Abstract: Abstract： In order to solve the optimization problem of average delay and energy consumption caused by the uncertainty of the dynamic arrival and channel conditions of the elderly health data tasks during task unloading in the edge computing environment， an online task computing offloading optimization algorithm based on Lyapunov optimization and deep reinforcement learning was proposed. In a multi-user mobile edge computing network， the user task data arrived randomly. Lyapunov optimization method was applied to constrain and model the queue length in the process of task offloading. Then， the model information was utilized by deep reinforcement learning method to convert the input environment parameters into the process of learning the optimal binary offloading action， and the offloading action was accurately evaluated. The simulation results show that the proposed algorithm is superior to some deep reinforcement learning algorithms， and the energy consumption of task offloading is reduced effectively while the queue length is constrained reasonably.

Key words: Key words： smart senior care, Lyapunov optimization, deep reinforcement learing, edge computing offloading, mobile edge computing