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基于多源大数据融合的城市园林景观设计与维护管理方法。

Urban Landscaping Landscape Design and Maintenance Management Method Based on Multisource Big Data Fusion.

机构信息

Zhengzhou University of Aeronautics, Henan, Zhengzhou 450046, China.

出版信息

Comput Intell Neurosci. 2022 Aug 30;2022:1353668. doi: 10.1155/2022/1353668. eCollection 2022.

DOI:10.1155/2022/1353668
PMID:36082348
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9448568/
Abstract

In the process of continuous urbanization construction, the construction scale of urban landscaping projects is getting larger. At the same time, the design and the maintenance of the management is becoming more important. Recently, the rocketing development of the ternary world of many people, machines, and things has triggered the generation of multisource fusion data and the development of artificial intelligence technology, and the world has entered the era of multisource big data intelligence. Multisource data refer to the fusion of multiple types of data with effective characteristic information, which has richer, more comprehensive, more detailed, and more effective information than a single data source, and can provide high-quality data sources for various complex problems. Therefore, more effective data can be provided for the definition of urban fringe areas. From the moment Google's AlphaGo defeated Go world champion Li Shishi, the chess game has been occupied by AI, setting off an upsurge in the study, research, and application of AI technology. Colleges and universities around the world have followed suit and set up AI-related majors. Deep learning is one of the cutting-edge technologies in the field of artificial intelligence. It is a method to solve complex real-life problems by extracting effective information from the data and mining key features on the basis of a large amount of learning and computing data.

摘要

在不断推进城市化建设的过程中,城市园林绿化工程的建设规模越来越大。与此同时,设计和管理的维护变得更加重要。最近,许多人、机器和事物的三元世界的飞速发展引发了多源融合数据的产生和人工智能技术的发展,世界已经进入了多源大数据智能时代。多源数据是指多种类型的数据与有效特征信息的融合,比单一数据源具有更丰富、更全面、更详细、更有效的信息,可为各种复杂问题提供高质量的数据来源。因此,可以为城市边缘区的定义提供更有效的数据。自从谷歌的 AlphaGo 击败围棋世界冠军李世石以来,围棋就被人工智能占据了,掀起了人工智能技术研究、研究和应用的热潮。世界各地的高校也纷纷效仿,设立了人工智能相关专业。深度学习是人工智能领域的前沿技术之一,它是一种通过大量学习和计算数据,从数据中提取有效信息并挖掘关键特征,从而解决复杂现实问题的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/340e27b8db07/CIN2022-1353668.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/9ca046a7a28d/CIN2022-1353668.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/fd826b2bcaa2/CIN2022-1353668.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/77ba47707dcb/CIN2022-1353668.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/ea84442c7207/CIN2022-1353668.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/aa3e383226ce/CIN2022-1353668.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/603a4f5b588f/CIN2022-1353668.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/47cc022de409/CIN2022-1353668.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/7c543b410d73/CIN2022-1353668.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/340e27b8db07/CIN2022-1353668.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/9ca046a7a28d/CIN2022-1353668.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/fd826b2bcaa2/CIN2022-1353668.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/77ba47707dcb/CIN2022-1353668.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/ea84442c7207/CIN2022-1353668.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/aa3e383226ce/CIN2022-1353668.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/603a4f5b588f/CIN2022-1353668.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/47cc022de409/CIN2022-1353668.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/7c543b410d73/CIN2022-1353668.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/63b7/9448568/340e27b8db07/CIN2022-1353668.009.jpg

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引用本文的文献

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Retracted: Urban Landscaping Landscape Design and Maintenance Management Method Based on Multisource Big Data Fusion.撤回:基于多源大数据融合的城市园林绿化景观设计与维护管理方法
Comput Intell Neurosci. 2023 Aug 2;2023:9850256. doi: 10.1155/2023/9850256. eCollection 2023.

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