AI Sensor Model Retraining by Reference Sensor Switching

The patent segments sensors into groups based on their data characteristics (format, resolution, frequency) and assigns specific AI models to each group. This segmentation allows each model to be optimized for its specific sensor group while maintaining resource efficiency through shared infrastructure and model management.

The patent changes model parameters dynamically based on sensor group characteristics. Different sensor groups receive models with adjusted parameters suited to their specific data formats, resolutions, and frequencies, thereby maintaining high prediction accuracy while using a generalized model approach.

The system implements automated model management where the platform automatically assigns models to sensor groups, monitors performance, and retraining occurs without manual intervention. The system serves itself by detecting when retraining is needed and executing the retraining process automatically based on performance metrics.

The patent implements continuous feedback loops where model performance is monitored against actual sensor data, and when performance degradation is detected, the system automatically triggers retraining. This feedback mechanism maintains high accuracy while eliminating manual management complexity.

The patent implements periodic retraining based on performance monitoring rather than continuous retraining. Models are retrained at intervals determined by performance degradation thresholds, ensuring adaptability to environmental changes while minimizing unnecessary retraining that would waste time and computational resources.

The system changes retraining parameters dynamically based on environmental change detection. When environmental changes are detected through performance monitoring, retraining is triggered with adjusted parameters to adapt quickly; when stability is maintained, retraining is deferred to reduce time and computational costs.