發(fā)布時間：2018-05-12 07:16

  本文選題：溫室 + 模型　； 參考：《安徽農(nóng)業(yè)大學(xué)》2017年碩士論文

【摘要】：溫室環(huán)境控制是作物監(jiān)測的內(nèi)容之一,對于其品質(zhì)、產(chǎn)量等具有重要意義。合理控制溫室設(shè)備,使溫室內(nèi)的環(huán)境參數(shù)更好的滿足作物生長是目前溫室環(huán)境控制需要解決的重要問題。為了合理調(diào)控溫室環(huán)境控制設(shè)備,一定程度上節(jié)約用電成本。本文以安徽農(nóng)業(yè)大學(xué)農(nóng)萃園的茶樹育苗溫室為研究對象,通過引入人工控制因素,以擴展的自回歸模型(Auto Regressive eXogenous,ARX)為基礎(chǔ),構(gòu)建溫度、濕度及耗電量多目標(biāo)模型函數(shù)。在標(biāo)準(zhǔn)粒子群算法(Particle Swarm Optimization,PSO)基礎(chǔ)上,結(jié)合灰色關(guān)聯(lián)理論概念,面向溫室環(huán)境進行多目標(biāo)調(diào)控。主要研究內(nèi)容和結(jié)果如下:(1)針對溫室環(huán)境的空間特征,對茶苗溫室進行多源信息采集。多點采集溫室大棚環(huán)境溫度和濕度信息,運用自適應(yīng)加權(quán)融合估計算法對溫室多源采集信息進行融合,完成大棚環(huán)境多源因子在數(shù)據(jù)層的融合。運用LabVIEW開發(fā)軟件采集大棚環(huán)境信息,利用PH氣象站采集大氣環(huán)境信息。通過小波降噪和自適應(yīng)加權(quán)融合估計算法對采集數(shù)據(jù)進行預(yù)處理,有效去除信息采集過程中存在的噪聲,保證溫濕度數(shù)據(jù)的可信度,為溫室環(huán)境建模做準(zhǔn)備。(2)構(gòu)建育苗溫室環(huán)境的溫度、濕度及能耗模型。通過引入人工控制因素,圍繞ARX模型結(jié)構(gòu),運用系統(tǒng)辨識的方法辨識出模型的結(jié)構(gòu)和參數(shù),構(gòu)建育苗溫室環(huán)境的溫度、濕度模型。運用交叉驗證的方式檢驗溫度和濕度模型的準(zhǔn)確性,仿真結(jié)果表明模型計算得到的溫濕度與實測的溫濕度變化趨勢一致,說明ARX模型能有效模擬育苗溫室內(nèi)的溫度和濕度信息;以調(diào)控機構(gòu)運行消耗的電量為參考建立耗電量模型。(3)算法優(yōu)化控制。通過引入灰色關(guān)聯(lián)度理論,在標(biāo)準(zhǔn)PSO算法的基礎(chǔ)上,將調(diào)控設(shè)備組合種類視為粒子的解,以溫度模型、濕度模型及能耗模型為目標(biāo)函數(shù),以此完成溫室環(huán)境控制的多目標(biāo)優(yōu)化控制。將本文算法優(yōu)化得到的溫濕度與線性加權(quán)和法、單目標(biāo)PSO優(yōu)化得到的結(jié)果相比對,發(fā)現(xiàn)選取本文方法不僅能夠使溫室環(huán)境的溫濕度在作物適宜的生長范圍之內(nèi),相對于其余兩種優(yōu)化方法在一定程度上節(jié)約了用電成本。
[Abstract]:Greenhouse environmental control is one of the contents of crop monitoring, which is of great significance to its quality and yield. It is an important problem for greenhouse environmental control to control the greenhouse equipment reasonably and make the environmental parameters of greenhouse better meet the crop growth. In order to control the greenhouse environment control equipment reasonably, save electricity cost to a certain extent. In this paper, based on the expanded autoregressive model (Auto Regressive eXogenous-ARX), the temperature, humidity and power consumption multiobjective model functions are constructed by introducing artificial control factors into the tea seedling greenhouse in the Sui Garden of Anhui Agricultural University. Based on the standard particle swarm optimization algorithm (PSO) and the concept of grey correlation theory, multi-objective regulation is carried out in greenhouse environment. The main contents and results are as follows: (1) according to the spatial characteristics of greenhouse, the tea seedling greenhouse is collected with multi-source information. Multi-point collection of greenhouse environment temperature and humidity information, using adaptive weighted fusion estimation algorithm to the greenhouse multi-source information fusion, the greenhouse environment multi-source factors in the data level fusion. The environment information of greenhouse is collected by LabVIEW software and atmospheric environment information is collected by PH weather station. The wavelet denoising and adaptive weighted fusion estimation algorithm are used to preprocess the collected data, which can effectively remove the noise existing in the process of information acquisition and ensure the reliability of the temperature and humidity data. The temperature, humidity and energy consumption model of greenhouse environment were constructed. By introducing artificial control factors around the structure of ARX model, the structure and parameters of the model are identified by the method of system identification, and the temperature and humidity models of greenhouse environment are constructed. The accuracy of the temperature and humidity model was verified by cross validation. The simulation results showed that the temperature and humidity calculated by the model were consistent with the measured temperature and humidity, which indicated that the ARX model could effectively simulate the temperature and humidity information in seedling raising greenhouse. The optimal control algorithm is established based on the model of power consumption. Based on the standard PSO algorithm, this paper introduces the grey correlation degree theory, regards the type of the control equipment as the solution of particles, and takes the temperature model, humidity model and energy consumption model as the objective functions. Thus the multi-objective optimal control of greenhouse environment control is completed. Comparing the temperature and humidity obtained by this method with the linear weighted sum method and the results obtained by single objective PSO optimization, it is found that the selection of this method can not only make the temperature and humidity of greenhouse environment within the suitable growth range of crops. Compared with the other two optimization methods, the cost of electricity is saved to some extent.
【學(xué)位授予單位】：安徽農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;S625

