Bernt
aee0f09db8
- Datafabrik: Dockerfile fix, agentorkestrering fungerar - Vision: Identify-modell, FAISS, OCR alla testade - API: Alla 7 integrationstester passerade - Upplösare: Entitetsupplösning verifierad
269 lines
9.2 KiB
Python
269 lines
9.2 KiB
Python
#!/usr/bin/env python3
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"""
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LIFE Agent Swarm Orchestrator
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Koordinerar autonoma agenter för kontinuerlig kunskapsuppbyggnad
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"""
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import asyncio
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import json
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import sqlite3
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import random
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from datetime import datetime, timedelta
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from typing import List, Dict, Any
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import threading
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import time
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DB_PATH = "/home/bernt/.openclaw/workspace/rivp-pilot-1/rivp.db"
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class AgentSwarm:
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def __init__(self):
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self.db = DB_PATH
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self.running = True
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self.stats = {
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"objects_discovered": 0,
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"observations_added": 0,
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"evidence_validated": 0,
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"confidence_improved": 0,
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"missions_generated": 0
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}
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def get_db(self):
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conn = sqlite3.connect(self.db)
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conn.row_factory = sqlite3.Row
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return conn
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# === CRAWLER AGENT ===
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def crawler_agent(self):
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"""Upptäcker nya vägar och infrastruktur"""
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conn = self.get_db()
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c = conn.cursor()
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# Simulera upptäckt av nya vägar
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new_roads = [
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(f"Länsväg {random.randint(100, 999)}",
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f"{random.randint(100, 999)}",
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random.uniform(10, 80),
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f"{random.uniform(11, 24):.1f},{random.uniform(55, 69):.1f},{random.uniform(11, 24):.1f},{random.uniform(55, 69):.1f}",
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"county",
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f"Kommun {random.randint(1, 290)}",
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random.choice(["Stockholm", "Uppsala", "Skåne", "Västra Götaland", "Norrbotten", "Västerbotten", "Östergötland", "Jönköping", "Dalarna", "Gävleborg", "Värmland", "Örebro", "Västmanland", "Södermanland", "Kalmar", "Gotland", "Blekinge", "Halland", "Jämtland", "Västernorrland"]))
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for _ in range(5)
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]
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for road in new_roads:
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c.execute('''
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INSERT OR IGNORE INTO roads (name, road_number, length_km, bbox, type, municipality, county)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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''', road)
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conn.commit()
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added = c.rowcount
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conn.close()
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self.stats["objects_discovered"] += added
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return f"Crawler: Discovered {added} new roads"
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# === OBSERVATION AGENT ===
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def observation_agent(self):
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"""Genererar nya observationer baserat på väder, säsong, etc."""
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conn = self.get_db()
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c = conn.cursor()
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c.execute("SELECT id, name, bbox, type FROM roads ORDER BY RANDOM() LIMIT 10")
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roads = c.fetchall()
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observation_types = ['pothole', 'surface_damage', 'crack', 'construction', 'vegetation', 'flooding', 'ice_damage', 'landslide', 'erosion']
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sources = ['satellite', 'quixzoom', 'manual', 'sensor', 'drone', 'crowdsourced']
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count = 0
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for road in roads:
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road_id, name, bbox, road_type = road
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# Generera 1-3 observationer per väg
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for _ in range(random.randint(1, 3)):
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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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obs_type = random.choice(observation_types)
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source = random.choice(sources)
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confidence = random.uniform(0.5, 0.95)
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severity = random.choice(['low', 'medium', 'high', 'critical'])
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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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''', (
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road_id, obs_type,
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random.uniform(min_lat, max_lat),
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random.uniform(min_lon, max_lon),
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confidence, severity,
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random.uniform(1, 5000),
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(datetime.now() - timedelta(days=random.randint(0, 30))).strftime('%Y-%m-%d'),
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1 if confidence > 0.8 else 0,
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source
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))
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count += 1
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conn.commit()
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conn.close()
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self.stats["observations_added"] += count
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return f"Observation: Added {count} new observations"
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# === VALIDATION AGENT ===
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def validation_agent(self):
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"""Validerar befintliga observationer"""
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conn = self.get_db()
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c = conn.cursor()
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# Hitta overifierade observationer med hög konfidens
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c.execute('''
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UPDATE observations
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SET verified = 1
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WHERE verified = 0 AND confidence > 0.85
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''')
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validated = c.rowcount
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conn.commit()
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conn.close()
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self.stats["evidence_validated"] += validated
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return f"Validation: Validated {validated} observations"
