苏州昆山市智慧工地系统合同预警管理数据

采集苏州昆山市范围内合同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合同风险走势情况。推送给班组长、企业、劳务公司、监管部门。（1）数据采集：采集苏州昆山市范围内合同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：首先对采集的合同确认数据进行清洗，包括合同为0的或者为null的。然后对数据在时间维度按年，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。计算各工地的合同确认总数量，已签署数为已经确认合同的数量，待签署数为未确认合同数量， 离职申请中数为已签署合同申请离职中数量，已离职数为已签署合同离职数量，超时未处理数量指合同发起后七天未完成的数量，超时未处理比例=超时未处理数量/总数量，超时未离职数量指提出离职七天未完成数量，超时未离职比例=超时未离职数/已离职数。 （3）数据分析: 低风险数=0%<超时未处理比例<5%、0%<超时未离职比例<5%，中风险数=5%<超时未处理比例<10%、5%<超时未离职比例<10%，高风险数=超时未处理比例>10%、超时未离职比例>10%。

The original data source was established by collecting contract information within the scope of Kunshan City, Suzhou via both manual entry and device-aided offline input into the database. Subsequently, Business Intelligence (BI) tools were utilized to conduct classified statistics by region and job type, and line charts were employed to visualize the contract risk trends of job types in each region, with the results pushed to team leaders, enterprises, labor service companies, and regulatory authorities. (1) Data Collection: Contract information within the scope of Kunshan City, Suzhou was collected via manual entry and device-aided offline input into the database as the original data source. (2) Data Processing: First, clean the collected contract confirmation data, including removing entries with zero contract quantity or null values. Then, perform aggregation at the finest granularity level: by year in the time dimension, by enterprise in the project dimension, and by region in the regional dimension. Calculate the total number of contract confirmations for each construction site, where the signed contract count refers to the number of confirmed contracts, the pending signature count refers to the number of unconfirmed contracts, the in-resignation-review count refers to the number of signed contracts with resignation applications under review, and the resigned count refers to the number of signed contracts where employees have already resigned. The overdue unprocessed count refers to the number of contracts that have not been completed within 7 days after initiation, with the overdue unprocessing rate calculated as overdue unprocessed count / total contract count. The overdue unresigned count refers to the number of resignation applications that have not been completed within 7 days after submission, with the overdue unresignation rate calculated as overdue unresigned count / resigned count. (3) Data Analysis: Define low-risk count as the scenario where 0% < overdue unprocessing rate < 5% and 0% < overdue unresignation rate < 5%; medium-risk count as the scenario where 5% < overdue unprocessing rate < 10% and 5% < overdue unresignation rate < 10%; high-risk count as the scenario where overdue unprocessing rate > 10% and overdue unresignation rate > 10%.

搜集汇总

数据集介绍

特点

该数据集包含苏州昆山市智慧工地系统的合同预警管理数据，涵盖项目名称、地址、合同状态及风险等级等信息，用于监控和分析合同风险。数据每月更新，通过BI工具进行可视化分析，服务于班组长、企业和监管部门。