big data technology
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2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Qin Yang

The arrival of the big data era not only provides corresponding technical support for the development of Educational Networking but also promotes the acceleration of Educational Networking. Therefore, this paper puts forward the research on the impact of big data on the development of education network and constructs a Bayesian knowledge tracking model to collect and analyze the behavior data of teachers and learners in network education. The experimental results show that big data technology provides greater development space for Education Networking. Its market scale has reached 502.47 billion yuan in 2021, and there is a trend of continuous growth. At the same time, the increase in the number of users also makes its teaching content richer and teaching methods more diversified and personalized. And, through the analysis of relevant data, learners and teachers can more comprehensively and truly understand their own level, achieve the purpose of accurate assistance to learners and teachers, and help learners and teachers find their own problems and make targeted adjustments. In addition, the campus intelligent management system based on big data technology can achieve the purpose of multipurpose and information management.


2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Li Xue ◽  
Chuangjian Yang

In order to improve the effect of copying and recreation of painting works, this paper combines mobile digital multimedia big data technology to improve the image coding algorithm, identify the characteristics of existing works, apply the algorithm to the detailed analysis of painting works, and construct the main functional structure modules of the system. Moreover, this paper combines the existing hardware equipment to construct the painting works’ recreation system and obtains the image processing module. After the system is constructed, the effect of copying and recreating painting works is analyzed through the mobile digital multimedia big data analysis technology. Finally, this paper constructs the system of this paper through simulation methods and uses experiments to calculate the feature recognition effect and copy effect of the painting works of the system. Through experimental analysis, it can be known that the copying and recreation system of painting works based on mobile digital multimedia big data analysis proposed in this paper can help painters effectively improve the effect of recreation.


Author(s):  
Zhenna Chen

This exploration aims to transfer, process and store multimedia information timely, accurately and comprehensively through computer comprehensive technology processing, and organically combine various elements under the background of big data analysis, so as to form a complete intelligent platform design for multimedia information processing and application. In this exploration, the intelligent vehicle monitoring system is taken as an example. Data acquisition, data transmission, real-time data processing, data storage and data application are realized through the real-time data stream processing framework of [Formula: see text] of big data technology. Data interaction is realized through Spring, Spring MVC, VUE front-end framework, and Ajax asynchronous communication local update technology. Data storage is achieved through Red is cache database, and intelligent vehicle operation supervision system is achieved through multimedia information technology processing. Its purpose is to manage the vehicle information, real-time monitor the running state of the vehicle and give an alarm when there are some problems. The basic functions of vehicle operation monitoring and management system based on big data analysis are realized. The research on the design of vehicle operation monitoring and management system based on big data analysis shows that big data technology can be applied to the design of computer multimedia intelligent platform, and provides a reference case for the development of computer multimedia intelligent platform based on big data analysis.


2022 ◽  
Vol 2152 (1) ◽  
pp. 012009
Author(s):  
Bo Tian ◽  
Hao Zhang ◽  
Jie Liu ◽  
Yibin Liu ◽  
Chaohe Yang

Abstract With the continuous improvement of big data technology, my country’s coal liquefaction technology has also continued to mature, maintaining a stable industrial development. Traditional coal pyrolysis technology for tar production with the purpose of increasing tar production, such as coal hydropyrolysis, has problems such as high cost of pure hydrogen atmosphere and complex process and equipment operations, which severely restrict its industrial operation process. Based on this, this paper proposes a new technology of coal pyrolysis and depolymerization coupled with oil increase by using hydrogen precipitated by the condensation polymerization reaction at relatively high temperature under big data technology to study the effect of this process on coal pyrolysis for oil production. Experiments show that at 700°C, the tar yield reaches 21.5wt.%, which is 6% and 7% higher than the pyrolysis tar yield under the same conditions under hydrogen and nitrogen atmospheres. At 600°C, the methane aromatization reaction is relatively weak, and it can be seen that the tar yield is only slightly higher than that under hydrogen and nitrogen atmospheres. As the temperature of the methane anaerobic aromatization reaction increases, the equilibrium conversion rate increases accordingly. Therefore, as the reaction temperature increases, the tar yield also begins to increase.


2022 ◽  
Vol 355 ◽  
pp. 02025
Author(s):  
Yiyi Yin ◽  
Yong Zhang ◽  
Zhengzheng Wei ◽  
Xiang Zhao

In order to solve the limitation of traditional offline forecasting application scenarios, the author uses a variety of big data open source frameworks and tools to combine with railway real-time data, and proposes a real-time prediction model of railway passenger flow. The model architecture is divided into four levels from bottom to top: data source layer, data transmission layer, prediction calculation layer and application layer. The main components of the model are data flow and prediction flow. Through message queue and ETL, the data process part realizes the synchronization of offline data and real-time data; through the big data technology frameworks such as Spark, Redis and Hive and the GBDT (Gradient Boosting Tree) algorithm, the prediction process partially realizes the real-time passenger flow of the train OD section prediction. The experimental results show that the model proposed by the author has certain practicability and accuracy both in performance and prediction accuracy.


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