Engine Air Brace

import tensorflow as tf
from tensorflow.lite import Interpreter
import numpy as np

# Load optimized TensorFlow Lite model
interpreter = Interpreter('maf_model.tflite')
interpreter.allocate_tensors()

def create_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(128, activation='relu',
                            input_shape=(8,)),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(32, activation='relu'),
        tf.keras.layers.Dense(1, activation='linear')
    ])

    model.compile(
        optimizer='adam',
        loss='mse',
        metrics=['mae', 'accuracy']
    )
    return model

def predict_maf_realtime(sensor_data):
    # Preprocess inputs for neural network
    normalized_input = preprocess_sensors(sensor_data)

    # TensorFlow Lite inference
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    interpreter.set_tensor(
        input_details[0]['index'],
        normalized_input
    )
    interpreter.invoke()

    prediction = interpreter.get_tensor(
        output_details[0]['index']
    )

    return denormalize_maf(prediction[0][0])

........

import obd
import time
import threading
from collections import deque

# OBD2 connection with live monitoring
connection = obd.OBD("/dev/ttyUSB0", baudrate=38400)

# Real-time data buffer
live_data = deque(maxlen=100)

OBD_COMMANDS = {
    'rpm': obd.commands.RPM,
    'throttle': obd.commands.THROTTLE_POS,
    'maf': obd.commands.MAF,
    'intake_temp': obd.commands.INTAKE_TEMP,
    'map': obd.commands.INTAKE_PRESSURE,
    'engine_load': obd.commands.ENGINE_LOAD
}

def stream_obd2_data():
    """Continuous OBD2 data acquisition"""
    while connection.is_connected():
        sensor_readings = {}

        for sensor, command in OBD_COMMANDS.items():
            response = connection.query(command)
            if response.value:
                sensor_readings[sensor] = float(
                    response.value.magnitude
                )

        # Add timestamp for real-time tracking
        sensor_readings['timestamp'] = time.time()
        live_data.append(sensor_readings)

        # Real-time TensorFlow prediction
        if len(live_data) >= 10:
            predicted_maf = predict_maf_realtime(
                sensor_readings
            )

            # Performance comparison
            actual_maf = sensor_readings.get('maf', 0)
            efficiency = calculate_efficiency(
                predicted_maf, actual_maf
            )

            print(f"Live MAF: {actual_maf:.2f} g/s")
            print(f"Predicted: {predicted_maf:.2f} g/s")
            print(f"Efficiency: {efficiency:.1f}%")

        time.sleep(0.05)  # 20Hz sampling rate

# Start real-time monitoring
threading.Thread(
    target=stream_obd2_data,
    daemon=True
).start()

........