| 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,2022,41(1):57-61. |
| 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,2022,41(1):57-61. |
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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 |
| DOI：10.13522/j.cnki.ggps.2021094 |
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| A Smart Control System for Greenhouse Designed Based on Grey Entropy Correlation Analysis |
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LI Wei, JIN Liang, DU Li.
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North China University of Water Resources and Electric Power, Zhengzhou 450046, China
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| Abstract: |
| 【Background and objective】Sustaining agricultural production in regions with water scarcity is a concern in many countries. Developing water-saving irrigation and restructuring cropping systems offer one solution. The purpose of this paper is to propose a method showing how cropping system in a region can be optimized to balance water supply and demand for water from different sectors.【Method】We took Anyang in north Henan province as an example, with objective of the optimization to balance economic, social ecological benefits from limited water resources. We used the strategies of inertia weight decay and the particle mutation to establish the multi-objective agricultural planting structure, and solved it by an improved particle swarm optimization method. The optimal results were obtained from analytic hierarchy process (AHP) by processing the pareto solution set and preference-selecting.【Result】To balance water use for all sectors, the studied region should reduce the areas of staple crops, including wheat and corn, which are more water-demanding, and increased the areas of cash crops, such as oil-bearing, vegetables and edible fungus. This adjustment can improve the overall benefits and ameliorate the current imbalance between water supply and water demand, and meet demand of the crops for water in most of their growth seasons. Implementation of the optimized cropping systems can reduce water shortage ratio by 9.02%, 9.56% and 9.95% in the base year (2018), and 2025 and 2035 respectively, with their associated overall benefits increased by 13.59%, 10.90%, 9.82%, respectively. The optimized cropping systems still meet the demand of 386.60 kg/a per capita for grains. The downside of the optimized cropping systems is that the increased cash-crop areas would be labor-intensive and, depending on the cash crop market, could compromise farmers’ profits. In the long term, the optimized systems will reduce fertilizer use compared with the level in 2018, but fertilizer application in total will still exceed the limit of 225 kg/ha deemed to be the safe threshold. It hence could risk soil and environmental pollution.【Conclusion】We proposed a method to help optimize cropping structure with the aim to reduce agricultural water use and ensure food security. Case study shows the pros and cons of the optimized results. Its implementation needs to consider the fluctuation in both food and labor markets, as increasing planting areas of cash crops will be labor intensive. |
| Key words: planting structure; water resources; multi-objective optimization; agricultural water saving; particle swarming optimization |
