杭州市桐庐县智慧工地系统工资预警管理数据

采集杭州市桐庐县范围内工资信息线下通过设备和手动录入的方式采集数据到数据库作为原始数据源。最后通过BI工具，按区域工种进行分类统计，利用折线图体现每个地区工种的工资风险走势情况。推送给班组长、企业、劳务公司、监管部门。（1）数据采集：采集杭州市桐庐县范围内工资信息线下通过设备和手动录入的方式采集数据到数据库作为原始数据源。（2）数据处理：首先对采集的工资确认数据进行清洗，包括工资金额为0的或者为null的。然后对数据在时间维度按日，项目维度按企业，区域维度按区域，进行最细级别粒度的聚合。计算得到各工地的工资确认总数量为X，未确认工资人数/X=未确认人数在总工资发放中的占比， 待处理数为已经采集且并未介入处理数， 已处理数为已经开始处理的数量，已完成数为已经确认工资发放的数量，超时未处理数为超时未处理数指超过1天未处理数，X=待处理数+已处理数+已完成数。（3）数据分析: 低危风险数=企业待处理数低于总比10%且超时未处理数低于5%，中危风险数=企业待处理数低于总比20%且超时未处理数低于10%，高危风险数=企业待处理数低于总比30%且超时未处理数低于15%。

This dataset collects wage information within the scope of Tonglu County, Hangzhou City. Data is collected offline via equipment and manual entry and stored in a database as the original data source. Finally, classified statistics are conducted by region and job type using BI tools, and line charts are used to visualize the wage risk trends of job types in each region. The analysis results are pushed to team foremen, enterprises, labor service companies, and regulatory authorities.
(1) Data Collection: Wage information within the scope of Tonglu County, Hangzhou City is collected offline via equipment and manual entry and stored in the database as the original data source.
(2) Data Processing: First, clean the collected wage confirmation data, including records with zero or null wage amounts. Then, perform finest-grained aggregation of the data: along the time dimension by day, along the project dimension by enterprise, and along the regional dimension by region. Calculate the total number of wage confirmations for each construction site as X; the proportion of unconfirmed wage recipients is (number of unconfirmed wage recipients)/X. The pending processing count refers to the number of records that have been collected but not yet processed; the processed count refers to the number of records that have started processing; the completed count refers to the number of records with confirmed wage payments; the overtime unprocessed count refers to the number of records that have not been processed for more than 1 day. The formula X = pending processing count + processed count + completed count applies.
(3) Data Analysis: Low-risk count: the enterprise's pending processing count is less than 10% of the total and the overtime unprocessed count is less than 5%; Medium-risk count: the enterprise's pending processing count is less than 20% of the total and the overtime unprocessed count is less than 10%; High-risk count: the enterprise's pending processing count is less than 30% of the total and the overtime unprocessed count is less than 15%.

搜集汇总

数据集介绍

特点

该数据集为杭州市桐庐县智慧工地系统的工资预警管理数据，包含36条记录，每月更新。数据来源于企业，主要用于采集、处理和统计分析工地工资信息，帮助识别和管理工资发放中的风险。