This study aims to improve the accuracy of fire source detection, the efficiency of path planning, and the precision of firefighting operations in drone swarms during fire emergencies. It proposes an intelligent firefighting technology for drone swarms based on multi-sensor integrated path planning. The technology integrates the You Only Look Once version 8 (YOLOv8) algorithm and its optimization strategies to enhance real-time fire source detection capabilities. Additionally, this study employs multi-sensor data fusion and swarm cooperative path-planning techniques to optimize the deployment of firefighting materials and flight paths, thereby improving firefighting efficiency and precision. First, a deformable convolution module is introduced into the backbone network of YOLOv8 to enable the detection network to flexibly adjust its receptive field when processing targets, thereby enhancing fire source detection accuracy. Second, an attention mechanism is incorporated into the neck portion of YOLOv8, which focuses on fire source feature regions, significantly reducing interference from background noise and further improving recognition accuracy in complex environments. Finally, a new High Intersection over Union (HIoU) loss function is proposed to address the challenge of computing localization and classification loss for targets. This function dynamically adjusts the weight of various loss components during training, achieving more precise fire source localization and classification. In terms of path planning, this study integrates data from visual sensors, infrared sensors, and LiDAR sensors and adopts the Information Acquisition Optimizer (IAO) and the Catch Fish Optimization Algorithm (CFOA) to plan paths and optimize coordinated flight for drone swarms. By dynamically adjusting path planning and deployment locations, the drone swarm can reach fire sources in the shortest possible time and carry out precise firefighting operations. Experimental results demonstrate that this study significantly improves fire source detection accuracy and firefighting efficiency by optimizing the YOLOv8 algorithm, path-planning algorithms, and cooperative flight strategies. The optimized YOLOv8 achieved a fire source detection accuracy of 94.6% for small fires, with a false detection rate reduced to 5.4%. The wind speed compensation strategy effectively mitigated the impact of wind on the accuracy of material deployment. This study not only enhances the firefighting efficiency of drone swarms but also enables rapid response in complex fire scenarios, offering broad application prospects, particularly for urban firefighting and forest fire disaster rescue.

In response to the growing demand for sustainable aviation, a fuel consumption prediction model based on Radial Basis Function (RBF) Neural Networks was proposed. Using high-resolution onboard Quick Access Recorder (QAR) data, which contains richer flight parameters and higher accuracy, RBF models were constructed based on the extracted key influencing factors for different flight phases, including takeoff/climb, cruise, and descent/approach. The model provides a lightweight and computationally efficient solution for high-dimensional, nonlinear flight data, ensuring accuracy with lower computational burdens. It is suitable both for pre-flight ground-based fuel consumption prediction and deployment in resource-constrained onboard environments, enabling real-time prediction during flight operations. Experimental results showed that the RBF model’s prediction errors for the takeoff/climb, cruise, and descent/approach phases were 5.73%, 3.36%, and 14.04%, respectively, significantly outperforming the comparison models. The error variances from ten-fold cross-validation were 0.31%, 0.15%, and 0.29%, respectively, confirming the robustness of the model. Further analysis indicated that the model can be employed to evaluate the “fuel penalty for carrying additional fuel” patterns and enhance fuel efficiency. This study provided valuable insights and theoretical support for airlines in optimizing flight planning and minimizing fuel consumption, thereby contributing to the sustainable development of green aviation.

Accurate 4D trajectory prediction plays an important role in the sustainable management of future air traffic. Aiming at the problems of inadequate feature utilization, unbalanced overall prediction (OP) result, and weak real-time response in 4D trajectory prediction by machine learning, a fractal dimension feature-prediction (FDFP) model is proposed, starting from the airborne quick access recorder (QAR) trajectory data. Firstly, the trajectory features are classified and transformed according to the aircraft operation characteristics. Then, the long short-term memory (LSTM) network is used to construct the prediction model by fractional dimensions; based on the fractal dimension feature (FDF), the different combinations of influencing factors are selected as the feature matrix, and the optimal prediction model of each dimension is obtained. Finally, 671 city pair trajectory data are used to conduct simulation experiments to verify the accuracy and effectiveness of the model. The experimental results show that the FDFP model performs well, with the mean absolute error (MAE) of longitude and latitude both less than 0.0015°, and the MAE of altitude less than 3 m. Compared with the OP model, the MAE of the FDFP model in these three dimensions decreased by 92%, 81% and 79%, respectively. Compared with experiments without feature transformation, the MAE of the FDFP model is reduced by 75%, 82%, and 69%, respectively. Each prediction of the model takes about 30 ms, which satisfies the real-time prediction conditions and can provide a reference for air traffic operation assessment.

To improve the safety of routes, reduce route delays, and shorten the latency time, 4D trajectory prediction is carried out based on deep learning, and a 4D trajectory prediction method for aircraft in point merge approach mode is proposed. This paper takes a certain airport as an example for research. Firstly, the acquired ADS-B data is preprocessed based on the principle of point merge. Secondly, the point merge data are screened out by dividing the route data. Finally, the two models of CNN and LSTM are used to carry out the simulation and results analysis. In the comparison of the prediction ability of the two models in terms of time, latitude, longitude, and altitude, the results of the research show that the deep learning-based trajectory prediction is feasible in point merge approach mode. Furthermore, compared with the CNN model, the LSTM model can better capture the dynamic changes of the aircraft and show higher accuracy in prediction.

In recent years, the remarkable success of deep neural networks (DNNs) in computer vision is largely due to large-scale, high-quality labeled datasets. Training directly on real-world datasets with label noise may result in overfitting. The traditional method is limited to deal with closed set label noise, where noisy training data has true class labels within the known label space. However, there are some real-world datasets containing open set label noise, which means that some samples belong to an unknown class outside the known label space. To address the open set label noise problem, we introduce a method based on Robust Sample Selection and Margin-Guided Module (RSS-MGM). Firstly, unlike the prior clean sample selection approach, which only select a limited number of clean samples, a robust sample selection module combines small loss selection or high-confidence sample selection to obtain more clean samples. Secondly, to efficiently distinguish open set label noise and closed set ones, margin functions are designed to filter open-set data and closed set data. Thirdly, different processing methods are selected for different types of samples in order to fully utilize the data's prior information and optimize the whole model. Furthermore, extensive experimental results with noisy labeled data from benchmark datasets and real-world datasets, such as CIFAR-100N-C, CIFAR80N-O, WebFG-469, and Food101N, indicate that our approach outperforms many state-of-the-art label noise learning methods. Especially, it can more accurately divide open set label noise samples and closed set ones.