基于检索增强大语言模型的MBSE智能设计方法

doi:10.11996/JG.j.2095-302X.2024061188

图学学报 ›› 2024, Vol. 45 ›› Issue (6): 1188-1199.DOI: 10.11996/JG.j.2095-302X.2024061188

• “大模型与图学技术及应用”专题 • 上一篇下一篇

基于检索增强大语言模型的MBSE智能设计方法

于晗¹(), 陈治源¹, 熊熙瑞¹, 戴原星², 蔡鸿明¹()

1.上海交通大学软件学院，上海 200240
2.中国船舶及海洋工程设计研究院，上海 200011

收稿日期:2024-07-18 接受日期:2024-10-10 出版日期:2024-12-31 发布日期:2024-12-24
通讯作者:蔡鸿明(1975-)，男，教授，博士。主要研究方向为工业软件、计算机辅助设计等。E-mail：hmcai@sjtu.edu.cn
第一作者:于晗(1994-)，女，助理研究员，博士。主要研究方向为基于模型的系统工程、知识图谱、工业软件。E-mail：han_yu@sjtu.edu.cn
基金资助:
上海市青年科技英才扬帆计划项目(23YF1420600);上海交通大学新进教师启动计划项目(22X010503836)

Intelligent MBSE design approach based on retrieval augmented large language model

YU Han¹(), CHEN Zhiyuan¹, XIONG Xirui¹, DAI Yuanxing², CAI Hongming¹()

1. School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
2. Marine Design & Research Institute of China, Shanghai 200011, China

Received:2024-07-18 Accepted:2024-10-10 Published:2024-12-31 Online:2024-12-24
Contact: CAI Hongming (1975-), professor, Ph.D. His main research interests cover industrial software and computer-aided design, etc. E-mail：hmcai@sjtu.edu.cn
First author：YU Han (1994-), assistant researcher, Ph.D. Her main research interests cover MBSE, knowledge graph and industrial software. E-mail：han_yu@sjtu.edu.cn
Supported by:
Shanghai Sailing Program(23YF1420600);SJTU New Faculty Initiation Program(22X010503836)

摘要/Abstract

摘要：

基于模型的系统工程(MBSE)是当今产品数字化设计的重要方法之一。然而由于系统工程极高的专业性和产品极高的复杂关联性，在复杂产品上应用基于模型的系统工程十分困难。针对这一问题，一种基于检索增强大语言模型的智能化设计方法被首次提出。方法首先建立了面向模型对象的多模态向量表示方法，通过检索增强生成技术，引入领域知识和建模规则，引导大模型更准确地生成MBSE模型视图；其次，提出了基于MBSE元素关联的视图优化方法，通过上下文交互结果交叉验证模型准确性；再次，通过大语言模型对建模工具接口调用和对候选零件的选择，实现设计模型和物料树的生成；最后，构建了一个包含24个场景模型的数据集对方法进行验证，实验结果表明该方法具有较高的准确性和可用性。以喷水推进装置为建模对象的案例研究也表明该方法能在保持可用性的基础上有效提升建模效率，对于基于MBSE方法的智能化具有重要意义。

关键词: 基于模型的系统工程, 大语言模型, 智能设计, 提示词工程, 计算机辅助设计

Abstract:

Model-based systems engineering (MBSE) is one of the most important methods for today’s digital design of products. However, due to the high specialization of systems engineering and the complex interrelationships within products, the application of MBSE to complex products has proven challenging. To address this problem, an intelligent design method based on retrieval-augmented large language model was proposed for the first time. The method first established an object-oriented multi-modal vector representation for models, leveraging retrieval-augmented generation techniques that incorporate domain knowledge and modeling rules to guide the model in more accurately generating MBSE model diagrams. Secondly, a diagram optimization method based on the MBSE model relations was proposed, cross-validating the model accuracy through the results of contextual interaction. Thirdly, the large language model was employed to call modelling APIs and to select the proper materials to generate design models and eBOM. Finally, a dataset containing 24 scenario models was constructed for method validation. Experimental results showed that the approach possessed high accuracy and usability. A case study with water jet propulsion as the modelling object further demonstrated that the approach can effectively enhance the modelling efficiency while maintaining usability, marking an important step toward intelligent application of model-based systems engineering.

