机械工程学报 ›› 2025, Vol. 61 ›› Issue (24): 267-284.doi: 10.3901/JME.2025.24.267
宁方伟1, 鲁嘉星1, 王一轩1,2, 马玉山2, 黎磊1, 李赫然1, 石岩1
收稿日期:2025-02-02
修回日期:2025-10-08
出版日期:2025-12-20
发布日期:2026-01-26
作者简介:宁方伟,男,1991年出生,博士,副教授,硕士研究生导师。主要研究方向为机械设计制造、生成式建模、知识工程。E-mail:nfangwei@buaa.edu.cn基金资助:NING Fangwei1, LU Jiaxing1, WANG Yixuan1,2, MA Yushan2, LI Lei1, LI Heran1, SHI Yan1
Received:2025-02-02
Revised:2025-10-08
Online:2025-12-20
Published:2026-01-26
摘要: 随着生成式人工智能的快速发展,机械设计领域迎来新的变革。设计理念正逐渐由传统的“计算机辅助+人工经验”发展为具备高级智能化的“历史设计数据与知识+生成式模型”,具体设计行为由“人工建模”发展为“生成式建模”,机械产品设计驱动由人工经验发展为数据知识。针对这一种发展趋势,提出了新的机械设计理念:智能生成式设计,阐述了智能生成式设计的内容组成、核心运行机制、设计特点、关键技术等。在此基础上,探讨了智能生成式设计在机械产品设计上的应用价值,为机械产品的设计指明新趋势和发展方向。
中图分类号:
宁方伟, 鲁嘉星, 王一轩, 马玉山, 黎磊, 李赫然, 石岩. 智能生成式设计——一种新的机械设计理念[J]. 机械工程学报, 2025, 61(24): 267-284.
NING Fangwei, LU Jiaxing, WANG Yixuan, MA Yushan, LI Lei, LI Heran, SHI Yan. Intelligent Generative Design—A New Mechanical Design Concept[J]. Journal of Mechanical Engineering, 2025, 61(24): 267-284.
| [1] CAMBA J D,COMPANY P,NAYA F. Sketch-based modeling in mechanical engineering design:Current status and opportunities[J]. Computer-Aided Design,2022,150:103283. [2] CHALAMALASETTI G S. A novel application of computer vision and deep learning for assessing mechanical engineering drawings[M]. Regina:The University of Regina,2021. [3] MA W,CHEN S,LOU Y,et al. Draw step by step:reconstructing cad construction sequences from point clouds via multimodal diffusion[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,16-22 June,2024,Seattle,WA,USA. NewYork:IEEE, 2024:27154-27163. [4] 程锦,叶虎强,冯劲松,等. 三维CAD软件性能自动化测试与变粒度可视评价[J]. 西安交通大学学报,2023,57(8):92-104. CHENG Jin,YE Huqiang,FENG Jinsong. Automatic testing and variable-granularity visual evaluation of the performance of 3D CAD software[J]. Journal of Xi'an Jiaotong University. 2023,57(8):92-104. [5] HUNDE B R,WOLDEYOHANNES A D. Future prospects of computer-aided design (CAD)-A review from the perspective of artificial intelligence (AI),extended reality,and 3D printing[J]. Results in Engineering,2022,14:100478. [6] 龙辉,郝佳,牛红伟,等. 基于情境的CAD模型手势操控技术[J]. 机械工程学报,2022,58(10):374-382. LONG Hui,HAO Jia,NIU Hongwei,et al. Gesture control technology of CAD model based on situation model[J]. Journal of Mechanical Engineering,2022,58(10):374-382. [7] 程锦,吕昊,刘振宇,等. 考虑多源不确定性的三维CAD软件质量综合评价[J]. 机械工程学报,2024,60(13):235-246. CHENG Jin,LÜ Hao,LIU Zhenyu,et al. Comprehensive quality evaluation of 3D CAD software considering multi-source uncertainties[J]. Journal of Mechanical Engineering,2024,60(13):235-246. [8] 程锦,叶虎强,谭建荣,等. 三维CAD技术研究进展及其发展趋势综述[J]. 机械工程学报,2023,59(23):158-185. CHENG Jin,YE Huqiang,TAN Jianrong,et al. Review of research progress and development trends of 3D CAD technology[J]. Journal of Mechanical Engineering,2023,59(23):158-185. [9] MA L,SHARMA A. Research on 3D CAD design of manufacturing domain integration system based on cloud computing[J]. Computer-Aided Design & Applications,2022. [10] 华顺刚,谢守广,刘斌,等. 基于CAD模型参数和MHD度量的装配体检索研究[J]. 