The application provides an energy efficiency service deployment and delivery method and
system for an
edge computing network, and relates to the technical field of
edge computing.The method comprises the following steps: extracting multi-dimensional service features, encoding and fusing the extracted multi-dimensional service features to generate a global context feature representation; combining
system load
dynamic prediction results to adaptively adjust an upper-layer decision time scale based on the global context feature representation; executing service deployment decisions and
base station sleep / activation switching decisions under the adjusted upper-layer time scale; executing service delivery decisions and
resource allocation decisions based on real-time service requests under a fine-grained lower-layer time scale; and coordinating the decision-making process through a double-time-scale hierarchical learning framework.The method can effectively reduce long-term
network overhead, effectively respond to dynamic service requests, improve the
processing capacity for time
coupling relationships between deployment and delivery cycles, and reduce
system costs.