基于加速度分布特征的快递暴力分拣识别方法

目的 针对我国烟草物流配送中心包装效率低、人工成本高、条烟差错率高等问题，设计一种异型烟全自动码垛的控制系统。方法 设计基于SIMATIC S7-1200 PLC、步科MT4523TE触摸屏、YAMAHA机器人控制器RCX240和工控机的控制系统，PLC作为主控制器，与触摸屏和工控机采用PROFINET通讯，与YAMAHA控制器采用PROFIBUS通讯，实现信息的交互。外接的控制按钮、传感器、伺服驱动器与PLC的SM1223和SM1231信号模块连接。PLC将传感器采集的信号和工控机发送的数据进行处理，然后与机器人控制器配合发出各种控制指令，通过各个机构相互协调动作实现对异型烟的快速、自动、精确码垛。结果 该套控制系统可对异型烟长、高进行自动识别，码垛差错率≤0.006%，对垛型进行自动整理功能，损烟率≤0.005%，设备故障率≤0.5%，码垛效率可达3861条/h，系统运行稳定。结论 该套控制系统大大提高了异型烟的码垛效率，降低了生产成本，具有良好的可靠性和稳定性，满足烟草物流配送中心高效稳定作业的要求。

Recognition Method for Rough Handling of Express Parcels Based on Acceleration Distribution Features

The prerequisite of solving the problem of rough handling of express parcels in logistics process is to effectively identify behaviors of rough handling of express parcels. For this reason, the work aims to propose an intelligent recognition method of rough handling of express parcels based on acceleration distribution characteristics. The data acquisition device integrated with the three-axis acceleration sensor was used to collect and intercept the acceleration data of the express parcels in the case of potential abnormal operation in real time. And then the potential abnormal data were uploaded to the server and the distribution features were extracted on the server. Finally the feature matrix was sent to the neural network for classification to get the result of the operation category of the express parcels. Experiments showed that the multi threshold interception method proposed in this paper can effectively intercept the potential abnormal data, and the acceleration distribution used as the feature can effectively classify the rough behavior. Among them, when using BP network as pattern recognition classifier, the classification accuracy can reach 93.6%, and when using CNN as pattern recognition classifier, the classification accuracy can reach 95.3%. The rough handling recognition method proposed in this paper is accurate, fast and has excellent online real-time performance. Based on this method, a database of recognition results for rough handling of parcels can be constructed to provide data support for further improving the quantitative evaluation system of the service level of express delivery enterprises. These data can also be used for in-depth analysis of the causes of rough handling of parcels, thereby proposing targeted solutions to reduce these behaviors.