食堂食品安全风险与食物中心温度监测数据

基于食物中心温度统计的食堂食品安全风险指数是一个创新的量化工具，可用于评估因食物烹饪不当导致食物中心温度偏低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情况，根据指数的变化及时发现因食物未煮熟导致的潜在食品安全问题，从而做出针对性的措施。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物加工规范性的重视和承诺，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。5.本数据还能为食品中心温度测试仪厂家对仪器进行功能改进或提升提供依据。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食物烹饪后的中心温度数据，包括日期、时间、食堂编号、所在地区、食物中心温度。（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.基于食物中心温度统计数据预测食堂食品安全风险数据： （1）食物中心温度状态判定：若食物中心温度＜72℃，则判定为“异常”，反之则判定为“正常”；（2）计算近30日的总判定次数、异常次数和连续异常次数：利用SUM函数对近30日的总判定次数进行累加；利用CountIf函数分别对近30日的异常次数和连续异常次数进行累加；（3）计算近30日异常率和连续异常次数占比：近30日异常率=近30日异常次数÷近30日总判定次数×100%；近30日连续异常次数占比=近30日连续异常次数÷近30日总判定次数×100%；（4）建立食堂食品安全风险评估模型：基于食物中心温度统计的食堂食品安全风险指数=近30日异常率×a+近30日连续异常次数占比×b；a和b为对应的系数，属于我司商业秘密，故不作详细列举。

The canteen food safety risk index based on food core temperature statistics is an innovative quantitative tool used to assess the degree of risk to food safety that may be caused by insufficient food core temperature due to improper food cooking.
1. Canteens can use this data to understand the overall risk status of current canteen food safety, timely detect potential food safety issues caused by undercooked food based on changes in the index, and then take targeted measures.
2. Food and beverage regulatory authorities can use this data as one of the basis for supervising canteen food safety, timely identify canteens with high food safety risks based on index changes, and carry out early intervention and guidance.
3. Canteens or regulatory authorities can publicly disclose this data to demonstrate the attention and commitment of their unit or region to the standardization of food processing, which is conducive to enhancing the trust of diners.
4. Insurance companies can use this data 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.
5. This data can also provide a basis for food core temperature tester manufacturers to improve or upgrade the functions of their instruments.

1. Data extraction and preprocessing:
(1) Data extraction: Extract the core temperature data of post-cooked food from relevant canteens from the database of the self-developed 5G Smart Food Safety Industrial Internet of Things digital management platform, including date, time, canteen number, location, and food core temperature.
(2) Data preprocessing: Clean the extracted data to remove duplicate, incorrect or irrelevant information for subsequent analysis and modeling.

2. Prediction of canteen food safety risk data based on food core temperature statistics:
(1) Food core temperature status judgment: If the food core temperature is less than 72°C, it is judged as "abnormal", otherwise it is judged as "normal";
(2) Calculate the total number of judgments, number of abnormal judgments and number of consecutive abnormal judgments in the past 30 days: Use the SUM function to accumulate the total number of judgments in the past 30 days; Use the COUNTIF function to accumulate the number of abnormal judgments and the number of consecutive abnormal judgments in the past 30 days respectively;
(3) Calculate the abnormal rate and the proportion of consecutive abnormal times in the past 30 days: Abnormal rate in the past 30 days = (number of abnormal times in the past 30 days ÷ total number of judgments in the past 30 days) × 100%; Proportion of consecutive abnormal times in the past 30 days = (number of consecutive abnormal times in the past 30 days ÷ total number of judgments in the past 30 days) × 100%;
(4) Establish a canteen food safety risk assessment model: Canteen food safety risk index based on food core temperature statistics = Abnormal rate in the past 30 days × a + Proportion of consecutive abnormal times in the past 30 days × b; Coefficients a and b are trade secrets of our company and will not be listed in detail.