發(fā)布時(shí)間：2018-04-12 12:28

  本文選題：無(wú)線傳感器網(wǎng)絡(luò) + 傳感器部署��； 參考：《北京郵電大學(xué)》2017年碩士論文

【摘要】：農(nóng)業(yè)是國(guó)家現(xiàn)代化的基礎(chǔ),也是全面建成小康社會(huì)的重點(diǎn)和難點(diǎn)。當(dāng)前我國(guó)仍處于由傳統(tǒng)農(nóng)業(yè)向現(xiàn)代農(nóng)業(yè)轉(zhuǎn)變的關(guān)鍵時(shí)期。大田狀態(tài)感知的數(shù)據(jù)量多、結(jié)構(gòu)復(fù)雜,引入數(shù)據(jù)聚類方法能夠有效挖掘感知數(shù)據(jù)的內(nèi)在聯(lián)系,從而為過(guò)濾冗余數(shù)據(jù)和合理優(yōu)化傳感器部署提供了可行方案。為此,本文在國(guó)家支撐計(jì)劃的支持下,針對(duì)大田狀態(tài)感知的數(shù)據(jù)冗余大和傳感器部署混疊的問(wèn)題,研究融合特征距離與信息熵的大田土壤數(shù)據(jù)聚類方法,主要研究?jī)?nèi)容如下:1、面向大田作業(yè)的數(shù)據(jù)效能分析和聚類算法支撐。研究當(dāng)前大田作業(yè)的空氣和土壤的特征變化情況,得到大田作業(yè)感知數(shù)據(jù)的維度和時(shí)效需求。研究藍(lán)牙、射頻、Zigbee等不同傳感器網(wǎng)絡(luò)在大田作業(yè)中的數(shù)據(jù)感知能力。分析BIRCH、STING、DENCLUE以及k-means等四種數(shù)據(jù)聚類算法的數(shù)據(jù)處理能力。2、融合特征距離與信息熵?cái)?shù)據(jù)聚類算法。針對(duì)現(xiàn)有聚類算法的聚類簇難以確定,以及初始聚類中心敏感的問(wèn)題,本文提出融合特征距離與信息熵?cái)?shù)據(jù)聚類算法,對(duì)多維擴(kuò)散的感知進(jìn)行數(shù)據(jù)標(biāo)準(zhǔn)量化,降低原始數(shù)據(jù)的偏差值�；跉W式聚類因子對(duì)感知數(shù)據(jù)進(jìn)行初級(jí)聚類,得到概略的聚類簇,限定數(shù)據(jù)的分類范圍�；诟怕跃垲惔�,仍需要進(jìn)一步提升數(shù)據(jù)的聚類程度。針對(duì)非均勻稀疏數(shù)據(jù)的處理效果差的問(wèn)題,提出融合熵增減的聚類優(yōu)化,構(gòu)建基于熵增減的多目標(biāo)聚類準(zhǔn)則函數(shù),通過(guò)級(jí)聯(lián)熵增減的多目標(biāo)聚類收斂方法,實(shí)現(xiàn)面向大田作業(yè)狀態(tài)感知數(shù)據(jù)的最優(yōu)聚類。通過(guò)仿真表明,融合特征距離與信息熵?cái)?shù)據(jù)聚類算法能夠提升數(shù)據(jù)能效2.3%。3、研制特征距離與信息熵?cái)?shù)據(jù)聚類的大田墑情監(jiān)測(cè)與預(yù)測(cè)系統(tǒng)。設(shè)計(jì)并完成大田墑情監(jiān)測(cè)與預(yù)測(cè)系統(tǒng)的總體架構(gòu)、硬件架構(gòu)、軟件架構(gòu)和數(shù)據(jù)庫(kù)字典。基于級(jí)聯(lián)熵增減的非均勻稀疏數(shù)據(jù)聚類算法,優(yōu)化大田傳感器部署。通過(guò)搭建大田墑情監(jiān)測(cè)與預(yù)測(cè)系統(tǒng)對(duì)融合特征距離與信息熵的大田土壤數(shù)聚類方法進(jìn)行測(cè)試和性能分析。利用河南長(zhǎng)葛試驗(yàn)田采集到的數(shù)據(jù)為聚類樣本進(jìn)行聚類,根據(jù)聚類結(jié)果指導(dǎo)當(dāng)?shù)氐膫鞲衅鞑渴?大田覆蓋信息度為93%,利用優(yōu)化前后的數(shù)據(jù)對(duì)大田墑情進(jìn)行預(yù)測(cè)。實(shí)測(cè)數(shù)據(jù)表明,基于融合特征距離與信息熵的數(shù)據(jù)聚類方法的傳感器部署方案采集的數(shù)據(jù)對(duì)大田土壤墑情預(yù)測(cè)平均誤差為0.016。
[Abstract]:Agriculture is the foundation of national modernization and the key point and difficulty of building a well-off society in an all-round way.At present, our country is still in the key period of transition from traditional agriculture to modern agriculture.Because of the large amount of data and complex structure, the data clustering method can effectively mine the internal relationship of the perceived data, which provides a feasible scheme for filtering redundant data and optimizing the sensor deployment.Therefore, in this paper, with the support of the State support Plan, aiming at the problem of large data redundancy in field state perception and the problem of sensor deployment aliasing, a field soil data clustering method based on the fusion of feature distance and information entropy is studied.The main research contents are as follows: 1, data efficiency analysis and clustering algorithm support for field operations.The changes of air and soil characteristics of field operations were studied, and