Detect Industrial Equipment Anomalies in Real Time with Flink Agents and Apache Kafka

                      
                     
"""
Production implementation for detecting industrial equipment anomalies in real-time using Flink agents and Apache Kafka.
This module provides a secure and scalable architecture for processing streaming data and identifying anomalies.
"""
from typing import Dict, Any, List
import os
import logging
import json
import requests
import time
from kafka import KafkaConsumer, KafkaProducer

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    kafka_bootstrap_servers: str = os.getenv('KAFKA_BOOTSTRAP_SERVERS', 'localhost:9092')
    kafka_topic: str = os.getenv('KAFKA_TOPIC', 'equipment_data')

class AnomalyDetection:
    def __init__(self):
        self.consumer = KafkaConsumer(self.kafka_topic, bootstrap_servers=self.kafka_bootstrap_servers)
        self.producer = KafkaProducer(bootstrap_servers=self.kafka_bootstrap_servers)
        logger.info('Kafka consumer and producer initialized.')

    def validate_input(self, data: Dict[str, Any]) -> bool:
        """Validate input data for anomalies.
        
        Args:
            data: Input data to validate
        Returns:
            True if valid
        Raises:
            ValueError: If validation fails
        """
        required_fields = ['temperature', 'vibration', 'pressure']
        for field in required_fields:
            if field not in data:
                raise ValueError(f'Missing field: {field}')
        return True

    def normalize_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Normalize input data for processing.
        
        Args:
            data: Raw input data
        Returns:
            Normalized data
        """
        # Example normalization logic
        data['temperature'] = float(data['temperature'])
        data['vibration'] = float(data['vibration'])
        data['pressure'] = float(data['pressure'])
        return data

    def process_batch(self, records: List[Dict[str, Any]]) -> None:
        """Process a batch of records for anomaly detection.
        
        Args:
            records: List of input records
        """
        for record in records:
            try:
                self.validate_input(record)  # Validate data
                normalized_record = self.normalize_data(record)  # Normalize data
                if self.detect_anomaly(normalized_record):  # Check for anomalies
                    logger.warning(f'Anomaly detected: {normalized_record}')
                    self.save_to_db(normalized_record)  # Save anomaly
            except Exception as e:
                logger.error(f'Error processing record: {record}, error: {e}')

    def detect_anomaly(self, data: Dict[str, Any]) -> bool:
        """Detect anomalies based on threshold values.
        
        Args:
            data: Normalized data to analyze
        Returns:
            True if anomaly detected, else False
        """
        # Example anomaly detection logic
        if data['temperature'] > 100 or data['vibration'] > 5:
            return True
        return False

    def save_to_db(self, record: Dict[str, Any]) -> None:
        """Save anomaly record to a database.
        
        Args:
            record: Anomaly record to save
        """
        # Placeholder for database save logic
        logger.info(f'Saving to database: {record}')

    def fetch_data(self) -> None:
        """Fetch data from Kafka and process.
        
        This method continuously fetches data from Kafka and processes it in batches.
        """
        logger.info('Starting to fetch data from Kafka...')
        for message in self.consumer:
            try:
                record = json.loads(message.value.decode('utf-8'))
                self.process_batch([record])  # Process each record
            except json.JSONDecodeError:
                logger.error('Failed to decode JSON from Kafka message')

if __name__ == '__main__':
    anomaly_detector = AnomalyDetection()
    anomaly_detector.fetch_data()  # Start fetching and processing data