杭州市重要公共场所不同电梯故障率分析数据

采集和统计杭州市重要公共场所内（市政等）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，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，采集杭州市重要公共场所内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，重要公共场所），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）。杭州市重要公共场所电梯故障总数量＝∑重要公共场所全部序号电梯故障数量（∑为求和公式符号，非字段代称），杭州市重要公共场所某类型电梯故障率＝杭州市重要公共场所某类型电梯故障数量/ 杭州市重要公共场所电梯故障总数量。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，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 counts elevator malfunctions in key public places (including municipal facilities) in Hangzhou, and analyzes the failure rates of different types of elevators. Since the key 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 content, 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 places, with distinct maintenance priorities. Therefore, the data is classified by different places. The dataset is updated annually, and dynamic big data models are built through superposition and coverage calculations. 1. Data Collection: Collect elevator malfunction data in key public places in Hangzhou via elevator IoT devices and alarm receiving platforms. 2. Data Processing: Clean, analyze and organize the collected raw data to obtain required information: elevator type, manufacturing date, service life, number of malfunctions, and malfunction names. 3. Data calculation is performed using the IF function and SUM formula in the system: the condition IF(Hangzhou, Key Public Places) is applied. If the condition is met, the elevator is assigned a serial number for entry; each malfunction is recorded as 1 and accumulated, and 0 is recorded if no malfunction occurs (unit of quantity-related fields: times). The total number of elevator malfunctions in Hangzhou's key public places = ∑(number of malfunctions of all sequenced elevators in key public places) (∑ here refers to the summation formula symbol, not a field alias). The failure rate of a specific type of elevator in Hangzhou's key public places = (number of malfunctions of that specific elevator type in Hangzhou's key public places) / (total number of elevator malfunctions in Hangzhou's key public places). 4. Data Analysis: Use software such as Finereport to create visual charts of malfunction occurrences, which intuitively reflect the failure rates of various elevator types. Classify failure rates as: 0 ≤ failure rate ≤ 5% as "Monitor & Maintain", 5% < failure rate ≤ 100% as "Monitor & Early Warning". Continue to use the existing supervision mode for elevator types categorized as "Monitor & Maintain", and issue warning notices to the relevant persons in charge of elevator types categorized as "Monitor & Early Warning" to urge maintenance units to strengthen supervision. The data is classified by different places due to the varying types and quantities of elevators and distinct maintenance priorities in different locations: for example, residential areas mostly have 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 "certain year" is inconsistent with the actual data situation, so the field names do not include specific year range restrictions. The dataset is updated annually, and dynamic big data models are built through superposition and coverage calculations.