the dimension and time requirement of field job perceptual data were obtained.The data sensing ability of different sensor networks such as Bluetooth and RF Zigbee in field operation is studied.This paper analyzes the data processing ability of four data clustering algorithms, such as Birch and STINGNCLUE and k-means, and combines the feature distance and information entropy data clustering algorithm.In view of the difficulty to determine the clustering clusters of the existing clustering algorithms and the sensitivity of the initial clustering centers, this paper proposes a data clustering algorithm based on the fusion of feature distance and information entropy, which quantifies the perception of multidimensional diffusion.Reduces the deviation value of the original data.Based on the Euclidean clustering factor, the perceptual data is preliminarily clustered, and a general clustering cluster is obtained, which limits the classification range of the data.Based on the general clustering, it is necessary to further improve the clustering degree of data.Aiming at the problem of poor processing effect of non-uniform sparse data, the clustering optimization of fusion entropy increase or decrease is proposed, and the multi-objective clustering criterion function based on entropy increase and subtraction is constructed, and the multi-objective clustering convergence method based on cascade entropy increase or decrease is proposed.The optimal clustering for field job state perception data is realized.The simulation results show that the feature distance and information entropy data clustering algorithm can improve the data energy efficiency of 2.33. 3. A field soil moisture monitoring and forecasting system based on feature distance and information entropy data clustering is developed.The overall structure, hardware structure, software architecture and database dictionary of the field moisture monitoring and forecasting system are designed and completed.A nonuniform sparse data clustering algorithm based on concatenated entropy increases and decreases to optimize sensor deployment in the field.The field soil moisture monitoring and forecasting system was set up to test and analyze the performance of the field soil number clustering method which combines the characteristic distance and the information entropy.The data collected from Changge experimental field in Henan Province were used as clustering samples. According to the clustering results, the local sensor deployment was guided. The information degree of field coverage was 933. The soil moisture content was predicted by the data before and after optimization.The measured data show that the average error of soil moisture prediction based on the sensor deployment scheme based on data clustering method based on feature distance and information entropy is 0.016.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：S126;TP311.13

