规模化肉牛场数字化管控平台的开发与应用——以阳信亿利源5G数字化牧场为例

doi:10.19788/j.issn.2096-6369.000009

摘要/Abstract

摘要：

随着我国肉牛规模化设施养殖模式的逐步普及，对牛只的精细化甚至智慧化管控已提上议事日程；同时，物联网、大数据、人工智能（AI）甚至大模型的快速发展正不断渗透到各行各业，使得对包括传统养殖在内的智慧管控也成为可能。本研究以阳信亿利源5G数字化牧场为研究对象，集成应用智能电子耳标及智能项圈，以及包括温湿度、氨气、二氧化碳、风向风速、光照及空气质量（H₂S，PM2.5，PM10，TSP）等多种环境传感器。同时进行全面动态感知牛只个体的生理指标如运动量、反刍量等，并通过对上述2个重要的生理指标的分析，判断繁殖母牛的发情情况并预测最佳的配种时间，或通过监测牛只反刍量的变化，判断饲料的调整是否合适；全面感知牛舍包括温湿度及空气质量指标，为牛舍的精准通风及环境质量的精准控制提出数据支撑；并采用MY SQL数据库技术及DELPHI语言进行相关数据的分析。本研究构建了集养殖环节关键数据自动采集、数据的自动转换计算、数据的有线及无线传输、数据的远程贮存及处理控制为一体的物联网管控平台，实现了对肉牛养殖环境、健康状态、防疫及饲料调度等数字化管控平台。随着系统不断运行与迭代、养殖过程数据维度及数据量的不断积累与拓展，基础数据及派生数据的挖掘及升值空间越大，将为建立肉牛养殖的大模型奠定基础。

关键词: 肉牛, 数字化牧场, 电子耳标, 传感器, 管控平台

Abstract:

As large-scale beef cattle facilities become more and more common in China, sophisticated and even intelligent management of cattle has gained prominence. In the meantime, the Internet of Things, big data, artificial intelligence (AI) and even large models are developing at a rapid pace and are constantly permeating in all industries, making the intelligent management, including traditional breeding, possible. In this study, Yangxin Yi Liyuan 5G digital farm was used as the research object, which integrated application of intelligent electronic ear tags and intelligent collars, as well as a variety of environmental sensors for temperature and humidity, ammonia, carbon dioxide, wind direction and speed, light and air quality (H₂S, PM2.5, PM10, TSP). Simultaneously, a comprehensive dynamic perception of individual physiological indicators, such as the degree of exercise and rumination, was utilized to determine the estrus period of breeding cattle and forecast the ideal breeding period, and was employed to ascertain the estrus period of breeding cattle, forecast the ideal mating period, and determine whether the feed adjustment was necessary by monitoring variations in rumination time. Comprehensively monitoring the environmental indicators, such as temperature, humidity and air quality, is necessary to accurately manage and ventilate the cattle house. The pertinent data was analyzed using the MY SQL database technology and DELPHI language technology. In order to create a digital control platform for the breeding environment, health status, epidemic prevention, and feed management of beef cattle, this research built a digital control platform that integrated automatic collection of key data in breeding links, automatic conversion and calculation of data, wired and wireless transmission of data, remote storage and processing control of data, and so on. This study also shown that the more the system was used and improved, the more data was collected and extended in both size and type during the breeding process, and the more fundamental and derived data could be mined and appreciated. This paved way for the eventual development of a large-scale model for breeding beef cattle in the future.

Key words: beef cattle, digital pasture, electronic ear tag, sensor, management and control platform

张帆, 周梦婷, 唐湘方, 刘民泽, 杨振刚, 熊本海. 规模化肉牛场数字化管控平台的开发与应用——以阳信亿利源5G数字化牧场为例[J]. 农业大数据学报, 2024, 6(1): 68-81.

ZHANG Fan, ZHOU MengTing, LIU MinZe, TANG XiangFang, XIONG BenHai. Development and Application of Digital Control Platform in Large- scale Beef Cattle Farm——Take the 5G digital ranch of Yangxin Yi Liyuan Halal Meat Co., Ltd as an example[J]. Journal of Agricultural Big Data, 2024, 6(1): 68-81.

