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import random
from datetime import datetime, timedelta
from typing import Dict, List
import uuid
class MetroSyntheticDataGenerator:
"""Generate synthetic data for metro trainset scheduling system"""
def __init__(self, num_trainsets: int = 25):
self.num_trainsets = num_trainsets
self.trainset_ids = [f"TS-{str(i+1).zfill(3)}" for i in range(num_trainsets)]
self.departments = ["Rolling Stock", "Signalling", "Telecom"]
self.brands = ["Brand-A", "Brand-B", "Brand-C", "Brand-D", "Brand-E"]
def generate_trainset_status(self) -> List[Dict]:
"""Generate current operational status for all trainsets"""
statuses = []
for ts_id in self.trainset_ids:
status = {
"trainset_id": ts_id,
"current_location": random.choice(["Depot-A", "Depot-B", "In-Service", "IBL", "Cleaning-Bay"]),
"operational_status": random.choice(["Available", "In-Service", "Maintenance", "Standby"]),
"last_service_date": (datetime.now() - timedelta(days=random.randint(1, 30))).isoformat(),
"total_mileage_km": random.randint(50000, 200000),
"daily_mileage_km": random.randint(200, 400),
"operational_hours": random.randint(5000, 15000),
"last_updated": datetime.now().isoformat()
}
statuses.append(status)
return statuses
def generate_fitness_certificates(self) -> List[Dict]:
"""Generate fitness certificates from different departments"""
certificates = []
for ts_id in self.trainset_ids:
for dept in self.departments:
cert = {
"certificate_id": str(uuid.uuid4()),
"trainset_id": ts_id,
"department": dept,
"issue_date": (datetime.now() - timedelta(days=random.randint(1, 60))).isoformat(),
"expiry_date": (datetime.now() + timedelta(days=random.randint(-5, 90))).isoformat(),
"status": random.choice(["Valid", "Valid", "Valid", "Expired", "Expiring-Soon"]),
"inspector_id": f"INS-{random.randint(100, 999)}",
"compliance_score": random.randint(75, 100),
"remarks": random.choice(["All systems operational", "Minor issues noted", "Requires follow-up", ""])
}
certificates.append(cert)
return certificates
def generate_job_cards(self) -> List[Dict]:
"""Generate IBM Maximo job cards"""
job_types = ["Preventive", "Corrective", "Breakdown", "Inspection"]
priorities = ["Critical", "High", "Medium", "Low"]
job_cards = []
for ts_id in self.trainset_ids:
# Random number of job cards per trainset
num_jobs = random.randint(0, 5)
for _ in range(num_jobs):
job = {
"job_card_id": f"JC-{random.randint(10000, 99999)}",
"trainset_id": ts_id,
"work_order_number": f"WO-{random.randint(100000, 999999)}",
"job_type": random.choice(job_types),
"priority": random.choice(priorities),
"status": random.choice(["Open", "Open", "Closed", "In-Progress", "Pending-Parts"]),
"created_date": (datetime.now() - timedelta(days=random.randint(1, 30))).isoformat(),
"estimated_completion": (datetime.now() + timedelta(hours=random.randint(2, 48))).isoformat(),
"assigned_technician": f"TECH-{random.randint(100, 999)}",
"component": random.choice(["Brakes", "HVAC", "Doors", "Bogies", "Pantograph", "Electrical"]),
"description": "Routine maintenance required",
"estimated_hours": random.randint(2, 24),
"cost_estimate": random.randint(5000, 50000)
}
job_cards.append(job)
return job_cards
def generate_component_health(self) -> List[Dict]:
"""Generate IoT sensor data for component health"""
components = {
"Bogie": {"wear_threshold": 80, "unit": "% wear"},
"Brake_Pad": {"wear_threshold": 70, "unit": "% remaining"},
"HVAC": {"wear_threshold": 85, "unit": "% efficiency"},
"Door_System": {"wear_threshold": 90, "unit": "cycles"},
"Pantograph": {"wear_threshold": 75, "unit": "% condition"},
"Battery": {"wear_threshold": 80, "unit": "% capacity"}
}
health_data = []
for ts_id in self.trainset_ids:
for comp, meta in components.items():
health = {
"trainset_id": ts_id,
"component": comp,
"health_score": random.randint(60, 100),
"wear_level": random.randint(0, 100),
"threshold": meta["wear_threshold"],
"unit": meta["unit"],
"status": random.choice(["Good", "Good", "Good", "Fair", "Warning"]),
"next_maintenance_km": random.randint(1000, 5000),
"last_maintenance_date": (datetime.now() - timedelta(days=random.randint(1, 60))).isoformat(),
