feat(site-finder): distance to EKB center + success quartirography recommendation
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lekss361
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1 changed files with 86 additions and 4 deletions
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@ -214,11 +214,25 @@ def _fetch_weather_sync(lat: float, lon: float) -> dict | None:
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return None
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# Координаты центра ЕКБ — Площадь 1905 года
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EKB_CENTER_LAT: float = 56.838011
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EKB_CENTER_LON: float = 60.597474
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# Эмпирические пороги score для ЕКБ: средний диапазон 15-30, max редко >40.
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SCORE_THRESHOLDS: dict[str, float] = {"плохо": 5.0, "средне": 15.0, "хорошо": 25.0, "отлично": 40.0}
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SCORE_MAX_REFERENCE: float = 40.0
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def _haversine_km(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
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"""Расстояние по формуле гаверсинуса между двумя точками (км)."""
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earth_r = 6371.0
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phi1, phi2 = math.radians(lat1), math.radians(lat2)
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dphi = math.radians(lat2 - lat1)
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dlam = math.radians(lon2 - lon1)
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a = math.sin(dphi / 2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(dlam / 2) ** 2
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return 2 * earth_r * math.atan2(math.sqrt(a), math.sqrt(1 - a))
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def _score_label(s: float) -> str:
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"""Текстовая интерпретация POI-score по эмпирическим порогам ЕКБ."""
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if s < SCORE_THRESHOLDS["средне"]:
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@ -517,6 +531,17 @@ def analyze_parcel(
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centroid_lat: float = float(centroid_row["lat"]) if centroid_row else 56.838
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centroid_lon: float = float(centroid_row["lon"]) if centroid_row else 60.605
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# 6b) Distance to EKB center + center bonus
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dist_to_center_km = _haversine_km(centroid_lat, centroid_lon, EKB_CENTER_LAT, EKB_CENTER_LON)
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if dist_to_center_km < 5:
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center_bonus = 3.0
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elif dist_to_center_km < 10:
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center_bonus = 1.5
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elif dist_to_center_km < 15:
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center_bonus = 0.5
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else:
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center_bonus = 0.0
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# 7) Noise score — шумовые источники в радиусе 2 км
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noise_rows = (
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db.execute(
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@ -789,7 +814,54 @@ def analyze_parcel(
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logger.warning("zoning query failed for %s: %s", cad_num, e)
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zoning = None
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# 10c) Geology stub — реальные данные требуют ВСЕГЕИ-200/1000 шейпы в PostGIS
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# 10c) Success recommendation — топ квартирография по district из v_bucket_success_score
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success_recommendation: dict[str, Any] | None = None
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if district_row:
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try:
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success_rows = (
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db.execute(
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text("""
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SELECT bucket, success_score, n_deals, avg_price_per_m2, avg_area_m2,
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velocity_z, price_z, area_z
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FROM v_bucket_success_score
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WHERE district_name = :dn
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ORDER BY success_score DESC
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LIMIT 5
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"""),
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{"dn": district_row["district_name"]},
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)
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.mappings()
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.all()
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)
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if success_rows:
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success_recommendation = {
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"district": district_row["district_name"],
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"ranking": [
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{
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"bucket": r["bucket"],
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"success_score": round(float(r["success_score"]), 2),
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"n_deals": int(r["n_deals"]),
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"avg_price_per_m2": (
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int(r["avg_price_per_m2"]) if r["avg_price_per_m2"] else None
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),
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"avg_area_m2": (
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round(float(r["avg_area_m2"]), 1) if r["avg_area_m2"] else None
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),
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}
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for r in success_rows
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],
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"top_bucket": dict(success_rows[0]) if success_rows else None,
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"note": (
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"Топ комнатность по 'успешности' = z-scores: velocity×0.5 + price×0.3 "
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"- area×0.2. Min 30 сделок в группе за 24 мес. "
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"Используй для квартирографии проекта."
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),
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}
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except Exception as e:
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logger.warning("success_recommendation query failed for %s: %s", cad_num, e)
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success_recommendation = None
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# 10d) Geology stub — реальные данные требуют ВСЕГЕИ-200/1000 шейпы в PostGIS
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karpinsky_url = (
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f"https://www.karpinskyinstitute.ru/ru/gisatlas/web-gisatlas/"
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f"?lat={centroid_lat:.6f}&lon={centroid_lon:.6f}&zoom=12"
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@ -811,20 +883,29 @@ def analyze_parcel(
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"lon": centroid_lon,
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}
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score_final = score + center_bonus
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return {
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"cad_num": cad_num,
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"source": source,
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"geom_geojson": json.loads(geom_geojson) if geom_geojson else None,
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"district": dict(district_row) if district_row else None,
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"score": round(score, 2),
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"score_label": _score_label(score),
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"score": round(score_final, 2),
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"score_without_center": round(score, 2),
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"score_label": _score_label(score_final),
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"score_max_reference": SCORE_MAX_REFERENCE,
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"score_explanation": (
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"Сумма close-distance POI (школы/сады/парки +, трамваи -). "
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"Сумма close-distance POI (школы/сады/парки +, трамваи -) + center_bonus. "
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">40 = редко, типичный город. центр 15-30."
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),
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"score_breakdown": by_category,
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"poi_count": len(poi_rows),
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"location": {
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"distance_to_center_km": round(dist_to_center_km, 2),
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"center_bonus": center_bonus,
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"ekb_center": {"lat": EKB_CENTER_LAT, "lon": EKB_CENTER_LON},
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"note": "Бонус к score: <5км +3.0, 5-10км +1.5, 10-15км +0.5, >15км 0",
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},
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"competitors": [dict(c) for c in competitor_rows],
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"noise": {
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"score": round(noise_score, 2),
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@ -842,6 +923,7 @@ def analyze_parcel(
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"geotech_risk": _geotech_risk(66, db, geom_wkt),
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"market_trend": market_trend,
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"zoning": zoning,
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"success_recommendation": success_recommendation,
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"isochrones_available": bool(settings.openrouteservice_api_key),
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}
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