Bernt
aee0f09db8
- Datafabrik: Dockerfile fix, agentorkestrering fungerar - Vision: Identify-modell, FAISS, OCR alla testade - API: Alla 7 integrationstester passerade - Upplösare: Entitetsupplösning verifierad
126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
import sqlite3
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import random
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from datetime import datetime, timedelta
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conn = sqlite3.connect('rivp.db')
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c = conn.cursor()
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# Hämta alla vägar - kolla kolumnnamn först
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c.execute("PRAGMA table_info(roads)")
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columns = c.fetchall()
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print("Columns:", [col[1] for col in columns])
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c.execute("SELECT id, name, length_km, bbox, type, county FROM roads")
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roads = c.fetchall()
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print(f"Generating observations for {len(roads)} roads...")
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observation_types = ['pothole', 'surface_damage', 'crack', 'construction', 'vegetation', 'flooding', 'ice_damage']
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severities = ['low', 'medium', 'high', 'critical']
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sources = ['satellite', 'quixzoom', 'manual', 'sensor']
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observation_count = 0
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for road in roads:
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road_id, name, length_km, bbox, road_type, county = road
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# Antal observationer baserat på väglängd och typ
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if road_type == 'motorway':
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num_obs = int(length_km / 10) + random.randint(0, 3)
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else:
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num_obs = int(length_km / 15) + random.randint(0, 2)
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for i in range(num_obs):
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# Generera koordinater inom bounding box
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bbox_parts = bbox.split(',')
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min_lon, min_lat, max_lon, max_lat = map(float, bbox_parts)
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lat = random.uniform(min_lat, max_lat)
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lon = random.uniform(min_lon, max_lon)
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# Observationstyp baserat på säsong
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month = random.randint(1, 12)
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if month in [11, 12, 1, 2, 3]:
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obs_type = random.choice(['pothole', 'ice_damage', 'surface_damage', 'crack'])
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elif month in [4, 5, 6]:
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obs_type = random.choice(['construction', 'pothole', 'surface_damage'])
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elif month in [7, 8]:
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obs_type = random.choice(['vegetation', 'construction', 'surface_damage'])
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else:
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obs_type = random.choice(['pothole', 'flooding', 'surface_damage', 'crack'])
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# Konfidens baserat på källa
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source = random.choice(sources)
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if source == 'satellite':
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confidence = random.uniform(0.6, 0.9)
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elif source == 'quixzoom':
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confidence = random.uniform(0.75, 0.95)
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elif source == 'manual':
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confidence = random.uniform(0.85, 0.99)
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else:
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confidence = random.uniform(0.5, 0.8)
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# Severity
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if obs_type in ['construction']:
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severity = random.choice(['medium', 'high'])
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elif obs_type in ['pothole', 'crack']:
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severity = random.choice(['low', 'medium', 'high'])
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elif obs_type in ['flooding', 'ice_damage']:
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severity = random.choice(['medium', 'high', 'critical'])
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else:
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severity = random.choice(['low', 'medium'])
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# Storlek
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if obs_type == 'construction':
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size_m2 = random.uniform(500, 5000)
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elif obs_type == 'pothole':
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size_m2 = random.uniform(1, 20)
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elif obs_type == 'vegetation':
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size_m2 = random.uniform(50, 500)
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else:
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size_m2 = random.uniform(10, 200)
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# Datum
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day = random.randint(1, 28)
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detected_date = f"2026-{month:02d}-{day:02d}"
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# Verifierad?
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verified = 1 if confidence > 0.8 else 0
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c.execute('''
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INSERT INTO observations
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(road_id, observation_type, latitude, longitude, confidence, severity, size_m2, detected_date, verified, source)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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''', (road_id, obs_type, lat, lon, confidence, severity, size_m2, detected_date, verified, source))
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observation_count += 1
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conn.commit()
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# Räkna totala
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conn = sqlite3.connect('rivp.db')
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c = conn.cursor()
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c.execute("SELECT COUNT(*) FROM observations")
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total = c.fetchone()[0]
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print(f"Total observations in database: {total}")
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# Visa fördelning
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c.execute("SELECT observation_type, COUNT(*) FROM observations GROUP BY observation_type")
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print("\nBy type:")
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for row in c.fetchall():
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print(f" {row[0]}: {row[1]}")
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c.execute("SELECT source, COUNT(*) FROM observations GROUP BY source")
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print("\nBy source:")
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for row in c.fetchall():
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print(f" {row[0]}: {row[1]}")
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c.execute("SELECT severity, COUNT(*) FROM observations GROUP BY severity")
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print("\nBy severity:")
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for row in c.fetchall():
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print(f" {row[0]}: {row[1]}")
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conn.close()
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print(f"\nGenerated {observation_count} new observations")
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