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数字低碳渔仓智能化控制系统框架设计
梁晨1, 张华1, 周志刚2, 陈楠3, 暴愿达1
1.中国农业科学院农业资源与农业区划研究所;2.中国农业科学院饲料研究所;3.黄冈市农业科学院
摘要:
【目的】本文旨在利用物联网技术、大数据分析和人工智能等技术,设计开发数字渔仓系统,实现对水质参数和鱼类生长状况的实时监测,以应对传统水产养殖方式所面临的效率低、质量不稳定、环境污染等问题,提高养殖效率和产品品质,推动水产养殖业向更加高效和可持续方向发展。【方法】在现状分析与前景展望的基础上,本文综合利用物联网技术、大数据分析和人工智能等技术的方法,设计并开发了数字渔仓系统。该系统能够实时监测养殖水环境参数,全面了解鱼类生长状况,并为养殖者提供科学的决策依据,配套自动化设备还能实现水环境自动调控。【结果】经过实验验证,数字渔仓系统可以有效监测水质参数和鱼类生长状况,为养殖者提供了准确可靠的数据支持。养殖者可以根据系统提供的信息,及时调整养殖环境,提高养殖效率和产品品质。【结论】数字渔仓系统的设计和应用为传统水产养殖业带来了新的发展思路。利用先进技术手段,不仅可以有效解决传统养殖方式存在的问题,还能推动品质调控、无人渔场、表型组学与遗传育种等领域的研究进程。
关键词:  物联网  大数据  深度学习  人工智能  数字渔仓
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基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
Framework Design for the Intelligent Control System of Digital Low-Carbon Fish Farm
Liang Chen1, ZHANG HUA1, ZHOU ZHIGANG2, CHEN NAN3, BAO YUANDA1
1.Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences;2.Institute of Feed Research of Chinese Academy of Agricultural Sciences;3.Huanggang Academy of Agricultural Sciences
Abstract:
[Purpose] This paper aims to utilize technologies such as the Internet of Things, big data analysis, and artificial intelligence to design and develop a digital fish farm system. The goal is to achieve real-time monitoring of water quality parameters and fish growth conditions, addressing issues faced by traditional aquaculture, such as low efficiency, unstable quality, and environmental pollution. The objective is to enhance aquaculture efficiency, improve product quality, and propel the aquaculture industry towards a more efficient and sustainable direction. [Methods] Building upon the analysis of the current situation and future prospects, this paper comprehensively employs technologies such as the Internet of Things, big data analysis, and artificial intelligence to design and develop the digital fish farm system. This system allows real-time monitoring of aquaculture water environment parameters, comprehensive understanding of fish growth conditions, and provides scientific decision-making support for fish farmers. The system is equipped with automated devices for water environment regulation.[Results] Through experimental verification, the digital fish farm system effectively monitors water quality parameters and fish growth conditions, providing accurate and reliable data support for fish farmers. Farmers can promptly adjust the aquaculture environment based on the information provided by the system, thereby enhancing aquaculture efficiency and product quality.[Conclusion] The design and application of the digital fish farm system present new development prospects for the traditional aquaculture industry. By leveraging advanced technological means, this system not only effectively addresses issues associated with traditional aquaculture but also promotes research progress in quality control, unmanned fishing farm, phenomics, and genetic breeding..
Key words:  Internet of Things  big data  deep learning  artificial intelligence  digital fish farm