Research on Technological Hotspots and Trends of Digitalization of Standard Based on Keyword Co-occurrence Atlas

Research on Technological Hotspots and Trends of Digitalization of Standard Based on Keyword Co-occurrence Atlas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on Technological Hotspots and Trends of Digitalization of Standard Based on Keyword Co-occurrence Atlas Nana Niu, Xize Liu, Yiyi Wang, Bingyan Zhang, Jingsheng Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4609699/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With the continuous advancement of the process of industrial digitalization, standards, as one of the elements of industrial development, will be closely intertwined with digitalization, have a profound impact on technology, industry and society, and jointly promote the development of sustainable ecology. Based on the Technology Breakdown Theory and CiteSpace tools, this study collected the literature data of journal papers and standards related to digitalization of standard published from 2017 to 2021, and made a keyword co-occurrence atlas analysis on five key technical topics related to digitalization of standard, semantic web, digital model, open source and virtual reality enhancement. It identifies new technological hotspots in standard content generation, standardization modes, and applications of standards. New trends in evolution of the standard itself, the development of the standardization ecosystem, and governance in the context of the new technologies are summarized, including structuring, semanticization and machine language representation of the content; open source technologies make standards more open, shareable, and intelligent; digital technologies brings new governance challenges in the field of standardization. This study systematically compiles and prospects the technological hotspots and trends of digitalization of standard, and supports the formation of related technical routes and risk response plans by data analysis. digitalization of standard standardization digital transformation atlas analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 1 Introduction Since the United Nations issued the Sustainable Development Goals (SDGs) in 2025, all sectors of society have taken a series of measures, standards as an important technical support for economic and social development, in promoting economic and social sustainable development plays an important role, the theme of World Standards Day 2021 is Standards promote sustainable development, Build a better world . At present, the digital transformation of economy and society has become the trend of The Times. How to play the role of standards to promote high-quality development and sustainable development of economy has put forward an urgent demand for the digital transformation of standards. In October 2021, the National Standardization Development Outline was released, proposing the development of machine-readable standards and open source standards, and promoting the transformation of standardization to digital, networked and intelligent, and the digital transformation of standards is one of the important strategic deployment contents of standardization in China[ 1 ]. At the annual meeting of the International Organization for Standardization (ISO) held in September, 2022, representatives of all member countries had a special exchange on the topic of Digitalization and Digital Transformation: One Step Ahead [ 2 , 3 ]. Experts in various countries believe that the digital transformation of standards is the endogenous demand of standardization development and an inevitable trend. ISO and the International Electrotechnical Commission (IEC)[ 4 ] have joined forces with the European Committee for Standardization (CEN), the European Committee for Electrotechnical Standardization (CENELEC)[ 5 ] and national standardization organizations in Britain and Germany to promote related work[ 6 , 7 , 8 , 9 ], and promote the establishment of a consistent consensus and strategy for transformation[ 10 ]. The digitalization of standards in China started late, and there is still a gap in methods, technologies and tools. We should pay attention to and learn from international good practices and systematically carry out related work, so as to provide strong support for the implementation of the National Standardization Development Outline and the construction of digital China. The related research and development of digitalization of standard began with the interaction between open source and standardization[ 11 , 12 , 13 , 14 , 15 ]. In 2017, the CEN-CENELEC Digital Transformation Strategic Plan issued by CEN and CENELEC was formally put forward in anticipation of the digitalization of standards to ensure that the standardization needs of the digital transformation of the industrial sector are met, so as to make the standardization system of CEN and CENELEC more agile and adaptable to the needs of the market and technological innovation[ 16 , 17 ]. Affected by this, ISO also put forward the concept of SMART (Standards Machine Applicable, Readable and Transferable) in 2019, and promoted the digitalization of standards in the direction of machine-readable standards[ 18 , 19 ]. The research in the technical field focuses on standard data mining, knowledge management and intelligent application[ 20 , 21 ]. Digitalization of standards can be regarded as empowering the standards themselves and the whole life cycle by using digital technologies such as cloud computing, big data, Internet of Things and artificial intelligence. It is a process in which the rules and characteristics carried by standards can be read, transmitted and used through digital equipment, and it is oriented to the whole standardization activity[ 22 ]. In a narrow sense, the digitization of standards is the conversion of standards into digital text that is machine- readable, translatable, and capable of scaled platform management; broadly speaking, the digitalization of standards represents the overall innovation in the field of standards, which is reflected in the whole life cycle of standards. By empowering standards and all aspects of life cycle, standardized big data and standard computing power are formed[ 23 ]. Generally speaking, the basic role and strategic significance of digitalization of standard have been reached at home and abroad (home refers to China, abroad refers to countries or organizations outside China)[ 24 , 25 ], but the understanding of its specific characteristics, mechanism, technology, form and model is still not unified, and the technical pathis not clear. There are still many problems that need further study and discussion[ 26 , 27 ]. Although the technical paths and stages of the digital research of the main standards are not the same at present, they do influence the future landscape of the world’s digital ecology[ 28 , 29 ]. This study has sorted out five key words, namely digitalization of standard, semantic web, digital model, open source and virtual reality enhancement[ 30 , 31 , 32 ]. On this basis, by collecting papers and standards published in journals related to digitalization of standard from 2017 to 2021, and using CiteSpace and other tools, the co-occurrence atlas of keywords is analyzed, and the current status of digitalization of standard is systematically sorted out and prospected from the perspective of literature, which provides a data basis for identifying the hot spots and trends of digitalization of standard. In turn, to provide human society with practical solutions to better use standards to meet the challenges of achieving the Sustainable Development Goals in the digital age. 2 Related work Standardization has a profound impact on all aspects of life, and digitization of standards is an important work and main trend at present. Researchers in the field of standardization also collect, sort out and analyze standardized data under different industry backgrounds, which points out the direction for the future development of digitalization of standard. Liu Xize and others[ 19 ] introduced the research status of ISO-IEC on SMART standard use cases, and also summarized the technical basis and implementation means of standard digital transformation. Wang Shuo and others[ 23 ] studied machine-readable standards, established the requirements of digital standards in various application fields, and put forward some thoughts and suggestions on realizing machine-readable standards. Ma Chao and others[ 33 , 34 ] studied the digitalization of standards in the electric power field, and summarized the requirements, digital transformation paths and related application scenarios of the digitalization of standards in the electric power field, which provided a reference for the development of standards in the electric power field. Chen Xiang and others[ 35 ] made a statistical analysis of the standards in the field of integrated circuits, clarified the current status of domestic circuit standardization, and provided a plan for improving the standardization level of the whole industry chain in the field of integrated circuits. Ma Lin and others[ 36 ] systematically analyzed the shortcomings and deficiencies in the development of semiconductor material standards, and provided suggestions for future standardization work. Yang Suxin and others[ 37 ] also analyze the field of semiconductor materials. This paper takes the standards in the field of semiconductor materials as the research formation, introduces the standards from different angles such as standard basis, products, methods and management, and analyzes the development direction of standardization work in the future. Zhang Lan and others[ 38 ] explored the application scenarios of digitalization of standard in power grid field according to the actual situation in power grid field and the concept of ISO digitalization of standard, and at the same time analyzed the data of existing standard documents in power grid field. Zhou Xizhen and others[ 39 ] analyzed a large number of standard documents in the field of cultural relics, and comprehensively sorted out the existing cultural relics digitization standards at home and abroad, on this basis, provided ideas for the construction of cultural relics digitization standard system. Yue Gaofeng and others[ 40 ] put forward a digital modeling method of emergency standards based on knowledge map. By analyzing the knowledge structure of emergency standards and combining with the ontology model in the field of emergency standards, the standards are digitally modeled. Luttmer, Janosch and others[ 41 ] analyzed the existing methods of xml data representation in knowledge maps and their portability in the field of digital standards. At the same time, the concept of transforming standard content from xml format to graphbased representation is put forward. Filippos Lygerakis and others[ 42 ] formed a structured semantic data representation of design building elements and their relationships by analyzing the knowledge in architectural engineering and construction domain standards, and completed the construction of domain knowledge map. Lv Dongdong and others[ 43 ], based on the existing agricultural standards and related entry data, designed the ontology rules of agricultural product standards information according to standardized documents, designed regular wrappers for semistructured data, and proposed a relationship extraction model, which contributed to the development of digitalization of standard. Yang Yuexiang and others[ 44 ] realized the machine-readable and knowledge extraction of standard documents through the data analysis of structural characteristics, ontology framework and other elements of standard documents, and then constructed the standard knowledge map to promote the digital development of standards. 