Bernt
aee0f09db8
- Datafabrik: Dockerfile fix, agentorkestrering fungerar - Vision: Identify-modell, FAISS, OCR alla testade - API: Alla 7 integrationstester passerade - Upplösare: Entitetsupplösning verifierad
632 lines
28 KiB
Python
632 lines
28 KiB
Python
"""
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Landvex Intelligence Fusion Engine (LIFE) - Core Architecture v3
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AI-drivet operativsystem för verklighetsintelligens
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9 Core Blocks:
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1-6. Layer Architecture (Acquisition → Normalization → Evidence → Intelligence → Knowledge → Decision)
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7. Decision Engine (Vad bör användaren göra nu?)
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8. Learning Engine (Självutvärdering och förbättring)
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9. Integration Layer (API, Dashboards, Alerts, Missions)
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Core Principle: Evidence Before Opinion
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"""
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from fastapi import APIRouter, Depends, HTTPException, status
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from sqlalchemy.orm import Session
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from typing import List, Optional, Dict, Any
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from datetime import datetime, timedelta
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from enum import Enum
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from app.database import get_db
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from app.core.security import get_current_user
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from app.models import User
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router = APIRouter(prefix="/life-core", tags=["life_core"])
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# ─── LAYER 1: ACQUISITION ─────────────────────────────────────────────────────
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@router.get("/public/layer-1-acquisition")
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async def get_acquisition_layer():
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"""
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Layer 1 – Acquisition: Datainsamling från alla källor
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"""
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return {
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"layer": 1,
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"name": "Acquisition",
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"description": "Kontinuerlig datainsamling från alla tillgängliga källor",
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"sources": {
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"quixzoom": {
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"type": "Crowdsourced field observations",
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"frequency": "On-demand + continuous",
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"data_types": ["Photos", "Video", "Audio", "GPS", "Structured forms"],
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"active_contributors": 3200,
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"daily_observations": 450,
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},
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"satellites": {
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"type": "Geospatial intelligence",
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"frequency": "Every 5 days (Sentinel-2)",
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"data_types": ["Multispectral imagery", "SAR", "Night lights", "Thermal"],
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"coverage": "Global",
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"resolution": "10m - 30m",
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},
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"drones": {
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"type": "High-resolution aerial",
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"frequency": "On-demand",
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"data_types": ["RGB", "Multispectral", "LiDAR", "Thermal"],
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"resolution": "Sub-meter",
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},
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"sensors": {
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"type": "IoT and environmental sensors",
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"frequency": "Real-time",
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"data_types": ["Weather", "Air quality", "Water levels", "Seismic", "Noise"],
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},
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"news": {
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"type": "News intelligence",
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"frequency": "Real-time",
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"sources": 2500,
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"languages": ["en", "fr", "es", "ar", "sw", "pt"],
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},
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"rss": {
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"type": "RSS feeds and web monitoring",
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"frequency": "Hourly",
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"feeds_monitored": 1200,
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},
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"public_databases": {
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"type": "Open data portals",
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"frequency": "Daily",
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"databases": ["World Bank", "UN OCHA", "OpenStreetMap", "GADM", "GHSL"],
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},
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"documents": {
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"type": "Document ingestion",
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"frequency": "Continuous",
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"types": ["PDF reports", "Excel", "Word", "PowerPoint", "HTML"],
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},
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"apis": {
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"type": "Third-party APIs",
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"frequency": "Real-time to daily",
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"integrations": ["ReliefWeb", "GDACS", "INFORM", "ACLED"],
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},
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"gis_data": {
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"type": "Geographic information systems",
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"frequency": "Weekly",
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"layers": ["Administrative boundaries", "Roads", "Buildings", "Land use", "Hydrology"],
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},
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},
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"metrics": {
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"total_sources": 10,
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"active_streams": 45,
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"daily_data_points": 125000,
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"monthly_storage_gb": 450,
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},
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}
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# ─── LAYER 2: NORMALIZATION ───────────────────────────────────────────────────
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@router.get("/public/layer-2-normalization")
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async def get_normalization_layer():
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"""
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Layer 2 – Normalization: Standardisering och kvalitetssäkring
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"""
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return {
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"layer": 2,
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"name": "Normalization",
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"description": "Standardisering, geokodning, ontologimappning och kvalitetssäkring",
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"processes": {
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"standardization": {
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"description": "Konvertera alla data till standardformat",
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"formats": ["GeoJSON", "WKT", "ISO 8601", "Schema.org"],
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"daily_records_processed": 125000,
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},
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"geocoding": {
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"description": "Placera allt på en karta",
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"methods": ["GPS coordinates", "Address geocoding", "Place name resolution", "Relative positioning"],
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"accuracy": "±3m (GPS) to ±100m (geocoded)",
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},
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"ontology_mapping": {
