Classic Engine Air Optimization

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 RPi.GPIO as GPIO
import spidev
import time
import threading
from collections import deque

# SPI interface for TBI sensor data
spi = spidev.SpiDev()
spi.open(0, 0)  # Bus 0, Device 0
spi.max_speed_hz = 1000000

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

TBI_SENSORS = {
    'tps': {'channel': 0, 'scale': 5.0/1023},  # Throttle Position
    'rpm': {'channel': 1, 'scale': 8000/1023}, # RPM
    'map': {'channel': 2, 'scale': 100/1023},  # Manifold Pressure
    'iat': {'channel': 3, 'scale': 150/1023},  # Intake Air Temp
    'ect': {'channel': 4, 'scale': 200/1023},  # Engine Coolant Temp
    'o2': {'channel': 5, 'scale': 1.0/1023}    # O2 Sensor
}

def read_adc_channel(channel):
    """Read analog sensor data via SPI ADC"""
    adc = spi.xfer2([1, (8 + channel) << 4, 0])
    return ((adc[1] & 3) << 8) + adc[2]

def stream_tbi_data():
    """Continuous TBI sensor data acquisition"""
    while True:
        sensor_readings = {}

        for sensor, config in TBI_SENSORS.items():
            raw_value = read_adc_channel(config['channel'])
            sensor_readings[sensor] = raw_value * config['scale']

        # 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
            )

            # TBI optimization feedback
            current_tps = sensor_readings.get('tps', 0)
            optimization_factor = calculate_tbi_optimization(
                predicted_maf, current_tps
            )

            print(f"TPS: {current_tps:.1f}%")
            print(f"Predicted MAF: {predicted_maf:.2f} g/s")
            print(f"TBI Optimization: {optimization_factor:.1f}%")

        time.sleep(0.05)  # 20Hz sampling rate

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

........