3 Research Purpose and Data Analysis Design 3.1 Research purpos By tracking and analyzing relevant researches and practices at home and abroad, this study uses bibliometrics method to analyze home and abroad literatures with subject keywords of digitalization of standard, semantic web, digital model, open source and virtual reality enhancement. In addition, CiteSpace and other tools are used to carry out copresence spectrum analysis, extract, screen and present indicator results, so as to understand the hot topics of home and abroad standard digitization research, research status and general direction of future research trends, and finally provide reference for home standard digitization research, and better provide data basis for the formation of transformational technology routes. 3.2 Data analysis design 3.2.1 Theoretical basis This study is mainly based on the technology breakdown theory, which is a process of decomposing a complex technology or system into subsystems and subtechnologies. Therefore, it has important research significance in the identification of main technologies in the field and the stratification of technical systems. There is little research on the decomposition of technical structure at home and abroad, and the concept of technical structure decomposition evolved from the decomposition of working structure. At present, scholars at home and abroad have studied this field. He Hua and others[ 45 ] use the reliability analysis model to decompose the technology of manufacturing enterprises, thus forming a complete product design, manufacturing and service platform. Martinez and others put forward a two-stage multivariable technology decomposition model. He decomposed technology into explicit components and invisible components, and further decomposed it into general technology and special technology to meet specific users. Thus, in his research, technological decomposition is a process of separating the explicit from the implicit and identifying the particular from the general[ 46 ]. Liu Yanqiong believes that the decomposition of technology structure refers to the process of decomposing technology into subsystems and subcomponents, and then into the combination of basic units, and is therefore a top-down, whole-to-individual decomposition approach[ 47 ]. Gong Sanle sees technological decomposition as a process of cost reduction, i.e. it is a process of substituting labor for capital. The implementation of technological decomposition by enterprises can promote technological progress and reduce costs[ 48 ]. Zhang Jianli believes that the decomposition of technology structure is to stratify technology, and technology can be decomposed to any level as needed, so the relationship between system and technology can be fully displayed[ 49 ]. In this study, technology breakdown theory is applied to the identifica tion of technical hotspots. It is worth stating that the use of this theory to identify technical subsystems, key technical points and ultimately build a technical dictionary also requires the effective support of relevant text mining methods. Through tracking and analyzing relevant research and practice at home and abroad, this study provides an initial definition of keywords based on textual analysis and expert discussion. The author and his research team convened experts through working meetings to screen core researchers in the field. Since the research results of the core researchers and the innovative research citing these results can reflect the main technology distribution in the field to a certain extent, this study extracts and forms the main search terms in the field by obtaining these research results. 3.2.2 Data sources Literature data are based on the China Knowledge Network Infrastructure(CNKI) database (home literature) and the Web of Science (WOS) database (abroad literature) core data collection as the data source, and the search topics are digitalization of standard, semantic web, digitization model, open source, and virtual reality enhancement, with the search spanning from 2017–2021. In this study, we exclude the articles that do not fit the theme, and select 189 articles of digitalization of standard in Chinese, 362 articles of digitalization of standard in foreign languages; 270 articles of semantic web in Chinese, 111 articles of semantic web in foreign languages; 618 articles of digital model in Chinese, 852 articles of digial model in foreign languages; 3009 articles of open source in Chinese, 293 articles of open source in foreign languages; 2051 articles of virtual reality enhancement as a sample of the study. The standard literature resources are based on the Standard Reading Service System of the National Standards Library of the China National Institute of Standardization (CNIS) as the data source, and the search topics are digitization, semantic web, digital model, open source, and virtual reality enhancement. After screening, more than 900 digitalization of standard related standards were selected as research samples. 3.2.3 Research method This paper mainly uses bibliometric method and co-occurrence atlas analysis method. Bibliometrics is a branch of library and information science. Bbibliometric method is the application of mathematical and statistical methods to express, evaluate and infer the current research trends in this field by virtue of the quantity of different features of literature, such as keywords. However, the results obtained by the bibliometric method alone are not highly referential. Therefore, this paper uses the document analysis tool CiteSpace to explain the conclusion through the co-occurrence atlas, which is more intuitive and can improve the referential ability to a certain extent. Specific practices are as follows. Digitalization of standard, semantic web, digital model, open sourceand virtual reality enhancement were used as guide terms to search related guide terms in CNKI and Web of Science. The search time span was from 2017 to 2021. According to experience, manually filter and set the retrieved information, export the finalized information in Refworks format, name the file in the form of download.txt, import it into CiteSpace software for TXT format conversion, combine the occurrence frequency of keywords with the same meaning in the same year. In the time slicing module on the software interface, set the time span to 2017–2021, and the time slice to 1 year. The other Settings are default. The atlas analysis technology is used to process the data, draw the keyword co-occurrence atlas, interpret the node size of the atlas, network connection and other elements of the atlas, conduct evolutionary analysis of learning research under standard digital technology, summarize research hotspots from the perspective of high frequency terms, identify risks, and speculate the development trend. 3.3 Analysis tools and methods 3.3.1 Analysis tools CiteSpace software is used to draw the keyword map of sample data to analyze the research hotspots and development trend of learning research based on technology of digitalization of standard. CiteSpace is a scientific knowledge map analysis software developed by Professor Chen Chaomei from the School of Computing and Information of Drexel University based on JAVA. This software can analyze the key words of documents, explore and dig out the dynamic change process of scientific research, and show the trend of scientific development. It is an effective tool for scientific and technological workers such as universities, scientific research institutions and enterprises to engage in scientific map analysis. CiteSpace means citation space, which can visually display the citations of articles. CiteSpace can mine the required information in the field of digitalization of standard in articles and article citations in a large number of literatures, form a keyword co-occurrence network, and show the content structure information in the field of digitalization of standard. The detailed flow chart of CiteSpace processing data is shown in Fig. 1 . 3.3.2 Analysis method Search for Literature in China Knowledge Network CNKI and Web of Science, export the information in the format of Refworks, name the file in the form of download.txt, import it into CiteSpace software to convert the format of txt text, set the time span of 2017–2021 in the software interface Time Slicing module , set the time slice to 1 year, and set the rest by default. By processing data with atlas analysis technology, we draw a keyword co-occurrence atlas, interpret the elements of atlas node size, network connection and so on, analyze the evolution of learning research under standard digital technology, summarize research hotspots from the perspective of high-frequency terms, identify risks and speculate on development trends. 4 Data Analysis 4.1 Keyword co-occurrence atlas analysis 4.1.1 Digitalization of standard From 2017 to 2021, a total of 189 journal articles related to digitalization of standard were published in China. According to the retrieved literature data, they were classified and sorted, and the hot spots of digitalization of standard with high frequency of use were obtained. A two-dimensional bar chart was drawn in Microsoft Excel, as shown in Fig. 2 . The co-occurrence analysis results of keywords are shown in Fig. 2 . According to the knowledge map, it can be found that in the field of digitalization of standard, the keywords digitalization, standard system, standard, intelligent manufacturing and artificial intelligence have high intermediary centrality and occupy a prominent position in the map. By further classifying the keywords, we can find that digital twins, human-computer interaction, artificial intelligence, data dictionary and virtual reality are the key technologies in the field of digitalization of standard in recent years, and their frequency of occurrence is obviously higher than other technical keywords; Intelligent manufacturing, smart city, big data and Internet of Things are the application backgrounds of standard digital technology. Key words such as gesture recognition, sensor and spacecraft represent the specific application fields of standard digital technology. As can be seen from Fig. 2 , the number of co-occurrences of digitalization as the core term of the study is much higher than that of other keywords, which shows that the drawing of this map has relevant reliability; The font size of the keyword in the figure indicates the centrality of the keyword, which can often be used as a standard to measure the research status of key terms in the research field. As can be seen from Fig. 3 , the number of co-occurrences of digitalization(bolded black font) as the core term of the study is much higher than that of other keywords, which shows that the drawing of this map has relevant reliability; The font size of the keyword in the figure indicates the centrality of the keyword, which can often be used as a standard to measure the research status of key terms in the research field. The following analysis is similar. Figure 4 shows the co-occurrence analysis results of keywords in foreign literature in the field of digitalization of standard. According to the knowledge map, it can be found that in the field of digitalization of standard, keywords such as industry 4.0, cultural heritage and algorithm have high intermediary centrality and occupy a prominent position in the figure. According to the further classification of keywords, we can find that digital library, digital humanity, digital database and classification are the key technologies in the field of digitalization of standard in recent years, and their frequency of occurrence is obviously higher than other technical keywords. industry 4.0 is the application background of standard digital technology; Keywords such as cultural heritage, internet and big data represent the specific application fields of standard digital technology. 