Abstract: The development process of heavy-duty truck products is a systematic engineering endeavor, where the efficiency of issue management directly impacts the project development cycle. Traditional issue management and development primarily relies on manual experience and offline operations, making it difficult to achieve informatized collaborative control. Additionally, the lack of data-driven decision support results in low efficiency and insufficient scientific rigor in decisionmaking. The article proposes an information-based approach for comprehensive management of issue feedback, decomposition, and closure throughout heavy-duty truck development processes, while correlating problem resolution status with project progress. Leveraging big data visualization models, it enables categorized statistics, screening, early warnings, and delay tracking of development issues. By presenting complex datasets through intuitive graphics and visualizations,this methodology streamlines data analysis processes, enhances issue control efficiency, and provides robust support for research and development decision-making, project development productivity, and valve review processes.

Key words: big data; heavy-duty truck; development problem; efficiency improvement

摘要： 重型卡车产品开发过程是一项系统工程，开发过程中问题管控效率直接影响项目开发 周期。传统的问题管理和开发主要依赖人工经验和线下操作，难以实现信息化协同管控，且 缺乏数据驱动的决策支持，导致效率低下和决策科学性不足。文章提出一种采用信息化手段， 用于对重型卡车开发过程中问题的反馈、分解与关闭进行全流程管理，并将开发问题关闭情 况与项目进展进行关联。基于大数据可视化的模式，对开发问题进行按类别统计、筛选、预 警及拖期，将复杂的数据集以直观的图形或图像形式展现出来，简化数据分析的过程，提升 开发问题管控效率，为研发组织决策及项目开发效率及阀门评审提供强有力的支持。

关键词: 大数据；重型卡车；开发问题；效率提升