基于双层复杂网络的产品架构设计核心驱动因素识别

doi:10.3785/j.issn.1008-973X.2026.08.004

浙江大学学报(工学版)

2026, Vol. 60

Issue (8): 1650-1661 DOI: 10.3785/j.issn.1008-973X.2026.08.004

能源工程、机械工程

基于双层复杂网络的产品架构设计核心驱动因素识别

刘世锋1(

),苏建宁2,*(

),张书涛2,王世杰1,邱凯2,杨文瑾2

1. 兰州理工大学机电工程学院，甘肃兰州 730050
2. 兰州理工大学建筑与艺术设计学院，甘肃兰州 730050

Identification of core driving factors for product architecture design based on dual-layer complex network

Shifeng LIU1(

),Jianning SU2,*(

),Shutao ZHANG2,Shijie WANG1,Kai QIU2,Wenjin YANG2

1. School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2. School of Architecture and Art Design, Lanzhou University of Technology, Lanzhou 730050, China

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摘要：

为了解决产品设计中物理组件的功能结构解与外观美学解难协同、多源驱动因素与物理组件之间“多对多”复杂映射关系不清晰的问题，提出双层复杂网络分析多源驱动因素对产品架构设计的复杂影响关系. 基于产品架构本体特性和自然语言处理技术，分别构建表征物理组件之间拓扑关联的物理组件网络和反映多源驱动因素之间语义关系的驱动因素网络；基于设计认知特性建立“物理组件-驱动因素”映射的双层复杂网络，定量表征设计认知约束下多源驱动因素对物理组件的复杂作用关系；通过多维拓扑特征融合分析，识别物理组件在层内与层间节点双重作用下的核心驱动因素及其权重. 结果表明，多源驱动因素对产品架构设计的影响具有多层级链式驱动和协同交互驱动的特征；应该关注功能结构和外观美学关系的协同作用. 以汽车架构为例进行分析，验证了提出方法的可行性.

关键词： 产品架构; 多源驱动因素; 物理组件; 设计认知; 双层复杂网络

Abstract:

A method based on a dual-layer complex network was proposed to resolve two problems in product design: the difficulty in synergizing functional structure solutions with aesthetic appearance solutions, and the ambiguous and complex many-to-many mappings between multi-source driving factors and physical components. First, based on the ontological characteristics of product architecture and the natural language processing techniques, a physical component network was constructed to represent the topological relationships among physical components, and a driving factor network was built to reflect the semantic relationships among multi-source driving factors. Then, based on design cognitive characteristics, a dual-layer complex network was established to map the relationships between physical components and driving factors, enabling the quantitative representation of the complex influence of multi-source driving factors on physical components under design cognitive constraints. Finally, through multi-dimensional topological feature fusion analysis, the core driving factors and their weights for physical components were identified under the combined effects of intra-layer and inter-layer nodes on physical components. The results indicate that the influence of multi-source driving factors on the product architecture design exhibits characteristics of multi-level chain effects and synergistic interactions. Additionally, the synergistic relationship between functional structure and aesthetic appearance should be considered. A case study on automotive architecture demonstrates the feasibility of the proposed method.

Key words: product architecture multi-source driving factor physical component design cognition dual-layer complex network

收稿日期: 2025-07-10 出版日期: 2026-07-16

CLC:

TB 472

基金资助: 国家自然科学基金资助项目（52165033）；甘肃省青年科技计划资助项目（24JRRA968）；甘肃省教育厅高校教师创新基金资助项目（2023A-024）.

通讯作者: 苏建宁 E-mail: liusf@lut.edu.cn;sujn@lut.edu.cn

作者简介: 刘世锋（1993—），男，博士生，从事创新设计和智能设计研究. orcid.org/0000-0002-7095-0576. E-mail：liusf@lut.edu.cn

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引用本文:

刘世锋,苏建宁,张书涛,王世杰,邱凯,杨文瑾. 基于双层复杂网络的产品架构设计核心驱动因素识别[J]. 浙江大学学报(工学版), 2026, 60(8): 1650-1661.

Shifeng LIU,Jianning SU,Shutao ZHANG,Shijie WANG,Kai QIU,Wenjin YANG. Identification of core driving factors for product architecture design based on dual-layer complex network. Journal of ZheJiang University (Engineering Science), 2026, 60(8): 1650-1661.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2026.08.004 或 https://www.zjujournals.com/eng/CN/Y2026/V60/I8/1650

图 1 多源驱动因素对产品架构设计复杂影响的分析框架

表 1 功能结构关系评价准则[34]

