Rural Ecological Environment Monitoring and VR Visualization Analysis of Jilin Province Supported By Big Data
Authors: 
Chao Ju, Han HuangAuthors Info & Claims
Chao Ju, Han HuangAuthors Info & ClaimsAbstract
With the development and progress of society, Chinese people have higher and higher requirements for the quality of life. The rapid development of urbanization and industrialization has led to a large number of environmental pollution problems, which has made people realize the need to seek a more environmentally friendly and sustainable way of life. The development of ecological rural areas is to solve this problem. The rural environment is the basis for the survival and development of rural areas. Therefore, protecting the ecological environment, improving farmers' environmental awareness, promoting urban-rural integration, and improving the quality of rural residents are all inseparable. This article uses data collection methods to analyze the changes and spatial distribution characteristics of the population in different regions in the large database, conducts a statistical descriptive analysis based on the current situation of agricultural production in Jilin Province, establishes a regression equation model, studies the impact of virtual reality visualization on the rural ecological environment in rural environmental monitoring under a large database, and conducts relevant tests on the environmental monitoring system. The test results show that the accuracy is judged by the changing trend of the data before and after the experiment. Its accuracy has increased by about 14%, and the consistency of the original survey information collected by the system at different locations is basically above 80%, which is very acceptable.
References
[1]
Yanzhen Wang, Xiaofen Wang, Lihua Han, Research on Great Wall section protection and user VR experience innovation based on GIS data visualization, Soft Comput 27 (13) (2023) 9041–9050.
[2]
Florian Weidner, Gerd Boettcher, Stephanie Arévalo Arboleda, Chenyao Diao, Luljeta Sinani, Christian Kunert, Christoph Gerhardt, Wolfgang Broll, Alexander Raake, A Systematic Review on the Visualization of Avatars and Agents in AR & VR displayed using Head-Mounted Displays, IEEE Trans. Vis. Comput. Graph. 29 (5) (2023) 2596–2606.
[3]
Julia Smith, Minh Nguyen, Nicole Allison, Braedon Carr, Kody Wood, Alvaro Uribe-Quevedo, Sharman Perera, Akira Tokuhiro, Edward Waller, Seeing the Invisible: A VR Approach to Radiation Attenuation Visualization for Nuclear Engineering Laboratory Practices, IEEE Trans. Games 14 (3) (2022) 502–510.
[4]
Yi Gao, Cheng Chang, Xiaxia Yu, Pengjin Pang, Nian Xiong, Chuan Huang, A VR-based volumetric medical image segmentation and visualization system with natural human interaction, Virtual Real 26 (2) (2022) 415–424.
[5]
François Ancien, Fabrizio Pucci, Wim F. Vranken, Marianne Rooman, MutaFrame - an interpretative visualization framework for deleteriousness prediction of missense variants in the human exome, Bioinform 38 (1) (2021) 265–266.
[6]
Marius N. Varga, Robert Merrison-Hort, Paul Watson, Roman Borisyuk, Dan Livingstone, Tadpole VR: virtual reality visualization of a simulated tadpole spinal cord, Virtual Real 25 (1) (2021) 1–17.
[7]
Yongxin Wang, Evaluation of rural ecological environment development based on PCA-GA-BP model, J. Comput. Methods Sci. Eng. 23 (4) (2023) 1869–1882.
[8]
Wen Wei, Qiwen Zhang, Evaluation of rural financial ecological environment based on machine learning and improved neural network, Neural Comput. Appl. 34 (12) (2022) 9335–9352.
[9]
Jianmin Song, Senmao Xia, Demetris Vrontis, Arun Sukumar, Bing Liao, Qi Li, Kun Tian, Nengzhi Yao, The Source of SMEs' Competitive Performance in COVID-19: Matching Big Data Analytics Capability to Business Models, Inf. Syst. Frontiers 24 (4) (2022) 1167–1187.
[10]
Zigui Jiang, Qihao Yuan, Rongheng Lin, Fangchun Yang: Canonical Correlation Analysis and Visualization for Big Data in Smart Grid, IEEE J. Emerg. Sel. Topics Circuits Syst. 13 (3) (2023) 702–711.
[11]
Nikos Bikakis, Panos K. Chrysanthis, George Papastefanatos, Tobias Schreck, Special Issue on Machine Learning Approaches in Big Data Visualization, IEEE Computer Graphics and Applications 42 (3) (2022) 39–40.
[12]
Long Shi, Application of big data language recognition technology and GPU parallel computing in English teaching visualization system, Int. J. Speech Technol. 25 (3) (2022) 667–677.
[13]
Nishaal J. Parmar, Hazem H. Refai, Thordur Runolfsson: A Survey on the Methods and Results of Data-Driven Koopman Analysis in the Visualization of Dynamical Systems, IEEE Trans. Big Data 8 (3) (2022) 723–738.
[14]
Afzal Badshah, Ateeqa Jalal, Umar Farooq, Ghani-Ur Rehman, Shahab S. Band, Celestine Iwendi, Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds, Big Data 11 (5) (2023) 339–354.
[15]
Jia Ren, Xu Wang, Guangfu Li, Bayesian Method for Water Quality Emergency Monitoring in Environmental Pollution Accident Disposal, Big Data 11 (2) (2023) 117–127.
[16]
Yuxin Dong, Teaching Quality Monitoring and Evaluation in Higher Education through a Big Data Analysis, Int. J. Emerg. Technol. Learn. 18 (8) (2023) 61–78.
[17]
Zhenghong Huang, Chunguang Mao, Shiyu Guan, Hui Tang, Guanghua Chen, Zaohong Liu, Security threshold setting algorithm of distributed optical fiber monitoring and sensing system based on big data in smart city, Soft Comput 27 (8) (2023) 5147–5157.
[18]
Amisha Gangwar, Sudhakar Singh, Richa Mishra, Shiv Prakash, The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems Using IoT, Big Data, and Machine Learning, Wirel. Pers. Commun. 130 (3) (2023) 1699–1729.
[19]
Panagiotis Tampakis, Eva Chondrodima, Andreas Tritsarolis, Aggelos Pikrakis, Yannis Theodoridis, Kostis Pristouris, Harry Nakos, Panagiotis Kalampokis, Theodore Dalamagas:i4sea: a big data platform for sea area monitoring and analysis of fishing vessels activity, Geo spatial Inf. Sci. 25 (2) (2022) 132–154.
[20]
K. Venkatachalam, P. Prabu, Ala Saleh D. Alluhaidan, Stepán Hubálovský, Pavel Trojovský, Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT, Mob. Networks Appl. 27 (3) (2022) 1060–1069.
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Published: 01 January 2024
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