餐饮企业食品留样状态可信管理数据

本数据可用于餐饮企业食堂食品留样工作状态的每日监测，为食堂加强食品安全管理提供辅助依据，对食品安全事故进行有效分析依据。若每日留样状态出现“异常”，则表明食物留样工作存在操作违规或操作失误，食堂管理人员应立即介入并加以纠正。若近30日留样异常率偏高，则表明食堂食物留样工作的合规性偏弱，需要对相关人员进行有效的教育和培训。1.数据采集：在原始数据授权的前提下从本单位运营的5G智慧食安工业物联网数字化管理平台上获得餐饮企业的食品留样数据。2.算法加工步骤：（1）第一步，对采集到的原始数据进行去重、脱敏；第二步，对今日留样状态S（T）进行判定：①若留样数量是否满足要求、留样重量是否满足要求、留样温度是否满足要求均为是则判定为正常 ，出现一次否则判定为异常；② 若实际留样数量等于应留样数量则判断留样数量是否满足要求为是否则为否；③ 若留样样品最小重量克（G）≥125G 则判断留样重量是否满足要求为是否则为否；④若留样时样品最高温度（°C）≤25°C 则判断留样温度是否满足要求为是否则为否；第三步，计算近30日留样异常天数A；第四步，计算近30日留样异常率R：近30日留样异常率R=近30日留样异常天数A÷30×100%。（2）整合算法：R=（∑[S（Ti）=异常]，i=1,2,3,…,30）÷30×100%S（Ti）=异常，if 留样数量是否满足要求、留样重量是否满足要求、留样温度是否满足要求出现一次否；正常，if 留样数量是否满足要求、留样重量是否满足要求、留样温度是否满足要求均为是

This dataset can be used for daily monitoring of the food sample retention status of canteens in catering enterprises, providing auxiliary basis for strengthening food safety management in canteens and effective analytical basis for food safety incident investigation. If the daily sample retention status is marked as "Abnormal", it indicates that there are operational violations or mistakes in the food sample retention work, and canteen managers should immediately intervene and correct the issues. If the abnormal sample retention rate in the past 30 days is relatively high, it means that the compliance of the canteen's food sample retention work is relatively weak, and relevant personnel need to receive effective education and training.

1. Data Collection: Obtain food sample retention data of catering enterprises from the 5G Smart Food Safety Industrial Internet of Things (IIoT) digital management platform operated by our unit, with the authorization of the original data.

2. Algorithm Processing Steps:
(1) Step 1: Perform deduplication and data desensitization on the collected raw data; Step 2: Determine today's sample retention status S(T):
① If all three conditions (whether the sample retention quantity meets the requirements, whether the sample retention weight meets the requirements, and whether the sample retention temperature meets the requirements) are "yes", the status is judged as "Normal"; otherwise, it is judged as "Abnormal";
② If the actual sample retention quantity is equal to the required sample retention quantity, the judgment result of "whether the sample retention quantity meets the requirements" is "yes", otherwise it is "no";
③ If the minimum weight of the retained sample (in grams, G) ≥ 125G, the judgment result of "whether the sample retention weight meets the requirements" is "yes", otherwise it is "no";
④ If the maximum temperature of the sample during retention (in °C) ≤ 25°C, the judgment result of "whether the sample retention temperature meets the requirements" is "yes", otherwise it is "no";
Step 3: Calculate the number of abnormal sample retention days in the past 30 days, denoted as A;
Step 4: Calculate the abnormal sample retention rate in the past 30 days, denoted as R: R = (A / 30) × 100%.

(2) Integrated Algorithm:
R = (∑[S(T_i) = "Abnormal"], where i=1,2,3,…,30) / 30 × 100%
Where S(T_i) = "Abnormal" if at least one of the three conditions (whether the sample retention quantity meets the requirements, whether the sample retention weight meets the requirements, and whether the sample retention temperature meets the requirements) is "no"; S(T_i) = "Normal" if all three conditions are "yes"