基于无线传感器网络的猪运动行为监测系统研究
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摘要
养猪产业是我国最重要的畜牧产业。随着现代养猪产业规模化发展,对养猪自动化管理水平提出了越来越高的要求。现有的RFID电子耳标技术通讯距离短、不具有扩展性,难以满足精准智能化猪养殖的迫切需求。因此将猪的环境、饮食、行为纳入到监测的范围,实现养猪过程的全过程、全方位地跟踪监测,能有效推动智能化猪饲养水平,提高养猪产业的管理效率,进而提高养猪场的经济收益。
本文研究了将无线传感器网络(WSN)应用于养猪运动行为监测分析系统中的几个关键问题,包括圈养猪舍内无线传感器节点的边缘部署问题；猪舍内圈养猪的定位问题；猪舍内圈养猪的运动行为监测问题；基于WSN的养猪综合监测系统的架构和实体-关系(E-R)模型。研究结果如下：
1)评价圈养猪舍内无线传感器节点的边缘部署。在已有的覆盖率、覆盖效率、以及覆盖方差的基础上,提出了K重覆盖率、K重覆盖效率、K重全覆盖所需最小半径三个针对K重覆盖问题的评价指标,确立了猪舍内无线传感器节点部署方案的评价指标体系,并用该评价指标体系对4种可行性的部署方案进行了评价,得出最优部署策略,形成一套最优部署策略评价分析方法。
2)首次建立了K=1边缘覆盖问题中最少结点部署方案的计算方法。针对不同猪舍面积,不同节点通讯半径,以K=1全覆盖为目标,提出一套系统地以最少节点为目标的部署算法。在Adobe Dreamweaver软件开发平台及PHPnow互联网应用框架上,开发了猪舍无线网络节点部署边界条件及最小节点计算软件,对部署节点数进行计算验证；在Matlab软件平台上,开发了猪舍无线网络节点部署仿真分析软件,对部署方案进行仿真分析。
3)猪舍的三边直角加权质心算法的确立。对不同厂家的无线传感器节点进行研究和测试,运用曲线拟合,取得了0-80m和0-20m接收信号强度值(RSSI)与距离关系的最优模型；在三边加权质心算法的基础上,开发出针对猪舍的三边直角加权质心算法,并设计了该算法的具体运算步骤；10.43m×5.72m室内空间的测试验证表明,该算法可以达到平均误差1.346m,去掉两个特殊位置点后,平均定位误差可降低到1.0875m,每次定位时间间隔为10s。
4)研究了圈养猪运动行为的监测方法。设计了无线传感器节点的三轴加速度传感器硬件,以Z-stack协议栈为基础开发了加速度数据采集软件,并确定了与PC机通讯的通讯方法和通讯协议,实现了三轴加速度数据采集和通讯。
5)建立了神经网络模式识别分类算法。在神经网络模式识别算法的理论基础上构建了二层运动行为识别神经网络分类器,并设计了Matlab软件实现对四种运动行为(分别为走、跑、跳、静止)的识别。模拟试验证明,在85组训练样本试验时识别率达到100%,在439组大样本时,神经网络方法对走和静止两种状态的识别率达到100%,而跑和跳两种运动行为由于实际数据特征比较接近,其识别率分别达到99.1%和96.1%,整体识别率达98.9%。
6)圈养猪综合运动行为监测的研究。首次采用短时能量和短时过零率计算方法分析计算三轴加速度运动数据,设计了短时能量和短时过零率运动行为分类算法、FFT快速傅立叶运动行为分类算法和标准差运动行为分类算法。并基于VS2010平台开发了实时综合猪运动行为监测软件,通过实时模拟试验证明,采用短时能量分类法优于FFT快速傅立叶分类算法和标准差算法,对走、静止、跳和跑三大类运动行为进行识别分类时,识别率约为100%左右,区分跳、跑两种运动行为时,识别率约80%左右。
7)基于WSN的养猪综合精准监测系统框架的分析的设计。确立了基于WSN的养猪综合精准监测系统的框架结构和工作原理,并设计了网络化综合监测信息系统的实体-关系(E-R)模型。
前期研究了染色竹条分级流水线的自动控制系统及高通量下水稻育种网络信息管理系统,在此研究的技术基础上,提出了基于WSN的养猪综合监测系统关键技术的研究。本文研究结果为无线传感器网络应用于养猪综合监测系统寻求了科学的解决方案,为后期进一步实现养猪综合精准监测系统提供了技术基础,并有着广阔的应用前景。
With the industrial scale of development of pig production, the most important animal husbandry industry in China, the requirement of husbandry auto-management is put forward to higher level. Due to the limited communication distance and poor extension ability, the commonly-used RFID ear tag cannot meet the requirement of intelligent precise auto-husbandry. Thus, it will advantageous to better the management and increase the income of pig production by incorporating the environment of pig house, the behavior and diet of pigs into monitoring system to track the process timely and dynamically. The main research results are as follows:
This dissertation studies several key technologies in behavior monitoring system of pig using the wireless sensor network (WSN). It includs the WSN nodes deployment issue in pig house, the location issue of scatter-feed pigs, the movinig modes monitoring of scatter-feed pigs, and the E-R mode and structure of comprehensive monitoring system for pig production. The descriptions are as follows:
1) Peripheral deployment issue in pig house for scatter-feed pigs is studied. The evaluation index system for wireless sensor nodes deployment in pig house is established Evaluation indexes of K-Coverage Ratio, Efficiency of K-Coverage and Minimum radius for K-coverage are put forward based on existing indexes of coverage, efficiency and coverage of variance. An evaluation index system for different nodes deployments is built. Then the system is used to evaluate and optimize four deployed strategies. According to the results, a series of analysis methods of evaluating are created.
