976 lines
35 KiB
Python
976 lines
35 KiB
Python
import math
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from copy import deepcopy
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from decimal import Decimal
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from django.conf import settings
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from kombu.exceptions import OperationalError
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from django.db import transaction
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from django.db.models import Prefetch
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from sensor_hub.models import Sensor
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from external_api_adapter.adapter import request as external_request
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from .mock_data import AREA_RESPONSE_DATA, PRODUCTS_RESPONSE_DATA
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from .models import (
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CropArea,
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CropProduct,
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CropZone,
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CropZoneAnalysis,
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CropZoneCriteria,
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CropZoneCultivationRiskLayer,
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CropZoneRecommendation,
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CropZoneSoilQualityLayer,
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CropZoneWaterNeedLayer,
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)
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EARTH_RADIUS_METERS = 6378137.0
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PRODUCT_DEFAULTS = PRODUCTS_RESPONSE_DATA["products"]
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DEFAULT_CELL_SIDE_KM = 0.15
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RULE_BASED_ALGORITHM = "rule_based_v1"
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RULE_BASED_PRODUCTS = {
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"wheat": {
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"water_need": "۴۵۰۰-۵۵۰۰ m³/ha",
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"water_need_level": "medium",
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"estimated_profit": "۱۵-۲۵ میلیون/هکتار",
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"reason": "دمای مناسب، خاک حاصلخیز، دسترسی به آب کافی",
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},
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"canola": {
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"water_need": "۵۰۰۰-۶۰۰۰ m³/ha",
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"water_need_level": "high",
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"estimated_profit": "۲۰-۳۵ میلیون/هکتار",
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"reason": "پایداری بهتر در برابر نوسان دما و پتانسیل سود اقتصادی مناسب",
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},
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"saffron": {
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"water_need": "۳۰۰۰-۴۰۰۰ m³/ha",
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"water_need_level": "low",
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"estimated_profit": "۵۰-۱۵۰ میلیون/هکتار",
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"reason": "اقلیم خشکتر و نیاز آبی کمتر این زون برای زعفران مناسبتر است",
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},
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}
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RULE_BASED_CROP_IDS = tuple(RULE_BASED_PRODUCTS.keys())
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def get_default_cell_side_km():
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raw_value = getattr(settings, "CROP_ZONE_CELL_SIDE_KM", None)
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try:
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cell_side_km = float(raw_value)
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except (TypeError, ValueError):
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cell_side_km = 0
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if cell_side_km > 0:
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return cell_side_km
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raw_value = getattr(settings, "CROP_ZONE_CHUNK_AREA_SQM", 0)
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try:
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chunk_area = float(raw_value)
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except (TypeError, ValueError):
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chunk_area = 0
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if chunk_area > 0:
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return math.sqrt(chunk_area) / 1000.0
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return DEFAULT_CELL_SIDE_KM
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def get_cell_side_km(cell_side_km=None):
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if cell_side_km is None or cell_side_km == "":
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resolved_value = get_default_cell_side_km()
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else:
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try:
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resolved_value = float(cell_side_km)
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except (TypeError, ValueError) as exc:
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raise ValueError("cell_side_km must be a positive number.") from exc
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if resolved_value <= 0:
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raise ValueError("cell_side_km must be a positive number.")
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return resolved_value
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def get_chunk_area_sqm(cell_side_km=None):
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resolved_cell_side_km = get_cell_side_km(cell_side_km)
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return (resolved_cell_side_km * 1000.0) ** 2
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def get_default_area_feature():
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return deepcopy(AREA_RESPONSE_DATA["area"])
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def normalize_area_feature(area_feature):
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if area_feature is None:
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raise ValueError("Area polygon coordinates are required.")
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if not isinstance(area_feature, dict):
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raise ValueError("Area GeoJSON must be an object.")
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if area_feature.get("type") == "Feature":
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geometry = deepcopy(area_feature.get("geometry") or {})
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normalized_feature = {
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"type": "Feature",
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"properties": deepcopy(area_feature.get("properties") or {}),
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"geometry": geometry,
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}
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else:
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normalized_feature = {
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"type": "Feature",
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"properties": {},
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"geometry": deepcopy(area_feature),
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}
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geometry = normalized_feature.get("geometry") or {}
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if geometry.get("type") != "Polygon":
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raise ValueError("Area GeoJSON geometry type must be Polygon.")
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ring = get_polygon_ring(normalized_feature)
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if len(ring) < 4:
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raise ValueError("Area polygon must contain at least four coordinates.")
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return normalized_feature
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def ensure_products_exist():
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for product in PRODUCT_DEFAULTS:
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CropProduct.objects.update_or_create(
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product_id=product["id"],
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defaults={"label": product["label"], "color": product["color"]},
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)
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def get_products_payload():
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ensure_products_exist()
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products = CropProduct.objects.order_by("id")
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return {
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"products": [
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{"id": product.product_id, "label": product.label, "color": product.color}
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for product in products
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]
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}
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def get_polygon_ring(area_feature):
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geometry = (area_feature or {}).get("geometry", {})
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coordinates = geometry.get("coordinates", [])
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if not coordinates or not coordinates[0]:
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raise ValueError("Area polygon coordinates are required.")