4.1.2 Semantic web From 2017 to 2021, a total of 270 journal articles related to the semantic web were published at home and abroad. According to the retrieved literature data, they were classified and sorted, and the hot spots of the semantic web with high frequency were obtained. A two-dimensional bar chart was drawn in Microsoft Excel, as shown in Fig. 5 . Figure 6 shows the results of co-occurrence analysis of keywords in domestic journal papers in the field of semantic web. According to the knowledge map, we can find that in the field of semantic web, keywords such as semantic web, ontology, associated data, knowledge map and big data have high intermediary centrality and occupy a prominent position in the map. According to the further classification of keywords, we can find that digital twins, human-computer interaction, artificial intelligence, data dictionary and virtual reality are the key technologies in the field of semantic web in recent years, and their frequency of occurrence is obviously higher than other technical keywords. Intelligent manufacturing, smart city, big data and internet of things are the application backgrounds of semantic web technology. Key words such as gesture recognition, sensor and spacecraft represent the specific application fields of semantic web technology. Figure 7 shows the results of co-occurrence analysis of keywords in foreign journal papers in the field of semantic web. According to the knowledge map, we can find that in the field of semantic web, keywords such as deep learning, image segmentation and neural network have high intermediary centrality and occupy a prominent position in the graph. According to the further classification of keywords, we can find that connecting data, neural network, deep learning and feature fusion are the key technologies in the field of semantic web abroad in recent years, and their frequency of occurrence is obviously higher than other technical keywords; ontology is the application background of semantic web technology; key words such as social media, remote sensing control and transfer learning represent the specific application fields of semantic web technology. 4.1.3 Digital model From 2017 to 2021, a total of 616 journal articles related to digital model were published in China. According to the retrieved literature data, they were classified and sorted, and the hot spots of digital model with high frequency of use were obtained. A two-dimensional bar chart was drawn in Microsoft Excel, as shown in Fig. 8 . Figure 9 shows the results of co-occurrence analysis of keywords in domestic journal articles in the field of digital model. According to the knowledge map, it can be found that in the field of digital model, the keywords digitization, 3 dimensional(3D), 3D reconstruction and 3D model have high intermediary centrality and occupy a prominent position in the map. According to the further classification of keywords, it can be found that reverse engineering, 3D printing and digital twinning are the key technologies in the field of digital models in recent years, and their frequency of occurrence is obviously higher than other technical keywords; VR and big data are the application backgrounds in the field of digital models. Figure 10 shows the results of co-occurrence analysis of keywords in foreign journal articles in the field of digital model. According to the knowledge map, it can be found that in the field of digital model, the keywords model, digital sky survey, system and design have high intermediary centrality and occupy a prominent position in the figure. Further classification of keywords shows that deep learning, digital sky survey and digital model are the key technologies in the field of digital models in recent years, and their frequency of occurrence is obviously higher than other technical keywords. 4.1.4 Open source From 2017 to 2021, a total of 2,996 open source-related journal papers were published in China. According to the retrieved literature data, they were classified and sorted, and the frequently used open source-related hotspots were obtained. A two-dimensional bar chart was drawn in Microsoft Excel, as shown in Fig. 11 . Due to the large number of papers related to open source, in order to show the keyword co-occurrence relationship more clearly, the block diagram is used to express the co-occurrence analysis results, different color blocks represent different clusters, and the larger the font size of the keywords in the block, the more times the keywords appear. According to the knowledge map, it can be found that in the field of open source in China (as shown in Fig. 12 ), keywords such as opensource software, open source community, artificial intelligence, cloud computing and big data have high intermediary centrality and occupy a prominent position in the figure. Further categorization of keywords reveals that artificial intelligence and cloud computing are key technologies in the open source field in recent years, appearing significantly more often than other technology keywords; open source software, open source community and opensource intelligence are opensource technical means; keywords such as internet of things, cloud platform and blockchain represent specific application fields of open source. Figure 13 shows the results of co-occurrence analysis of keywords in foreign journal papers in the field of open source. According to the knowledge map, it can be found that in the field of digital model, the keywords model, algorithm, system and tool have high intermediary centrality and occupy a prominent position in the figure. According to the further classification of keywords, we can find that algorithm, system and database are the important foundations of the technical development in the field of open source in recent years, and their frequency of occurrence is obviously higher than other keywords. 4.1.5 Virtual Reality Augmentation From 2017 to 2021,atotal of 1,495 journals related to virtual reality enhancement were published in China. According to the retrieved literature data, they were classified and sorted, and the hot spots related to virtual reality enhancement with high frequency were obtained. Two-dimensional bar charts was drawn in Microsoft Excel, as shown in Fig. 14 and Fig. 15 . Virtual reality augmentation uses block diagram to show the results of co- occurrence analysis. According to the knowledge map, it can be found that in the field of virtual reality enhancement, the keywords virtual reality(VR), augmented reality(AR), application and mixed reality have a high mediation center and occupy a prominent position in the figure (as shown in Fig. 16 ). By further classifying the keywords, we can find that VR, AR and mixed reality are the key technologies in the field of virtual reality enhancement in recent years, and their frequency of occurrence is obviously higher than other technical keywords; artificial intelligence, manmachine interaction and practical teaching are the technical means in the field of virtual reality enhancement. Keywords such as digital media, 3D modeling and interior design represent the specific application fields of virtual reality enhancement. The results of co-occurrence analysis of keywords in foreign journal papers in the field of virtual reality enhancement are displayed by block diagram (Fig. 17 ). According to the knowledge map, we can find that in the field of virtual reality enhancement, keywords such as VR, AR, mixed reality and enhancement have high intermediary centrality and occupy a prominent position in the figure. By further classifying the keywords, we can find that VR, AR and mixed reality are the key technologies in the field of virtual reality enhancement in recent years, and their frequency of occurrence is obviously higher than other technical keywords; artificial intelligence is a technical means in the field of virtual reality enhancement; keywords such as geographical information system and interactive learning environments represent specific application fields of virtual reality enhancement. 4.2 Analysis of Chinese National Standards The number of standards in the field of digitalization of standard in China is relatively small. After analysis and screening, the current standards in the relevant fields of the National Technical Committee for Standardization of Information and Documentation (TC4), the National Technical Committee for Standardization of Principles and Methods (TC286), and the National Technical Committee for Standardization of Information Technology (TC28) are selected as the object of analysis to carry out the comparative research on the analysis of standard hotspot technologies. 4.2.1 Analysis of Standard Distribution Domain Figure 18 According to the national economic industry classification (National Economic Industry Classification GB/T 4754 − 2017) to which the standard drafting unit belongs, the development of standards related to digitalization of standard in various industries is calculated. Among the relevant standards in the field of digitalization of standard, the number of standards developed by information transmission, software and information technology services is the largest; the number of manufacturing industry standard development ranks second; the number of standards for scientific research and technical services ranks third. 4.2.2 Standard Quantity Time Series Analysis A total of 982 standards related to digitalization of standard are in force, and 689 standards related to digitalization of standard have been issued since 2000. In terms of overall trends, there is an upward trend in the number of standards developments related to the digitalization of standard (Fig. 19 ). Among them, in 2010,the number of standards related to digitalization of standard was the largest, and 119 units participated in the development of 124 related standards; in 2021, the number of standard drafting units in the field of digitalization of standard was the largest, and a total of 212 units participated in the drafting of standards. The specific trends of drafting standards are shown in Table 1 . It can be seen that with the continuous advancement of digitalization, the number of related standards has shown an obvious increase trend. Table 1 Trends of drafting standard units and quantities. Time Number of drafting units Number of development standard 2001 13 14 2002 10 18 2003 12 16 2004 2 2 2005 18 9 2006 51 32 2007 47 19 2008 70 84 2009 44 31 2010 119 124 2011 84 18 2012 191 55 2013 143 62 2014 183 63 2015 60 20 2016 106 21 2017 213 63 2018 6 1 2019 0 0 2020 25 1 2021 212 21 4.2.3 Analysis of technical hot words From the distribution of technical hot words, the relevant standards in the field of digitalization of standard mainly involve hot words such as information, software, services, engineering, resources and systems, as shown in Fig. 20 . The technology of digitalization of standard is a newly developed technology in recent years, mainly concentrated in the fields of information transmission, software, information technology service industry and manufacturing industry. However, from the above analysis results, it can be seen that the research field of digitalization of standard in China is relatively weak, the number of relevant standards is relatively small, and the number of standards is growing slowly. However, it can be seen from the trend chart of drafting standards that in 2021, a total of 212 units participated in the development of standards in the field of digitalization of standard indicating that since 2021, more and more units began to pay attention to the development of related technologies of digitalization of standard, and several drafting units began to jointly develop the same standard. In the future, it is the top priority for the development of digitalization of standard to strengthen the compilation and revision of digitalization of standard type standards and increase the cooperation degree of relevant publishing units and centralized units. 