Research on virtual collection method of layer house temperature for the construction requirements of digital twin system

Abstract

At present, in the context of the highly intensive development of livestock and poultry breeding, digital management is becoming increasingly important, and digital twin systems are gradually being applied. To solve the contradiction between data acquisition and sensor network congestion, a virtual acquisition method based on historical data and real-time reference of point data is proposed when constructing a digital twin system. Firstly, computational fluid dynamics (CFD) simulation was used to analyze and determine the temperature distribution and environmental characteristics inside the layer house, and the collection area was preliminarily divided according to the CFD simulation results. Then, combined with gray correlation degree and cosine similarity analysis, it can effectively identify the reference points highly correlated with the temperature of the key unmonitored area. Finally, WOA was used to optimize the BiLSTM hyperparameters and construct a WOA-BiLSTM virtual acquisition model. It is based on the XGBoost algorithm to determine the actual data collection points, predict the current value based on the actual data of the reference point and the historical data of the test point, and complete virtual collection. Through the test in a farm, the average absolute error between the data of 10 virtual collection points and the actual data was within 0.25 °C, which ensured the reliability of the data. It analyzes the data volume requirements for digital twin modeling and theoretically verifies the supporting role of virtual collection in the construction of digital twin systems.