机械工程学报,2022,58(16):384-390. HUA Shungang,XIE Shouguang,LIU Bin,et al. Study on assembly retrieval based on CAD model parameters and MHD metric[J]. Journal of Mechanical Engineering,2022,58(16):384-390. [11] NING F,SHI Y,CAI M,et al. Manufacturing cost estimation based on a deep-learning method[J]. Journal of Manufacturing Systems,2020,54:186-195. [12] NING F,SHI Y,CAI M,et al. Part machining feature recognition based on a deep learning method[J]. Journal of Intelligent Manufacturing,2023,34(2):809-821. [13] HOU J,LUO C,QIN F,et al. FuS-GCN:Efficient B-rep based graph convolutional networks for 3D-CAD model classification and retrieval[J]. Advanced Engineering Informatics,2023,56:102008. [14] ALZUBAIDI L,ZHANG J,HUMAIDI A J,et al. Review of deep learning:Concepts,cnn architectures,challenges,applications,future directions[J]. Journal of big Data,2021,8:1-74. [15] VASWANI A,SHAZEER N,PARMAR N,et al. Attention is all you need[J]. Advances in Neural Information Processing Systems,2017,30:1706. [16] 白辰甲,许华哲,李学龙. 大模型驱动的具身智能:发展与挑战[J]. 中国科学:信息科学,2024,54(9):1-48. BAI Chenjia,XU Huazhe,LI Xuelong. Embodied-AI with large models:Research and challenges[J]. Scientia Sinica(Informationis),2024,54(9):1-48. [17] CAMBA J D,CONTERO M,COMPANY P. Parametric CAD modeling:An analysis of strategies for design reusability[J]. Computer-Aided Design,2016,74:18-31. [18] GAI Z,YANG T. The application of CAD combined deep learning algorithms in advertising creative design[J]. Computer-Aided Design & Applications, 2024,21(S18):290-305. [19] XU P,ZHU X,CLIFTON D A. Multimodal learning with transformers:A survey[J]. IEEE Transactions on Pattern Analysis Machine Intelligence,2023,45(10):12113-12132. [20] 车万翔,窦志成,冯岩松,等. 大模型时代的自然语言处理:挑战、机遇与发展[J]. 中国科学:信息科学,2023,53(9):1645-1687. CHE Wanxiang,DOU Zhicheng,FENG Yansong,et al. Towards a comprehensive understanding of the impact of large language models on natural language processing:Challenges,opportunities and future directions[J]. Scientia Sinica(Informationis),2023,53(9):1645-1687. [21] 周涛,李鑫,周俊临,等. 大模型智能体:概念、前沿和产业实践[J]. 电子科技大学学报,2024,26(4):57-62. ZHOU Tao,LI Xin,ZHOU Junlin,et al. AI large model ai-agents large-model-based agents[J]. Journal of University of Electronic Science and Technology of China,2024,26(4):57-62. [22] DING Y,YU J,LIU B,et al. Mukea:Multimodal knowledge extraction and accumulation for knowledge-based visual question answering[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition,June 18-24,2022,New Orleans,LA,USA. NewYork:IEEE,2022:5089-5098. [23] 何俊,张彩庆,李小珍,等. 面向深度学习的多模态融合技术研究综述[J]. 计算机工程与科学,2020,46(5):11. HE Jun,ZHANG Caiqing,LI Xiaozhen,et al. Survey of research on multimodal fusion technology for deep learning[J]. Computer Engineering,2020,46(5):11. [24] 张正,徐勇,卢光明. 数据分析的结构化表征学习[M]. 北京:人民邮电出版社,2022. ZHANG Zheng,XU Yong,LU Guangming. Structured reprentation learning for data analys[M]. Beijing:Posts & Telecom Press,2022. [25] 胡志强,刘鸣飞,李琦,等. 基于多源异构数据的风机多模态装配工艺知识图谱建模[J]. 上海交通大学学报,2024,58(8):1249-1263. HU Zhiqiang,LIU Mingfei,LI Qi,et al. Modeling of multi-modal knowledge graph for assembly process of wind turbines with multi-source heterogeneous data[J]. Journal of Shanghai Jiao Tong University,2024,58(8):1249-1263. [26] 杜鹏飞,李小勇,高雅丽. 