绍兴市嵊州市智慧工地系统工资预警管理数据

采集绍兴市嵊州市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：采集绍兴市嵊州市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%。

Original data source is established by collecting wage information within the jurisdiction of Shengzhou City, Shaoxing City via offline equipment and manual entry, and storing the collected data into a database. Subsequently, classified statistics are conducted by region and job type using BI tools, and a line chart is utilized to demonstrate the wage risk trend of each region and job type. The analysis results will be pushed to team leaders, enterprises, labor service companies and regulatory departments.

(1) Data Collection: Collect wage information within the jurisdiction of Shengzhou City, Shaoxing City through offline equipment and manual entry, and store the collected data into a database as the original data source.

(2) Data Processing: First, clean the collected wage confirmation data, including removing records with a wage amount of 0 or null. Then, aggregate the data at the finest granularity across three dimensions: time (daily), project (by enterprise), and region (by region). Calculate the total number of wage confirmation tasks for each construction site as X. The proportion of unconfirmed wage workers in total wage payments is calculated as (number of unconfirmed wage workers / X). Define the following metrics: pending tasks refer to collected but unprocessed records; processed tasks refer to records that have started processing; completed tasks refer to records with confirmed wage payments; overdue unprocessed tasks refer to records that have not been processed for more than 1 day. The following formula must be satisfied: X = pending tasks + processed tasks + completed tasks.

(3) Data Analysis: Classify enterprise risk levels based on the following criteria:
- Low-risk count: The number of pending tasks of the enterprise is less than 10% of the total, and the number of overdue unprocessed tasks is less than 5%;
- Medium-risk count: The number of pending tasks of the enterprise is less than 20% of the total, and the number of overdue unprocessed tasks is less than 10%;
- High-risk count: The number of pending tasks of the enterprise is less than 30% of the total, and the number of overdue unprocessed tasks is less than 15%.

搜集汇总

数据集介绍

特点

绍兴市嵊州市智慧工地系统工资预警管理数据集包含35条记录，每月更新，涵盖项目名称、地址、工资确认情况、处理状态及风险等级等信息。数据通过设备和手动录入采集，用于监控工资发放风险，服务于建筑行业相关方。