【摘要】 目的 设计领域引入蓬勃发展的AIGC技术以期提高效率，在此基础上结合大数据技术和双钻模型，以提高AIGC应用于复杂产品工业设计的质量。方法 提出了一种AIGC协同的“1+2”钻工业设计流程模型，通过三次发散、三次收敛来提高AIGC协同工业设计的流程效率与方案质量。首先，在ChatGPT和Python大数据爬取辅助下发散需求问题，在Python高频词分析、竞品分析、专家评审下收敛设计目标；其次，运用Midjourney的Stop参数模糊生图进行创意发散，通过对项目人员问卷调查收敛设计方向；最后，联合使用DALL·E、Stable Diffusion和Midjourney发散设计细节，通过焦点小组收敛以确定最终方案并建模渲染。结果 实现全面、专业、准确的长期卧床老人气道廓清的需求分析，且辅具设计方案的在用户友好程度、造型美观度、技术可行性和功能可用性方面获得了设计与工程双方专业人员的认可。结论 AIGC、大数据与传统工业设计方法相结合的“1+2”钻设计流程，能够通过人工控制AIGC的参考内容、处理AIGC的生成内容，以及阶段性专家评估有效地提升AIGC协同的工业设计方案质量，为规范化的AIGC协同工业设计流程提供思路。更多还原

【Abstract】 Since the booming AIGC technology has been introduced into the design field to improve efficiency, the work aims to improve the application quality of AIGC in complex product industrial design combined with big data technology and double diamond model. An AIGC collaborative "1+2 diamond" industrial design process model was proposed to improve the process efficiency and solution quality of AIGC collaborative industrial design through three divergences and three convergences. Firstly, the demand issues were diverged with the assistance of ChatGPT and big data crawling by Python, and the design goals were converged with the help of high-frequency word analysis by Python, competitive product analysis and expert review. Secondly, creative ideas were diverged through fuzzy drawing by Midjourney’s stop parameter, and the design direction was converged through a questionnaire survey of project personnel. Finally, DALL·E,Stable Diffusion and Midjourney were employed in conjunction to generate divergent design details. The final solution was determined through focus group convergence and the modeling rendering was completed. A comprehensive, professional and accurate demand analysis was conducted on the airway clearance for long-term bedridden elderly people. The assistive device design solution was recognized by design and engineering professionals in terms of user-friendliness,aesthetic appeal, technical feasibility and functional usability. The "1+2 diamond" design process which integrates AIGC,big data and conventional industrial design methodologies, can efficaciously enhance the quality of AIGC collaborative industrial design by manually controlling AIGC’s reference content, processing AIGC’s generated content and conducting periodic expert evaluation, which offers insights into the standardized AIGC collaborative industrial design process.更多还原