本文關(guān)鍵詞： 養(yǎng)豬 無線傳感器網(wǎng)絡(luò) 部署 定位 運動行為監(jiān)測　出處：《華中農(nóng)業(yè)大學》2014年博士論文　論文類型：學位論文

【摘要】：養(yǎng)豬產(chǎn)業(yè)是我國最重要的畜牧產(chǎn)業(yè)。隨著現(xiàn)代養(yǎng)豬產(chǎn)業(yè)規(guī)�；l(fā)展,對養(yǎng)豬自動化管理水平提出了越來越高的要求。現(xiàn)有的RFID電子耳標技術(shù)通訊距離短、不具有擴展性,難以滿足精準智能化豬養(yǎng)殖的迫切需求。因此將豬的環(huán)境、飲食、行為納入到監(jiān)測的范圍,實現(xiàn)養(yǎng)豬過程的全過程、全方位地跟蹤監(jiān)測,能有效推動智能化豬飼養(yǎng)水平,提高養(yǎng)豬產(chǎn)業(yè)的管理效率,進而提高養(yǎng)豬場的經(jīng)濟收益。 本文研究了將無線傳感器網(wǎng)絡(luò)(WSN)應用于養(yǎng)豬運動行為監(jiān)測分析系統(tǒng)中的幾個關(guān)鍵問題,包括圈養(yǎng)豬舍內(nèi)無線傳感器節(jié)點的邊緣部署問題；豬舍內(nèi)圈養(yǎng)豬的定位問題；豬舍內(nèi)圈養(yǎng)豬的運動行為監(jiān)測問題；基于WSN的養(yǎng)豬綜合監(jiān)測系統(tǒng)的架構(gòu)和實體-關(guān)系(E-R)模型。研究結(jié)果如下： 1)評價圈養(yǎng)豬舍內(nèi)無線傳感器節(jié)點的邊緣部署。在已有的覆蓋率、覆蓋效率、以及覆蓋方差的基礎(chǔ)上,提出了K重覆蓋率、K重覆蓋效率、K重全覆蓋所需最小半徑三個針對K重覆蓋問題的評價指標,確立了豬舍內(nèi)無線傳感器節(jié)點部署方案的評價指標體系,并用該評價指標體系對4種可行性的部署方案進行了評價,得出最優(yōu)部署策略,形成一套最優(yōu)部署策略評價分析方法。 2)首次建立了K=1邊緣覆蓋問題中最少結(jié)點部署方案的計算方法。針對不同豬舍面積,不同節(jié)點通訊半徑,以K=1全覆蓋為目標,提出一套系統(tǒng)地以最少節(jié)點為目標的部署算法。在Adobe Dreamweaver軟件開發(fā)平臺及PHPnow互聯(lián)網(wǎng)應用框架上,開發(fā)了豬舍無線網(wǎng)絡(luò)節(jié)點部署邊界條件及最小節(jié)點計算軟件,對部署節(jié)點數(shù)進行計算驗證；在Matlab軟件平臺上,開發(fā)了豬舍無線網(wǎng)絡(luò)節(jié)點部署仿真分析軟件,對部署方案進行仿真分析。 3)豬舍的三邊直角加權(quán)質(zhì)心算法的確立。對不同廠家的無線傳感器節(jié)點進行研究和測試,運用曲線擬合,取得了0-80m和0-20m接收信號強度值(RSSI)與距離關(guān)系的最優(yōu)模型；在三邊加權(quán)質(zhì)心算法的基礎(chǔ)上,開發(fā)出針對豬舍的三邊直角加權(quán)質(zhì)心算法,并設(shè)計了該算法的具體運算步驟；10.43m×5.72m室內(nèi)空間的測試驗證表明,該算法可以達到平均誤差1.346m,去掉兩個特殊位置點后,平均定位誤差可降低到1.0875m,每次定位時間間隔為10s。 4)研究了圈養(yǎng)豬運動行為的監(jiān)測方法。設(shè)計了無線傳感器節(jié)點的三軸加速度傳感器硬件,以Z-stack協(xié)議棧為基礎(chǔ)開發(fā)了加速度數(shù)據(jù)采集軟件,并確定了與PC機通訊的通訊方法和通訊協(xié)議,實現(xiàn)了三軸加速度數(shù)據(jù)采集和通訊。 5)建立了神經(jīng)網(wǎng)絡(luò)模式識別分類算法。在神經(jīng)網(wǎng)絡(luò)模式識別算法的理論基礎(chǔ)上構(gòu)建了二層運動行為識別神經(jīng)網(wǎng)絡(luò)分類器,并設(shè)計了Matlab軟件實現(xiàn)對四種運動行為(分別為走、跑、跳、靜止)的識別。模擬試驗證明,在85組訓練樣本試驗時識別率達到100%,在439組大樣本時,神經(jīng)網(wǎng)絡(luò)方法對走和靜止兩種狀態(tài)的識別率達到100%,而跑和跳兩種運動行為由于實際數(shù)據(jù)特征比較接近,其識別率分別達到99.1%和96.1%,整體識別率達98.9%。 6)圈養(yǎng)豬綜合運動行為監(jiān)測的研究。首次采用短時能量和短時過零率計算方法分析計算三軸加速度運動數(shù)據(jù),設(shè)計了短時能量和短時過零率運動行為分類算法、FFT快速傅立葉運動行為分類算法和標準差運動行為分類算法。并基于VS2010平臺開發(fā)了實時綜合豬運動行為監(jiān)測軟件,通過實時模擬試驗證明,采用短時能量分類法優(yōu)于FFT快速傅立葉分類算法和標準差算法,對走、靜止、跳和跑三大類運動行為進行識別分類時,識別率約為100%左右,區(qū)分跳、跑兩種運動行為時,識別率約80%左右。 7)基于WSN的養(yǎng)豬綜合精準監(jiān)測系統(tǒng)框架的分析的設(shè)計。確立了基于WSN的養(yǎng)豬綜合精準監(jiān)測系統(tǒng)的框架結(jié)構(gòu)和工作原理,并設(shè)計了網(wǎng)絡(luò)化綜合監(jiān)測信息系統(tǒng)的實體-關(guān)系(E-R)模型。 前期研究了染色竹條分級流水線的自動控制系統(tǒng)及高通量下水稻育種網(wǎng)絡(luò)信息管理系統(tǒng),在此研究的技術(shù)基礎(chǔ)上,提出了基于WSN的養(yǎng)豬綜合監(jiān)測系統(tǒng)關(guān)鍵技術(shù)的研究。本文研究結(jié)果為無線傳感器網(wǎng)絡(luò)應用于養(yǎng)豬綜合監(jiān)測系統(tǒng)尋求了科學的解決方案,為后期進一步實現(xiàn)養(yǎng)豬綜合精準監(jiān)測系統(tǒng)提供了技術(shù)基礎(chǔ),并有著廣闊的應用前景。
[Abstract]:The pig breeding industry is one of the most important animal husbandry industries in China . With the development of modern pig breeding industry , the demand for automatic management of pig is put forward . The existing RFID electronic ear tag technology has short communication distance and no expansibility , it is difficult to meet the urgent need of precision intelligent pig breeding . Therefore , the whole process of pig breeding process is realized , the monitoring can be carried out in all directions , the intelligent pig raising level can be effectively promoted , the management efficiency of the pig industry is improved , and the economic benefit of the pig farm is improved . This paper studies several key problems in the monitoring and analysis system of pig ' s movement behavior by applying wireless sensor network ( WSN ) to pig ' s movement behavior monitoring and analysis system , including the problem of edge deployment of pig house wireless sensor node , the problem of pig ' s movement behavior monitoring in pig house , and the structure and entity - relation ( E - R ) model of pig integrated monitoring system based on WSN . The results are as follows : 1 ) Based on the existing coverage , coverage efficiency and coverage variance , three evaluation indexes of K weight coverage , K weight covering efficiency and minimum radius required for K weight full coverage are put forward . The evaluation index system of wireless sensor node deployment scheme in pigsty is established , and four feasible deployment schemes are evaluated by using the evaluation index system , and an optimal deployment strategy evaluation method is formed . 