The application discloses a dynamic self-adaptive edge-cloud
collaborative computing segmentation point and compression strategy adjustment method. The core of the method is to deploy a dynamic decision engine on the
edge device. The engine real-time perceives network state (such as bandwidth,
delay) and application specified task priority, and according to the preset decision logic (such as query table or lightweight prediction model), dynamically selects the optimal model segmentation point and feature compression level combination from a
deep learning model supporting multiple segmentation points and equipped with variable
bit rate layered quantization feature compression model for each segmentation point. The combination is transmitted to the cloud, and the corresponding backend model is loaded to complete the calculation. Through the dynamic adjustment mechanism, the application realizes the real-
time optimal balance among
inference delay, task accuracy and
network overhead, and significantly improves the performance stability,
resource utilization and adaptability of the
system in a variable environment.