数据与计算发展前沿2026，Vol.8Issue(1)：119-128,10.DOI:10.11871/jfdc.issn.2096-742X.2026.01.010
农业科学数据自动挖掘框架设计与实践
Design and Practice of an Automated Mining Framework for Agricultural Science Data
摘要
Abstract
[Background]The digital transformation of agriculture has accelerated widespread adoption of big data technologies,yet conventional data processing approaches continue to grapple with challenges,including intricate workflow complexities and integration difficulties with legacy tools.[Objective]This study proposes an automated mining framework for agricultural science data employing intelligent pipeline architecture PiFlow to address dynamic adaptation between data processing and application scenarios.[Methods]The architecture integrates streaming en-gines with Directed Acyclic Graph(DAG)task orchestration to construct heterogeneous data pipelines supporting unified stream-batch computation.Utilizing modular service design and containerized elastic scaling mechanisms,it establishes a standardized operator abstraction lay-er with unified interfaces that incorporates both general-purpose processing operators and spe-cialized mining tools.Through integration of visual interactive engines and predefined operator templates,the framework enables low-code development of complex analytical workflows.A prototype system was subsequently implemented using a six-layer subsystem architecture encompassing processing pipelines,exe-cution engines,scheduling,monitoring,logging,and visualization components.[Results]Validation through agri-cultural genomics selection and arable land resource assessment demonstrates significant enhancements in multi-dimensional data analysis efficiency and cross-scenario reusability,establishing an extensible technical infrastruc-ture for precision agricultural decision-making systems.关键词
农业大数据/异构数据流水线处理/动态组件扩展/可视化任务编排/预置模板库Key words
agricultural big data/heterogeneous data pipeline processing/elastic operator scaling/visualized task orchestra-tion/preset templates library引用本文复制引用
蓝晨阳,路长发,朱小杰,段军磊,任浩..农业科学数据自动挖掘框架设计与实践[J].数据与计算发展前沿,2026,8(1):119-128,10.基金项目
国家重点研发计划"场景驱动的农业科学数据挖掘分析技术与应用"(2022YFF0711800) （2022YFF0711800）
