宁波市电动汽车充电桩布局密度优化数据

本数据通过分析宁波市内充电桩使用情况，为充电基础设施优化提供决策支持。主要应用于：指导运营商优化充电桩布局，识别高负荷站点优先扩容，发现闲置设备可迁移区域，优化运维资源分配，合理配置公共资源，提升整体运营效率。同时可为城市规划部门提供充电设施使用热力图参考，助力实现充电资源的高效利用。
"1.数据采集
采集企业自有充电桩设备管理数据，包括城市名称、充电站编号、统计周期、近30天充电总时长、充电桩数量等数据。
2.数据处理与加工计算
通过数据清洗剔除不足5分钟的充电记录等其他异常值与无效记录，计算需求指数=近30天充电总时长/充电桩数量，使用Python scipy.spatial.ConvexHull包（一种Python中用于计算覆盖范围的工具）计算服务半径R，得出服务面积=πR^2。计算密度指数=充电桩数量/服务面积。计算匹配度=需求指数/密度指数。
3.优化策略
进行匹配度分类并给出优化策略：
匹配度＞200：匹配度分类为""供给不足""，优化策略为""建议新建""。
80≤匹配度≤200：匹配度分类为""供需平衡""，优化策略为""建议保持现状""。
匹配度＜80：匹配度分类为""供给过剩""，优化策略为""建议迁移""。"

This dataset is developed based on the analysis of charging pile usage within Ningbo City, aiming to provide decision-making support for the optimization of charging infrastructure. Its core applications are as follows: guiding operators to optimize the layout of charging piles, identifying high-load stations for priority capacity expansion, locating areas where idle equipment can be relocated, optimizing the allocation of operation and maintenance resources, rationally deploying public resources, and improving overall operational efficiency. In addition, it can provide reference heatmaps of charging facility usage for urban planning departments, helping to achieve efficient utilization of charging resources.

1. Data Collection
Data is collected from the enterprise's self-owned charging pile equipment management system, including city name, charging station ID, statistical cycle, total charging duration over the past 30 days, number of charging piles, and other related metrics.

2. Data Processing and Calculation
First, data cleaning is conducted to eliminate abnormal values and invalid records such as charging sessions with a duration of less than 5 minutes. The demand index is calculated as: total charging duration in the past 30 days divided by the number of charging piles. The service radius R is calculated using the Python `scipy.spatial.ConvexHull` package, a Python tool for computing coverage scope, and the service area is derived as πR². The density index is calculated as: number of charging piles divided by the service area. The matching degree is calculated as: demand index divided by density index.

3. Optimization Strategies
Classification of matching degree and corresponding optimization strategies are formulated as follows:
- If matching degree > 200: Classified as "Insufficient Supply", with the optimization strategy of "Recommended for new construction".
- If 80 ≤ matching degree ≤ 200: Classified as "Supply-Demand Balance", with the optimization strategy of "Recommended to maintain current status".
- If matching degree < 80: Classified as "Excess Supply", with the optimization strategy of "Recommended for relocation".

搜集汇总

背景与挑战

背景概述

该数据集聚焦于宁波市电动汽车充电桩的布局密度优化，可能包含充电桩分布、密度分析或优化建议等数据，旨在支持城市充电基础设施的规划与改进。由于访问限制，详细内容暂不可见。