Design And Implementation Of Business Intelligence System Based On Mobile Shopping Cart

With the development of science and technology and economy,the expansion of people's retail purchase channels has brought about online and offline retail models.For online or offline retail operators,increasing sales to increase profits is the ultimate goal of business operations.The business intelligence system can integrate a large number of multi-source data,flexible multi-angle analysis,and mining out unknown rules from a large amount of data can be a very good service and retail the ultimate goal.For the offline retail industry,in the context of the large number of applications of POS machines and retail systems,various merchants have precipitated a huge amount of sales-related inventory data,which can be used for data mining through business intelligence to give practical business advice.However,compared with online retail,data mining of business intelligence has been difficult to apply to online retail browsing and shopping basket interest processes due to the difficulty of offline retail customer location tracking.This paper improves the indoor positioning of Bluetooth and uses the shielding factor method to improve the accuracy of the indoor positioning method,so that the operators in offline and offline retail can track and record the browsing of user products.The specific implementation method is to place the electronic price tag on the shopping cart and collect the RSSI of each mobile shopping basket through the smart AP.After Gaussian filtering,the Bluetooth intensity of each electronic price tag is uploaded to the mobile shopping basket in a segmented manner at an interval of 5 seconds.The management system and the positioning system use the triangulation method to calculate the Bluetooth accuracy,thereby obtaining the user's real-time coordinates,traveling speed,and other data.In order to analyze the user location data,this paper implements a business intelligence system based on a mobile shopping basket,which is used to integrate the data in the shopping cart positioning system,the commercial super POS retail system,the user management system,and other subsystems for unified analysis.Excavate.The implementation method is to integrate the user positioning points in the shopping cart positioning system into path data,and clean the data according to the characteristics of the data,and then use the DBSCAN clustering algorithm to classify the path data,and the list of goods purchased by the user in the super business POS management system.Combine data mining and use the Apriori algorithm and FP-Tree algorithm to discover the relationship between user route categories and purchased products.In order to analyze and display the results of the business intelligence system,this paper analyzes and mines the data by implementing the business intelligence display system of the mobile shopping basket.The results are visualized and displayed,including the heat map display for the user's density,according to the user's The analysis of the user interest by the comparison of the speed of travel and the adjacent product categories,and the user's buying habits and occupation age inference based on the analysis of the relationship between the user's purchase path and the purchased goods and time,and corresponding commercial recommendations according to the corresponding scenarios.