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https://github.com/BigBodyCobain/Shadowbroker.git
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BigBodyCobain
af9b3d08cc
Add Telegram OSINT with hourly incremental t.me scraping, metro geocoding separate from news centroids, threat-intercept popup UI with inline media, and HTML markers above alert boxes so pins stay clickable. Expose GFW_API_TOKEN in onboarding and Settings Maritime; harden GFW/CCTV/geo fetchers. Port Osiris- derived recon, SCM, entity graph, malware/cyber feeds, sanctions, and submarine cable layers with tests and documentation. Co-authored-by: Cursor <cursoragent@cursor.com>
155 lines
7.5 KiB
Python
155 lines
7.5 KiB
Python
"""SCM supplier risk overlay (Osiris port, uses in-memory dashboard data)."""
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from __future__ import annotations
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import math
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from datetime import datetime, timezone
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from typing import Any
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from services.fetchers._store import _data_lock, _mark_fresh, get_latest_data_subset_refs, is_any_active, latest_data
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from services.network_utils import fetch_with_curl
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SUPPLIERS: list[dict[str, Any]] = [
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{"id": "sup-tsmc-hsinchu", "name": "TSMC Fab 12 (Tier 1)", "city": "Hsinchu", "country": "Taiwan", "lat": 24.774, "lng": 120.992, "category": "Semiconductor"},
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{"id": "sup-tsmc-tainan", "name": "TSMC Fab 14 (Tier 1)", "city": "Tainan", "country": "Taiwan", "lat": 23.111, "lng": 120.273, "category": "Semiconductor"},
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{"id": "sup-sec-giheung", "name": "Samsung Electronics (Tier 1)", "city": "Giheung", "country": "South Korea", "lat": 37.221, "lng": 127.098, "category": "Semiconductor"},
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{"id": "sup-sk-icheon", "name": "SK Hynix (Tier 1)", "city": "Icheon", "country": "South Korea", "lat": 37.256, "lng": 127.483, "category": "Semiconductor"},
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{"id": "sup-sony-kumamoto", "name": "Sony Semiconductor (Tier 2)", "city": "Kikuyo", "country": "Japan", "lat": 32.883, "lng": 130.825, "category": "Electronics"},
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{"id": "sup-mlcc-murata", "name": "Murata MLCC (Tier 2)", "city": "Izumo", "country": "Japan", "lat": 35.361, "lng": 132.756, "category": "Electronics"},
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{"id": "sup-bosch-stuttgart", "name": "Bosch Auto Parts (Tier 1)", "city": "Stuttgart", "country": "Germany", "lat": 48.815, "lng": 9.176, "category": "Automotive"},
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{"id": "sup-zf-bavaria", "name": "ZF Friedrichshafen (Tier 1)", "city": "Friedrichshafen", "country": "Germany", "lat": 47.662, "lng": 9.489, "category": "Automotive"},
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{"id": "sup-valeo-paris", "name": "Valeo R&D (Tier 2)", "city": "Paris", "country": "France", "lat": 48.878, "lng": 2.308, "category": "Automotive"},
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{"id": "sup-magna-celaya", "name": "Magna Assembly (Tier 2)", "city": "Celaya", "country": "Mexico", "lat": 20.525, "lng": -100.814, "category": "Automotive"},
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{"id": "sup-denso-monterrey", "name": "Denso Corp (Tier 1)", "city": "Monterrey", "country": "Mexico", "lat": 25.772, "lng": -100.174, "category": "Automotive"},
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{"id": "sup-catl-ningde", "name": "CATL Battery HQ (Tier 1)", "city": "Ningde", "country": "China", "lat": 26.666, "lng": 119.544, "category": "Battery"},
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{"id": "sup-byd-shenzhen", "name": "BYD Gigafactory (Tier 1)", "city": "Shenzhen", "country": "China", "lat": 22.684, "lng": 114.341, "category": "Battery"},
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{"id": "sup-panasonic-nevada", "name": "Panasonic Giga (Tier 1)", "city": "Sparks", "country": "US", "lat": 39.539, "lng": -119.439, "category": "Battery"},
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]
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def _distance_km(lat1: float, lng1: float, lat2: float, lng2: float) -> float:
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dx = (lng1 - lng2) * math.cos(math.radians((lat1 + lat2) / 2))
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dy = lat1 - lat2
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return math.sqrt(dx * dx + dy * dy) * 111.32
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def _seismic_risk_level(distance_km: float, magnitude: float) -> str | None:
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"""Meaningful fab impact only — ignore routine micro-quakes (e.g. Taiwan M3.x)."""
