Research on an Intelligent Perception and Early Warning System for Biosafety Risks in Pig Farms Based on Multi-Source Data Fusion

In this paper, the design of a biosafety development based on an intelligent perception and early warning system of biosafety hazards in pig farms that consist of multi-source data fusion and modern artificial intelligence technologies is introduced. The system receives and analyzes diverse environmental monitoring data, video monitoring data, animal and operational data, records, etc., so as to be able to thoroughly track possible biosafety violations. To overcome the shortcomings associated with Convolutional Neural Networks (CNN) and the Long Short-Term Memory (LSTM) based networks, a hybrid deep learning model is designed to efficiently extract the spatial representations along with capturing temporal dynamics in the data. CNNs would analyses visual and environmental data to detect anomalies including unusual pig behaviour or environmental contamination signs and LSTM networks would use previously acquired sequential patterns to predict changing risks like disease outbreaks or environmental risks. This integration model has the capability to provide real-time risk analysis and produce preliminary warning signs with the help of an intelligent interface that gives workable feedback to farm managers.