ساخت سیستم های محاسباتی محیط: راهنمای توسعه دهنده UX نامرئی

# Ambient system sensor processor - Raspberry Pi Pico example
import mmWave
from edge_ai import BehaviorPredictor

sensor = mmWave.PresenceDetector()
ai_model = BehaviorPredictor.load('home_patterns.onnx')

while True:
    movement = sensor.scan_environment()
    predicted_action = ai_model.predict(movement)
    if predicted_action == 'wake_up':
        home_api.trigger_scene('morning_routine')

// ESP32-C3 mmWave radar snippet
void handleDetection() {
  if (mmWave.readPresence() > THRESHOLD) {
    mqttClient.publish("home/kitchen/occupancy", "true");
  }
}

# Convert TensorFlow model for edge devices
tflite_convert --saved_model_dir=behavior_model \
               --output_file=home_ai.tflite \
               --optimize=latency

// Ambient light notification (Node-RED flow)
[{"id":"a1","type":"mqtt in","z":"flow1","name":"","topic":"alert/water"},
 {"id":"a2","type":"color light","z":"flow1","name":"Kitchen Strip",
  "effect":"pulse_amber","duration":3000}]

# 1. Check sensor raw feeds
mmWave-cli --dump-raw /dev/ttyUSB0

# 2. Model performance monitoring
edge_ai_monitor --model=home_ai.tflite --latency=200ms