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Exploring the Development of Chinese Digital Resources under Lightweight Deep Learning

机译:轻量级深度学习下中国数字资源开发探索

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摘要

From 2019, countries worldwide have been negatively affected by the corona virus disease 2019 (COVID-19) in all aspects of social life. The high-tech digital industry represented by emerging digital technologies is still vigorous, and correspondingly, the digital economy has become an important force to promote the stable recovery and re-prosperity of the national economy. The digital economy plays a memorable role in preventing and controlling COVID-19, the resumption of work and production, and the creation of new business formats and models. Urban big data (UBD) involves a wide range of dynamic and static data with high dimensions, but there are no mature and clear data classification and grading standards. Currently, it is urgent to strengthen the security protection of high-value datasets. Therefore, a UBD classification and grading method is proposed based on the lightweight (LWT) deep learning (DL) clustering algorithm. It uses a semi-intelligent path based on partial artificial to form data classification (DC) and hierarchical thesaurus, corpus, rule base, and model base. Subsequently, a big data analysis system is built for unstructured and structured data association analysis based on deep learning, spatiotemporal correlation, and big data technology to improve data value and adapt to multiscenario applications. Meanwhile, with the help of data and graphics processing tool Tableau, the present work analyzes the development status and existing problems of digital resources in China. The results show that although China's digital infrastructure is the top in the world, the trading infrastructure is still only 41.65 percentage points. This shows that China's digital economy still has a lot of room for growth in distribution and trading. The analysis of the ownership of data resources indicates that the scores of China's digital economy in accounting, privacy, and security are very low, only 2.4 points, 5.1 points, and 11 points, respectively. This study has solved the problems of distribution and trade in China's digital economy through research and put forward corresponding suggestions for the current development of China's digital economy market. Hence, a preliminary summary and suggestions are made on the development of China's data resources, to promote the open sharing of data, strengthen the management of data quality, activate the data resource market, strengthen data security, and enhance the vitality of the market economy.
机译:从 2019 年开始,世界各国在社会生活的各个方面都受到了 2019 年冠状病毒病 (COVID-19) 的负面影响。以新兴数字技术为代表的高科技数字产业依然生机勃勃,与之相对应的是,数字经济已成为推动国民经济稳定复苏、再繁荣的重要力量。数字经济在防控COVID-19、复工复产、创造新业态和新模式方面发挥着令人难忘的作用。城市大数据(UBD)涉及的动静态数据种类繁多,维度较高,但尚无成熟明确的数据分类分级标准。当前,加强高价值数据集的安全防护迫在眉睫。因此,该文提出一种基于轻量级(LWT)深度学习(DL)聚类算法的UBD分类分级方法。它使用基于部分人工的半智能路径来形成数据分类(DC)和分层词库、语料库、规则库和模型库。随后,基于深度学习、时空关联、大数据技术构建大数据分析系统,进行非结构化和结构化数据关联分析,提升数据价值,适应多场景应用。同时,本文借助数据和图形处理工具Tableau,分析了我国数字资源的发展现状和存在的问题。结果显示,尽管中国的数字基础设施位居世界前列,但贸易基础设施仍仅为41.65个百分点。这表明,中国数字经济在分销和贸易方面仍有很大的增长空间。对数据资源所有权的分析表明,中国数字经济在会计、隐私和安全方面的得分非常低,分别只有2.4分、5.1分和11分。本研究通过研究解决了我国数字经济中的流通和贸易问题,并对当前我国数字经济市场的发展提出了相应的建议。因此,对我国数据资源开发、促进数据开放共享、加强数据质量管理、激活数据资源市场、加强数据安全、增强市场经济活力等工作进行了初步总结和建议。

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