食品安全数字治理应用数据

针对阳光工厂、商超溯源、智慧餐饮、阳光农场、智慧菜场，5个场景的日常线下的隐患排查数据；以及针对5大场景的物联数据的采集，如：物联设备信息、隐患排查数据、生产企业关键控制点（CCP的数据）等数据采集。其中，物联设备信息采集的是智慧餐饮中，部分学校场景的物联数据，如：物联设备型号、单位名称、设备类型、设备编号；隐患排查采集的是智慧餐饮中，部分餐饮店场景的物联数据，如：评价内容、排查项目、排查日期；生产企业关键控制点（CCP的数据）采集的是阳光工厂中，部分CCP点位数据，如：企业名称、设备类型、设备编号、日期、温度值、湿度值。通过对设备的物联数据及线下人员采集的现场隐患问题对企业开展风险等级评定，一是物联数据及预警数据采集；二是排查隐患问题数据采集，运用食品相关法律法规对企业进行分级评定。针对这些信息进行风险指数评定，对风险指数高的单位及设备进行预警。

This dataset includes daily offline hidden danger inspection data for five scenarios: Sunshine Factory, Supermarket Traceability, Smart Catering, Sunshine Farm, and Smart Market, alongside IoT data collection for these five scenarios. The collected IoT data covers multiple categories: IoT device information, hidden danger inspection data, and data from Critical Control Points (CCP) of production enterprises, etc.

Specifically, the IoT device information is collected from partial school scenarios under Smart Catering, including IoT device model, affiliated unit name, device type, and device number. The hidden danger inspection data is collected from partial restaurant scenarios under Smart Catering, including inspection evaluation content, inspection items, and inspection date. The CCP data is collected from partial CCP points under Sunshine Factory, including enterprise name, device type, device number, collection date, temperature value, and humidity value.

Based on the IoT data of devices and on-site hidden danger problems collected by offline personnel, risk level assessment will be conducted for enterprises. The assessment workflow includes two core parts: first, collecting IoT data and early warning data; second, collecting hidden danger problem data. Enterprises are graded and evaluated in accordance with relevant food safety laws and regulations, and a unified risk index evaluation is performed for all collected information. Early warnings will be issued for units and devices with high risk indexes.

搜集汇总

数据集介绍

特点

该数据集聚焦于食品安全领域的数字治理，涵盖多个场景的隐患排查和物联数据采集，数据规模较大且每日更新，主要用于企业风险等级评定和预警。