基于DQN和K-means聚类算法的天然气站场仪表智能识别研究

天然气站场中的仪表是工人和设备交互的窗口，可以反映工厂的运行状况。但是站场很多老式仪表不能远程读取示数，采用人工方法读取则浪费人力，需要对其进行智能化的读数研究。针对上述问题，采用了一种基于四足机器人作为载体运动控制，并通过深度强化学习（DQN）进行目标追踪任务和图像处理来读取仪表示数的新方法。首先通过改进的 DQN 算法的深度网络模型，根据仿真的环境中机器人学习效果，设计并调整动作奖励函数，设计机器人顶层决策控制系统。实现一维与二维状态参数输入下的仪表目标追踪任务。其次在仪表定位和仪表配准的基础上，通过K-means聚类二值化处理得到刻度分明的表盘；将图像进行内切圆处理，再在图像中间添加一根指针进行旋转，旋转过程中精确计算指针与表盘重合度最高的角度来得到对应刻度。经过实验表明，此算法可实现运动过程中仪表目标的精准追踪和降低计算时间，并大大提高了仪表追踪与识别的精度和效率，为天然气站场的仪表安全监控提供了有效保障。

Research on Intelligent Recognition of Natural Gas Station Meters Based on K-means Clustering Algorithm

The meters in the natural gas station are the windows for the interaction between workers and equipment, which can reflect the operation status of the plant. However, many old-fashioned instruments in the station yard cannot read the readings remotely, and manual reading is a waste of manpower, and it is necessary to carry out intelligent reading research on them. Aiming at the above problems, a new method based on quadruped robot as carrier motion control, and target tracking task and image processing through deep reinforcement learning (DQN) to read the instrument representation number is adopted. Firstly, through the deep network model of the improved DQN algorithm, according to the robot learning effect in the simulated environment, the action reward function is designed and adjusted, and the top-level decision control system of the robot is designed. The instrument target tracking task under the input of one-dimensional and two-dimensional state parameters is realized. Secondly, on the basis of meter positioning and meter registration, K-means clustering binarization is used to obtain a dial with clear scale; the image is inscribed circle, and then a pointer is added in the middle of the image to rotate, during the rotation process Accurately calculate the angle with the highest coincidence between the pointer and the dial to obtain the corresponding scale. Experiments show that this algorithm can achieve accurate tracking of instrument targets and reduce calculation time during the movement process, and greatly improve the accuracy and efficiency of instrument tracking and identification, providing an effective guarantee for instrument safety monitoring in natural gas stations.