东阳共享电动车健康预警监测数据

通过分析车辆历史租用频次、近期订单量变化趋势及投放时长等数据，识别疑似故障或损坏的车辆。当触发预警时，自动推送车辆信息，指导运维人员优先检修或回收问题车辆，减少用户因故障车辆导致的订单取消、骑行安全隐患等问题，同时降低车辆因未及时维护造成的资产损耗。1、数据来源：从东阳创享汽车服务有限公司的创享出行共享电动车运营平台系统中，采集车辆数据、用户骑行订单数据。 2、数据处理：对采集的数据进行数据清洗，基于车辆唯一标识整合车辆和用户骑行订单数据。计算每个车辆的车辆投放时长（当前年份-开始投放年份） 3、数据计算： （1）通过最近30天的订单数据，基于count函数，计算最近30天各车辆日均订单量的基准值O（即最近30天日均单量）。 （2）通过最近7天的订单数据，基于count函数，计算最近7天各车辆的日均订单量R（即最近7天日均单量） （3）根据O和R计算车辆的衰减系数【衰减系数=（O-R)/O】。 （4）根据预警模型对车辆的健康度进行判断，区分高危车辆（衰减系数大于0.5且最近7天无订单且投放时长不超过3年 或投放时间超过4年）、中危车辆（衰减系数大于0.5 且最近3天无订单，且投放时长超过1年）、低危车辆（衰减系数大于0.5且最近1天订单或投放时长超过2年）、正常车辆。 4、数据应用：结果数据接入平台，每天对车辆的状态进行更新并提供给运维人员，针对不同健康度的车辆采用不同的运维策略。By analyzing data such as historical rental frequency, recent order volume change trends, and deployment duration, vehicles suspected of malfunction or damage can be identified. When an alert is triggered, vehicle information will be automatically pushed to guide operation and maintenance (O&M) personnel to prioritize maintenance or recovery of problematic vehicles, thereby reducing order cancellations and riding safety hazards caused by faulty vehicles for users, and lowering asset depreciation caused by delayed maintenance of vehicles. 1. Data Source: Vehicle data and user riding order data are collected from the Chuangxiang Travel shared electric vehicle operation platform system of Dongyang Chuangxiang Automobile Service Co., Ltd. 2. Data Processing: Perform data cleaning on the collected data, and integrate vehicle data and user riding order data based on the unique vehicle identifier. Calculate the deployment duration of each vehicle (current year - start deployment year). 3. Data Calculation: (1) Based on the order data of the past 30 days and using the count() function, calculate the benchmark value O of the average daily order volume of each vehicle in the past 30 days (i.e., the average daily order volume over the past 30 days). (2) Based on the order data of the past 7 days and using the count() function, calculate the average daily order volume R of each vehicle in the past 7 days (i.e., the average daily order volume over the past 7 days). (3) Calculate the vehicle attenuation coefficient based on O and R [attenuation coefficient = (O - R)/O]. (4) Judge the health status of vehicles based on the alert model, and classify them into four categories: high-risk vehicles (attenuation coefficient > 0.5 AND no orders in the past 7 days AND deployment duration ≤ 3 years OR deployment duration > 4 years), medium-risk vehicles (attenuation coefficient > 0.5 AND no orders in the past 3 days AND deployment duration > 1 year), low-risk vehicles (attenuation coefficient > 0.5 AND have orders in the past 1 day OR deployment duration > 2 years), and normal vehicles. 4. Data Application: Integrate the resulting data into the platform, update the vehicle status daily and provide it to O&M personnel, and adopt different O&M strategies for vehicles with different health statuses.

搜集汇总

背景与挑战

背景概述

东阳共享电动车健康预警监测数据是一个包含8003条记录的企业数据集，每日更新，用于分析共享电动车的健康状况和预警潜在故障。数据集包含车辆唯一号、投放时间、最近订单时间、日均单量等关键字段，通过计算衰减系数和预警模型，识别高危、中危、低危和正常车辆，指导运维人员采取相应措施。