{"title":"制造业多价值链协同下的数据采集与处理研究——以电动汽车制造业为例","authors":"Shiping Geng, Yuntian Liu, D. Niu, Xiao-dan Guo","doi":"10.1109/CCIS53392.2021.9754677","DOIUrl":null,"url":null,"abstract":"With the rapid development of economic globalization and the continuous changes of digital technology and the Internet, the electric vehicle manufacturing industry has become an effective way to improve its own competitive advantage by strengthening the collaborative management of many industrial chains and value chains, while the construction of data space is to increase the company’s An effective tool for chain collaboration. Based on this, this paper first summarizes the characteristics of the data sources of the electric vehicle manufacturing industry from three aspects: “large quantity”, “multiple categories”, and “rapid change”. Secondly, based on the characteristics of the data sources of electric vehicle manufacturers, the theory of data acquisition and processing is explained, and big data methods are proposed to solve the problem of data acquisition in the electric vehicle manufacturing industry. Then, in the context of the rapid growth of the data volume of electric vehicle manufacturers and the increasingly complex data types, the main problems of using big data to obtain data from electric vehicle manufacturers are analyzed. Finally, the countermeasures for data acquisition methods of electric vehicle manufacturers in the future are proposed.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Data Acquisition and Processing Under the Coordination of Multiple Value Chains in the Manufacturing Industry—Taking the Electric Vehicle Manufacturing Industry as an Example\",\"authors\":\"Shiping Geng, Yuntian Liu, D. Niu, Xiao-dan Guo\",\"doi\":\"10.1109/CCIS53392.2021.9754677\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the rapid development of economic globalization and the continuous changes of digital technology and the Internet, the electric vehicle manufacturing industry has become an effective way to improve its own competitive advantage by strengthening the collaborative management of many industrial chains and value chains, while the construction of data space is to increase the company’s An effective tool for chain collaboration. Based on this, this paper first summarizes the characteristics of the data sources of the electric vehicle manufacturing industry from three aspects: “large quantity”, “multiple categories”, and “rapid change”. Secondly, based on the characteristics of the data sources of electric vehicle manufacturers, the theory of data acquisition and processing is explained, and big data methods are proposed to solve the problem of data acquisition in the electric vehicle manufacturing industry. Then, in the context of the rapid growth of the data volume of electric vehicle manufacturers and the increasingly complex data types, the main problems of using big data to obtain data from electric vehicle manufacturers are analyzed. Finally, the countermeasures for data acquisition methods of electric vehicle manufacturers in the future are proposed.\",\"PeriodicalId\":191226,\"journal\":{\"name\":\"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCIS53392.2021.9754677\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCIS53392.2021.9754677","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Data Acquisition and Processing Under the Coordination of Multiple Value Chains in the Manufacturing Industry—Taking the Electric Vehicle Manufacturing Industry as an Example
With the rapid development of economic globalization and the continuous changes of digital technology and the Internet, the electric vehicle manufacturing industry has become an effective way to improve its own competitive advantage by strengthening the collaborative management of many industrial chains and value chains, while the construction of data space is to increase the company’s An effective tool for chain collaboration. Based on this, this paper first summarizes the characteristics of the data sources of the electric vehicle manufacturing industry from three aspects: “large quantity”, “multiple categories”, and “rapid change”. Secondly, based on the characteristics of the data sources of electric vehicle manufacturers, the theory of data acquisition and processing is explained, and big data methods are proposed to solve the problem of data acquisition in the electric vehicle manufacturing industry. Then, in the context of the rapid growth of the data volume of electric vehicle manufacturers and the increasingly complex data types, the main problems of using big data to obtain data from electric vehicle manufacturers are analyzed. Finally, the countermeasures for data acquisition methods of electric vehicle manufacturers in the future are proposed.