC/SCADA data through a standardized protocol.
  3. Kafka (Red Panda): A high-throughput messaging system like Red Panda ingests data from the OPC-UA server.
  4. RisingWave (Real-Time Feature Engineering): RisingWave processes the data stream in real-time, performing feature engineering operations to create relevant features for training.
  5. TinyML (On-Device Training): TinyML tools are used to train the Small Language Model (PHI-3) on the engineered features. This training could occur on edge devices (e.g., industrial gateways) for localized model updates.

Python Code Snippets (Illustrative)

# Example 1: Data Ingestion from OPC-UA Server (Conceptual)
import opcua

client = opcua.Client("opc.tcp://<PLC_IP_ADDRESS>")
# ... Connect to server, browse data nodes, read values

# Example 2: Feature Engineering in RisingWave (Conceptual)
from risingwave import RWQuery

query = RWQuery("SELECT temperature, pressure, time_stamp FROM sensor_data") 
# ... Define feature calculations (e.g., rolling averages, ratios)

# Example 3: TinyML Training (Conceptual)
from tinyml import Trainer
# ... Load pre-trained PHI-3 model
# ... Feed engineered features into the trainer for fine-tuning

Working Inputs and Outputs

  • Input (PLC/SCADA): Sensor readings (temperature, pressure, flow rate, motor speed, etc.), timestamps, event logs.

  • Output (Fine-Tuned Model):

    • A custom PHI-3 model that understands the specific context of your manufacturing environment.
    • The model can generate predictions (e.g., equipment failure risk, optimal process settings) or answer natural language questions about the manufacturing process.

Important Considerations

  • Data Security and Privacy: Implement robust security measures to protect sensitive process data.
  • Data Quality: Ensure your data is accurate, consistent, and complete. Implement data validation and cleaning processes.
  • Feature Selection: Carefully select features that are relevant and informative for your specific use case.
  • Model Interpretability: Strive for models that are interpretable and explainable to operations teams.
  • Deployment and Monitoring: Deploy your models to edge devices or on-premises servers. Continuously monitor performance and retrain models as needed.

You're asking for a complete end-to-end Python code example, which is a very ambitious request given the complexity of integrating various systems like OPC-UA, Kafka, RisingWave, and TinyML.

Providing fully functional code for such a complex system is beyond the scope of a single response.

However, I can give you more detailed, structured code snippets with explanations to guide you in the right direction.

1. OPC-UA Data Access (Conceptual)

import opcua

# Connect to your OPC-UA server
client = opcua.Client("opc.tcp://<PLC_IP_ADDRESS>") 

# Browse for nodes (data points)
root = client.get_root_node()
nodes = root.get_children()

for node in nodes:
    print(f"Node name: {node.get_node_id()}")
    # ... Read data values from the nodes

client.disconnect()

2. Kafka Data Ingestion (Conceptual, Using Red Panda)

from redpanda_client import Client

# Connect to Red Panda
client = Client("redpanda://<RED_PANDA_HOST>:<RED_PANDA_PORT>")

# Subscribe to a topic (replace "manufacturing_data" with your topic)
topic = client.topic("manufacturing_data")
for message in topic.consume():
    data = message.value.decode()  
    print(f"Received data: {data}")

client.close()

3. RisingWave Feature Engineering (Conceptual)

# Connect to RisingWave (using their Python SDK)
import risingwave

connection = risingwave.connect("localhost:9090")

# Create a SQL query
query = "SELECT temperature, pressure FROM sensor_data WHERE time_stamp > now() - INTERVAL '1 minute'"

# Execute the query and process the results
result = connection.execute(query)
for row in result:
    temperature = row["temperature"]
    pressure = row["pressure"]
    # ... Perform feature engineering calculations (e.g., calculate average pressure)

connection.close()

4. TinyML Model Training (Conceptual)

from tinyml import Trainer

# Load pre-trained PHI-3 model
model = Trainer.load_model("phi-3-model")

# Prepare your engineered features (from RisingWave)
features = [temperature, pressure, average_pressure] 

# Fine-tune the model
model.fit(features)

# Save the fine-tuned model
model.save("fine_tuned_phi_3_model")

Important Notes:

  • These code snippets are highly simplified and illustrative. You'll need to adapt them to your specific environment and technology stack.
  • Refer to the documentation of each tool (OPC-UA, Kafka, RisingWave, TinyML) for detailed instructions and examples.

example 2

Creating a custom fine-tuned model for manufacturing operations using Federated AI involves several key components, including data collection from PLC/SCADA systems, feature engineering, model training, and deployment. Below is an end-to-end example of how to generate a training dataset from PLC or SCADA systems, using the technology stack you provided (OPC-UA Server, Red Panda for Kafka, RisingWave for Read-time Feature Engineering, TinyML for Model Training, PHI-3 Small Language Model, MicroK8S for platform, Ubuntu MicroCloud).

Step 1: Data Collection from PLC/SCADA Systems

PLCs and SCADAs generate data that is sent to an OPC-UA Server. The Red Panda Kafka service can be used as a bridge between the SCADA systems and the feature store.