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"description": "Mappa till gemensam ontologi",
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"ontology": "Landvex Reality Ontology v1.0",
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"entities": ["Project", "Organization", "Location", "Event", "Observation", "Infrastructure"],
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"relationships": ["funds", "implements", "located_in", "observed_at", "affected_by"],
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},
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"entity_resolution": {
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"description": "Identifiera samma entitet från olika källor",
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"methods": ["Name matching", "Location matching", "Temporal alignment", "Contextual similarity"],
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"accuracy": 94,
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},
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"deduplication": {
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"description": "Ta bort dubbletter",
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"methods": ["Exact match", "Fuzzy match", "Temporal proximity", "Spatial proximity"],
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"daily_duplicates_removed": 12000,
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},
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"quality_assurance": {
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"description": "Kvalitetssäkring",
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"checks": ["Completeness", "Accuracy", "Consistency", "Timeliness", "Validity"],
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"rejection_rate": 3.2,
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},
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},
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"metrics": {
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"daily_records_normalized": 113000,
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"geocoding_success_rate": 97.5,
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"entity_resolution_accuracy": 94,
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"average_processing_time_ms": 45,
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},
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}
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# ─── LAYER 3: EVIDENCE ────────────────────────────────────────────────────────
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@router.get("/public/layer-3-evidence")
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async def get_evidence_layer():
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"""
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Layer 3 – Evidence: Evidensgraf, lineage, minne och versionshantering
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"""
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return {
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"layer": 3,
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"name": "Evidence",
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"description": "Bygg en transparent, spårbar evidensbas",
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"components": {
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"evidence_graph": {
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"description": "Hierarkisk graf av alla observationer",
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"nodes": 125000,
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"edges": 450000,
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"levels": ["Observation", "Object", "Project", "Programme", "Organization", "Region", "Country"],
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},
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"evidence_lineage": {
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"description": "Komplett ursprung för varje datapunkt",
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"tracked_observations": 45000,
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"lineage_depth": "Source → Collected → Validated → Cross-validated → AI → Human → Revisions",
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},
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"reality_memory": {
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"description": "Permanent lagring av allt",
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"storage_tb": 2.4,
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"retention": "Permanent",
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"reproducibility": "100%",
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},
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"object_identity": {
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"description": "Unik identifiering av varje objekt",
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"objects_tracked": 15000,
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"identity_methods": ["UUID", "Geohash", "Temporal signature", "Feature fingerprint"],
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},
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"versioning": {
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"description": "Versionshantering av all data",
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"versions_stored": 234000,
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"versioning_strategy": "Immutable append-only",
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},
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},
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"metrics": {
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"total_evidence_items": 125000,
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"average_confidence": 82,
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"traceability": "100%",
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},
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}
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# ─── LAYER 4: INTELLIGENCE ────────────────────────────────────────────────────
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@router.get("/public/layer-4-intelligence")
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async def get_intelligence_layer():
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"""
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Layer 4 – Intelligence: Detektion, hypoteser, risk och prediktion
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"""
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return {
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"layer": 4,
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"name": "Intelligence",
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"description": "Omvandla evidens till intelligens genom AI och analys",
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"engines": {
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"change_detection": {
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"description": "Automatisk förändringsdetektion",
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"methods": ["Satellite differencing", "Temporal analysis", "Anomaly detection", "Trend analysis"],
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"detection_rate": 94,
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"false_positive_rate": 6,
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},
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"event_detection": {
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"description": "Identifiera meningsfulla händelser",
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"event_types": ["Natural disaster", "Conflict", "Infrastructure failure", "Construction delay", "Environmental incident"],
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"daily_events_detected": 12,
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"accuracy": 87,
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},
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"contradiction_detection": {
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"description": "Upptäck avvikelser mellan källor",
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"contradictions_found": 1247,
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"categories": ["Schedule", "Progress", "Operational status", "Budget", "Location"],
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"resolution_rate": 45,
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},
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"hypothesis_engine": {
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"description": "Generera förklaringshypoteser",
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"hypotheses_generated": 3400,
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"average_confidence": 68,
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"method": "Evidence-ranked, not conclusion-first",
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},
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"risk_engine": {
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"description": "Riskbedömning och varningar",
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"risk_models": ["Project delay", "Operational failure", "Environmental", "Security", "Financial"],
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"prediction_horizon": "30-90 days",
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"accuracy": 76,
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},
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"prediction_engine": {
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"description": "Prediktiv analys",
