杭州市拱墅区智慧工地系统合同预警管理数据

采集杭州市拱墅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：采集杭州市拱墅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%。

Contract information within the Gongshu District of Hangzhou was collected into the database via offline devices and manual entry as the original data source. Subsequently, classification statistics were conducted by region and occupation using BI tools, and line charts were utilized to visualize the contract risk trends of occupations in each region, with the analysis results pushed to team leaders, enterprises, labor service companies, and regulatory authorities.
(1) Data Collection: Contract information within the Gongshu District of Hangzhou was collected into the database via offline devices and manual entry as the original data source.
(2) Data Processing: First, clean the collected contract confirmation data by removing entries with 0 or null contract values. Then, perform finest-grained aggregation of the data across three dimensions: time dimension (by year), project dimension (by enterprise), and regional dimension (by district). Calculate the following indicators 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 pending resignation applications (i.e., signed contracts under resignation review), number of resigned employees (i.e., signed contracts with employees who have already resigned), number of overdue unprocessed contracts (i.e., contracts that have not been completed within 7 days after initiation), overdue unprocessing rate = number of overdue unprocessed contracts / total number of contracts, number of overdue unresigned resignation applications (i.e., resignation applications that have not been completed within 7 days after submission), and overdue unresignation rate = number of overdue unresigned resignation applications / number of resigned employees.
(3) Data Analysis: Define risk levels as follows: Low-risk count corresponds to cases where 0% < overdue unprocessing rate < 5% and 0% < overdue unresignation rate < 5%; Medium-risk count corresponds to cases where 5% < overdue unprocessing rate < 10% and 5% < overdue unresignation rate < 10%; High-risk count corresponds to cases where overdue unprocessing rate > 10% and overdue unresignation rate > 10%.

搜集汇总

数据集介绍

特点

该数据集聚焦于杭州市拱墅区智慧工地系统的合同预警管理，涵盖合同签署状态、风险等级等信息，数据规模为161条，每月更新。数据通过设备和手动录入采集，应用于合同风险走势的统计分析，服务于多个相关方。