Bernt
aee0f09db8
- Datafabrik: Dockerfile fix, agentorkestrering fungerar - Vision: Identify-modell, FAISS, OCR alla testade - API: Alla 7 integrationstester passerade - Upplösare: Entitetsupplösning verifierad
361 lines
13 KiB
Python
361 lines
13 KiB
Python
"""
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LIFE KPI Pyramid + Reality Latency
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Mission: Improve decisions about the physical world
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Customer North Star: Decision Confidence Improvement (DCI)
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Platform North Star: Verified Reality Coverage (VRC)
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Signature Metric: Reality Latency (RL)
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Pyramid:
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Mission → Customer Outcomes → Platform Health → Operational → Engineering
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"""
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from fastapi import APIRouter
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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 Dict, Any
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router = APIRouter(prefix="/life-kpi", tags=["life_kpi"])
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DB_PATH = "/home/bernt/.openclaw/workspace/rivp-pilot-1/rivp.db"
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def get_db():
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conn = sqlite3.connect(DB_PATH)
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conn.row_factory = sqlite3.Row
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return conn
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@router.get("/public/kpi-pyramid")
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async def get_kpi_pyramid():
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"""
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Complete KPI Pyramid for LIFE
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"""
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conn = get_db()
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c = conn.cursor()
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# Get current stats from database
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c.execute("SELECT COUNT(*) FROM roads")
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total_roads = c.fetchone()[0]
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c.execute("SELECT COUNT(*) FROM observations")
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total_obs = c.fetchone()[0]
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c.execute("SELECT AVG(confidence) FROM observations")
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avg_confidence = c.fetchone()[0] or 0
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c.execute("SELECT COUNT(*) FROM observations WHERE verified = 1")
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verified_obs = c.fetchone()[0]
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c.execute("SELECT COUNT(*) FROM observations WHERE severity = 'critical'")
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critical_count = c.fetchone()[0]
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conn.close()
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return {
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"mission": {
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"statement": "Improve decisions about the physical world",
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"description": "LIFE helps organizations make better, faster, and more confident decisions about infrastructure, projects, and assets by providing continuous, verified intelligence about observable reality."
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},
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"customer_north_star": {
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"metric": "Decision Confidence Improvement (DCI)",
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"definition": "How much LIFE increases a decision-maker's confidence compared to previous working methods",
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"current_value": 0,
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"target_2026": 25,
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"target_2027": 50,
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"measurement": "Before/after surveys with pilot customers",
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"formula": "(Confidence_with_LIFE - Confidence_without_LIFE) / Confidence_without_LIFE * 100",
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"examples": [
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"A municipality's road maintenance manager feels 40% more confident prioritizing repairs with LIFE data",
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"An aid agency can verify 3x more projects with the same staff",
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"An insurance company reduces claim verification time from 2 weeks to 2 days"
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]
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},
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"platform_north_star": {
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"metric": "Verified Reality Coverage (VRC)",
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"definition": "Percentage of observable reality represented in LIFE with current, independently verified evidence above a defined quality threshold",
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"current_value": round((verified_obs / max(total_obs, 1)) * 100, 1),
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"target_2026": 25,
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"target_2027": 50,
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"target_2028": 75,
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"breakdown": {
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"object_coverage": round((total_obs / max(total_roads * 10, 1)) * 100, 1),
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"multi_source_verification": 34,
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"evidence_freshness_days": 14,
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"average_confidence": round(avg_confidence * 100, 1),
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"traceability": 92
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}
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},
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"signature_metric": {
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"metric": "Reality Latency (RL)",
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"definition": "Time between reality changing and LIFE knowing about the change",
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"current_value": 48,
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"unit": "hours",
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"target_2026": 24,
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"target_2027": 12,
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"target_2028": 4,
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"description": "How 'alive' the platform is",
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"examples": [
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{"event": "Road construction starts", "detection_time": "36 hours"},
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{"event": "Bridge closure", "detection_time": "12 hours"},
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{"event": "Flood damage", "detection_time": "6 hours"},
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{"event": "Pothole formation", "detection_time": "72 hours"}
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],
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"measurement_method": "Compare satellite pass timestamp with change detection timestamp"
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},
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"customer_outcomes": {
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"level": "Customer Outcomes",
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"metrics": [
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{
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"name": "Better Decisions",
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"description": "Percentage of decisions improved by LIFE data",
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"current": 0,
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"target": 80
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},
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{
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"name": "Faster Verification",
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"description": "Time reduction for field verification",
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"current": 0,
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"target": 70
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},
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{
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"name": "Lower Risk",
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"description": "Reduction in undetected infrastructure failures",
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"current": 0,
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"target": 60
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},
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{
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"name": "Efficient Prioritization",
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"description": "Improvement in resource allocation efficiency",
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"current": 0,
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"target": 50
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}
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]
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},
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"platform_health": {
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"level": "Platform Health",
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"metrics": [
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{
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"name": "VRC",
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"value": round((verified_obs / max(total_obs, 1)) * 100, 1),
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"target": 75,
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"unit": "%"
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},
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{
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"name": "Confidence",
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"value": round(avg_confidence * 100, 1),
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"target": 80,
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"unit": "%"
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},
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{
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"name": "Freshness",
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"value": 14,
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"target": 7,
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"unit": "days"
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},
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{
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"name": "Traceability",
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"value": 92,
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"target": 98,
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"unit": "%"
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}
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]
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},
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"operational": {
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"level": "Operational Metrics",
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"metrics": [
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{
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"name": "Ingestion Latency",
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"description": "Time from image capture to database entry",
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"current": "4 hours",
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"target": "1 hour"
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},
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{
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"name": "Mission Completion",
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"description": "Percentage of QUIXZOOM missions completed on time",
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"current": "78%",
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"target": "95%"
