北方设施农业气象灾害监测预警智能服务系统设计与实现
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摘要
该文针对设施农业种养殖企业用户的设施农业气象灾害直通式服务需求,运用云计算、物联网、移动互联网等信息化技术,基于JavaEE技术框架、SOA(service oriented architecture)云服务技术,通过多重因素关联规则学习方法,构建基于互联网气象数据、设施农业小气候环境数据及作物生育期等多重因素的设施农业气象灾害预警和生产管理专家知识规则,依托气象部门一体化智能网格气象预报预警平台和未来3~7 d的精细化气象要素预报,开发基于互联网数据挖掘和专家知识决策技术的设施农业气象灾害监测预警及智能决策推送服务系统,对寒潮、大风、低温寡照、暴雪等北方主要设施农业气象灾害进行早期预警提醒,系统于2017年秋冬季在天津津南区部分农业园区推广应用,基于移动互联网通过智能手机APP对5次强冷空气过程提前3~5 d自动研判并实时推送设施农业生产管理决策和防灾减灾提醒建议,便于生产管理者及时关注天气变化和提前采取生产管理措施,避免重大灾害损失,探索应用互联网、云技术、大数据挖掘等信息手段开展气象灾害早期预警,为研究满足设施农业互动式、个性化、智能化和专业化气象信息服务和推动农业现代化和现代农业发展提供借鉴。
In recent years, facility agriculture that features high-efficiency has become an important part of agricultural production in the North of China. However, it was suffered some damages from severe weather such as cold wave, strong winds, blizzards, low temperature and less sunshine hazard. This study was aimed to cater for the straight-through demands of facility agricultural breeding enterprises and large agricultural breeding families for facility Agra-meteorological disasters resisting and early warning. Based on artificial intelligence means such as internet data mining and expert knowledge decision-making system, we established an intelligent service system for monitoring and warning of meteorological disasters in facility agriculture to guarantee the security and stability of facility agriculture production. First of all, the meteorological knowledge that was urgently needed for the production of crops such as cucumber, strawberry, tomato, and sweet pepper etc. was summarized by using information technologies such as cloud computing, Internet of things, mobile Internet, the Java EE technology framework, SOA(service oriented architecture) cloud service technology, and multi-factor association rule learning method. Then we defined the rules of agricultural meteorological disaster warning and production management expert knowledge based on the location-based weather forecasting data, microclimate environment data of facility agriculture, dynamic planting information and growth period data of facility crops, which would effectively improve the integration of intelligent services and actual production needs. Finally, the facility Agra-meteorological disaster monitoring and early warning and intelligent decision-making pushing service system was built depending on the integrated platform for intelligent grid weather forecast and warning of meteorological department, forecast of refined meteorological elements in the next 3 to 7 days, Internet data mining and expert knowledge decision techniques, which would provide interactive, individualized, intelligent and straight-through meteorological information service for agricultural parks and large farming households. It indicated that real-time warning of meteorological disasters and intelligent decision-making services for production management had been working well. The system could not only provide real-time, personalized guidance for production practices, but also realize automatic warnings for Agra-meteorological disasters in major Northern facility agriculture such as cold wave, strong wind, low temperature and less sunshine hazard, and heavy snow. It would timely send information on the important turning weather in next 7 days, meteorological disaster warning, facility agricultural production management decision-making, and disaster prevention recommendations to agricultural technicians through smart phone APP. It was convenient for production managers to pay attention to weather changes and adopt corresponding production management measures according to the type of planting crops in time. Therefore, they would be in early preparation for meteorological disasters, avoiding major disaster losses. Providing interactive, personalized and intelligent straight-through meteorological information services for agricultural parks and large agricultural breeding families would effectively solve the pre-disaster early warning and disaster prevention problems of major facilities agricultural meteorological disasters. It would significantly improve the efficiency of modern agricultural production and be of far-reaching significance for promoting the development of agricultural modernization and modern agriculture.
