AI-Powered Industrial Data Hub with UNS Framework
Leveraging the Unified Namespace (UNS) architecture, it enables real-time data flow between industrial devices, applications, and AI models, delivering a complete data-to-decision loop for AI-powered smart manufacturing.
Bridging the Gap of IT and OT with AI-Enabled Unified Namespace (UNS)
Powered by UNS, the EMQ IIoT solution integrates EMQX, NeuronEX industry gateways, and domain LLMs into a cognitive data pipeline, enabling AI-enhanced smart factory operations.
Capabilities
Industrial Connectivity
Connect diverse industrial devices with 100+ protocols, including Modbus, OPC-UA, EtherNet/IP, S7, and others. Convert industrial protocol seamlessly to MQTT to enable real-time data exchange with the cloud.
Unified Namespace
Unlock seamless IIoT advancement with a unified namespace architecture, bridging the OT and IT systems from edge to cloud, and eliminating data silos.
MQTT Sparkplug
Use MQTT as the reliable messaging hub to connect everything. Standardize data communication and streamline industrial device management with the Sparkplug specification.
Real-time Stream Processing
Cleanse, normalize, aggregate, and enrich data on the fly using 160+ built-in functions and a flexible rule engine.
AI/ML Integration
Analyze data in motion, identify trends, automate processes, gain real-time insights, and make data-driven decisions with stream processing and AI/ML on the factory floor.
Multimodal Data Integration & LLM Orchestration
Combines real-time production data acquisition with knowledge base solutions, leveraging LLM-powered analytics to identify anomalies, generate proactive solutions, and enable cognitive decision-making.
Why EMQ
Breaking Down Data Silos
- Enable unified access to factory data, including real-time collection of all industrial equipment data
- Multi-source data access and enterprise system integration
Operational Efficiency Improvement
- Gain deep insights into every stage of production processes to identify potential bottlenecks
- Optimize workflows to enhance overall operational efficiency
Cost Reduction
- Effective predictive maintenance and optimized resource utilization
- Minimize unplanned downtime and waste, reducing production and operational costs
Enhanced Industrial Intelligence
- Integrating IoT, AI/ML, big data, and large language models (LLMs)
- Achieve predictive analytics and maintenance, automated decision-making, adaptive control, and anomaly detection
Use Cases
Predictive Maintenance
- Vibration Analysis: Edge FFT feature extraction + cloud-based anomaly detection models
- Lifespan Prediction: RUL (Remaining Useful Life) forecasting combining historical equipment data with real-time operating conditions
- Auto Work Order Generation: AI diagnostics directly trigger MES maintenance work orders
Intelligent Production Scheduling
- Real-time Capacity Analysis: Aggregate OEE data across production lines to calculate overall plant capacity
- Dynamic Scheduling: Real-time scheduling optimization based on order priorities and equipment status
- Material Coordination: Automatically trigger AGV replenishment requests to reduce line-side inventory
Digital Twin
- Virtual-Physical Synchronization: Low-latency data transmission ensures digital twin-physical entity alignment
- Simulation Optimization: Feed twin data back to process optimization algorithms
- AR Maintenance: Real-time equipment data access via UNS topics supports AR remote assistance