酒店充电桩用户充电急迫度标签分析数据

本数据通过分析酒店充电桩用户充电行为特征，为充电服务优化提供决策支持。主要应用于：指导运营商根据急迫度分级实施差异化服务策略，对紧急用户优先分配充电资源并缩短排队时间，对随意补电用户引导错峰充电；为导航平台提供用户急迫度标签数据，助力优化充电站推荐算法；为充电基础设施规划提供参考依据。同时可为电力调度系统提供高峰时段充电需求预测，助力电网负荷平衡。
"1.数据采集与处理
采集企业自有充电桩设备管理数据，包括充电站编号、城市名称、部署区域、用户ID、当前车辆SOC（剩余电量百分比）、至充电站的距离、采集时间、时段类型（早高峰/晚高峰/平峰/低谷）等数据。对原始数据进行清洗，剔除充电时长小于3分钟或SOC>90%的异常记录。将时段划分为：早高峰（7:00-9:00）、晚高峰（17:00-19:00）、低谷（0:00-6:00）、平峰（其余时段）
2.核心计算
进行特征指标计算：
电量焦虑指数：
当前SOC≤20%时，为(20%-当前SOC)/20%；当前SOC＞20%时，为0
距离系数=min{至充电站的距离/5km，1}
进行急迫度值计算：
急迫度=电量焦虑指数×0.7+距离系数×0.2+时段系数×0.1
其中时段系数根据所处时段条件取值，早高峰：1.3，晚高峰：1.2，低谷：0.8，平峰：1.0
3.急迫度分级标签：
S级（紧急）：急迫度≥0.9
A级（高度需求）：0.7≤急迫度＜0.9
B级（常规需求）：0.4≤急迫度＜0.7
C级（随意补电）：急迫度＜0.4"

This dataset analyzes the charging behavior characteristics of users at hotel charging piles to provide decision support for charging service optimization. Its main application scenarios are as follows: guiding operators to implement differentiated service strategies based on urgency levels, prioritizing the allocation of charging resources for urgent users and shortening their queuing time, and guiding users who conduct random top-up charging to charge during off-peak hours; providing user urgency tag data for navigation platforms to help optimize charging station recommendation algorithms; providing reference basis for charging infrastructure planning. Additionally, it can supply peak-period charging demand forecasting data for power dispatching systems to facilitate grid load balancing.

1. Data Collection and Processing
Collect the enterprise's self-owned charging pile equipment management data, including charging station ID, city name, deployment area, user ID, current vehicle SOC (State of Charge percentage), distance to the charging station, collection timestamp, and time period type (morning peak, evening peak, flat peak, off-peak). Clean the raw data by removing abnormal records with charging duration less than 3 minutes or SOC > 90%. Divide the time periods into the following categories: morning peak (7:00-9:00), evening peak (17:00-19:00), off-peak (0:00-6:00), and flat peak (remaining time periods).

2. Core Calculations
Perform feature indicator calculations:
- Electricity anxiety index: When current SOC ≤ 20%, the index is calculated as (20% - current SOC) / 20%; when current SOC > 20%, the index is 0.
- Distance coefficient = min{distance to charging station / 5km, 1}
Calculate the urgency value using the formula:
Urgency = 0.7 × Electricity Anxiety Index + 0.2 × Distance Coefficient + 0.1 × Time Period Coefficient
The time period coefficient takes values based on the corresponding time period: morning peak: 1.3, evening peak: 1.2, off-peak: 0.8, flat peak: 1.0.

3. Urgency Level Tags:
- Level S (Emergency): Urgency ≥ 0.9
- Level A (High Demand): 0.7 ≤ Urgency < 0.9
- Level B (Regular Demand): 0.4 ≤ Urgency < 0.7
- Level C (Random Top-up Charging): Urgency < 0.4

搜集汇总

背景与挑战

背景概述

该数据集基于酒店充电桩用户行为数据，通过分析剩余电量、距离和时段等特征，计算急迫度值并划分为S级到C级标签，旨在识别用户充电需求的紧急程度。它主要用于指导运营商实施差异化服务策略，如优先分配资源给紧急用户，同时为导航平台和电力调度系统提供数据支持，以优化充电推荐和负荷平衡。数据集的特点在于结合了多维度指标进行量化评估，为充电服务优化和基础设施规划提供决策依据。