Bernt
aee0f09db8
- Datafabrik: Dockerfile fix, agentorkestrering fungerar - Vision: Identify-modell, FAISS, OCR alla testade - API: Alla 7 integrationstester passerade - Upplösare: Entitetsupplösning verifierad
148 lines
4.5 KiB
Python
148 lines
4.5 KiB
Python
#!/usr/bin/env python3
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"""
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Weather Intelligence Pipeline
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Huvudflöde för väderintelligens
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"""
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import sys
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import json
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import logging
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from datetime import datetime
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from pathlib import Path
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sys.path.insert(0, '/home/bernt/.openclaw/workspace/life-weather')
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from providers.weather_provider import WeatherProvider
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from storage.observation_store import ObservationStore
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from engine.risk_engine import RiskEngine
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# Konfigurera loggning
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LOG_DIR = Path("/home/bernt/.openclaw/workspace/life-weather/logs")
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LOG_DIR.mkdir(exist_ok=True)
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler(LOG_DIR / "pipeline.log"),
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logging.StreamHandler()
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]
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)
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logger = logging.getLogger(__name__)
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class WeatherPipeline:
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def __init__(self):
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self.provider = WeatherProvider()
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self.store = ObservationStore()
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self.engine = RiskEngine()
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def run(self, road_id=None, road_type="primary"):
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"""
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Kör hela pipelinen
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Flöde:
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1. Hämta väderdata
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2. Spara rådata
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3. Skapa observationer
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4. Beräkna risk
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5. Returnera resultat
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"""
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logger.info(f"Startar pipeline för väg {road_id}")
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# Steg 1: Hämta väderdata
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logger.info("Hämtar väderdata...")
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weather = self.provider.get_current_weather()
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if not weather["temperature"]:
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logger.error("Kunde inte hämta väderdata")
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return None
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# Steg 2: Spara rådata
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raw_file = self.store.save_raw_data(weather)
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logger.info(f"Rådata sparad: {raw_file}")
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# Steg 3: Skapa observationer
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observations = []
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if weather["temperature"]:
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obs_id = self.store.save_observation(
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road_id=road_id or 0,
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observation_type="temperature",
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value=weather["temperature"]["value"],
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unit="celsius",
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timestamp=weather["timestamp"],
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raw_data=weather["temperature"]
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)
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if obs_id:
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observations.append(obs_id)
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logger.info(f"Temperaturobservation sparad: {obs_id}")
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if weather["precipitation"]:
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obs_id = self.store.save_observation(
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road_id=road_id or 0,
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observation_type="precipitation",
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value=weather["precipitation"]["value"],
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unit="mm",
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timestamp=weather["timestamp"],
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raw_data=weather["precipitation"]
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)
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if obs_id:
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observations.append(obs_id)
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logger.info(f"Nederbördsobservation sparad: {obs_id}")
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if weather["wind"]:
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obs_id = self.store.save_observation(
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road_id=road_id or 0,
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observation_type="wind",
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value=weather["wind"]["value"],
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unit="m/s",
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timestamp=weather["timestamp"],
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raw_data=weather["wind"]
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)
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if obs_id:
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observations.append(obs_id)
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logger.info(f"Vindobservation sparad: {obs_id}")
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# Steg 4: Beräkna risk
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temp = weather["temperature"]["value"] if weather["temperature"] else None
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precip = weather["precipitation"]["value"] if weather["precipitation"] else None
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wind = weather["wind"]["value"] if weather["wind"] else None
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risks = self.engine.calculate_risk(
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road_id=road_id or 0,
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road_type=road_type,
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temperature=temp,
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precipitation=precip,
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wind=wind
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)
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logger.info(f"Risker beräknade: {len(risks)}")
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# Resultat
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result = {
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"timestamp": weather["timestamp"],
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"road_id": road_id,
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"weather": weather,
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"observations": observations,
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"risks": [
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{
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"type": r.risk_type,
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"severity": r.severity,
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"score": r.score,
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"description": r.description
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}
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for r in risks
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]
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}
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logger.info("Pipeline klar")
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return result
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if __name__ == "__main__":
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pipeline = WeatherPipeline()
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result = pipeline.run(road_id=1, road_type="primary")
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if result:
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print(json.dumps(result, indent=2))
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else:
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print("Pipeline misslyckades")
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