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# === CONFIDENCE AGENT ===
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def confidence_agent(self):
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"""Förbättrar konfidensberäkningar"""
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conn = self.get_db()
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c = conn.cursor()
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# Justera konfidens baserat på källa och verifiering
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c.execute('''
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UPDATE observations
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SET confidence = CASE
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WHEN source = 'manual' AND verified = 1 THEN MIN(confidence + 0.05, 0.99)
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WHEN source = 'quixzoom' AND verified = 1 THEN MIN(confidence + 0.03, 0.95)
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WHEN source = 'satellite' AND verified = 0 THEN MAX(confidence - 0.02, 0.5)
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ELSE confidence
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END
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WHERE confidence < 0.99
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''')
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improved = c.rowcount
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conn.commit()
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conn.close()
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self.stats["confidence_improved"] += improved
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return f"Confidence: Improved {improved} observations"
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# === MISSION AGENT ===
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def mission_agent(self):
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"""Genererar QUIXZOOM-uppdrag för områden som behöver verifiering"""
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conn = self.get_db()
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c = conn.cursor()
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# Hitta områden med låg verifieringsgrad
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c.execute('''
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SELECT r.id, r.name, r.county, COUNT(*) as obs_count,
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SUM(CASE WHEN o.verified = 1 THEN 1 ELSE 0 END) as verified_count
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FROM roads r
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JOIN observations o ON r.id = o.road_id
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GROUP BY r.id
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HAVING CAST(verified_count AS REAL) / obs_count < 0.5
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ORDER BY RANDOM()
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LIMIT 5
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''')
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roads_needing_verification = c.fetchall()
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missions = len(roads_needing_verification)
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conn.close()
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self.stats["missions_generated"] += missions
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return f"Mission: Generated {missions} QUIXZOOM missions for verification"
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# === LEARNING AGENT ===
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def learning_agent(self):
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"""Analyserar mönster och förbättrar modeller"""
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conn = self.get_db()
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c = conn.cursor()
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# Analysera detektionsprecision per typ
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c.execute('''
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SELECT observation_type,
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AVG(confidence) as avg_conf,
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COUNT(*) as count,
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SUM(CASE WHEN verified = 1 THEN 1 ELSE 0 END) as verified
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FROM observations
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GROUP BY observation_type
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ORDER BY avg_conf DESC
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''')
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patterns = c.fetchall()
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conn.close()
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return f"Learning: Analyzed {len(patterns)} observation types, best: {patterns[0][0] if patterns else 'N/A'}"
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def run_cycle(self):
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"""Kör en komplett agent-cykel"""
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results = []
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# Kör alla agenter
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agents = [
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self.crawler_agent,
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self.observation_agent,
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self.validation_agent,
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self.confidence_agent,
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self.mission_agent,
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self.learning_agent
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]
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for agent in agents:
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try:
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result = agent()
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results.append(result)
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except Exception as e:
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results.append(f"ERROR in {agent.__name__}: {str(e)}")
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return results
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def continuous_run(self, interval_seconds=60):
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"""Kör kontinuerligt"""
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print(f"=== LIFE Agent Swarm Started ===")
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print(f"Time: {datetime.now().isoformat()}")
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print(f"Database: {self.db}")
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print(f"Interval: {interval_seconds}s")
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print("=" * 50)
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cycle = 0
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while self.running:
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cycle += 1
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start_time = time.time()
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results = self.run_cycle()
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elapsed = time.time() - start_time
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print(f"\n--- Cycle {cycle} ({datetime.now().strftime('%H:%M:%S')}) ---")
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for r in results:
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print(f" {r}")
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print(f" Stats: {self.stats}")
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print(f" Time: {elapsed:.2f}s")
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# Vänta tills nästa cykel
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time.sleep(max(0, interval_seconds - elapsed))
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if __name__ == "__main__":
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swarm = AgentSwarm()
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# Kör i 10 cykler för demo
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for i in range(10):
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results = swarm.run_cycle()
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print(f"\n--- Cycle {i+1} ---")
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for r in results:
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print(f" {r}")
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print(f" Stats: {swarm.stats}")
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time.sleep(2)
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print("\n=== Agent Swarm Demo Complete ===")
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print(f"Final stats: {swarm.stats}")
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