OPCUA Fusion - OPC UA Client and Data Aggregator

OPCUA Fusion is a comprehensive Django-based web application designed to connect to, monitor, and interact with multiple OPC UA servers. It provides a real-time dashboard for each connected server, logs historical data to Google Firebase, enables complex process scheduling across machines, and offers tools for data visualization and download.

📸 User Interface Preview

✨ Features

• 🌐 Multi-Server Connectivity: Connect to multiple OPC UA servers simultaneously from a central hub.
• 📊 Real-time Dashboard: For each connection, a dedicated dashboard provides:
  • A hierarchical tree view of the server's address space.
  • Detailed node information (ID, class, description, data type).
  • Live monitoring of variable values, updated every 500ms.
• ⚙️ Method Execution: Call methods on the OPC UA server directly from the web interface, with support for input arguments.
• ☁️ Cloud Data Logging: All connection events, node structures, real-time variable data, and method calls are logged to Google Firebase Firestore, creating a historical data record.
• 🔗 Process Scheduling & Aggregation:
  • Design and execute complex process chains involving method calls on different machines in a specific sequence.
  • Use the output of one process step as an input for a subsequent step.
• 🎨 Data Visualization: Generate 2D plots of historical data stored in Firestore by selecting variables for the X and Y axes and specifying a time range.
• 💾 Data Export: Download historical variable data as a CSV file for a given time period.
• 🧊 3D Machine Visualization: The main hub displays an interactive 3D model (using Three.js) for each connected machine type (CNC, Cobot, Conveyor).

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🛠️ Technology Stack

• 🐍 Backend: Django 5.0, Python 3.x
• 🤝 OPC UA Communication: freeopcua/opcua library
• 🗄️ Database:
  • Primary Data Store: Google Firebase Firestore
  • Django Backend: SQLite (default)
• 🖥️ Frontend: HTML, CSS, JavaScript (Vanilla)
• ⚡ Asynchronous Tasks: asyncio, ThreadPoolExecutor, and threading for non-blocking data collection and process execution.
• 🧊 3D Rendering: Three.js
• 🐼 Data Handling & Plotting: Pandas, Matplotlib

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✅ Prerequisites

• Python 3.10+
• pip package manager
• An active OPC UA Server for testing.
• A Firebase project enabled.

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🚀 Setup and Installation

Follow these steps to set up and run the project locally.

1. 🔥 Firebase Setup (Critical Step)

This project relies on Google Firebase to store all operational data.

1. Create a Firebase Project:

   • Go to the Firebase Console.
   • Click "Add project" and follow the on-screen instructions.

2. Generate a Service Account Key:

   • In your Firebase project, go to Project Settings (click the gear icon ⚙️ next to "Project Overview").
   • Navigate to the Service accounts tab.
   • Click Generate new private key. A JSON file will be downloaded.
   • Rename this file to serviceAccountKey.json.
   • Place this file in the root directory of the Django project (the same folder that contains manage.py).

3. Set up Firestore Database:

   • In the Firebase Console, go to Build > Firestore Database.
   • Click Create database.
   • Start in Test mode for initial setup. This allows open read/write access.

   ⚠️ Security Note: For production, you must configure Security Rules to protect your data.

   • Choose a location for your database.

4. Set up Firebase Storage:

   • The application uses a storage bucket name in the configuration.
   • In the Firebase Console, go to Build > Storage.
   • Click Get started and follow the setup wizard (you can use the default security rules for now).
   • Once created, you will see your storage bucket URL at the top (e.g., gs://my-project.appspot.com).
   • Open opcua_client/views.py and find this line:

     firebase_admin.initialize_app(
         cred,
         {"storageBucket": "opcuafusion.appspot.com"}, # <-- CHANGE THIS
     )

   • Replace "opcuafusion.appspot.com" with your actual bucket name (without the gs:// prefix).

2. 💻 Project Installation

1. Clone the Repository:

   git clone <your-repository-url>
   cd opcua_fusion

2. Create and Activate a Virtual Environment:

   # For Windows
   python -m venv venv
   .\venv\Scripts\activate
   
   # For macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Apply Database Migrations: (This sets up the local SQLite database for Django's internal use)

   python manage.py migrate

5. Run the Development Server:

   python manage.py runserver

The application will now be running at http://127.0.0.1:8000/.

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📖 How to Use the Application

🌐 Main Hub: Connecting Machines

1. Open your browser to http://127.0.0.1:8000/.
2. In the form, enter:
   • Endpoint: The URL of your OPC UA server.
   • Namespace URI: The namespace identifier for your server's objects.
   • Machine: Select a machine type (CNC, Cobot, etc.).
3. Click Connect. A new browser tab will open with the dashboard for that machine.
4. The main hub page will now show a 3D model of the connected machine. You can interact with the model and connect to more machines.

