| 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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| 引用本文： | 孙治贵,王元胜,张禄,郭旺.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[J].农业工程学报,2018,34(23):149-156. |
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| 作者姓名： | 孙治贵 王元胜 张禄 郭旺 |
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| 作者单位： | 1. 天津海洋中心气象台，天津 300074;,2. 北京农业信息技术研究中心，北京 100097;,3. 天津市津南区气象局，天津 300050;,2. 北京农业信息技术研究中心，北京 100097; |
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| 基金项目： | 国家自然科学基金资助项目（61571051，61471067）；天津市气象局科技专项（201717qxzx02） |
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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。
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| 关 键 词： | 监测 预警系统 设施农业 专家决策 气象灾害 数据挖掘 信息技术 |
| 收稿时间： | 2018/7/13 0:00:00 |
| 修稿时间： | 2018/10/10 0:00:00 |
Design and realization of intelligent service system for monitoring and warning of meteorological disasters in facility agriculture in North China |
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| Sun Zhigui,Wang Yuansheng,Zhang Lu and Guo Wang.Design and realization of intelligent service system for monitoring and warning of meteorological disasters in facility agriculture in North China[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(23):149-156. |
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| Authors: | Sun Zhigui Wang Yuansheng Zhang Lu and Guo Wang |
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| Institution: | 1. Tianjin Marine Meteorological Center, Tianjin 300074, China;,2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China;,3. Tianjin Jinnan District Meteorological Bureau, Tianjin 300050, China and 2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China; |
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| Abstract: | Abstract: 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. |
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| Keywords: | monitoring warning systems facility agriculture expert decision-making meteorological disaster data mining information technology |
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