基于食堂员工手部卫生违规行为倾向指数数据

员工手部卫生主动违规倾向指数是一个创新的量化指标，用于评估食堂工作人员在手部卫生管理方面的主动（或故意）违规倾向。 1.本数据可用于食堂判断勤务人员在手部卫生方面的违规倾向，对主动违规程度较为严重的人员进行重点培训和教育，及时采取措施预防食品污染。2.食堂可向用餐者披露本数据，体现食堂对工作人员卫生的严格管控和承诺，有助于增强用餐者的信任。3.保险公司可在推行食品安全责任险过程中，根据本数据来评估食堂的保险风险，制定合理的保险费率和条款。4.本数据可以体现食堂的卫生管理水平，园区、大型企业、学校、医院等单位在引进食堂承包商时，可将本数据作为食堂卫生管理能力的考察依据之一。1.数据抽取和预处理： （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食堂用户的部分考勤数据，包括日期、食堂编号、所在地区、人员编号、双手十指是否超出手指头边缘1mm（T1-10）、左右手手腕是否戴有手镯（T11-12）、双手十指是否戴有戒指（T13-22）、左右手手掌正反面是否有伤口（T23-26）、左右手手掌正反面是否有创可贴（T27-30）；本数据对食堂和人员用编号形式进行匿名化，充分保障隐私。（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.基于手部卫生情况推测勤务人员的主动违规倾向： （1）计算近30日手部卫生各类违规次数及总违规次数：基于原始数据对手部卫生情况进行判定，若T1-30中有任何一条为是，则判定为违规，反之则为正常；利用CountIf函数分别对近30日违规原因为指甲类、饰品类、伤口类的次数和总违规次数进行累加。（2）建立主动违规倾向评估模型：主动违规倾向指数=近30日违规原因为指甲类的累计次数×x+近30日违规原因为饰品类的累计次数×y；x和y为对应的系数，属于商业秘密，故不作详细列举。

The Active Non-Compliance Tendency Index for Hand Hygiene of Employees is an innovative quantitative indicator used to evaluate the active (or intentional) non-compliance tendency of canteen staff in hand hygiene management.
1. This data can be used by canteens to identify the non-compliance tendency of service personnel in hand hygiene, provide targeted training and education for those with severe active non-compliance, and take timely measures to prevent food contamination.
2. Canteens can disclose this data to diners, demonstrating the canteen's strict control and commitment to staff hygiene, which helps enhance diners' trust.
3. Insurance companies can evaluate the insurance risk of canteens based on this data when promoting food safety liability insurance, and formulate reasonable insurance premiums and clauses.
4. This data can reflect the hygiene management level of canteens. When introducing canteen contractors, units such as parks, large enterprises, schools, and hospitals can take this data as one of the inspection bases for the canteen's hygiene management capabilities.
1. Data Extraction and Preprocessing:
(1) Data Extraction: Extract partial attendance data of canteen users from the database of the self-developed 5G Smart Food Safety Industrial Internet of Things (IIoT) digital management platform, including date, canteen number, location, personnel number, whether all ten fingers of both hands exceed the edge of the fingertips by 1mm (T1-10), whether bracelets are worn on the wrists of left and right hands (T11-12), whether rings are worn on all ten fingers of both hands (T13-22), whether there are wounds on the front and back of the palms of left and right hands (T23-26), and whether there are adhesive bandages on the front and back of the palms of left and right hands (T27-30); This data is anonymized using numbers for canteens and personnel, fully protecting personal privacy.
(2) Data Preprocessing: Clean the extracted data, remove duplicate, erroneous or irrelevant information to facilitate subsequent analysis and modeling.
2. Estimation of Service Personnel's Active Non-Compliance Tendency Based on Hand Hygiene Status:
(1) Calculate the number of various hand hygiene violations and total violations in the past 30 days: Determine the hand hygiene status based on the original data. If any item in T1-30 is "Yes", it is judged as a violation; otherwise, it is normal; Use the COUNTIF function to accumulate the number of violations caused by nails, jewelry, and wounds and the total number of violations in the past 30 days respectively.
(2) Establish an active non-compliance tendency evaluation model: Active Non-Compliance Tendency Index = Cumulative number of nail-related violations in the past 30 days × x + Cumulative number of jewelry-related violations in the past 30 days × y; x and y are corresponding coefficients, which are trade secrets and therefore not listed in detail.

搜集汇总

数据集介绍

特点

该数据集记录了食堂员工的手部卫生违规行为，包含516条每日更新的数据，通过算法计算员工的违规倾向指数，适用于食堂管理、食品安全评估等场景。