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rfcp/backend/app/services/coverage_service.py

465 lines
15 KiB
Python

import numpy as np
import asyncio
from typing import List, Optional, Tuple
from pydantic import BaseModel
from app.services.terrain_service import terrain_service, TerrainService
from app.services.los_service import los_service
from app.services.buildings_service import buildings_service, Building
from app.services.materials_service import materials_service
from app.services.dominant_path_service import dominant_path_service
from app.services.street_canyon_service import street_canyon_service, Street
from app.services.reflection_service import reflection_service
class CoveragePoint(BaseModel):
lat: float
lon: float
rsrp: float # dBm
distance: float # meters from site
has_los: bool
terrain_loss: float # dB
building_loss: float # dB
reflection_gain: float = 0.0 # dB (NEW)
class CoverageSettings(BaseModel):
radius: float = 10000 # meters
resolution: float = 200 # meters
min_signal: float = -120 # dBm threshold
# Layer toggles
use_terrain: bool = True
use_buildings: bool = True
use_materials: bool = True
use_dominant_path: bool = False
use_street_canyon: bool = False
use_reflections: bool = False
# Preset
preset: Optional[str] = None # fast, standard, detailed, full
# Propagation model presets
PRESETS = {
"fast": {
"use_terrain": True,
"use_buildings": False,
"use_materials": False,
"use_dominant_path": False,
"use_street_canyon": False,
"use_reflections": False,
},
"standard": {
"use_terrain": True,
"use_buildings": True,
"use_materials": True,
"use_dominant_path": False,
"use_street_canyon": False,
"use_reflections": False,
},
"detailed": {
"use_terrain": True,
"use_buildings": True,
"use_materials": True,
"use_dominant_path": True,
"use_street_canyon": False,
"use_reflections": False,
},
"full": {
"use_terrain": True,
"use_buildings": True,
"use_materials": True,
"use_dominant_path": True,
"use_street_canyon": True,
"use_reflections": True,
},
}
def apply_preset(settings: CoverageSettings) -> CoverageSettings:
"""Apply preset configuration to settings"""
if settings.preset and settings.preset in PRESETS:
for key, value in PRESETS[settings.preset].items():
setattr(settings, key, value)
return settings
class SiteParams(BaseModel):
lat: float
lon: float
height: float = 30 # antenna height meters
power: float = 43 # dBm (20W)
gain: float = 15 # dBi
frequency: float = 1800 # MHz
azimuth: Optional[float] = None # degrees, None = omni
beamwidth: Optional[float] = 65 # degrees
class CoverageService:
"""
RF Coverage calculation with terrain, buildings, materials,
dominant path, street canyon, and reflections
"""
EARTH_RADIUS = 6371000
def __init__(self):
self.terrain = terrain_service
self.buildings = buildings_service
self.los = los_service
async def calculate_coverage(
self,
site: SiteParams,
settings: CoverageSettings
) -> List[CoveragePoint]:
"""
Calculate coverage grid for a single site
Returns list of CoveragePoint with RSRP values
"""
# Apply preset if specified
settings = apply_preset(settings)
points = []
# Generate grid
grid = self._generate_grid(
site.lat, site.lon,
settings.radius,
settings.resolution
)
# Calculate bbox for data fetching
lat_delta = settings.radius / 111000
lon_delta = settings.radius / (111000 * np.cos(np.radians(site.lat)))
# Fetch buildings for coverage area (if enabled)
buildings: List[Building] = []
if settings.use_buildings:
buildings = await self.buildings.fetch_buildings(
site.lat - lat_delta, site.lon - lon_delta,
site.lat + lat_delta, site.lon + lon_delta
)
# Fetch streets (if street canyon enabled)
streets: List[Street] = []
if settings.use_street_canyon:
streets = await street_canyon_service.fetch_streets(
site.lat - lat_delta, site.lon - lon_delta,
site.lat + lat_delta, site.lon + lon_delta
)
# Calculate coverage for each point
for lat, lon in grid:
point = await self._calculate_point(
site, lat, lon,
