物联网技术下农业水产养殖水质在线监测方法OA
Method of agricultural aquaculture water quality online monitoring based on Internet of Things technology
由于高温、水体中生物活动增强、放养密度过高等因素的影响,当前水产养殖业存在关键水质因子溶解氧无法有效监测的问题,这不仅显著增加了养殖水质恶化与生物患病的风险,甚至可能导致鱼类厌食乃至大面积死亡.为此,提出一种基于物联网技术下农业水产养殖水质在线监测方法.构建基于物联网的农业水产养殖水质在线监测框架,采用感知层的智能数据采集终端实时获取养殖池塘pH值、水温、溶解氧含量等关键水质数据;利用通信层的5G网络和基站将其传输至应用层,应用层基于改进BiLSTM和状态转移约束方法处理水质监测数据;再利用远程监控和管理模块远程操控水质调节装置,实现对养殖水质环境的精准控制,并通过Profile配置文件和编写数据编解码工具集中管理物联网传感器,完成农业水产养殖水质的远程监测.实验结果表明,所提方法可有效监测农业水产养殖水质状态,且监测结果的F1分数达到0.935,均方根误差为0.087.
Due to factors such as high temperature,increased biological activity in water bodies,and high stocking density,the current aquaculture industry faces the problem of ineffective monitoring of key water quality factors such as dissolved oxygen,which increases the risk of deteriorating aquaculture water quality,biological diseases,and even leads to loss of appetite and large-scale mortality of fish.Therefore,a method of agricultural aquaculture water quality online monitoring based on Internet of Things technology is proposed.An online monitoring framework for agricultural aquaculture water quality based on the Internet of Things is built.The intelligent data collection terminal of the perception layer is used to obtain key water quality data such as pH value,water temperature,and dissolved oxygen in the aquaculture pond in real time,and the 5G network of communication layer and base station are used to transmit these to the application layer.The application layer can be used to process the water quality monitoring data based on improved BiLSTM and state transition constraint methods.The remote monitoring and management module are used to remotely control the water quality adjustment device,achieving precise control of the aquaculture water environment.IoT sensors are centrally managed by means of Profile configuration files and data encoding and decoding tools,so as to realize remote monitoring of agricultural and aquaculture water quality.The experimental results show that the proposed method can effectively monitor the water quality status of agricultural aquaculture,and the F1 score of the monitoring results can reach 0.935,with a root mean square error of 0.087.
慕光宇;乔璐;曾雯茜;韩宇
大连海洋大学 机械与动力工程学院,辽宁 大连 116023大连海洋大学 机械与动力工程学院,辽宁 大连 116023大连海洋大学 机械与动力工程学院,辽宁 大连 116023大连海洋大学 机械与动力工程学院,辽宁 大连 116023
信息技术与安全科学
农业水产养殖水质监测物联网技术水质调节装置改进BiLSTM状态转移约束
agricultural aquaculturewater quality monitoringInternet of Things technologywater quality adjustment deviceimproved BiLSTMstate transition constraint
《现代电子技术》 2026 (2)
29-34,6
辽宁省教育厅科研项目(JYTMS20230495)国家自然科学基金项目(42406198)辽宁省教育厅科研项目(LJ212410158025)辽宁省属本科高校基本科研业务费专项资金(2024JBYBZ006)设施渔业教育部重点实验室(大连海洋大学)资助项目(KLECA202405)
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