苏州张家港市智慧工地系统合同预警管理数据

采集苏州张家港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：采集苏州张家港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 is established by collecting contract information within the scope of Zhangjiagang City, Suzhou via offline equipment and manual entry, then storing the data into a database. Subsequently, BI tools are used to conduct classified statistics by region and job type, and line charts are 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 Zhangjiagang City, Suzhou is collected offline via equipment and manual entry, then stored into the database as the original data source.
(2) Data Processing: First, clean the collected contract confirmation data, eliminating entries with zero or null contract values. Then perform finest-granularity aggregation on the data along the time dimension (by year), project dimension (by enterprise) and regional dimension (by region). Calculate the following metrics for each construction site: total number of contract confirmations; number of signed contracts (i.e., confirmed contracts); number of pending signatures (i.e., unconfirmed contracts); number of applications in resignation (i.e., signed contracts under resignation processing); number of resigned employees (i.e., signed contracts with employees having completed resignation procedures). The number of overdue unprocessed contracts refers to contracts that have not been completed within 7 days after initiation. The overdue unprocessed rate = number of overdue unprocessed contracts / total number of contracts. The number of overdue unresigned items refers to resignation applications that have not been completed within 7 days after submission. The overdue unresigned rate = number of overdue unresigned items / number of signed contracts.
(3) Data Analysis: Low-risk count: 0% < overdue unprocessed rate < 5% and 0% < overdue unresigned rate < 5%; Medium-risk count: 5% < overdue unprocessed rate < 10% and 5% < overdue unresigned rate < 10%; High-risk count: overdue unprocessed rate > 10% and overdue unresigned rate > 10%.