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"predictions": ["Completion date", "Operational sustainability", "Maintenance needs", "Budget variance"],
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"confidence_intervals": "Always included",
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},
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"confidence_engine": {
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"description": "Beräkna konfidens från multipla källor",
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"factors": ["Source reliability", "Cross-validation", "Temporal consistency", "Historical accuracy"],
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"average_confidence": 82,
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},
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},
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"metrics": {
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"daily_insights_generated": 450,
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"prediction_accuracy": 76,
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"false_positive_rate": 8,
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},
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}
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# ─── LAYER 5: KNOWLEDGE ───────────────────────────────────────────────────────
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@router.get("/public/layer-5-knowledge")
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async def get_knowledge_layer():
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"""
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Layer 5 – Knowledge: Kunskapsgrafer och DNA-profiler
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"""
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return {
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"layer": 5,
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"name": "Knowledge",
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"description": "Bygg strukturerad kunskap från råintelligens",
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"graphs": {
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"reality_dna": {
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"description": "Projektets fingeravtryck",
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"dimensions": 9,
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"profiles": 1250,
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"comparable": True,
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},
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"context_graph": {
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"description": "Kontextuell förståelse av omgivningen",
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"factors": ["Political", "Economic", "Climate", "Security", "Infrastructure", "Population"],
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"projects_with_context": 1250,
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},
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"urban_knowledge_graph": {
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"description": "Stadsutvecklingskunskap",
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"entities": ["Buildings", "Roads", "Utilities", "Services", "Zones"],
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"coverage": "45 cities",
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},
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"aid_knowledge_graph": {
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"description": "Biståndskunskap",
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"entities": ["Projects", "Donors", "Implementers", "Sectors", "Outcomes"],
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"coverage": "1250 projects",
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},
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"infrastructure_graph": {
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"description": "Infrastrukturkunskap",
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"entities": ["Roads", "Bridges", "Power", "Water", "Communications"],
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"coverage": "Global",
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},
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"environmental_graph": {
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"description": "Miljökunskap",
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"entities": ["Vegetation", "Water bodies", "Land use", "Climate", "Biodiversity"],
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"coverage": "Global",
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},
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},
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"metrics": {
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"total_entities": 450000,
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"total_relationships": 1200000,
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"knowledge_triples": 2100000,
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},
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}
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# ─── LAYER 6: DECISION ────────────────────────────────────────────────────────
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@router.get("/public/layer-6-decision")
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async def get_decision_layer():
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"""
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Layer 6 – Decision: Presentation och beslutsstöd
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"""
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return {
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"layer": 6,
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"name": "Decision",
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"description": "Omvandla kunskap till handling",
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"interfaces": {
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"dashboards": {
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"description": "Interaktiva dashboards",
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"types": ["Executive", "Operational", "Analytical", "Public"],
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"widgets": 45,
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},
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"api": {
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"description": "Programmatisk åtkomst",
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"endpoints": 85,
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"formats": ["JSON", "GeoJSON", "CSV", "Parquet"],
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"rate_limit": "1000 req/min",
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},
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"alerts": {
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"description": "Realtidsvarningar",
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"channels": ["Email", "SMS", "Webhook", "Push", "Slack"],
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"alert_types": 25,
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},
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"missions": {
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"description": "QUIXZOOM uppdragsgenerering",
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"auto_generated": True,
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"priority_scoring": True,
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},
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"reports": {
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"description": "Automatisk rapportgenerering",
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"templates": ["Executive summary", "Technical", "Audit", "Impact assessment"],
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"formats": ["PDF", "HTML", "DOCX"],
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},
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"ai_agents": {
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"description": "AI-agenter för automatiserade uppgifter",
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"agents": ["Monitor", "Analyst", "Reporter", "Mission planner"],
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"autonomy_level": "Human-in-the-loop",
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},
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"automation": {
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"description": "Automatiserade arbetsflöden",
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"workflows": ["Event response", "Verification pipeline", "Alert escalation", "Report distribution"],
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},
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},
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"metrics": {
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"daily_api_calls": 45000,
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"active_dashboard_users": 320,
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"alerts_sent_daily": 1200,
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"reports_generated_monthly": 450,
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},
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}
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# ─── BLOCK 7: DECISION ENGINE ─────────────────────────────────────────────────
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@router.get("/public/decision-engine")
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async def get_decision_engine():
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"""
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Decision Engine: Vad bör användaren göra nu?
