| 引用本文: | 李 伟,金 梁,杜 丽.基于灰熵关联分析的温室智能调控系统研究[J].灌溉排水学报,0,():-. |
| LI Wei,JIN Liang,DU Li.基于灰熵关联分析的温室智能调控系统研究[J].灌溉排水学报,0,():-. |
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| 摘要: |
| 【目的】解决传统温室环境参数较难控制、水肥利用效率低等问题。【方法】引入灰色熵权理论，将各种环境要素视为不确定型多属性决策，计算与理想情况的接近度，建立了评价植物生长环境要素的数学模型；通过无线传感网络片上系统CC2530与传感器、物联网相结合，设计温室环境调控系统，调配温湿度、光照等各生长要素达到最优值，从而给植物配置最优生长环境。【结果】温室生产环境实地试验表明，无线网络通信丢包率不超过1%，满足环境信息实时采集和稳定传输要求，育苗植株成活率提高3.1%，同时节水率约为14%。【结论】该系统与传统温室手动调节环境参数的方法相比，运行稳定、可靠、有效，为提高温室育苗成活率、减少水资源浪费提供理论支持和实践指导，具有较高的推广价值。 |
| 关键词: 智能调控系统;育苗温室;多属性决策;灰熵;物联网 |
| DOI： |
| 分类号:S275.6; TP273 |
| 基金项目:教育部“云数融合科教创新”基金课题（2018A10004） |
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| The Study on Greenhouse Intelligent Control System based on Grey Entropy Correlation Analysis |
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LI Wei1, JIN Liang1, DU Li2
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1.Henan Polytechnic Institute;2.Henan Rose Germplasm Innovation and Cultivation Technology Engineering Research Center
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| Abstract: |
| 【Objective】In view of the problems of difficult control of environmental parameters and low efficiency of water and fertilizer utilization in traditional greenhouse, 【Method】a mathematical model for the evaluation of plant growth environment factors is established, and the grey entropy weight theory is introduced. The influencing factors are regarded as multiple attributes of uncertain decision-making, and the approach degree to the ideal situation is calculated. Finally, a greenhouse environment monitoring system is designed by wireless sensor network system on chip CC2530, sensors and Internet of things, to allocate the temperature, humidity, light and other factors are close to the optimal value, so as to allocate the optimal growth environment for plants. 【Result】The field test of greenhouse production environment shows that the packet loss rate of wireless network communication does not exceed 1%, which meets the requirements of real-time environmental information collection and stable transmission, and the survival rate of seedling plants is increased by 3.1%, while the water saving rate is about 14%. 【Conclusion】The system is stable, reliable and effective compared with the traditional method of manually adjusting environmental parameters in the greenhouse, and provides theoretical support and practical guidance for improving the survival rate of greenhouse seedlings and reducing water waste. |
| Key words: intelligent control system; seedling greenhouse; multiple attribute decision making; grey entropy; Internet of things |
