种鹅舍环境智能监控系统的研制和试验

发布时间：2018-05-27 10:48

  本文选题：环境控制 + 智能监控系统　； 参考：《农业工程学报》2017年09期

【摘要】：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(P0.05)。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。
[Abstract]:In view of the low function of the hardware equipment in the off-season breeding production of goose breeding, it is difficult to carry out the timely and fine control of the environmental operation in the shed, and it is difficult to obtain the environmental data in the house and trace the problem to the source, and so on. This paper presents an environment intelligent monitoring system which is specially used in the breeding house of breeding geese. The intelligent control model of temperature and humidity is established by BP neural network instead of manual operation to meet the environmental requirements. Wireless transmission of environmental parameters through the GPRS module, and the use of its GSM function through the mobile terminal remote control of fans, lighting, water pumps and other equipment. Based on Ext hibernate and Spring as the basic frame technology, a lightweight and strong multilevel cache J2EE enterprise Web application program is constructed to realize remote monitoring of environment parameters of geese shed, and the field test and performance comparison with existing commercial artificial controller are carried out. The test results show that the intelligent monitoring system is stable and reliable for a long time and can meet the requirements of environment regulation of light and temperature and humidity for off-season breeding of geese. Compared with the manual rough control, the control precision of Shanghai Fanlong's livestock and poultry controller was increased by 5.49% and 2.83%, respectively. The root-mean-square error of the measured indoor temperature relative to the set value is 0.202 ~ (0.494) ~ 0.372 鈩，

本文编号：1941740