Anshun University School of Mathematics and Computer Science, Anshun, Guizhou 561300, China.
Guizhou University of Finance and Economics, Guiyang, Guizhou 550026, China.
Comput Intell Neurosci. 2022 Sep 10;2022:6377043. doi: 10.1155/2022/6377043. eCollection 2022.
Currently, livestock and poultry farming is gradually developing towards modernization and scale, and closed livestock and poultry farms are widely used for poultry feeding management, but at the same time, the farming risks of large-scale farms are increasing. In this paper, based on the study of wireless sensor networks and biological neural network models, the environmental factors that mainly affect the growth of domestic rabbits are analyzed as an example, and the technology is used to design and implement an environmental monitoring system for modern farms. The design of the system is divided into three main parts: hardware design of each node, software design, and upper computer monitoring software design. The hardware part of the system uses coordinator nodes, router nodes, sensor nodes, and control nodes to form a wireless sensor network in the farm, carries out the hardware circuit design of each node, and based on the protocol stack, designs the software program of each node to realize the collection, transmission, and regulation of environmental information in the farm. In the upper computer part, the design and development of the upper computer monitoring software interface are used to complete the real-time display of environmental data, historical query, database storage, and curve drawing, and to design a remote client data query system based on the architecture to realize the query of environmental data of the farm by remote users and to carry out monitoring fault intelligent identification alarm. At the same time, the paper investigates the optimal deployment of wireless sensor network nodes and searches for the optimal location of sensor nodes through an improved biological neural network algorithm to maximize the network coverage and reduce the coverage of blind areas, and conducts simulation experiments with the coverage rate of a rabbit farm as the optimization target.
目前，畜牧业正逐渐向现代化和规模化方向发展，封闭式养殖场被广泛应用于家禽饲养管理，但与此同时，大规模养殖场的养殖风险也在增加。本文以影响家兔生长的主要环境因素为例，基于无线传感器网络和生物神经网络模型的研究，利用相关技术设计并实现了现代化养殖场环境监测系统。系统设计分为三个主要部分：各节点的硬件设计、软件设计和上位机监控软件设计。系统的硬件部分采用协调器节点、路由器节点、传感器节点和控制节点在养殖场内组成无线传感器网络，进行各节点的硬件电路设计，并基于协议栈设计各节点的软件程序，实现养殖场内环境信息的采集、传输和调节。在上位机部分，完成了上位机监控软件界面的设计与开发，实现了环境数据的实时显示、历史查询、数据库存储和曲线绘制，并基于该架构设计了远程客户端数据查询系统，实现了远程用户对养殖场环境数据的查询和监测故障智能识别报警。同时，本文还对无线传感器网络节点的最优部署进行了研究，通过改进的生物神经网络算法搜索传感器节点的最优位置，以最大化网络的覆盖范围，减少盲区的覆盖，以兔场的覆盖率作为优化目标进行了仿真实验。
