基于食堂食品安全违规行为预测食堂风险数据

基于人员违规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，用于评估食堂人员的违规行为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，指数的变化也可以反映人员的违规状态，结合对违规次数和违规原因的分析，食堂可以针对性地加强特定方面的培训，如卫生习惯、操作规范等。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相关食堂的人员违规数据，包括日期、食堂编号、所在地区、当日总违规次数、七种不同原因的当日违规次数。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.基于人员违规情况统计数据预测食堂食品安全风险数据： （1）计算每家食堂近30日总违规次数和七种不同原因的近30日累计违规次数和：利用SUM函数分别对每家食堂近30日总违规次数和七种不同原因近30日的违规次数进行累加；（2）建立食堂食品安全风险评估模型：基于人员违规情况统计的食堂食品安全风险指数=近30日总违规次数×a+近30日不戴工帽累计违规次数×b+近30日不穿工衣累计违规次数×c+近30日不戴口罩累计违规次数×d+近30日抽烟吃东西累计违规次数×e+近30日看手机累计违规次数×f+近30日垃圾桶不盖累计违规次数×g+近30日菜品放置不正确累计违规次数×h；a~h为对应的系数，根据违规原因的恶劣程度确定，为我司商业秘密，故不作详细列举。

The Canteen Food Safety Risk Index based on statistical data of personnel violations is an innovative quantitative tool for evaluating the degree of risk that personnel violations in canteens may pose to food safety. 1. Canteens can use this dataset to understand the overall risk status of current canteen food safety and identify potential food safety issues in a timely manner. Changes in the index can also reflect the status of personnel violations. Combined with the analysis of violation times and causes, canteens can provide targeted training on specific aspects such as hygiene habits and operation specifications. 2. Catering supervision departments can use this dataset as one of the bases for supervising canteen food safety, and timely identify canteens with high food safety risks through changes in the index to carry out intervention and guidance in advance. 3. Canteens or regulatory agencies can publicly disclose this dataset to demonstrate their attention to and commitment to food safety work, which is conducive to enhancing the trust of diners. 4. Insurance companies can use this dataset to identify the insurance 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 and Preprocessing: (1) Data Extraction: Extract personnel violation data of relevant canteens from the database of the self-developed 5G Smart Food Safety Industrial Internet of Things (IIoT) Digital Management Platform, including date, canteen number, location, total daily violation times, and daily violation counts for seven different violation causes. To fully protect personal privacy, personal information of service personnel will not be extracted. (2) Data Preprocessing: Clean the extracted data to remove duplicate, erroneous or irrelevant information for subsequent analysis and modeling. 2. Prediction of Canteen Food Safety Risk Data Based on Statistical Personnel Violation Data: (1) Calculate the total violation times in the past 30 days and the cumulative violation counts of seven different violation causes in the past 30 days for each canteen: Use the SUM function to accumulate the total violation times in the past 30 days and the violation times of seven different causes in the past 30 days for each canteen respectively; (2) Establish a canteen food safety risk assessment model: The canteen food safety risk index based on statistical data of personnel violations = Total violation times in the past 30 days × a + Cumulative violation times of not wearing work caps in the past 30 days × b + Cumulative violation times of not wearing work clothes in the past 30 days × c + Cumulative violation times of not wearing masks in the past 30 days × d + Cumulative violation times of smoking or eating in the past 30 days × e + Cumulative violation times of using mobile phones in the past 30 days × f + Cumulative violation times of not covering trash bins in the past 30 days × g + Cumulative violation times of incorrect placement of dishes in the past 30 days × h; a~h are the corresponding coefficients, which are determined based on the severity of each violation cause and are trade secrets of our company, so detailed enumeration will not be provided.