餐饮业高温消毒有效管控数据

餐饮企业高温消毒有效管控数据是一个创新的量化工具，可用于评估餐具消毒严格符合《餐饮服务食品安全操作规范》，保障食品安全。 1.餐饮企业可以通过实时温度数据及时发现消毒不达标情况，从而立即停止使用问题批次餐具，避免食源性疾病发生。也可以根据历史数据分析找出消毒薄弱环节，从而优化消毒流程安排，确保全过程食品安全。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，通过区域消毒数据比对发现高风险单位，从而实施重点监管，防范群体性食品安全事故。3.保险公司可通过消毒达标率预测投保客户食安事故概率，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部署在高温消毒设备内的WIFI温度传感器的温度数据（精度±1°C，每30秒1次），包括时间、所在区域、设备编号、温度°C、数据状态、处理状态等。（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.基于高温消毒数据保障食品安全： （1消毒合规判定：若温度≥120°C且持续≥30分钟，则判定为“正常”；若温度＜100°C，则判定为“严重异常”；若100°C≤温度＜120°C或时长＜30min ，则判定为 "异常"；（2）处理状态判定：若数据状态为“正常”，则判定为“无需处理”，反之则为“未处理”；（3）利用CountIf函数分别对单日温度状态为异常的次数和近30日温度状态为异常的次数进行累加，分别算出单日异常总次数和总监测次数，计算近30日异常率：近30日异常率= ∑[单日异常总次数] ÷ 总监测次数 × 100%。

The high-temperature disinfection effective control data for catering enterprises is an innovative quantitative tool for evaluating whether tableware disinfection strictly complies with the *Catering Service Food Safety Operation Specifications*, so as to ensure food safety.
1. Catering enterprises can timely identify non-compliant disinfection via real-time temperature data, immediately stop using tableware of problematic batches, and prevent the occurrence of foodborne diseases. They can also identify weak links in disinfection through historical data analysis, optimize disinfection process arrangements, and guarantee food safety throughout the entire process.
2. Food safety supervision departments can use this data as one of the bases for supervising food safety in canteens, compare regional disinfection data to identify high-risk entities, implement targeted supervision, and prevent group food safety accidents.
3. Insurance companies can predict the probability of food safety accidents of insured customers based on the disinfection compliance rate, identify the underwriting risks of target canteen customers in advance, and determine the pricing of relevant insurance products such as food safety liability insurance.

1. Data extraction and preprocessing:
(1) Data extraction: Extract temperature data from WiFi temperature sensors deployed in high-temperature disinfection equipment of relevant canteens (accuracy: ±1°C, sampled every 30 seconds) from the database of the self-developed 5G Smart Food Safety Industrial IoT digital management platform. The collected data includes time, location, equipment number, temperature (°C), data status, processing status, etc.
(2) Data preprocessing: Clean the extracted data to remove duplicate, incorrect or irrelevant information, so as to facilitate subsequent analysis and modeling.
2. Ensuring food safety based on high-temperature disinfection data:
(1) Disinfection compliance judgment: If the temperature is ≥120°C and lasts for ≥30 minutes, it is judged as "normal"; if the temperature is <100°C, it is judged as "serious abnormality"; if 100°C ≤ temperature <120°C or duration <30 minutes, it is judged as "abnormality";
(2) Processing status judgment: If the data status is "normal", it is judged as "no action required", otherwise it is judged as "unprocessed";
(3) Use the COUNTIF function to accumulate the number of abnormal temperature statuses in a single day and the number of abnormal temperature statuses in the past 30 days respectively, calculate the total number of single-day abnormalities and the total number of monitoring times, and then calculate the 30-day abnormality rate: 30-day abnormality rate = (∑[total number of single-day abnormalities] ÷ total number of monitoring times) × 100%.

搜集汇总

背景与挑战

背景概述

该数据集聚焦餐饮业高温消毒管控，包含1417条每日更新的温度监测数据，用于实时评估餐具消毒合规性，保障食品安全。数据通过算法判定消毒状态（如温度≥120°C且持续30分钟为正常），并计算异常率，支持餐饮企业自查、监管部门监督和保险公司风险评估等应用场景。