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论文编号：	15829
作者编号：	2320234121
上传时间：	2025/12/10 16:43:22
中文题目：	基于人工智能的大型化工企业研发流程管理优化研究
英文题目：	Research on the Optimization of Research and Development Process Management in Large-scale Chemical Enterprises Based on Artificial Intelligence
指导老师：	姚欣林
中文关键字：	人工智能；研发流程管理；化工新材料；大语言模型；流程优化
英文关键字：	Artificial intelligence; R&D process management; New chemical materials; Large Language Model; Process Optimization
中文摘要：	本研究聚焦于大型化工企业研发流程管理的优化，特别是通过人工智能（Artificial Intelligence，AI）技术的应用来提升研发效率、降低成本并增强创新能力。中国将人工智能视为国家战略，推动其在能源化工等领域的应用，T化工研究院作为大型化工企业的核心研发机构，需要通过智能化转型提升研发效率和创新能力。 本研究通过对T化工研究院现状的深入分析，识别出其在研发流程管理中项目管理不明确、资源分配机制僵化、知识数据不清晰、风险管控机制不完善以及人工智能应用不全面等问题。基于以上问题提出了基于人工智能的研发流程管理优化方案，构建基于人工智能的研发流程管理平台，主要包括AI化工大模型、机器学习平台和实验室管理系统三部分，旨在通过智能化手段解决传统研发流程中的瓶颈问题。 优化方案的设计基于集成产品开发（Integrated Product Development，IPD）模型、流程优化与再造理论，形成以人工智能技术为引擎的研发流程管理平台。该平台通过数据整合、智能检索、跨域共享等功能，打破了数据壁垒，提升了数据共享率。同时，通过线上化流程重构和跨部门协同机制，显著缩短了研发周期，提升了项目管理效率。 在实施效果评估方面，本研究通过在新材料研发项目组的试点实施，验证了优化方案的有效性。研究结论指出，通过构建基于人工智能的研发流程管理优化体系，显著提升了T化工研究院的研发效率和创新能力。验证了“项目管理-资源配置-知识复用-风险管控”四维优化模型的有效性，并嵌入PDCA循环机制，实现了研发管理的闭环优化与持续改进，为化工行业的智能化转型提供了可复制的解决方案。研究展望部分提出了全面推广优化方案、进一步拓展AI技术在研发全链条的应用以及建立长期跟踪机制的建议，以持续评估平台的经济效益和技术成熟度。总体而言，本研究为大型化工企业研发流程管理的智能化转型提供了理论支持和实践指导，具有重要的理论和实践意义。
英文摘要：	This study focuses on optimizing R&D process management in large chemical enterprises, particularly through the application of artificial intelligence (AI) to enhance R&D efficiency, reduce costs, and strengthen innovation capabilities. As China positions AI as a national strategic priority and promotes its application in sectors such as energy and chemicals, T Chemical Research Institute, as a core R&D entity within a large chemical enterprise, must undergo intelligent transformation to improve its R&D efficiency and innovation capacity. Through an in-depth analysis of the current situation at T Chemical Research Institute, this study identifies key issues in its R&D process management, including unclear project management, rigid resource allocation mechanisms, fragmented knowledge and data, inadequate risk control mechanisms, and insufficient application of AI technologies. In response, an AI-driven R&D process optimization solution is proposed, which involves the construction of an intelligent R&D management platform comprising three main components: an AI-powered chemical large model, a machine learning platform, and a laboratory management system. This platform is designed to address bottlenecks in traditional R&D processes through intelligent means. The optimization scheme is developed based on the Integrated Product Development (IPD) model and process optimization and reengineering theory, forming an AI-powered R&D process management platform. By enabling functions such as data integration, intelligent retrieval, and cross-domain sharing, the platform breaks down data barriers and improves the data sharing rate. Furthermore, through online process reengineering and cross-department collaboration mechanisms, it significantly shortens the R&D cycle and enhances project management efficiency. The effectiveness of the optimization scheme was validated through a pilot implementation in a new material R&D project team. The results demonstrate that the AI-based R&D process management optimization system significantly improves T Chemical Research Institute's R&D efficiency and innovation capability. It verifies the effectiveness of a four-dimensional optimization model—"Project Management - Resource Allocation - Knowledge Reuse - Risk Control"—and incorporates a PDCA (Plan-Do-Check-Act) cycle mechanism to achieve closed-loop optimization and continuous improvement in R&D management. This provides a replicable solution for the intelligent transformation of the chemical industry. The research outlook includes recommendations for comprehensively promoting the optimization scheme, further expanding the application of AI technology across the entire R&D chain, and establishing a long-term tracking mechanism to continuously evaluate the platform's economic benefits and technological maturity. Overall, this study offers theoretical support and practical guidance for the intelligent transformation of R&D process management in large chemical enterprises, holding significant theoretical and practical importance.
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