基于多模态融合与AI辅助的高质量数据集构建方法与实践OA
A methodological framework for high-quality dataset construction based on multimodal fusion and AI assistance
针对企业高质量数据集建设面临的目标定位模糊、实施路径碎片化、技术底座薄弱、标注成本高昂四大瓶颈,提出一套基于多模态融合与人工智能辅助的高质量数据集构建方法论.该方法论以中国电信知识服务平台为技术载体,构建了"需求映射—智能治理—价值释放"三层架构.并且,该方法论在高端装备制造、消费品行业的规模化实践中的有效性与可复制性得到验证,数据集构建周期大幅缩短,为数据要素市场化配置背景下的企业数据资产建设提供了可操作的工程范式.
Building high-quality datasets for AI applications often faces four practical challenges:unclear alignment with business goals,fragmented implementation,limited technical infrastructure,and excessive annotation costs,this paper presents a methodology that addresses these issues through a three-layer framework-demand mapping,intelligent governance,and value realization-implemented on China Telecom's Knowledge Service Platform.The methodology has been validated in high-end equipment manufacturing and consumer goods industries,cutting dataset construction time,offering a practical pathway for enterprise data asset development in the era of data marketization.
王栋;杨华锋;刘威辰;李康;刘敬谦;刘世伟
中国电信集团有限公司,北京 100033中国电信集团有限公司,北京 100033中国电信集团有限公司,北京 100033中国电信集团有限公司,北京 100033中国电信集团有限公司,北京 100033中国电信集团有限公司,北京 100033
信息技术与安全科学
高质量数据集多模态融合AI辅助标注数据资产管理
high-quality datasetmultimodal fusionAI-assisted annotationdata asset management
《信息通信技术与政策》 2026 (5)
22-31,10
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