【相似文獻(xiàn)】

相關(guān)期刊論文 前3條

1 張麗娜;姜新華;那日蘇;;基于改進(jìn)的ISODATA算法的大樣本數(shù)據(jù)聚類方法研究[J];內(nèi)蒙古農(nóng)業(yè)大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年01期

2 汪雪紅;焦清局;常盼盼;黃繼風(fēng);;基于最小編碼長(zhǎng)度的基因數(shù)據(jù)聚類(英文)[J];Agricultural Science & Technology;2012年06期

3 汪雪紅;焦清局;常盼盼;黃繼風(fēng);;基于最小編碼長(zhǎng)度的基因數(shù)據(jù)聚類[J];安徽農(nóng)業(yè)科學(xué);2012年19期

相關(guān)會(huì)議論文 前6條

1 武森;張桂瓊;王瑩;吳玲玉;;容差集合差異度高維不完備數(shù)據(jù)聚類[A];第十二屆中國(guó)管理科學(xué)學(xué)術(shù)年會(huì)論文集[C];2010年

2 吳繼兵;李心科;;基于分治融合的混合屬性數(shù)據(jù)聚類算法研究[A];全國(guó)第20屆計(jì)算機(jī)技術(shù)與應(yīng)用學(xué)術(shù)會(huì)議(CACIS·2009)暨全國(guó)第1屆安全關(guān)鍵技術(shù)與應(yīng)用學(xué)術(shù)會(huì)議論文集（下冊(cè)）[C];2009年

3 高學(xué)東;周磊;;Web點(diǎn)擊流數(shù)據(jù)聚類算法[A];全國(guó)第十屆企業(yè)信息化與工業(yè)工程學(xué)術(shù)年會(huì)論文集[C];2006年

4 陶紅霞;車建華;劉國(guó)華;;基于聚類的個(gè)性化推薦算法[A];2005年全國(guó)理論計(jì)算機(jī)科學(xué)學(xué)術(shù)年會(huì)論文集[C];2005年

5 王琳;羅可;羅永紅;;一種基于混合的IGA & PSO的數(shù)據(jù)聚類算法[A];2008'中國(guó)信息技術(shù)與應(yīng)用學(xué)術(shù)論壇論文集（二）[C];2008年

6 武森;高學(xué)東;;高維稀疏數(shù)據(jù)聚類的CABOSFV算法[A];第七屆北京青年科技論文評(píng)選獲獎(jiǎng)?wù)撐募痆C];2003年

相關(guān)博士學(xué)位論文 前4條

1 程媛;多關(guān)系數(shù)據(jù)聚類算法及其應(yīng)用研究[D];哈爾濱工程大學(xué);2014年

2 白天;生物醫(yī)學(xué)數(shù)據(jù)聚類方法研究[D];吉林大學(xué);2012年

3 彭柳青;高維高噪聲數(shù)據(jù)聚類中關(guān)鍵問(wèn)題研究[D];西安電子科技大學(xué);2011年

4 孫志偉;空間數(shù)據(jù)聚類的研究[D];天津大學(xué);2007年

相關(guān)碩士學(xué)位論文 前10條

1 谷云霞;融合特征距離與信息熵的大田土壤數(shù)據(jù)聚類方法[D];北京郵電大學(xué);2017年

2 李凱;基于網(wǎng)絡(luò)優(yōu)化最小樹(shù)的大數(shù)據(jù)聚類分析研究[D];蘭州大學(xué);2015年

3 楊果利;基于模糊聚類的大規(guī)模數(shù)據(jù)聚類算法及其應(yīng)用研究[D];西安電子科技大學(xué);2014年

4 宋翰瀛;基于C-MDP的決策優(yōu)化系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[D];南京大學(xué);2014年

5 曹陽(yáng);基于復(fù)雜網(wǎng)絡(luò)的網(wǎng)絡(luò)大數(shù)據(jù)聚類應(yīng)用研究[D];蘭州交通大學(xué);2016年

6 譚佳;高維分類數(shù)據(jù)聚類方法研究與實(shí)現(xiàn)[D];東華大學(xué);2017年

7 王建新;符號(hào)型數(shù)據(jù)聚類算法的研究[D];山西大學(xué);2016年

8 王_g欣;有標(biāo)簽的數(shù)據(jù)聚類算法[D];天津工業(yè)大學(xué);2017年

9 羅陽(yáng);基于大數(shù)據(jù)處理的森林生態(tài)站數(shù)據(jù)聚類研究[D];北京林業(yè)大學(xué);2016年

10 張小宇;基于圖論的符號(hào)型數(shù)據(jù)聚類算法研究[D];山西大學(xué);2009年

，

本文編號(hào)：1739763