ZONY Real-Time Satellite Integration™

# satellite/ingestion.py
import requests

def get_ndvi(lat, lon):
    # simulação (substituir por API real)
    return {
        "ndvi": 0.72,
        "soil_moisture": 35,
        "temperature": 29
    }

def fetch_satellite_data(field):
    data = get_ndvi(field["lat"], field["lon"])
    return data

# ai/satellite_decision.py

def analyze_satellite(data):
    ndvi = data["ndvi"]

    if ndvi < 0.4:
        return "CRITICAL"     # planta com sérios problemas

    elif ndvi < 0.6:
        return "ATTENTION"    # monitorar / intervir

    else:
        return "HEALTHY"      # nenhuma ação necessária

# ai/auto_action.py
import requests

def act_on_field(status):
    if status == "CRITICAL":
        print("IA: aplicar irrigação urgente")
        requests.post("http://api:8000/irrigate?zone=1&volume=200")

    elif status == "ATTENTION":
        print("IA: aplicar fertilizante")
        requests.post("http://api:8000/fertilize?zone=1&amount=50")

    else:
        print("IA: campo saudável — nenhuma ação")

# satellite/main_loop.py
import time
from ingestion import fetch_satellite_data
from ai.satellite_decision import analyze_satellite
from ai.auto_action import act_on_field

FIELD = {"lat": -19.2, "lon": -40.1}

while True:
    sat_data = fetch_satellite_data(FIELD)
    status   = analyze_satellite(sat_data)

    print(f"NDVI: {sat_data['ndvi']} → Status: {status}")

    act_on_field(status)

    time.sleep(3600)  # a cada 1 hora

# Integração Satélite + AutoPilot
# satellite/autopilot_integration.py
from ingestion import fetch_satellite_data
from ai.satellite_decision import analyze_satellite
from autopilot.path_planner import generate_lines
from autopilot.navigation import calculate_heading, steering_control

def satellite_to_field_action(field):
    # 1. Satélite detecta problema
    sat_data = fetch_satellite_data(field)
    status   = analyze_satellite(sat_data)

    if status in ["CRITICAL", "ATTENTION"]:
        # 2. IA decide irrigar/fertilizar
        lines = generate_lines(field["width"], spacing=0.5)

        for line_x in lines:
            target      = (line_x, field["lat"])
            current     = get_gps_position()

            # 3. Autopilot navega até o ponto
            target_hdg  = calculate_heading(current, target)
            current_hdg = get_imu_heading()
            steering    = steering_control(current_hdg, target_hdg)

            # 4. Executa ação no campo via CAN
            send_can(0x200, [int((steering + 30) * 4)])

# Estrutura para APIs reais de satélite
import requests

# ESA Copernicus / Sentinel Hub
def get_sentinel_ndvi(lat, lon, token):
    url = "https://services.sentinel-hub.com/api/v1/statistics"
    headers = {"Authorization": f"Bearer {token}"}
    payload = {
        "input": {
            "bounds": {"geometry": {
                "type": "Point",
                "coordinates": [lon, lat]
            }},
            "data": [{"type": "sentinel-2-l2a"}]
        },
        "aggregation": {"evalscript": "NDVI"}
    }
    return requests.post(url, json=payload, headers=headers).json()

# Planet Labs
def get_planet_image(lat, lon, api_key):
    url = "https://api.planet.com/data/v1/quick-search"
    headers = {"Authorization": f"api-key {api_key}"}
    payload = {
        "item_types": ["PSScene"],
        "filter": {
            "type": "GeometryFilter",
            "field_name": "geometry",
            "config": {"type": "Point", "coordinates": [lon, lat]}
        }
    }
    return requests.post(url, json=payload, headers=headers).json()