Building an AI4S-Oriented Intelligent Data Service System for Astronomy: Practices from the National Astronomical Data Center

doi:10.11871/jfdc.issn.2096-742X.2026.04.001

Abstract

Abstract:

[Objective] Astronomy has fully entered the era of data-intensive scientific discovery. Meanwhile, the rapid emergence of the artificial intelligence-driven scientific research paradigm, namely AI for Science (AI4S), has imposed unprecedented requirements for enhancing the capability of scientific data centers as integral components of national strategic scientific and technological forces. Confronted with the challenges of governing massive and heterogeneous datasets, the efficiency bottlenecks of traditional data service models, and the mission of supporting cutting-edge AI4S innovation, the National Astronomical Data Center (NADC) has systematically planned and implemented a next-generation astronomical data service system characterized by “Digital-Intelligent Integration”. [Methods] This paper first presents a comprehensive description of the system's “trinity” overall architecture. A standardized and intelligent data service layer is established with application programming interfaces (APIs) and the Model Context Protocol (MCP) as dual cores. A one-stop research support layer is built upon the cloud-based scientific research platform, the China-VO Science Platform, which deeply integrates data, software, computing resources, and models. An open and collaborative application ecosystem layer is cultivated through deep engagement with major scientific projects, innovation in science-education integration models, disciplinary and talent development, expansion of citizen science initiatives, and strengthened international collaboration. [Results] The paper focuses on in-depth analyses of successful practices within this system, including standardized and intelligent services for the LAMOST astronomical spectral database, AI agent-driven time-domain astronomical discovery with EP satellites, and citizen science programs. These cases demonstrate the system's significant effectiveness in enhancing intelligent data provision, lowering the barriers to adopting advanced AI technologies, and empowering discoveries in time-domain astronomy. [Conclusions] The explorations of NADC provide a systematic and demonstrative solution for functional transformation, capacity building, and service model innovation of national scientific data centers in the era of big data and artificial intelligence.

Key words: National Astronomical Data Center (NADC), data-intensive scientific discovery, AI for Science (AI4S), Virtual Observatory (VO), intelligent data service, open science, Model Context Protocol (MCP), agent

CUI Chenzhou, XU Yunfei, FAN Dongwei, LI Changhua, LI Shanshan, MI Linying, GAN Qingbo, ZHAO Gongbo, China-VO Collaboration. Building an AI4S-Oriented Intelligent Data Service System for Astronomy: Practices from the National Astronomical Data Center[J]. Frontiers of Data and Computing, 2026, 8(4): 1-18, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.04.001.

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协议名称	主要用途
VOTable	IVOA标准数据交换格式，用于存储和传输LAMOST星表、光谱元数据与观测参数，支持多平台、多软件的数据互通与解析，保障数据结构的标准化与可重用性。
SAMP	简单应用消息协议，用于天文软件的互操作，支持LAMOST数据在TOPCAT、Aladin等主流天文软件间的实时数据共享与协同分析，实现检索结果的跨软件同步可视化。
ConeSearch	天球锥形区域检索协议，支持用户通过指定赤经、赤纬与搜索半径，快速检索目标天区内的LAMOST天体数据。
SSAP	简单光谱访问协议，专门用于光谱数据的标准化检索与访问，返回光谱数据及配套元信息。
TAP	数据表访问协议，支持用户通过天文数据查询语言ADQL语句对LAMOST光谱数据库进行复杂检索，可实现多条件组合筛选、跨表关联查询，满足深度数据挖掘需求。

API名称	功能描述	访问方式	访问地址（URL）
数据版本查询	获取LAMOST所有已公开的数据版本列表	GET请求	https://www.lamost.org/openapi/dr_versions
表数据查询	针对指定数据表执行结构化条件查询	POST请求	https://www.lamost.org/openapi/{dr_version}/{sub_version}/query/{table_name}
SQL语句查询	支持通过ADQL/SQL语句执行复杂的自定义查询	GET请求	https://www.lamost.org/openapi/{dr_version}/{sub_version}/sql
星表文件下载	下载指定分辨率光谱的全量星表文件	GET请求	https://www.lamost.org/openapi/{dr_version}/{sub_version}/{resolution}/catalog
光谱FITS获取	获取指定天体的光谱数据FITS文件	GET请求	https://www.lamost.org/openapi/{dr_version}/{sub_version}/{resolution}/spectrum/fits
光谱CSV转换	将光谱数据转换为CSV格式	GET请求	https://www.lamost.org/openapi/{dr_version}/{sub_version}/{resolution}/spectrum/fits2csv