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基于物联网和产业供应链的绿色技术创新对促进企业数字经济的影响。

Impact of green technology innovation based on IoT and industrial supply chain on the promotion of enterprise digital economy.

作者信息

Song Ruilin, Hu Hui

机构信息

Economics and Management Division, Wuhan City College, Wuhan, Hube, China.

Hubei Science and Technology Innovation High Quality Development Research Center, Wuhan, Hubei, China.

出版信息

PeerJ Comput Sci. 2023 May 30;9:e1416. doi: 10.7717/peerj-cs.1416. eCollection 2023.

Abstract

With the gradual deterioration of the natural environment, a green economy has become a competing goal for all countries. As a trend of green innovation development, the digital economy has become a research hotspot for scientists. In this article, we study the supply chain management of enterprises in green innovation and digital economy development and complete the identification and demand prediction of warehouse goods through the Internet of Things (IoT) and artificial intelligence (AI). As the stuff meets the goods detection and storage, we employ an intelligent method to detect and classify the goods. The demand prediction analysis is carried out based on historical data on goods demand in the enterprise. The absolute error between the prediction result and the actual demand within 1 week is less than 30 goods by the particle swarm optimization-support vector machine (PSO-SVM) method used in this article. First, the goods identification task is completed based on video surveillance data using YOLOv4, and the recognition rate is as high as 98.3%. This article realises enterprises' intelligent supply chain management through the intelligent identification of goods and the demand forecasting analysis of goods in the warehouse, which provides new ideas for green innovation and digital economy development.

摘要

随着自然环境的逐渐恶化,绿色经济已成为各国竞相追求的目标。作为绿色创新发展的一种趋势,数字经济已成为科学家们的研究热点。在本文中,我们研究了绿色创新和数字经济发展中企业的供应链管理,并通过物联网(IoT)和人工智能(AI)完成了仓库货物的识别与需求预测。在货物检测与存储方面,我们采用智能方法对货物进行检测和分类。需求预测分析基于企业货物需求的历史数据进行。本文采用粒子群优化支持向量机(PSO-SVM)方法,预测结果与1周内实际需求的绝对误差小于30件货物。首先,基于视频监控数据使用YOLOv4完成货物识别任务,识别率高达98.3%。本文通过货物的智能识别和仓库货物需求预测分析,实现了企业的智能供应链管理,为绿色创新和数字经济发展提供了新思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5281/10280464/880e5d8bf46a/peerj-cs-09-1416-g001.jpg

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