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return coordinates[0]
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def polygon_area_sqm(ring):
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if len(ring) < 4:
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return 0.0
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latitudes = [point[1] for point in ring]
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mean_latitude = math.radians(sum(latitudes) / len(latitudes))
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projected_points = []
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for longitude, latitude in ring:
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x = math.radians(longitude) * EARTH_RADIUS_METERS * math.cos(mean_latitude)
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y = math.radians(latitude) * EARTH_RADIUS_METERS
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projected_points.append((x, y))
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area = 0.0
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for index in range(len(projected_points) - 1):
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x1, y1 = projected_points[index]
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x2, y2 = projected_points[index + 1]
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area += (x1 * y2) - (x2 * y1)
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return abs(area) / 2.0
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def normalize_points(ring):
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if len(ring) > 1 and ring[0] == ring[-1]:
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ring = ring[:-1]
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return [[point[0], point[1]] for point in ring]
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def calculate_center(points):
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if not points:
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return {"longitude": 0.0, "latitude": 0.0}
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longitude = sum(point[0] for point in points) / len(points)
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latitude = sum(point[1] for point in points) / len(points)
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return {
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"longitude": round(longitude, 8),
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"latitude": round(latitude, 8),
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}
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def get_bbox(points):
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longitudes = [point[0] for point in points]
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latitudes = [point[1] for point in points]
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return {
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"min_lng": min(longitudes),
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"max_lng": max(longitudes),
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"min_lat": min(latitudes),
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"max_lat": max(latitudes),
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}
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def meters_to_latitude_delta(meters):
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return meters / 111320.0
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def meters_to_longitude_delta(meters, latitude):
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longitude_factor = 111320.0 * math.cos(math.radians(latitude))
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if abs(longitude_factor) < 1e-9:
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longitude_factor = 1.0
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return meters / longitude_factor
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def point_in_polygon(point, polygon_points):
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x, y = point
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inside = False
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point_count = len(polygon_points)
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if point_count < 3:
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return False
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for index in range(point_count):
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x1, y1 = polygon_points[index]
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x2, y2 = polygon_points[(index + 1) % point_count]
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intersects = ((y1 > y) != (y2 > y)) and (
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x < ((x2 - x1) * (y - y1) / ((y2 - y1) or 1e-12)) + x1
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)
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if intersects:
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inside = not inside
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return inside
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def _orientation(point_a, point_b, point_c):
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value = ((point_b[1] - point_a[1]) * (point_c[0] - point_b[0])) - (
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(point_b[0] - point_a[0]) * (point_c[1] - point_b[1])
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)
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if abs(value) < 1e-12:
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return 0
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return 1 if value > 0 else 2
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def _on_segment(point_a, point_b, point_c):
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return (
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min(point_a[0], point_c[0]) - 1e-12 <= point_b[0] <= max(point_a[0], point_c[0]) + 1e-12
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and min(point_a[1], point_c[1]) - 1e-12 <= point_b[1] <= max(point_a[1], point_c[1]) + 1e-12
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)
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def segments_intersect(point_a, point_b, point_c, point_d):
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orientation_1 = _orientation(point_a, point_b, point_c)
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orientation_2 = _orientation(point_a, point_b, point_d)
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orientation_3 = _orientation(point_c, point_d, point_a)
