Digital Twins

import asyncio
import json
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from enum import Enum
import pandas as pd
 
class TwinSyncStatus(Enum):
    SYNCHRONIZED = "synchronized"
    STALE = "stale"
    ERROR = "error"
    INITIALIZING = "initializing"
 
@dataclass
class SensorReading:
    sensor_id: str
    timestamp: datetime
    value: float
    unit: str
    quality: float = 1.0  # Data quality score 0-1
    metadata: Dict[str, Any] = field(default_factory=dict)
 
@dataclass
class TwinState:
    entity_id: str
    timestamp: datetime
    properties: Dict[str, Any]
    computed_metrics: Dict[str, float]
    sync_status: TwinSyncStatus
    confidence_score: float
 
class DigitalTwin:
    def __init__(self, entity_id: str, configuration: Dict):
        self.entity_id = entity_id
        self.config = configuration
        self.current_state = None
        self.historical_states = []
        self.sensors = self.initialize_sensors()
        self.models = self.initialize_models()
        self.sync_manager = SynchronizationManager()
        self.analytics_engine = AnalyticsEngine()
        self.prediction_models = PredictionModels()
 
    def initialize_sensors(self) -> Dict[str, Any]:
        """Initialize sensor configurations and connections"""
        sensors = {}
 
        for sensor_config in self.config.get('sensors', []):
            sensor = {
                'id': sensor_config['id'],
                'type': sensor_config['type'],
                'location': sensor_config.get('location'),
                'sampling_rate': sensor_config.get('sampling_rate', 1.0),
                'data_processor': self.create_sensor_processor(sensor_config),
                'connection': self.establish_sensor_connection(sensor_config),
                'calibration': sensor_config.get('calibration', {}),
                'last_reading': None,
                'status': 'active'
            }
            sensors[sensor_config['id']] = sensor
 
        return sensors
 
    def initialize_models(self) -> Dict[str, Any]:
        """Initialize physics and behavioral models"""
        models = {}
 
        for model_config in self.config.get('models', []):
            if model_config['type'] == 'physics':
                models[model_config['name']] = PhysicsModel(model_config)
            elif model_config['type'] == 'behavioral':
                models[model_config['name']] = BehavioralModel(model_config)
            elif model_config['type'] == 'statistical':
                models[model_config['name']] = StatisticalModel(model_config)
 
        return models
 
    async def update_from_sensors(self):
        """Continuously update digital twin from sensor data"""
        while True:
            try:
                # Collect sensor readings
                sensor_data = await self.collect_sensor_readings()
 
                # Process and validate data
                validated_data = await self.validate_sensor_data(sensor_data)
 
                # Update twin state
                await self.update_twin_state(validated_data)
 
                # Run analytics and predictions
                analytics_results = await self.run_analytics(validated_data)
 
                # Check for anomalies and alerts
                await self.check_anomalies(analytics_results)
 
                # Log state history
                await self.log_state_history()
 
            except Exception as e:
                await self.handle_update_error(e)
 
            # Wait for next update cycle
            await asyncio.sleep(1.0 / self.config.get('update_frequency', 1.0))
 
    async def collect_sensor_readings(self) -> List[SensorReading]:
        """Collect data from all connected sensors"""
        readings = []
 
        for sensor_id, sensor in self.sensors.items():
            try:
                # Read from sensor
                raw_value = await sensor['connection'].read_value()
 
                # Apply calibration
                calibrated_value = self.apply_calibration(
                    raw_value, sensor['calibration']
                )
 
                # Create sensor reading
                reading = SensorReading(
                    sensor_id=sensor_id,
                    timestamp=datetime.now(),
                    value=calibrated_value,
                    unit=sensor.get('unit', ''),
                    quality=await self.assess_data_quality(sensor_id, calibrated_value),
                    metadata={'sensor_type': sensor['type']}
                )
 
                readings.append(reading)
                sensor['last_reading'] = reading
 
            except Exception as e:
                # Handle sensor errors
                await self.handle_sensor_error(sensor_id, e)
 
        return readings
 
    async def update_twin_state(self, sensor_data: List[SensorReading]):
        """Update the digital twin's state based on sensor data"""
 
        # Data fusion - combine multiple sensor readings
        fused_data = await self.fuse_sensor_data(sensor_data)
 
        # Update physics models
        physics_state = {}
        for model_name, model in self.models.items():
            if isinstance(model, PhysicsModel):
                model_state = await model.update_state(fused_data)
                physics_state.update(model_state)
 
        # Update behavioral models
        behavioral_state = {}
        for model_name, model in self.models.items():
            if isinstance(model, BehavioralModel):
                model_state = await model.predict_behavior(fused_data, physics_state)
                behavioral_state.update(model_state)
 
        # Compute derived metrics
        computed_metrics = await self.compute_derived_metrics(
            sensor_data, physics_state, behavioral_state
        )
 
