【正文】 rezArias et al 2020 for the estimation of yield and profits the optimization is performed to calculate the setpoint trajectories of the inside greenhouse temperature and the EC along the considered control horizon typically 65 days for a short season 260 decision variables or 120 days for a long season 480 decision variables Models for irrigation have also been developed for control and optimization purposes The longterm weather prediction which is logically one of the elements with a higher degree of uncertainty and is performed using a software tool that accesses the weather predictions given by the Spanish National Institute of Meteorology for the next eight days forward generates patterns based on several indexes clarity imum mean and minimum temperatures and solar radiation and searches within a local historical database for a climatic sequence that better fits the generated patterns In this way taking the selected sequence as a short term weather prediction the estimation for the rest of the crop cycle is generated starting from this short sequence and using a data window from the historical database The associated high degree of uncertainties is reduced through the receding horizon approach and modifications performed in the second layer 32 Setpoint adaptation layer In this layer the setpoints to be sent to the lower layer for the next day are modified and updated in order to avoid unfeasibility problems and allow reaching the reference values These modifications are performed considering the trajectories generated in the upper layer the short term weather prediction that has a lower degree of uncertainty the current state of the crop and the short term grower goals considering hisher skill and the crop status this being a necessary degree of freedom to let the grower interact with the hierarchical control system Then this information is used within the models described above in order to simulate the greenhouse behavior and to evaluate if the provided setpoints can be reached The optimization process is repeated modifying the constraints diminishing or increasing the setpoints according to the simulation results When the setpoints are reachable they are sent to the lower layer 33 Climate control and nutrition layer Using the temperature and EC setpoints from the upper layers the controllers pute the adequate control signals for the actuators The control algorithms developed include a wide range from feedforward control adaptive control predictive control and hybrid control This list of references is evidently limited and many important papers on temperature control are not mentioned due to space constraints 4 Conclusion In this work an MO optimization problem has been proposed and tested for greenhouse crop growth management obtaining tradeoff solutions of three objectives imization of economic benefits fruit quality and wateruse