This patent, based on the "Method, Apparatus, and Storage Medium for Predicting Tool Wear State," innovatively proposes a smart monitoring solution based on the Internet of Things and cloud collaboration.  By integrating multi-source sensor data (such as vibration and cutting force) and applying adaptive sampling technology and machine learning algorithms, it achieves high-precision real-time monitoring of machine tool status. Its core innovation lies in constructing an anomaly detection model that combines dynamic thresholds with real-time cutting feature feedback, effectively predicting tool wear trends and providing timely warnings of tool breakage risks.  Simultaneously, it automatically adjusts monitoring parameters based on the cutting process, significantly improving monitoring accuracy and adaptability. The system employs a layered architecture and edge computing, ensuring data security while supporting remote monitoring and cross-platform access. This helps precision machining enterprises in the 3C industry transition from reactive maintenance to predictive maintenance, reducing tool wear and unplanned downtime, and improving production efficiency and overall equipment utilization.