Network Architecture for Intelligent Identification of Faults in Rabbit Farm Environment Monitoring Based on a Biological Neural Network Model.

Abstract

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.