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"""
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return {
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"block": 7,
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"name": "Decision Engine",
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"description": "Omvandla intelligens till konkreta rekommendationer",
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"principle": "LIFE vet vad som händer, varför det händer, och hur säker modellen är. Decision Engine svarar på: Vad bör användaren göra nu?",
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"recommendation_types": {
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"send_quixzoom_mission": {
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"description": "Skicka QUIXZOOM-verifieringsuppdrag",
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"trigger": "Otillräcklig field evidence eller avvikelse detekterad",
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"priority_calculation": "Risk × Uncertainty × Cost of verification",
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"example": "Projekt visar 85% completion men rapporterar 95%. Skicka mission för att fotografera interiör.",
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},
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"order_satellite_imagery": {
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"description": "Beställ ny satellitbild",
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"trigger": "Behov av uppdaterad geospatial data",
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"priority_calculation": "Data age × Change probability × Decision urgency",
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"example": "Senaste bilden är 10 dagar gammal och området har nyligen drabbats av översvämning.",
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},
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"flag_for_manual_review": {
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"description": "Markera för manuell granskning",
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"trigger": "Högkonfidens avvikelse eller komplex situation",
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"priority_calculation": "Confidence × Severity × Strategic importance",
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"example": "Stor avvikelse mellan rapporterad budget och observerad aktivitet. Kräver analytikergranskning.",
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},
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"notify_project_owner": {
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"description": "Informera projektägare om observerad avvikelse",
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"trigger": "Verifierad avvikelse som kan påverka projektet",
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"priority_calculation": "Severity × Verification confidence × Response time sensitivity",
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"example": "Observerad försening på 2 veckor. Informera implementerande partner.",
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},
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"wait": {
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"description": "Avvakta - evidensen är otillräcklig",
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"trigger": "För låg konfidens för att agera",
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"priority_calculation": "Monitoring continues, action deferred until confidence threshold reached",
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"example": "Endast en källa rapporterar avvikelse. Vänta på korsvalidering.",
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},
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"prioritize_followup": {
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"description": "Prioritera uppföljning baserat på risk och osäkerhet",
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"trigger": "Flera projekt kräver uppmärksamhet",
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"priority_calculation": "Risk score × Evidence gap × Strategic value × Resource availability",
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"example": "15 projekt flaggade. Prioritera de 5 med högst risk × osäkerhet.",
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},
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},
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"decision_matrix": {
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"high_confidence_high_risk": "Omedelbar åtgärd - hög konfidens och hög risk",
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"high_confidence_low_risk": "Dokumentera - låg prioritet men välunderbyggd",
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"low_confidence_high_risk": "Samla mer evidens - hög risk men osäker",
