西南地区城镇燃气用户呼叫指数及应答预警数据

通过大数据处理某时间段内用户呼叫情况，结合历史高峰呼叫数据指数，评估是否存在异常风险，通过算法输出预警结论。1、获取某时间段内呼叫用户的应答时间、主叫号码、呼叫时长字段数据。 2、使用K-shape时间聚类，对应答时间区间进行聚类，根据动态特性和趋势获取呼叫指数。 3、结合近48个小时，每个小时的呼叫数、话务员数，计算小时人均接听数，按48小时内呼入的平均指数计算。 4、如呼叫指数大于60，则为趋势波动过大，需要预警。This dataset processes user call records within a specified time period via big data technologies, combines with historical peak call data indices to assess potential abnormal risks, and generates early warning conclusions through algorithms. 1. Collect field data including answer time, calling number and call duration of calling users within the target time period. 2. Conduct clustering on answer time intervals using K-Shape time series clustering, and derive call indices based on their dynamic characteristics and trends. 3. Calculate the hourly average answered calls per operator by utilizing the number of calls and the number of operators per hour over the past 48 hours, and compute the average incoming call index based on the call records within these 48 hours. 4. If the call index exceeds 60, it signals excessive trend volatility and warrants an early warning.

搜集汇总

数据集介绍

特点

该数据集记录了西南地区城镇燃气用户的呼叫信息及应答预警数据，包含582条每日更新的记录，主要用于通过大数据分析和算法评估呼叫异常风险。数据集涵盖地区、呼叫时长、呼叫指数等多个关键字段，并采用K-shape时间聚类等算法进行预警分析。