efficiency This optimization scheme has been integrated into a hierarchical control architecture that allows the automatic generation of setpoints for diurnal and nocturnal temperatures and EC through a whole crop cycle using a receding horizon strategy The obtained results show logical trajectories both in short and long crop cycles The work summarizes research performed on modeling simulation control and optimization of greenhouse crop production during eight years providing real results in an industrial greenhouse 畢業(yè)設(shè)計(jì) 。外文翻譯 農(nóng)業(yè)大棚溫室智能化自動(dòng)控制 附錄 3 外文文獻(xiàn)翻譯譯文 農(nóng)業(yè)大棚溫室智能化自動(dòng)控制 摘要確定控制溫室作物生長(zhǎng)歷來(lái)使用約束優(yōu)化或應(yīng)用人工智能技術(shù)解決了軌跡的問(wèn)題已被用作經(jīng)濟(jì)利潤(rùn)的最優(yōu)化研究的主要標(biāo)準(zhǔn)以獲得足夠的作物生長(zhǎng)的氣候控制設(shè)定值本文針對(duì)溫室作物生長(zhǎng)的問(wèn)題通過(guò)分層控制體系結(jié)構(gòu)由一個(gè)高層次的多目標(biāo)優(yōu)化方法在解決這個(gè)問(wèn)題的辦法是找到白天和夜間溫度參考軌跡氣候相關(guān)的設(shè)定值和電導(dǎo)率 fertirrigation 相關(guān)設(shè)定值的目標(biāo) 是利潤(rùn)最大化果實(shí)品質(zhì)水分利用效率這些目前正在培育的國(guó)際規(guī)則結(jié)果說(shuō)明選擇從那些獲得工業(yè)的溫室在過(guò)去的八年中示出和描述 關(guān)鍵詞農(nóng)業(yè)分層系統(tǒng)過(guò)程控制優(yōu)化方法產(chǎn)量?jī)?yōu)化 1 介紹 現(xiàn)代農(nóng)業(yè)是時(shí)下在質(zhì)量和環(huán)境影響方面的規(guī)定因此它是一個(gè)自動(dòng)控制技術(shù)的應(yīng)用領(lǐng)域已經(jīng)增加了很多在過(guò)去的幾年里溫室產(chǎn)生的空氣系統(tǒng)的是一個(gè)復(fù)雜的物理化學(xué)和生物學(xué)過(guò)程同時(shí)使具有不同的響應(yīng)時(shí)間和模式的環(huán)境因素其特征在于由許多相互作用它必須加以控制以以獲得最佳效果的種植者作物生長(zhǎng)過(guò)程是最重要的主要受周?chē)h(huán)境的氣候變量光合有效輻射 PAR 溫度濕度二氧化碳濃 度里面的空氣水和化肥灌溉病蟲(chóng)害提供量和文化的勞動(dòng)力如修剪和農(nóng)藥的治療等等溫室是適合作物生長(zhǎng)因?yàn)樗鼧?gòu)成了一個(gè)封閉的環(huán)境中可以控制氣候和肥料灌溉變量氣候和肥料灌溉是兩個(gè)獨(dú)立的系統(tǒng)不同的控制問(wèn)題和目標(biāo)根據(jù)經(jīng)驗(yàn)不同作物品種的水和養(yǎng)分的要求是已知的在實(shí)際上第一個(gè)自動(dòng)化系統(tǒng)控制這些變量另一方面市場(chǎng)價(jià)格的波動(dòng)和環(huán)境的規(guī)則以提高水的利用效率或其他方面加以考慮減少肥料殘留在土壤中的如硝酸鹽含量因此優(yōu)化生產(chǎn)過(guò)程可概括為一個(gè)溫室大氣系統(tǒng)的問(wèn)題達(dá)到以下目標(biāo)的最佳作物生長(zhǎng)一個(gè)更大的生產(chǎn)與質(zhì)量更好聯(lián)營(yíng)公司的成本主要是燃料電力和化肥減少 減少殘留物主要是殺蟲(chóng)劑和離子在土壤中和水的利用效率的提高許多方法已被應(yīng)用到這個(gè)問(wèn)題例如處理的溫室氣候管理中的最優(yōu)控制字段 2 M0 優(yōu)化作物生產(chǎn) 一個(gè) MO 優(yōu)化問(wèn)題可以定義為尋找決策變量的向量它滿足約束條件和優(yōu)化的目標(biāo)函數(shù)一個(gè)向量其元素特點(diǎn)是競(jìng)爭(zhēng)的措施表現(xiàn)或目標(biāo)的問(wèn)題被視為 MO 優(yōu)化問(wèn)題其中 n 目標(biāo)姬 p 在變量的向量 P∈ P 的同時(shí)最小化或最大化 問(wèn)題往往沒(méi)有最佳的解決方案同時(shí)優(yōu)化所有目標(biāo)但它有一組作為一個(gè)Pareto 最優(yōu)集其中一個(gè)折衷的解決方案可以選自已知的不理想的或不占主導(dǎo)地位的替代解決方案設(shè)置一個(gè)決策 過(guò)程不同的標(biāo)準(zhǔn)如物理產(chǎn)量作物品質(zhì)產(chǎn)品質(zhì)量生產(chǎn)過(guò)程中的時(shí)間不同或生產(chǎn)成本和風(fēng)險(xiǎn)可配制于溫室作物管理這些標(biāo)準(zhǔn)往往會(huì)產(chǎn)生有爭(zhēng)議的的氣候和肥料灌溉要求必須要解決的或明或暗地在所謂的戰(zhàn)術(shù)層面上種植者有幾個(gè)相互沖突的目標(biāo)做出決定該解決方案的這個(gè) MO 優(yōu)化過(guò)程的p∈ P 是最佳的日間和夜間的當(dāng)前和未來(lái)的參考軌跡的溫度 XTA 導(dǎo)電性 XEC 作物周期的其余部分即沿著優(yōu)化的時(shí)間間隔內(nèi)的空氣溫度是一個(gè)向量并沿著優(yōu)化的時(shí)間間隔的電導(dǎo)率 EC 是一個(gè)矢量請(qǐng)注意在植物生長(zhǎng)的 PAR 輻射晝夜的條件的影響下進(jìn)行光合作用過(guò)程此外溫度成為影響糖的生產(chǎn)速度通過(guò) 光合作用從而輻射和溫度具有較高的輻射水平