金华市公交车载终端设备事件管理数据

通过金华市公交集团的指挥调度监控系统，收集公交车辆的车载终端设备事件数据，实时掌握公交车辆的设备终端运行情况，有助于更加精准地评估车辆正常运行时间段，为实时监控车辆轨迹偏离情况和临时调度导航提供可靠支撑，确保公共交通服务的高效性和便捷性，助力城市道路交通有序运行。1、数据采集：从金华市公交集团的指挥调度监控系统实时采集公交车的车载终端设备的状态数据、通信数据等； 2、数据处理：对采集到的数据按公交车牌号进行汇总分类、排列清洗，根据车载终端的设备事件数据按照更新时间进行降序处理，同时对车载终端SIM卡进行匿名化处理； 3、数据加工：计算设备不同状态的累计数量∑Rk，其中k=1~8，状态包括开机上线、客户登陆、客户注销、连接中断、重启上线、关机消息、关机失败消息、终端ID冲突；计算设备不同状态的累计占比Xi=备不同状态的累计数量∑Rk/所有状态的累计数∑R；计算设备所有状态的日增长率Y=当日设备所有状态的累计占比Xi/前一日设备所有状态的累计占比X(i-1)，判断客户注销、连接中断、重启上线、关机失败消息的日均占比增长率Y的情况，若Y大于200%说明该设备大概率出现异常情况；Y在100%~200%之间说明该设备很有可能出现异常情况。 4、数据应用：通过以上数据的统计加工分析，获得公交车的终端设备异常监测信息，作为及时发现设备异常情况的重要指标，同时关联GPS定位数据为分析车辆是否超速、是否偏离路线等提供支持，进而为提升驾驶员的车辆安全驾驶习惯。This dataset collects on-board terminal device event data of public transit buses via the command, dispatch and monitoring system of Jinhua Public Transport Group. By real-time monitoring of the operating status of bus on-board terminals, this dataset enables accurate assessment of vehicle normal operating periods, provides reliable support for real-time trajectory deviation monitoring and temporary dispatching navigation, ensures the efficiency and convenience of public transport services, and promotes the orderly operation of urban road traffic. 1. Data Collection: Real-time acquisition of status data, communication data and other relevant data of bus on-board terminals from the command, dispatch and monitoring system of Jinhua Public Transport Group. 2. Data Processing: The collected data are summarized, classified, sorted and cleaned based on bus license plate numbers; the terminal device event data are sorted in descending order according to their update time, and anonymization processing is performed on the SIM cards of the on-board terminals. 3. Data Processing and Calculation: Calculate the cumulative count ∑Rk of different device states, where k ranges from 1 to 8. The states include: boot online, customer login, customer logout, connection interruption, restart online, shutdown notification, shutdown failure notification, and terminal ID conflict. Calculate the cumulative proportion of each state as Xi = ∑Rk (cumulative count of a single state) / ∑R (total cumulative count of all states). Calculate the daily growth rate Y of the cumulative proportion of all device states on the current day, defined as Y = Xi_current / Xi_previous (where Xi_previous refers to the cumulative proportion of all states on the previous day, i.e., X(i-1)). Then judge the daily average growth rate Y of four types of events: customer logout, connection interruption, restart online, and shutdown failure notification. Specifically, if Y > 200%, the device is highly likely to be abnormal; if 100% ≤ Y ≤ 200%, the device is very likely to be abnormal. 4. Data Application: Through statistical processing and analysis of the above data, abnormal monitoring information of bus terminal devices can be derived, which serves as a critical indicator for timely detection of device abnormalities. Additionally, this information is associated with GPS positioning data to support analysis of vehicle overspeed and route deviation, thereby helping to improve the safe driving habits of drivers.