本文選題：精準(zhǔn)農(nóng)業(yè) + 數(shù)據(jù)挖掘　； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：伴隨著物聯(lián)網(wǎng)技術(shù)的興起和應(yīng)用,基于位置服務(wù)的數(shù)據(jù)交換方式不斷出現(xiàn),越來越多的領(lǐng)域開始將重心放在數(shù)據(jù)本身,目前精準(zhǔn)農(nóng)業(yè)領(lǐng)域正是這樣一個(gè)以信息技術(shù)為基礎(chǔ)的領(lǐng)域。利用傳感器檢測和數(shù)據(jù)挖掘技術(shù),人們能夠準(zhǔn)確、及時(shí)地控制農(nóng)業(yè)耕作,實(shí)現(xiàn)生產(chǎn)效益最大化。本文所研究的數(shù)據(jù)挖掘方法著手于地理信息系統(tǒng)中的農(nóng)機(jī)作業(yè)信息,對(duì)相關(guān)耕作指標(biāo)進(jìn)行精準(zhǔn)測算,同時(shí)對(duì)耕作質(zhì)量進(jìn)行監(jiān)測評(píng)估,使農(nóng)機(jī)能夠盡快地調(diào)整耕作狀態(tài),從而減小不合理耕作現(xiàn)象對(duì)于農(nóng)業(yè)資源的浪費(fèi)。首先,本文對(duì)現(xiàn)有農(nóng)機(jī)管理系統(tǒng)中的設(shè)備耕作軌跡進(jìn)行數(shù)據(jù)預(yù)處理,利用高斯克呂格投影變換將原有橢球坐標(biāo)在數(shù)值和方向上盡量減少失真地投影成直角坐標(biāo);建立完善的農(nóng)機(jī)軌跡數(shù)據(jù)集,利用軌跡點(diǎn)的深度信息獲取采集軌跡坐標(biāo)的耕作路段,利用距離和時(shí)間間隔將軌跡坐標(biāo)做分段處理,利用段內(nèi)線性插值方式彌補(bǔ)了實(shí)際設(shè)備采樣不足的缺陷,提高了算法性能。其次,針對(duì)農(nóng)機(jī)平臺(tái)運(yùn)行過程中的耕作面積及相關(guān)計(jì)算指標(biāo),本文提出了基于采集軌跡坐標(biāo)的面積測算方法,能夠在移動(dòng)端精確測算農(nóng)機(jī)設(shè)備當(dāng)天耕作面積,同時(shí)利用等效軌跡乘以幅寬面積數(shù)值可以有效衡量軌跡重耕率,提出一種基于網(wǎng)格的面積測算算法覆蓋計(jì)算耕作區(qū)域面積,從而獲得耕作漏耕率�；诟鬈壽E的時(shí)間序列數(shù)據(jù)隨著時(shí)間的不斷推進(jìn),其體量會(huì)變得十分巨大,本文采用改進(jìn)的網(wǎng)格聚類算法,將這些軌跡通過聚類形成地塊,利用邊緣檢測算法提取地塊邊緣點(diǎn)和幾何中心點(diǎn),并用其代替原始耕作記錄。采用基于特定數(shù)據(jù)索引的地圖疊加算法將指定區(qū)域不同時(shí)間段圖層疊加分析,進(jìn)而檢測歷史重耕問題,提出采用區(qū)域交集方法快速計(jì)算歷史重耕面積。最后,本文提出一種基于支持向量機(jī)分類的耕作質(zhì)量評(píng)估方法,通過比較和篩選確定影響農(nóng)機(jī)耕作質(zhì)量的特征信息,將這些特征信息作為農(nóng)機(jī)耕作質(zhì)量的參考輸入序列最小化支持向量機(jī)訓(xùn)練模型中,經(jīng)過參數(shù)調(diào)優(yōu)處理最終得到適用于當(dāng)前數(shù)據(jù)平臺(tái)的質(zhì)量評(píng)估模型,為耕作提供客觀的評(píng)估標(biāo)準(zhǔn)。
[Abstract]:With the rise and application of the Internet of things technology, data exchange based on location services is emerging, and more fields begin to focus on the data itself. At present, precision agriculture is such an area based on information technology. By using sensor detection and data mining technology, people can accurately and timely control agricultural tillage and maximize the benefit of production. The data mining method studied in this paper is based on the information of agricultural machinery operation in GIS, and measures the relevant tillage index accurately. At the same time, it can monitor and evaluate the tillage quality so that the agricultural machinery can adjust the tillage status as soon as possible. In order to reduce the unreasonable farming phenomenon to the waste of agricultural resources. Firstly, this paper preprocesses the track of equipment tillage in the existing agricultural machinery management system, and uses the Gao Si Kruger projection transformation to reduce the original ellipsoid coordinates to Cartesian coordinates in the numerical and direction as far as possible. A perfect track data set of agricultural machinery is established, and the depth information of the locus points is used to obtain the cultivation section where the track coordinates are collected, and the trajectory coordinates are segmented by distance and time intervals. Intra-segment linear interpolation is used to make up for the shortage of sampling in practical equipment, and the performance of the algorithm is improved. Secondly, aiming at the tillage area and related calculation indexes in the running process of agricultural machinery platform, this paper puts forward an area measurement method based on the collection track coordinate, which can accurately measure the farming area of agricultural machinery and equipment on the moving end. At the same time, the equivalent track multiplied by the width area value can be used to measure the track retillage rate effectively. A grid-based area calculation algorithm is proposed to calculate the area of the tillage area, so as to obtain the tillage leakage rate. The volume of time series data based on tillage trajectory will become very large with the development of time. In this paper, the improved grid clustering algorithm is used to form the plots by clustering these tracks. Edge detection algorithm is used to extract the edge points and geometric center points and replace the original tillage records with them. The map overlay algorithm based on specific data index is used to analyze the layer overlay of different time periods of the designated area, and then the problem of historical retillage is detected, and the regional intersection method is proposed to calculate the area of historical retillage quickly. Finally, a method of farming quality evaluation based on support vector machine classification is proposed in this paper. The characteristic information that affects farming quality is determined by comparison and screening. The characteristic information is used as the reference input sequence of agricultural machinery tillage quality minimization support vector machine training model. After parameter optimization processing, the quality evaluation model suitable for current data platform is obtained. Provide objective assessment criteria for farming.
【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
【分類號(hào)】：S22;TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 黃文明;莫陽;;基于文本加權(quán)KNN算法的中文垃圾短信過濾[J];計(jì)算機(jī)工程;2017年03期