2 ) The calculation method of minimum node deployment scheme in K = 1 edge coverage problem is established for the first time . For different pigsty area , different node communication radius and K = 1 full coverage , a set of deployment algorithm is proposed , which is based on minimum node . On the framework of Adobe Dreamweaver software development platform and PHPnow Internet application framework , the deployment boundary conditions and minimum node computing software are developed , and the deployment node number is calculated and verified . On the Matlab software platform , the deployment simulation analysis software of the wireless node of the pigsty is developed , and the deployment plan is simulated and analyzed . 3 ) The establishment of a three - sided right - angle weighted centroid algorithm for pigsty . The wireless sensor nodes of different manufacturers are studied and tested . Based on the three - side weighted centroid algorithm , a three - sided right - angle weighted centroid algorithm is developed for pigsty . The algorithm can achieve the average error of 1.346m , and the average positioning error can be reduced to 1.0875m after removing two special position points , and the time interval is 10s . 4 ) The monitoring method of the movement behavior of the loop pig is studied . The hardware of the triaxial acceleration sensor of the wireless sensor node is designed , the acceleration data acquisition software is developed based on the Z - stack protocol stack , and the communication method and the communication protocol for communication with the PC are determined , and the three - axis acceleration data acquisition and communication are realized . 5 ) The neural network pattern recognition classification algorithm is established . Based on the theory of neural network pattern recognition algorithm , two - layer motion behavior recognition neural network classifier is constructed , and Matlab software is designed to realize the recognition of four kinds of motion behaviors ( walking , running , jumping and rest ) . 6 ) The research on the monitoring of the comprehensive sports behavior of the pigs . The short - time energy and the short - time zero - crossing rate calculation method are used to analyze the three - axis acceleration motion data , the short - time energy and the short - time zero - crossing rate motion behavior classification algorithm , the FFT fast Fourier motion behavior classification algorithm and the standard deviation motion behavior classification algorithm are designed . The real - time comprehensive swine motion behavior monitoring software is developed based on the VS2010 platform . 7 ) The design of the framework of the pig comprehensive precision monitoring system based on WSN is designed . The frame structure and working principle of the pig integrated precision monitoring system based on WSN are established , and the entity - relation ( E - R ) model of the networked integrated monitoring information system is designed . This paper studies the automatic control system and the information management system of rice breeding network under high flux . Based on this research , the key technology of pig comprehensive monitoring system based on WSN is studied in this paper . The result is that wireless sensor network is applied to pig comprehensive monitoring system to seek scientific solution , which provides the technical basis for further realization of pig comprehensive precision monitoring system in the later stage , and has wide application prospect .

【學位授予單位】：華中農(nóng)業(yè)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TP212.9;TN929.5;S828

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