2) The calculation method of the issue of least number of nodes used for K=1peripheral covering is formed. Two softwares, one for calculating the least number of nodes, the other for simulating the deployment, are designed and programmed. Three deployment examples show the evaluation and the calculation are efficient. Based on Adobe Dreamweaver software development platforms and PHPnow on Internet application framework, a pigsty boundary condition and minimum node calculation software is developed, which can verity the number of the deployment nodes; based on the Matlab software platform, a pigsty node deployment simulation software is developed, which can analyze and simulate the deployment strategies
3) A novel piggery trilateral rectangular weighted centroid algorithm is stablished. The measured experiments is studied, using commercial sensor nodes made by different manufacturer. The optimal mode of the function of RSSI and distance within0-80m and0-20m is derived by curve-fitting. On the basis of the trilateral weighted centroid algorithm, a novel piggery trilateral rectangular weighted centroid algorithm is developed, and the procedure is designed further. Experiments of indoor space test in0.43m*5.72m show that the even location error is1.346m, and further shrink to1.0875m when two location data relative with unnormal location are removed from the data set, The time interval for locating is10s.
4) The pig moving modes monitoring is realized by three-axis accelerometer A three-axis accelerometer circuit on wireless sensor node is designed. In the wireless sensor node, an software based on Zigbee protocol stack is developed for acquiring acceleration data and sending them to the PC. The protocol for communication is studied.
5) A method of moving mode recognition neural network is studied. The two-layer moving mode recognition neural network classifier is built based on the classical theory of the neural network pattern recognition algorithm. A software based on Matlab platform is designed to achieve distinguishing four actions (walk, run, jump, stand up). Simulation experiments proved that the recognition rate for85training samples reachs100%; for439group samples, the recognition rate of neural network method in two moving status, walking and standing up, is still100%, but in running and jumping, recognition rates of the two acts, because the data characteristics more akin to each other, up to99.1%and96.1%respectively, and98.9%of overall resolution is obtained.
6) Comprehensive monitoring of swine behaviors are studied. For the first time the algorithm of short-time energy and short-time zero crossing ratio are used to classify the tri-axial acceleration data. Based on VS2010platform, a comprehensive real-time pig moving modes monitoring software is developed. Algorithms used in the software include the short-time energy and short-time zero crossing ratio classification algorithm, fast Fourier translating algorithm and a standard deviation algorithm. Real-time simulated tests proved that the classification rate of the short-time energy classification algorithm is higher than that of FFT fast Fourier classification algorithm and the standard deviation algorithm. The three categories of acting modes, including walking, standing, jumping and running, can be distinguished by the software almost complemently. The recognition rate of jumping mode and running mode is about80%.
7) Analysis and design of the pig comprehensive precise monitoring system based on WSN frame structure are performed. Subsequently, E-R model for the networked comprehensive monitoring information system.
