本文選題：物流派件異常偵測(cè)　切入點(diǎn)：異常預(yù)警　出處：《浙江工業(yè)大學(xué)》2014年碩士論文

【摘要】：當(dāng)前,物流派件異常偵測(cè)已經(jīng)成為各大企業(yè)愈加關(guān)注的領(lǐng)域,尤其是最近幾年在企業(yè)電子商務(wù)領(lǐng)域中十分流行。不同于傳統(tǒng)行業(yè),隨著電子商務(wù)的迅猛發(fā)展,物流作為近年來(lái)新興的行業(yè),有著自身的業(yè)務(wù)特色。異常偵測(cè)對(duì)于企業(yè)尤其是電商來(lái)說(shuō),貨件的快速派送與貨件派送的優(yōu)質(zhì)服務(wù)是提升物流效率和強(qiáng)化企業(yè)形象等方面的關(guān)鍵決勝點(diǎn),但是當(dāng)前物流中存在大量的丟包延誤而被客戶(hù)投訴的事件,可見(jiàn)物流派件跟蹤的異常偵測(cè)需要改善。本課題根據(jù)目前的物流異常偵測(cè)系統(tǒng)或軟件的特色和業(yè)務(wù)流程,通過(guò)對(duì)派件時(shí)間范圍的設(shè)定加入異常預(yù)警、異常檢測(cè)、數(shù)據(jù)分析和數(shù)據(jù)可視化等新功能,并利用Python Web技術(shù),再開(kāi)發(fā)了一個(gè)異常偵測(cè)系統(tǒng)。 本文的工作主要包括以下幾個(gè)方面： (1)詳細(xì)介紹了物流派件異常偵測(cè)的發(fā)展過(guò)程和技術(shù)特點(diǎn),總結(jié)了物流派件異常偵測(cè)相關(guān)功能,利用Python Web技術(shù)完善物流派件過(guò)程中出現(xiàn)異�？蓪�(shí)現(xiàn)的偵測(cè)與告警。對(duì)目前市場(chǎng)上已有的物流ERP系統(tǒng)異常偵測(cè)系統(tǒng)或軟件,進(jìn)行了調(diào)研,綜合總結(jié)它們各自的功能特點(diǎn)和側(cè)重方向,作為本文開(kāi)發(fā)的參考。 (2)在傳統(tǒng)功能實(shí)現(xiàn)的基礎(chǔ)上加入異常預(yù)警和數(shù)據(jù)分析模塊,通過(guò)計(jì)算各信息節(jié)點(diǎn)的時(shí)間間隔,來(lái)預(yù)測(cè)是否可能存在異常,分析收集的異常數(shù)據(jù),反饋異常預(yù)警結(jié)果,調(diào)節(jié)預(yù)警閥值,增加異常預(yù)警的準(zhǔn)確性,從而實(shí)現(xiàn)對(duì)異常的預(yù)警,提升物流企業(yè)處理異常的及時(shí)性。 (3)學(xué)習(xí)和研究Python Web和其他Web開(kāi)發(fā)的相關(guān)技術(shù),包括：Python(服務(wù)器端編程語(yǔ)言),JavaScript(客戶(hù)端編程語(yǔ)言),jQuery (JavaScript框架),Django (Python集成框架)。利用上述技術(shù)和框架,完成了物流異常偵測(cè)系統(tǒng)的開(kāi)發(fā),并對(duì)該系統(tǒng)進(jìn)行了測(cè)試和評(píng)估。
[Abstract]:At present, the abnormal detection of material distribution has become an increasingly concerned area for large enterprises, especially in recent years, it is very popular in the field of enterprise e-commerce. Different from the traditional industry, with the rapid development of e-commerce, Logistics, as an emerging industry in recent years, has its own business characteristics. The quick delivery of goods and the high quality service of delivery of goods are the key decisive points in improving the efficiency of logistics and strengthening the image of the enterprise. However, there are a large number of incidents in the current logistics of losing packets and being complained by customers. It can be seen that the anomaly detection of logistics dispatch tracking needs to be improved. According to the characteristics and business process of the current logistics anomaly detection system or software, this subject adds anomaly warning and anomaly detection through setting the time range of the distribution. Based on the new functions of data analysis and data visualization, an anomaly detection system is developed using Python Web technology. The work of this paper mainly includes the following aspects:. In this paper, the development process and technical characteristics of abnormal detection of material distribution are introduced in detail, and the related functions of abnormal detection of logistics distribution are summarized. This paper makes use of Python Web technology to perfect the detection and alarm of abnormal in the process of logistics distribution. The existing abnormal detection system or software of logistics ERP system in the market is investigated. Summarize their respective functional characteristics and focus on the direction, as a reference for the development of this paper. 2) adding anomaly warning and data analysis module on the basis of traditional function realization, calculating the time interval of each information node to predict the possible existence of anomaly, analyzing the collected abnormal data, and feedback the abnormal warning result. Adjust the warning threshold, increase the accuracy of abnormal warning, so as to achieve the abnormal warning, improve the timeliness of logistics enterprises to deal with anomalies. 3) to study and study the relevant technologies of Python Web and other Web development, including: Python (client programming language / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java / Java). The system is tested and evaluated.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP393.09

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本文編號(hào)：1657891