表 2 意象风格关系评价准则

表 3 物理组件间的综合关联强度

表 4 驱动因素之间的相关性

表 5 “物理组件-驱动因素”映射关系

图 2 “物理组件-驱动因素”双层网络

图 3 物理组件综合重要度

图 4 驱动因素对模块影响强度

模块	组件	驱动因素1的路径	驱动因素2的路径	驱动因素3的路径	驱动因素4的路径	驱动因素5的路径	驱动因素6的路径
底盘模块	P₁	E₃→E₁₀→E₇→P₁ 权重：0.156 8	E₁₅→P₈→P₁ 权重：0.189 0	E₁₆→P₂₄→P₁ 权重：0.181 8	E₈→E₉→E₇→P₁ 权重：0.178 2	E₂→E₅→P₁ 权重：0.156 2	E₂₀→E₅→P₁ 权重：0.138 0
	P₃₂	E₃→E₁₀→P₃₂ 权重：0.154 6	E₁₅→P₃₂ 权重：0.194 5	E₁₆→E₁₈→P₃₂ 权重：0.174 0	E₈→P₂₆→P₃₂ 权重：0.180 5	E₂→P₃₂ 权重：0.155 5	E₂₀→E₅→P₃₂ 权重：0.140 9
	P₃₅	E₃→E₁₀→P₃₅ 权重：0.145 8	E₁₅→P₁₂→P₄→P₃₅ 权重：0.180 8	E₁₆→P₃₅ 权重：0.177 7	E₈→P₃₅ 权重：0.162 3	E₂→E₉→P₃₅ 权重：0.163 5	E₂₀→E₅→E₁₀→P₃₅ 权重：0.169 9
前脸模块	P₁₂	E₄→E₁→P₁₂ 权重：0.158 7	E₁₁→E₁→P₁₂ 权重：0.169 0	E₁₇→E₅→E₁→P₁₂ 权重：0.198 2	E₂₀→E₅→E₁→P₁₂ 权重：0.184 0	E₂→E₁→P₁₂ 权重：0.157 3	E₁₆→E₁₅→P₁₂ 权重：0.132 9
	P₂₄	E₄→E₁₀→P₂₄ 权重：0.156 2	E₁₁→E₁₀→P₂₄ 权重：0.156 0	E₁₇→P₂₄ 权重：0.191 7	E₂₀→E₅→E₁₀→P₂₄ 权重：0.182 7	E₂→P₂₄ 权重：0.156 8	E₁₆→P₂₄ 权重：0.156 8
	P₁₀	E₄→P₁₀ 权重：0.142 9	E₁₁→E₁₀→P₁₀ 权重：0.160 1	E₁₇→P₁₀ 权重：0.151 9	E₂₀→P₁₀ 权重：0.178 7	E₂→P₁₀ 权重：0.169 7	E₁₆→P₁₀ 权重：0.196 6
内室模块	P₆	E₁₆→P₂→P₄→P₆ 权重：0.199 2	E₂₀→E₅→P₁₉→P₆ 权重：0.168 0	E₁₈→E₁₀→E₇→P₆ 权重：0.164 2	E₄→P₂₈→P₆ 权重：0.147 4	E₈→P₃₀→P₆ 权重：0.161 9	E₃→P₆ 权重：0.159 3
	P₃	E₁₆→P₃₇→P₃ 权重：0.170 3	E₂₀→P₃₇→P₃ 权重：0.170 1	E₁₈→P₃₂→P₃ 权重：0.162 9	E₄→E₁₀→E₇→P₃ 权重：0.167 1	E₈→P₂₉→P₃ 权重：0.162 9	E₃→E₁₀→E₇→P₃ 权重：0.166 6
	P₅	E₁₆→E₁₈→E₁₂→P₅ 权重：0.199 5	E₂₀→E₅→P₅ 权重：0.172 7	E₁₈→E₁₂→P₅ 权重：0.148 8	E₄→E₁₀→E₇→P₅ 权重：0.156 6	E₈→P₂₀→P₅ 权重：0.166 3	E₃→E₁₀→E₇→P₅ 权重：0.156 1
侧身模块	P₁₉	E₁₆→P₂→P₁₉ 权重：0.183 5	E₄→E₁→P₁₉ 权重：0.160 8	E₁₈→E₁→P₁₉ 权重：0.160 0	E₁₇→E₅→P₁₉ 权重：0.159 4	E₂₀→E₅→P₁₉ 权重：0.145 7	E₈→E₉→E₇→P₁₉ 权重：0.190 6
	P₁₄	E₁₆→P₃₉→P₁₄ 权重：0.190 6	E₄→E₁₀→E₇→P₁₄ 权重：0.152 8	E₁₈→E₁₀→E₇→P₁₄ 权重：0.152 3	E₁₇→E₉→E₇→P₁₄ 权重：0.165 9	E₂₀→E₅→E₁₀→E₇→P₁₄ 权重：0.174 1	E₈→E₁₃→P₁₄ 权重：0.164 4
	P₁₈	E₁₆→P₁₁→P₁₈ 权重：0.187 6	E₄→E₁₀→P₁₈ 权重：0.148 4	E₁₈→E₁₀→P₁₈ 权重：0.147 9	E₁₇→E₉→E₇→P₁₈ 权重：0.168 7	E₂₀→E₅→E₁₀→P₁₈ 权重：0.171 0	E₈→E₉→E₇→P₁₈ 权重：0.176 4
尾部模块	P₂₅	E₄→P₂₅ 权重：0.170 7	E₂₀→E₅→E₉→P₂₅ 权重：0.181 8	E₁₆→P₂₅ 权重：0.170 7	E₈→E₉→P₂₅ 权重：0.169 5	E₁₉→E₉→P₂₅ 权重：0.146 1	E₁₇→E₉→P₂₅ 权重：0.161 2
	P₂₃	E₄→E₁₀→E₇→P₂₃ 权重：0.185 3	E₂₀→P₂₃ 权重：0.161 6	E₁₆→P₂₃ 权重：0.161 6	E₈→P₂₃ 权重：0.170 0	E₁₉→E₉→P₂₃ 权重：0.152 9	E₁₇→E₉→P₂₃ 权重：0.168 6
	P₂₀	E₄→E₁₀→P₂₀ 权重：0.161 2	E₂₀→E₅→E₁₄→P₂₀ 权重：0.176 7	E₁₆→E₁₈→E₁₀→P₂₀ 权重：0.214 8	E₈→P₂₀ 权重：0.124 9	E₁₉→P₂₀ 权重：0.139 6	E₁₇→E₁₄→P₂₀ 权重：0.182 8

表 6 关键驱动路径及权重

图 5 累计感染节点数随时间变化

图 6 物理组件的度中心性和聚类相关性系数

表 7 基于3类关联关系的物理组件模块划分结果

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