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一种基于物联网和多传感器融合的人工智能新闻特征挖掘系统。

An artificial intelligence based news feature mining system based on the Internet of Things and multi-sensor fusion.

作者信息

Xie Zhuozheng, Wang Junren

机构信息

Yiwu Industrial and Commercial College, Yiwu, China.

出版信息

PeerJ Comput Sci. 2023 Jun 21;9:e1428. doi: 10.7717/peerj-cs.1428. eCollection 2023.

DOI:10.7717/peerj-cs.1428
PMID:37409085
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10319266/
Abstract

The application of Internet of Things (IoT) technology in news media communication has significantly enhanced the effectiveness and coverage of news data releases. However, as the scale of news data continues to grow, traditional IoT approaches face challenges such as slow data processing speed and low mining efficiency. To address these issues, a novel news feature mining system combining IoT and Artificial Intelligence (AI) has been developed. The hardware components of the system include a data collector, a data analyzer, a central controller, and sensors. The GJ-HD data collector is utilized to gather news data. Multiple network interfaces are designed at the device terminal to ensure data extraction from the internal disk in case of device failure. The central controller integrates the MP/MC and DCNF interfaces for seamless information interconnection. In the software aspect of the system, the network transmission protocol of the AI algorithm is embedded, and a communication feature model is constructed. This enables fast and accurate mining of news data communication features. Experimental results demonstrate that the system achieves a mining accuracy of over 98%, enabling efficient processing of news data. Overall, the proposed IoT and AI-based news feature mining system overcomes the limitations of traditional approaches, allowing for efficient and accurate processing of news data in a rapidly expanding digital landscape.

摘要

物联网（IoT）技术在新闻媒体传播中的应用显著提高了新闻数据发布的有效性和覆盖面。然而，随着新闻数据规模的不断扩大，传统的物联网方法面临着数据处理速度慢和挖掘效率低等挑战。为了解决这些问题，开发了一种将物联网和人工智能（AI）相结合的新型新闻特征挖掘系统。该系统的硬件组件包括数据采集器、数据分析器、中央控制器和传感器。GJ - HD数据采集器用于收集新闻数据。在设备终端设计了多个网络接口，以确保在设备故障时能从内部磁盘提取数据。中央控制器集成了MP/MC和DCNF接口，实现无缝信息互连。在系统的软件方面，嵌入了人工智能算法的网络传输协议，并构建了通信特征模型。这使得能够快速准确地挖掘新闻数据通信特征。实验结果表明，该系统的挖掘准确率超过98%，能够高效处理新闻数据。总体而言，所提出的基于物联网和人工智能的新闻特征挖掘系统克服了传统方法的局限性，能够在快速扩展的数字环境中高效准确地处理新闻数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/b498d41920d1/peerj-cs-09-1428-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/0ee12183bb16/peerj-cs-09-1428-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/b3f707e2fb2d/peerj-cs-09-1428-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/567de1b1068d/peerj-cs-09-1428-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/fa8d0c6d7c98/peerj-cs-09-1428-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/1a6e6c0b663f/peerj-cs-09-1428-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/68f13f7e04d7/peerj-cs-09-1428-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/30591b02198a/peerj-cs-09-1428-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/020f21cf714e/peerj-cs-09-1428-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/b498d41920d1/peerj-cs-09-1428-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/0ee12183bb16/peerj-cs-09-1428-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/b3f707e2fb2d/peerj-cs-09-1428-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/567de1b1068d/peerj-cs-09-1428-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/fa8d0c6d7c98/peerj-cs-09-1428-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/1a6e6c0b663f/peerj-cs-09-1428-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/68f13f7e04d7/peerj-cs-09-1428-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/30591b02198a/peerj-cs-09-1428-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/020f21cf714e/peerj-cs-09-1428-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cc5/10319266/b498d41920d1/peerj-cs-09-1428-g009.jpg

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