settings, buildings, streets
)
if point.rsrp >= settings.min_signal:
points.append(point)
return points
async def calculate_multi_site_coverage(
self,
sites: List[SiteParams],
settings: CoverageSettings
) -> List[CoveragePoint]:
"""
Calculate combined coverage from multiple sites
Best server (strongest signal) wins at each point
"""
if not sites:
return []
# Apply preset once
settings = apply_preset(settings)
# Get all individual coverages
all_coverages = await asyncio.gather(*[
self.calculate_coverage(site, settings)
for site in sites
])
# Combine by best signal
point_map: dict[Tuple[float, float], CoveragePoint] = {}
for coverage in all_coverages:
for point in coverage:
key = (round(point.lat, 6), round(point.lon, 6))
if key not in point_map or point.rsrp > point_map[key].rsrp:
point_map[key] = point
return list(point_map.values())
def _generate_grid(
self,
center_lat: float, center_lon: float,
radius: float, resolution: float
) -> List[Tuple[float, float]]:
"""Generate coverage grid points"""
points = []
# Convert resolution to degrees
lat_step = resolution / 111000
lon_step = resolution / (111000 * np.cos(np.radians(center_lat)))
# Calculate grid bounds
lat_delta = radius / 111000
lon_delta = radius / (111000 * np.cos(np.radians(center_lat)))
lat = center_lat - lat_delta
while lat <= center_lat + lat_delta:
lon = center_lon - lon_delta
while lon <= center_lon + lon_delta:
# Check if within radius (circular, not square)
dist = TerrainService.haversine_distance(center_lat, center_lon, lat, lon)
if dist <= radius:
points.append((lat, lon))
lon += lon_step
lat += lat_step
return points
async def _calculate_point(
self,
site: SiteParams,
lat: float, lon: float,
settings: CoverageSettings,
buildings: List[Building],
streets: List[Street]
) -> CoveragePoint:
"""Calculate RSRP at a single point with all propagation models"""
# Distance
distance = TerrainService.haversine_distance(site.lat, site.lon, lat, lon)
if distance < 1:
distance = 1 # Avoid division by zero
# Base path loss (Okumura-Hata for urban)
path_loss = self._okumura_hata(
distance, site.frequency, site.height, 1.5 # 1.5m receiver height
)
# Antenna pattern loss (if directional)
antenna_loss = 0.0
if site.azimuth is not None and site.beamwidth:
antenna_loss = self._antenna_pattern_loss(
site.lat, site.lon, lat, lon,
site.azimuth, site.beamwidth
)
# Terrain loss (LoS check)
terrain_loss = 0.0
has_los = True
if settings.use_terrain:
los_result = await self.los.check_line_of_sight(
site.lat, site.lon, site.height,
lat, lon, 1.5 # receiver at 1.5m
)
has_los = los_result["has_los"]
if not has_los:
# Add diffraction loss based on clearance
clearance = los_result["clearance"]
terrain_loss = self._diffraction_loss(clearance, site.frequency)
# Building loss (with optional material awareness)
building_loss = 0.0
if settings.use_buildings and buildings:
if settings.use_materials:
# Material-aware building loss
for building in buildings:
intersection = self.buildings.line_intersects_building(
site.lat, site.lon, site.height + await self.terrain.get_elevation(site.lat, site.lon),
lat, lon, 1.5 + await self.terrain.get_elevation(lat, lon),
building
)
if intersection is not None:
material = materials_service.detect_material(building.tags)
building_loss += materials_service.get_penetration_loss(
material, site.frequency
)
has_los = False
break # One building is enough
else:
# Simple building loss (legacy behavior)
for building in buildings:
intersection = self.buildings.line_intersects_building(
site.lat, site.lon, site.height + await self.terrain.get_elevation(site.lat, site.lon),
lat, lon, 1.5 + await self.terrain.get_elevation(lat, lon),
building
)
if intersection is not None:
building_loss += 20.0 # Default concrete
has_los = False
break
# Dominant path analysis (find best route)
if settings.use_dominant_path and buildings:
paths = await dominant_path_service.find_dominant_paths(
site.lat, site.lon, site.height,
