Facility poultry breeding environment digital twin regulation method based on spatiotemporal data deep fusion

Abstract

This application discloses a digital twin control method for facility poultry farming environments based on deep spatiotemporal data fusion, relating to the field of poultry farming technology. It proposes a spatiotemporal sequence prediction model for environmental parameters using a bidirectional long short-term memory network (VMD-Attention-BiLSTM) that integrates variational mode decomposition and attention mechanisms, effectively overcoming the system's large time lag. A digital twin decision engine based on nonlinear model predictive control is constructed, and the optimal control sequence for energy consumption and environmental quality is solved under multiple constraints by establishing a joint state equation for thermodynamics and gas diffusion. Specifically, for complex winter conditions, a ventilation-heating collaborative game strategy based on real-time heat loss compensation is designed. Adaptive sliding mode control with radial basis function neural network compensation is introduced into the underlying execution unit, significantly enhancing the system's anti-disturbance capability. This achieves precise environmental control under complex conditions, improving poultry welfare and energy efficiency.