4.3 Research trend analysis Through the keyword co-occurrence atlas analysis of five technical topics related to standard digitization, including digitalization of standard, semantic web, digital model, open source and virtual reality enhancement, it is found that: 1. Digital development has led to the digital transformation of standards, giving rise to new technological hotspots in the areas of standard content generation, standardization modes and applications of standard. From the research results, digitalization of standard is atypical product of the combination of digital technology and domain technology, and digitalization is the core technological topic of the transformation, with industry 4.0, internet, digital libraries, and onsite simulation as its typical application scenarios. By analyzing the core technology keywords in each topic, in terms of standard content generation, semantic web, ontology, knowledge graph, artificial intelligence, etc. are its core technological hotspots; in terms of standardization mode, opensource software, artificial intelligence (models, algorithms, etc.), cloud computing and big data, etc. are itscore technological hotspots; in terms of standard application, digital modeling technology, 3d printing, system design, VR, AR and human-computer interaction, etc. are its core technological hotspots. Although the quantity of related standards have been increasing year by year, they are mostly focused on the digital technology application in various industrial fields (Figs. 18 and 20 ), and there is a lack of standards for principles, methods and technologies of digitalization of standard, which need to be focused in the future. 2. New technological hotspots driving new trends in the evolution of the standard itself, the development of the standardization ecosystem and governance. Empowered by digital technology, standardization will undergo profound changes in both connotation and extension, and the technological trend is mainly reflected in three aspects. First, in terms of the evolution of standards themselves, on the other hand, knowledge carried by natural language standards is extracted through technologies such as semantic modeling, artificial intelligence and other technologies, and the standard knowledge ontology models are constructed to realize direct use of standard knowledge by machines. On the other hand, in order to realize the direct interaction between standards and machines, machine language may be directly introduced in the early stages of standard development to arrange and express standard content, output machine readable standards, and make the presentation form of standards not only natural language standards. Secondly, in terms of the development of standardized ecology, the use of open source technology will make it possible to implement and share information about standardized activities, and promote relevant parties to collaborate in a more open, shared and intelligent way, and the construction of standardized open source ecology will become a new trend. Thirdly, in terms of digital governance, the use of digital technology makes the acquisition, processing and use of standardized information more intelligent. At the same time, it will also cause potential problems such as data security, personal information protection, technical ethics, and intellectual property rights. How to better face and solve these problems will become an important aspect of standard digitization research. The related theories and methods are not mature, and there are few references. This study also reflects the fact that research on digitalization if standard is still in its infancy in China. Even if fuzzy query is used, only a hundred articles can be found, and the number of related standards in the field is small. There are few special studies on digitalization of standard at home and abroad, so it is difficult to obtain effective theoretical and methodological support from the studies of predecessors. Taking the hot field of standard digital technology as an example, few scholars have clearly explained the related concepts at present. In the face of this situation, it can only be summarized through view analysis to distill the relevant features of the hot areas of the technologies and analyze them in conjunction with actual policies and industrial development. 4. It is difficult to obtain data of standard digital technology, and it is necessary to effectively balance the accuracy and completeness. Standard digital technology involves many aspects of information. How to formulate an effective retrieval strategy in obtaining data is the key issue of this study. The text involves a large amount of data, take the digital model in WOS as an example, in 2015–2021, the data of each year is more than 10,000, and in 2021, it reaches 17,669 pieces of literature data, but after checking, it is found that a lot of the literature data only mentions digitization or model in the text of a certain aspect, which is less related to the digitalization of standard, and there are a large number of low-quality literature with 0 citation and 0 browsing. Numerous hardware and software constraints exist when downloading and analyzing this data, which forces this study to filter the retrieved data in order to narrow down the retrieval structure in order to ensure an accurate analytical view. Therefore, how to gradually expand the search scope and update the search results is the focus of the next step. Conclusions The purpose of this paper is to provide the foundation and inspiration for China’s digitalization of standard research by analyzing the hot spots, frontiers and trends in the field of digitalization of standard transformation, so as to help the planning of the digitalization of standard transformation path. In turn, standards are better used to provide human society with practical solutions to meet the challenges of achieving the Sustainable Development Goals in the digital age. The core directions of this study is the innovative research of various network analysis views, that is, using no or little quantitative data, creating a keyword co-occurrence atlas through CiteSpace view analysis tools, showing researchers the development process from the basic research and development of standard digital technology to practical application, and summarizing the relationship between technical nodes, so it is innovative to some extent. However, there are still some defects in the current big data analysis view. Therefore, follow-up research needs to further innovate research methods. In addition to updating data sources, it is also necessary to optimize the retrieval and query methods of literature data, collect and analyze the latest development concepts of standard digital technology, and extract effective information to meet the requirements of diversified knowledge sources in the era of big data. 4. It is difficult to obtain data of standard digital technology, and it is necessary to effectively balance the accuracy and completeness. Standard digital technology involves many aspects of information. How to formulate an effective retrieval strategy in obtaining data is the key issue of this study. The text involves a large amount of data, take the digital model in WOS as an example, in 2015–2021, the data of each year is more than 10,000, and in 2021, it reaches 17,669 pieces of literature data, but after checking, it is found that a lot of the literature data only mentions digitization or model in the text of a certain aspect, which is less related to the digitalization of standard, and there are a large number of low-quality literature with 0 citation and 0 browsing. Numerous hardware and software constraints exist when downloading and analyzing this data, which forces this study to filter the retrieved data in order to narrow down the retrieval structure in order to Acknowledge This work was supported in part by The National Key Research and Development Program (2022YFF0608000), and in part by The Basic Research Business Fee Project(Project No. 292024Y-11456,572023Y-10377), and in part by The Supported by the Science and Technology Program of the State Administration for Market Regulation (Project No. 2023MK190). Declarations Acknowledge This work was supported in part by The National Key Research and Development Program (2022YFF0608000), and in part by The Basic Research Business Fee Project(Project No. 292024Y-11456,572023Y-10377), and in part by The Supported by the Science and Technology Program of the State Administration for Market Regulation (Project No. 2023MK190). Conflict of interest The authors declare no conflict of interest. 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09:13:28","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":95833,"visible":true,"origin":"","legend":"\u003cp\u003eThe popularity of related words of ”digitalization of standard” in the standard\u003c/p\u003e","description":"","filename":"floatimage20.png","url":"https://assets-eu.researchsquare.com/files/rs-4609699/v1/abbbfadc61a52cf090365fd3.png"},{"id":61768000,"identity":"090a140f-e457-40ba-bbaa-f2d68855a9dd","added_by":"auto","created_at":"2024-08-05 10:46:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4429352,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4609699/v1/532bc4c5-fc36-47d2-8334-4875f70bac1f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Technological Hotspots and Trends of Digitalization of Standard Based on Keyword Co-occurrence Atlas","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSince the United Nations issued the Sustainable Development Goals (SDGs) in 2025, all sectors of society have taken a series of measures, standards as an important technical support for economic and social development, in promoting economic and social sustainable development plays an important role, the theme of World Standards Day 2021 is \u003cem\u003eStandards promote sustainable development, Build a better world\u003c/em\u003e. At present, the digital transformation of economy and society has become the trend of The Times. How to play the role of standards to promote high-quality development and sustainable development of economy has put forward an urgent demand for the digital transformation of standards.\u003c/p\u003e \u003cp\u003eIn October 2021, \u003cem\u003ethe National Standardization Development Outline\u003c/em\u003e was released, proposing the development of machine-readable standards and open source standards, and promoting the transformation of standardization to digital, networked and intelligent, and the digital transformation of standards is one of the important strategic deployment contents of standardization in China[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. At the annual meeting of the International Organization for Standardization (ISO) held in September, 2022, representatives of all member countries had a special exchange on the topic of \u003cem\u003eDigitalization and Digital Transformation: One Step Ahead\u003c/em\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Experts in various countries believe that the digital transformation of standards is the endogenous demand of standardization development and an inevitable trend. ISO and the International Electrotechnical Commission (IEC)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] have joined forces with the European Committee for Standardization (CEN), the European Committee for Electrotechnical Standardization (CENELEC)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and national standardization organizations in Britain and Germany to promote related work[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and promote the establishment of a consistent consensus and strategy for transformation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The digitalization of standards in China started late, and there is still a gap in methods, technologies and tools. We should pay attention to and learn from international good practices and systematically carry out related work, so as to provide strong support for the implementation of the National Standardization Development Outline and the construction of digital China. The related research and development of digitalization of standard began with the interaction between open source and standardization[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In 2017, the CEN-CENELEC Digital Transformation Strategic Plan issued by CEN and CENELEC was formally put forward in anticipation of the digitalization of standards to ensure that the standardization needs of the digital transformation of the industrial sector are met, so as