landvex: Fixar och tester klara för alla komponenter

- Datafabrik: Dockerfile fix, agentorkestrering fungerar
- Vision: Identify-modell, FAISS, OCR alla testade
- API: Alla 7 integrationstester passerade
- Upplösare: Entitetsupplösning verifierad

life-agents/multi_source_pipeline.py

@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+"""
+LIFE Multi-Source Pipeline
+Hämtar data kontinuerligt från flera källor
+"""
+import sqlite3
+import json
+import random
+from datetime import datetime, timedelta
+import time
+
+DB_PATH = "/home/bernt/.openclaw/workspace/rivp-pilot-1/rivp.db"
+
+def get_db():
+    conn = sqlite3.connect(DB_PATH)
+    conn.row_factory = sqlite3.Row
+    return conn
+
+def fetch_trafikverket_data():
+    """Simulerar hämtning från Trafikverket"""
+    return [
+        {"road": "E4", "type": "ice", "severity": "high", "lat": 59.85, "lon": 17.65},
+        {"road": "E4", "type": "roadwork", "severity": "medium", "lat": 59.88, "lon": 17.72},
+        {"road": "272", "type": "flooding", "severity": "low", "lat": 59.92, "lon": 17.55},
+    ]
+
+def fetch_smhi_data():
+    """Simulerar hämtning från SMHI"""
+    return [
+        {"location": "Uppsala", "weather": "snow", "temperature": -5, "impact": "high"},
+        {"location": "Stockholm", "weather": "rain", "temperature": 8, "impact": "medium"},
+    ]
+
+def fetch_quixzoom_data():
+    """Simulerar hämtning från quiXzoom contributors"""
+    return [
+        {"road_id": 1, "type": "pothole", "confidence": 0.92, "lat": 59.85, "lon": 17.65},
+        {"road_id": 2, "type": "crack", "confidence": 0.78, "lat": 59.88, "lon": 17.72},
+    ]
+
+def process_and_save(source_name, data):
+    """Bearbeta och spara data från varje källa"""
+    conn = get_db()
+    c = conn.cursor()
+    
+    count = 0
+    for item in data:
+        if source_name == "trafikverket":
+            c.execute("SELECT id FROM roads WHERE road_number = ?", (item["road"],))
+            result = c.fetchone()
+            if result:
+                road_id = result[0]
+                c.execute('''
+                    INSERT INTO observations 
+                    (road_id, observation_type, latitude, longitude, confidence, severity, detected_date, source)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                ''', (road_id, item["type"], item["lat"], item["lon"], 0.9, item["severity"], datetime.now().strftime('%Y-%m-%d'), source_name))
+                count += 1
+        
+        elif source_name == "smhi":
+            # SMHI-data påverkar alla vägar i området
+            c.execute("SELECT id FROM roads WHERE county = ?", (item["location"],))
+            roads = c.fetchall()
+            for road in roads:
+                c.execute('''
+                    INSERT INTO observations 
+                    (road_id, observation_type, latitude, longitude, confidence, severity, detected_date, source)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                ''', (road[0], item["weather"], 0, 0, 0.85, item["impact"], datetime.now().strftime('%Y-%m-%d'), source_name))
+                count += 1
+        
+        elif source_name == "quixzoom":
+            c.execute('''
+                INSERT INTO observations 
+                (road_id, observation_type, latitude, longitude, confidence, severity, detected_date, source)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+            ''', (item["road_id"], item["type"], item["lat"], item["lon"], item["confidence"], "medium", datetime.now().strftime('%Y-%m-%d'), source_name))
+            count += 1
+    
+    conn.commit()
+    conn.close()
+    return count
+
+def run_pipeline():
+    """Kör komplett pipeline från alla källor"""
+    print(f"[{datetime.now().isoformat()}] Running multi-source pipeline...")
+    
+    sources = {
+        "trafikverket": fetch_trafikverket_data,
+        "smhi": fetch_smhi_data,
+        "quixzoom": fetch_quixzoom_data
+    }
+    
+    total = 0
+    for source_name, fetch_func in sources.items():
+        try:
+            data = fetch_func()
+            saved = process_and_save(source_name, data)
+            total += saved
+            print(f"  {source_name}: {saved} observations")
+        except Exception as e:
+            print(f"  {source_name}: ERROR - {e}")
+    
+    print(f"[{datetime.now().isoformat()}] Pipeline complete: {total} total observations")
+    return total
+
+if __name__ == "__main__":
+    print("="*60)
+    print("LIFE MULTI-SOURCE PIPELINE")
+    print("="*60)
+    
+    run_pipeline()
+    
+    print("="*60)