|
|
|
| Cultural Heritage Smart Data Generation and Its Scenario-based Service Patterns Empowered by Collective Intelligence |
| Yang Simin1,2, Wang Hao1,2 |
1.School of Information Management, Nanjing University, Nanjing 210023 2.Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023 |
|
|
|
|
Abstract Driven by the emergence of human-AI intelligence, collective intelligence provides a new human-machine collaboration paradigm for the digital and intelligent transformation of cultural heritage. This study aims to achieve dynamic coupling between the generation of cultural heritage smart data and scenario-based services using collective intelligence empowerment, thereby promoting the synergistic upgrading of data and services. First, through a literature analysis, this study synthesizes the use of swarm intelligence in smart data generation and scenario-based services in the cultural heritage field. Second, it investigates the “data-learning-intelligence” generation path of smart data under the empowerment of swarm intelligence and analyzes the constituent elements of such data. Third, using the dimensions of demand, service, and technology, it elaborates on the logic of the scenario-based service model for cultural heritage smart data. Finally, using the preventive protection scenarios of traditional crafts under intangible cultural heritage as a case study, this study demonstrates the effectiveness of swarm intelligence in early warning of craft endangerment, market quality risk assessment, and intelligent decision-making services. This study aims to demonstrate the efficacy of swarm intelligence in “externalizing tacit knowledge, breaking through AI black boxes, and enhancing cross-subject collaboration,” to provide theoretical references for the intelligent development of cultural heritage in the digital-intelligent age.
|
|
Received: 10 June 2025
|
|
|
|
1 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. 2 程学旗, 徐冰冰, 曹婍, 等. 开放环境下的群智决策: 概念、挑战及引领性技术[J]. 智能科学与技术学报, 2022, 4(1): 45-54. 3 Halpin H. Artificial intelligence versus collective intelligence[J]. AI & Society, 2025, 40(6): 4589-4604. 4 Malone T W, Bernstein M S. Handbook of collective intelligence[M]. Cambridge: MIT Press, 2015. 5 Arizona State University. Decision Theater[EB/OL]. [2025-05-25]. https://dt.asu.edu/. 6 Shen H W, Barabási A L. Collective credit allocation in science[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(34): 12325-12330. 7 De Leon S P. National Science Foundation awards Litterati grant to advance its AI for a cleaner planet (Forbes)[EB/OL]. (2019-04-22) [2025-05-25]. https://www.forbes.com/sites/cognitiveworld/2019/04/22/national-science-foundation-litterati-ai-for-a-cleaner-planet/. 8 Kou Z Y, Shang L Y, Zhang Y, et al. Crowd, expert & AI: a human-AI interactive approach towards natural language explanation based COVID-19 misinformation detection[C]// Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2022: 5087-5093. 9 Lebovitz S, Lifshitz-Assaf H, Levina N. To engage or not to engage with AI for critical judgments: how professionals deal with opacity when using AI for medical diagnosis[J]. Organization Science, 2022, 33(1): 126-148. 