多模态视觉语言表征学习研究综述[J]. 软件学报,2021,32(2):327-348. DU Pengfei,LI Xiaoyong,GAO Yali. Survey on multimodal visual language representation learning[J]Journal of Software,2021,32(2):327-348. [27] 杨宏宇,马建辉,侯旻,等. 基于多模态对比学习的代码表征增强预训练方法[J]. 软件学报,2024,35(4):1601-1617. YANG Hongyu,MA Jianhui,HOU Min,et al. Hou minpre-training method for enhanced code representation based on multimodal contrastive learning[J]. Journal of Software,2024,35(4):1601-1617. [28] 刘静,郭龙腾. GPT-4对多模态大模型在多模态理解,生成,交互上的启发[J]. 中国科学基金,2023,5:793-802. LIU Jing,GUO Longteng. Inspiration of GPT-4on multimodal foundation models in multimodal understanding,generation,and interaction[J]. Fundamental Research,2023,5:793-802. [29] 汪美玲,邵伟,张道强. 基于标签对齐的多模态一致性表型关联方法[J]. 软件学报,2022,33(12):4545-4558. WANG Meiling,SHAO Wei,ZHANG Daoqiang. Label-aligned multi-modality consistent phenotype association method[J]. Journal of Software,2022,33(12):4545-4558. [30] HU R,SINGH A. Unit:Multimodal multitask learning with a unified transformer[C]//Proceedings of the IEEE/CVF international conference on computer vision,October 10-17,2021,Montreal,QC,Canada. NewYork:IEEE,2021:1439-1449. [31] 曹建军,聂子博,郑奇斌,等. 跨模态数据实体分辨研究综述[J]. 软件学报,2023,34(12):5822-5847. CAO Jianjun,NIE Zibo,ZHENG Qibin,et al. Survey on cross-modal data entity resolution[J]. Journal of Software,2023,34(12):5822-5847. [32] CHEN H,JIANG D,SAHLI H. Transformer encoder with multi-modal multi-head attention for continuous affect recognition[J]. IEEE Transactions on Multimedia,2020,23:4171-4183. [33] 赵鹏,马泰宇,李毅,等. 融合全模态自编码器和生成对抗机制的跨模态检索[J]. 计算机辅助设计与图形学学报,2021,33(10):1486-1494. ZHAO Peng,MA Taiyu,LI Yi,et al. Cross-modal retrieval based on full-modal autoencoder with generative adversarial mechanism[J]. Journal of Computer-Aided Design & Computer Graphics,2021,33(10):1486-1494. [34] RICHARDSON E,ALALUF Y,PATASHNIK O,et al. Encoding in style:a stylegan encoder for image-to-image translation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition,June 19-25,2021,Nashville,USA. NewYork:IEEE Computer Society Press,2021:2287-2296. [35] 王德文,魏波涛. 基于孪生变分自编码器的小样本图像分类方法[J]. 智能系统学报,2021,16(2):254-262. WANG Dewen,WEI Botao. A small-sample image classification method based on a Siamese variational auto-encoder[J]. CAAI Transactions on Intelligent Systems,2021,16(2):254-262. [36] XIA M,ZHAO X,HU X. Machining feature and topological relationship recognition based on a multi-task graph neural network[J]. Advanced Engineering Informatics,2024,62:102721. [37] QIAO Z,ZHOU Y,YANG D,et al. Seed:Semantics enhanced encoder-decoder framework for scene text recognition[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. June 13-19,2020,Seattle,USA. NewYork:IEEE Computer Society Press,2020:13528-13537. [38] 吴福祥,程俊. 基于自编码器生成对抗网络的可配置文本图像编辑[J]. 软件学报,2022,33(9):3139-3151. WU Fuxiang,CHENG Jun. Configurable text-based image editing by autoencoder-based generative adversarial networks[J]. Journal of Software,2022,33(9):3139-3151. [39] 兰猛,张乐飞,杜博,等. 