Fundamentals of Artificial Intelligence Applications in the Internet of Things

AI technologies can extract valuable information from massive IoT data, enabling deep data mining and value transformation.

Achieve intelligent device control and autonomous decision-making through AI, reducing manual intervention and improving system efficiency.

Support forward-looking applications like failure prediction and trend analysis, identifying issues early and taking preventive measures.

Provide users with more accurate and personalized service experiences based on AI-powered recommendations and intelligent interaction.

• Lightweight AI model deployment
• Basic data preprocessing
• Real-time response processing

• Medium-complexity model execution
• Local data analysis
• Temporary decision-making

• Complex AI model training
• Global data analysis
• Model optimization and updates

• Classification Algorithms: Device state recognition
• Regression Algorithms: Sensor data prediction

• Clustering Algorithms: Device behavior analysis
• Anomaly Detection: Fault identification

• Intelligent control strategies
• Resource scheduling optimization

• Predictive Maintenance
• Quality Control
• Production Optimization

• Traffic Management
• Environmental Monitoring
• Energy Management

• Scene Control
• Security Monitoring
• Energy Optimization

• Model Compression
• Algorithm Optimization
• Distributed Computing

• Stream Processing
• Fast Inference
• Parallel Computing

• Data Cleaning
• Feature Engineering
• Incremental Learning