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"low_confidence_low_risk": "Övervaka - låg prioritet och osäker",
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},
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"example_decisions": [
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{
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"project": "Mogadishu Primary School",
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"situation": "85% observed vs 95% reported completion",
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"confidence": 78,
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"risk": "medium",
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"recommendation": "send_quixzoom_mission",
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"rationale": "Avvikelse verifierad av 3 oberoende källor. Interiör observation behövs för att klargöra.",
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"priority": 8.2,
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},
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{
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"project": "Lagos Road Rehabilitation",
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"situation": "No activity for 90 days, equipment removed",
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"confidence": 92,
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"risk": "high",
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"recommendation": "flag_for_manual_review",
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"rationale": "Högkonfidens indikation på projektavbrott. Kräver omedelbar analytikergranskning.",
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"priority": 9.5,
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},
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],
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}
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# ─── BLOCK 8: LEARNING ENGINE ─────────────────────────────────────────────────
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@router.get("/public/learning-engine")
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async def get_learning_engine():
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"""
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Learning Engine: Självutvärdering och kontinuerlig förbättring
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"""
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return {
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"block": 8,
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"name": "Learning Engine",
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"description": "LIFE utvärderar sina egna slutsatser och förbättrar sina modeller",
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"learning_loops": {
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"hypothesis_validation": {
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"description": "Vilka hypoteser visade sig stämma?",
|
||
"method": "Jämför prediktion med faktisk utveckling",
|
||
"metrics": {
|
||
"total_hypotheses": 3400,
|
||
"validated": 2150,
|
||
"partially_validated": 890,
|
||
"invalidated": 360,
|
||
"accuracy": 73,
|
||
},
|
||
},
|
||
"risk_indicator_performance": {
|
||
"description": "Vilka riskindikatorer var mest prediktiva?",
|
||
"method": "Korrelera tidiga varningssignaler med faktiska händelser",
|
||
"top_indicators": [
|
||
{"indicator": "Satellite activity decline", "predictive_power": 0.87},
|
||
{"indicator": "Contributor report frequency drop", "predictive_power": 0.82},
|
||
{"indicator": "News sentiment shift", "predictive_power": 0.78},
|
||
{"indicator": "Budget execution lag", "predictive_power": 0.75},
|
||
],
|
||
},
|
||
"source_accuracy": {
|
||
"description": "Vilka datakällor gav bäst träffsäkerhet?",
|
||
"method": "Utvärdera varje källas historiska noggrannhet",
|
||
"rankings": [
|
||
{"source": "Satellite imagery", "accuracy": 94, "reliability": "very_high"},
|
||
{"source": "QUIXZOOM field obs", "accuracy": 88, "reliability": "high"},
|
||
{"source": "Official reports", "accuracy": 82, "reliability": "high"},
|
||
{"source": "News monitoring", "accuracy": 65, "reliability": "medium"},
|
||
{"source": "Social media", "accuracy": 45, "reliability": "low"},
|
||
],
|
||
},
|
||
"environmental_adaptation": {
|
||
"description": "Hur presterar modeller i olika miljöer?",
|
||
"method": "Jämför prediktionsnoggrannhet per region och sektor",
|
||
"best_performing": [
|
||
{"environment": "Urban infrastructure", "accuracy": 89},
|
||
{"environment": "Rural agriculture", "accuracy": 76},
|
||
{"environment": "Conflict zones", "accuracy": 62},
|
||
],
|
||
},
|
||
},
|
||
"model_improvement": {
|
||
"description": "Kontinuerlig modellförbättring",
|
||
"process": [
|
||
"Samla utfall för alla prediktioner",
|
||
"Identifiera systematiska fel",
|
||
"Justera modellparametrar",
|
||