基于时空层级查询的指代视频目标分割[J]. 中国科学:信息科学,2024,54(3):674-91. LAN Meng,ZHANG Lefei,DU Bo,et al. Spatio-temporal hierarchical query for referring video object segmentation[J]. Scientia Sinica (Informationis),2024,54(3):674-691. [40] CHEN X,JIA S,XIANG Y. A review:Knowledge reasoning over knowledge graph[J]. Expert Systems with Applications,2020,141:112948. [41] 黄勃,吴申奥,王文广,等. 图模互补:知识图谱与大模型融合综述[J]. 武汉大学学报,2024,70(4):397-412. HUANG Bo,WU Shenao,WANG Wenguang,et al. KG-LLM-MCom:A survey on integration of knowledge graph and large language model[J]. Journal of Wuhan University,2024,70(4):397-412. [42] WANG Y,TANG S,ZHU F,et al. Revisiting the transferability of supervised pretraining:an MLP perspective[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-24,2022,New Orleans,LA,USA. NewYork:IEEE,2022:9183-9193. [43] LIN H,CHENG X,WU X,et al. Cat:Cross attention in vision transformer[C]//2022 IEEE International Conference on Multimedia and Expo (ICME),July 18-22,2022,Taipei,China. NewYork:IEEE,2022:1-6. [44] FENG J,LIN M,SHANG L,et al. Autonomous aspect-image instruction a2II:Q-Former guided multimodal sentiment classification[C]//Proceedings of the 2024 Joint International Conference on Computational Linguistics,Language Resources and Evaluation (2024),May 20-25,2024,Torino,Italy. Torino:LREC-COLING. 2024:1996-2005. [45] LIU C,SUN K,ZHOU Q,et al. Cpmi-chatglm:Parameter-efficient fine-tuning chatglm with chinese patent medicine instructions[J]. Scientific Reports,2024,14(1):6403. [46] SANDMANN S,RIEPENHAUSEN S,PLAGWITZ L,et al. Systematic analysis of ChatGPT,Google search and Llama 2 for clinical decision support tasks[J]. Nature Communications,2024,15(1):1-8. [47] ZHANG L,LIU Y,LUO Y,et al. Qwen-ig:A qwen-based instruction generation model for llm fine-tuning[C]//Proceedings of the 202413th International Conference on Computing and Pattern Recognition. Association for Computing Machinery,October 25-272024,Tianjin,China. NewYork:ACM,2025:295-302 [48] BARBIERI L,BRAMBILLA M,STEFANUTTI M,et al. A tiny transformer-based anomaly detection framework for IoT solutions[J]. IEEE Open Journal of Signal Processing,2023,4:462-478. [49] ALMEIDA L B. Multilayer perceptrons[M]. Handbook of Neural Computation. Carabas:CRC Press,2020. [50] HEDLIN E,SHARMA G,MAHAJAN S,et al. Unsupervised semantic correspondence using stable diffusion[J]. Advances in Neural Information Processing Systems,2023,36:8266-8279. [51] LIU H,YUAN Y,LIU X,et al. Audioldm 2:Learning holistic audio generation with self-supervised pretraining[J]. IEEE/ACM Transactions on Audio,2024,32:2871-2883. [52] JI S,PAN S,CAMBRIA E,et al. A survey on knowledge graphs:representation,acquisition,and applications[J]. IEEE Transactions on Neural Networks and Learning Systems,2021,33(2):494-514. [53] 缪青海,王兴霞,杨静,等. 从基础智能到通用智能:基于大模型的GenAI和AGI之现状与展望[J]. 自动化学报,2024,50(4):674-687. MIAO Qinghai,WANG Xingxia,YANG Jing,et al. From foundation intelligence to general intelligence:The state-of-art and perspectives of genai and agi based on foundation models[J]. Acta Automatica Sinica,2024,50(4):674-687. [54] TOMCZAK J M. Why deep generative modeling?