Complexity Measurement Model and Methods of Crowd Intelligence Collaborative Innovation for Design Activities
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摘要: 群智创新通过网络社区聚集企业外部大规模设计资源参与产品创新设计，成为互联网环境下企业拓展设计能力、解决设计难题、实现转型升级的重要方式. 针对群智协同设计活动的复杂性难以定量描述，导致互联网社区中设计活动的发布、推送和动态调整缺乏量化依据等问题，提出一种基于信息熵的群智协同设计活动复杂性度量方法. 通过分析互联网社区中群智协同设计过程特点、用户角色和能力特点，从过程复杂性、问题复杂性、可解复杂性3个维度建立群智协同设计活动复杂性度量模型，采用信息熵理论实现复杂性的量化分析；最后以Local Motors社区LM SF-01项目中的悬架设计活动为例，计算该设计活动3个维度的复杂性分别为318.15、477.66、134.46 bit，根据项目特点采用加权的方式得到其统一复杂性为331.76 bit，验证了该复杂性度量模型和方法的可行性和有效性.Abstract: Crowd intelligence (CI) innovation has become an important product innovation tool in the environment of Internet as it can amass huge design resources outside the enterprises through online community. It is an important way for enterprises to expand design capabilities, solve design problems, and realize transformation and upgrading. The design activity complexity of crowd intelligence collaborative innovation is difficult to quantitatively describe. As a result, it leads to the absence of a quantitative basis for the release, push, and dynamic adjustment of design activities in the Internet innovation community. To solve these problems, a design activity complexity measurement method based on information entropy theory is put forward. First, the characteristics of the collaborative design process in Internet innovation community, as well as user types and capacity characteristics are analyzed. Second, three dimension including process complexity, problem complexity, and solvability complexity, are proposed to valuate design activities. Then, the quantitative methods are proposed on the basis of information entropy theory. Finally, the suspension design of project LM SF-01 in the Local Motors community is used as an example to calculate its complexity. The complexity in the three dimensions are respectively 318.15, 477.66 bit, and 134.46 bit. According to the characteristics of user participation, the weighted complexity of the design activity is 331.76 bit, which validates the effectiveness and feasibility of the complexity measurement model.
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Key words:
- crowd intelligence /
- product innovation /
- design activity /
- complexity /
- information entropy
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图 2 群智协同设计活动复杂性模型
Figure 2. Complexity measurement model of design activity in CI collaborative design
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MICHELUCCI P, DICKINSON J L. The power of crowds[J]. Science, 2016, 351(6268): 32-33. doi: 10.1126/science.aad6499 AMERI F, SUMMERS J D, MOCKO G M, et al. Engineering design complexity:an investigation of methods and measures[J]. Research in Engineering Design, 2008, 19(2/3): 161-179. doi: 10.1007/s00163-008-0053-2 SUMMERS J D, SHAH J J. Mechanical engineering design complexity metrics:size,coupling,and solvability[J]. Journal of Mechanical Design, 2010, 132: 021004.1-021004.11. doi: 10.1115/1.4000759 张鹏,杨伯军,张换高,等. 面向CAI的设计过程复杂性理论冲突确定原理[J]. 计算机集成制造系统,2013,19(2): 330-337.ZHANG Peng, YANG Bojun, ZHANG Huangao, et al. Conflict determination oriented to CAI based on design-centric complexity[J]. Computer Integrated Manu- facturing Systems, 2013, 19(2): 330-337. 徐江,王修越,王奕,等. 基于确定性信息理论的设计认知复杂度计算方法[J]. 中国机械工程,2017,28(5): 596-602.XU Jiang, WANG Xiuyue, WANG Yi, et al. Complexity computation approach of design cognition using deterministic information theory[J]. China Mechanical Engineering, 2017, 28(5): 596-602. 张根保,曾海峰,王国强,等. 基于广义信息熵测度的制造过程质量评估[J]. 中国机械工程,2010,21(20): 2451-2458.ZHANG Genbao, ZENG Haifeng, WANG Guoqiang, et al. Effectiveness evaluation of manufacturing process quality by measurement of extended information entropy[J]. China Mechanical Engineering, 2010, 21(20): 2451-2458. 马靖,刘明周,王强,等. 基于物联熵的机械产品装配系统复杂性度量[J]. 计算机集成制造系统,2016,22(1): 248-256.MA Jing, LIU Mingzhou, WANG Qiang, et al. Complexity metrics for assembly system of mechanical products based on IOT-entropy[J]. Computer Integrated Manufacturing Systems, 2016, 22(1): 248-256. WANG H, ZHU X W, WANG H, et al. Multi-objective optimization of product variety and manufacturing complexity in mixed-model assembly systems[J]. Journal of Manufacturing Systems, 2011, 30(1): 16-27. doi: 10.1016/j.jmsy.2011.03.002 ANTORINI Y M, MUNIZ JR A M. Invited article:the benefits and challenges of collaborating with user communities[J]. Research-Technology Management, 2013, 56(3): 21-28. doi: 10.5437/08956308X5603931 LI W, WU W J, WANG H M, et al. Crowd intelligence in AI 2.0 era[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 15-43. 郑庆. 复杂产品群智协同创新过程建模分析理论及方法研究[D]. 天津: 天津大学, 2018. GUO W, ZHENG Q, AN, W J, et al. User roles and contributions during the new product development process in collaborative innovation communities[J]. Applied Ergonomics, 2017, 63: 106-114. doi: 10.1016/j.apergo.2017.04.013 张硕,李英姿,张晓冬. 创新扩散视角下众创设计社区参与者选择行为模型[J]. 科技进步与对策,2018,35(21): 15-22.ZHANG Shuo, LI Yingzi, ZHANG Xiaodong. Selection behavior model based on innovation diffusion in crowd innovation community[J]. Science & Technology Progress and Policy, 2018, 35(21): 15-22. SUH N P. Axiomatic design: advances and applications[M]. New York: Oxford University Press, 2001. SHANNON C E. A mathematical theory of communication[J]. The Bell System Technical Journal, 1948, 27(3): 379-423. 彭珍瑞,张楠,殷红,等. 基于频响函数的动车组构架传感器优化布置[J]. 西南交通大学学报,2019,54(2): 402-407, 414.PENG Zhenrui, ZHANG Nan, YIN Hong, et al. Optimal sensor placement of EMU frame based on frequency response function[J]. Journal of Southwest Jiaotong University, 2019, 54(2): 402-407, 414. BRAHA D, MAIMON O. The measurement of a design structural and functional complexity[J]. IEEE Transactions on Systems,Man,and Cybernetics—Part A:Systems and Humans, 1998, 28(4): 527-535. doi: 10.1109/3468.686715 ULLMAN D G. A taxonomy for mechanical design[J]. Research in Engineering Design, 1992, 3(3): 179-189. doi: 10.1007/BF01580519 -
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