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| import pandas as pd | |
| import numpy as np | |
| import datetime | |
| import uuid | |
| def simulate_data(n=50, faults=True): | |
| today = datetime.date.today() | |
| poles = [f"Pole_{i+1:03}" for i in range(n)] | |
| # Distribute poles across 4 locations | |
| locations = ["Hyderabad"] * 12 + ["Gadwal"] * 12 + ["Kurnool"] * 12 + ["Bangalore"] * 14 | |
| np.random.shuffle(locations) # Randomize for realism | |
| data = [] | |
| for i, (pole, location) in enumerate(zip(poles, locations)): | |
| solar = round(np.random.uniform(3.0, 7.5), 2) | |
| wind = round(np.random.uniform(0.5, 2.0), 2) | |
| required = round(np.random.uniform(1.0, 1.5), 2) | |
| total = solar + wind | |
| cam = np.random.choice(['Online', 'Offline'], p=[0.85, 0.15]) if faults else "Online" | |
| tilt = round(np.random.uniform(0, 12), 1) | |
| vib = round(np.random.uniform(0.1, 2.5), 2) | |
| sufficient = "Yes" if total >= required else "No" | |
| rfid = str(uuid.uuid4())[:16] # 16-digit unique RFID | |
| anomaly = [] | |
| if faults: | |
| if solar < 4.0: | |
| anomaly.append("Low Solar Output") | |
| if wind < 0.7: | |
| anomaly.append("Low Wind Output") | |
| if tilt > 10: | |
| anomaly.append("High Pole Tilt Risk") | |
| if vib > 2.0: | |
| anomaly.append("Excessive Vibration") | |
| if cam == "Offline": | |
| anomaly.append("Camera Offline") | |
| if sufficient == "No": | |
| anomaly.append("Power Insufficient") | |
| alert = "Green" | |
| if len(anomaly) == 1: | |
| alert = "Yellow" | |
| elif len(anomaly) > 1: | |
| alert = "Red" | |
| data.append({ | |
| "PoleID": pole, | |
| "RFID": rfid, | |
| "Location": location, | |
| "Date": today, | |
| "SolarGen(kWh)": solar, | |
| "WindGen(kWh)": wind, | |
| "PowerRequired(kWh)": required, | |
| "PowerSufficient": sufficient, | |
| "CameraStatus": cam, | |
| "Tilt(°)": tilt, | |
| "Vibration(g)": vib, | |
| "Anomalies": ";".join(anomaly) if anomaly else "None", | |
| "AlertLevel": alert | |
| }) | |
| return pd.DataFrame(data) |