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orientation_4 = _orientation(point_c, point_d, point_b)
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if orientation_1 != orientation_2 and orientation_3 != orientation_4:
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return True
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if orientation_1 == 0 and _on_segment(point_a, point_c, point_b):
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return True
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if orientation_2 == 0 and _on_segment(point_a, point_d, point_b):
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return True
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if orientation_3 == 0 and _on_segment(point_c, point_a, point_d):
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return True
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if orientation_4 == 0 and _on_segment(point_c, point_b, point_d):
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return True
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return False
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def rectangle_contains_point(point, cell_points):
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min_lng = min(vertex[0] for vertex in cell_points)
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max_lng = max(vertex[0] for vertex in cell_points)
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min_lat = min(vertex[1] for vertex in cell_points)
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max_lat = max(vertex[1] for vertex in cell_points)
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return min_lng <= point[0] <= max_lng and min_lat <= point[1] <= max_lat
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def polygon_intersects_cell(polygon_points, cell_points):
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cell_center = calculate_center(cell_points)
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if point_in_polygon([cell_center["longitude"], cell_center["latitude"]], polygon_points):
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return True
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if any(point_in_polygon(point, polygon_points) for point in cell_points):
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return True
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if any(rectangle_contains_point(point, cell_points) for point in polygon_points):
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return True
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polygon_edges = list(zip(polygon_points, polygon_points[1:] + polygon_points[:1]))
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cell_edges = list(zip(cell_points, cell_points[1:] + cell_points[:1]))
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return any(
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segments_intersect(start_a, end_a, start_b, end_b)
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for start_a, end_a in polygon_edges
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for start_b, end_b in cell_edges
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)
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def build_square_points(left_lng, bottom_lat, right_lng, top_lat):
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return [
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[round(left_lng, 8), round(bottom_lat, 8)],
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[round(right_lng, 8), round(bottom_lat, 8)],
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[round(right_lng, 8), round(top_lat, 8)],
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[round(left_lng, 8), round(top_lat, 8)],
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]
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def build_zone_square(area_points, center, zone_area_sqm):
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if len(area_points) < 4:
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return area_points
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width = math.sqrt(max(zone_area_sqm, 1))
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half_width = width / 2.0
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delta_lat = meters_to_latitude_delta(half_width)
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delta_lng = meters_to_longitude_delta(half_width, center["latitude"])
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return build_square_points(
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center["longitude"] - delta_lng,
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center["latitude"] - delta_lat,
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center["longitude"] + delta_lng,
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center["latitude"] + delta_lat,
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)
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def split_area_into_zones(area_feature, cell_side_km=None):
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area_ring = get_polygon_ring(area_feature)
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area_points = normalize_points(area_ring)
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area_center = calculate_center(area_points)
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total_area_sqm = polygon_area_sqm(area_ring)
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resolved_cell_side_km = get_cell_side_km(cell_side_km)
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chunk_area_sqm = get_chunk_area_sqm(resolved_cell_side_km)
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cell_side_meters = resolved_cell_side_km * 1000.0
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bbox = get_bbox(area_points)
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latitude_step = meters_to_latitude_delta(cell_side_meters)
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zones = []
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sequence = 0
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current_lat = bbox["min_lat"]
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while current_lat < bbox["max_lat"] - 1e-12:
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next_lat = current_lat + latitude_step
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row_center_lat = current_lat + (latitude_step / 2.0)
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longitude_step = meters_to_longitude_delta(cell_side_meters, row_center_lat)