        # Create new twin state
        new_state = TwinState(
            entity_id=self.entity_id,
            timestamp=datetime.now(),
            properties={
                'sensor_values': {r.sensor_id: r.value for r in sensor_data},
                'physics_state': physics_state,
                'behavioral_state': behavioral_state
            },
            computed_metrics=computed_metrics,
            sync_status=await self.assess_sync_status(sensor_data),
            confidence_score=await self.calculate_confidence_score(sensor_data)
        )
 
        self.current_state = new_state
 
        # Notify subscribers of state change
        await self.notify_state_change(new_state)
 
    async def run_predictive_analysis(self, prediction_horizon: timedelta) -> Dict[str, Any]:
        """Run predictive analysis for specified time horizon"""
 
        if not self.current_state:
            raise ValueError("No current state available for prediction")
 
        # Prepare historical data for prediction models
        historical_features = await self.prepare_prediction_features()
 
        # Run various prediction models
        predictions = {}
 
        # Performance predictions
        performance_prediction = await self.prediction_models.predict_performance(
            current_state=self.current_state,
            historical_data=historical_features,
            horizon=prediction_horizon
        )
        predictions['performance'] = performance_prediction
 
        # Failure predictions
        failure_prediction = await self.prediction_models.predict_failures(
            current_state=self.current_state,
            historical_data=historical_features,
            horizon=prediction_horizon
        )
        predictions['failures'] = failure_prediction
 
        # Maintenance predictions
        maintenance_prediction = await self.prediction_models.predict_maintenance_needs(
            current_state=self.current_state,
            historical_data=historical_features,
            horizon=prediction_horizon
        )
        predictions['maintenance'] = maintenance_prediction
 
        # Optimization recommendations
        optimization_recommendations = await self.generate_optimization_recommendations(
            predictions=predictions,
            current_state=self.current_state
        )
        predictions['optimization'] = optimization_recommendations
 
        return predictions
 
    async def simulate_scenarios(self, scenarios: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Simulate various what-if scenarios"""
 
        simulation_results = {}
 
        for scenario in scenarios:
            scenario_id = scenario['id']
 
            # Create scenario-specific model configuration
            scenario_models = await self.configure_scenario_models(scenario)
 
            # Run simulation
            simulation_result = await self.run_scenario_simulation(
                scenario_models=scenario_models,
                scenario_parameters=scenario['parameters'],
                simulation_duration=scenario.get('duration', timedelta(hours=24))
            )
 
            simulation_results[scenario_id] = simulation_result
 
        return simulation_results
 
class PhysicsModel:
    def __init__(self, config: Dict):
        self.config = config
        self.model_type = config['model_type']
        self.parameters = config.get('parameters', {})
        self.state_variables = config.get('state_variables', [])
 
    async def update_state(self, sensor_data: List[SensorReading]) -> Dict[str, float]:
        """Update physics model state based on sensor data"""
 
        if self.model_type == 'thermal':
            return await self.update_thermal_model(sensor_data)
        elif self.model_type == 'mechanical':
            return await self.update_mechanical_model(sensor_data)
        elif self.model_type == 'electrical':
            return await self.update_electrical_model(sensor_data)
        elif self.model_type == 'fluid_dynamics':
            return await self.update_fluid_dynamics_model(sensor_data)
        else:
            return await self.update_generic_model(sensor_data)
 
    async def update_thermal_model(self, sensor_data: List[SensorReading]) -> Dict[str, float]:
        """Update thermal physics model"""
 
        # Extract temperature readings
        temperature_readings = [
            r for r in sensor_data
            if 'temperature' in r.sensor_id.lower()
        ]
 
        if not temperature_readings:
            return {}
 
        # Simple thermal model - heat transfer equations
        thermal_state = {}
 
        # Calculate temperature gradients
        if len(temperature_readings) >= 2:
            temp_values = [r.value for r in temperature_readings]
            thermal_state['temperature_gradient'] = np.gradient(temp_values).mean()
 
        # Estimate heat flux
        ambient_temp = self.parameters.get('ambient_temperature', 20.0)
        for reading in temperature_readings:
            heat_flux = self.parameters.get('heat_transfer_coefficient', 10.0) * (
                reading.value - ambient_temp
            )
            thermal_state[f'heat_flux_{reading.sensor_id}'] = heat_flux
 
        # Calculate thermal capacity effects
        if 'thermal_mass' in self.parameters:
            thermal_time_constant = (
                self.parameters['thermal_mass'] *
                self.parameters.get('specific_heat', 1000) /
                self.parameters.get('heat_transfer_coefficient', 10.0)
            )
            thermal_state['thermal_time_constant'] = thermal_time_constant
 
        return thermal_state
 
    async def update_mechanical_model(self, sensor_data: List[SensorReading]) -> Dict[str, float]:
        """Update mechanical physics model"""
 
