## 1 Introduction
The paper takes into account the transition phase of the UAV flight, including acceleration and deceleration in horizontal motion, to make the model more accurate and complete. However, the model requires significant computational resources for training and inference, which demands high-performance equipment and high-precision data support. In general, the black-box method uses data-driven machine learning or statistical models to estimate UAV energy consumption without relying on detailed physical modeling or parametric analysis. This adaptive approach accommodates different operating scenarios and workload conditions but requires significant time and resources for data acquisition and pre-processing, while failing to capture the interrelationships within the model. Regarding the white-box method, [PERSON] et al. [29] developed a quadrotor UAV energy consumption model by integrating rotor dynamics and battery electronics. They used polynomial interpolation to determine the rotor efficiency function, which improved the accuracy of the model. However, this approach does not consider aerodynamics and lacks practical experimental validation. [PERSON] et al. [30] developed a comprehensive rotor UAV propulsion energy consumption model that considers variations in the thrust-to-weight ratio under different speed and direction change conditions. This model provides a more realistic representation of propulsion energy consumption during actual flight scenarios. However, the proposed model is challenging as it involves a non-convex programming problem, making it difficult to obtain an optimal solution. [PERSON] et al. [31] derived a model for UAV flight energy consumption that incorporates the UAV acceleration and deceleration rates as a function of duration. The model is presented as a closed-form expression that accounts for the energy consumed during the flight acceleration and deceleration processes. This inclusion improves the reliability and accuracy of the model. However, because as it does not include external factors such as air turbulence, it may be subject to bias in practical applications. In contrast to black-box methods, white-boxmethods use physical or mathematical models to estimate UAV energy consumption. This approach provides a deeper insight into the inner workings of the model, ensuring better interpretability and adaptability to specific needs and scenarios. It also allows further optimization of UAV parameters to achieve energy savings. In addition, previous research has presented various optimization methods to minimize the energy consumption of UAVs during flight paths. [PERSON] et al. [32] considered propulsion energy consumption as a measure of total energy and introduced a system parameter to balance mission time and maximum flight speed. They also developed an energy-efficient deployment strategy based on trajectory planning to maximize the efficiency of propulsion energy consumption. However, their study does not consider how different combinations of mission time and maximum flight velocity affect the optimization results for a given propulsion energy consumption efficiency. [PERSON] et al. [33] proposed an improved path planning method by integrating the classical particle swarm optimization (PSO) algorithm with the deep deterministic policy gradient (DDPG) model. Their objective was to generate an optimal path with minimum energy consumption in complex terrain environments. However, this approach increases complexity and limits practicality for real-world applications. [PERSON] et al. [34] proposed a set of efficient energy-optimized speed control algorithms to investigate the influence of aerodynamics on the overall energy consumption of UAVs and demonstrated superior optimization performance under stable aerodynamic conditions. As demonstrated in their series of studies, the collective works of [PERSON] et al. provided a comprehensive perspective on optimizing complex systems.