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current_lng = bbox["min_lng"]
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while current_lng < bbox["max_lng"] - 1e-12:
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next_lng = current_lng + longitude_step
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zone_points = build_square_points(current_lng, current_lat, next_lng, next_lat)
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if polygon_intersects_cell(area_points, zone_points):
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zone_geometry = {
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"type": "Polygon",
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"coordinates": [[*zone_points, zone_points[0]]],
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}
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zone_area_sqm = polygon_area_sqm(zone_geometry["coordinates"][0])
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zones.append(
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{
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"zone_id": f"zone-{sequence}",
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"geometry": zone_geometry,
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"points": zone_points,
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"center": calculate_center(zone_points),
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"area_sqm": round(zone_area_sqm, 2),
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"area_hectares": round(zone_area_sqm / 10000, 4),
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"sequence": sequence,
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}
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)
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sequence += 1
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current_lng = next_lng
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current_lat = next_lat
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if not zones:
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zone_points = build_zone_square(area_points, area_center, max(total_area_sqm, chunk_area_sqm))
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zone_geometry = {
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"type": "Polygon",
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"coordinates": [[*zone_points, zone_points[0]]],
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}
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zone_area_sqm = polygon_area_sqm(zone_geometry["coordinates"][0])
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zones.append(
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{
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"zone_id": "zone-0",
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"geometry": zone_geometry,
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"points": zone_points,
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"center": area_center,
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"area_sqm": round(zone_area_sqm, 2),
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"area_hectares": round(zone_area_sqm / 10000, 4),
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"sequence": 0,
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}
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)
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zone_count = len(zones)
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area_geometry = {
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"type": "Feature",
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"properties": {},
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"geometry": deepcopy(area_feature.get("geometry", {})),
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}
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area_geometry.setdefault("properties", {})
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area_geometry["properties"].update(
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{
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"center": area_center,
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"area_sqm": round(total_area_sqm, 2),
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"area_hectares": round(total_area_sqm / 10000, 4),
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"cell_side_km": round(resolved_cell_side_km, 4),
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}
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)
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return {
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"area": {
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"geometry": area_geometry,
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"points": area_points,
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"center": area_center,
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"area_sqm": total_area_sqm,
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"area_hectares": total_area_sqm / 10000,
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"chunk_area_sqm": chunk_area_sqm,
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"cell_side_km": resolved_cell_side_km,
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"zone_count": zone_count,
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},
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"zones": zones,
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}
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def build_rule_based_zone_metrics(index, coords):
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if coords:
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first_longitude, first_latitude = coords[0]
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else:
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first_longitude, first_latitude = (0.0, 0.0)
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seed = int((index * 7) + math.floor(first_latitude * 100) + math.floor(first_longitude * 100))
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crop_id = RULE_BASED_CROP_IDS[abs(seed) % len(RULE_BASED_CROP_IDS)]
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crop_metadata = RULE_BASED_PRODUCTS[crop_id]
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match_percent = 60 + (abs(seed) % 35)
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criteria = [
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{"name": "دما", "value": 55 + (abs(seed + 11) % 40)},
