Smart Factory Data Generator for Real-Time Insights
detail.loadingPreview
Automate the generation of realistic factory data for testing and simulation. This workflow simulates machine status and environmental readings, sending them to an AMQP broker for real-time analysis and monitoring.
About This Workflow
The Smart Factory Data Generator workflow is designed to bring your factory monitoring and analytics to life. It leverages n8n's automation capabilities to continuously generate simulated machine data, including unique identifiers, simulated temperatures, uptime, and precise timestamps. This data can be crucial for developing and testing IoT platforms, data processing pipelines, and predictive maintenance models without requiring access to live production systems. By dynamically creating these data points at regular intervals and publishing them via AMQP, you gain a powerful tool for building resilient and intelligent manufacturing solutions. Ensure your systems are ready for any operational scenario by practicing with high-fidelity simulated data.
Key Features
- Automated Data Simulation: Generates realistic machine IDs, temperature, and uptime data.
- Real-Time Data Streaming: Publishes generated data to an AMQP message broker.
- Customizable Generation Intervals: Easily control the frequency of data generation.
- Dynamic Timestamping: Includes accurate
time_stampfor time-series analysis. - Flexible Machine Identification: Uses a predefined
n8n_cr8machine ID for easy tracking.
How To Use
- Configure the
IntervalNode: Set the desired frequency for data generation (e.g., every 5 seconds, 1 minute). This node triggers the workflow at specified intervals. - Customize the
SetNode:machine_id.name: Set a static machine identifier or use expressions for dynamic values.temperature_celsius: Define the random range for temperature simulation using JavaScript'sMath.random()andMath.floor().machine_id.uptime: Simulate machine uptime using similar random number generation techniques.time_stamp: Ensure accurate time tracking by usingDate.now()to generate Unix timestamps.
- Configure the
AMQP SenderNode:sink: Specify the target AMQP queue or topic (e.g.,berlin_factory_01).credentials: Provide your AMQP connection details (username, password, host, port) using n8n's credential management.options.dataAsObject: Set totrueto send data as a JSON object.
Apps Used
Workflow JSON
{
"id": "896eabe9-613f-46aa-a15b-6306b2dd4230",
"name": "Smart Factory Data Generator for Real-Time Insights",
"nodes": 15,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 896eabe9-613f...
About the Author
Free n8n Workflows Official
System Admin
The official repository for verified enterprise-grade workflows.
Statistics
Related Workflows
Discover more workflows you might like
Universal CSV to JSON API Converter
Effortlessly transform CSV data into structured JSON with this versatile n8n workflow. Integrate it into any application as a custom API endpoint, supporting various input methods including file uploads and raw text.
Instant WooCommerce Order Notifications via Telegram
When a new order is placed on your WooCommerce store, instantly receive detailed notifications directly to your Telegram chat. Stay on top of your e-commerce operations with real-time alerts, including order specifics and a direct link to view the order.
On-Demand Microsoft SQL Query Execution
This workflow allows you to manually trigger and execute any SQL query against your Microsoft SQL Server database. Perfect for ad-hoc data lookups, administrative tasks, or quick tests, giving you direct control over your database operations.