In recent years, facility agriculture that features high-efficiency has become an important part of agricultural production in the North of China. However, it was suffered some damages from severe weather such as cold wave, strong winds, blizzards, low temperature and less sunshine hazard. This study was aimed to cater for the straight-through demands of facility agricultural breeding enterprises and large agricultural breeding families for facility Agra-meteorological disasters resisting and early warning. Based on artificial intelligence means such as internet data mining and expert knowledge decision-making system, we established an intelligent service system for monitoring and warning of meteorological disasters in facility agriculture to guarantee the security and stability of facility agriculture production. First of all, the meteorological knowledge that was urgently needed for the production of crops such as cucumber, strawberry, tomato, and sweet pepper etc. was summarized by using information technologies such as cloud computing, Internet of things, mobile Internet, the Java EE technology framework, SOA(service oriented architecture) cloud service technology, and multi-factor association rule learning method. Then we defined the rules of agricultural meteorological disaster warning and production management expert knowledge based on the location-based weather forecasting data, microclimate environment data of facility agriculture, dynamic planting information and growth period data of facility crops, which would effectively improve the integration of intelligent services and actual production needs. Finally, the facility Agra-meteorological disaster monitoring and early warning and intelligent decision-making pushing service system was built depending on the integrated platform for intelligent grid weather forecast and warning of meteorological department, forecast of refined meteorological elements in the next 3 to 7 days, Internet data mining and expert knowledge decision techniques, which would provide interactive, individualized, intelligent and straight-through meteorological information service for agricultural parks and large farming households. It indicated that real-time warning of meteorological disasters and intelligent decision-making services for production management had been working well. The system could not only provide real-time, personalized guidance for production practices, but also realize automatic warnings for Agra-meteorological disasters in major Northern facility agriculture such as cold wave, strong wind, low temperature and less sunshine hazard, and heavy snow. It would timely send information on the important turning weather in next 7 days, meteorological disaster warning, facility agricultural production management decision-making, and disaster prevention recommendations to agricultural technicians through smart phone APP. It was convenient for production managers to pay attention to weather changes and adopt corresponding production management measures according to the type of planting crops in time. Therefore, they would be in early preparation for meteorological disasters, avoiding major disaster losses. Providing interactive, personalized and intelligent straight-through meteorological information services for agricultural parks and large agricultural breeding families would effectively solve the pre-disaster early warning and disaster prevention problems of major facilities agricultural meteorological disasters. It would significantly improve the efficiency of modern agricultural production and be of far-reaching significance for promoting the development of agricultural modernization and modern agriculture.
引文
[1]李春,郭晶,薛庆禹,等.天津近郊设施农业气候资源与气象灾害变化特征[J].北方园艺,2015,39(6):190-193.Li Chun,Guo Jing,Xue Qingyu,et al.Variation characteristics of facility agro-climatic resources and meteorological disaster in Tianjin suburb area[J].Northern Horticulture,2015,39(6):190-193.(in Chinese with English abstract)
[2]高浩,黎贞发,潘学标,等.中国设施农业气象业务服务现状与对策[J].中国农业气象,2010,31(3):402-406.Gao Hao,Li Zhenfa,Pan Xuebiao,et al.Situation and countermeasures to agro-meteorological services on controlled environment agriculture in china[J].Chinese Journal of Agrometeorology,2010,31(3):402-406.(in Chinese with English abstract)
[3]魏瑞江,赵春雷.基于GIS的河北果菜日光温室最佳发展区域确定[J].中国农业资源与区划,2005,26(1):35-38.Wei Ruijiang,Zhao Chunlei.Confirmation of most suitable area for developing fruit and vegetable solar green house in hebei province by using gis technology[J].Journal of China Agricultural Resources and Regional Planning,2005,26(1):35-38.(in Chinese with English abstract)
[4]赵子征,彭高军,辛本胜.我国西北地区节能型日光温室蔬菜生产气候区划[J].农机化研究,2006(5):87-90.Zhao Zizheng,Peng Gaojun,Xin Bensheng.The climatic regionalization for vegetable production in energy-saving solar greenhouse in northwest of China[J].Journal of Agricultural Mechanization Research,2006(5):87-90.(in Chinese with English abstract)
[5]贺延梅,李化龙,尚小宁,等.日光温室灾害性天气及防御[J].陕西农业科学,2008(6):81-83.
