Abstract: A rigid-flexible coupling multi-degree-of-freedom walking position-adjusting leg unit and a hybrid robot platform thereof is provided and includes a vertical rigid-flexible coupling multi-degree-of-freedom walking position-adjusting leg unit and a horizontal rigid-flexible coupling multi-degree-of-freedom walking position-adjusting leg unit, which both include a moving device, a moving drive, a steering frame, a lifting frame, a spring device and a driving differential wheel set, in combination with a frame, a driver set, a battery pack and a control box, forming a multi-degree-of-freedom parallel mechanism platform. The hybrid robot platform including the rigid-flexible coupling multi-degree-of-freedom walking position-adjusting leg unit as provided by the present disclosure has functions of a rigid position adjustment, an elastic suspension and a rigid-flexible coupling position adjustment, and can automatically adapt to a working condition of the uneven ground.

Abstract: A deep reinforcement learning-based information processing method includes: determining whether a target edge computing server enters an alert state according to a quantity of service requests received by the target edge computing server within a preset time period; when the target edge computing server enters the alert state, obtaining preset system status information from a preset memory library; computing an optimal action value corresponding to the target edge computing server based on a preset deep reinforcement learning model according to the preset system status information and preset strategy information; and generating an action corresponding to the target edge computing server according to the optimal action value, and performing the action on the target edge computing server. A deep reinforcement learning-based information processing apparatus for an edge computing server includes a first determining module, an acquisition module, a first computing module, a first generation module.

Abstract: A deep reinforcement learning-based information processing method includes: determining whether a target edge computing server enters an alert state according to a quantity of service requests received by the target edge computing server within a preset time period; when the target edge computing server enters the alert state, obtaining preset system status information from a preset memory library; computing an optimal action value corresponding to the target edge computing server based on a preset deep reinforcement learning model according to the preset system status information and preset strategy information; and generating an action corresponding to the target edge computing server according to the optimal action value, and performing the action on the target edge computing server. A deep reinforcement learning-based information processing apparatus for an edge computing server includes a first determining module, an acquisition module, a first computing module, a first generation module.

Abstract: A rigid-flexible coupling multi-degree-of-freedom walking position-adjusting leg unit and a hybrid robot platform thereof is provided and includes a vertical rigid-flexible coupling multi-degree-of-freedom walking position-adjusting leg unit and a horizontal rigid-flexible coupling multi-degree-of-freedom walking position-adjusting leg unit, which both include a moving device, a moving drive, a steering frame, a lifting frame, a spring device and an driving differential wheel set, in combination with a frame, a driver set, a battery pack and a control box, forming a multi-degree-of-freedom parallel mechanism platform. The hybrid robot platform including the rigid-flexible coupling multi-degree-of-freedom walking position-adjusting leg unit as provided by the present disclosure has functions of a rigid position adjustment, an elastic suspension and a rigid-flexible coupling position adjustment, and can automatically adapt to a working condition of the uneven ground.