SPESIFIKASI FAVORIOT

FAVORIOT menawarkan satu penyelesaian menyeluruh berasaskan IoT Enterprise Platform yang membolehkan pemantauan suhu dan kelembapan secara masa nyata dengan pemasangan sensor tanpa wayar serta integrasi penuh melalui cloud persendirian yang selamat.

1. Platform IoT FAVORIOT (Enterprise Edition)

Platform ini direka khusus untuk menyokong aplikasi pemantauan suhu dan kelembapan di lokasi sensitif seperti arkib, stor bahan kritikal, makmal, dan gudang penyimpanan.

Ciri-Ciri Utama Platform:

• Awan Persendirian dan diperakui oleh MySTI (Malaysia Science, Technology and Innovation)
• Pengurusan Peranti Tanpa Had – menyokong pelbagai jenis peranti dan gateway secara selamat
• Pemantauan Masa Nyata status peranti dan data suhu/kelembapan
• Konfigurasi Jarak Jauh serta kawalan peranti secara terus melalui platform
• Sokongan Protokol IoT: MQTT, CoAP, REST
• Struktur Hierarki: Projek → Aplikasi → Kumpulan → Peranti
• Storan Fleksibel untuk pelbagai jenis dan format data
• Visualisasi Data melalui dashboard tersuai
• Analitik Data: Statistik, Corak Trend, Korelasi, Ramalan Siri Masa
• Peraturan & Notifikasi: Tetapan amaran dan automasi tindakan melalui e-mel & Telegram
• API RESTful Tanpa Had untuk sambungan ke sistem lain
• Edge Gateway Processing – menyokong pemprosesan awal di peringkat gateway
• Keselamatan Data melalui token akses & penyulitan
• Perkongsian Data secara awam melalui pautan atau ke pelayan pihak ketiga
• Muat Turun Data dalam format CSV/JSON
• Penghantaran Data Berkala ke e-mel yang ditetapkan

2. Sensor Suhu & Kelembapan Dalam Ruang (Wireless Indoor Sensor)

Model: UN-LORA-AT-RH-BATT
Teknologi: LoRaWAN®

3. Gateway IoT (4G LTE + LoRaWAN)

Model: UN-IOT-G-4G-W
Teknologi: LoRaWAN® + 4G LTE

4. Perkhidmatan Tambahan

• Sokongan & Penyelenggaraan: Sehingga 3 tahun (termasuk sokongan e-mel, telefon dan lawatan tapak)
• Latihan & Dokumentasi: Disediakan untuk pentadbir sistem dan pengguna akhir
• Pensijilan Pemasang: Semua pemasangan dilakukan oleh jurutera bertauliah yang mempunyai sijil pemasang rasmi FAVORIOT

FAVORIOT memberi tumpuan kepada kebolehpercayaan, keselamatan, dan fleksibiliti dalam membina penyelesaian IoT tempatan yang menyeluruh untuk pemantauan persekitaran secara pintar.

This guide will show you how to build an AI-powered quality inspection system using a camera and send inspection results to the FAVORIOT IoT platform in real time.

🔧 Step 1: What You Need

Hardware:

• Raspberry Pi (or any computer with a camera)
• Camera (USB or Pi Camera)
• Internet connection

Software:

• Python 3
• Libraries: opencv-python, tensorflow, numpy, requests

🛠️ Step 2: Install the Required Software

Open Terminal and run:

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip -y
pip3 install opencv-python numpy requests tensorflow

🧠 Step 3: Train an AI Model to Detect Defects

Create a folder called dataset_defects with 2 subfolders: defect and normal.

Now, use this Python code to train the model:

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

dataset_path = "dataset_defects"
batch_size = 32
img_size = (224, 224)

datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)

train_data = datagen.flow_from_directory(
    dataset_path,
    target_size=img_size,
    batch_size=batch_size,
    class_mode="binary",
    subset="training"
)

val_data = datagen.flow_from_directory(
    dataset_path,
    target_size=img_size,
    batch_size=batch_size,
    class_mode="binary",
    subset="validation"
)

base_model = tf.keras.applications.MobileNetV2(input_shape=(224, 224, 3), include_top=False, weights="imagenet")
base_model.trainable = False

model = tf.keras.Sequential([
    base_model,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(1, activation="sigmoid")
])

model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(train_data, validation_data=val_data, epochs=10)

model.save("defect_detection_model.h5")

🎥 Step 4: Real-Time Defect Detection Using Camera

Once the model is trained and saved, run this script:

import cv2
import numpy as np
import tensorflow as tf

model = tf.keras.models.load_model("defect_detection_model.h5")
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    img = cv2.resize(frame, (224, 224))
    img = np.expand_dims(img, axis=0) / 255.0
    prediction = model.predict(img)[0][0]

    label = "Defect Detected!" if prediction > 0.5 else "Product OK"
    color = (0, 0, 255) if prediction > 0.5 else (0, 255, 0)

    cv2.putText(frame, label, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
    cv2.imshow("Quality Inspection", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

🌐 Step 5: Send Defect Results to FAVORIOT

✅ 1. Set Up a Device in FAVORIOT

1. Log in to Favoriot Platform
2. Go to Devices → Add Device
3. Note down your Device Developer ID and API Key

✅ 2. Add Code to Send Data

Below is the function to send results:

import requests
import json

DEVICE_ID = "YOUR_DEVICE_ID"
API_KEY = "YOUR_FAVORIOT_API_KEY"
URL = "https://apiv2.favoriot.com/v2/streams"

def send_data_to_favoriot(status):
    payload = {
        "device_developer_id": DEVICE_ID,
        "data": {
            "status": status
        }
    }

    headers = {
        "Content-Type": "application/json",
        "Apikey": API_KEY
    }

    response = requests.post(URL, data=json.dumps(payload), headers=headers)
    print("Response from Favoriot:", response.json())

✅ 3. Combine with Real-Time Detection

Add this snippet inside your prediction logic:

if prediction > 0.5:
    send_data_to_favoriot("Defect Detected!")
else:
    send_data_to_favoriot("Product OK")

📊 Step 6: View Data on FAVORIOT Dashboard

• Go to your device on the FAVORIOT Dashboard
• Click on Streams to view defect data
• You can also create graphs or alert rules for monitoring

🚀 Bonus Tips

• Add Telegram Alerts using Telegram Bot API
• Add Dashboard Charts using Favoriot’s visualization
• Improve accuracy with better dataset or model tuning

✅ Summary

With this project, you have:

✅ Built a real-time defect detection system
✅ Displayed results on screen
✅ Sent reports to FAVORIOT cloud platform

References

• Favoriot Platform Registration (FREE and Paid)
• Favoriot Full Documentation (Official)
• Favoriot Github
• How-To Use Favoriot Platform Video Playlist

Disclaimer

This article provides a step-by-step guide and only serves as a guideline. The source code may need adjustments to fit the final project design.

Favoriot Edge Gateway is a service that connects IoT gateways directly to the Favoriot Platform. It bridges the “edge” (local devices) and the Favoriot Platform, enabling data from local devices to be processed, managed, and stored. Additionally, it allows for the selection of specific parameters in the payload to be stored, providing greater control over transmitted data.

Key References

• Official documentation: https://platform.favoriot.com/tutoria…
• Favoriot Platform: https://platform.favoriot.com
• Favoriot Website: https://www.favoriot.com/