一种融合双粒度知识库的仿生设计方案智能生成方法

张晓明; 刘成响; 钟凯; 谢锟; 钱护磊

doi:10.12263/DZXB.20250456

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一种融合双粒度知识库的仿生设计方案智能生成方法

学术论文 | 更新时间：2026-06-16

- 一种融合双粒度知识库的仿生设计方案智能生成方法
  增强出版
- An Intelligent Bio-Inspired Design Solution Generation Method Integrating Dual-Granularity Knowledge Bases
- 电子学报 2026年54卷第3期页码：1161-1177
- 作者机构：
  
  1.安徽大学物质科学与信息技术研究院，安徽合肥 230601
  2.青海省科学技术信息研究所有限公司，青海西宁 810008
  3.青海理工学院，青海西宁 810016
- 作者简介：
  
  张晓明男，1979年12月出生于山东省济南市。现为安徽大学物质科学与信息技术研究院教授、博士生导师。主要研究方向为知识图谱与智能决策。E-mail: zxm@ahu.edu.cn
  刘成响男，2002年8月出生于安徽省安庆市。现为安徽大学物质科学与信息技术研究院人工智能专业学生。主要研究方向为大模型与知识图谱。E-mail: cxbetter825@gmail.com
  钟凯男，1990年4月出生于安徽省含山县。现为安徽大学物质科学与信息技术研究院副教授。主要研究方向为复杂系统建模与智能运维、人工智能方法及其工程应用。E-mail: kaizhong0402@ahu.edu.cn
  谢锟男，1968年出生于青海省湟源县。现为青海理工学院教务处负责人、副教授。主要研究方向为网络动力学、概率论与数理统计、高校教学管理。E-mail: kxie@qhit.edu.cn
  钱护磊男，2000年10月出生于安徽省无为市。现为安徽大学物质科学与信息技术研究院人工智能专业学生。主要研究方向为大模型与知识图谱。E-mail: qianhulei1@gmail.com
- 基金信息：
  
  中国科学院委托技术开发项目（No.动合（科）2025-069）
- DOI：10.12263/DZXB.20250456
  中图分类号： TP182;TP391
- 收稿：2025-05-31，
  
  录用：2026-03-18，
  
  纸质出版：2026-03-25
- 稿件说明：
移动端阅览
张晓明, 刘成响, 钟凯, 等. 一种融合双粒度知识库的仿生设计方案智能生成方法[J]. 电子学报, 2026, 54(03): 1161-1177.

ZHANG Xiaoming, LIU Chengxiang, ZHONG Kai, et al. An Intelligent Bio-Inspired Design Solution Generation Method Integrating Dual-Granularity Knowledge Bases[J]. Acta Electronica Sinica, 2026, 54(03): 1161-1177.
张晓明, 刘成响, 钟凯, 等. 一种融合双粒度知识库的仿生设计方案智能生成方法[J]. 电子学报, 2026, 54(03): 1161-1177. DOI：10.12263/DZXB.20250456

ZHANG Xiaoming, LIU Chengxiang, ZHONG Kai, et al. An Intelligent Bio-Inspired Design Solution Generation Method Integrating Dual-Granularity Knowledge Bases[J]. Acta Electronica Sinica, 2026, 54(03): 1161-1177. DOI：10.12263/DZXB.20250456