[M]. NewYork:Springe,2021. [55] 宋雪萌,聂礼强,申恒涛,等. 融合预训练技术的多模态学习研究专题前言[J]. 软件学报,2023,34(5):1997-1999. SONG Xuemeng,NIE Liqiang,SHEN Hengtao,et al. Introduction to the research on multimodal learning with fusion of pre-training techniques[J]. Journal of Software,2023,34(5):1997-1999. [56] WANG P,LIU W,YOU Y. A hybrid framework for manufacturing feature recognition from CAD models of 3-axis milling parts[J]. Advanced Engineering Informatics,2023,57:102073. [57] 姚鑫骅,于涛,封森文,等. 基于图神经网络的零件机加工特征识别方法[J]. 浙江大学学报,2024,58(2):349-359. YAO Xinhua, YU Tao, FENG Senwen,et al. Recognition method of parts machining features based on graph neural network[J]. Journal of Zhejiang University,2024,58(2):349-359. [58] LEE H,LEE J,KWON S,et al. Simplification of 3d cad model in voxel form for mechanical parts using generative adversarial networks[J]. Computer-Aided Design,2023,163:103577. [59] 龚靖渝,楼雨京,柳奉奇,等. 三维场景点云理解与重建技术[J]. 中国图象图形学报,2023,28(6):1741-1766. GONG Jingyu,LOU Yujing,LIU Fengqi,et al. Scene point cloud understanding and reconstruction technologies in 3D space[J]. Journal of Image and Graphics,28(6):1741-1766. [60] 张虎成,李雷孝,刘东江. 多模态数据融合研究综述[J]. 计算机科学与探索,2024,18(10):2501-2520. ZHANG Hucheng,LI Leixiao,LIU Dongjiang. Survey of multimodal data fusion research[J]. Journal of Frontiers of Computer Science and Technology,2024,18(10):2501-2520. [61] 田枫,宗内丽,刘芳,等. 多模态融合的三维目标检测方法研究[J]. 计算机工程与应用,2024,60(13):113-123. TIAN Feng,ZONG Neili,LIU Fang,et al. Research on 3D object detection method based on multi-modal fusion[J]. Computer Engineering and Applications,2024,60(13):113-123. [62] 叶志鸿,吴运兵,戴思翀,等. 多级融合知识图谱补全模型[J]. 计算机科学与探索,2025,19(3):1-15. YE Zhihong,WU Yunbing,DAI Sichong,et al. Multi-level fusion knowledge graph completion model[J]. Journal of Frontiers of Computer Science and Technology,2025,19(3):1-15. [63] 吕友豪,贾袁骏,庄圆,等. 基于多模态信息融合的四足机器人避障方法[J]. 工程科学学报,2024,46(8):1426-1433. LÜ Youhao,JIA Yuanjun,ZHUANG Yuan,et al. Obstacle avoidance approach for quadruped robot based on multi-modal information fusion[J]. Chinese Journal of Engineering,2024,46(8):1426-1433. [64] 李雄,苏建宁,张志鹏,等. 特征迁移的细粒度产品形态智能决策方法[J]. 计算机辅助设计与图形学学报,2024,36(1):47-62. LI Xiong,SU Jianning,ZHANG Zhipeng,et al. Intelligent decision-making of fine-grained product form with feature transfer[J].Journal of Computer-Aided Design & Computer Graphics,2024,36(1):47-62. [65] 刘学博,户保田,陈科海,等. 大模型关键技术与未来发展方向——从ChatGPT谈起[J]. 中国科学基金,2023,37(5):758-766. LIU Xuebo,HU Baotian,CHEN Kehai,et al. Key technologies and future development directions of largelanguage models:Insights from chatgpt[J]. Fundamental Research,2023,37(5):758-766. [66] 李国杰. 智能化科研(AI4R):第五科研范式[J]. 中国科学院院刊,2024,39(1):1-9. LI Guojie. AI4R:The fifth scientific research paradigm[J]. Bulletin of Chinese Academy of Sciences,2024,39(1):1-9. [67] 袁小锋,王雅琳,阳春华,等. 深度学习在流程工业过程数据建模中的应用[J]. 