Key words: model based systems engineering, large language model, intelligent design, prompt engineering, computer-aided design

中图分类号:

于晗, 陈治源, 熊熙瑞, 戴原星, 蔡鸿明. 基于检索增强大语言模型的MBSE智能设计方法[J]. 图学学报, 2024, 45(6): 1188-1199.

YU Han, CHEN Zhiyuan, XIONG Xirui, DAI Yuanxing, CAI Hongming. Intelligent MBSE design approach based on retrieval augmented large language model[J]. Journal of Graphics, 2024, 45(6): 1188-1199.

图/表 15

图1 基于检索增强大模型的MBSE智能设计方法流程框架

Fig. 1 The process framework for MBSE intelligent modelling approach based on LLM interaction

图2 面向实体的多模态向量表示框架

Fig. 2 The multi-modal vector representation framework for entities

表1 SysML语法规则键值对示例

Table 1 Examples of key-values for SysML grammar rules

语法元素	描述
bdd	块定义图，用以说明系统结构的信息，其中显示的模型元素(模块、执行者、值类型、约束模块、流说明、接口)都是其他模型元素的类型
package	包，用以说明系统中的包结构
block	模块，用以说明系统结构的基本单元，包含模块名称、约束属性、值属性
constraints	约束属性，代表模块值的约束，通常是数学关系

图3 基于MBSE建模流程的一致性检测与迭代优化过程

Fig. 3 The process for consistency checking and iterative optimization based on standard MBSE modelling process

表2 方法在视图级的建模结果准确性

Table 2 Modelling accuracy of four approaches on view layer

方法	视图平均
方法	元素数量	Recall	Precision	F1
LLM	11.7	0.362	0.268	0.279
LLM+case	12.6	0.542	0.417	0.449
LLM+case+object	30.8	0.787	0.633	0.673
MbseLLM	28.3	0.786	0.663	0.697

表3 方法在项目级的建模结果准确性

Table 3 Modelling accuracy of four approaches on project layer

方法	项目元素数量	Recall	Precision	F1
LLM	31.3	0.096	0.085	0.090
LLM+case	41.1	0.469	0.316	0.378
LLM+case+object	52.2	0.816	0.432	0.565
MbseLLM	65.6	0.967	0.407	0.573

表4 MbseLLM方法不同建模阶段的视图准确性

Table 4 Modelling accuracy in different modeling stages of the MbseLLM method

建模阶段	阶段视图数量	视图平均
建模阶段	阶段视图数量	Recall	Precision	F1
需求图	17	0.886	0.854	0.870
模块定义图	20	0.723	0.504	0.594
内部模块图	23	0.622	0.456	0.526
活动图	18	0.900	0.591	0.714
参数图	12	0.363	0.289	0.321

图4 需求图输入

Fig. 4 The input for requirement diagram

图5 需求图建模结果

Fig. 5 The output model of requirement diagram

图6 块结构图输入

Fig. 6 The input for block diagram

图7 块结构图建模结果

Fig. 7 The output model of block diagram

图8 内部结构图建模结果(为阅读方便已做简化)

Fig. 8 The output model of internal diagram (Simplified for easy reading)

图9 行为图建模结果

Fig. 9 The output model of behavior diagram

图10 参数图建模结果

Fig. 10 The output model of parametric diagram

图11 eBOM树自动构建结果

Fig. 11 The results of automated eBOM construction

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基于检索增强大语言模型的MBSE智能设计方法

Intelligent MBSE design approach based on retrieval augmented large language model

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