为了解决基于DQN算法进行智能船舶全局路径规划时，存在规划路径距离障碍物过近、拐点过多、大拐角以及算法收敛速度较慢等问题，提出了基于噪声DQN(NoisyNet-DQN)的全局路径规划方法。首先，为了使智能船舶与障碍物保持安全距离，减少路径拐点和大拐角，在传统奖励函数的基础上增加了额外的航向奖励函数、时间奖励函数、拐点奖励函数和安全奖励函数；然后针对在复杂航行场景中，算法收敛速度慢的问题，在DQN神经网络的输出层引入了参数噪声，提高了DQN网络收敛速度。最后，针对大连和舟山实际海域环境开展了仿真研究。仿真结果表明，提出的Noise-DQN算法相比于传统DQN算法，其算法收敛速度得到了显著提升，规划的全局路径在安全性和经济性方面得到了大幅度提高，更加符合船舶的实际航行需求。研究成果可为智能船舶全局路径规划提供一定参考。

 In order to address the challenges encountered in intelligent ship global path planning using the DQN algorithm, such as paths being planned too close to obstacles, excessive turning points, large turning angles, and slow algorithm convergence, a method based on Noisy DQN (NoisyNet-DQN) for global path planning is proposed. Firstly, to maintain a safe distance between intelligent ships and obstacles, and to reduce path turning points and large turning angles, additional reward functions including heading reward, time reward, turning point reward, and safety reward are incorporated on top of the traditional reward function. Secondly, to tackle the slow convergence issue in complex navigation scenarios, parameter noise is introduced into the output layer of the DQN neural network, thereby enhancing the convergence speed of the DQN network. Simulation studies are conducted in the actual maritime environments of Dalian and Zhoushan. The simulation results indicate that compared to the traditional DQN algorithm, the proposed Noise-DQN algorithm significantly improves the convergence speed and, greatly enhances the safety and economy of the planned global path, better aligning with the actual navigation requirements of ships. The research results can provide a certain reference for global path planning in intelligent ship navigation.