图/表 13

图1

图2

图3

表1

图4

图5

图6

图7

图8

图9

图10

图11

图12

参考文献 36

[1]	农业农村部市场预警专家委员会. 中国农业展望报告(2022-2030)[M]: 中国农业科学技术出版社, 2022.
[2]	农小蜂智库. 2023年中国肉牛产业分析简报[EB/OL]. https://baijiahao.baidu.com/s?id=1776372548098153560&wfr=spider&for=pc.
[3]	农业农村部畜牧兽医局, 全国畜牧总站. 2020年肉牛产业发展形势与2021年展望[J]. 2021(2):40-42.
[4]	韩硕. 规模肉牛养殖现状及发展对策探究[J]. 2023(13):32-33.
[5]	中国报告大厅. 肉牛养殖前景[EB/OL]. https://www.chinabgao.com/freereport/78706.
[6]	杨亮, 熊本海, 王辉, 等. 家畜饲喂机器人研究进展与发展展望[J]. 智慧农业(中英文), 2022, 4(2):86-98.
[7]	ALPROTM Herd Management System[EB/OL]. [2013]. http://www.delaval.com/en/-/Product-Information1/Management/Systems/ALPRO/.
[8]	Agritec[EB/OL]. [2023]. https://www.agritecsoft.com/zh-chs/.
[9]	何勇. 智慧农业[M]. 北京: 科学出版社, 2023.
[10]	熊本海, 王朝元, 沈维政. 人工智能与智能养猪[M]. 北京: 中国农业大学出版社, 2023.
[11]	肖建华, 胡玉龙, 范福祥, 等. 奶牛繁殖决策支持系统的构建[J]. 中国农业科学, 2012, 45(10):2012-2021. doi: 10.3864/j.issn.0578-1752.2012.10.014
[12]	杨亮, 杨振刚, 熊本海. 对我国肉牛及其产品溯源的编码规则设计的初步建议[C]// 中国畜牧兽医学会信息技术分会2014年学术研讨会. 中国畜牧兽医学会信息技术分会2014年学术研讨会论文集. 中国江苏无锡, 2014: 8.
[13]	杨亮, 杨振刚, 吕健强, 等. 规模化肉牛场数字化网络平台开发与应用[J]. 2014, 16(6):74-80.
[14]	王秀娟. 数字赋能为现代农业装上“智慧大脑”(下)[N]. 山西日报, 2023-03-21(007).
[15]	黄成龙, 柯宇曦, 华向东, 等. 边缘计算在智慧农业中的应用现状与展望[J]. 农业工程学报, 2022, 38(16):224-234.
[16]	游战清. 无线射频识别技术 (RFID) 理论与应用[M]. 北京: 电子工业出版社, 2004.
[17]	熊本海, 钱平, 罗清尧, 等. 基于奶牛个体体况的精细饲养方案的设计与实现[J]. 农业工程学报, 2005, 21(10):118-123..
[18]	杜欣怡, 滕光辉, 杜晓冬, 等. 基于雷达图的蛋鸡舍综合环境舒适度评价及应用[J]. 农业工程学报, 2020, 36(15):202-209.
[19]	石家庄龙腾伟业科技有限公司[EB/OL]. (2023). http://www.ltwy-iot.com/case.html?ext_fild=%E7%8E%AF%E5%A2%83%E7%9B%91%E6%B5%8B.2023.
[20]	无锡富华科技有限责任公司[EB/OL]. (2023). http://www.fofia.com/.
[21]	Reith S, Hoy S. Relationship between daily rumination time and estrus of dairy cows[J]. Journal of Dairy Science, 2012, 95(11): 6416-6420. doi: 10.3168/jds.2012-5316 pmid: 22939783
[22]	Dolecheck K A, Silvia W J, Heersche G, et al. Behavioral and physiological changes around estrus events identified using multiple automated monitoring technologies[J]. Journal of Dairy Science, 2015, 98(12):8723-8731. doi: 10.3168/jds.2015-9645 pmid: 26427547
[23]	Pahl C, Hartung E, Mahlkow-Nerge K, et al. Feeding characteristics and rumination time of dairy cows around estrus[J]. Journal of Dairy Science, 2015, 98(1):148-154. doi: 10.3168/jds.2014-8025 pmid: 25465539
