基于应急避难场所的医疗物资配送数据集

基于应急避难场所的医疗物资配送数据集主要面向疫情早期阻断的医疗物资分配和协同运输方案研究，为实现兼顾运输成本和风险成本的高质量路径规划提供支持。数据集通过对深圳市应急避难场所信息进行采集、整理与风险评估后生成。具体内容包括深圳市430处可用应急避难场所的详细信息（如地址、经纬度、容量、面积等），以及基于此生成的风险等级划分和六种应急医疗资源协同运输算法模型（如MOACO、NSGA-II、MOLS等）。原始数据来源于深圳市应急管理局发布的室内应急避难场所公开信息。在对数据进行清洗和标准化后，通过谱聚类等算法结合风险指标（如当地感染率）为各避难场所进行“高风险”或“低风险”的聚类标注。最后，基于此风险划分，开发并应用多种多目标优化算法，以生成差异化的应急物资配送方案，从而形成最终的数据集。数据量约为3.2MB。

This medical supply distribution dataset based on emergency shelters is primarily intended for research on medical supply allocation and collaborative transportation schemes for early-stage epidemic containment, providing support for high-quality path planning that balances both transportation costs and risk costs.

This dataset is generated by collecting, organizing, and conducting risk assessments on information of emergency shelters in Shenzhen. Its specific contents include detailed information of 430 available emergency shelters in Shenzhen (such as address, longitude and latitude, capacity, area, etc.), as well as the risk level classification derived from this data and six collaborative transportation algorithm models for emergency medical resources (e.g., MOACO, NSGA-II, MOLS, etc.).

The original data is sourced from public information on indoor emergency shelters released by the Shenzhen Emergency Management Bureau. After data cleaning and standardization, cluster labeling of "high-risk" or "low-risk" is performed for each shelter using algorithms such as spectral clustering combined with risk indicators (e.g., local infection rate). Finally, based on this risk classification, multiple multi-objective optimization algorithms are developed and applied to generate differentiated emergency supply distribution schemes, thus forming the final dataset. The total size of the dataset is approximately 3.2 MB.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于应急避难场所的医疗物资配送，包含深圳市430处避难场所的详细信息（如地址、经纬度、容量等）及六种协同运输算法模型（如MOACO、NSGA-II等），旨在支持疫情早期阻断的医疗物资分配和路径规划研究。数据来源于深圳市应急管理局公开信息，经过清洗、标准化和风险评估处理，适用于多目标优化车辆路径问题的研究。