杭州市富阳区智慧工地系统工资预警管理数据

采集杭州市富阳区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：采集杭州市富阳区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%。

The original data source is salary information collected offline within Fuyang District, Hangzhou City via equipment and manual entry into a database. Subsequently, Business Intelligence (BI) tools are used to classify and aggregate data by region and job type, generate line charts to visualize the wage risk trends of each job type in different regions, and push the results to team leaders, enterprises, labor service companies and regulatory authorities.

(1) Data Collection: Salary information within Fuyang District, Hangzhou City is collected offline through equipment and manual entry into the database as the original data source.

(2) Data Processing: First, clean the collected salary confirmation data, including records with a salary amount of 0 or null. Next, perform finest-grained aggregation of the data: by day in the time dimension, by enterprise in the project dimension, and by region in the regional dimension. Calculate the total number of salary confirmations for each construction site as X; the proportion of unconfirmed wage recipients in the total salary confirmations is calculated as unconfirmed personnel / X. The pending processing count refers to the number of collected salary confirmation data that has not yet been processed; the processed count refers to the number of salary confirmation data that has started processing; the completed count refers to the number of salary confirmation data for which salary payment has been confirmed; the overtime unprocessed count refers to the number of salary confirmation data that has not been processed for more than 1 day. The formula X = pending processing count + processed count + completed count holds true.

(3) Data Analysis: Low-risk count is defined as enterprises where the proportion of pending processing count in the total is less than 10% and the proportion of overtime unprocessed count in the total is less than 5%; Medium-risk count is defined as enterprises where the proportion of pending processing count in the total is less than 20% and the proportion of overtime unprocessed count in the total is less than 10%; High-risk count is defined as enterprises where the proportion of pending processing count in the total is less than 30% and the proportion of overtime unprocessed count in the total is less than 15%.

搜集汇总

数据集介绍

特点

该数据集聚焦于杭州市富阳区智慧工地系统的工资预警管理，包含详细的工资发放和风险分析数据，每月更新，用于监控和管理工资发放情况，支持通过BI工具进行数据可视化分析。