[24]	Reith S, Brandt H, Hoy S. Simultaneous analysis of activity and rumination time, based on collar-mounted sensor technology, of dairy cows over the peri-estrus period[J]. Livestock Science, 2014, 170: 219-227. doi: 10.1016/j.livsci.2014.10.013
[25]	Erasmus L M, van Marle-Köster E, Masenge A, et al. Exploring the effect of auditory stimuli on activity levels, milk yield and faecal glucocorticoid metabolite concentrations in Holstein cows[J]. Domestic Animal Endocrinology, 2023, 82:106767. doi: 10.1016/j.domaniend.2022.106767
[26]	Dhungana S, Khanal D R, Sharma M, et al. Effect of music on animal behavior: A Review[J]. Nepalese Veterinary Journal, 2018(35): 142-149.
[27]	张振乾, 汪澍, 宋琦, 等. 人工智能大模型在智慧农业领域的应用[J]. 智慧农业导刊, 2023, 3(10):9-12, 17.
[28]	王群, 李馥娟, 倪雪莉, 等. 区块链数据形成与隐私威胁[J]. 计算机工程, 2023, 49(8):1-12. doi: 10.19678/j.issn.1000-3428.0065926
[29]	杨亮, 熊本海, 吕健强, 等. 山黑猪繁殖数据网络数据库平台的开发[J]. 中国农业科学, 2013, 46(12):2550-2557. doi: 10.3864/j.issn.0578-1752.2013.12.016
[30]	李鸿强, 李亚敏, 曾立华, 等. 基于JDBC技术的奶牛场信息管理系统[J]. 农机化研究, 2007(5):127-128.
[31]	熊本海. 种猪场精细饲养综合技术平台[M]. 北京: 中国农业科学技术出版社, 2005.
[32]	杨亮, 高华杰, 夏阿林, 等. 智能化猪场数字化管控平台创制及应用[J]. 农业大数据学报, 2022, 4(3):135-146.
[33]	熊本海, 赵一广, 罗清尧, 等. 中国饲料营养大数据分析平台研制[J]. 智慧农业(中英文), 2022, 4(2):110-120.
[34]	赵春平, 马云, 昝林森, 等. 利用盈亏平衡分析法对奶牛场经济效益的分析[J]. 畜牧兽医杂志, 2008, 27(6):31-34.
[35]	Hansen B G, Stokstad G. Measuring financial performance on dairy farms[J]. Acta Agricultura Scandinavica, 2005, 2(2):99-109.
[36]	Mertens K, Decuypere E, De Baerdemaeker J, et al. Statistical control charts as a support tool for the management of livestock production[J]. Journal of Agricultural Science, 2011, 149:369-384.

类型	型号	阈值范围	测量精度	分辨率
温湿度	T-001-A0020-12	-40－80 ℃/0－100 RH%	±0.5 ℃/±3 RH%	0.1 ℃/0.1% RH
光照	D-001-A0120-12	0－65535LUX	±7 %	1 LUX
风速	T-001-A2220-12	0－40 m/s	±1 m/s	0.1 m/s
风向	T-001-A2320-12	360°全方位	±2 %(线性度)	0.36 °
CO₂	D-001-A0620-12	0－5000 ppm	<±4%	1 ppm
NH₃	D-001-A2720-12	0－100 ppm	<± 4%	0.1 ppm
H₂S	T-001-A2820-12	0－50 ppm	<± 4%	0.1 ppm
PM_2.5/PM₁₀	T-001-A7020-2-12-V1.2	0－999 μg/m^-3	<± 10%	0.1 μg m^-3
TSP	T-001-A5220-12	0－20000 m^-3	<± 15%	1 μg/m^-3