🖥️ Machine Dashboard

The new tab is the command center for a single machine.

• Address Space: Browse the server's nodes. Click ▶ to expand. Click a node name to see its details.
• Display Panel: Shows details of the last-clicked node and the results of method calls.
• Variables Real Time: A live feed of all variable values from the server, refreshing automatically.
• Methods: Lists all callable methods. Provide inputs and click Run to execute.
• Disconnect: Click the "Disconnect" button or close the tab to end the session.

📈 Data Visualization and Download

On the machine dashboard, you will find two buttons:

• Generate Graph: Opens a new tab to plot historical data from Firestore.
• Download Data: Opens a new tab to download historical variable data as a CSV file.

🔗 Scheduling a Process Chain

Automate tasks across multiple connected machines.

1. From the main hub, click Schedule the Machines.
2. A new window opens. Enter the number of steps in your process.
3. Define the Chain: For each step, select a machine and the method to call.
4. Provide Inputs: For each step, provide static values or use the output from a previous step as input.
5. Schedule and Execute: Start the process and monitor the live log. An Emergency Stop button is available.

🧪 Testing & Complementary Applications

• Test Environment: The OPCUA Fusion application can be tested using a set of sample OPC UA servers and virtual hardware simulations available at: opcua_fusion_test_servers_and_hardware.
• Customer-Facing Application: A complementary customer-facing cloud application that works with the data logged by OPCUA Fusion is available here: opcua_fusion_cloud.

🔌 Connecting OPCUA Test Server Cobot and Conveyor Hardware Directly via Ethernet (Network Bridge)

To connect the Raspberry Pi-based hardware directly to the laptop running this application without an external router, you can create a network bridge. This allows the laptop to share its Wi-Fi connection with devices connected via its Ethernet port.

Network Topology

(Internet) --- Wi-Fi ---> [ Laptop (Running OPCUA Fusion) ] --- Ethernet ---> [ Switch ] --- Ethernet ---> [ Pi 1: Conveyor (192.168.1.2) ]
                                                                                   |
                                                                                    --- Ethernet ---> [ Pi 2: Cobot (192.168.1.3) ]

Setup Guide

1. Configure the Laptop's Network

Your laptop must have a static IP address on the same network as the hardware. We will use the 192.168.1.x subnet.

On Windows:

1. Open Network Connections: Press Win + R, type ncpa.cpl, and press Enter.
2. Set Static IP on Ethernet Port:
   • Right-click your Ethernet adapter and select Properties.
   • Select Internet Protocol Version 4 (TCP/IPv4) and click Properties.
   • Select "Use the following IP address".
   • Enter the following:
     • IP address: 192.168.1.1
     • Subnet mask: 255.255.255.0
   • Leave the "Default gateway" and "DNS server" fields blank. Click OK.
3. Share Wi-Fi Connection:
   • Right-click your Wi-Fi adapter and select Properties.
   • Go to the Sharing tab.
   • Check the box "Allow other network users to connect through this computer's Internet connection".
   • From the "Home networking connection" dropdown, select your Ethernet adapter.
   • Click OK.

On macOS:

1. Open System Settings > Network.
2. Set Static IP on Ethernet Port:
   • Select your Ethernet adapter (or USB-to-Ethernet adapter).
   • Click Details... > TCP/IP.
   • Set "Configure IPv4" to Manually.
   • Enter the following:
     • IP Address: 192.168.1.1
     • Subnet Mask: 255.255.255.0
   • Leave the "Router" field blank. Click OK.
3. Share Wi-Fi Connection:
   • Go to System Settings > General > Sharing.
   • Turn on Internet Sharing.
   • Click the info icon (i) next to it.
   • Set "Share your connection from:" to Wi-Fi.
   • Set "To computers using:" to your Ethernet adapter.
   • Click Done.

2. Connect and Verify

1. Connect an Ethernet cable from your laptop's Ethernet port to a network switch.
2. Connect both Raspberry Pis (Cobot and Conveyor) to the same switch using Ethernet cables.
3. Open a terminal or Command Prompt on your laptop and test the connection using ping:

   # Test connection to the Conveyor Pi
   ping 192.168.1.2
   
   # Test connection to the Cobot Pi
   ping 192.168.1.3

4. If you receive replies, the network is configured correctly. You can now run the OPCUA Fusion application and connect to the hardware using their endpoints:
   • Conveyor: opc.tcp://192.168.1.2:4840/conveyor
   • Cobot: opc.tcp://192.168.1.3:4840/cobot_arm