本文選題：數(shù)據(jù)采集 + 排序?qū)W習(xí)　； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：我們國(guó)家農(nóng)業(yè)的體量比較大,但是實(shí)力并不強(qiáng),在農(nóng)業(yè)現(xiàn)代化方面還有很長(zhǎng)的路要去走,在國(guó)家的第十三個(gè)五年規(guī)劃(2016-2020)中,將農(nóng)業(yè)的信息化和現(xiàn)代化作為重大的農(nóng)業(yè)發(fā)展項(xiàng)目,現(xiàn)代農(nóng)業(yè)同傳統(tǒng)農(nóng)業(yè)相比,不再“靠天吃飯,聽(tīng)天由命”,而是通過(guò)現(xiàn)代科技采集農(nóng)田中的作物與作業(yè)信息,因地制宜地提出種植和培育方案,利用大型農(nóng)機(jī)進(jìn)行統(tǒng)一作業(yè),從而提高單位產(chǎn)出,同時(shí)使得農(nóng)業(yè)生產(chǎn)實(shí)現(xiàn)可持續(xù)。信息采集屬于精準(zhǔn)農(nóng)業(yè)實(shí)施的第一個(gè)步驟,大型農(nóng)機(jī)是實(shí)施農(nóng)田作業(yè)的主體,對(duì)農(nóng)業(yè)生產(chǎn)實(shí)現(xiàn)現(xiàn)代化起著至關(guān)重要的作用,本文首先關(guān)注農(nóng)業(yè)機(jī)械在進(jìn)行農(nóng)田作業(yè)過(guò)程中的數(shù)據(jù)采集,主要以深松作業(yè)為背景,深松就是利用深松機(jī)械鏟使得農(nóng)田的深層土壤松動(dòng),提高土壤對(duì)水肥的吸收,在深松作業(yè)中主要關(guān)注農(nóng)機(jī)經(jīng)過(guò)的路線,深松作業(yè)情況以及農(nóng)機(jī)運(yùn)動(dòng)狀態(tài),接著為了對(duì)作業(yè)結(jié)果進(jìn)行評(píng)價(jià),以采集到的數(shù)據(jù)為基礎(chǔ),提出了一種針對(duì)深松作業(yè)的評(píng)價(jià)方法。首先,本文設(shè)計(jì)了一種基于深松作業(yè)的農(nóng)機(jī)信息采集終端,信息采集終端在數(shù)據(jù)采集功能的基礎(chǔ)上也要對(duì)數(shù)據(jù)進(jìn)行上傳和本地備份,由功能需求設(shè)計(jì)了系統(tǒng)的模塊化結(jié)構(gòu),設(shè)計(jì)硬件電路編寫(xiě)軟件程序,主要工作流程是:由不同種類(lèi)的傳感器模塊獲取農(nóng)機(jī)的運(yùn)行軌跡信息、作業(yè)深度信息和運(yùn)動(dòng)狀態(tài)信息,經(jīng)數(shù)據(jù)提取和整合后經(jīng)GPRS網(wǎng)絡(luò)傳送給遠(yuǎn)端的服務(wù)器平臺(tái),同時(shí)將數(shù)據(jù)備份到本地的存儲(chǔ)介質(zhì)中。接下來(lái)對(duì)獲取到的數(shù)據(jù)實(shí)施分析,包括數(shù)據(jù)的預(yù)處理和特征提取,數(shù)據(jù)預(yù)處理包括對(duì)軌跡數(shù)據(jù)進(jìn)行坐標(biāo)系變換和軌跡分段,對(duì)運(yùn)動(dòng)狀態(tài)數(shù)據(jù)進(jìn)行濾波與加速度修正,從其中提取出軌跡規(guī)整度、危險(xiǎn)駕駛指數(shù)、油耗以及深度變異系數(shù)這些能表示深松作業(yè)質(zhì)量的特征值,同時(shí)進(jìn)行歸一化處理,用特征值構(gòu)造訓(xùn)練樣本集。最后,選取排序?qū)W習(xí)方法中的排序支持向量機(jī)對(duì)構(gòu)建的訓(xùn)練樣本集進(jìn)行訓(xùn)練得到深松評(píng)價(jià)模型,通過(guò)對(duì)選取的不同核函數(shù)進(jìn)行參數(shù)尋優(yōu),提高了在該評(píng)價(jià)體系中的模型的準(zhǔn)確率。
[Abstract]:Our country's agricultural mass is relatively large, but its strength is not strong. There is still a long way to go in the area of agricultural modernization. In the 13th Five-Year Plan of the State, 2016-2020), the informationization and modernization of agriculture are regarded as major agricultural development projects. Compared with the traditional agriculture, modern agriculture is no longer "relying on heaven to eat and leave to fate". Instead, it collects information on crops and operations in farmland through modern science and technology, puts forward cultivation and cultivation programs according to local conditions, and makes use of large-scale agricultural machinery to carry out unified operations. In order to improve unit output, while making agricultural production to achieve sustainability. Information collection is the first step in the implementation of precision agriculture. Large-scale agricultural machinery is the main body of agricultural operations, which plays a vital role in the modernization of agricultural production. In this paper, first of all, we pay attention to the data collection of agricultural machinery in the process of farmland operation, mainly in the background of deep loosening, which is to use deep loosening machinery to loosen the deep soil of farmland, and to improve the absorption of water and fertilizer by soil. In order to evaluate the operation results, an evaluation method for deep loosening operation is put forward based on the collected data. First of all, this paper designs a kind of agricultural machinery information collection terminal based on deep loosening operation. The information collection terminal also need to upload and backup the data on the basis of the data acquisition function. The modular structure of the system is designed according to the function requirement. The hardware circuit is designed to program the software. The main work flow is to obtain the running track information, the operation depth information and the motion state information of agricultural machinery by different kinds of sensor modules. After data extraction and integration, the data is transferred to the remote server platform via the GPRS network, and the data is backed up to the local storage medium. Then the acquired data is analyzed, including data preprocessing and feature extraction. The data preprocessing includes coordinate system transformation and trajectory segmentation, filtering and acceleration correction of moving state data. The characteristic values, such as trajectory regularity, dangerous driving index, fuel consumption and depth variation coefficient, which can represent the quality of deep loosening operation, are extracted from them. At the same time, the training sample set is constructed by normalized processing. Finally, the sorting support vector machine (SVM) is selected to train the set of training samples to obtain the deep loosening evaluation model, and the parameters of the selected kernel functions are optimized. The accuracy of the model in the evaluation system is improved.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：S222;S126

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