用AI构建基建与采购数据架构，提升决策效率与透明度。
An AI-Driven Data Mesh Architecture Enhancing Decision-Making in Infrastructure Construction and Public Procurement
- 融合数据网格与服务网格，构建统一智能平台。
- 基于超1000亿标记的训练数据，实现知识结构化。
- 适合政府与工程机构用于数字化转型与智能决策。
基础设施建设作为“产业之产业”，与政府支出和公共采购密切相关，通过提升透明度和信息可及性，有望显著提高生产率、节省成本并带来更广泛的经济效益。本文提出一种集成软件生态系统，融合数据网格与服务网格架构，包含涵盖超过1000亿个标记的大型基础设施与采购训练数据集，整合科学文献、项目活动与风险数据，并通过系统性AI框架进行结构化处理。平台基于知识图谱，连接领域特定多智能体任务与问答能力，实现异构数据源的标准化接入与知识转化。借助大语言模型（LLMs）与自动化技术，系统革新数据组织与知识生成方式，支持早期项目规划、深度研究、市场趋势分析及定性评估中的决策支持。其可扩展的Web架构提供领域定制化信息，使AI智能体能够进行推理并应对不确定性，同时为未来部署专业智能体以解决特定挑战预留空间。该体系将人工智能与领域专长结合，不仅提升基建与工程领域的效率与决策质量，也为政府效能提升和传统产业数字化转型提供范式参考。本工作有望深刻影响该领域的智能化进程，并引领人工智能运维（AI Ops）最佳实践。
原文摘要 · Abstract (English)
Infrastructure construction, often dubbed an "industry of industries," is closely linked with government spending and public procurement, offering significant opportunities for improved efficiency and productivity through better transparency and information access. By leveraging these opportunities, we can achieve notable gains in productivity, cost savings, and broader economic benefits. Our approach introduces an integrated software ecosystem utilizing Data Mesh and Service Mesh architectures. This system includes the largest training dataset for infrastructure and procurement, encompassing over 100 billion tokens, scientific publications, activities, and risk data, all structured by a systematic AI framework. Supported by a Knowledge Graph linked to domain-specific multi-agent tasks and Q&A capabilities, our platform standardizes and ingests diverse data sources, transforming them into structured knowledge. Leveraging large language models (LLMs) and automation, our system revolutionizes data structuring and knowledge creation, aiding decision-making in early-stage project planning, detailed research, market trend analysis, and qualitative assessments. Its web-scalable architecture delivers domain-curated information, enabling AI agents to facilitate reasoning and manage uncertainties, while preparing for future expansions with specialized agents targeting particular challenges. This integration of AI with domain expertise not only boosts efficiency and decision-making in construction and infrastructure but also establishes a framework for enhancing government efficiency and accelerating the transition of traditional industries to digital workflows. This work is poised to significantly influence AI-driven initiatives in this sector and guide best practices in AI Operations.
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