"predicted_failure_date": (datetime.now() + timedelta(days=random.randint(30, 180))).isoformat(),
"timestamp": datetime.now().isoformat()
}
health_data.append(health)
return health_data
def generate_iot_sensors(self) -> List[Dict]:
"""Generate real-time IoT sensor readings"""
sensor_data = []
for ts_id in self.trainset_ids:
sensors = {
"trainset_id": ts_id,
"timestamp": datetime.now().isoformat(),
"vibration": {
"bogie_1": round(random.uniform(0.5, 3.5), 2),
"bogie_2": round(random.uniform(0.5, 3.5), 2),
"unit": "mm/s"
},
"pressure": {
"brake_system": round(random.uniform(5.5, 8.5), 2),
"pneumatic_doors": round(random.uniform(6.0, 8.0), 2),
"unit": "bar"
},
"electrical": {
"voltage": round(random.uniform(730, 770), 1),
"current": round(random.uniform(100, 400), 1),
"power_consumption": round(random.uniform(200, 600), 1),
"battery_voltage": round(random.uniform(70, 85), 1)
},
"door_cycles": {
"door_1": random.randint(50000, 200000),
"door_2": random.randint(50000, 200000),
"door_3": random.randint(50000, 200000),
"door_4": random.randint(50000, 200000)
},
"gps": {
"latitude": round(random.uniform(9.9, 10.1), 6),
"longitude": round(random.uniform(76.2, 76.4), 6),
"speed_kmh": round(random.uniform(0, 80), 1) # Max tested speed: 80 km/h
}
}
sensor_data.append(sensors)
return sensor_data
def generate_branding_contracts(self) -> List[Dict]:
"""Generate branding/advertisement contract data"""
contracts = []
for ts_id in random.sample(self.trainset_ids, random.randint(10, 15)):
contract = {
"trainset_id": ts_id,
"brand": random.choice(self.brands),
"contract_id": f"ADV-{random.randint(1000, 9999)}",
"start_date": (datetime.now() - timedelta(days=random.randint(30, 180))).isoformat(),
"end_date": (datetime.now() + timedelta(days=random.randint(30, 365))).isoformat(),
"contracted_exposure_hours": random.randint(2000, 5000),
"actual_exposure_hours": random.randint(1500, 4500),
"daily_target_hours": random.randint(8, 12),
"contract_value": random.randint(500000, 2000000),
"penalty_per_hour_shortfall": random.randint(500, 2000),
"status": random.choice(["Active", "Active", "Active", "At-Risk", "Compliant"]),
"priority_level": random.choice(["High", "Medium", "Low"])
}
contracts.append(contract)
return contracts
def generate_maintenance_schedule(self) -> List[Dict]:
"""Generate planned maintenance schedules"""
maintenance_types = ["A-Check", "B-Check", "C-Check", "D-Check", "Overhaul"]
schedules = []
for ts_id in self.trainset_ids:
schedule = {
"trainset_id": ts_id,
"maintenance_type": random.choice(maintenance_types),
"scheduled_date": (datetime.now() + timedelta(days=random.randint(1, 60))).isoformat(),
"estimated_duration_hours": random.randint(4, 72),
"bay_required": random.choice(["IBL-1", "IBL-2", "Cleaning-Bay", "Workshop"]),
"priority": random.choice(["Mandatory", "Scheduled", "Optional"]),
"km_since_last_maintenance": random.randint(5000, 20000),
"days_since_last_maintenance": random.randint(15, 90),
"status": random.choice(["Scheduled", "Pending", "Overdue"])
}
schedules.append(schedule)
return schedules
def generate_performance_metrics(self) -> List[Dict]:
"""Generate historical performance data"""
metrics = []
for ts_id in self.trainset_ids:
# Last 30 days of performance
for days_ago in range(30):
date = datetime.now() - timedelta(days=days_ago)
metric = {
"trainset_id": ts_id,
"date": date.date().isoformat(),
"service_availability": random.choice([True, True, True, True, False]),
"punctuality_percent": round(random.uniform(95, 100), 2),
"km_traveled": random.randint(150, 450),
"trips_completed": random.randint(15, 35),
"breakdown_count": random.randint(0, 2),
"delay_minutes": random.randint(0, 30),
"passenger_count": random.randint(5000, 15000),
"energy_consumed_kwh": round(random.uniform(300, 800), 2),
"average_speed_kmh": round(random.uniform(35, 55), 1)
}
metrics.append(metric)
return metrics
def generate_cleaning_slots(self) -> List[Dict]:
"""Generate cleaning bay availability and schedules"""
bays = ["Cleaning-Bay-1", "Cleaning-Bay-2", "Cleaning-Bay-3"]
shifts = ["Morning", "Afternoon", "Night"]
slots = []
for bay in bays:
for shift in shifts:
slot = {
"bay_name": bay,
"date": datetime.now().date().isoformat(),
"shift": shift,
"capacity": random.randint(2, 4),
"occupied": random.randint(0, 3),
"available": random.randint(0, 2),
"scheduled_trainsets": random.sample(self.trainset_ids, random.randint(0, 3)),
"manpower_available": random.randint(2, 6),
"estimated_duration_hours": random.randint(2, 4)
}
slots.append(slot)
return slots
def generate_manual_overrides(self) -> List[Dict]:
"""Generate supervisor manual override entries"""
overrides = []
for _ in range(random.randint(3, 8)):
override = {
"override_id": str(uuid.uuid4()),
"trainset_id": random.choice(self.trainset_ids),
"timestamp": datetime.now().isoformat(),
"supervisor_id": f"SUP-{random.randint(100, 999)}",
"action": random.choice(["Force-Induction", "Hold-Back", "Priority-Change", "IBL-Delay"]),
"reason": random.choice([
"Emergency service requirement",
"VIP movement",
"Component inspection needed",
"Branding priority",
"Safety precaution"
]),
"priority": random.choice(["Critical", "High", "Medium"]),
"expiry": (datetime.now() + timedelta(hours=24)).isoformat()
}
overrides.append(override)
return overrides
def generate_external_factors(self) -> Dict:
"""Generate external factors affecting operations"""
return {
"date": datetime.now().date().isoformat(),
"weather": {
"humidity": random.randint(60, 90),
"rainfall_mm": round(random.uniform(0, 50), 1),
"condition": random.choice(["Clear", "Cloudy", "Rainy", "Stormy"])
},
"special_events": random.choice([
None,
"Festival - High ridership expected",
"VIP visit - Route restrictions",
"Maintenance window - Track work"
]),
"ridership_forecast": {
"expected_passengers": random.randint(80000, 150000),
"peak_hours": ["08:00-10:00", "17:00-20:00"],
"load_factor": round(random.uniform(0.6, 0.9), 2)
},
"track_conditions": {
"status": random.choice(["Normal", "Caution", "Restricted"]),
"maintenance_zones": random.randint(0, 3),
"speed_restrictions": random.randint(0, 2)
}
}
def generate_complete_dataset(self, include_job_cards: bool = False) -> Dict:
"""Generate complete synthetic dataset for metro scheduling.
Args:
include_job_cards: Whether to include job cards in the dataset. Default False.
"""
dataset = {
"metadata": {
"generated_at": datetime.now().isoformat(),
"num_trainsets": self.num_trainsets,
"system": "Kochi Metro Rail",
"data_version": "1.0"
},
"trainset_status": self.generate_trainset_status(),
"fitness_certificates": self.generate_fitness_certificates(),
"job_cards": self.generate_job_cards() if include_job_cards else [],
"component_health": self.generate_component_health(),
"iot_sensors": self.generate_iot_sensors(),
"branding_contracts": self.generate_branding_contracts(),
"maintenance_schedule": self.generate_maintenance_schedule(),
"performance_metrics": self.generate_performance_metrics(),
"cleaning_slots": self.generate_cleaning_slots(),
"manual_overrides": self.generate_manual_overrides(),
"external_factors": self.generate_external_factors()
}
return dataset
def save_to_json(self, filename: str = "metro_synthetic_data.json", include_job_cards: bool = False):
"""Save generated data to JSON file.
Args:
filename: Output filename.
include_job_cards: Whether to include job cards in the dataset. Default False.
"""
data = self.generate_complete_dataset(include_job_cards=include_job_cards)
with open(filename, 'w') as f:
json.dump(data, f, indent=2)
print(f"Synthetic data generated and saved to {filename}")
return data
# Usage example
if __name__ == "__main__":
generator = MetroSyntheticDataGenerator(num_trainsets=25)
# Generate and save complete dataset
data = generator.save_to_json("metro_synthetic_data.json")
# Print summary
print(f"\nDataset Summary:")
print(f"Trainsets: {len(data['trainset_status'])}")
print(f"Fitness Certificates: {len(data['fitness_certificates'])}")
print(f"Job Cards: {len(data['job_cards'])}")
print(f"Component Health Records: {len(data['component_health'])}")
print(f"IoT Sensor Readings: {len(data['iot_sensors'])}")
print(f"Branding Contracts: {len(data['branding_contracts'])}")
print(f"Performance Metrics: {len(data['performance_metrics'])}") |