Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud Platform
Recently, the development and utilization of industrial heritage resources by using big data has gradually attracted attention. This paper proposes a real-time visualization optimization management simulation of an industrial heritage cloud platform, which realizes the high reliability and diversifi...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8885191 |
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| _version_ | 1832546795842961408 |
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| author | Mengya Gao |
| author_facet | Mengya Gao |
| author_sort | Mengya Gao |
| collection | DOAJ |
| description | Recently, the development and utilization of industrial heritage resources by using big data has gradually attracted attention. This paper proposes a real-time visualization optimization management simulation of an industrial heritage cloud platform, which realizes the high reliability and diversified storage and utilization of industrial big data by the cloud data distributed storage subsystem. The big data prediction model of the GRU neural network based on a spark distributed framework is constructed to realize the prediction of industrial genetic data. Finally, visualization technology can provide information supporting for industrial production by displaying effective information intuitively. The model’s effectiveness and reliability are verified by simulation. |
| format | Article |
| id | doaj-art-3a4fb43993e5454b898f71ed02f50521 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3a4fb43993e5454b898f71ed02f505212025-02-03T06:47:00ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88851918885191Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud PlatformMengya Gao0School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, ChinaRecently, the development and utilization of industrial heritage resources by using big data has gradually attracted attention. This paper proposes a real-time visualization optimization management simulation of an industrial heritage cloud platform, which realizes the high reliability and diversified storage and utilization of industrial big data by the cloud data distributed storage subsystem. The big data prediction model of the GRU neural network based on a spark distributed framework is constructed to realize the prediction of industrial genetic data. Finally, visualization technology can provide information supporting for industrial production by displaying effective information intuitively. The model’s effectiveness and reliability are verified by simulation.http://dx.doi.org/10.1155/2020/8885191 |
| spellingShingle | Mengya Gao Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud Platform Complexity |
| title | Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud Platform |
| title_full | Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud Platform |
| title_fullStr | Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud Platform |
| title_full_unstemmed | Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud Platform |
| title_short | Real-Time Visualization Optimization Management Simulation of Big Data Stream on Industrial Heritage Cloud Platform |
| title_sort | real time visualization optimization management simulation of big data stream on industrial heritage cloud platform |
| url | http://dx.doi.org/10.1155/2020/8885191 |
| work_keys_str_mv | AT mengyagao realtimevisualizationoptimizationmanagementsimulationofbigdatastreamonindustrialheritagecloudplatform |