In all the results in this paper, the hardware design is beased on the method used in automatic control system for grading pipeline of dyeing bamboo sticks; the design of E-R model for the networked comprehensive monitoring information system is built on the basis of Research of network information management system for high-throughput rice breeding. The results provide a scientific solution for application of wireless sensor network (WSN) in pig comprehensive monitoring system. They can lay good foundation for realizing the pig comprehensive accurate monitoring system in future further research works. And they will bring a broad application prospect and development of intelligent auto-husbandry pigs in the future.
本文研究了将无线传感器网络(WSN)应用于养猪运动行为监测分析系统中的几个关键问题,包括圈养猪舍内无线传感器节点的边缘部署问题；猪舍内圈养猪的定位问题；猪舍内圈养猪的运动行为监测问题；基于WSN的养猪综合监测系统的架构和实体-关系(E-R)模型。研究结果如下：
1)评价圈养猪舍内无线传感器节点的边缘部署。在已有的覆盖率、覆盖效率、以及覆盖方差的基础上,提出了K重覆盖率、K重覆盖效率、K重全覆盖所需最小半径三个针对K重覆盖问题的评价指标,确立了猪舍内无线传感器节点部署方案的评价指标体系,并用该评价指标体系对4种可行性的部署方案进行了评价,得出最优部署策略,形成一套最优部署策略评价分析方法。
2)首次建立了K=1边缘覆盖问题中最少结点部署方案的计算方法。针对不同猪舍面积,不同节点通讯半径,以K=1全覆盖为目标,提出一套系统地以最少节点为目标的部署算法。在Adobe Dreamweaver软件开发平台及PHPnow互联网应用框架上,开发了猪舍无线网络节点部署边界条件及最小节点计算软件,对部署节点数进行计算验证；在Matlab软件平台上,开发了猪舍无线网络节点部署仿真分析软件,对部署方案进行仿真分析。
3)猪舍的三边直角加权质心算法的确立。对不同厂家的无线传感器节点进行研究和测试,运用曲线拟合,取得了0-80m和0-20m接收信号强度值(RSSI)与距离关系的最优模型；在三边加权质心算法的基础上,开发出针对猪舍的三边直角加权质心算法,并设计了该算法的具体运算步骤；10.43m×5.72m室内空间的测试验证表明,该算法可以达到平均误差1.346m,去掉两个特殊位置点后,平均定位误差可降低到1.0875m,每次定位时间间隔为10s。
4)研究了圈养猪运动行为的监测方法。设计了无线传感器节点的三轴加速度传感器硬件,以Z-stack协议栈为基础开发了加速度数据采集软件,并确定了与PC机通讯的通讯方法和通讯协议,实现了三轴加速度数据采集和通讯。
5)建立了神经网络模式识别分类算法。在神经网络模式识别算法的理论基础上构建了二层运动行为识别神经网络分类器,并设计了Matlab软件实现对四种运动行为(分别为走、跑、跳、静止)的识别。模拟试验证明,在85组训练样本试验时识别率达到100%,在439组大样本时,神经网络方法对走和静止两种状态的识别率达到100%,而跑和跳两种运动行为由于实际数据特征比较接近,其识别率分别达到99.1%和96.1%,整体识别率达98.9%。
6)圈养猪综合运动行为监测的研究。首次采用短时能量和短时过零率计算方法分析计算三轴加速度运动数据,设计了短时能量和短时过零率运动行为分类算法、FFT快速傅立叶运动行为分类算法和标准差运动行为分类算法。并基于VS2010平台开发了实时综合猪运动行为监测软件,通过实时模拟试验证明,采用短时能量分类法优于FFT快速傅立叶分类算法和标准差算法,对走、静止、跳和跑三大类运动行为进行识别分类时,识别率约为100%左右,区分跳、跑两种运动行为时,识别率约80%左右。
7)基于WSN的养猪综合精准监测系统框架的分析的设计。确立了基于WSN的养猪综合精准监测系统的框架结构和工作原理,并设计了网络化综合监测信息系统的实体-关系(E-R)模型。
前期研究了染色竹条分级流水线的自动控制系统及高通量下水稻育种网络信息管理系统,在此研究的技术基础上,提出了基于WSN的养猪综合监测系统关键技术的研究。本文研究结果为无线传感器网络应用于养猪综合监测系统寻求了科学的解决方案,为后期进一步实现养猪综合精准监测系统提供了技术基础,并有着广阔的应用前景。
With the industrial scale of development of pig production, the most important animal husbandry industry in China, the requirement of husbandry auto-management is put forward to higher level. Due to the limited communication distance and poor extension ability, the commonly-used RFID ear tag cannot meet the requirement of intelligent precise auto-husbandry. Thus, it will advantageous to better the management and increase the income of pig production by incorporating the environment of pig house, the behavior and diet of pigs into monitoring system to track the process timely and dynamically. The main research results are as follows:
This dissertation studies several key technologies in behavior monitoring system of pig using the wireless sensor network (WSN). It includs the WSN nodes deployment issue in pig house, the location issue of scatter-feed pigs, the movinig modes monitoring of scatter-feed pigs, and the E-R mode and structure of comprehensive monitoring system for pig production. The descriptions are as follows:
1) Peripheral deployment issue in pig house for scatter-feed pigs is studied. The evaluation index system for wireless sensor nodes deployment in pig house is established Evaluation indexes of K-Coverage Ratio, Efficiency of K-Coverage and Minimum radius for K-coverage are put forward based on existing indexes of coverage, efficiency and coverage of variance. An evaluation index system for different nodes deployments is built. Then the system is used to evaluate and optimize four deployed strategies. According to the results, a series of analysis methods of evaluating are created.