[6]李宁,申双和,黎贞发,等.基于主成分回归的日光温室内低温预测模型[J].中国农业气象,2013,34(3):306-311.Li Ning,Shen Shuanghe,Li Zhenfa,et al.Forecast model of minimum temperature inside greenhouse based on principal component regression[J].Chinese Journal of Agrometeorology,2013,34(3):306?311.(in Chinese with English abstract)
[7]杨小利,周安宁.高效节能日光温室蔬菜生产气候区划[J].干旱气象,1999,17(3):40-41.Yang Xiaoli,Zhou Anning.The climatic demacration for high efficient heliogreenhouse vegetable production[J].Journal of Arid Meteorology,1999,17(3):40-41.(in Chinese with english abstract)
[8]黎贞发,王铁,宫志宏,等.基于物联网的日光温室低温灾害监测预警技术及应用[J].农业工程学报,2013,29(4):229-236.Li Zhenfa,Wang Tie,Gong Zhihong,et al.Forewarning technology and application for monitoring low temperature disaster in solar greenhouses based on internet of things[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(4):229-236.(in Chinese with English abstract)
[9]陈思宁,黎贞发,刘淑梅.设施农业气象灾害研究综述及研究方法展望[J].中国农学通报,2014,30(20):302-307.Chen Sining,Li Zhenfa,Liu Shumei.Review of facilities agriculture meteorological disasters and prospect of associated study methods[J].Chinese Agricultural Science Bulletin,2014,30(20):302-307.(in Chinese with English abstract)
[10]中国气象局,地面气象观测规范[M].北京:气象出版社,2003.
[11]地面气象观测规范:QX/T 51-2007[S].
[12]重大气象灾害应急响应启动等级:QX/T116-2010[S].
[13]余文韬,吴振玲,何群英,等.天津天气预报手册[M].北京:气象出版社,2015.
[14]冷空气等级:GB/T20484-2006[S].
[15]降雨量等级:GB/T28592-2012[S].
[16]魏瑞江.日光温室低温寡照灾害指标[J].气象科技,2003,31(1):50-53.Wei Ruijiang.The disaster grades of low temperature and spare sunlight in greenhouse[J].Meteorological Science and Technology,2003.31(1):50-53.(in Chinese with English abstract)
[17]胡俊峰.应对北方设施农业灾害性天气的防御措施[J].蔬菜,2017(1):72-73.
[18]祝青林,王丽娜,徐梅,等.大连冬季设施农业气象灾害风险及趋势分析[J].中国农学通报,2015,31(26):235-240.Zhu Qinglin,Wang Lina,Xu Mei,et al.Trend analysis of meteorological disaster risk of facility agriculture of dalian in winter[J].Chinese Agricultural Science Bulletin,2015,31(26):235-240.(in Chinese with English abstract)
[19]张宗郁,张亚平,张静远,等.改进关联规则算法在高校教学管理中的应用[J].计算机工程,2012,1(38):75-77.Zhang Zongyu,Zhang Yaping,Zhang Jingyuan,et al.Application of improved association rule algorithm in college teaching management[J].Computer Engineering,2012,1(38):75-77.(in Chinese with English abstract)
[20]韩家玮.数据挖掘概念与技术[M].北京:机械工业出版社,2007.
[21]秦亮曦,史忠植.关联规则研究综述[J].广西大学学报:自然科学版,2005,30(4):310-317.Qin Liangxi,Shi Zhongzhi.A survey on the research of association rules[J].Journal of Guangxi University:Natural Science Edition,2005,30(4):310-317.(in Chinese with English abstract)
[22]毕建欣,张岐山.关联规则挖掘算法综述[J].中国工程科学,2005(7):88-94.Bi Jianxin,Zhang Qishan.Survey of the algorithms on association rule mining[J].Engineering Science,2005(7):88-94.(in Chinese with English abstract)
[23]朱庆,恰汗·合孜尔.一种改进的Apriori算法[J].计算机与数字工程,2010,38(4):30-32.Zhu Qing,Qiahan·Hezier.An improved apriori algorithm[J].Computer&Digital Engineering,2010,38(4):30-32.(in Chinese with English abstract)
[24]敖志刚.人工智能及专家系统[M].北京:机械工业出版社,2010.
[25]史忠植.高级人工智能(第二版)[M].北京:科学出版社,2006.
[26]孙想,冯臣,吴华瑞.基于语义Web的农业生产知识集成技术[J].农业工程学报,2008,24(2):186-190.Sun Xiang,Feng Chen,Wu Huarui.Technology of agricultural production knowledge integration based on semantic web[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2008,24(2):186-190.(in Chinese with English abstract)
[27]刘文,杜景林.基于LBS的气象预警Android平台设计[J].湖北农业科学,2013(24):6161-6165.Liu Wen,Du Jinglin.Design of meteorological warning based on LBS using android platform[J].Hubei Agricultural Sciences,2013(24):6161-6165.(in Chinese with English abstract)
[28]张艮龙,尹蓉.设施农业远程监控与智能决策支持系统的研究[J].山西电子技术,2017,5:80-83.Zhang Genlong,Yin Rong.Study on remote monitoring and intelligent decision support system for facility agriculture[J].Shanxi Electronic Technology,2017,5:80-83.(in Chinese with English abstract)
[29]Wang Yuansheng,Wu Huarui,Luo Lei,et al.Research on cloud storage technologies of typical crop growth environment monitoring data[J].Computer Modelling&New Technologies,2014,16(2):13-22.