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if magnitude < 4.5:
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return None
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if magnitude >= 6.0 and distance_km <= 200:
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return "CRITICAL"
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if magnitude >= 5.5 and distance_km <= 75:
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return "CRITICAL"
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if magnitude >= 5.0 and distance_km <= 100:
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return "HIGH"
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if magnitude >= 4.5 and distance_km <= 40:
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return "HIGH"
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return None
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def _apply_seismic_threats(suppliers: list[dict[str, Any]], earthquakes: list[dict[str, Any]]) -> None:
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for sup in suppliers:
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best: tuple[str, float] | None = None
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for eq in earthquakes:
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lat = eq.get("lat")
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lng = eq.get("lng") or eq.get("lon")
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mag = float(eq.get("mag") or eq.get("magnitude") or 0)
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if lat is None or lng is None or mag < 4.5:
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continue
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dist = _distance_km(sup["lat"], sup["lng"], float(lat), float(lng))
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level = _seismic_risk_level(dist, mag)
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if not level:
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continue
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severity = {"HIGH": 1, "CRITICAL": 2}
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if best is None:
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best = (level, mag)
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else:
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cur = severity[level]
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prev = severity[best[0]]
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if cur > prev or (cur == prev and mag > best[1]):
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best = (level, mag)
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if best:
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level, mag = best
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if sup["risk_level"] == "NORMAL" or (
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level == "CRITICAL" and sup["risk_level"] != "CRITICAL"
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):
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sup["risk_level"] = level
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elif level == "CRITICAL" and sup["risk_level"] == "HIGH":
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sup["risk_level"] = "CRITICAL"
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sup["active_threats"].append(f"SEISMIC PROXIMITY (M{mag:.1f})")
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def build_scm_payload() -> dict[str, Any]:
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suppliers = [{**s, "risk_level": "NORMAL", "active_threats": []} for s in SUPPLIERS]
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refs = get_latest_data_subset_refs("earthquakes", "firms_fires", "gdelt")
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earthquakes = refs.get("earthquakes") or []
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_apply_seismic_threats(suppliers, earthquakes)
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fires = refs.get("firms_fires") or []
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for sup in suppliers:
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count = 0
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for fire in fires:
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lat = fire.get("lat") or fire.get("latitude")
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lng = fire.get("lng") or fire.get("lon") or fire.get("longitude")
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if lat is None or lng is None:
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continue
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if _distance_km(sup["lat"], sup["lng"], float(lat), float(lng)) < 50:
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count += 1
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if count:
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if sup["risk_level"] == "NORMAL":
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sup["risk_level"] = "HIGH"
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sup["active_threats"].append(f"WILDFIRE PROXIMITY ({count} hotspots)")
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conflicts = refs.get("gdelt") or []
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for sup in suppliers:
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for event in conflicts:
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lat = event.get("lat")
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lng = event.get("lng") or event.get("lon")
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if lat is None or lng is None:
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continue
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if _distance_km(sup["lat"], sup["lng"], float(lat), float(lng)) < 100:
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sup["risk_level"] = "CRITICAL"
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sup["active_threats"].append("ARMED CONFLICT / RIOT")
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break
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# USGS fallback if earthquakes empty
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if not earthquakes:
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try:
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resp = fetch_with_curl(
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"https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/4.5_day.geojson",
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timeout=5,
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)
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if resp.status_code == 200:
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features = resp.json().get("features") or []
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usgs_quakes = [
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{
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"lat": f.get("geometry", {}).get("coordinates", [None, None])[1],
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"lng": f.get("geometry", {}).get("coordinates", [None, None])[0],
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"mag": f.get("properties", {}).get("mag") or 0,
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}
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for f in features
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if len(f.get("geometry", {}).get("coordinates") or []) >= 2
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]
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_apply_seismic_threats(suppliers, usgs_quakes)
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except Exception:
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pass
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critical = sum(1 for s in suppliers if s["risk_level"] == "CRITICAL")
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return {
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"suppliers": suppliers,
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"total": len(suppliers),
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"critical_count": critical,
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"timestamp": datetime.now(timezone.utc).isoformat(),
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}
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def fetch_scm_suppliers() -> dict[str, Any]:
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if not is_any_active("scm_suppliers"):
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return latest_data.get("scm_suppliers") or {}
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payload = build_scm_payload()
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with _data_lock:
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latest_data["scm_suppliers"] = payload
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_mark_fresh("scm_suppliers")
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return payload
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