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  • 标题:Research on Village Planning and Rural Architectural Design Based on Discrete Dynamic Modeling Technology
  • 本地全文:下载
  • 作者:Junhang Lin
  • 期刊名称:Scientific Programming
  • 印刷版ISSN:1058-9244
  • 出版年度:2022
  • 卷号:2022
  • DOI:10.1155/2022/9031664
  • 语种:English
  • 出版社:Hindawi Publishing Corporation
  • 摘要:With the advent of the era of big data, the combination of science and technology and urban and rural planning has become the focus of many countries. The improvement of village planning and the redesign of rural buildings can promote the rapid development of villages and strengthen rural cohesion. Based on the above situation, this paper proposes a dynamic programming algorithm combined with discrete dynamic modeling technology to improve rural planning. Firstly, the dynamic programming algorithm is used to reconstruct the village layout and optimize the original village model. The dynamic monitoring technology is used to update the dynamic data in real time to provide specific information for the follow-up rural architectural design. Secondly, the dynamic modeling technology is used to build the building model, which can calculate the building location and building angle of the village. In order to further improve village development, we also put forward the concept of green building design. The performance of traditional modeling technology and discrete dynamic modeling technology in the green building design model is compared. The results show that the discrete dynamic modeling technology can improve the overall performance of rural buildings and improve the operation efficiency of the system in the batch design of green buildings. The village layout improved by dynamic planning reduces the specific travel distance of villagers and provides effective help for rural construction and economic development. Compared with traditional modeling technology, dynamic modeling technology has a shorter workflow and less time. Discrete dynamic modeling technology can realize dynamic batch architecture design and has higher applicability than traditional algorithms.
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