宁波鄞州区智慧工地系统合同预警管理数据

采集宁波鄞州区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：采集宁波鄞州区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%。

Data was collected as the original data source for contract information within Yinzhou District, Ningbo via manual input and equipment, and stored into the database. Subsequently, Business Intelligence (BI) tools were employed to conduct classified statistics by region and job type, with a line chart used to visualize the contract risk trends of job types in each region. The analysis results were pushed to team leaders, enterprises, labor service companies, and regulatory authorities.
(1) Data Collection: Collect contract information within the scope of Yinzhou District, Ningbo via manual input and equipment, and store the collected data into the database as the original data source.
(2) Data Processing: First, clean the collected contract confirmation data, including removing entries with zero or null contract values. Then, perform finest-grained aggregation of the data along three dimensions: 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 number of signed contracts refers to the count of confirmed contracts, the number of pending signatures refers to the count of unconfirmed contracts, the number of pending resignation applications refers to the count of signed contracts under active resignation application, and the number of resigned cases refers to the count of signed contracts that have completed resignation procedures. The number of overdue unprocessed cases refers to the number of contracts that have not been completed within 7 days after initiation; the overdue unprocessing rate is calculated as (number of overdue unprocessed cases / total number of contracts). The number of overdue unresigned cases refers to the number of submitted resignation applications that have not been completed within 7 days; the overdue unresignation rate is calculated as (number of overdue unresigned cases / number of resigned cases).
(3) Data Analysis: Define low-risk count as cases where 0% < overdue unprocessing rate < 5% and 0% < overdue unresignation rate < 5%; medium-risk count as cases where 5% < overdue unprocessing rate < 10% and 5% < overdue unresignation rate < 10%; high-risk count as cases where overdue unprocessing rate > 10% and overdue unresignation rate > 10%.

搜集汇总

数据集介绍

特点

宁波鄞州区智慧工地系统合同预警管理数据集包含104条记录，每月更新，涵盖项目名称、地址、合同数量及风险等级等信息，通过BI工具进行区域和工种分类统计，用于合同风险监控和预警。