杭州市小型超市不同电梯故障率综合分析数据

采集和统计杭州市小型超市内电梯出现故障的情况，分析不同类型的电梯故障率。因为不同类型的电梯维保重点各不相同，本数据可应用于帮助各维保单位优化安全管理作业人员配置；加强监管，提高日常维护频率，精准加强维保内容，减少电梯安全隐患，协助政府对民生基础设施这一重要组成部分进行统筹。（不同场所下电梯的种类数量各不相同，其维保重点具有差异，因此数据以不同场所分类：例如住宅区多客梯，商场常用扶梯等；统计日期为数据包提取进行算法应用的时间，整体时间跨度为第一台电梯投入使用至今，单纯以“某一年”进行限定与数据情况不符，因此字段名称不出现具体年份集合的限定；数据包每年更新，叠加覆盖计算构建动态大数据模型）1、数据采集：通过电梯物联网设备和接警平台，采集杭州市小型超市电梯发生故障的情况。 2、数据处理：对采集到的原始数据进行清洗、分析、整理等方式，获取所需要的数据：电梯类型，出厂日期，使用年限，故障数量，故障名称。3.系统通过IF函数以及SUM公式进行数据计算，IF（杭州市，小型超市），满足则对机器进行编号录入，出现1次故障则数量记1，累加计算，未出现故障则记0（数量相关字段单位为次）。杭州市小型超市电梯故障总数量＝∑小型超市全部序号电梯故障数量（∑为求和公式符号，非字段代称），杭州市小型超市某类型电梯故障率＝杭州市小型超市某类型电梯故障数量/ 杭州市小型超市电梯故障总数量。4、数据分析：通过Finereport等软件将故障发生情况制作成可视化图表,直观反应各类型电梯的故障率，例如，示例数据中字段11、12的“自动扶梯”就是电梯的类型之一（对应公式中的某类型），数据包内还包括其它电梯类型；0≤故障率≤5%记为“关注维持”，5%＜故障率≤100%记为“关注预警”。对“关注维持”的电梯种类继续沿用既往监管模式，对“关注预警”的电梯类型相关负责人进行示警通知，督促维保单位加强监管。（不同场所下电梯的种类数量各不相同，其维保重点具有差异，因此数据以不同场所分类：例如住宅区多客梯，商场常用扶梯等；统计日期为数据包提取进行算法应用的时间，整体时间跨度为第一台电梯投入使用至今，单纯以“某一年”进行限定与数据情况不符，因此字段名称不出现具体年份集合的限定；数据包每年更新，叠加覆盖计算构建动态大数据模型）

This dataset collects and statistically analyzes the fault situations of elevators in small supermarkets in Hangzhou, and analyzes the failure rates of different types of elevators. Since the maintenance priorities vary across different elevator types, this dataset can be applied to help maintenance units optimize the allocation of safety management personnel, strengthen supervision, increase daily maintenance frequency, precisely enhance maintenance contents, reduce elevator safety hazards, and assist the government in overall planning of this important component of people’s livelihood infrastructure.

(The types and quantities of elevators differ across various venues, with distinct maintenance priorities, so the data is categorized by different venues: for example, residential areas have more passenger elevators, while shopping malls commonly use escalators; the statistical date is the time when the data package is extracted for algorithm application, and the overall time span spans from the commissioning of the first elevator to the present. Limiting the data to a single "year" is inconsistent with the actual data situation, so no specific year set restriction is included in the field names. The data package is updated annually, and overlay calculation is performed to build a dynamic big data model.)

1. Data Collection: Collect the fault situations of elevators in small supermarkets in Hangzhou via elevator Internet of Things (IoT) devices and alarm receiving platforms.

2. Data Processing: Clean, analyze and organize the collected raw data to obtain the required data: elevator type, manufacturing date, service life, number of faults, and fault name.

3. Data calculation is performed via the IF function and SUM formula: For the IF (Hangzhou, Small Supermarket) condition, if met, the machines are numbered and entered into the system. For each fault occurrence, the count is recorded as 1, with cumulative calculation; if no fault occurs, the count is recorded as 0 (the unit for quantity-related fields is "occurrence"). The total number of elevator faults in small supermarkets in Hangzhou = ∑ (number of faults of all sequentially numbered elevators in small supermarkets) (∑ here is the summation formula symbol, not a field alias). The failure rate of a specific type of elevator in small supermarkets in Hangzhou = (number of faults of that elevator type) / (total number of elevator faults in small supermarkets in Hangzhou).

4. Data Analysis: Use software such as Finereport to create visual charts of fault occurrences, which intuitively reflect the failure rates of various elevator types. For example, "escalator" in fields 11 and 12 in the sample data is one type of elevator (corresponding to the "specific type" in the aforementioned formula), and the data package also includes other elevator types. Failures with a failure rate of 0 ≤ x ≤ 5% are classified as "Monitor and Maintain", those with 5% < x ≤ 100% are classified as "Monitor and Alert". Continue to use the existing supervision mode for elevator types categorized as "Monitor and Maintain", and issue warning notifications to the relevant persons in charge of elevator types categorized as "Monitor and Alert" to urge maintenance units to strengthen supervision.

(The types and quantities of elevators differ across various venues, with distinct maintenance priorities, so the data is categorized by different venues: for example, residential areas have more passenger elevators, while shopping malls commonly use escalators; the statistical date is the time when the data package is extracted for algorithm application, and the overall time span spans from the commissioning of the first elevator to the present. Limiting the data to a single "year" is inconsistent with the actual data situation, so no specific year set restriction is included in the field names. The data package is updated annually, and overlay calculation is performed to build a dynamic big data model.)