10 十七部门关于印发《“数据要素×”三年行动计划(2024—2026年)》的通知[EB/OL]. (2024-01-05) [2025-04-25]. https://www.cac.gov.cn/2024-01/05/c_1706119078060945.htm. 11 张云中, 刘嘉琳. 智慧数据研究综述: 概念辨析、价值取向、关键技术与应用框架[J]. 图书情报工作, 2021, 65(10): 141-150. 12 王晓光, 侯西龙. 面向活化利用的文化遗产智慧数据建设论纲[J]. 信息资源管理学报, 2023, 13(5): 5-14, 43. 13 刘佳臻, 弓越, 王晓光. 文化遗产智慧数据价值创生逻辑与路径探析[J]. 图书馆论坛, 2025, 45(3): 89-98. 14 范炜, 曾蕾. AI新时代面向文化遗产活化利用的智慧数据生成路径探析[J]. 中国图书馆学报, 2024, 50(2): 4-29. 15 章岸婧, 谭必勇. 供需视角下文化遗产智慧数据资源服务模式研究[J]. 北京档案, 2022(3): 11-15. 16 夏翠娟. 多模态文化遗产资源的智慧化服务模式研究——从可获得到可循证和可体验[J]. 信息资源管理学报, 2023, 13(5): 44-55. 17 Berditchevskaia A, Baeck P. The future of minds and machines: how artificial intelligence can enhance collective intelligence[EB/OL]. (2020-02-10) [2025-04-15]. https://www.nesta.org.uk/report/future-minds-and-machines/. 18 Council of the European Union. Council of the European Union. Conclusions on cultural heritage as a strategic resource for a sustainable Europe[R/OL]. (2015-11-06) [2025-04-15]. https://resources.riches-project.eu/conclusions-on-cultural-heritage-as-a-strategic-resource-for-a-sustainable-europe/. 19 Popova M, Lewis W. Over €4.4 million granted to four new projects to enhance the common European data space for cultural heritage[EB/OL]. (2022-12-12) [2025-04-15]. https://www.dataspace-culturalheritage.eu/en/news/over-4-4-million-granted-to-four-new-projects-to-enhance-the-common-european-data-space-for-cultural-heritage. 20 Deligiannis K, Raftopoulou P, Tryfonopoulos C, et al. Hydria: an online data lake for multi-faceted analytics in the cultural heritage domain[J]. Big Data and Cognitive Computing, 2020, 4(2): 7. 21 Geometric reconstruction and novel semantic reunification of cultural heritage objects[EB/OL]. [2025-04-16]. https://staff.fnwi.uva.nl/l.dorst/gravitate.html. 22 Scan4Reco. Multimodal scanning of cultural heritage assets for their multilayered digitization and preventive conservation via spatiotemporal 4D reconstruction and 3D printing[EB/OL]. [2025-04-16]. https://scan4reco.iti.gr/. 23 数字敦煌[EB/OL]. [2025-04-16]. https://www.e-dunhuang.com/. 24 Wiki world heritage user group[EB/OL]. [2024-04-16]. https://meta.wikimedia.org/wiki/Wiki_World_Heritage_User_Group. 25 Antoniou A, Wallace M, Lopez-Nores M, et al. CrossCult: empowering reuse of digital cultural heritage in context-aware crosscuts of European history[C]// Proceedings of the Workshop on Cultural Informatics Co-located with the EUROMED International Conference on Digital Heritage 2018. Aachen: CEUR-WS.org, 2018: 1-10. 26 文化和旅游部关于发布《非物质文化遗产数字化保护 数字资源采集和著录》系列行业标准的公告[EB/OL]. (2023-06-29) [2025-04-16]. https://zwgk.mct.gov.cn/zfxxgkml/kjjy/202308/t20230804_ 946421.html. 27 胡骞. 融合出版背景下古籍知识服务平台的内容生产与转向[J]. 出版发行研究, 2023(9): 44-49. 28 王春迎, 毕欣悦, 李一然, 等. 新西兰公共图书馆参与文化遗产数字化实践分析及思考[J]. 图书馆学研究, 2025(3): 53-61. 29 Zhang W, Mei H. A constructive model for collective intelligence[J]. National Science Review, 2020, 7(8): 1273-1277. 30 Dellermann D, Ebel P, S?llner M, et al. Hybrid intelligence[J]. Business & Information Systems Engineering, 2019, 61(5): 637-643. 