杭州市互联网+废旧物资回收市场决策数据

通过采集分析余杭地区不同品类回收物品的销售重量，销售金额数据，分析各个品类的销售价格。能直观反应不同时段各个回收品类的价格走势，为垃圾回收市场，二次销售和采购提供可靠的分析指导依据（1）数据采集：线下通过设备和手动录入的方式采集销售数据到数据库作为原始数据源。（2）数据处理：首先对采集销售数据进行清洗，包括销售金额为0的或者为null的。然后对销售数据在时间维度按日，品类维度按品类，小类维度按小类，进行最细级别粒度的聚合。（3）数据分析：按照公式：p = sum(O)/sum(w)；其中o为每日小类销售金额；w为每日小类销售重量；p为销售均价；每天每个小类总销售金额，通过将每个订单的销售金额相加来实现；每天每个小类总销售重量，即该天内的订单总重量；使用上述公式，按照销售均价=每日销售小类销售金额/每日销售小类销售重量的算法进行统计分析。（4）数据应用：最后通过BI工具，按品类进行分类统计，利用折线图体现每个品类的历史价格走势情况。

This dataset collects and analyzes sales weight and sales amount data of various categories of recycled items in Yuhang District, to derive the selling prices of each category. It can intuitively reflect the price trends of different recycled item categories across different time periods, providing reliable analytical guidance for the waste recycling market, resale and procurement activities.
(1) Data Collection: Sales data is collected offline via physical equipment and manual entry into a database, serving as the original data source.
(2) Data Processing: First, clean the collected sales data by removing entries with sales amount equal to 0 or null. Then, aggregate the sales data at the finest granularity: by day in the time dimension, by main category in the product category dimension, and by sub-category in the sub-category dimension.
(3) Data Analysis: Calculate using the formula: p = sum(O)/sum(w), where O denotes the daily sales amount of the sub-category, w denotes the daily sales weight of the sub-category, and p denotes the average selling price. The total daily sales amount of each sub-category is calculated by summing the sales amounts of all its orders on that day; the total daily sales weight of each sub-category is the total weight of all orders generated on that day. Statistical analysis is performed according to the algorithm that average selling price = total daily sales amount of the sub-category / total daily sales weight of the sub-category.
(4) Data Application: Finally, use Business Intelligence (BI) tools to conduct categorized statistics by product category, and visualize the historical price trends of each category using line charts.