Bernt
aee0f09db8
- Datafabrik: Dockerfile fix, agentorkestrering fungerar - Vision: Identify-modell, FAISS, OCR alla testade - API: Alla 7 integrationstester passerade - Upplösare: Entitetsupplösning verifierad
114 lines
3.2 KiB
Python
114 lines
3.2 KiB
Python
#!/usr/bin/env python3
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"""
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Analysera väderkonsekvenser för vägar
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"""
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import sqlite3
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from datetime import datetime
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DB_PATH = "/home/bernt/.openclaw/workspace/rivp-pilot-1/rivp.db"
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def analyze_weather_impact(temperature, precipitation):
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"""
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Analysera väderpåverkan på vägar
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Returnerar risknivå och rekommendationer
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"""
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risks = []
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# Temperaturanalys
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if temperature < 0:
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risks.append({
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"type": "ice_risk",
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"severity": "high",
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"description": "Risk för halka och isbildning"
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})
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elif temperature < 5:
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risks.append({
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"type": "frost_risk",
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"severity": "medium",
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"description": "Risk för frostskador på vägbanan"
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})
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# Nederbördsanalys
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if precipitation > 10:
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risks.append({
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"type": "flooding_risk",
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"severity": "high",
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"description": "Risk för översvämning och nedsatt framkomlighet"
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})
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elif precipitation > 5:
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risks.append({
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"type": "wet_road",
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"severity": "medium",
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"description": "Blöt vägbana, ökad bromssträcka"
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})
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# Kombinerade effekter
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if temperature < 0 and precipitation > 0:
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risks.append({
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"type": "black_ice",
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"severity": "critical",
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"description": "Mycket hög risk för svartis"
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})
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return risks
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def get_affected_roads(county="Uppsala"):
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"""Hämta vägar i ett specifikt län"""
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conn = sqlite3.connect(DB_PATH)
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c = conn.cursor()
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c.execute("SELECT id, road_number, name, type FROM roads WHERE county = ?", (county,))
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roads = c.fetchall()
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conn.close()
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return roads
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def generate_weather_alerts():
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"""Generera vädervarningar för vägar"""
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# Hämta senaste väderdata
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import sys
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sys.path.insert(0, '/home/bernt/.openclaw/workspace/life-prod/ingestion')
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from smhi_weather import fetch_temperature, fetch_precipitation
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temp_data = fetch_temperature()
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precip_data = fetch_precipitation()
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if not temp_data or not precip_data:
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print("Ingen väderdata tillgänglig")
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return []
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temperature = temp_data["temperature"]
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precipitation = precip_data["precipitation_mm"]
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# Analysera risker
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risks = analyze_weather_impact(temperature, precipitation)
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# Hämta påverkade vägar
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roads = get_affected_roads()
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alerts = []
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for road in roads:
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for risk in risks:
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alerts.append({
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"road_id": road[0],
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"road_number": road[1],
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"road_name": road[2],
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"road_type": road[3],
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"risk_type": risk["type"],
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"severity": risk["severity"],
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"description": risk["description"],
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"temperature": temperature,
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"precipitation": precipitation,
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"timestamp": datetime.now().isoformat()
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})
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return alerts
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if __name__ == "__main__":
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alerts = generate_weather_alerts()
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print(f"Genererade {len(alerts)} vädervarningar")
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# Visa första 3
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for alert in alerts[:3]:
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print(f" {alert['road_number']}: {alert['risk_type']} ({alert['severity']})")
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