Vacuum Cleaner AI Floor Type Detection for Adaptive Suction Control

The patent combines multiple detection parameters (current load, voltage, cleaning speed, acceleration) into a composite feature set for floor type prediction. This merging of multiple data sources enables the AI model to distinguish between genuine floor type changes and transient variations caused by obstacles or user behavior, thereby improving prediction accuracy without requiring a single complex detection mechanism.

The patent transforms the detection approach from using a single parameter (current load threshold) to using multiple varying parameters (current, voltage, speed, acceleration) that change dynamically during cleaning. This parameter transformation allows the system to capture the nuanced differences between floor types while filtering out noise from obstacles and user patterns through temporal and multi-dimensional analysis.

The patent implements self-service through the AI model that automatically processes multiple data parameters and makes autonomous floor type predictions without requiring manual intervention or complex external processing systems. The model learns from training data and independently handles the complexity of multi-parameter analysis, converting what would be a complex processing requirement into an intelligent autonomous function.

The patent applies preliminary action by pre-training the AI model with labeled floor type data before deployment. This preliminary training phase allows the system to learn the relationships between multiple parameters and floor types in advance, so that during actual operation, the complex data processing is already optimized and the system can reliably predict floor types using the pre-learned patterns without requiring complex real-time processing architecture.