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基于深度神经网络的共享经济下社区生活服务构建。

Construction of Community Life Service in the Sharing Economy Based on Deep Neural Network.

机构信息

South China University of Technology, Guangzhou 510640, Guangdong, China.

出版信息

Comput Intell Neurosci. 2021 Sep 11;2021:7703152. doi: 10.1155/2021/7703152. eCollection 2021.

DOI:10.1155/2021/7703152
PMID:34545283
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8449718/
Abstract

Currently, the development of sharing economy and interconnection also has a profound impact on community life services. This study is based on the deep neural network theory, combined with the evolution mechanism of the commercial network of the community life service industry, link prediction theory, and the latest deep neural network algorithm, referring to the evolution model of merger and stripping, and the network structure is optimized on this basis. Through simulation experiments and result analysis, the model is used to deeply study the evolution trend and dynamics of the community life service business network from the perspective of quantitative analysis. Then the business network structure is optimized and development is promoted at the same time. At the same time, it can also upgrade those old scattered industries and provide theoretical and decision-making guidance for the future transformation and upgrading of the innovative community life service industry.

摘要

目前，共享经济和互联互通的发展也对社区生活服务业产生了深远的影响。本研究基于深度神经网络理论，结合社区生活服务业商业网络的演化机制、链接预测理论和最新的深度神经网络算法，借鉴合并和剥离的演化模型，在此基础上对网络结构进行优化。通过仿真实验和结果分析，利用该模型从定量分析的角度深入研究社区生活服务业商业网络的演化趋势和动态，进而优化商业网络结构，促进其发展。同时，还可以对老旧分散的产业进行升级，为未来创新型社区生活服务业的转型和升级提供理论和决策指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ace/8449718/97b0e794ea95/CIN2021-7703152.008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ace/8449718/97b0e794ea95/CIN2021-7703152.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ace/8449718/8c38c2550893/CIN2021-7703152.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ace/8449718/3b2f15149e15/CIN2021-7703152.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ace/8449718/46e752def7ec/CIN2021-7703152.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ace/8449718/756c1a1a6242/CIN2021-7703152.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ace/8449718/305884850064/CIN2021-7703152.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ace/8449718/4b8578402fad/CIN2021-7703152.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ace/8449718/c4ac6051bc3d/CIN2021-7703152.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ace/8449718/97b0e794ea95/CIN2021-7703152.008.jpg

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Comput Intell Neurosci. 2023 Jun 28;2023:9792478. doi: 10.1155/2023/9792478. eCollection 2023.

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