基于AHP-熵权法的货舱码垛效果模糊综合评价

目的 解决快递包裹物流过程中暴力分拣问题的前提是对暴力分拣行为进行有效识别，为此提出一种基于加速度分布特征的快递包裹暴力分拣行为智能识别方法。方法 利用集成三轴加速度传感器的数据采集设备实时采集并截取潜在异常操作情况下快递包裹的加速度数据。然后将潜在异常数据上传服务器，在服务器端执行分布特征的提取。最后将特征矩阵送入神经元网络分类器，得到当前包裹所受操作类别的结果。结果 实验证明，文中提出的多阈值截取方法可以有效截取潜在异常数据，使用加速度分布作为特征可以有效对暴力分拣行为进行分类。其中，使用BP网络作为模式识别分类器时，分类正确率可以达到93.6%;使用CNN作为模式识别分类器时，分类正确率可以达到95.3%。结论 文中提出的暴力分拣识别方法准确、快速，具有良好的在线实时性。基于此方法可以构建暴力分拣行为识别数据库，为进一步完善快递企业服务水平定量化评价体系提供数据支撑。这些数据还可以用于对暴力分拣产生的原因进行深入分析，从而提出减少暴力分拣行为的针对性解决方案。

Fuzzy Comprehensive Evaluation of Cargo Palletizing Effect Based on AHP-Entropy Weight Method

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.