UAV trajectory prediction is the core technology for autonomous UAV flight and is a prerequisite for control and navigation. In this paper, the UAV flight path prediction model is established by collecting the flight data of the actual UAV. Firstly, the UAV flight information collection and data preprocessing are carried out; Secondly, the UAV flight state recognition model is established based on the PCA-SVM model to identify five UAV flight states; Finally, the flight path prediction model of UAV based on flight state recognition is established, and the neural network model is established based on the flight path of five flight state recognition. The experimental results show that: 1) The accuracy of UAV flight state recognition based on PCA-SVM is more than 90%. 2) The average prediction error of the traditional neural network UAV trajectory is 0.422m, and the maximum error of the circling state is 0.84m. 3) The average prediction error of the UAV flight path based on flight state recognition is 0.214m, and the maximum error of the circling state is 0.41m. The model error is less than 0.5m. The results show that the prediction model with flight state recognition has significantly less error than the direct UAV trajectory prediction, and the prediction model with flight state recognition predicts better than the traditional Unscented Kalman Filter method.

With the rapid development of electronic science and technology, the research on wearable devices is constantly updated. Still, the current wearable devices are not yet comprehensive in recognizing and analyzing the movement patterns of special sports. Based on this, this paper improves wearable devices for table tennis, a single sport, and realizes the recognition and evaluation of table tennis players’ motor skills based on the pattern recognition method and hierarchical evaluation system. First, a table tennis player’s motion information acquisition device was designed and the collected motion data were processed. Secondly, a sliding window was designed to segment the collected motion data and establish a feature database of six table tennis benchmark movements. Secondly, the motion features were constructed based on feature engineering, and after dimensionality reduction, the different models were used for action skill recognition. Finally, a hierarchical evaluation system of movement skills was established and the loss functions of different evaluation indexes were given. The experimental results show that in table tennis players’ action skill recognition, the neural network model based on motor feature extraction proposed in this paper improves the recognition accuracy by 6.5% than the traditional convolutional neural network, and in table tennis players’ action skill evaluation, the ANOVA shows that the hierarchical evaluation system proposed in this paper has a significance level of p<0.05 for the athletes’ professionalism level. The conclusion of the study indicates that the proposed paper table tennis players’ motor skills recognition and hierarchical evaluation assessment system can realize the accurate recognition of athletes’ motor skills and the precise determination of their professional level.

With the impact of artificial intelligence on the traditional UAV industry, autonomous UAV flight has become a popular research field at present. Based on the demand for research on key technologies for autonomous UAV flight, this paper studies UAV flight state recognition. This paper is based on multi-sensor acquisition of UAV on-board information, and uses the collected information for data fusion to complete UAV flight state identification. Firstly, UAV flight data acquisition and data preprocessing are carried out; secondly, UAV flight trajectory features are extracted based on multidimensional data fusion; finally, UAV flight state recognition model based on PCA-DAGSVM model is established. The results show that the algorithm based on multi-sensor data fusion has good recognition effect in the UAV flight state recognition problem. The recognition accuracy of the algorithm exceeds that of the random forest model, and its accuracy in the training set of UAV flight state recognition is more than 90%, and its accuracy in the test set is more than 80%.

With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm of UAV flight state recognition and trajectory prediction. Presenting an innovative approach focused on improving the precision of unmanned aerial vehicle (UAV) path forecasting via the identification of flight states, this study demonstrates its efficacy through the implementation of two prediction models. Firstly, UAV flight data acquisition was realized in this paper by the use of multi-sensors. Finally, two models for UAV trajectory prediction were designed based on machine learning methods and classical mathematical prediction methods, respectively, and the results before and after flight pattern recognition are compared. The experimental results show that the prediction error of the UAV trajectory prediction method based on multiple flight modes is smaller than the traditional trajectory prediction method in different flight stages.

The joint optimization of fleet size and task allocation presents a critical challenge in deploying Unmanned Aerial Vehicles (UAVs) for time-sensitive missions such as emergency logistics. Conventional approaches often rely on pre-determined fleet sizes or computationally intensive centralized optimizers, which can lead to suboptimal performance. To address this gap, this paper proposes a novel two-stage hierarchical framework that integrates the Grey Wolf Optimizer (GWO) with the Consensus-Based Bundle Algorithm (CBBA). At the strategic level, the GWO determines the optimal number of UAVs by minimizing a comprehensive cost function that balances mission efficiency and operational costs. Subsequently, at the tactical level, the CBBA performs decentralized, real-time task allocation for the optimally sized fleet. We validated our GWO-CBBA framework through extensive simulations against three benchmarks: a standard CBBA with a fixed fleet, a centralized Particle Swarm Optimization (PSO) approach, and a Greedy Heuristic algorithm. The results are compelling: our framework demonstrates superior performance across all key metrics, reducing the overall scheduling cost by 13.2–36.5%, minimizing UAV mileage cost and significantly decreasing total task waiting time. This work provides a robust and efficient solution that effectively balances operational costs with service quality for dynamic multi-UAV scheduling problems.

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system’s capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties.