发表机构
China Agricultural University（中国农业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对个性化膳食规划中约束与营养目标冲突的问题，提出ShanLiangRen营养智能体，通过全量化多目标建模、检索增强生成和帕累托原则细化，输出可执行的膳食计划与合规报告。
AI 中文摘要
膳食营养规划在慢性病管理和维持健康身体方面发挥着重要作用。在实际应用中，它必须同时满足个性化约束和合理的多维营养目标。这两个方面往往相互冲突，且用户约束会随着反馈而演变，导致通用指南与可执行计划之间存在显著差距。为弥合这一差距，我们首先提出了个性化全量化多目标膳食规划问题（MDP）。为解决MDP，我们开发了一个营养智能体ShanLiangRen。该系统首先将膳食规格、营养数据、用户属性和自然语言需求转化为个体化的约束规划实例。然后，它采用精确的检索增强生成方法，从大规模食材和食谱空间中缩小可行候选集。最后，它采用基于帕累托原则的细化方法，其中大语言模型在确定性营养计算和约束验证反馈下迭代修订候选计划。系统输出具有明确食材和份量的全量化膳食计划，以及显示约束满足和营养区间达标的营养合规报告。我们已将系统作为微信小程序ShanLiangRen在线发布。演示视频可在此https URL获取。
英文摘要
Dietary nutrition planning plays an important role in chronic disease management and maintaining a healthy body. In applications, it must simultaneously satisfy personalized constraints and reasonable multidimensional nutritional goals. These two aspects often conflict, and user constraints evolve with feedback, resulting in a substantial gap between generic guidelines and executable plans. To bridge this gap, we first propose the personalized fully quantified multiobjective dietary planning problem (MDP). To tackle MDP, we develop a nutrition agent, ShanLiangRen. The system first transforms dietary specifications, nutrient data, user attributes and natural language requirements into an individualized constrained planning instance. It then employs an exact retrieval-augmented generation method to shrink the feasible candidate set from a large scale ingredient and recipe space. Finally, it adopts a refinement guided by Pareto principles, where an LLM iteratively revises candidate plans under deterministic nutrition computation and feedback from constraint verification. The system outputs fully quantified meal plans with explicit ingredients and portion sizes, together with reports on nutrition compliance that show constraint satisfaction and nutrient interval attainment. We have released the system online as a WeChat Program, ShanLiangRen. A demo video is available at https://www.youtube.com/watch?v=652OtY5VlGA.