# Import necessary libraries
import opcua
from kafka import KafkaProducer

def create_opcua_client(uri):
    # Connect to OPC UA server
    try:
        return opcua.Client(uri)
    except Exception as e:
        print(f"Could not connect to OPC-UA Server: {e}")
        exit(1)

# Create an OPC-UA client
uri = "opc.tcp://your-opcua-server-address:4840"
client = create_opcua_client(uri)

def read_data_from_opcua(client, node_id):
    node = client.get_node(node_id)
    value = node.get_value()
    return value

# Example node ID for reading temperature data
node_id = "ns=2;i=3"  # Adjust based on your OPC-UA server configuration
temperature_data = read_data_from_opcua(client, node_id)

# Publish the collected data to Kafka topic
producer = KafkaProducer(bootstrap_servers='your-kafka-broker:9092')
topic_name = 'opc-data'
producer.send(topic_name, temperature_data)

Step 2: Data Transformation and Feature Engineering

Use Red Panda Kafka Consumer to consume OPC-UA data and send it to RisingWave for feature engineering.

# Import necessary libraries for Kafka consumption
from kafka import KafkaConsumer

# Create a Kafka consumer
consumer = KafkaConsumer(
    'opc-data',
    bootstrap_servers='your-kafka-broker:9092',
    auto_offset_reset='earliest',
    enable_auto_commit=True,
    group_id='my-group',
)

for message in consumer:
    data = message.value  # This will be the OPC-UA data from previous step
    print(f"Received Data: {data}")

    # Example transformation to create a feature store entry (e.g., TemperatureReading)
    feature_store_entry = {
        'timestamp': datetime.now(),
        'temperature_value': float(data) / 100.0,  # Normalizing value if necessary
        # Add more features as needed for your model
    }

    # Publish to RisingWave Kafka topic
    risingwave_topic_name = 'feature-store'
    producer_risingwave = KafkaProducer(bootstrap_servers='your-rising-wave-broker:9092')
    producer_risingwave.send(risingwave_topic_name, json.dumps(feature_store_entry).encode())

Step 3: Federated Feature Engineering and Model Training

RisingWave can perform read-time feature engineering by ingesting data from multiple sources (including the feature store created in Red Panda).

# Import necessary libraries for RisingWave Kafka consumption
from kafka import KafkaConsumer

risingwave_topic_name = 'feature-store'
consumer_risingwave = KafkaConsumer(
    risingwave_topic_name,
    bootstrap_servers='your-rising-wave-broker:9092',
)

# Example feature engineering pipeline using Red Panda and RisingWave (pseudo code)
for message in consumer_risingwave:
    data = json.loads(message.value.decode())

    # Feature Engineering
    features = {
        'timestamp': data['timestamp'],
        'temperature_value_normalized': data['temperature_value'] * 0.1,  # Example feature transformation
        # Add more features as needed for your model
    }

    # Send to TinyML Kafka topic for federated training
    tinyml_topic_name = 'federated-training-data'
    producer_tinyml = KafkaProducer(bootstrap_servers='your-tinyml-broker:9092')
    producer_tinyml.send(tinyml_topic_name, json.dumps(features).encode())

Step 4: Model Training and Deployment

TinyML can perform model training in a federated manner across multiple devices.

# Import necessary libraries for TinyML Kafka consumption
from kafka import KafkaConsumer

tinyml_topic_name = 'federated-training-data'
consumer_tinyml = KafkaConsumer(
    tinyml_topic_name,
    bootstrap_servers='your-tinyml-broker:9092',
)

# Model Training Pipeline (pseudo code)
for message in consumer_tinyml:
    data = json.loads(message.value.decode())

    # Extract features and labels
    feature_vector = [data['timestamp'], data['temperature_value_normalized']]
    label = data['correct_response']  # Hypothetical labeled data

    # Train PHI-3 (or any other model) with the federated dataset
    trained_model = phi_3_model.train(feature_vector, label)

    # Save trained model and deploy it
    trained_model.save('trained_model.pkl')
    deployed_model_name = 'model_for_deploy'
    print(f"Model {deployed_model_name} is now ready for deployment.")

Step 5: On-Device ML Deployment

Once the model is trained, it can be deployed on device using MicroK8S and Ubuntu MicroCloud.

# Create a Kubernetes deployment YAML file (pseudo code)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: phi-3-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      app: phi-3
  template:
    metadata:
      labels:
        app: phi-3
    spec:
      containers:
      - name: phi-3-container
        image: your-tinyml-image-repo:latest
        ports:
          - containerPort: 8080

Step 6: Model Serving and Monitoring

Use MicroK8S to manage the Kubernetes deployment of the model.

# Apply the YAML file for deployment
kubectl apply -f phi-3-deployment.yaml

# Access the deployed model (example assumes service is exposed)
echo "http://<your-service-name>"

# Monitor model performance and traffic using appropriate monitoring tools

Summary

This example covers data collection, feature engineering, federated training, and deployment. Ensure that all Kafka brokers, RisingWave instances, TinyML servers, and MicroK8S are properly configured to integrate seamlessly.

Please adjust the code according to your specific environment and requirements.