深圳市大鹏新区某学校校园无人零售智能货柜商品销售分析数据

应用场景： 调查了学校教学楼和食堂附近的无人售货机，发现学生最常买的是饮料（比如矿泉水、茶饮料）和零食（比如辣条、薯片），尤其是午休和下午放学时段销量最高。课间操前很多同学会买功能饮料，考试周速溶咖啡和巧克力卖得也多。不过，像泡面、咸菜这类商品几乎没人买，经常缺货的反而是一些新到的网红零食。通过分析数据，发现饮料和零食能占到总销售额的80%，但不同时间卖的东西不太一样——比如夏天冰棍销量暴增，冬天热饮更受欢迎。这些数据能帮助无人售货机的运营方调整商品种类，比如在考试周多备咖啡，夏天增加冰柜数量，或者在课间人流量大的时候减少泡面进货，避免浪费。

Application Scenario: This study investigated vending machines located near school classroom buildings and campus cafeterias. Findings indicate that the most frequently purchased goods by students are beverages (e.g., mineral water, tea drinks) and snacks (e.g., spicy gluten strips, potato chips), with peak sales observed during lunch break and afternoon after-school periods. A significant number of students also purchase functional beverages prior to morning exercise breaks, while sales of instant coffee and chocolate increase notably during exam weeks. In contrast, products such as instant noodles and pickled vegetables yield almost no sales, and newly launched viral snacks frequently suffer from stockouts. Data analysis reveals that beverages and snacks collectively account for 80% of total sales, yet the top-selling product categories vary across different time periods: for example, ice pop sales surge in summer, while hot beverages are more popular in winter. These datasets can assist vending machine operators in optimizing their product portfolios: specifically, increasing stock of coffee during exam weeks, adding more freezers in summer, or reducing instant noodle inventory during peak inter-class pedestrian flows to minimize unnecessary waste.

搜集汇总

背景与挑战

背景概述

该数据集记录了深圳市大鹏新区某学校校园内无人售货机一个月的消费情况，包含货柜编号、商品类别、售卖数量等字段，共12113条数据。数据通过多源采集和清洗处理，应用ABC分类法对商品需求等级进行评价，适用于分析学生消费习惯和优化商品配置。