共享电单车故障风险评估数据

通过对共享单车最后一次使用时间后停车时间的判定，对单车的故障风险进行实时计分，基于使用较少或长时间不使用的共享单车的损坏的可能性较大的前提，将损坏的概率标记在服务器上，方便用户在寻找共享单车时更方便寻找到好的共享单车并及时对风险高的车辆状态进行确认。1. 数据采集：由中控系统上报共享单车的实时位置、停车驿站，最后一次骑行时间等数据。
2. 数据预处理：对采集到的数据进行清洗、去重、格式转换等处理，使其满足后续分析和建模的要求。
3. 数据分析：通过记录信息，根据单车停放时间(t)进行初始分数进行调整，生成实时分数，实时分数用于反映共享单车的损坏概率；故障率风险分数=100-k*（t-t1）,k为自定义时间系数；停放时间不包括夜间时间（t1），夜间时间（t1）为每日0时至8时。利用IFS函数对车辆故障风险分数进行分类，故障风险=IFS(车辆故障风险分数>=80, "低",车辆故障风险分数>=40, "中",车辆故障风险分数<40, "高")。
4. 数据应用：根据共享单车的使用状态记录，从而判断出共享单车损坏的概率，并且在服务器上进行标记，方便用户在寻找共享单车时更好地找到损坏概率小和公司维修人员对故障风险高的单车及时进行确认和修复。

This dataset enables real-time scoring of shared bike failure risk by calculating the parking duration after the last usage time. Based on the premise that less frequently used or long-unused shared bikes have a higher probability of damage, the failure probability is marked on the server, which helps users find well-conditioned bikes more conveniently during their search and enables timely status checks for high-risk vehicles.
1. Data Collection: Real-time data including shared bikes' real-time locations, parking stations, and last ride time are reported to the server via the central control system.
2. Data Preprocessing: Clean, deduplicate, and perform format conversion on the collected data to meet the requirements of subsequent analysis and modeling.
3. Data Analysis: Adjust the initial score based on the bike's parking duration (t) to generate a real-time score that reflects the failure probability of the shared bike. The failure risk score is calculated as: Failure Risk Score = 100 - k*(t - t1), where k is a custom time coefficient; parking duration excludes nighttime hours (t1), which are defined as 00:00 to 08:00 daily. The IFS function is used to classify the vehicle failure risk score: Failure Risk Level = IFS(vehicle failure risk score >=80, "Low", vehicle failure risk score >=40, "Medium", vehicle failure risk score <40, "High").
4. Data Application: Determine the failure probability of shared bikes based on their recorded usage status, and mark the result on the server. This helps users find bikes with low failure probability when searching, and enables the company's maintenance personnel to promptly confirm and repair high-risk bikes.

搜集汇总

数据集介绍

特点

该数据集为共享电单车故障风险评估数据，包含1143条记录，每日更新，用于实时评估共享单车的故障风险，方便用户和维修人员快速定位高风险车辆。