A Mode of Intelligent Equipment Monitoring Optimization Based on Dynamic Programming Algorithm
With the continuous development of the national economy and scientific productivity, urban construction and people’s living standards are also getting higher and higher. Although people enjoy increasingly convenient life, the demand for intelligence is getting higher and higher. Digital intelligent...
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| Vydané v: | Computational intelligence and neuroscience Ročník 2022; s. 1 - 11 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Hindawi
18.03.2022
John Wiley & Sons, Inc |
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| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
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| Abstract | With the continuous development of the national economy and scientific productivity, urban construction and people’s living standards are also getting higher and higher. Although people enjoy increasingly convenient life, the demand for intelligence is getting higher and higher. Digital intelligent equipment has the functions of data collection, calculation and analysis, diagnostic and early warning, and communication functions. Analyze the status quo and existing problems of the development of intelligent equipment, as well as analyze and research key monitoring technologies in the use and development of digital intelligent equipment and provide optimal solutions for intelligent equipment hardware development requirements, software development, and model algorithms. Intelligent equipment monitoring is related to all aspects of people’s livelihood, and its intelligent development is related to the public role in this field in the future. Accurate results of monitoring can provide data support for schools, research institutions, the public, and the government. At the same time, it is also an important basis for formulating social policies. At present, the commonly used monitoring method usually adopts time series algorithm. Through literature review, it is found that the algorithm has the problem of distortion of correct data, which affects the accuracy of monitoring results. Based on the above reasons, this article combines the wavelet function with the planning algorithm and proposes a dynamic programming algorithm, which removes the redundant monitoring data in turn and clusters the distortion monitoring data with the wavelet function, which improves the accuracy and computational efficiency of the algorithm and gives full play to the monitoring of intelligence. The simulation results using MATLAB show that the planning algorithm can eliminate 90% of redundant monitoring data and improve the extraction rate of characteristic monitoring data. At the same time, the accuracy of the planning algorithm reaches 95%, and the calculation time is less than 25 s, which is better than the static planning algorithm. Therefore, the dynamic programming algorithm can better utilize the intelligence, convenience, and efficiency of the equipment to optimize the monitoring model. |
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| AbstractList | With the continuous development of the national economy and scientific productivity, urban construction and people's living standards are also getting higher and higher. Although people enjoy increasingly convenient life, the demand for intelligence is getting higher and higher. Digital intelligent equipment has the functions of data collection, calculation and analysis, diagnostic and early warning, and communication functions. Analyze the status quo and existing problems of the development of intelligent equipment, as well as analyze and research key monitoring technologies in the use and development of digital intelligent equipment and provide optimal solutions for intelligent equipment hardware development requirements, software development, and model algorithms. Intelligent equipment monitoring is related to all aspects of people's livelihood, and its intelligent development is related to the public role in this field in the future. Accurate results of monitoring can provide data support for schools, research institutions, the public, and the government. At the same time, it is also an important basis for formulating social policies. At present, the commonly used monitoring method usually adopts time series algorithm. Through literature review, it is found that the algorithm has the problem of distortion of correct data, which affects the accuracy of monitoring results. Based on the above reasons, this article combines the wavelet function with the planning algorithm and proposes a dynamic programming algorithm, which removes the redundant monitoring data in turn and clusters the distortion monitoring data with the wavelet function, which improves the accuracy and computational efficiency of the algorithm and gives full play to the monitoring of intelligence. The simulation results using MATLAB show that the planning algorithm can eliminate 90% of redundant monitoring data and improve the extraction rate of characteristic monitoring data. At the same time, the accuracy of the planning algorithm reaches 95%, and the calculation time is less than 25 s, which is better than the static planning algorithm. Therefore, the dynamic programming algorithm can better utilize the intelligence, convenience, and efficiency of the equipment to optimize the monitoring model. With the continuous development of the national economy and scientific productivity, urban construction and people's living standards are also getting higher and higher. Although people enjoy increasingly convenient life, the demand for intelligence is getting higher and higher. Digital intelligent