[30]王元胜,李瑜玲,吴华瑞,等.农业园区全景GIS数字化系统设计与应用[J].农业工程学报,2018,34(11):143-149.Wang Yuansheng,Li Yuling,Wu Huarui,et al.Design and application of digital system for agricultural park based on panorama GIS[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(11):143-149.(in Chinese with English abstract)
[31]刘艺,张海涛,刘奇燕,等.基于分解数据库的FP-growth算法关联规则研究[J].计算机与数字工程,2018,46(7):1306-1310,1416.Liu Yi,Zhang Haitao,Liu Qiyan,et al.Research on association rules of FP-growth algorithm based on decomposition database[J].Computer&Digital Engineering,2018,46(7):1306-1310,1416.(in Chinese with English abstract)
[2]高浩,黎贞发,潘学标,等.中国设施农业气象业务服务现状与对策[J].中国农业气象,2010,31(3):402-406.Gao Hao,Li Zhenfa,Pan Xuebiao,et al.Situation and countermeasures to agro-meteorological services on controlled environment agriculture in china[J].Chinese Journal of Agrometeorology,2010,31(3):402-406.(in Chinese with English abstract)
[3]魏瑞江,赵春雷.基于GIS的河北果菜日光温室最佳发展区域确定[J].中国农业资源与区划,2005,26(1):35-38.Wei Ruijiang,Zhao Chunlei.Confirmation of most suitable area for developing fruit and vegetable solar green house in hebei province by using gis technology[J].Journal of China Agricultural Resources and Regional Planning,2005,26(1):35-38.(in Chinese with English abstract)
[4]赵子征,彭高军,辛本胜.我国西北地区节能型日光温室蔬菜生产气候区划[J].农机化研究,2006(5):87-90.Zhao Zizheng,Peng Gaojun,Xin Bensheng.The climatic regionalization for vegetable production in energy-saving solar greenhouse in northwest of China[J].Journal of Agricultural Mechanization Research,2006(5):87-90.(in Chinese with English abstract)
[5]贺延梅,李化龙,尚小宁,等.日光温室灾害性天气及防御[J].陕西农业科学,2008(6):81-83.
[6]李宁,申双和,黎贞发,等.基于主成分回归的日光温室内低温预测模型[J].中国农业气象,2013,34(3):306-311.Li Ning,Shen Shuanghe,Li Zhenfa,et al.Forecast model of minimum temperature inside greenhouse based on principal component regression[J].Chinese Journal of Agrometeorology,2013,34(3):306?311.(in Chinese with English abstract)
[7]杨小利,周安宁.高效节能日光温室蔬菜生产气候区划[J].干旱气象,1999,17(3):40-41.Yang Xiaoli,Zhou Anning.The climatic demacration for high efficient heliogreenhouse vegetable production[J].Journal of Arid Meteorology,1999,17(3):40-41.(in Chinese with english abstract)
[8]黎贞发,王铁,宫志宏,等.基于物联网的日光温室低温灾害监测预警技术及应用[J].农业工程学报,2013,29(4):229-236.Li Zhenfa,Wang Tie,Gong Zhihong,et al.Forewarning technology and application for monitoring low temperature disaster in solar greenhouses based on internet of things[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(4):229-236.(in Chinese with English abstract)
[9]陈思宁,黎贞发,刘淑梅.设施农业气象灾害研究综述及研究方法展望[J].中国农学通报,2014,30(20):302-307.Chen Sining,Li Zhenfa,Liu Shumei.Review of facilities agriculture meteorological disasters and prospect of associated study methods[J].Chinese Agricultural Science Bulletin,2014,30(20):302-307.(in Chinese with English abstract)
[10]中国气象局,地面气象观测规范[M].北京:气象出版社,2003.
[11]地面气象观测规范:QX/T 51-2007[S].
[12]重大气象灾害应急响应启动等级:QX/T116-2010[S].
[13]余文韬,吴振玲,何群英,等.天津天气预报手册[M].北京:气象出版社,2015.
