供应链上下游厂商主数据
收藏资源简介：
该数据集面向供应链知识建模与关系挖掘需求建设，聚焦笔记本产品供应链上下游厂商的业务信息整合与关联分析，针对企业知识图谱构建、供应商关系分析、上下游风险评估等任务的数据支撑缺口，填补了供应链领域结构化厂商关系数据集的空白，对提升供应链链路追溯效率、优化供应商管理、辅助风险决策具有重要意义，可广泛服务于学术研究、教学实践及非商业性质的人工智能技术研发。 数据集来源于联想公司业务数据库（采集地点为中国北京市海淀区西北旺东路 10 号院 2 号楼），数据采集时间跨度为 2022 年 12 月至 2025 年 1 月，按月同步更新。原始数据经结构化归一化、实体对齐、清洗与三元组化建模等标准化处理，确保了数据来源的权威性与一致性，具备极强的工业场景真实性与实用性。 数据集核心内容为笔记本产品供应链上游零部件供应商与下游采购商的多维度业务信息，以 “实体–关系–属性” 的三元组形式存储于单一文本文件中。每条三元组记录通过制表符分隔，涵盖厂商基本信息（公司名称、所在地、业务范围、公司介绍等）、上游供应关系（主营产品、具体零部件名称等）、下游采购数据（采购产品类型、采购频率等），形成完整的供应链厂商知识网络。数据无分级目录，文件命名规范，便于快速读取与解析。 数据体量方面，数据集共收录 30518 家厂商的 7591340 条标准化三元组记录，规模庞大且覆盖全面，能充分支撑供应链网络分析、知识图谱构建等复杂任务，为供应链上下游关系挖掘提供充足的数据基础。 该数据集为公开共享资源，采用纯文本格式存储，支持 Python、Java、C++ 等主流编程语言解析，可灵活转化为知识图谱、表格、向量等多种输入形式，适配图数据库存储与机器学习模型训练，为供应链领域的智能决策研究提供了高质量、结构化的核心数据支撑。
This dataset is developed to cater to the requirements of supply chain knowledge modeling and relationship mining, focusing on the integration and correlation analysis of business information for upstream and downstream manufacturers in the laptop product supply chain. It fills the gap of structured manufacturer relationship datasets in the supply chain domain by addressing the shortage of data support for tasks such as enterprise knowledge graph construction, supplier relationship analysis, and upstream-downstream risk assessment. This dataset holds significant value for improving supply chain traceability efficiency, optimizing supplier management, and aiding risk decision-making, and can be widely applied to academic research, teaching practice, and non-commercial artificial intelligence technology development. The dataset is sourced from the business database of Lenovo, with data collection conducted at Building 2, Courtyard 10, Xibeiwang East Road, Haidian District, Beijing, China. The data collection period spans from December 2022 to January 2025, with updates synchronized on a monthly basis. Raw data has undergone standardized processing including structured normalization, entity alignment, data cleaning, and triple-based modeling, which ensures the authority and consistency of the data source, and endows the dataset with strong authenticity and practicality in industrial scenarios. The core content of the dataset consists of multi-dimensional business information of upstream component suppliers and downstream purchasers in the laptop product supply chain, stored in a single text file in the "entity-relation-attribute" triple format. Each tab-separated triple record covers basic manufacturer information (company name, location, business scope, company introduction, etc.), upstream supply relationships (main products, specific component names, etc.), and downstream procurement data (procured product types, procurement frequency, etc.), forming a complete knowledge network of supply chain manufacturers. The dataset has no hierarchical directory, and the standardized file naming convention enables rapid reading and parsing. In terms of data scale, the dataset contains 7,591,340 standardized triple records covering 30,518 manufacturers. With its large scale and comprehensive coverage, it can sufficiently support complex tasks such as supply chain network analysis and knowledge graph construction, providing a solid data foundation for upstream and downstream relationship mining in the supply chain. This is a publicly available shared dataset stored in plain text format. It supports parsing via mainstream programming languages including Python, Java, and C++, and can be flexibly converted into various input formats such as knowledge graphs, tables, and vectors, making it compatible with graph database storage and machine learning model training. It provides high-quality, structured core data support for intelligent decision-making research in the supply chain domain.
