| 摘要: |
| 【目的】 文章旨在利用物联网技术、大数据分析和人工智能等技术，设计开发数字低碳渔仓系统，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，该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，设计并开发了数字低碳渔仓系统。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，数字低碳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】 数字低碳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 |
| DOI：10.12105/j.issn.1672-0423.20240406 |
| 分类号: |
| 基金项目:国家自然科学基金重点项目“肠道菌群调控养殖鱼类生长性状的机制研究”（32330110），国自然区域创新发展联合基金“草鱼细菌性肠道炎症发生的调控网络及营养干预机制研究”（U21A20267） |
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| Framework design for the intelligent control system of digital low-carbon fish farm |
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Liang Chen1,2, Zhou Zhigang2, Chen Nan2,3, Bao Yuanda1
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1.State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Nothern China/Institute of Agricultural Resources and Regional Planning，Chinese Academy of Agricultural Sciences，Beijing 100081，China;2.Institute of Feed Research of Chinese Academy of Agricultural Sciences，Beijing 100081，China;3.Huang Gang Academy of Agricultural Sciences，Huanggang 438000，Hubei，China
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| 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 low-carbon 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.Method Building upon the analysis of the current situation and future prospects，this paper comprehensively employed technologies such as the Internet of Things，big data analysis，and artificial intelligence to design and develop the digital low-carbon fish farm system. This system allowed real-time monitoring of aquaculture water environment parameters，comprehensive understanding of fish growth conditions，and provided scientific decision-making support for fish farmers. The system was equipped with automated devices for water environment regulation.Result Through experimental verification，the digital low-carbon fish farm system effectively monitored water quality parameters and fish growth conditions，providing accurate and reliable data support for fish farmers. Farmers could 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 low-carbon 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 low-carbon fish farm |
