基于变压器的兔舍环境多参数预测研究

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING International Journal of Data Warehousing and Mining Pub Date : 2024-01-10 DOI:10.4018/ijdwm.336286
Feiqi Liu, Dong Yang, Yuyang Zhang, Chengcai Yang, Jingjing Yang
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引用次数: 0

摘要

养兔业展现出巨大的经济潜力和发展机遇。然而,对兔舍环境条件的无效预测往往会导致传染病的传播,造成兔子生病和死亡。本文针对兔舍的温度、湿度、光照、二氧化碳浓度、NH3 浓度和灰尘状况等环境条件提出了一个多参数预测模型。该模型善于区分白天和夜晚的预测,从而改进了对环境数据趋势的适应性调整。重要的是,鉴于参数之间的高度相互关系,该模型囊括了多参数环境预测,从而提高了预测精度。该模型的性能通过 RMSE、MAE 和 MAPE 指标进行评估,在预测兔舍环境因素方面,其值分别为 0.018、0.031 和 6.31%。通过与 Bert、Seq2seq 和传统变压器模型并列实验,该方法表现出卓越的性能。
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Research on Multi-Parameter Prediction of Rabbit Housing Environment Based on Transformer
The rabbit breeding industry exhibits vast economic potential and growth opportunities. Nevertheless, the ineffective prediction of environmental conditions in rabbit houses often leads to the spread of infectious diseases, causing illness and death among rabbits. This paper presents a multi-parameter predictive model for environmental conditions such as temperature, humidity, illumination, CO2 concentration, NH3 concentration, and dust conditions in rabbit houses. The model adeptly distinguishes between day and night forecasts, thereby improving the adaptive adjustment of environmental data trends. Importantly, the model encapsulates multi-parameter environmental forecasting to heighten precision, given the high degree of interrelation among parameters. The model's performance is assessed through RMSE, MAE, and MAPE metrics, yielding values of 0.018, 0.031, and 6.31% respectively in predicting rabbit house environmental factors. Experimentally juxtaposed with Bert, Seq2seq, and conventional transformer models, the method demonstrates superior performance.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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