As industries develop, the automation and intelligence level of power plants is constantly improving, and the application of patrol robots is also increasingly widespread. This research combines computer vision technology and particle swarm optimization algorithm to build an obstacle recognition model and obstacle avoidance model of an intelligent patrol robot in a power plant respectively. Firstly, the traditional convolutional recurrent neural network is optimized, and the obstacle recognition model of an intelligent patrol robot is built by combining the connection timing classification algorithm. Then, the artificial potential field method optimizes the traditional particle swarm optimization algorithm, and an obstacle avoidance model of an intelligent patrol robot is built. The performance of the two models was tested, and it was found that the highest precision, recall, and F1 values of the identification model were 0.978, 0.974, and 0.975. The highest precision, recall, and F1 values of the obstacle avoidance model were 0.97, 0.96, and 0.96 respectively. The two optimization models designed in this research have better performance. In conclusion, the two models in this study are superior to the traditional methods in recognition effect and obstacle avoidance efficiency, providing an effective technical scheme for intelligent patrol inspection of power plants.

随着工业的发展，电厂的自动化、智能化水平不断提高，巡检机器人的应用也日益广泛。本研究结合计算机视觉技术和粒子群优化算法，分别构建了电厂智能巡检机器人的障碍物识别模型和避障模型。首先对传统的卷积循环神经网络进行优化，结合连接时序分类算法构建智能巡逻机器人的障碍物识别模型。然后，利用人工势场法对传统粒子群优化算法进行优化，建立了智能巡逻机器人的避障模型。对两个模型的性能进行测试，发现识别模型的最高查准率、查全率和F1值分别为0.978、0.974和0.975。避障模型的最高精度、召回率和F1值分别为0.97、0.96和0.96。本研究设计的两种优化模型具有较好的性能。综上所述，本研究的两种模型在识别效果和避障效率上均优于传统方法，为电厂智能巡检提供了有效的技术方案。