针对外塘养殖的复杂环境下采集到的水质参数无法解释的缺失、参数间时滞强耦合关系导致水质评估及水质预测精度不高进而引起养殖水产大量死亡的问题,设计了一款基于ESP32、OneNet物联网和MATLAB应用程序的水质监测设备,该设备能够实时采集塘中氨氮、溶解氧、pH值、水温和水深参数并上传到云平台。在此基础上,基于VMD-LSTM-XGBoost的参数分解重构并采用麻雀搜索算法(SSA)进行优化,实现参数间的时序信息挖掘及缺失数据补足;设计基于AHP-CV-正态云组合的水质评估方法,结合主观权重及客观权重方法实现权重的全局优化,最终通过构建集成学习模型的水质预测方法进一步提升预测精度。基于所采集的数据,本研究将4组参数的分解优化为34组时序数据并完成缺失参数补足,经实验验证,所提出的AHP-CV-正态云水质评估方法分类准确率大于98%,具有较好可行性;所设计的VMD-LSTM-XGBoost组合模型在验证集上测试效果达到了96.209%,具有较好的预测精度,为外塘养殖复杂环境下水质参数监测、数据补足、水质评估及水质预测提供了有效的参考途径,能够为投喂策略提供理论支撑。

Outdoor pond aquaculture feeding yields significantly vary with water quality evaluation, estimation, and control. However, several issues challenge the accuracy of water evaluation, such as inexplicable missing data in acquired parameters and strong coupling and time-lag correlations among multiple parameters. Inaccurate water evaluation results further introduce errors into the estimation and control processes, potentially leading to sudden losses in aquaculture. Therefore, a real-time water parameter monitoring device is firstly designed in this research, featuring an ESP32 microcontroller and an embedded MATLAB application. This device allows real-time data on ammonia nitrogen, dissolved oxygen, pH value, water temperature, and water depth to be transmitted to the cloud platform, specifically the OneNet IoT platform. Based on the device design, an innovative method with VMD-LSTM-XGBoost structure for parameter decomposition and reconstruction to extraction of temporal information among parameters and the supplementation of missing data. Meanwhile, the Sparrow Search Algorithm (SSA) is employed for decomposition numbers optimization. Furthermore, the combination of AHP-CV-normal cloud model is designed to improve the accuracy of water quality evaluation. Finally, an integrated learning model is constructed to improve the accuracy of water quality prediction. This research optimized the decomposition of 4 parameter groups into 34 sets of time-series data based on collected data and completed missing parameter supplementation. The experimental validation shows that the proposed AHP-CV-normal cloud model for water quality assessment achieves a classification accuracy rate of over 98%, demonstrating good feasibility. The designed VMD-LSTM-XGBoost hybrid model achieves a test accuracy of 96.209% on the validation set, demonstrating strong predictive performance. This research provides an effective solution for monitoring water quality parameters, data imputation, water quality assessment, and prediction in the complex environment of outdoor pond aquaculture, offering theoretical support for feeding strategies.