lat, lon, 1.5,
site.frequency, buildings
)
if paths:
best_path = paths[0]
# Use best path's loss if it's better
if best_path.is_valid and best_path.path_loss < (path_loss + terrain_loss + building_loss):
path_loss = best_path.path_loss
terrain_loss = 0
building_loss = 0
has_los = best_path.path_type == "direct" and not best_path.materials_crossed
# Street canyon model
if settings.use_street_canyon and streets:
canyon_loss, street_path = await street_canyon_service.calculate_street_canyon_loss(
site.lat, site.lon, site.height,
lat, lon, 1.5,
site.frequency, streets
)
# Use canyon loss if better than current total
if canyon_loss < (path_loss + terrain_loss + building_loss):
path_loss = canyon_loss
terrain_loss = 0
building_loss = 0
# Reflections
reflection_gain = 0.0
if settings.use_reflections and buildings:
reflection_paths = await reflection_service.find_reflection_paths(
site.lat, site.lon, site.height,
lat, lon, 1.5,
site.frequency, buildings
)
if reflection_paths:
# Combine direct and reflected signals
direct_rsrp = site.power + site.gain - path_loss - antenna_loss - terrain_loss - building_loss
combined_rsrp = reflection_service.combine_paths(
direct_rsrp, reflection_paths, site.power + site.gain
)
reflection_gain = max(0, combined_rsrp - direct_rsrp)
# Final RSRP
rsrp = site.power + site.gain - path_loss - antenna_loss - terrain_loss - building_loss + reflection_gain
return CoveragePoint(
lat=lat,
lon=lon,
rsrp=rsrp,
distance=distance,
has_los=has_los,
terrain_loss=terrain_loss,
building_loss=building_loss,
reflection_gain=reflection_gain
)
def _okumura_hata(
self,
distance: float, # meters
frequency: float, # MHz
tx_height: float, # meters
rx_height: float # meters
) -> float:
"""
Okumura-Hata path loss model (urban)
Returns path loss in dB
"""
d_km = distance / 1000
if d_km < 0.1:
d_km = 0.1 # Minimum distance
# Mobile antenna height correction (urban)
a_hm = (1.1 * np.log10(frequency) - 0.7) * rx_height - (1.56 * np.log10(frequency) - 0.8)
# Path loss
L = (69.55 + 26.16 * np.log10(frequency) - 13.82 * np.log10(tx_height) - a_hm +
(44.9 - 6.55 * np.log10(tx_height)) * np.log10(d_km))
return L
def _antenna_pattern_loss(
self,
site_lat: float, site_lon: float,
point_lat: float, point_lon: float,
azimuth: float, beamwidth: float
) -> float:
"""Calculate antenna pattern attenuation"""
# Calculate bearing from site to point
bearing = self._calculate_bearing(site_lat, site_lon, point_lat, point_lon)
# Angle difference from main lobe
angle_diff = abs(bearing - azimuth)
if angle_diff > 180:
angle_diff = 360 - angle_diff
# Simple cosine pattern approximation
# 3dB beamwidth = angle where power drops to half
half_beamwidth = beamwidth / 2
if angle_diff <= half_beamwidth:
# Within main lobe - minimal loss
loss = 3 * (angle_diff / half_beamwidth) ** 2
else:
# Outside main lobe - significant loss
loss = 3 + 12 * ((angle_diff - half_beamwidth) / half_beamwidth) ** 2
loss = min(loss, 25) # Cap at 25dB (back lobe)
return loss
def _calculate_bearing(
self,
lat1: float, lon1: float,
lat2: float, lon2: float
) -> float:
"""Calculate bearing from point 1 to point 2 (degrees)"""
lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])
dlon = lon2 - lon1
x = np.sin(dlon) * np.cos(lat2)
y = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(dlon)
bearing = np.degrees(np.arctan2(x, y))
return (bearing + 360) % 360
def _diffraction_loss(self, clearance: float, frequency: float) -> float:
"""
Knife-edge diffraction loss
Args:
clearance: Clearance in meters (negative = obstructed)
frequency: Frequency in MHz
Returns:
Additional loss in dB
"""
if clearance >= 0:
return 0.0 # No obstruction
# Fresnel parameter approximation
v = abs(clearance) / 10 # Normalize
# Knife-edge loss approximation
if v <= 0:
loss = 0
elif v < 2.4:
loss = 6.02 + 9.11 * v - 1.27 * v**2
else:
loss = 13.0 + 20 * np.log10(v)
return min(loss, 40) # Cap at 40dB
# Singleton
coverage_service = CoverageService()