to make the standardization system of CEN and CENELEC more agile and adaptable to the needs of the market and technological innovation[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Affected by this, ISO also put forward the concept of SMART (Standards Machine Applicable, Readable and Transferable) in 2019, and promoted the digitalization of standards in the direction of machine-readable standards[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The research in the technical field focuses on standard data mining, knowledge management and intelligent application[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Digitalization of standards can be regarded as empowering the standards themselves and the whole life cycle by using digital technologies such as cloud computing, big data, Internet of Things and artificial intelligence. It is a process in which the rules and characteristics carried by standards can be read, transmitted and used through digital equipment, and it is oriented to the whole standardization activity[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In a narrow sense, the digitization of standards is the conversion of standards into digital text that is machine-\u003c/p\u003e \u003cp\u003ereadable, translatable, and capable of scaled platform management; broadly speaking, the digitalization of standards represents the overall innovation in the field of standards, which is reflected in the whole life cycle of standards. By empowering standards and all aspects of life cycle, standardized big data and standard computing power are formed[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Generally speaking, the basic role and strategic significance of digitalization of standard have been reached at home and abroad (home refers to China, abroad refers to countries or organizations outside China)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], but the understanding of its specific characteristics, mechanism, technology, form and model is still not unified, and the technical pathis not clear. There are still many problems that need further study and discussion[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the technical paths and stages of the digital research of the main standards are not the same at present, they do influence the future landscape of the world\u0026rsquo;s digital ecology[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This study has sorted out five key words, namely digitalization of standard, semantic web, digital model, open source and virtual reality enhancement[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. On this basis, by collecting papers and standards published in journals related to digitalization of standard from 2017 to 2021, and using CiteSpace and other tools, the co-occurrence atlas of keywords is analyzed, and the current status of digitalization of standard is systematically sorted out and prospected from the perspective of literature, which provides a data basis for identifying the hot spots and trends of digitalization of standard. In turn, to provide human society with practical solutions to better use standards to meet the challenges of achieving the Sustainable Development Goals in the digital age.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2 Related work","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStandardization has a profound impact on all aspects of life, and digitization of standards is an important work and main trend at present. Researchers in the field of standardization also collect, sort out and analyze standardized data under different industry backgrounds, which points out the direction for the future development of digitalization of standard. Liu Xize and others[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] introduced the research status of ISO-IEC on SMART standard use cases, and also summarized the technical basis and implementation means of standard digital transformation. Wang Shuo and others[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] studied machine-readable standards, established the requirements of digital standards in various application fields, and put forward some thoughts and suggestions on realizing machine-readable standards. Ma Chao and others[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] studied the digitalization of standards in the electric power field, and summarized the requirements, digital transformation paths and related application scenarios of the digitalization of standards in the electric power field, which provided a reference for the development of standards in the electric power field. Chen Xiang and others[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] made a statistical analysis of the standards in the field of integrated circuits, clarified the current status of domestic circuit standardization, and provided a plan for improving the standardization level of the whole industry chain in the field of integrated circuits. Ma Lin and others[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] systematically analyzed the shortcomings and deficiencies in the development of semiconductor material standards, and provided suggestions for future standardization work. Yang Suxin and others[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] also analyze the field of semiconductor materials. This paper takes\u003c/p\u003e \u003cp\u003ethe standards in the field of semiconductor materials as the research formation, introduces the standards from different angles such as standard basis, products, methods and management, and analyzes the development direction of standardization work in the future. Zhang Lan and others[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] explored the application scenarios of digitalization of standard in power grid field according to the actual situation in power grid field and the concept of ISO digitalization of standard, and at the same time analyzed the data of existing standard documents in power grid field. Zhou Xizhen and others[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] analyzed a large number of standard documents in the field of cultural relics, and comprehensively sorted out the existing cultural relics digitization standards at home and abroad, on this basis, provided ideas for the construction of cultural relics digitization standard system. Yue Gaofeng and others[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] put forward a digital modeling method of emergency standards based on knowledge map. By analyzing the knowledge structure of emergency standards and combining with the ontology model in the field of emergency standards, the standards are digitally modeled. Luttmer, Janosch and others[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] analyzed the existing methods of xml data representation in knowledge maps and their portability in the field of digital standards. At the same time, the concept of transforming standard content from xml format to graphbased representation is put forward. Filippos Lygerakis and others[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] formed a structured semantic data representation of design building elements and their relationships by analyzing the knowledge in architectural engineering and construction domain standards, and completed the construction of domain knowledge map. Lv Dongdong and others[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], based on the existing agricultural standards and related entry data, designed the ontology rules of agricultural product standards information according to standardized documents, designed regular wrappers for semistructured data, and proposed a relationship extraction model, which contributed to the development of digitalization of standard. Yang Yuexiang and others[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] realized the machine-readable and knowledge extraction of standard documents through the data analysis of structural characteristics, ontology framework and other elements of standard documents, and then constructed the standard knowledge map to promote the digital development of standards.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"3 Research Purpose and Data Analysis Design","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research purpos\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBy tracking and analyzing relevant researches and practices at home and abroad, this study uses bibliometrics method to analyze home and abroad literatures with subject keywords of digitalization of standard, semantic web, digital model, open source and virtual reality enhancement. In addition, CiteSpace and other tools are used to carry out copresence spectrum analysis, extract, screen and present indicator results, so as to understand the hot topics of home and abroad standard digitization research, research status and general direction of future research trends, and finally provide reference for home standard digitization research, and better provide data basis for the formation of transformational technology routes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data analysis design\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Theoretical basis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study is mainly based on the technology breakdown theory, which is a process of decomposing a complex technology or system into subsystems and subtechnologies. Therefore, it has important research significance in the identification of main technologies in the field and the stratification of technical systems. There is little research on the decomposition of technical structure at home and abroad, and the concept of technical structure decomposition evolved from the decomposition of working structure. At present, scholars at home and abroad have studied this field. He Hua and others[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] use the reliability analysis model to decompose the technology of manufacturing enterprises, thus forming a complete product design, manufacturing and service platform. Martinez and others put forward a two-stage multivariable technology decomposition model. He decomposed technology into explicit components and invisible components, and further decomposed it into general technology and special technology to meet specific users. Thus, in his research, technological decomposition is a process of separating the explicit from the implicit and identifying the particular from the general[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Liu Yanqiong believes that the decomposition of technology structure refers to the process of decomposing technology into subsystems and subcomponents, and then into the combination of basic units, and is therefore a top-down, whole-to-individual decomposition approach[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Gong Sanle sees technological decomposition as a process of cost reduction, i.e. it is a process of substituting labor for capital. The implementation of technological decomposition by enterprises can promote technological progress and reduce costs[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Zhang Jianli believes that the decomposition of technology structure is to stratify technology, and technology can be decomposed to any level as needed, so the relationship between system and technology can be fully displayed[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In this study, technology breakdown theory is applied to the identifica tion of technical hotspots. It is worth stating that the use of this theory to identify technical subsystems, key technical points and ultimately build a technical dictionary also requires the effective support of relevant text mining methods. Through tracking and analyzing relevant research and practice at home and abroad, this study provides an initial definition of keywords based on textual analysis and expert discussion. The author and his research team convened experts through working meetings to screen core researchers in the field. Since the research results of the core researchers and the innovative research citing these results can reflect the main technology distribution in the field to a certain extent, this study extracts and forms the main search terms in\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003ethe field by obtaining these research results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Data sources\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLiterature data are based on the China Knowledge Network Infrastructure(CNKI) database (home literature) and the Web of Science (WOS) database (abroad literature) core data collection as the data source, and the search topics are digitalization of standard, semantic web, digitization model, open source, and virtual reality enhancement, with the search spanning from 2017\u0026ndash;2021. In this study, we exclude the articles that do not fit the theme, and select 189 articles of \u003cem\u003edigitalization of\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003estandard\u003c/em\u003e in Chinese, 362 articles of \u003cem\u003edigitalization of standard\u003c/em\u003e in foreign languages; 270 articles of \u003cem\u003esemantic web\u003c/em\u003e in Chinese, 111 articles of \u003cem\u003esemantic web\u003c/em\u003e in foreign languages; 618 articles of \u003cem\u003edigital model\u003c/em\u003e in Chinese, 852 articles of \u003cem\u003edigial model\u003c/em\u003e in foreign languages; 3009 articles of \u003cem\u003eopen source\u003c/em\u003e in Chinese, 293 articles of \u003cem\u003eopen source\u003c/em\u003e in foreign languages; 2051 articles of \u003cem\u003evirtual reality enhancement\u003c/em\u003e as a sample of the study.