equipment has the functions of data collection, calculation and analysis, diagnostic and early warning, and communication functions. Analyze the status quo and existing problems of the development of intelligent equipment, as well as analyze and research key monitoring technologies in the use and development of digital intelligent equipment and provide optimal solutions for intelligent equipment hardware development requirements, software development, and model algorithms. Intelligent equipment monitoring is related to all aspects of people's livelihood, and its intelligent development is related to the public role in this field in the future. Accurate results of monitoring can provide data support for schools, research institutions, the public, and the government. At the same time, it is also an important basis for formulating social policies. At present, the commonly used monitoring method usually adopts time series algorithm. Through literature review, it is found that the algorithm has the problem of distortion of correct data, which affects the accuracy of monitoring results. Based on the above reasons, this article combines the wavelet function with the planning algorithm and proposes a dynamic programming algorithm, which removes the redundant monitoring data in turn and clusters the distortion monitoring data with the wavelet function, which improves the accuracy and computational efficiency of the algorithm and gives full play to the monitoring of intelligence. The simulation results using MATLAB show that the planning algorithm can eliminate 90% of redundant monitoring data and improve the extraction rate of characteristic monitoring data. At the same time, the accuracy of the planning algorithm reaches 95%, and the calculation time is less than 25 s, which is better than the static planning algorithm. Therefore, the dynamic programming algorithm can better utilize the intelligence, convenience, and efficiency of the equipment to optimize the monitoring model.With the continuous development of the national economy and scientific productivity, urban construction and people's living standards are also getting higher and higher. Although people enjoy increasingly convenient life, the demand for intelligence is getting higher and higher. Digital intelligent equipment has the functions of data collection, calculation and analysis, diagnostic and early warning, and communication functions. Analyze the status quo and existing problems of the development of intelligent equipment, as well as analyze and research key monitoring technologies in the use and development of digital intelligent equipment and provide optimal solutions for intelligent equipment hardware development requirements, software development, and model algorithms. Intelligent equipment monitoring is related to all aspects of people's livelihood, and its intelligent development is related to the public role in this field in the future. Accurate results of monitoring can provide data support for schools, research institutions, the public, and the government. At the same time, it is also an important basis for formulating social policies. At present, the commonly used monitoring method usually adopts time series algorithm. Through literature review, it is found that the algorithm has the problem of distortion of correct data, which affects the accuracy of monitoring results. Based on the above reasons, this article combines the wavelet function with the planning algorithm and proposes a dynamic programming algorithm, which removes the redundant monitoring data in turn and clusters the distortion monitoring data with the wavelet function, which improves the accuracy and computational efficiency of the algorithm and gives full play to the monitoring of intelligence. The simulation results using MATLAB show that the planning algorithm can eliminate 90% of redundant monitoring data and improve the extraction rate of characteristic monitoring data. At the same time, the accuracy of the planning algorithm reaches 95%, and the calculation time is less than 25 s, which is better than the static planning algorithm. Therefore, the dynamic programming algorithm can better utilize the intelligence, convenience, and efficiency of the equipment to optimize the monitoring model. |
| Audience | Academic |
| Author | Jiang, Chuan Zheng, Zhilei |
| AuthorAffiliation | Department of Physical Education, Shandong University, Weihai 264200, China |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35341191$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/jiot.2020.3037759 10.1016/j.nanoen.2021.106775 10.1016/j.neucom.2021.10.081 10.3389/fspor.2021.788165 10.3934/naco.2021049 10.1109/jsen.2021.3127989 10.1016/j.cor.2021.105585 10.1016/j.sna.2021.113271 10.1016/j.cej.2021.132653 10.1016/j.uclim.2021.101020 10.1109/tac.2021.3050440 10.1016/j.ejor.2021.05.018 10.1016/j.comnet.2021.108508 10.1016/j.cej.2021.132635 10.1016/j.nanoen.2021.106612 10.1109/tfuzz.2020.3032794 10.1088/1361-6463/ac3c73 10.1016/j.ymssp.2021.108343 |
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| Copyright | Copyright © 2022 Zhilei Zheng and Chuan Jiang. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Zhilei Zheng and Chuan Jiang. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Zhilei Zheng and Chuan Jiang. 2022 |
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| References | 11 12 13 14 15 16 R. Granberry (5) 2022; 6 17 18 19 1 2 3 4 6 7 8 9 H. Zhen (20) 2022; 6 10 37538688 - Comput Intell Neurosci. 2023 Jul 26;2023:9758926 |
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| Title | A Mode of Intelligent Equipment Monitoring Optimization Based on Dynamic Programming Algorithm |
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