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{"name": "بارش", "value": 55 + (abs(seed + 17) % 40)},
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{"name": "خاک", "value": 55 + (abs(seed + 23) % 40)},
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{"name": "آب", "value": 55 + (abs(seed + 29) % 40)},
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]
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soil_quality_score = criteria[2]["value"]
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soil_level = _pick_level(soil_quality_score, 65, 85)
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cultivation_risk_score = max(1, min(100, round(100 - match_percent + ((abs(seed) % 9) - 4))))
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cultivation_risk_level = "low" if cultivation_risk_score <= 30 else "medium" if cultivation_risk_score <= 60 else "high"
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return {
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"soil_quality_score": soil_quality_score,
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"soil_level": soil_level,
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"water_need_level": crop_metadata["water_need_level"],
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"water_need_value": crop_metadata["water_need"],
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"cultivation_risk_level": cultivation_risk_level,
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"recommended_crop": crop_id,
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"match_percent": match_percent,
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"estimated_profit": crop_metadata["estimated_profit"],
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"reason": crop_metadata["reason"],
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"criteria": criteria,
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"algorithm": RULE_BASED_ALGORITHM,
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}
|
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|
|
|
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def build_initial_zone_payload(zone):
|
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recommendation = getattr(zone, "recommendation", None)
|
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return {
|
|
"zoneId": zone.zone_id,
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"geometry": zone.geometry,
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"crop": recommendation.product.product_id if recommendation else "",
|
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"matchPercent": recommendation.match_percent if recommendation else 0,
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|
"waterNeed": recommendation.water_need if recommendation else "",
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"estimatedProfit": recommendation.estimated_profit if recommendation else "",
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}
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|
|
def persist_zone_analysis_metrics(zone, metrics):
|
|
ensure_products_exist()
|
|
product = CropProduct.objects.get(product_id=metrics["recommended_crop"])
|
|
recommendation, _ = CropZoneRecommendation.objects.update_or_create(
|
|
crop_zone=zone,
|
|
defaults={
|
|
"product": product,
|
|
"match_percent": metrics["match_percent"],
|
|
"water_need": metrics["water_need_value"],
|
|
"estimated_profit": metrics["estimated_profit"],
|
|
"reason": metrics["reason"],
|
|
},
|
|
)
|
|
CropZoneCriteria.objects.filter(recommendation=recommendation).delete()
|
|
CropZoneCriteria.objects.bulk_create(
|
|
[
|
|
CropZoneCriteria(
|
|
recommendation=recommendation,
|
|
name=item["name"],
|
|
value=item["value"],
|
|
sequence=index,
|
|
)
|
|
for index, item in enumerate(metrics["criteria"])
|
|
]
|
|
)
|
|
CropZoneWaterNeedLayer.objects.update_or_create(
|
|
crop_zone=zone,
|
|
defaults={
|
|
"level": metrics["water_need_level"],
|
|
"value": metrics["water_need_value"],
|
|
"color": _get_level_color_map("water", metrics["water_need_level"]),
|
|
},
|
|
)
|
|
CropZoneSoilQualityLayer.objects.update_or_create(
|
|
crop_zone=zone,
|
|
defaults={
|
|
"level": metrics["soil_level"],
|
|
"score": metrics["soil_quality_score"],
|
|
"color": _get_level_color_map("soil", metrics["soil_level"]),
|
|
},
|
|
)
|
|
CropZoneCultivationRiskLayer.objects.update_or_create(
|
|
crop_zone=zone,
|
|
defaults={
|
|
"level": metrics["cultivation_risk_level"],
|
|
"color": _get_level_color_map("risk", metrics["cultivation_risk_level"]),
|
|
},
|
|
)
|
|
return recommendation
|
|
|
|
|
|
def ensure_rule_based_zone_data(zone, force=False):
|
|
has_recommendation = CropZoneRecommendation.objects.filter(crop_zone=zone).exists()
|
|
if has_recommendation and not force:
|
|
return zone
|
|
|
|
metrics = build_rule_based_zone_metrics(zone.sequence, zone.points)
|
|
persist_zone_analysis_metrics(zone, metrics)
|
|
return zone
|
|
|
|
|
|
def _get_level_color_map(layer_name, level):
|
|
mappings = {
|
|
"water": {"low": "#7dd3fc", "medium": "#0ea5e9", "high": "#0369a1"},
|
|
"soil": {"low": "#ef4444", "medium": "#eab308", "high": "#22c55e"},
|
|
"risk": {"low": "#22c55e", "medium": "#f59e0b", "high": "#ef4444"},
|
|
}
|
|
return mappings[layer_name][level]
|
|
|
|
|
|
def _pick_level(score, low_threshold, high_threshold):
|
|
if score >= high_threshold:
|
|
return "high"
|
|
if score >= low_threshold:
|
|
return "medium"
|
|
return "low"
|
|
|
|
|
|
def _format_range(start, end, suffix):
|
|
return f"{start}-{end} {suffix}"
|
|
|
|
|
|
def _derive_analysis_metrics(depths):
|
|
if not depths:
|
|
return {
|
|
"soil_quality_score": 0,
|
|
"soil_level": "low",
|
|
"water_need_level": "high",
|
|
"water_need_value": "0-0 m³/ha",
|
|
"cultivation_risk_level": "high",
|
|
"recommended_crop": PRODUCT_DEFAULTS[0]["id"],
|
|
"match_percent": 0,
|
|
"estimated_profit": "0-0 میلیون/هکتار",
|
|
"reason": "داده تحلیل خاک موجود نیست",
|
|
"criteria": [],
|
|
}
|
|
|
|
avg_ph = sum(item.get("phh2o", 0) for item in depths) / len(depths)
|
|
avg_soc = sum(item.get("soc", 0) for item in depths) / len(depths)
|
|
avg_clay = sum(item.get("clay", 0) for item in depths) / len(depths)
|
|
avg_nitrogen = sum(item.get("nitrogen", 0) for item in depths) / len(depths)
|
|
avg_wv0033 = sum(item.get("wv0033", 0) for item in depths) / len(depths)
|
|
|
|
soil_quality_score = max(0, min(100, round((avg_soc * 20) + (avg_nitrogen * 120) + (avg_wv0033 * 120) + (20 - abs(avg_ph - 7) * 10))))
|
|
soil_level = _pick_level(soil_quality_score, 50, 80)
|
|
|
|
water_base = round(3000 + (avg_clay * 70))
