食堂食品安全风险与员工健康状况关联分析数据

基于人员健康情况统计的食堂食品安全风险指数是一个创新的量化工具，用于评估食堂工作人员的整体健康状况可能对食品安全造成的风险程度。 1.食堂可以通过本数据了解当前食堂食品安全的整体风险状况，及时发现潜在的食品安全问题，指数的变化也可以作为人员健康状态的参考，帮助食堂关注人员健康，及时采取措施。2.餐饮监管部门可以利用本数据作为监管食堂食品安全的依据之一，可通过指数的变化及时发现食品安全风险较高的食堂，提前进行干预和指导。3.食堂或和监管机构可以将本数据对外披露公开，体现本单位或本地区对食品安全工作的重视和承诺，有利于增强用餐者的信任。4.保险公司可根据本数据提前识别目标食堂客户的投保风险，从而确定相关保险产品的定价，如食品安全责任险。（1）数据抽取：在自研的5G智慧食安工业物联网数字化管理平台数据库中抽取相关食堂的考勤数据，包括日期、食堂编号、所在地区、当日测温序号、体温。为充分保障个人隐私，不抽取勤务人员的个人信息数据。（2）数据预处理：对抽取的数据进行清洗，去除重复、错误或无关的信息，以便后续的分析和建模。 2.基于人员健康统计数据预测食堂食品安全风险数据： （1）计算食堂人员整体健康状态异常率：对每个测温序号对应体温的状态进行判定，若体温＜36.2℃或＞37.2℃，则判定为异常，反之为正常；利用CountIf函数分别对当日体温状态为异常的次数、近7天体温状态为异常的次数进行累加；利用SUM函数对近7天的总测温次数进行累加；当日食堂人员整体健康状态异常率=当日体温状态异常次数÷对应的当日测温序号×100%；近7天食堂人员整体健康状态异常率=近7天体温状态异常次数÷近7天测温次数×100%。（2）建立食堂食品安全风险评估模型：基于人员健康情况统计的食堂食品安全风险指数=当日食堂人员整体健康状态异常率×100×0.6+近7天食堂人员整体健康状态异常率×100×0.4；指数数值的上限是100。

The Canteen Food Safety Risk Index based on staff health statistics is an innovative quantitative tool for assessing the level of risk posed to food safety by the overall health conditions of canteen employees.
1. Canteens: They can use this dataset to understand the overall food safety risk status of their current canteen, identify potential food safety issues in a timely manner. Changes in the index can also serve as a reference for staff health status, helping canteens pay attention to staff health and take timely measures.
2. Food and Beverage Supervision Departments: They can use this dataset as one of the bases for supervising canteen food safety, identify canteens with relatively high food safety risks in a timely manner through changes in the index, and conduct early intervention and guidance.
3. Canteens or Regulatory Agencies: They can publicly disclose this dataset to demonstrate the emphasis and commitment of their unit or region to food safety work, which is conducive to enhancing the trust of diners.
4. Insurance Companies: They can use this dataset to identify the underwriting risks of target canteen customers in advance, so as to determine the pricing of relevant insurance products, such as food safety liability insurance.

(1) Data Extraction: Extract attendance data of relevant canteens from the database of the self-developed 5G Smart Food Safety Industrial Internet of Things digital management platform, including date, canteen number, location, daily temperature measurement serial number, and body temperature. To fully protect personal privacy, no personal information of service personnel shall be extracted.
(2) Data Preprocessing: Clean the extracted data to remove duplicate, erroneous or irrelevant information, so as to facilitate subsequent analysis and modeling.

2. Canteen Food Safety Risk Data Prediction Based on Staff Health Statistics:
(1) Calculation of the overall health status abnormality rate of canteen staff: Determine the body temperature status corresponding to each temperature measurement serial number. If the body temperature is <36.2°C or >37.2°C, it is judged as abnormal; otherwise, it is normal. Use the COUNTIF function to accumulate the number of abnormal body temperature statuses on the same day and the number of abnormal body temperature statuses in the past 7 days respectively; use the SUM function to accumulate the total number of temperature measurements in the past 7 days. The overall health status abnormality rate of canteen staff on the same day = (number of abnormal body temperature statuses on the same day ÷ corresponding number of temperature measurements on the same day) × 100%; The overall health status abnormality rate of canteen staff in the past 7 days = (number of abnormal body temperature statuses in the past 7 days ÷ total number of temperature measurements in the past 7 days) × 100%.
(2) Establishment of the canteen food safety risk assessment model: The canteen food safety risk index based on staff health statistics = (daily overall health status abnormality rate of canteen staff) × 100 × 0.6 + (overall health status abnormality rate of canteen staff in the past 7 days) × 100 × 0.4; The upper limit of the index value is 100.