基于商户维度的电动汽车充电场站充电占用影响经营金额预估数据

本数据集以商户为基本分析单元，聚焦因充电枪被占用（如停车未充电、离线未释放等）而造成的潜在营收损失，结合商户等级因子，构建“占用行为-金额损失”核算模型。具体应用场景如下： 1.对平台（即申请人）而言：可助力形成商户运营绩效中的非故障型损耗识别模块，支持商户画像精细化、考核机制多元化与平台治理能力提升； 2.对场站商家而言：可据此量化站内非收益性占位对财务端的影响，为优化车位利用策略与制定超时付费规则提供依据； 3.对政府而言：可辅助评估区域内经营主体的收益稳定性，作为推动城市充电设施公平运营、建立用户信用激励机制的重要数据支撑。1.数据采集：原始数据经授权合法获取，采集统计日期、站点ID、商户ID、商户等级（根据商户历史经营规模排序进行赋级）、该日该站点占用分钟数、该日该站点占用分钟占比、该日该站点未占用时段经营金额等字段。 2.设置影响因子-商户等级因子β：若商户等级为A级，则β赋值1.14，B级则β赋值1.07，C级则β赋值1，D级则β赋值0.93。 3.建立充电占用影响经营金额的预估模型：（1）计算该日该站点未占用分钟占比：该日该站点未占用分钟占比=1-该日该站点占用分钟占比；（2）计算该日该站点基准占用经营金额预估值：该日该站点基准占用经营金额预估值=该日该站点未占用时段经营金额/该日该站点未占用分钟占比-该日该站点未占用时段经营金额；（3）修正计算：修正后的该日该站点占用经营金额预估值=该日该站点占用时段的基准经营金额预估值*商户等级因子β。This dataset takes merchants as the basic unit of analysis, focuses on potential revenue losses caused by charging ports being occupied (e.g., parking without charging, failing to release the port while offline, etc.), and combines the merchant level factor to develop the "occupancy behavior - revenue loss" accounting model. Specific application scenarios are as follows: 1. For the platform (i.e., the applicant): It can help establish a non-fault loss identification module for merchant operation performance, supporting refined merchant profiling, diversified assessment mechanisms, and improved platform governance capabilities; 2. For charging station merchants: It can be used to quantify the impact of non-revenue-generating occupancy on their financial status, providing a basis for optimizing parking space utilization strategies and formulating overtime charging rules; 3. For the government: It can assist in evaluating the revenue stability of business entities in the region, serving as an important data support for promoting the fair operation of urban charging facilities and establishing user credit incentive mechanisms. 1. Data collection: Original data is legally obtained with authorization. Collected fields include statistical date, site ID, merchant ID, merchant level (graded based on the ranking of merchants' historical operating scale), daily occupied minutes of the site, daily occupied minutes proportion of the site, and daily operating revenue during unoccupied periods of the site, etc. 2. Set the impact factor - merchant level factor β: Assign β=1.14 for Grade A merchants, β=1.07 for Grade B merchants, β=1 for Grade C merchants, and β=0.93 for Grade D merchants. 3. Establish the estimation model for the impact of charging port occupancy on operating revenue: (1) Calculate the unoccupied minutes proportion of the site on the day: Unoccupied minutes proportion of the site on the day = 1 - Occupied minutes proportion of the site on the day; (2) Calculate the baseline estimated operating revenue from occupied periods of the site on the day: Baseline estimated operating revenue from occupied periods of the site on the day = (Daily operating revenue during unoccupied periods of the site / Unoccupied minutes proportion of the site on the day) - Daily operating revenue during unoccupied periods of the site; (3) Corrected calculation: Corrected estimated operating revenue from occupied periods of the site on the day = Baseline estimated operating revenue from occupied periods of the site * merchant level factor β.

搜集汇总

背景与挑战

背景概述

该数据集聚焦电动汽车充电场站，以商户维度分析充电枪占用对经营金额的影响，包含1327条每日更新的记录，通过商户等级因子构建预估模型，量化非收益性占位导致的潜在营收损失，应用于平台商户绩效评估、场站优化策略制定和政府监管支持。