【摘要】：近年來，隨著信息技術(shù)的不斷發(fā)展，我國對山羊畜群的健康養(yǎng)殖、安全生產(chǎn)、質(zhì)量監(jiān)管及質(zhì)量溯源的要求越來越嚴(yán)格，但是對山羊畜群行為特征監(jiān)測依然要依賴飼養(yǎng)員的直覺和經(jīng)驗來判斷，不但耗費大量人力，而且工作效率也低，已不再適合規(guī)�；�、集約化的現(xiàn)代家畜養(yǎng)殖業(yè)。 為了快速并精準(zhǔn)的判定動物行為與疫病診斷的關(guān)系，各國研究人員引入了機器視覺技術(shù)、視頻或圖像處理技術(shù)、聲學(xué)探測等行為特征識別技術(shù)，，但是這些識別技術(shù)對環(huán)境光線、攝像機的固定位置及角度有較高要求。如果想要連續(xù)不間斷地記錄動物（特別是散養(yǎng)放牧的家畜動物）的行為數(shù)據(jù)，就必須依賴于傳感器信息采集與處理技術(shù)。為此本論文設(shè)計的核心就是利用三軸加速度傳感器對山羊典型日常行為特征進行識別研究。主要內(nèi)容包括： （1）本文有針對性的對山羊的三軸加速度數(shù)據(jù)進行采集并分析，通過K-means均值聚類算法和SVM算法分別對采集的山羊三軸加速度數(shù)據(jù)集進行迭代訓(xùn)練，分析比較兩種算法對分類模型精度的影響，并結(jié)合動作發(fā)生的時間識別并驗證山羊的4種典型日常行為對應(yīng)的加速度數(shù)據(jù)模型。研究結(jié)果顯示：依據(jù)K-means均值聚類算法對山羊的躺臥、站立或慢走、采食、跨跳等典型日常行為識別的準(zhǔn)確率達(dá)87.76%，但經(jīng)過SVM算法優(yōu)化之后的典型行為識別率提高了近6個百分點。研究結(jié)果表明該算法對山羊典型日常行為的特征分類與識別能夠達(dá)到較高的分類精度，具有更高的尋優(yōu)效率，在行為識別領(lǐng)域有非常高的應(yīng)用價值。 （2）本文對山羊的生活習(xí)性進行了詳細(xì)分析，研究了三軸加速度傳感器在山羊身體部署位置與試驗結(jié)果的關(guān)系，得出了三軸加速度傳感器最佳部署位置。 （3）本文還研究了傳感器記錄間隔時間對分類精度及數(shù)據(jù)處理的影響，結(jié)果表明當(dāng)記錄間隔時間選定為2s時，可以在不影響分類精度的前提下，減少數(shù)據(jù)處理量，提高數(shù)據(jù)處理速度。 （4）本文通過建立山羊典型日常行為加速度數(shù)據(jù)模型，可以進一步判定山羊的異常行為特征，改善山羊生產(chǎn)管理系統(tǒng)中的動物福利，為判定山羊典型日常行為與疾病關(guān)系以及山羊疾病預(yù)測模型的建立提供基礎(chǔ)。
[Abstract]:In recent years, with the continuous development of information technology, the requirements of healthy breeding, safe production, quality supervision and quality traceability of goat herds in China are becoming more and more stringent. However, the monitoring of goat herd behavior still depends on the intuition and experience of the breeder, which not only consumes a lot of manpower, but also has low working efficiency, so it is no longer suitable for the large-scale and intensive modern livestock breeding industry. To quickly and accurately determine the relationship between animal behavior and disease diagnosis, researchers from all over the world have introduced behavioral feature recognition techniques such as machine vision, video or image processing, acoustic detection, etc. The fixed position and angle of the camera have high requirements. If we want to continuously record the behavior data of animals, especially those in bulk grazing, we must rely on the technology of collecting and processing information from sensors. Therefore, the core of this paper is to identify the typical daily behavior of goats by using three-axis acceleration sensor. The main contents are as follows: (1) this paper collects and analyzes the goat's triaxial acceleration data, and trains the goat's triaxial acceleration data set by K-means mean clustering algorithm and SVM algorithm. The effects of the two algorithms on the accuracy of the classification models are analyzed and compared, and the acceleration data models corresponding to the four typical daily behaviors of goats are identified and verified in combination with the time of action. The results show that the recognition accuracy of typical daily behaviors such as lying down, standing or walking, feeding and jumping is 87.76% according to the K-means mean clustering algorithm, but the recognition rate of typical behavior optimized by SVM algorithm is improved by nearly 6 percentage points. The results show that the algorithm can achieve higher classification accuracy and better optimization efficiency for the classification and recognition of goat typical daily behavior. It has very high application value in the field of behavior recognition. (2) the life habits of goats are analyzed in detail, and the relationship between the position of three-axis acceleration sensor in goat body and the experimental results is studied. The optimal deployment position of triaxial acceleration sensor is obtained. (3) the effect of recording interval time on classification accuracy and data processing is also studied. The results show that when the recording interval time is chosen as 2 s, Without affecting the classification accuracy, we can reduce the amount of data processing and improve the speed of data processing. (4) by establishing the acceleration data model of typical daily behavior of goats, we can further determine the abnormal behavior characteristics of goats. The improvement of animal welfare in goat production management system provides a basis for judging the relationship between goat typical daily behavior and disease and the establishment of goat disease prediction model.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：S827;TP212

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本文編號：2197958