Study Of The Automatic Detection Of Abnormality Of Express Delivery In Express Systems

Currently, the logistics to send pieces anomaly detection has become increasingly concerned about the major companies, especially in recent years is more and more popular in e-commerce enterprises. Unlike traditional industries, with the rapid development of e-commerce, logistics is an emerging industry in recent years, the business has its own characteristics. Anomaly detection for enterprises, especially for e-commerce enterprises, fast delivery and quality service in delivery is the most important key to improve logistics efficiency and strengthen corporate image and so on, but a lot of customer complaints cause by packets loss and delay are often exist in the current logistics, so the anomaly detection about sending pieces of the logistics trace needs to be improved. This subject of logistics anomaly detection based on the current system or software features and business processes, set the abnormal warning, abnormal testing, data analysis and data visualization by ranging the time of the sending pieces, and other new features, and then developed an anomaly detection system with Python Web.The main work of this paper including the following aspects:(1) Introducing the development of anomaly detection in logistics sending process and technical characteristics, summed the functions about the anomaly detection in logistics sending, using Python Web technologies to improve the technology about the alarm would appear during the logistics sending.,Research the ERP for logistics systems currently available on the market anomaly detection system or software, summing their respective features and focused direction, as developed in this paper reference.(2) Join the abnormal warning and data analysis module in the traditional functions, by calculating the time interval of each node information to predict whether there may be an exception, analysis of collected anomal data, feedback abnormal warning results, adjust warning threshold to increase the accuracy of the abnormal warning in order to achieve early warning of abnormal to help logistics enterprises handle exceptions.(3) Study and research of Python Web developers and Web-related technologies, including: Python (server-side programming language), JavaScript (client-side programming language), jQuery (JavaScript framework), Django (Python Integration Framework). With these technologies and frameworks, complete the development of the logistics anomaly detection system, and test and evaluate the system was.