2 劉二輝;姚錫凡;;基于改進(jìn)遺傳算法的自動(dòng)導(dǎo)引小車路徑規(guī)劃及其實(shí)現(xiàn)平臺(tái)[J];計(jì)算機(jī)集成制造系統(tǒng);2017年03期

3 馬躍峰;梁循;周小平;;一種基于全局代表點(diǎn)的快速最小二乘支持向量機(jī)稀疏化算法[J];自動(dòng)化學(xué)報(bào);2017年01期

4 李斌;王勁松;黃瑋;;一種大數(shù)據(jù)環(huán)境下的新聚類算法[J];計(jì)算機(jī)科學(xué);2015年12期

5 李學(xué)龍;龔海剛;;大數(shù)據(jù)系統(tǒng)綜述[J];中國科學(xué):信息科學(xué);2015年01期

6 楊盛琴;;不同國家精準(zhǔn)農(nóng)業(yè)的發(fā)展模式分析[J];世界農(nóng)業(yè);2014年11期

7 周開樂;楊善林;丁帥;羅賀;;聚類有效性研究綜述[J];系統(tǒng)工程理論與實(shí)踐;2014年09期

8 何清;李寧;羅文娟;史忠植;;大數(shù)據(jù)下的機(jī)器學(xué)習(xí)算法綜述[J];模式識(shí)別與人工智能;2014年04期

9 范俊甫;馬廷;周成虎;周玉科;許濤;;分治法在GIS多邊形快速合并算法中的應(yīng)用及效率提升評(píng)價(jià)模型[J];地球信息科學(xué)學(xué)報(bào);2014年02期

10 羅丹;徐艷;王躍朋;張鳳榮;;基于地塊面積的土地整理耕作效率測算方法研究[J];中國土地科學(xué);2013年06期

，

本文編號(hào)：1949112