[14]冷空气等级:GB/T20484-2006[S].
[15]降雨量等级:GB/T28592-2012[S].
[16]魏瑞江.日光温室低温寡照灾害指标[J].气象科技,2003,31(1):50-53.Wei Ruijiang.The disaster grades of low temperature and spare sunlight in greenhouse[J].Meteorological Science and Technology,2003.31(1):50-53.(in Chinese with English abstract)
[17]胡俊峰.应对北方设施农业灾害性天气的防御措施[J].蔬菜,2017(1):72-73.
[18]祝青林,王丽娜,徐梅,等.大连冬季设施农业气象灾害风险及趋势分析[J].中国农学通报,2015,31(26):235-240.Zhu Qinglin,Wang Lina,Xu Mei,et al.Trend analysis of meteorological disaster risk of facility agriculture of dalian in winter[J].Chinese Agricultural Science Bulletin,2015,31(26):235-240.(in Chinese with English abstract)
[19]张宗郁,张亚平,张静远,等.改进关联规则算法在高校教学管理中的应用[J].计算机工程,2012,1(38):75-77.Zhang Zongyu,Zhang Yaping,Zhang Jingyuan,et al.Application of improved association rule algorithm in college teaching management[J].Computer Engineering,2012,1(38):75-77.(in Chinese with English abstract)
[20]韩家玮.数据挖掘概念与技术[M].北京:机械工业出版社,2007.
[21]秦亮曦,史忠植.关联规则研究综述[J].广西大学学报:自然科学版,2005,30(4):310-317.Qin Liangxi,Shi Zhongzhi.A survey on the research of association rules[J].Journal of Guangxi University:Natural Science Edition,2005,30(4):310-317.(in Chinese with English abstract)
[22]毕建欣,张岐山.关联规则挖掘算法综述[J].中国工程科学,2005(7):88-94.Bi Jianxin,Zhang Qishan.Survey of the algorithms on association rule mining[J].Engineering Science,2005(7):88-94.(in Chinese with English abstract)
[23]朱庆,恰汗·合孜尔.一种改进的Apriori算法[J].计算机与数字工程,2010,38(4):30-32.Zhu Qing,Qiahan·Hezier.An improved apriori algorithm[J].Computer&Digital Engineering,2010,38(4):30-32.(in Chinese with English abstract)
[24]敖志刚.人工智能及专家系统[M].北京:机械工业出版社,2010.
[25]史忠植.高级人工智能(第二版)[M].北京:科学出版社,2006.
[26]孙想,冯臣,吴华瑞.基于语义Web的农业生产知识集成技术[J].农业工程学报,2008,24(2):186-190.Sun Xiang,Feng Chen,Wu Huarui.Technology of agricultural production knowledge integration based on semantic web[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2008,24(2):186-190.(in Chinese with English abstract)
[27]刘文,杜景林.基于LBS的气象预警Android平台设计[J].湖北农业科学,2013(24):6161-6165.Liu Wen,Du Jinglin.Design of meteorological warning based on LBS using android platform[J].Hubei Agricultural Sciences,2013(24):6161-6165.(in Chinese with English abstract)
[28]张艮龙,尹蓉.设施农业远程监控与智能决策支持系统的研究[J].山西电子技术,2017,5:80-83.Zhang Genlong,Yin Rong.Study on remote monitoring and intelligent decision support system for facility agriculture[J].Shanxi Electronic Technology,2017,5:80-83.(in Chinese with English abstract)
[29]Wang Yuansheng,Wu Huarui,Luo Lei,et al.Research on cloud storage technologies of typical crop growth environment monitoring data[J].Computer Modelling&New Technologies,2014,16(2):13-22.
[30]王元胜,李瑜玲,吴华瑞,等.农业园区全景GIS数字化系统设计与应用[J].农业工程学报,2018,34(11):143-149.Wang Yuansheng,Li Yuling,Wu Huarui,et al.Design and application of digital system for agricultural park based on panorama GIS[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(11):143-149.(in Chinese with English abstract)
[31]刘艺,张海涛,刘奇燕,等.基于分解数据库的FP-growth算法关联规则研究[J].计算机与数字工程,2018,46(7):1306-1310,1416.Liu Yi,Zhang Haitao,Liu Qiyan,et al.Research on association rules of FP-growth algorithm based on decomposition database[J].Computer&Digital Engineering,2018,46(7):1306-1310,1416.(in Chinese with English abstract)