基于数据分析的生鲜超市业务系统的设计与实现

发布时间：2018-09-09 17:23

【摘要】：随着社会的发展和人们生活水平的不断提高,生鲜农产品作为餐桌上不可或缺的食物和营养来源,人们对它的需求也与日俱增。生鲜连锁超市作为一种新的生鲜农产品经营方式,由于其统一经营管理,生鲜农产品的安全和质量更有保障,因而越来越受到消费者的欢迎与认可。然而随着生鲜超市业务的不断发展和扩张,生鲜超市的业务信息系统却发展缓慢。企业现有的超市业务系统往往没有考虑到生鲜农产品的独特性和经营中的特殊需求,缺少对整个生鲜业务的全过程覆盖,同时并没对生鲜超市连锁企业在经营过程中产生的大量数据进行数据分析和挖掘。本文针对长春市某生鲜连锁超市企业的实际情况,设计和开发了更加贴合企业需求的生鲜超市业务系统,并将数据挖掘中的聚类分析和关联规则运用于企业的销售数据分析中。将数据挖掘中的相关技术与业务系统相结合,从而帮助企业制定更加合理的经营管理和营销策略。主要工作分为两部分:第一部分为生鲜超市业务系统基础功能的实现工作,系统采用当前较为流行的Java语言,开源的SSH框架来完成系统中各个模块的开发。系统的基础业务模块主要包括用户管理、接受销售流水、验收入库、大库分货、门店调拨、商品报损、采购计划、门店订货、商品管理、在途商品信息管理、退货处理等,让生鲜超市经营中的每一环节都可控可追踪,完成数据共享和整个业务的信息化,提高生鲜流转过程中信息化水平。在途商品信息管理中使用开源的Open Layers构建了基于JavaScript的轻量级Web GIS模块,能够更加直观的获取运输地理位置信息。此外在考虑到采购人员对移动办公的迫切需求后,完成了采购管理移动App的设计与开发,帮助采购员更好的开展采购工作;第二部分工作为设计和实现对生鲜连锁超市的销售数据分析,由于生鲜超市在经营过程中会积累大量数据以及每个门店的销售数据不同,因此可以使用各个门店的销售数据来对门店进行聚类分析。但是在对聚类分析中的模糊C均值聚类算法进行研究后,发现该算法在聚类前需要指定聚类数目的缺点后引入了统计学中的混合F分布,提出了一种能获取最佳聚类数目的改进方案,能够解决模糊C均值聚类算法需要事先指定聚类数目的缺点,并将改进后的算法应用到生鲜超市销售特征的分析中。然后针对生鲜超市顾客购买商品之间的潜在关联,设计和实现了顾客购物篮分析。生鲜连锁超市顾客每次购买商品时的交易数据可以称为购物篮数据,由于数据挖掘技术中的关联规则较为适用于生鲜超市顾客购物篮交易数据的分析中,因此采用Apriori关联规则挖掘算法来对顾客的购物篮数据进行商品间关联规则的挖掘。通过两个模块中的实验结果进行分析,能够发现企业积累的大量数据中潜在的信息,为企业的实际经营管理提供帮助。
[Abstract]:With the development of society and the improvement of people's living standards, fresh agricultural products as an indispensable source of food and nutrition on the table, the demand for it is also increasing. As a new mode of management of fresh agricultural products, fresh supermarket chain is more and more popular and accepted by consumers because of its unified management, and the safety and quality of fresh agricultural products are more and more guaranteed. However, with the development and expansion of fresh supermarket business, the business information system of fresh supermarket is developing slowly. The existing supermarket business systems of enterprises often do not take into account the uniqueness of fresh agricultural products and the special needs in operation, and lack the whole process of covering the whole fresh products business. At the same time, there is no data analysis and mining on a large number of data generated in the operation process of fresh supermarket chain enterprises. In view of the actual situation of a fresh supermarket enterprise in Changchun, this paper designs and develops a fresh supermarket business system that meets the needs of the enterprise, and applies the clustering analysis and association rules in data mining to the sales data analysis of the enterprise. The related technology in data mining is combined with business system to help enterprises to formulate more reasonable management and marketing strategies. The main work is divided into two parts: the first part is the realization of the basic functions of the fresh supermarket business system. The system adopts the popular Java language and the open source SSH framework to complete the development of each module of the system. The basic business modules of the system mainly include user management, acceptance of sales flow, acceptance of warehousing, distribution of stores, allocation of stores, reporting of loss of goods, purchase plan, store ordering, commodity management, information management of goods in transit, return handling, etc. Make every link of fresh supermarket controllable and traceable, complete data sharing and the information of the whole business, improve the level of information in the process of fresh and fresh circulation. In the course of commodity information management, open source Open Layers is used to construct lightweight Web GIS module based on JavaScript, which can obtain geographic location information of transportation more intuitively. In addition, after considering the urgent need of purchasing staff for mobile office, we completed the design and development of the procurement management mobile App, to help buyers to better carry out the procurement work; The second part of the work is the design and implementation of fresh supermarket sales data analysis, because the fresh supermarket in the business process will accumulate a large number of data and the sales data of each store is different. Therefore, we can use the sales data of each store to cluster the stores. However, after studying the fuzzy C-means clustering algorithm in clustering analysis, it is found that the fuzzy C-means clustering algorithm has introduced the mixed F distribution in statistics after it needs to specify the number of clusters before clustering. An improved scheme to obtain the best number of clusters is proposed, which can solve the problem that the fuzzy C-means clustering algorithm needs to specify the number of clusters in advance, and the improved algorithm is applied to the analysis of the sales characteristics of fresh supermarkets. Then the analysis of customer shopping basket is designed and implemented in allusion to the potential relationship between the customers of fresh supermarket and the purchase of goods. The transaction data of fresh supermarket customers every time they buy goods can be called shopping basket data, because the association rules in data mining technology are more suitable for the analysis of shopping basket transaction data of fresh supermarket customers. Therefore, the Apriori association rule mining algorithm is used to mine the association rules between the items of the shopping basket data. Through the analysis of the experimental results in the two modules, we can find the potential information in a large amount of data accumulated by the enterprise, and provide help for the actual management of the enterprise.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.13

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