基于AI视觉的井工煤矿井下新型辅助运输系统设计

Design of New Type of Auxiliary Transportation System for Underground Coal Mines Based on AI Vision

为提高煤矿井下辅助运输的效率,提出基于AI视觉的井工煤矿井下新型辅助运输系统设计,以增强避障效果,达到较高的运输效率。硬件部分,选取无极绳绞车牵引+卡轨车的运输方式,并配置传感器、控制模块、通信与协作模块等进行数据采集、传输和控制;软件部分,预处理所采集传输的图像数据,再利用卷积神经网络实现对图像的特征提取,并通过全连接层对图像数据进行分类,以实现对障碍物的检测。最终将检测结果输出至监控中心,由PLC控制器来控制电控绞车,实现避障,至此,完成井工煤矿井下新型辅助运输系统的设计。结果表明,所设计系统的运输效率较高,具有可靠的应用性。

In order to improve the efficiency of coal mine underground auxiliary transportation,a new auxiliary transportation system design based on AI vision is proposed to enhance the effect of obstacle avoidance and achieve higher transportation efficiency.In the hardware part,the transportation mode of endless rope winch traction+rail car is selected,and the sensor,control module,communication and cooperation module and other modules are configured for data acquisition,transmission and control.In the software part,the collected and transmitted image data are preprocessed,and then the convolutional neural network is used to extract the image features,and the image data are classified through the full connection layer to realize the detection of obstacles.Finally,the detection results will be output to the monitoring center,and the PLC controller will control the electric winch to avoid obstacles.So far,the design of a new auxiliary transportation system for underground mine is completed.The results show that the designed system has high transportation efficiency and reliable applicability.