智能科学与技术学报,2020,2(2):107-115. YUAN Xiaofeng,WANG Yalin,YANG Chunhua,et al. The application of deep learning in data-driven modeling of process industries[J]. Chinese Journal of Intelligent Science and Technology,2020,2(2):107-115. [68] CHAI C P. The importance of data cleaning:Three visualization examples[J]. Chance,2020,33(1):4-9. [69] FALLAHIAN M,DORODCHI M,KRETH K. Gan-based tabular data generator for constructing synopsis in approximate query processing:Challenges and solutions[J]. Machine Learning and Knowledge Extraction,2024,6(1):171-198. [70] 刘冬,袁利恒,丛明. 齿轮机器人复杂装配过程在线建模与参数优化[J]. 机械工程学报,2021,57(13):124-131. LIU Dong,YUAN Liheng,CONG Ming. Online modeling and parameter optimization method for robotic complex assembly process of gear[J]. Journal of Mechanical Engineering,2021,57(13):124-131. [71] DEWI C,CHEN R C,LIU Y T,et al. Synthetic data generation using dcgan for improved traffic sign recognition[J]. Neural Computing and Applications,2022,34(24):21465-21480. [72] 葛轶洲,许翔,杨锁荣,等. 序列数据的数据增强方法综述[J]. 计算机科学与探索,2021,15(7):1207-1219. GE Yizhou,XU Xiang,YANG Suorong,et al. Survey on sequence data augmentation[J]. Journal of Frontiers of Computer Science and Technology,2021,15(7):1207-1219. [73] HUANG J,CUI K,GUAN D,et al. Masked generative adversarial networks are data-efficient generation learners[J]. Advances in Neural Information Processing Systems,2022,35:2154-2167. [74] ZEBARI R,ABDULAZEEZ A,ZEEBAREE D,et al. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction[J]. Journal of Applied Science Technology Trends,2020,1(1):56-70. [75] 吕天根,洪日昌,何军,等. 多模态引导的局部特征选择小样本学习方法[J]. 软件学报,2022,34(5):2068-2082. LÜ Tiangen,HONG Richang,HE Jun,et al. Multimodal-guided local feature selection for few-shot learning[J]. Journal of Software,2022,34(5):2068-2082. [76] DUAN H,SUN Y,CHENG W,et al. Gesture recognition based on multi-modal feature weight[J]. Concurrency and Computation:Practice and Experience,2021,33(5):e5991. [77] HOU Z,LIU Y,ZHANG L. POS-GIFT:A geometric and intensity-invariant feature transformation for multimodal images[J]. Information Fusion,2024,102:102027. [78] ZHENG Y,HUANG D,LIU S,et al. Cross-domain object detection through coarse-to-fine feature adaptation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. June 13-19,2020,Seattle,USA. NewYork:IEEE Computer Society Press,2020:13766-13775. [79] ZHONG L,WU J,LI Q,et al. A comprehensive survey on automatic knowledge graph construction[J]. ACM Computing Surveys,2023,56(4):1-62. [80] 陈强,张栋,李寿山,等. 融合任务知识的多模态知识图谱补全[J]. 软件学报,2024(4):1590-1603. CHEN Qiang,ZHANG Dong,LI Shoushan,et al. Task knowledge fusion for multimodal knowledge graph completion[J]. Journal of Software,2024(4):1590-1603. [81] 韩铖山,李红辉,闫佳和,等. 基于知识图谱的多源异构信息通道耦合技术研究[J]. 集成技术,2023,12(3):48-60. HAN Chengshan,LI Honghui,YAN Jiahe,et al. Research on coupling technology of multi-source heterogeneous information channels based on knowledge graph[J]. Journal of Integration Technology,2023,12(3):48-60. [82] 毕忠勤,张锴,单美静,等. 基于图神经网络的多源异构知识增强对话模型[J]. 科学技术与工程,2024,24(17):7196-7204. BI Zhongqin,ZHANG Kai,SHAN Meijing,et al. Multi-source heterogeneous knowledge enhanced dialogue model based on graph neural network[J]. Science Technology and Engineering,2024,24(17):7196-7204. [83] 郝小芳,张超群,李晓翔,等. 