种鹅舍环境智能监控系统的研制和试验

针对种鹅反季节繁殖生产中硬件设备功能低下、难以实施舍内环境操作的适时精细调控、难以获取记录舍内环境数据进行问题溯源等问题,提出一种专门应用于种鹅反季节繁殖生产舍的环境智能监控系统。该系统通过BP神经网络建立温湿度智能调控模型,取代人工手动操作以满足舍内环境要求。通过GPRS模块无线传输舍内环境参数,并利用其GSM功能通过移动终端远程控制风机、照明、水泵等设备。以EXT、Hibernate和Spring为基本框架技术,构建了轻量级、强壮的多级缓存的J2EE企业级Web应用程序,实现鹅舍环境参数的远程监控,并与现有商用人工控制器进行了现场试验和性能对比。试验结果表明：该智能监控系统长期运行稳定、可...

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Bibliographic Details
Published in农业工程学报 Vol. 33; no. 9; pp. 180 - 186
Main Author 郭彬彬 孙爱东 丁为民 施振旦 赵三琴 杨红兵
Format Journal Article
LanguageChinese
Published 南京农业大学工学院,南京,210031%江苏省农业科学院,南京,210014 2017
Subjects
BP神经网络
GPRS
Web应用
智能监控系统
温度
环境控制
鹅反季节繁殖
鹅反季节繁殖
environmental control
智能监控系统
geese out-of-season breeding
GPRS
环境控制
Web应用
温度
BP neural network
BP神经网络
Web application
temperature
intelligent monitoring system
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ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2017.09.023

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Summary:针对种鹅反季节繁殖生产中硬件设备功能低下、难以实施舍内环境操作的适时精细调控、难以获取记录舍内环境数据进行问题溯源等问题,提出一种专门应用于种鹅反季节繁殖生产舍的环境智能监控系统。该系统通过BP神经网络建立温湿度智能调控模型,取代人工手动操作以满足舍内环境要求。通过GPRS模块无线传输舍内环境参数,并利用其GSM功能通过移动终端远程控制风机、照明、水泵等设备。以EXT、Hibernate和Spring为基本框架技术,构建了轻量级、强壮的多级缓存的J2EE企业级Web应用程序,实现鹅舍环境参数的远程监控,并与现有商用人工控制器进行了现场试验和性能对比。试验结果表明：该智能监控系统长期运行稳定、可靠,能够满足鹅反季节繁殖对光照和温湿度的环境调控要求。与人工粗略控制、上海梵龙的畜禽控制器相比,控制精度分别提高5.49%和2.83%。在夏季风机湿帘负压通风降温时测定的舍内温度相对于设定值的均方根误差分别为0.202、0.494、0.372℃,相对湿度相对于设定值的均方根误差分别为1.745%、3.166%、2.621%,控制效果显著优于人工粗略控制和现有控制器（P〈0.05）。在精准的光照调控下,种鹅均能按预期的时间开产,并在高峰期长期维持产蛋率35%~45%,表现出稳定、良好的产蛋性能。
Bibliography:11-2047/S
environmental control; intelligent monitoring system; temperature; geese out-of-season breeding; BP neuralnetwork; GPRS; Web application
Out-of-season breeding technology for goose was proposed due to their obviously seasonality of production. Precisely time control is the most critical factor in this technology. Simultaneously, temperature and humidity control are also needed in case of heat stress. The application of the new technology brings challenges to the traditional breeding methods. Environment control in goose house was mostly artificial or semi mechanized. Manual operation was of poor real-time performance, and could not achieve the organic connection between the various environmental factors. Light, temperature, relative humidity and other environmental parameters could not be recorded real-time. When diseases or some abnormal reproduction happen, managers could not trace causes from the aspect of environment. This study therefore was conducted to develop an intelligent geese house environ
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2017.09.023