|
|
water_need_value = _format_range(water_base, water_base + 1000, "m³/ha")
|
|
water_need_level = "low" if water_base < 4000 else "medium" if water_base < 5000 else "high"
|
|
|
|
cultivation_risk_score = max(0, min(100, round(100 - soil_quality_score + abs(avg_ph - 7) * 8)))
|
|
cultivation_risk_level = "low" if cultivation_risk_score <= 30 else "medium" if cultivation_risk_score <= 55 else "high"
|
|
|
|
if water_need_level == "low" and soil_quality_score >= 85:
|
|
recommended_crop = "saffron"
|
|
estimated_profit = "۵۰-۱۵۰ میلیون/هکتار"
|
|
elif soil_quality_score >= 70:
|
|
recommended_crop = "wheat"
|
|
estimated_profit = "۱۵-۲۵ میلیون/هکتار"
|
|
else:
|
|
recommended_crop = "canola"
|
|
estimated_profit = "۲۰-۳۵ میلیون/هکتار"
|
|
|
|
match_percent = max(1, min(100, round((soil_quality_score * 0.55) + ((100 - cultivation_risk_score) * 0.45))))
|
|
reason = "خاک و شرایط رطوبتی این زون برای محصول پیشنهادی مناسب ارزیابی شده است"
|
|
criteria = [
|
|
{"name": "دما", "value": max(1, min(100, round(70 + (avg_ph - 6.5) * 10)))},
|
|
{"name": "بارش", "value": max(1, min(100, round(60 + avg_wv0033 * 100)))},
|
|
{"name": "خاک", "value": soil_quality_score},
|
|
{"name": "آب", "value": max(1, min(100, round(100 - ((water_base - 3000) / 30))))},
|
|
]
|
|
|
|
return {
|
|
"soil_quality_score": soil_quality_score,
|
|
"soil_level": soil_level,
|
|
"water_need_level": water_need_level,
|
|
"water_need_value": water_need_value,
|
|
"cultivation_risk_level": cultivation_risk_level,
|
|
"recommended_crop": recommended_crop,
|
|
"match_percent": match_percent,
|
|
"estimated_profit": estimated_profit,
|
|
"reason": reason,
|
|
"criteria": criteria,
|
|
}
|
|
|
|
|
|
def fetch_soil_data_for_zone(zone):
|
|
center = zone.center or calculate_center(zone.points)
|
|
payload = {
|
|
"lon": center["longitude"],
|
|
"lat": center["latitude"],
|
|
"zone": {
|
|
"id": zone.zone_id,
|
|
"geometry": zone.geometry,
|
|
"center": center,
|
|
"area_sqm": zone.area_sqm,
|
|
"area_hectares": zone.area_hectares,
|
|
},
|
|
}
|
|
return external_request("ai", "/soil-data", method="POST", payload=payload).data
|
|
|
|
|
|
def analyze_and_store_zone_soil_data(zone_id):
|
|
ensure_products_exist()
|
|
zone = CropZone.objects.select_related("crop_area").get(id=zone_id)
|
|
if zone.processing_status == CropZone.STATUS_COMPLETED:
|
|
return zone
|
|
|
|
zone.processing_status = CropZone.STATUS_PROCESSING
|
|
zone.processing_error = ""
|
|
zone.save(update_fields=["processing_status", "processing_error", "updated_at"])
|
|
|
|
try:
|
|
adapter_data = fetch_soil_data_for_zone(zone)
|
|
soil_data = adapter_data.get("data", {}) if isinstance(adapter_data, dict) else {}
|
|
depths = soil_data.get("depths", [])
|
|
metrics = _derive_analysis_metrics(depths)
|
|
product = CropProduct.objects.get(product_id=metrics["recommended_crop"])
|
|
|
|
CropZoneAnalysis.objects.update_or_create(
|
|
crop_zone=zone,
|
|
defaults={
|
|
"source": soil_data.get("source", ""),
|
|
"external_record_id": str(soil_data.get("id", "")),
|
|
"longitude": Decimal(str(soil_data.get("lon", zone.center.get("longitude", 0)))),
|
|
"latitude": Decimal(str(soil_data.get("lat", zone.center.get("latitude", 0)))),
|
|
"raw_response": adapter_data if isinstance(adapter_data, dict) else {},
|
|
"depths": depths,
|
|
},
|
|
)
|
|
persist_zone_analysis_metrics(zone, metrics)
|
|
zone.processing_status = CropZone.STATUS_COMPLETED
|
|
zone.processing_error = ""
|
|
zone.save(update_fields=["processing_status", "processing_error", "updated_at"])
|
|
except Exception as exc:
|
|
zone.processing_status = CropZone.STATUS_FAILED
|
|
zone.processing_error = str(exc)
|
|
zone.save(update_fields=["processing_status", "processing_error", "updated_at"])
|
|
raise
|
|
|
|
return zone
|
|
|
|
|
|
def dispatch_zone_processing_tasks(crop_area_id=None, zone_ids=None):
|
|
from .tasks import process_zone_soil_data
|
|
|
|
queryset = CropZone.objects.select_related("crop_area").all()
|
|
if crop_area_id is not None:
|
|
queryset = queryset.filter(crop_area_id=crop_area_id)
|
|
if zone_ids is not None:
|
|
queryset = queryset.filter(id__in=zone_ids)
|
|
|
|
zones = list(queryset.only("id", "task_id", "processing_status", "crop_area__sensor_id"))
|
|
sensor_task_ids = {}
|
|
for zone in zones:
|
|
sensor_id = zone.crop_area.sensor_id
|
|
existing_task_id = sensor_task_ids.get(sensor_id) or zone.task_id
|
|
if existing_task_id and zone.processing_status in {CropZone.STATUS_PENDING, CropZone.STATUS_PROCESSING}:
|
|
sensor_task_ids[sensor_id] = existing_task_id
|
|
if zone.task_id != existing_task_id:
|
|
CropZone.objects.filter(id=zone.id).update(task_id=existing_task_id)
|
|
continue
|
|
|
|
try:
|
|
async_result = process_zone_soil_data.delay(zone.id)
|
|
task_identifier = getattr(async_result, "id", "") or str(uuid.uuid4())
|
|
processing_error = ""
|
|
except OperationalError as exc:
|
|
task_identifier = str(uuid.uuid4())
|
|
processing_error = f"Celery broker unavailable: {exc}"
|
|
except Exception as exc:
|
|
task_identifier = str(uuid.uuid4())
|
|
processing_error = f"Celery dispatch failed: {exc}"
|
|
|
|
update_fields = {"task_id": task_identifier}
|
|
if zone.processing_status == CropZone.STATUS_FAILED:
|
|
update_fields["processing_status"] = CropZone.STATUS_PENDING
|
|
if processing_error:
|
|
update_fields["processing_error"] = processing_error
|
|
elif zone.processing_status == CropZone.STATUS_FAILED:
|
|
update_fields["processing_error"] = ""
|
|
CropZone.objects.filter(id=zone.id).update(**update_fields)
|
|
if sensor_id and task_identifier:
|
|
sensor_task_ids[sensor_id] = task_identifier
|
|
|
|
|
|
def create_missing_zones_for_area(crop_area):
|
|
if crop_area.zones.exists():
|
|
return list(crop_area.zones.order_by("sequence", "id"))
|
|
|
|
area_feature = normalize_area_feature(crop_area.geometry)
|
|
zoning_result = split_area_into_zones(
|
|
area_feature,
|
|
cell_side_km=math.sqrt(max(crop_area.chunk_area_sqm, 1)) / 1000.0,
|
|
)
|
|
zones = CropZone.objects.bulk_create(
|
|
[
|
|
CropZone(
|
|
crop_area=crop_area,
|
|
zone_id=zone["zone_id"],
|
|
geometry=zone["geometry"],
|
|
points=zone["points"],
|
|
center=zone["center"],
|
|
area_sqm=round(zone["area_sqm"], 2),
|
|
area_hectares=round(zone["area_hectares"], 4),
|
|
sequence=zone["sequence"],
|
|
)
|
|
for zone in zoning_result["zones"]
|
|
]
|
|
)
|
|
crop_area.zone_count = len(zones)
|
|
crop_area.save(update_fields=["zone_count", "updated_at"])
|
|
return list(crop_area.zones.order_by("sequence", "id"))
|
|
|
|
|
|
def get_sensor_for_uuid(sensor_uuid):
|
|
if not sensor_uuid:
|
|
raise ValueError("sensor_uuid is required.")