"A/B-testa nya modeller",
|
||
"Deploya förbättrade modeller",
|
||
],
|
||
"current_improvements": [
|
||
{"model": "Construction progress estimator", "improvement": "+12% accuracy", "status": "deployed"},
|
||
{"model": "Risk predictor", "improvement": "+8% precision", "status": "testing"},
|
||
{"model": "Event detector", "improvement": "-15% false positives", "status": "training"},
|
||
],
|
||
},
|
||
"feedback_integration": {
|
||
"description": "Integrera mänsklig feedback",
|
||
"sources": ["Analyst corrections", "Customer feedback", "Expert reviews", "Ground truth validation"],
|
||
"feedback_items_processed": 4500,
|
||
"incorporation_rate": 87,
|
||
},
|
||
}
|
||
|
||
|
||
# ─── BLOCK 9: INTEGRATION LAYER ───────────────────────────────────────────────
|
||
|
||
@router.get("/public/integration")
|
||
async def get_integration_layer():
|
||
"""
|
||
Integration Layer: Anslutning till externa system och arbetsflöden
|
||
"""
|
||
return {
|
||
"block": 9,
|
||
"name": "Integration Layer",
|
||
"description": "Anslut LIFE till omvärlden",
|
||
"interfaces": {
|
||
"rest_api": {
|
||
"description": "REST API för alla LIFE-funktioner",
|
||
"version": "v1",
|
||
"endpoints": 120,
|
||
"authentication": "OAuth 2.0 + API keys",
|
||
"rate_limits": "1000 req/min standard, 10000 req/min enterprise",
|
||
"formats": ["JSON", "GeoJSON", "CSV"],
|
||
},
|
||
"webhooks": {
|
||
"description": "Realtidsnotifieringar",
|
||
"events": ["new_observation", "change_detected", "risk_alert", "hypothesis_generated"],
|
||
"delivery": "Guaranteed delivery with retry",
|
||
},
|
||
"streaming": {
|
||
"description": "Realtidsdataströmmar",
|
||
"protocols": ["WebSocket", "SSE", "MQTT"],
|
||
"topics": ["observations", "events", "alerts", "insights"],
|
||
},
|
||
"sdk": {
|
||
"description": "SDK för enkel integration",
|
||
"languages": ["Python", "JavaScript", "Java", "Go"],
|
||
"features": ["Data ingestion", "Query builder", "Visualization helpers"],
|
||
},
|
||
"plugins": {
|
||
"description": "Plugin-arkitektur för anpassning",
|
||
"types": ["Data source", "Analyzer", "Visualizer", "Exporter"],
|
||
"marketplace": "Coming Q2 2027",
|
||
},
|
||
},
|
||
"enterprise_integrations": {
|
||
"gis": ["ArcGIS", "QGIS", "Google Earth Engine"],
|
||
"bi": ["Tableau", "Power BI", "Looker"],
|
||
"crm": ["Salesforce", "HubSpot"],
|
||
"erp": ["SAP", "Oracle"],
|
||
"communication": ["Slack", "Teams", "Email"],
|
||
},
|
||
}
|
||
|
||
|
||
# ─── SYSTEM OVERVIEW ──────────────────────────────────────────────────────────
|
||
|
||
@router.get("/public/architecture")
|
||
async def get_architecture_overview():
|
||
"""
|
||
Komplett arkitekturöversikt
|
||
"""
|
||
return {
|
||
"system_name": "Landvex Intelligence Fusion Engine (LIFE)",
|
||
"version": "3.0.0",
|
||
"tagline": "AI-drivet operativsystem för verklighetsintelligens",
|
||
"description": "Genom att kontinuerligt integrera geospatiala data, crowdsourcade observationer, offentliga datakällor och maskininlärning skapar LIFE en levande, spårbar och förklarbar digital representation av den fysiska världen. Plattformen omvandlar observerbara signaler till evidens, evidens till kunskap och kunskap till beslutsunderlag.",
|
||
"layers": [
|
||
{"number": 1, "name": "Acquisition", "function": "Datainsamling", "sources": 10},
|
||
{"number": 2, "name": "Normalization", "function": "Standardisering", "processes": 6},
|
||
{"number": 3, "name": "Evidence", "function": "Evidenshantering", "components": 5},
|
||
{"number": 4, "name": "Intelligence", "function": "AI-analys", "engines": 7},
|
||
{"number": 5, "name": "Knowledge", "function": "Kunskapsbyggande", "graphs": 6},
|
||
{"number": 6, "name": "Decision", "function": "Beslutsstöd", "interfaces": 7},
|
||
],
|
||
"core_blocks": [
|
||
{"number": 7, "name": "Decision Engine", "function": "Rekommendationsgenerering"},
|
||
{"number": 8, "name": "Learning Engine", "function": "Självförbättring"},
|
||
{"number": 9, "name": "Integration Layer", "function": "Extern anslutning"},
|
||
],
|
||
"strategic_positioning": "LIFE är ett AI-drivet operativsystem för verklighetsintelligens. Biståndsanalys är en tillämpning, inte identitet. Samma motor kan användas för infrastruktur, miljö, försäkring, kommunal tillsyn, katastrofhantering, fastighetsförvaltning och andra områden där beslutsfattare behöver kontinuerlig, evidensbaserad lägesbild.",
|
||
"applications": [
|
||
"Development aid monitoring",
|
||
"Infrastructure management",
|
||
"Urban planning",
|
||
"Environmental monitoring",
|
||
"Insurance risk assessment",
|
||
"Municipal supervision",
|
||
"Disaster response",
|
||
"Property management",
|
||
"Supply chain tracking",
|
||
"Security analysis",
|
||
],
|
||
"metrics": {
|
||
"projects_monitored": 1250,
|
||
"observations_processed": 45000,
|
||
"data_sources": 10,
|
||
"active_streams": 45,
|
||
"ai_engines": 7,
|
||
"knowledge_graphs": 6,
|
||
"api_endpoints": 120,
|
||
"daily_insights": 450,
|
||
},
|
||
}
|