摘要
随着工业4.0与智能制造的深入推进,人工智能已成为制造业数字化转型的核心驱动力。然而,技术落地仍面临应用场景模糊、数据治理薄弱、模型适配不足、安全管控缺位与投资回报不确定等系统性挑战。本文基于对辽宁省沈阳市、大连市等地30余家工业企业的实地调研,系统分析人工智能在生产制造环节的应用现状与实施障碍,识别出企业在技术认知、数据基础、模型定制、安全风险与成本结构等方面的关键瓶颈。在此基础上,提出“场景驱动-数据治理-模型构建-价值实现”四环主路径与“全流程安全管控”保障层协同的“4+1”循环演进体系模型,强调通过多维反馈机制实现动态迭代。该体系为理解人工智能在传统制造业中的融合逻辑提供了系统性分析视角。
With the advancement of Industry 4.0 and smart manufacturing,artificial intelligence(AI)has become a core driver of digital transformation in the manufacturing sector.However,the practical implementation of AI still faces systemic challenges,including ambiguous application scenarios,weak data governance,poor model adaptability,inadequate security controls,and uncertain return on investment.Based on field research conducted at over 30 industrial enterprises in Shenyang,Dalian,and other cities in Liaoning Province,this study systematically examines the current status and implementation barriers of AI in manufacturing processes,identifying key bottlenecks in technological understanding,data infrastructure,model customization,security risks,and cost structures.Building on this analysis,the paper proposes a“4+1”cyclical evolution framework,integrating four core phases-scenario-driven,data governance,model construction,and value realization-with a cross-cutting layer of end-to-end security control,supported by multi-dimensional feedback mechanisms.This framework provides a systematic analytical perspective for understanding the integration of AI into traditional manufacturing systems.
作者
孙诗华
SUN Shihua(Liaoning Province Industrial Internet Development Research Center,Shenyang 110000,China)
出处
《智能制造》
2026年第1期25-30,共6页
Intelligent Manufacturing
关键词
人工智能
数字化转型
数据
大模型
artificial intelligence
digital transformation
data
large model