【參考文獻】

相關(guān)期刊論文 前10條

1 張雪花;張武;李葉云;蔡芮瑩;朱小倩;;基于灰色粒子群算法的溫室環(huán)境多目標(biāo)優(yōu)化控制[J];湖南農(nóng)業(yè)大學(xué)學(xué)報(自然科學(xué)版);2017年02期

2 張雪花;張武;楊旭;王露皎;馬慧敏;范瓊;;農(nóng)業(yè)溫室環(huán)境控制方法研究綜述[J];控制工程;2017年01期

3 范瓊;張武;張雪花;謝濤;;溫室小氣候環(huán)境監(jiān)測預(yù)警技術(shù)及應(yīng)用研究[J];中國農(nóng)機化學(xué)報;2016年05期

4 吳曉強;黃云戰(zhàn);趙永杰;;基于模糊神經(jīng)網(wǎng)絡(luò)的溫室溫濕度智能控制系統(tǒng)研究[J];中國農(nóng)機化學(xué)報;2016年04期

5 張鵬鵬;俞阿龍;孫詩裕;徐冬平;;多傳感器數(shù)據(jù)融合在礦井安全監(jiān)測中的應(yīng)用[J];工礦自動化;2015年12期

6 王華東;王大羽;;一種改進的多無線傳感器數(shù)據(jù)分批估計自適應(yīng)加權(quán)融合算法[J];傳感技術(shù)學(xué)報;2015年08期

7 任哲;陳懷亮;王連喜;李穎;李琪;;利用交叉驗證的小麥LAI反演模型研究[J];國土資源遙感;2015年04期

8 溫靜;程茂;;多傳感器數(shù)據(jù)融合技術(shù)在溫室控制中的應(yīng)用[J];河北農(nóng)業(yè)大學(xué)學(xué)報;2015年04期

9 王立舒;李巖;梁秋艷;董守田;唐麗靜;;基于改進肖維涅算法的溫室環(huán)境數(shù)據(jù)采集[J];農(nóng)業(yè)工程學(xué)報;2015年05期

10 唐亞鵬;;基于自適應(yīng)加權(quán)數(shù)據(jù)融合算法的數(shù)據(jù)處理[J];計算機技術(shù)與發(fā)展;2015年04期

相關(guān)博士學(xué)位論文 前1條

1 王君;基于模糊控制策略的溫室遠程智能控制系統(tǒng)的研究[D];吉林大學(xué);2015年

相關(guān)碩士學(xué)位論文 前6條

1 劉永星;WSN中的一種多傳感器數(shù)據(jù)融合算法及在森林防火中的應(yīng)用[D];太原理工大學(xué);2015年

2 郝光健;基于LabVIEW和單片機的室內(nèi)環(huán)境監(jiān)測預(yù)警系統(tǒng)的設(shè)計[D];山東農(nóng)業(yè)大學(xué);2014年

3 侯濤;基于改進多目標(biāo)進化算法的溫室環(huán)境 PID控制的仿真研究[D];東北農(nóng)業(yè)大學(xué);2014年

4 秘立鵬;基于模糊神經(jīng)網(wǎng)絡(luò)的設(shè)施農(nóng)業(yè)溫室大棚自適應(yīng)控制系統(tǒng)的開發(fā)[D];太原理工大學(xué);2014年

5 于殿龍;基于積溫和經(jīng)濟最優(yōu)的夏季Venlo型溫室環(huán)境控制技術(shù)的研究[D];江蘇大學(xué);2008年

6 陳紹新;多目標(biāo)優(yōu)化的粒子群算法及其應(yīng)用研究[D];大連理工大學(xué);2007年

，

本文編號：1877658