杭州市文体娱场馆不同电梯故障率分析数据
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采集和统计杭州市文体娱场馆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。1、数据采集：通过电梯物联网设备和接警平台，采集杭州市文体娱场馆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，文体娱场馆），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）。杭州市文体娱场馆电梯故障总数量＝∑文体娱场馆全部序号电梯故障数量（∑为求和公式符号，非字段代称），杭州市文体娱场馆某类型电梯故障率＝杭州市文体娱场馆某类型电梯故障数量/ 杭州市文体娱场馆电梯故障总数量。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 failures in culture, sports and entertainment venues in Hangzhou, and analyzes the failure rates of different types of elevators. Since different types of elevators have different key maintenance priorities, this data can be applied to help maintenance units optimize the allocation of safety management personnel, strengthen supervision, increase daily maintenance frequency, accurately strengthen maintenance content, reduce elevator safety hazards, and assist the government in overall planning of this important component of people's livelihood infrastructure. Given that the types and quantities of elevators vary across different venues and their key maintenance priorities differ, the data is classified by venue. 1. Data Collection: Collect elevator failure data in culture, sports and entertainment venues in Hangzhou through elevator IoT devices and alarm receiving platforms. 2. Data Processing: Clean, analyze and organize the collected raw data to obtain the required information: elevator type, manufacturing date, service life, number of failures, and failure name. 3. Data Calculation: The system uses the IF function and SUM formula for data calculation: when the IF function judges that the venue belongs to Hangzhou's culture, sports and entertainment venues, the elevator will be assigned a unique number and recorded; each occurrence of a failure will be counted as 1 and accumulated, and if no failure occurs, it will be recorded as 0 (the unit of quantity-related fields is "times"). The total number of elevator failures in Hangzhou's culture, sports and entertainment venues = ∑ of the failure quantities of all numbered elevators in these venues (∑ is the summation formula symbol, not a field alias). The failure rate of a specific type of elevator in Hangzhou's culture, sports and entertainment venues = (number of failures of this type of elevator) / (total number of elevator failures in all Hangzhou culture, sports and entertainment venues). 4. Data Analysis: Use software such as Finereport to create visual charts of failure occurrences to intuitively reflect the failure rates of various types of elevators. Elevator types are categorized into two groups based on their failure rates: those with a failure rate of 0 ≤ r ≤ 5% are labeled as "Attention & Maintenance", while those with 5% < r ≤ 100% are labeled as "Attention & Early Warning". For elevator types labeled "Attention & Maintenance", the existing supervision mode will continue to be applied; for those labeled "Attention & Early Warning", relevant responsible persons will receive warning notifications to urge maintenance units to strengthen supervision. Additional notes: The types and quantities of elevators vary across different venues, and their key maintenance priorities differ, so the data is classified by venue: 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 covers from the commissioning of the first elevator to the present. Limiting the data to a specific single year does not align with the actual data situation, so no specific year restrictions will be included in the field names; the data package is updated annually, and superposition and coverage calculations are used to construct a dynamic big data model.