2) The calculation method of the issue of least number of nodes used for K=1peripheral covering is formed. Two softwares, one for calculating the least number of nodes, the other for simulating the deployment, are designed and programmed. Three deployment examples show the evaluation and the calculation are efficient. Based on Adobe Dreamweaver software development platforms and PHPnow on Internet application framework, a pigsty boundary condition and minimum node calculation software is developed, which can verity the number of the deployment nodes; based on the Matlab software platform, a pigsty node deployment simulation software is developed, which can analyze and simulate the deployment strategies
3) A novel piggery trilateral rectangular weighted centroid algorithm is stablished. The measured experiments is studied, using commercial sensor nodes made by different manufacturer. The optimal mode of the function of RSSI and distance within0-80m and0-20m is derived by curve-fitting. On the basis of the trilateral weighted centroid algorithm, a novel piggery trilateral rectangular weighted centroid algorithm is developed, and the procedure is designed further. Experiments of indoor space test in0.43m*5.72m show that the even location error is1.346m, and further shrink to1.0875m when two location data relative with unnormal location are removed from the data set, The time interval for locating is10s.
4) The pig moving modes monitoring is realized by three-axis accelerometer A three-axis accelerometer circuit on wireless sensor node is designed. In the wireless sensor node, an software based on Zigbee protocol stack is developed for acquiring acceleration data and sending them to the PC. The protocol for communication is studied.
5) A method of moving mode recognition neural network is studied. The two-layer moving mode recognition neural network classifier is built based on the classical theory of the neural network pattern recognition algorithm. A software based on Matlab platform is designed to achieve distinguishing four actions (walk, run, jump, stand up). Simulation experiments proved that the recognition rate for85training samples reachs100%; for439group samples, the recognition rate of neural network method in two moving status, walking and standing up, is still100%, but in running and jumping, recognition rates of the two acts, because the data characteristics more akin to each other, up to99.1%and96.1%respectively, and98.9%of overall resolution is obtained.
6) Comprehensive monitoring of swine behaviors are studied. For the first time the algorithm of short-time energy and short-time zero crossing ratio are used to classify the tri-axial acceleration data. Based on VS2010platform, a comprehensive real-time pig moving modes monitoring software is developed. Algorithms used in the software include the short-time energy and short-time zero crossing ratio classification algorithm, fast Fourier translating algorithm and a standard deviation algorithm. Real-time simulated tests proved that the classification rate of the short-time energy classification algorithm is higher than that of FFT fast Fourier classification algorithm and the standard deviation algorithm. The three categories of acting modes, including walking, standing, jumping and running, can be distinguished by the software almost complemently. The recognition rate of jumping mode and running mode is about80%.
7) Analysis and design of the pig comprehensive precise monitoring system based on WSN frame structure are performed. Subsequently, E-R model for the networked comprehensive monitoring information system.
In all the results in this paper, the hardware design is beased on the method used in automatic control system for grading pipeline of dyeing bamboo sticks; the design of E-R model for the networked comprehensive monitoring information system is built on the basis of Research of network information management system for high-throughput rice breeding. The results provide a scientific solution for application of wireless sensor network (WSN) in pig comprehensive monitoring system. They can lay good foundation for realizing the pig comprehensive accurate monitoring system in future further research works. And they will bring a broad application prospect and development of intelligent auto-husbandry pigs in the future.
引文
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