IoT sensing: Uses technologies such as sensors, RFID, and industrial IoT (IIoT) to collect raw data such as equipment status, material flow, and environmental parameters in real time.
Data processing: Use big data and cloud computing techniques to cleanse, store, and analyze vast amounts of data and extract useful information.
Knowledge extraction: Combining artificial intelligence (such as machine learning and deep learning) with expert systems to identify patterns, predict trends, and diagnose faults in data.
Service integration: Based on knowledge networks and service-oriented architecture (SOA), manufacturing resources (equipment, processes, inventory, etc.) are encapsulated as callable services.
Collaborative human-machine decision-making: Human experts and intelligent systems work together to develop optimal production strategies and response plans.
Closed-loop execution: Issue decision instructions to execution layers such as robots, MES, and APS to automatically adjust production operations and provide feedback to optimize models.