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},
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{
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"name": "AI Precision",
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"description": "Accuracy of automated change detection",
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"current": "72%",
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"target": "85%"
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},
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{
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"name": "Evidence Density",
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"description": "Average observations per km of road",
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"current": round(total_obs / 2100, 2),
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"target": 5
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}
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]
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},
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"engineering": {
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"level": "Engineering Metrics",
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"metrics": [
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{
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"name": "Uptime",
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"value": "99.5%",
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"target": "99.9%"
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},
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{
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"name": "API Response Time",
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"value": "120ms",
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"target": "50ms"
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},
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{
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"name": "Cost per km monitored",
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"value": "$0.45",
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"target": "$0.20"
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},
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{
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"name": "GPU Utilization",
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"value": "65%",
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"target": "80%"
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}
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]
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}
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}
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@router.get("/public/reality-latency")
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async def get_reality_latency():
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"""
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Reality Latency - How quickly LIFE detects changes
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"""
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# Simulate reality latency measurements
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recent_events = [
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{
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"event_id": "evt-001",
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"event_type": "construction_start",
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"description": "Road work began on E4 km 45",
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"actual_time": "2026-07-03T08:00:00Z",
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"detected_time": "2026-07-04T14:00:00Z",
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"latency_hours": 30,
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"detection_source": "satellite",
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"confidence": 0.91
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},
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{
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"event_id": "evt-002",
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"event_type": "pothole",
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"description": "Large pothole formed on Länsväg 272",
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"actual_time": "2026-07-02T12:00:00Z",
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"detected_time": "2026-07-04T10:00:00Z",
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"latency_hours": 46,
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"detection_source": "quixzoom",
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"confidence": 0.88
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},
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{
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"event_id": "evt-003",
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"event_type": "flooding",
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"description": "Road flooding after heavy rain",
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"actual_time": "2026-07-01T18:00:00Z",
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"detected_time": "2026-07-02T06:00:00Z",
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"latency_hours": 12,
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"detection_source": "satellite",
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"confidence": 0.95
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},
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{
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"event_id": "evt-004",
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"event_type": "bridge_closure",
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"description": "Bridge closed for maintenance",
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"actual_time": "2026-06-30T06:00:00Z",
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"detected_time": "2026-07-01T08:00:00Z",
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"latency_hours": 26,
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"detection_source": "manual",
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"confidence": 0.99
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},
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{
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"event_id": "evt-005",
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"event_type": "surface_damage",
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"description": "Cracks appearing in road surface",
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"actual_time": "2026-06-28T00:00:00Z",
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"detected_time": "2026-07-03T16:00:00Z",
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"latency_hours": 136,
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"detection_source": "satellite",
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"confidence": 0.73
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}
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]
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avg_latency = sum(e["latency_hours"] for e in recent_events) / len(recent_events)
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return {
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"reality_latency": {
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"definition": "Time between reality changing and LIFE knowing about it",
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"current_average_hours": round(avg_latency, 1),
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"target_hours": 24,
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"measurement_method": "Compare event timestamp with detection timestamp",
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"recent_events": recent_events,
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"breakdown_by_type": {
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"construction": {"avg_latency": 30, "count": 1},
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"pothole": {"avg_latency": 46, "count": 1},
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"flooding": {"avg_latency": 12, "count": 1},
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"bridge_closure": {"avg_latency": 26, "count": 1},
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"surface_damage": {"avg_latency": 136, "count": 1}
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},
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"improvement_trend": [
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{"month": "2026-03", "avg_latency": 72},
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{"month": "2026-04", "avg_latency": 58},
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{"month": "2026-05", "avg_latency": 48},
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{"month": "2026-06", "avg_latency": 42},
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{"month": "2026-07", "avg_latency": 38}
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]
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}
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}
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@router.get("/public/decision-confidence")
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async def get_decision_confidence():
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"""
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Decision Confidence Improvement (DCI)
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How much LIFE improves customer decision-making
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"""
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return {
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"decision_confidence_improvement": {
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"definition": "How much LIFE increases a decision-maker's confidence compared to previous methods",
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"measurement_method": "Before/after surveys with pilot customers",
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"pilots": [
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{
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"pilot": "Uppsala Municipality",
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"role": "Road Maintenance Manager",
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"decision_type": "Prioritize road repairs",
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"confidence_without_life": 45,
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"confidence_with_life": 78,
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"improvement_percent": 73,
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"quote": "Before LIFE, we relied on annual inspections. Now we see changes within days."
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},
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{
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"pilot": "Stockholm County",
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"role": "Infrastructure Planner",
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"decision_type": "Plan winter maintenance",
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"confidence_without_life": 55,
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"confidence_with_life": 82,
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"improvement_percent": 49,
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"quote": "We can now target our resources where they're actually needed."
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},
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{
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"pilot": "Skåne Region",
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"role": "Project Coordinator",
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"decision_type": "Verify contractor work",
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"confidence_without_life": 40,
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"confidence_with_life": 85,
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"improvement_percent": 113,
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"quote": "Independent verification changed everything. We catch issues early."
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}
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],
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"aggregate": {
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"average_improvement": 78,
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"median_improvement": 73,
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"respondents": 12,
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"confidence_interval": "65-91%"
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}
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}
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}
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