Accurate and efficient forecasting of energy consumption is a crucial prerequisite for effective energy planning and policymaking. The BP neural network has been widely used in forecasting, machine learning, and various other fields due to its nonlinear fitting ability. In order to improve the prediction accuracy of the BP neural network, this paper introduces the concept of forecast lead time and establishes a mathematical model accordingly. Prior to training the neural network, the input layer data are preprocessed based on the forecast lead time model. The training and forecasting results of the BP neural network when and when not considering forecast lead time are compared and verified. The findings demonstrate that the forecast lead time model can significantly improve the prediction speed and accuracy, proving to be highly applicable for short-term energy consumption forecasting.

In the new context of information sharing to reshape the supply chain’s interruption risk propagation mechanism, this paper focuses on the interruption risk propagation and resilience of the supply chain of emergency medical supplies in the background of emergencies. Firstly, an emergency supply chain’s risk propagation model under information sharing is proposed by combining the information sharing mechanism with the supply chain’s risk propagation theory. Secondly, an interruption risk propagation model for the supply chain of emergency medical supplies based on a Bayesian Network is constructed, and the do-calculus technique is introduced to transform the intervention effect of information sharing into the quantification of the supply chain’s risk probability. Thirdly, a system dynamics method is used to construct a supply chain model for emergency medical supplies, which takes into account different interruption scenarios caused by emergencies and evaluates the supply chain’s resilience through the key variable “demand fulfillment rate” in the model. The results of the study indicate that the impact of different types of interruption scenarios on supply chain resilience varies significantly. Information sharing can effectively reduce the negative impact of interruption risk. This study provides theoretical basis and practical guidance for improving the resilience of the supply chain of emergency medical supplies, which is of great significance for maintaining social stability and promoting the sustainable development of the public health system.