|
|
try:
|
|
return Sensor.objects.get(uuid_sensor=sensor_uuid)
|
|
except Sensor.DoesNotExist as exc:
|
|
raise ValueError("Sensor not found.") from exc
|
|
|
|
|
|
def ensure_latest_area_ready_for_processing(sensor_uuid, area_feature=None):
|
|
sensor = get_sensor_for_uuid(sensor_uuid)
|
|
latest_area = CropArea.objects.filter(sensor=sensor).order_by("-created_at", "-id").first()
|
|
if latest_area is None:
|
|
latest_area, _ = create_zones_and_dispatch(area_feature or get_default_area_feature(), sensor=sensor)
|
|
return latest_area
|
|
|
|
zones = create_missing_zones_for_area(latest_area)
|
|
for zone in zones:
|
|
ensure_rule_based_zone_data(zone)
|
|
|
|
active_task_id = next((zone.task_id for zone in zones if zone.task_id and zone.processing_status in {CropZone.STATUS_PENDING, CropZone.STATUS_PROCESSING}), "")
|
|
zones_to_dispatch = []
|
|
for zone in zones:
|
|
if zone.processing_status == CropZone.STATUS_COMPLETED:
|
|
continue
|
|
if active_task_id:
|
|
if not zone.task_id:
|
|
CropZone.objects.filter(id=zone.id).update(task_id=active_task_id)
|
|
continue
|
|
if zone.processing_status == CropZone.STATUS_PROCESSING and zone.task_id:
|
|
active_task_id = zone.task_id
|
|
continue
|
|
if zone.processing_status == CropZone.STATUS_PENDING and zone.task_id:
|
|
active_task_id = zone.task_id
|
|
continue
|
|
zones_to_dispatch.append(zone.id)
|
|
|
|
if zones_to_dispatch:
|
|
dispatch_zone_processing_tasks(zone_ids=zones_to_dispatch)
|
|
|
|
return CropArea.objects.get(id=latest_area.id)
|
|
|
|
|
|
def create_zones_and_dispatch(area_feature, cell_side_km=None, sensor=None):
|
|
ensure_products_exist()
|
|
area_feature = normalize_area_feature(area_feature)
|
|
zoning_result = split_area_into_zones(area_feature, cell_side_km=cell_side_km)
|
|
area_data = zoning_result["area"]
|
|
|
|
with transaction.atomic():
|
|
crop_area = CropArea.objects.create(
|
|
sensor=sensor,
|
|
geometry=area_data["geometry"],
|
|
points=area_data["points"],
|
|
center=area_data["center"],
|
|
area_sqm=round(area_data["area_sqm"], 2),
|
|
area_hectares=round(area_data["area_hectares"], 4),
|
|
chunk_area_sqm=round(area_data["chunk_area_sqm"], 2),
|
|
zone_count=area_data["zone_count"],
|
|
)
|
|
zones = CropZone.objects.bulk_create(
|
|
[
|
|
CropZone(
|
|
crop_area=crop_area,
|
|
zone_id=zone["zone_id"],
|
|
geometry=zone["geometry"],
|
|
points=zone["points"],
|
|
center=zone["center"],
|
|
area_sqm=round(zone["area_sqm"], 2),
|
|
area_hectares=round(zone["area_hectares"], 4),
|
|
sequence=zone["sequence"],
|
|
)
|
|
for zone in zoning_result["zones"]
|
|
]
|
|
)
|
|
|
|
crop_area.refresh_from_db()
|
|
zones = list(crop_area.zones.order_by("sequence", "id"))
|
|
for zone in zones:
|
|
ensure_rule_based_zone_data(zone)
|
|
dispatch_zone_processing_tasks(crop_area.id)
|
|
return crop_area, zones
|
|
|
|
|
|
def _zones_queryset(zone_ids=None):
|
|
queryset = CropZone.objects.select_related(
|
|
"recommendation__product",
|
|
"water_need_layer",
|
|
"soil_quality_layer",
|
|
"cultivation_risk_layer",
|
|
).prefetch_related(
|
|
Prefetch("recommendation__criteria", queryset=CropZoneCriteria.objects.order_by("sequence", "id"))
|
|
).order_by("sequence", "id")
|
|
if zone_ids:
|
|
queryset = queryset.filter(zone_id__in=zone_ids)
|
|
return queryset
|
|
|
|
|
|
def get_latest_area_payload(area=None):
|
|
area = area or CropArea.objects.order_by("-created_at", "-id").first()
|
|
if area:
|
|
zones = list(area.zones.only("zone_id", "task_id", "processing_status", "processing_error"))
|
|
total_zones = len(zones)
|
|
completed_zones = sum(1 for zone in zones if zone.processing_status == CropZone.STATUS_COMPLETED)
|
|
processing_zones = sum(1 for zone in zones if zone.processing_status == CropZone.STATUS_PROCESSING)