融合交互注意力网络的实体和关系联合抽取模型[J]. 计算机工程与应用,2024,60(8):156-164. HAO Xiaofang,ZHANG Chaoqun,LI Xiaoxiang,et al. Joint entity relation extraction model based on interactive attention[J]. Computer Engineering and Applications,2024,60(8):156-164. [84] DAI G,WANG X,ZOU X,et al. Mrgat:Multi-relational graph attention network for knowledge graph completion[J]. Neural Networks,2022,154:234-245. [85] WANG M,QI G,WANG H,et al. Richpedia:A comprehensive multi-modal knowledge graph[C]//Semantic Technology:9th Joint International Conference,November 25-27,2019,Hangzhou,China. NewYork:Springer,2020:130-145. [86] 孔德明,李晓伟,杨庆鑫. 基于伪点云特征增强的多模态三维目标检测方法[J]. 计算机学报,2024,47(4):759-775. KONG Deming,LI Xiaowei,YANG Qingxin. Multimodal 3d obiect detection method based on pseudopoint cloud feature enhancement[J]. Chinese Journal of Computers,2024,47(4):759-775. [87] YANG Z,XU B,LUO W,et al. Autoencoder-based representation learning and its application in intelligent fault diagnosis:A review[J]. Measurement,2022,189:110460. [88] 管浩良,张广滨,王岩. 融合特征金字塔和自注意力机制的SAR三维点云目标识别方法[J]. 信号处理,2025,41(1):70-83. GUAN Haoliang,ZHANG Guangbin,WANG Yan. Fusing feature pyramid and self-attention mechanism for sar 3d point cloud target recognition[J]. Journal of Signal Processing,2025,41(1):70-83. [89] 吴萌,严瑞祺,孙增国,等. 三维点云双重补全网络[J]. 计算机工程与应用,2025,61(7):297-305. WU Meng,YAN Ruiqi,SUN Zengguo,et al. 3D point cloud dual completion network[J]. Computer Engineering and Applications,2025,61(7):297-305. [90] 杨军,张琛. 融合双注意力机制和动态图卷积神经网络的三维点云语义分割[J]. 北京航空航天大学学报,2023,50(10):2984-2994. YANG Jun,ZHANG Chen. Semantic segmentation of 3D point cloud by fusing dual attention mechanism and dynamic graph convolution neural network[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,50(10):2984-2994. [91] 吴亦奇,韩放,张德军,等. 基于特征通道和空间位置注意力的三维点云特征学习网络[J]. 计算机工程与科学,2022,44(7):1239. WU Yiqi,HAN Fang,ZHANG Dejun,et al. A 3D point cloud feature learning network based on feature channel and spatial position attentions[J]. Computer Engineering & Science,2022,44(7):1239. [92] NIU Z,ZHONG G,YU H. A review on the attention mechanism of deep learning[J]. Neurocomputing,2021,452:48-62. [93] 宫丽娜,周易人,乔羽,等. 预训练模型在软件工程领域应用研究进展[J]. 软件学报,2025,36(1):1-26. GONG Lina,ZHOU Yiren,QIAO Yu,et al. Research progress of pre-trained model in software engineering[J],Journal of Software,2025,36(1):1-26. [94] 曾焕强,胡浩麟,林向伟,等. 深度神经网络压缩与加速综述[J]. 信号处理,2022,38(1):183-194. ZENG Huanqiang,HU Haolin,LIN Xiangwei,et al. Deep neural network compression and acceleration:an overview[J]. Journal of Signal Processing,2022,38(1):183-194. [95] GANDHI A,ADHVARYU K,PORIA S,et al. Multimodal sentiment analysis:A systematic review of history,datasets,multimodal fusion methods,applications,challenges and future directions[J]. Information Fusion,2023,91:424-444. [96] ZHU Y,WU L,GUO Q,et al. Collaborative large language model for recommender systems[C]//Proceedings of the ACM Web Conference 2024,May 13-17,2024,Singapore:ICPS,2024:3162-3172. [97] WANG Y,REDDY R G,MUJAHID Z M,et al. Factcheck-bench:Fine-grained evaluation benchmark for automatic fact-checkers[C]//In Findings of the Association for Computational Linguistics,November,2024,Miami,Florida,USA. Miami:Association for Computational Linguistics, 2023:14199-14230. [98] 张明悦,金芝,刘坤. 