Research on technology and application of identiﬁcation resource search for industrial internet of things

doi:10.12267/j.issn.2096-5931.2025.02.012

Abstract

Abstract:

The amount of industrial data has grown exponentially with the development of the digital economy, and general search has been unable to meet the requirements of searching massive multi-source heterogeneous data in the industrial field. Identification resource search for industrial internet of things can connect to identification resolution nodes, and use identification to obtain object or user information efficiently. It can get much more valuable industrial data, and carry out more in-depth and professional information mining for industrial scenarios. It is an efficient way to transform industrial data to productive forces. First, this paper analyzes the necessity of developing identification resource search for industrial internet of things in detail. Then, it introduces key technologies such as data collection, knowledge construction, data search as well as identification resolution preprocessing. Finally, it gives suggestions on the application scenarios and development trends.

Key words: industrial internet of things, identification resolution, industrial big data, identi?cation resource search

CLC Number:

TP391.3

HOU Cong, HUO Ru, PENG Kailai, HUANG Tao. Research on technology and application of identiﬁcation resource search for industrial internet of things[J]. Information and Communications Technology and Policy, 2025, 51(2): 76-80.

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URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2025.02.012

http://ictp.caict.ac.cn/EN/Y2025/V51/I2/76

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References 18

[1]	陈洪军, 叶丽珠, 陈其龙, 等. 产品全生命周期工业大数据采集与管理范式探究[J]. 机电工程技术, 2024, 53(3):72-77.
[2]	罗立辉. 工业大数据的应用和实践研究[J]. 信息记录材料, 2022, 23(2):167-169.
[3]	刘欣, 李向东, 耿立校, 等. 工业互联网环境下的工业大数据采集与应用[J]. 物联网技术, 2021, 11(8):62-65,71.
[4]	曾大明, 唐波. 基于工业互联网标识解析的全生命周期管理应用实践[J]. 通信与信息技术, 2023(5):63-65.
[5]	杨震, 张东, 李洁, 等. 工业互联网中的标识解析技术[J]. 电信科学, 2017, 33(11):134-140. doi: 10.11959/j.issn.1000-0801.2017296
[6]	曾鹏, 刘阳. 工业生产中工业互联网标识解析技术应用研究[J]. 自动化博览, 2019(11):46-48.
[7]	张俊林. 这就是搜索引擎:核心技术详解[M]. 北京: 电子工业出版社, 2012.
[8]	梁晔, 须德. 基于焦点和角度的多维索引方法[J]. 北京交通大学学报, 2005(2):22-25.
[9]	孟猛, 朱庆华. 移动视觉搜索用户体验模型构建与实证研究[J]. 图书情报工作, 2022, 66(5):80-92. doi: 10.13266/j.issn.0252-3116.2022.05.009
[10]	VIRGINIA G, TJANDRA J C, SUSANTO B, et al. Search engine optimization by re-ranking the product search result based on user click data[C]// Proceedings of the 3rd International Conference on Advanced Information Science and System, 2021:1-5.
[11]	李映雪, 陈劭涵, 郑卫国. 知识驱动的交互式图搜索[J]. 北京大学学报:自然科学版, 2023, 59(5):735-746.
[12]	QUAN C, TAN J, ERYILMAZ A, et al. A new flexible multi-flow LRU cache management paradigm for minimizing misses[C]// Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2019, 3(2):1-30.
[13]	MEGIDDO N, MODHA D S. Outperforming LRU with an adaptive replacement cache algorithm[J]. Computer, 2004, 37(4):58-65.
[14]	MARKATOS E P. On caching search engine query results[J]. Computer Communications, 2001, 24(2):137-143.
[15]	陈汝鹏, 焦枫, 张洁心. 通信网络中的数据库缓存策略与性能优化[J]. 通信电源技术, 2024, 41(1):182-184.
[16]	李超, 谢坤武. 用户搜索体验质量及搜索结果排序[J]. 计算机工程与应用, 2014, 50(1):24-28.
[17]	FUHR N. Probabilistic models in information retrieval[J]. The Computer Journal, 1992, 35(3):243-255.
[18]	张钰雯, 池程, 朱斯语. 工业互联网标识解析体系发展趋势[J]. 电信网技术, 2019(8):43-46.