Due to its advantages of low cost,consumable,distributed deployment,agility and flexibility,UAVs have shown great success in many civil fields.However,due to the limitation of its intelligence,there are still significant challenges in how to autonomously and safely complete tasks under complex adversarial conditions.Aiming at the problems of intelligence and safety in UAV mission planning,based on safe reinforcement learning,a UAV intelligent planning method called SAC-Lagrangian is proposed.Considering the radar threats,no fly zone safety constraints and ground-to-air missile(SAM) countermeasure conditions,the mission planning problem is modeled as a Constrained Markov Decision Process (CMDP),which is transformed into a dual problem through Lagrangian multiplier method.The maximum entropy Soft Actor-Critic(SAC) algorithm is used to approximate the optimal policy,ensuring that the agent can maximize the expected return under the safety constraints.Compared with other baseline algorithms,simulation results show that the proposed method can ensure the safety while ensuring the task performance,adapt to the dynamical changing scenarios,and achieve a task completion rate of 96%.Therefore,the proposed method is efficient,robust and safe.