合作-竞争混合型多智能体系统的虚拟遗憾优势自博弈方法[J]. 软件学报,2024,35(2):739-757. ZHANG Mingyue,JIN Zhi,LIU Kun. Counterfactual regret advantage-based self-play approach for mixed cooperative-competitive multi-agent systems[J]. Journal of Software,2024,35(2):739-757. [99] ZHOU L,DENG X,WANG Z,et al. Semantic information extraction and multi-agent communication optimization based on generative pre-trained transformer[J]. IEEE Transactions on Cognitive Communications and Networking,2025,11(2):725-737. [100] KANNAN S S,VENKATESH V L N,MIN B C. Smart-llm:smart multi-agent robot task planning using large language models[C]//2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),14-18,October,2024,Abu Dhabi:IEEE. 2024:12140-12147. [101] YAO S,ZHAO J,YU D,et al. React:Synergizing reasoning and acting in language models[C]//International Conference on Learning Representations (ICLR),May 1-5,2023,Kigali,Rwanda. Kigali:OpenReview,2023:1-33. [102] WEI J,WANG X,SCHUURMANS D,et al. Chain-of-thought prompting elicits reasoning in large language models[J]. Advances in Neural Information Processing Systems,2022,35:24824-24837. [103] 黄峻,林飞,杨静,等. 生成式AI的大模型提示工程:方法,现状与展望[J]. 智能科学与技术学报,2024,6(2):115-133. HUANG Jun,LIN Fei,YANG Jing,et al. From prompt engineering to generative artificial intelligence for large models:The state of the art and perspective[J]. Chinese Journal of Intelligent Science and Technology,2024,6(2):115-133. [104] YIN X,NI C,WANG S. Multitask-based evaluation of open-source llm on software vulnerability[J]. IEEE Transactions on Software Engineering,2024,50(11):3071-3087. [105] 赵睿卓,曲紫畅,陈国英,等. 大语言模型评估技术研究进展[J]. 数据采集与处理,2024,39(3):502-523. ZHAO Ruizhuo,QU Zichang,CHEN Guoying,et al. Research progress in evaluation techniques for large language models[J]. Journal of Data Acquisition & Processing,2024,39(3):502-523. [106] ZHANG W,ALJUNIED M,GAO C,et al. M3exam:A multilingual,multimodal,multilevel benchmark for examining large language models[J]. Advances in Neural Information Processing Systems,2023,36:5484-5505. [107] STRUBELL E,GANESH A,MCCALLUM A. Energy and policy considerations for deep learning in NLP[C]//In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics,July 28th to August 2nd,2019,Florence,Italy. Florence:Association for Computational Linguistics,2019:3645-3650. [108] WANG J,HUANG Y,CHEN C,et al. Software testing with large language models:Survey,landscape,and vision[J]. IEEE Transactions on Software Engineering,2024,50(4):911-936. [109] DULNY A,HOTHO A,KRAUSE A. DynaBench:A benchmark dataset for learning dynamical systems from low-resolution data[C]//Machine Learning and Knowledge Discovery in Databases:Research Track,September 18-22,2023,Turin,Italy. NewYork:Springer,2023:438-555. |
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