Smart Factory Digital Twin System

The Smart Factory Digital Twin System is a core product independently developed by Shuzhilian, achieving visualization, monitoring, and optimization of the entire production process through real-time mapping between the physical world and virtual space. The system integrates IoT sensing, 3D modeling, AI algorithms, and other technologies, offering the following core functionalities:
1. Real-time Data Acquisition and Integration
Supports industrial protocols such as OPC UA/Modbus, enabling connectivity with PLCs, sensors, MES, and other devices/systems. The data collection frequency reaches 10ms-level with an accuracy rate exceeding 99.9%. The system comes pre-installed with 200+ industrial device drivers, compatible with mainstream brands like Siemens and Schneider, ensuring "plug-and-play" deployment.
2. High-Precision Digital Twin Modeling
Utilizes BIM+GIS fusion technology to achieve a modeling accuracy of 0.1mm, capable of replicating workshop layouts, equipment structures, material flows, and other details. Supports dynamic updates of model parameters (e.g., equipment temperature, rotation speed) synchronized with 3D models for intuitive production status visualization.
3. AI-Powered Predictive Maintenance
Employs machine learning algorithms to analyze vibration, current, and other characteristic data, building fault prediction models that identify potential failures 72 hours in advance with 92% accuracy. The system has processed over 100,000 equipment data samples, covering 50+ device types including motors and machine tools.
4. Intelligent Scheduling and Optimization
Integrates genetic algorithms and simulated annealing algorithms to automatically generate optimal production plans based on constraints like order priority, material inventory, and equipment capacity, reducing average delivery cycles by 30%. Supports multi-scenario simulations (e.g., urgent orders, equipment failures) for rapid impact assessment of plan adjustments.
5. Energy Consumption Analysis and Efficiency Recommendations
Monitors real-time energy data (water, electricity, gas, etc.), using neural network models to detect anomalies and pinpoint high-consumption phases. The system helped an automotive parts manufacturer save ¥1.2 million annually in electricity costs while reducing carbon emissions by 23%.
6. Mobile and VR Interaction
Provides iOS/Android mobile apps and VR headset compatibility for remote production data monitoring and anomaly alerts. VR mode enables immersive virtual factory tours, facilitating remote equipment debugging and training, cutting on-site personnel requirements by 30%.