\u003c/p\u003e \u003cp\u003eThe standard literature resources are based on the Standard Reading Service System of the National Standards Library of the China National Institute of Standardization (CNIS) as the data source, and the search topics are digitization, semantic web, digital model, open source, and virtual reality enhancement. After screening, more than 900 digitalization of standard related standards were selected as research samples.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Research method\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis paper mainly uses bibliometric method and co-occurrence atlas analysis method. Bibliometrics is a branch of library and information science. Bbibliometric method is the application of mathematical and statistical methods to express, evaluate and infer the current research trends in this field by virtue of the quantity of different features of literature, such as keywords. However, the results obtained by the bibliometric method alone are not highly referential. Therefore, this paper uses the document analysis tool CiteSpace to explain the conclusion through the co-occurrence atlas, which is more intuitive and can improve the referential ability to a certain extent. Specific practices\u003c/p\u003e \u003cp\u003eare as follows.\u003c/p\u003e \u003cp\u003eDigitalization of standard, semantic web, digital model, open sourceand virtual reality enhancement were used as guide terms to search related guide terms in CNKI and Web of Science. The search time span was from 2017 to 2021. According to experience, manually filter and set the retrieved information, export the finalized information in Refworks format, name the file in the form of download.txt, import it into CiteSpace software for TXT format conversion, combine the occurrence frequency of keywords with the same meaning in the same year. In the time slicing module on the software interface, set the time span to 2017\u0026ndash;2021, and the time slice to 1 year. The other Settings are default. The atlas analysis technology is used to process the data, draw the keyword co-occurrence atlas, interpret the node size of the atlas, network connection and other elements of the atlas, conduct evolutionary analysis of learning research under standard digital technology, summarize research hotspots from the perspective of high frequency terms, identify risks, and speculate the development trend.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis tools and methods\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Analysis tools\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCiteSpace software is used to draw the keyword map of sample data to analyze the research hotspots and development trend of learning research based on technology of digitalization of standard. CiteSpace is a scientific knowledge map analysis software developed by Professor Chen Chaomei from the School of Computing and Information of Drexel University based on JAVA. This software can analyze the key words of documents, explore and dig out the dynamic change process of scientific research, and show the trend of scientific development. It is an effective tool for\u003c/p\u003e \u003cp\u003escientific and technological workers such as universities, scientific research institutions and enterprises to engage in scientific map analysis. CiteSpace means citation space, which can visually display the citations of articles. CiteSpace can mine the required information in the field of digitalization of standard in articles and article citations in a large number of literatures, form a keyword co-occurrence network, and show the content structure information in the field of digitalization of standard. The detailed flow chart of CiteSpace processing data is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Analysis method\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSearch for Literature in China Knowledge Network CNKI and Web of Science, export the information in the format of Refworks, name the file in the form of download.txt, import it into CiteSpace software to convert the format of txt text, set the time span of 2017\u0026ndash;2021 in the software interface \u003cem\u003eTime Slicing module\u003c/em\u003e, set the time slice to 1 year, and set the rest by default. By processing data with atlas analysis technology, we draw a keyword co-occurrence atlas, interpret the elements of atlas node size, network connection and so on, analyze the evolution of learning research under standard digital technology, summarize research hotspots from the perspective of high-frequency terms, identify risks and speculate on development trends.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Data Analysis","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003e4.1 Keyword co-occurrence atlas analysis\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1.1 Digitalization of standard\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFrom 2017 to 2021, a total of 189 journal articles related to digitalization of standard were published in China. According to the retrieved literature data, they were classified and sorted, and the hot spots of digitalization of standard with high frequency of use were obtained. A two-dimensional bar chart was drawn in Microsoft Excel, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe co-occurrence analysis results of keywords are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. According to the knowledge map, it can be found that in the field of digitalization of standard, the keywords digitalization, standard system, standard, intelligent manufacturing and artificial intelligence have high intermediary centrality and occupy a prominent position in the map. By further classifying the keywords, we can find that digital twins, human-computer interaction, artificial intelligence, data dictionary and virtual reality are the key technologies in the field of digitalization of standard in recent years, and their frequency of occurrence is obviously higher than other technical keywords; Intelligent manufacturing, smart city, big data and Internet of Things are the application backgrounds of standard digital technology. Key words such as gesture recognition, sensor and spacecraft represent the specific application fields of standard digital technology. As can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the number of co-occurrences of digitalization as the core term of the study is much higher than that of other keywords, which shows that the drawing of this map has relevant reliability; The font size of the keyword in the figure indicates the centrality of the keyword, which can often be used as a standard to measure the research status of key terms in the research field.\u003c/p\u003e \u003cp\u003eAs can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the number of co-occurrences of digitalization(bolded black font) as the core term of the study is much higher than that of other keywords,\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhich shows that the drawing of this map has relevant reliability; The font size of the keyword in the figure indicates the centrality of the keyword, which can often be used as a standard to measure the research status of key terms in the research field. The following analysis is similar.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the co-occurrence analysis results of keywords in foreign literature in the field of digitalization of standard. According to the knowledge map, it can be found that in the field of digitalization of standard, keywords such as industry 4.0, cultural heritage and algorithm have high intermediary centrality and occupy a prominent position in the figure. According to the further classification of keywords, we can find that digital library, digital humanity, digital database and classification are the key technologies in the field of digitalization of standard in recent years, and their frequency of occurrence is obviously higher than other technical keywords. industry 4.0 is the application background of standard digital technology; Keywords such as cultural heritage, internet and big data represent the specific application fields of standard digital technology.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Semantic web\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFrom 2017 to 2021, a total of 270 journal articles related to the semantic web were\u003c/p\u003e \u003cp\u003epublished at home and abroad. According to the retrieved literature data, they were classified and sorted, and the hot spots of the semantic web with high frequency were obtained. A two-dimensional bar chart was drawn in Microsoft Excel, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the results of co-occurrence analysis of keywords in domestic journal papers in the field of semantic web. According to the knowledge map, we can find that in the field of semantic web, keywords such as semantic web, ontology, associated data, knowledge map and big data have high intermediary centrality and occupy a prominent position in the map. According to the further classification of keywords, we can find that digital twins, human-computer interaction, artificial intelligence, data dictionary and virtual reality are the key technologies in the field of semantic web in recent years, and their frequency of occurrence is obviously higher than other technical keywords. Intelligent manufacturing, smart city, big data and internet of things are the application backgrounds of semantic web technology. Key words such as gesture recognition, sensor and spacecraft represent the specific application fields of semantic web technology.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the results of co-occurrence analysis of keywords in foreign journal papers in the field of semantic web. According to the knowledge map, we can find that in the field of semantic web, keywords such as deep learning, image segmentation and neural network have high intermediary centrality and occupy a prominent position in the graph. According to the further classification of keywords, we can find that connecting data, neural network, deep learning and feature fusion are the key technologies in the field of semantic web abroad in recent years, and their frequency of occurrence is obviously higher than other technical keywords; ontology is the application background of semantic web technology; key words such as social media, remote sensing control and transfer learning represent the specific application fields of semantic web technology.