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工业大模型赋能纺织产业的路径、挑战与未来

Industrial Foundation Models Empowering the Textile Industry: Pathways, Challenges, and Future

  • 摘要: 在新一轮科技革命和产业变革的背景下,人工智能(AI)与工业互联网的深度融合正成为推动制造业高质量发展的核心动力。纺织业作为国民经济的重要支柱产业,正处于由传统制造向智能制造加速跃迁的关键阶段。本文以YOCSEF杭州观点论坛“工业大模型赋能纺织行业”的研讨成果为基础,系统分析了工业大模型在纺织产业中的应用现状、落地场景、实施难点及生态构建路径。研究指出,工业大模型通过深度学习织造工艺、设备参数及生产知识,可在设计创新、质量控制、供应链管理及市场服务等环节实现智能决策与协同优化,显著提升产业效率与竞争力。然而,其推广仍受制于数据标准不统一、技术适配性不足、复合型人才短缺及成本压力等因素。为此,本文提出以“数据治理标准化、人才体系复合化、技术方案轻量化、成本机制分层化”为核心的应用条件体系,并进一步探讨了“产政学研”多方协同机制、开源共性模型建设及行业标准体系完善等生态构建路径。研究认为,工业大模型在纺织行业的落地,不仅是技术变革的结果,更是产业协同与生态共生的体现。未来,随着算力基础设施与数据治理体系的完善,工业大模型将成为纺织产业数字化、智能化、绿色化高质量发展的关键引擎。

     

    Abstract: Against the backdrop of a new wave of scientific and technological industrial transformation, the deep integration of artificial intelligence (AI) and the industrial internet has become a core driving force for high-quality development of the manufacturing industry. As a key pillar of the national economy, the textile industry is undergoing a critical transition from traditional manufacturing to intelligent manufacturing. Based on the discussions at the YOCSEF Hangzhou Forum themed on “Industrial Foundation Models Empowering the Textile Industry”. This article systematically analyzes the current application status, implementation scenarios, challenges, and ecosystem construction paths of industrial foundation models within the textile sector. The study finds that industrial foundation models, through in-depth learning of weaving processes, equipment parameters, and production knowledge, can enable intelligent decision-making and collaborative optimization in fields such as design innovation, quality control, supply chain management, and market services—significantly enhancing industrial efficiency and competitiveness. However, their large-scale adoption remains constrained by factors such as non-standardized data, insufficient technological adaptability, shortages of interdisciplinary talents, and high implementation costs. Therefore, this article proposes an application framework centered on “data governance standardization, talent system integration, technology lightweighting, and cost stratification”, and further explores ecosystem construction paths including multi-stakeholder collaboration among industry, government, academia, and research institution, the development of open-source common models, and the improvement of industrial standards. The study concludes that the application of industrial foundation models in the textile industry represents not only a technological transformation but also a manifestation of industrial collaboration and ecological symbiosis. In the future, as computing infrastructure and data governance systems continue to improvement, industrial foundation models will become a key engine driving the digital, intelligent, and green transformation of the textile industry.

     

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