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Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic
Since industrialization, manufacturing has been an important pillar of a country's economic development. Under the dual pressure of the new trend of global manufacturing development and the loss of competitive advantage of manufacturing industry, it is especially important to accelerate the enh...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8731281/ https://www.ncbi.nlm.nih.gov/pubmed/35003238 http://dx.doi.org/10.1155/2021/1698089 |
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author | Wang, Tianxiang Ma, Qingqing Li, Jinxi |
author_facet | Wang, Tianxiang Ma, Qingqing Li, Jinxi |
author_sort | Wang, Tianxiang |
collection | PubMed |
description | Since industrialization, manufacturing has been an important pillar of a country's economic development. Under the dual pressure of the new trend of global manufacturing development and the loss of competitive advantage of manufacturing industry, it is especially important to accelerate the enhancement of national high technology innovation capacity and the optimization of high technology policy innovation management mechanism driven by advanced evolutionary Internet of Things (IoT) arithmetic. The main of this paper thus introduces the effective method of optimization of high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic. To study the optimization of high technology policy innovation management mechanism, a conceptual analysis of currently popular information technologies, such as big data technologies, artificial intelligence technologies, and Internet of Things technologies, and an overview of the application of these technologies in microgrids are given. In the paper, all factors are studied using the STP innovation management mechanism-based approach, and finally, all factors are classified into two categories of cause and effect factors by this approach, and the importance of all factors is ranked. Secondly, a wind power prediction algorithm based on data mining technology and an improved algorithm and a PV power prediction algorithm based on a deep neural network were established with the technical support of high-tech information technology such as big data and artificial intelligence. Finally, the majorization of high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic is proposed. |
format | Online Article Text |
id | pubmed-8731281 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-87312812022-01-06 Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic Wang, Tianxiang Ma, Qingqing Li, Jinxi Comput Intell Neurosci Research Article Since industrialization, manufacturing has been an important pillar of a country's economic development. Under the dual pressure of the new trend of global manufacturing development and the loss of competitive advantage of manufacturing industry, it is especially important to accelerate the enhancement of national high technology innovation capacity and the optimization of high technology policy innovation management mechanism driven by advanced evolutionary Internet of Things (IoT) arithmetic. The main of this paper thus introduces the effective method of optimization of high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic. To study the optimization of high technology policy innovation management mechanism, a conceptual analysis of currently popular information technologies, such as big data technologies, artificial intelligence technologies, and Internet of Things technologies, and an overview of the application of these technologies in microgrids are given. In the paper, all factors are studied using the STP innovation management mechanism-based approach, and finally, all factors are classified into two categories of cause and effect factors by this approach, and the importance of all factors is ranked. Secondly, a wind power prediction algorithm based on data mining technology and an improved algorithm and a PV power prediction algorithm based on a deep neural network were established with the technical support of high-tech information technology such as big data and artificial intelligence. Finally, the majorization of high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic is proposed. Hindawi 2021-12-29 /pmc/articles/PMC8731281/ /pubmed/35003238 http://dx.doi.org/10.1155/2021/1698089 Text en Copyright © 2021 Tianxiang Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Tianxiang Ma, Qingqing Li, Jinxi Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic |
title | Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic |
title_full | Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic |
title_fullStr | Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic |
title_full_unstemmed | Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic |
title_short | Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic |
title_sort | optimization of stp innovation management mechanisms driven by advanced evolutionary iot arithmetic |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8731281/ https://www.ncbi.nlm.nih.gov/pubmed/35003238 http://dx.doi.org/10.1155/2021/1698089 |
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