        # Extract mechanical sensor readings
        vibration_readings = [r for r in sensor_data if 'vibration' in r.sensor_id.lower()]
        force_readings = [r for r in sensor_data if 'force' in r.sensor_id.lower()]
        displacement_readings = [r for r in sensor_data if 'displacement' in r.sensor_id.lower()]
 
        mechanical_state = {}
 
        # Vibration analysis
        if vibration_readings:
            vibration_amplitudes = [r.value for r in vibration_readings]
            mechanical_state['rms_vibration'] = np.sqrt(np.mean(np.square(vibration_amplitudes)))
            mechanical_state['peak_vibration'] = max(vibration_amplitudes)
 
        # Force analysis
        if force_readings:
            total_force = sum(r.value for r in force_readings)
            mechanical_state['total_force'] = total_force
 
            # Estimate stress if cross-sectional area is known
            if 'cross_sectional_area' in self.parameters:
                stress = total_force / self.parameters['cross_sectional_area']
                mechanical_state['stress'] = stress
 
        # Dynamic response
        if displacement_readings and len(displacement_readings) >= 2:
            displacements = [r.value for r in displacement_readings]
            # Simple finite difference for velocity and acceleration
            dt = (displacement_readings[1].timestamp - displacement_readings[0].timestamp).total_seconds()
            if dt > 0:
                velocity = (displacements[-1] - displacements[0]) / (dt * (len(displacements) - 1))
                mechanical_state['velocity'] = velocity
 
        return mechanical_state
 
class PredictiveMaintenanceEngine:
    def __init__(self, twin: DigitalTwin):
        self.twin = twin
        self.failure_models = self.initialize_failure_models()
        self.maintenance_optimizer = MaintenanceOptimizer()
 
    async def predict_component_failures(self, prediction_horizon: timedelta) -> Dict[str, Any]:
        """Predict component failures within specified time horizon"""
 
        # Get current system state
        current_state = self.twin.current_state
        if not current_state:
            raise ValueError("No current state available")
 
        # Get historical failure data
        historical_failures = await self.get_historical_failure_data()
 
        # Prepare feature vectors for prediction
        feature_vectors = await self.prepare_failure_prediction_features(
            current_state, historical_failures
        )
 
        failure_predictions = {}
 
        # Predict failures for each critical component
        for component_id in self.get_critical_components():
            # Extract component-specific features
            component_features = self.extract_component_features(
                feature_vectors, component_id
            )
 
            # Run failure prediction model
            failure_probability = await self.failure_models[component_id].predict_failure_probability(
                features=component_features,
                time_horizon=prediction_horizon
            )
 
            # Calculate time to failure estimate
            time_to_failure = await self.estimate_time_to_failure(
                component_id, component_features, failure_probability
            )
 
            failure_predictions[component_id] = {
                'failure_probability': failure_probability,
                'estimated_time_to_failure': time_to_failure,
                'confidence_interval': await self.calculate_prediction_confidence(
                    component_id, component_features
                ),
                'contributing_factors': await self.identify_failure_factors(
                    component_id, component_features
                )
            }
 
        return failure_predictions
 
    async def optimize_maintenance_schedule(self, failure_predictions: Dict[str, Any],
                                          constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize maintenance schedule based on failure predictions"""
 
        # Define optimization objective
        optimization_objective = {
            'minimize_downtime': constraints.get('minimize_downtime', True),
            'minimize_cost': constraints.get('minimize_cost', True),
            'maximize_reliability': constraints.get('maximize_reliability', True)
        }
 
        # Create maintenance decision variables
        maintenance_decisions = {}
        for component_id, prediction in failure_predictions.items():
            # Consider maintenance options
            maintenance_options = [
                'preventive_maintenance',
                'condition_based_maintenance',
                'run_to_failure',
                'component_replacement'
            ]
 
            # Optimize maintenance decision for each component
            optimal_decision = await self.maintenance_optimizer.optimize_component_maintenance(
                component_id=component_id,
                failure_prediction=prediction,
                maintenance_options=maintenance_options,
                constraints=constraints,
                objective=optimization_objective
            )
 
            maintenance_decisions[component_id] = optimal_decision
 
        # Coordinate maintenance schedule across components
        coordinated_schedule = await self.maintenance_optimizer.coordinate_maintenance_schedule(
            individual_decisions=maintenance_decisions,
            system_constraints=constraints.get('system_constraints', {}),
            resource_constraints=constraints.get('resource_constraints', {})
        )
 
        return {
            'individual_decisions': maintenance_decisions,
            'coordinated_schedule': coordinated_schedule,
            'expected_benefits': await self.calculate_maintenance_benefits(coordinated_schedule),
            'risk_assessment': await self.assess_maintenance_risks(coordinated_schedule)
        }