摘要

仿生学通过借鉴自然界生物的优异特性，为工程创新提供了重要的方法支撑。然而，如何从丰富的生物信息中提取准确的知识并与设计需求有效对接，仍具有挑战。尽管大语言模型（Large Language Models，LLM）在自动化仿生设计过程中展现了潜力，但其依然存在局限性，包括：认知缺乏导致对仿生原理理解偏差；参数化知识约束引发时效性与准确性不足；外部知识库利用不足使得生成方案缺乏领域支撑。为此，本文提出一种融合双粒度知识库与LLM的智能化仿生设计方案生成方法（Intelligent Bio-Inspired Design，IBID），其核心是整合双粒度仿生知识库与LLM协同生成与评审机制，以实现从问题出发生成完整且有效的仿生设计方案。首先，针对知识表示与检索，本文收集生物优异特性及仿生实例，通过将每种生物策略拆分为连贯的片段，并依据主题相似性对其进行聚类，形成粗细两种粒度的知识表示，构建了双粒度仿生知识库，并设计了双粒度检索机制以提升知识检索的效率与准确性。其次，针对方案生成与优化，本文设计了分层协同的LLM工作流程：初始由基于LLM的仿生设计方案写作专家根据需求生成结构化大纲，进而设计了专家会商评审机制，由多个LLM形成普通专家组，基于知识证据独立扩展草案，再通过多轮德尔菲法进行意见交换、评估与迭代修订，以此集成多元视角、提升一致性与可靠性；最后，由基于LLM的高级专家进行终审，执行逻辑校验、细节补充与整体提升，避免观点漂移，提升方案的完整性与有效性。为验证IBID的有效性，本文在自主构建的仿生数据集上开展了实验分析，结果表明：IBID在Precision@1 （P@1）、Precision@3 （P@3）及Mean Average Precision （MAP）等检索指标上均优于最佳基线模型；在生成质量评估中，IBID在专业质量及内容相关性等大多数指标上优于GraphRAG、RAPTOR及其他基线模型。本方法有效缓解了LLM在专业领域的幻觉与知识局限问题，为高质量、高可靠的智能仿生设计提供了实用框架。

Abstract

Bio-inspired design leverages the superior characteristics of biological organisms in nature

providing essential methodological support for engineering innovation. However

extracting accurate knowledge from abundant biological information and effectively aligning it with design requirements remains challenging. Although large language models (LLMs) have demonstrated potential in automating the bio-inspired design process

they still exhibit notable limitations

including: cognitive deficiencies leading to biased understanding of bio-inspired principles; parametric knowledge constraints resulting in insufficient timeliness and accuracy; and underutilization of external knowledge bases

which leaves generated solutions lacking domain-specific support. To address these issues

this paper proposes an intelligent bio-inspired design (IBID) method that integrates a dual-granularity knowledge base with LLMs for automated design solution generation. The core of IBID lies in the synergistic integration of a dual-granularity bio-inspired knowledge base with an LLM-based collaborative generation and review mechanism

enabling the generation of complete and effective bio-inspired design solutions from problem statements. First

regarding knowledge representation and retrieval

we collect biological superior characteristics and bio-inspired instances

decompose each biological strategy into coherent segments

and cluster them based on thematic similarity to form coarse- and fine-grained knowledge representations

thereby constructing a dual-granularity bio-inspired knowledge base. A corresponding dual-granularity retrieval mechanism is designed to improve the efficiency and accuracy of knowledge retrieval. Second

regarding solution generation and optimization

we design a hierarchically collaborative LLM workflow: an LLM-based bio-inspired design writing expert initially generates a structured outline based on the requirements; subsequently

an expert consultation and review mechanism is introduced

wherein multiple LLMs form a panel of general experts that independently expand drafts based on knowledge evidence

followed by multi-round Delphi-method-based opinion exchange

evaluation

and iterative revision to integrate diverse perspectives and enhance consistency and reliability. Finally

an LLM-based senior expert conducts a final review

performing logical verification

detail supplementation

and overall refinement to prevent viewpoint drift and improve the completeness and effectiveness of the solution. To validate the effectiveness of IBID

experimental analyses are conducted on a self-constructed bio-inspired dataset. The results demonstrate that IBID outperforms the best baseline models across retrieval metrics including Precision@1 (P@1)

Precision@3 (P@3)

and Mean Average Precision (MAP). In the generation quality evaluation

IBID surpasses GraphRAG

RAPTOR

and other baseline models on most metrics

including professional quality and content relevance. The proposed method effectively mitigates hallucination and knowledge limitation issues of LLMs in specialized domains

providing a practical framework for high-quality and reliable intelligent bio-inspired design.

关键词

Keywords

references

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