|
|
failed_zones = sum(1 for zone in zones if zone.processing_status == CropZone.STATUS_FAILED)
|
|
pending_zones = sum(1 for zone in zones if zone.processing_status == CropZone.STATUS_PENDING)
|
|
|
|
if failed_zones:
|
|
task_status = "FAILURE"
|
|
elif total_zones and completed_zones == total_zones:
|
|
task_status = "SUCCESS"
|
|
elif processing_zones or completed_zones:
|
|
task_status = "PROCESSING"
|
|
else:
|
|
task_status = "PENDING"
|
|
|
|
return {
|
|
"task": {
|
|
"status": task_status,
|
|
"area_uuid": str(area.uuid),
|
|
"total_zones": total_zones,
|
|
"completed_zones": completed_zones,
|
|
"processing_zones": processing_zones,
|
|
"pending_zones": pending_zones,
|
|
"failed_zones": failed_zones,
|
|
"task_ids": [zone.task_id for zone in zones if zone.task_id],
|
|
"failed_zone_errors": [
|
|
{
|
|
"zoneId": zone.zone_id,
|
|
"error": zone.processing_error,
|
|
}
|
|
for zone in zones
|
|
if zone.processing_status == CropZone.STATUS_FAILED and zone.processing_error
|
|
],
|
|
"cell_side_km": round(math.sqrt(max(area.chunk_area_sqm, 1)) / 1000.0, 4),
|
|
},
|
|
"area": area.geometry,
|
|
}
|
|
return {
|
|
"task": {
|
|
"status": "IDLE",
|
|
"area_uuid": "",
|
|
"total_zones": 0,
|
|
"completed_zones": 0,
|
|
"processing_zones": 0,
|
|
"pending_zones": 0,
|
|
"failed_zones": 0,
|
|
"task_ids": [],
|
|
"failed_zone_errors": [],
|
|
"cell_side_km": round(get_default_cell_side_km(), 4),
|
|
},
|
|
"area": get_default_area_feature(),
|
|
}
|
|
|
|
|
|
def get_initial_zones_payload(crop_area):
|
|
zones = _zones_queryset().filter(crop_area=crop_area)
|
|
return {
|
|
"total_area_hectares": crop_area.area_hectares,
|
|
"total_area_sqm": crop_area.area_sqm,
|
|
"zone_count": crop_area.zone_count,
|
|
"zones": [build_initial_zone_payload(zone) for zone in zones],
|
|
}
|
|
|
|
|
|
def get_water_need_payload(zone_ids=None):
|
|
zones = _zones_queryset(zone_ids)
|
|
return {
|
|
"zones": [
|
|
{
|
|
"zoneId": zone.zone_id,
|
|
"geometry": zone.geometry,
|
|
"level": getattr(zone.water_need_layer, "level", ""),
|
|
"value": getattr(zone.water_need_layer, "value", ""),
|
|
"color": getattr(zone.water_need_layer, "color", ""),
|
|
}
|
|
for zone in zones
|
|
]
|
|
}
|
|
|
|
|
|
def get_soil_quality_payload(zone_ids=None):
|
|
zones = _zones_queryset(zone_ids)
|
|
return {
|
|
"zones": [
|
|
{
|
|
"zoneId": zone.zone_id,
|
|
"geometry": zone.geometry,
|
|
"level": getattr(zone.soil_quality_layer, "level", ""),
|
|
"score": getattr(zone.soil_quality_layer, "score", 0),
|
|
"color": getattr(zone.soil_quality_layer, "color", ""),
|
|
}
|
|
for zone in zones
|
|
]
|
|
}
|
|
|
|
|
|
def get_cultivation_risk_payload(zone_ids=None):
|
|
zones = _zones_queryset(zone_ids)
|
|
return {
|
|
"zones": [
|
|
{
|
|
"zoneId": zone.zone_id,
|
|
"geometry": zone.geometry,
|
|
"level": getattr(zone.cultivation_risk_layer, "level", ""),
|
|
"color": getattr(zone.cultivation_risk_layer, "color", ""),
|
|
}
|
|
for zone in zones
|
|
]
|
|
}
|
|
|
|
|
|
def get_zone_details_payload(zone_id):
|
|
zone = _zones_queryset().get(zone_id=zone_id)
|
|
recommendation = getattr(zone, "recommendation", None)
|
|
criteria = recommendation.criteria.all() if recommendation else []
|
|
return {
|
|
"zoneId": zone.zone_id,
|
|
"crop": recommendation.product.product_id if recommendation else "",
|
|
"matchPercent": recommendation.match_percent if recommendation else 0,
|
|
"waterNeed": recommendation.water_need if recommendation else "",
|
|
"estimatedProfit": recommendation.estimated_profit if recommendation else "",
|
|
"reason": recommendation.reason if recommendation else "",
|
|
"criteria": [{"name": item.name, "value": item.value} for item in criteria],
|
|
"area_hectares": zone.area_hectares,
|
|
}
|