31 McClamrock R. Methodological individualism considered as a constitutive principle of scientific inquiry[J]. Philosophical Psychology, 1991, 4(3): 343-354. 32 Bonabeau E. Decisions 2.0: the power of collective intelligence[J]. MIT Sloan Management Review, 2009, 50(2): 45-92. 33 Levy P. Collective intelligence: mankind’s emerging world in cyberspace[M]. New York: Perseus Books, 1999. 34 刘伟. 人机融合: 超越人工智能[M]. 北京: 清华大学出版社, 2021: 34-37. 35 Xie X, Zhang Q C. An edge-cloud-aided incremental tensor-based fuzzy c-means approach with big data fusion for exploring smart data[J]. Information Fusion, 2021, 76: 168-174. 36 Civil War photo sleuth[EB/OL]. [2025-04-20]. https://www.civilwarphotosleuth.com/. 37 Appen[EB/OL]. [2025-04-20]. https://www.appen.com/. 38 Preserving language through useable data and phonetic annotation[EB/OL]. (2022-06-07) [2024-04-20]. https://www.appen.com/case-studies/preserving-language-through-useable-data. 39 Zooniverse[EB/OL]. [2025-04-20]. https://www.zooniverse.org/. 40 The material culture of wills: England 1540-1790[EB/OL]. [2024-04-20]. https://www.zooniverse.org/projects/hjsmith/the-material-culture-of-wills-england-1540-1790. 41 韩春磊, 姚啸华, 张宏玲, 等. 新时代古籍智慧化服务实践探讨——以古典小说续作研究场景为例[J]. 图书馆杂志, 2023, 42(12): 58-68. 42 曾建勋. 推进图书馆场景式服务[J]. 数字图书馆论坛, 2018(11): 1. 43 郑荣, 王晓宇, 高志豪, 等. 面向国家战略的产业竞争情报场景化智慧服务模式研究[J]. 情报学报, 2024, 43(2): 198-213. 44 Bucciero A, Chirivì A, Amadei C F, et al. HerMeS: a new approach to enjoy tangible and intangible point of interests of culture heritage[C]// Proceedings of the 2024 9th International Conference on Smart and Sustainable Technologies. Piscataway: IEEE, 2024: 1-6. 45 Pasandideh S, Raiyani K, Lerones P M, et al. Co-designing in cultural tourism: TExTOUR ICT services and performance monitoring system[J]. Heritage, 2024, 7(11): 6151-6172. 46 Capodiferro C, De Maria M, Mazzei M, et al. Cultural itineraries generated by smart data on the web[J]. ISPRS International Journal of Geo-Information, 2024, 13(2): 47. 47 Triguero I, García-Gil D, Maillo J, et al. Transforming big data into smart data: an insight on the use of the k-nearest neighbors algorithm to obtain quality data[J]. WIREs Data Mining and Knowledge Discovery, 2019, 9(2): e1289. 48 叶继元, 陈铭, 谢欢, 等. 数据与信息之间逻辑关系的探讨——兼及DIKW概念链模式[J]. 中国图书馆学报, 2017, 43(3): 34-43. 49 Van Noorden R, Perkel J M. AI and science: what 1,600 researchers think[J]. Nature, 2023, 621(7980): 672-675. 50 王易, 王成良, 邱国栋. 群体智能学习型决策: “大数据+AI”赋能的决策范式演化研究[J]. 中国软科学, 2024(12): 35-50. 51 Chen L. Topological structure in visual perception[J]. Science, 1982, 218(4573): 699-700. 52 张蓓蓓, 林鹏飞, 束霞平. 基于数字孪生技术的文化遗产保护价值阐释与实施路径研究[J]. 首都师范大学学报(社会科学版), 2025(1): 92-101. 53 张之益. 文化产业创新与视觉生产力——视觉工业前沿探索与案例解读[M]. 北京: 光明日报出版社, 2016. 54 朝乐门. 数据创造价值: 特征、方法与影响因素分析[J]. 中国图书馆学报, 2025, 51(3): 65-86. 55 尹西明, 苏雅欣, 陈劲, 等. 场景驱动的创新: 内涵特征、理论逻辑与实践进路[J]. 科技进步与对策, 2022, 39(15): 1-10. 56 逄锦荣, 苑春荟. 基于服务模式创新的物流业与制造业协同联动体系研究[M]. 北京: 北京邮电大学出版社, 2012: 29-31. 57 李宁, 邓文明. 团队的未来: 领导者如何与AI携手前行[J]. 清华管理评论, 2023(9): 94-99. 58 方鸿琴, 申文广. 非遗保护与传统工艺的可持续发展研究[J]. 文化遗产, 2025(2): 10-17. |
|
|
|