Log anomaly detection (LAD) is essential to ensure the safe and stable operation of Cyeber-physical systems. Although current LAD methods exhibit significant potential in addressing challenges posed by unstable log events and temporal sequence patterns, their limitations in detection efficiency and generalization ability present a formidable challenge when dealing with evolving systems. To construct a real-time and reliable online log anomaly detection model, we propose OMLog, a semi-supervised online meta-learning method, to effectively tackle the distribution shift issue caused by changes in log event types and frequencies. Specifically, we introduce a maximum mean discrepancy-based distribution shift detection method to identify distribution changes in unseen log sequences. Depending on the identified distribution gap, the method can automatically trigger online fine-grained detection or offline fast inference. Furthermore, we design an online learning mechanism based on meta-learning, which can effectively learn the highly repetitive patterns of log sequences in the feature space, thereby enhancing the generalization ability of the model to evolving data. Extensive experiments conducted on two publicly available log datasets, HDFS and BGL, validate the effectiveness of the OMLog approach. When trained using only normal log sequences, the proposed approach achieves the F1-Score of 93.7% and 64.9%, respectively, surpassing the performance of the state-of-the-art (SOTA) LAD methods and demonstrating superior detection efficiency.

For intelligent unmanned aerial vehicles working in complex environments, it is necessary to have a certain autonomous flight control decision-making ability to adapt to complex and changeable environments. In order to realize the rapid path planning of intelligent unmanned aerial vehicle in complex flight environment and ensure its accurate positioning, we consider the constraints of error correction and turning radius and so on, and establish a multiobjective optimization model with the shortest path and the least correction times. In addition, a novel projection algorithm is proposed to solve this model. The evaluation of our proposed method is done from a dataset. We clearly show its effectiveness and its superiority compared to several state-of-the art approaches.