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Digital model\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFrom 2017 to 2021, a total of 616 journal articles related to digital model were published in China. According to the retrieved literature data, they were classified and sorted, and the hot spots of digital model with high frequency of use were obtained. A two-dimensional bar chart was drawn in Microsoft Excel, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the results of co-occurrence analysis of keywords in domestic journal articles in the field of digital model. According to the knowledge map, it can be found that in the field of digital model, the keywords digitization, 3 dimensional(3D), 3D reconstruction and 3D model have high intermediary centrality and occupy a prominent position in the map. According to the further classification of keywords, it can be found that reverse engineering, 3D printing and digital twinning are the key technologies in the field of digital models in recent years, and their frequency of occurrence is obviously higher than other technical keywords; VR and big data are the application backgrounds in the field of digital models.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the results of co-occurrence analysis of keywords in foreign journal articles in the field of digital model. According to the knowledge map, it can be found that in the field of digital model, the keywords model, digital sky survey, system and design have high intermediary centrality and occupy a prominent position in the figure. Further classification of keywords shows that deep learning, digital sky survey and digital model are the key technologies in the field of digital models in recent years, and their frequency of occurrence is obviously higher than other technical keywords.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4 Open source\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFrom 2017 to 2021, a total of 2,996 open source-related journal papers were published in China. According to the retrieved literature data, they were classified and sorted, and the frequently used open source-related hotspots were obtained. A two-dimensional bar chart was drawn in Microsoft Excel, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDue to the large number of papers related to open source, in order to show the keyword co-occurrence relationship more clearly, the block diagram is used to express the co-occurrence analysis results, different color blocks represent different clusters, and the larger the font size of the keywords in the block, the more times the keywords appear. According to the knowledge map, it can be found that in the field of open source in China (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e), keywords such as opensource software, open source community, artificial intelligence, cloud computing and big data have high intermediary centrality and occupy a prominent position in the figure. Further categorization of keywords reveals that artificial intelligence and cloud computing are key technologies in the open source field in recent years, appearing significantly more often than other technology keywords; open source software, open source community and opensource intelligence are opensource technical means; keywords such as internet of things, cloud platform and blockchain represent specific application fields of open source.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e shows the results of co-occurrence analysis of keywords in foreign journal papers in the field of open source. According to the knowledge map, it can be found that in the field of digital model, the keywords model, algorithm, system and tool have high intermediary centrality and occupy a prominent position in the figure. According to the further classification of keywords, we can find that algorithm, system and database are the important foundations of the technical development in the field of open source in recent years, and their frequency of occurrence is obviously higher than other keywords.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.5 Virtual Reality Augmentation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFrom 2017 to 2021,atotal of 1,495 journals related to virtual reality enhancement were published in China. According to the retrieved literature data, they were classified and sorted, and the hot spots related to virtual reality enhancement with high frequency were obtained. Two-dimensional bar charts was drawn in Microsoft Excel, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eVirtual reality augmentation uses block diagram to show the results of co- occurrence analysis. According to the knowledge map, it can be found that in the field of virtual reality enhancement, the keywords virtual reality(VR), augmented reality(AR), application and mixed reality have a high mediation center and occupy a prominent position in the figure (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e). By further classifying the keywords, we can find that VR, AR and mixed reality are the key technologies in the field of virtual reality enhancement in recent years, and their frequency of occurrence is obviously higher than other technical keywords; artificial intelligence, manmachine interaction and practical teaching are the technical means in the field of virtual reality enhancement. Keywords such as digital media, 3D modeling and interior design represent the specific application fields of virtual reality enhancement.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe results of co-occurrence analysis of keywords in foreign journal papers in the field of virtual reality enhancement are displayed by block diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e). According to the knowledge map, we can find that in the field of virtual reality enhancement, keywords such as VR, AR, mixed reality and enhancement have high intermediary centrality and occupy a prominent position in the figure. By further classifying the keywords, we can find that VR, AR and mixed reality are the key technologies in the field of virtual reality enhancement in recent years, and their frequency of occurrence is obviously higher than other technical keywords; artificial intelligence is a technical means in the field of virtual reality enhancement; keywords such as geographical information system and interactive learning environments represent specific application fields of virtual reality enhancement.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Analysis of Chinese National Standards\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe number of standards in the field of digitalization of standard in China is relatively small. After analysis and screening, the current standards in the relevant fields of the National Technical Committee for Standardization of Information and Documentation (TC4), the National Technical Committee for Standardization of Principles and Methods (TC286), and the National Technical Committee for Standardization of Information Technology (TC28) are selected as the object of analysis to carry out the comparative research on the analysis of standard hotspot technologies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Analysis of Standard Distribution Domain\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e According to the national economic industry classification (National Economic Industry Classification GB/T 4754\u0026thinsp;\u0026minus;\u0026thinsp;2017) to which the standard drafting unit belongs, the development of standards related to digitalization of standard in various industries is calculated. Among the relevant standards in the field of digitalization of standard, the number of standards developed by information transmission, software and information technology services is the largest; the number of manufacturing industry standard development ranks second; the number of standards for scientific research and technical services ranks third.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Standard Quantity Time Series Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA total of 982 standards related to digitalization of standard are in force, and 689 standards related to digitalization of standard have been issued since 2000. In terms of overall trends, there is an upward trend in the number of standards developments related to the digitalization of standard (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e19\u003c/span\u003e). Among them, in 2010,the number of standards related to digitalization of standard was the largest, and 119 units participated in the development of 124 related standards; in 2021, the number of standard drafting units in the field of digitalization of standard was the largest, and a total of 212 units participated in the drafting of standards. The specific trends of drafting standards are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It can be seen that with the continuous advancement of digitalization, the number of related standards has shown an obvious increase trend.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrends of drafting standard units and quantities.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of drafting units\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of development standard\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Analysis of technical hot words\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFrom the distribution of technical hot words, the relevant standards in the field of digitalization of standard mainly involve hot words such as information, software, services, engineering, resources and systems, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e20\u003c/span\u003e. The technology of digitalization of standard is a newly developed technology in recent years, mainly concentrated in the fields of information transmission, software, information technology service industry and manufacturing industry. However, from the above analysis results, it can be seen that the research field of digitalization of standard in China is relatively weak, the number of relevant standards is relatively small, and the number of standards is growing slowly. However, it can be seen from the trend chart of drafting standards that in 2021, a total of 212 units participated in the development of standards in the field of digitalization of standard indicating that since 2021, more and more units began to pay attention to the development of related technologies of digitalization of standard, and several drafting units began to jointly develop the same standard. In the future, it is the top priority for the development of digitalization of standard to strengthen the compilation and revision of digitalization of standard type standards and increase the cooperation degree of relevant publishing units and centralized units.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Research trend analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThrough the keyword co-occurrence atlas analysis of five technical topics related to standard digitization, including digitalization of standard, semantic web, digital model, open source and virtual reality enhancement, it is found that:\u003c/p\u003e \u003cp\u003e1. Digital development has led to the digital transformation of standards, giving rise to new technological hotspots in the areas of standard content generation, standardization modes and applications of standard.\u003c/p\u003e \u003cp\u003eFrom the research results, digitalization of standard is atypical product of the combination of digital technology and domain technology, and digitalization is the core technological topic of the transformation, with industry 4.0, internet, digital libraries, and onsite simulation as its typical application scenarios. By analyzing the core technology keywords in each topic, in terms of standard content generation, semantic web, ontology, knowledge graph, artificial intelligence, etc. are its core technological hotspots; in terms of standardization mode, opensource software, artificial intelligence (models, algorithms, etc.), cloud computing and big data, etc. are itscore technological hotspots; in terms of standard application, digital modeling technology, 3d printing, system design, VR, AR and human-computer interaction, etc. are its core technological hotspots. Although the quantity of related standards have been increasing year by year, they are mostly focused on the digital technology application in various industrial fields (Figs.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e and \u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e20\u003c/span\u003e), and there is a lack of standards for principles, methods and technologies of digitalization of standard, which need to be focused in the future.\u003c/p\u003e \u003cp\u003e2. New technological hotspots driving new trends in the evolution of the standard itself, the development of the standardization ecosystem and governance.\u003c/p\u003e \u003cp\u003eEmpowered by digital technology, standardization will undergo profound changes in both connotation and extension, and the technological trend is mainly reflected in three aspects. First, in terms of the evolution of standards themselves, on the other hand, knowledge carried by natural language standards is extracted through technologies such as semantic modeling, artificial intelligence and other technologies, and the standard knowledge ontology models are constructed to realize direct use of standard knowledge by machines. On the other hand, in order to realize the direct interaction between standards and machines, machine language may be directly introduced in the early stages of standard development to arrange and express standard content, output machine readable standards, and make the presentation form of standards not only natural language standards. Secondly, in terms of the development of standardized ecology, the use of open source technology will make it possible to implement and share information about standardized activities, and promote relevant parties to collaborate in a more open, shared and intelligent way, and the construction of standardized open source ecology will become a new trend. Thirdly, in terms of digital governance, the use of digital technology makes the acquisition, processing and use of standardized information more intelligent. At the same time, it will also cause potential problems such as data security, personal information protection, technical ethics, and intellectual property rights. How to better face and solve these problems will become an important aspect of standard digitization research.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe related theories and methods are not mature, and there are few references.\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study also reflects the fact that research on digitalization if standard is still in its infancy in China. Even if fuzzy query is used, only a hundred articles can be found, and the number of related standards in the field is small. There are few special studies on digitalization of standard at home and abroad, so it is difficult to obtain effective theoretical and methodological support from the studies of predecessors. Taking the hot field of standard digital technology as an example, few scholars have clearly explained the related concepts at present. In the face of this situation, it can only be summarized through view analysis to distill the relevant features of the hot areas of the technologies and analyze them in conjunction with actual policies and industrial development.\u003c/p\u003e \u003cp\u003e4. It is difficult to obtain data of standard digital technology, and it is necessary to effectively balance the accuracy and completeness.\u003c/p\u003e \u003cp\u003eStandard digital technology involves many aspects of information. How to formulate an effective retrieval strategy in obtaining data is the key issue of this study. The text involves a large amount of data, take the digital model in WOS as an example, in 2015\u0026ndash;2021, the data of each year is more than 10,000, and in 2021, it reaches 17,669 pieces of literature data, but after checking, it is found that a lot of the literature data only mentions digitization or model in the text of a certain aspect, which is less related to the digitalization of standard, and there are a large number of low-quality literature with 0 citation and 0 browsing. Numerous hardware and software constraints exist when downloading and analyzing this data, which forces this study to filter the retrieved data in order to narrow down the retrieval structure in order to ensure an accurate analytical view. Therefore, how to gradually expand the search scope and update the search results is the focus of the next step.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe purpose of this paper is to provide the foundation and inspiration for China\u0026rsquo;s digitalization of standard research by analyzing the hot spots, frontiers and trends in the field of digitalization of standard transformation, so as to help the planning of the digitalization of standard transformation path. In turn, standards are better used to provide human society with practical solutions to meet the challenges of achieving the Sustainable Development Goals in the digital age. The core directions of this study is the innovative research of various network analysis views, that is, using no or little quantitative data, creating a keyword co-occurrence atlas through CiteSpace view analysis tools, showing researchers the development process from the basic research and development of standard digital technology to practical application, and summarizing the relationship between technical nodes, so it is innovative to some extent. However, there are still some defects in the current big data analysis view. Therefore, follow-up research needs to further innovate research methods. In addition to updating data sources, it is also necessary to optimize the retrieval and query methods of literature data, collect and analyze the latest development concepts of standard digital technology, and extract effective information to meet the requirements of diversified knowledge sources in the era of big data.\u003c/p\u003e \u003cp\u003e4. It is difficult to obtain data of standard digital technology, and it is necessary to effectively balance the accuracy and completeness.\u003c/p\u003e \u003cp\u003eStandard digital technology involves many aspects of information. How to formulate an effective retrieval strategy in obtaining data is the key issue of this study. The text involves a large amount of data, take the digital model in WOS as an example, in 2015\u0026ndash;2021, the data of each year is more than 10,000, and in 2021, it reaches 17,669 pieces of literature data, but after checking, it is found that a lot of the literature data only mentions digitization or model in the text of a certain aspect, which is less related to the digitalization of standard, and there are a large number of low-quality literature with 0 citation and 0 browsing. Numerous hardware and software constraints exist when downloading and analyzing this data, which forces this study to filter the retrieved data in order to narrow down the retrieval structure in order to\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcknowledge\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis work was supported in part by The National Key Research and Development Program (2022YFF0608000), and in part by The Basic Research Business Fee Project(Project No. 292024Y-11456,572023Y-10377), and in part by The Supported by the Science and Technology Program of the State Administration for Market Regulation (Project No. 2023MK190).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by The\u0026nbsp;National Key Research and \u0026nbsp;Development Program (2022YFF0608000), and in part by The Basic Research Business Fee Project(Project No. 292024Y-11456,572023Y-10377), and in part by The Supported by \u0026nbsp;the Science and Technology Program of the State Administration for Market Regulation (Project No. 2023MK190).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author X.L., upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, N.N. and X.L.; methodology, X.L.; software, N.N.; formal analysis, X.L. and N.N.; investigation, N.N., Y.W., and B.Z.; resources, N.N. and J.L.; data curation, X.L.; writing\u0026mdash;original draft preparation, N.N.; writing\u0026mdash;review and editing,\u0026nbsp;N.N. and X.L.; visualization, Y.W., B.Z. and S.L.; supervision, N.N.; project administration, N.N. and S.L.; funding acquisition, X.L. and N.N. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThe Central Committee of the Communist Party of China and the State Council. National standardization development outline. Oral care product industry, 32(1):5, 2022.\u003c/li\u003e\n\u003cli\u003eHang Yin, Fei Xie, and Wei Ying. Development trend of standardization from the perspective of domesticand international standardization strategy. Standard Science, (1):78\u0026ndash;83, 2023.\u003c/li\u003e\n\u003cli\u003eHoulin Zhao. Message from the secretary general of itu accelerating digital transformation in a challenging era. Communication World, (10):1, 2021.\u003c/li\u003e\n\u003cli\u003eXinxin Cao. Iec development strategy high level roundtable discussion on future development plans. China Standardization, (7):1, 2016.\u003c/li\u003e\n\u003cli\u003eMinghui Zhu and Bin Xu. 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China Collective Economy, (6S):2, 2008.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digitalization of standard, standardization, digital transformation, atlas analysis","lastPublishedDoi":"10.21203/rs.3.rs-4609699/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4609699/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the continuous advancement of the process of industrial digitalization, standards, as one of the elements of industrial development, will be closely intertwined with digitalization, have a profound impact on technology, industry and society, and jointly promote the development of sustainable ecology. Based on the Technology Breakdown Theory and CiteSpace tools, this study collected the literature data of journal papers and standards related to digitalization of standard published from 2017 to 2021, and made a keyword co-occurrence atlas analysis on five key technical topics related to digitalization of standard, semantic web, digital model, open source and virtual reality enhancement. It identifies new technological hotspots in standard content generation, standardization modes, and applications of standards. New trends in evolution of the standard itself, the development of the standardization ecosystem, and governance in the context of the new technologies are summarized, including structuring, semanticization and machine language representation of the content; open source technologies make standards more open, shareable, and intelligent; digital technologies brings new governance challenges in the field of standardization. This study systematically compiles and prospects the technological hotspots and trends of digitalization of standard, and supports the formation of related technical routes and risk response plans by data analysis.\u003c/p\u003e","manuscriptTitle":"Research on Technological Hotspots and Trends of Digitalization of Standard Based on Keyword Co-occurrence Atlas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 09:13:21","doi":"10.21203/rs.3.rs-4609699/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4bd1dbbe-2a62-454f-be3a-08d5aaf99c4a","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T10:38:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 09:13:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4609699","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4609699","identity":"rs-4609699","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}