Owing to their controlled mobility, extensive coverage and low cost, unmanned aerial vehicles (UAVs) have significant promise in the field of military, civilian and commercial applications, which has sparked a lot of research interest. This paper explores the application of the UAV in the symbiotic radio (SR) system. Specifically, we study the multi-user SR system assisted by the UAV and intelligent reflecting surface (IRS), and the modulation scheme with high flexibility is adopted to embed the IRS symbols into the UAV signals. First, we focus on minimizing the maximum bit error rate (BER) among all ground devices (GDs) while meeting the minimum communication rate requirements by jointly designing the active transmit beamforming and flight trajectory of the UAV as well as IRS reflective phase shifts. Then, we develop an iterative algorithm based on block coordinate descent (BCD) technology to address this optimization problem. Specifically, the optimization problem is decoupled into three sub-problems to be alternately solved, which are solved by invoking the semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques. Moreover, the convergence performance and computational complexity of the proposed algorithm are analyzed. Finally, simulation results show the proposed algorithm can significantly improve the quality of IRS transmission.

Power equipment maintenance work orders are vital in power equipment management because they contain detailed information such as equipment specifications, defect reports, and specific maintenance activities. However, due to limited research into automated information extraction, valuable operational and maintenance data remain underutilized. A key challenge is recognizing unstructured Chinese maintenance texts filled with specialized and abbreviated terms unique to the power sector. Existing named entity recognition (NER) solutions often fail to effectively manage these complexities. To tackle this, this paper proposes a NER model tailored to power equipment maintenance work orders. First, a dataset called power equipment maintenance work orders (PE-MWO) is constructed, which covers seven entity categories. Next, a novel position- and similarity-aware attention module is proposed, where an innovative position embedding method and attention score calculation are designed to improve the model’s contextual understanding while keeping computational costs low. Further, with this module as the main body, combined with the BERT-wwm-ext and conditional random field (CRF) modules, an efficient NER model is jointly constructed. Finally, validated on the PE-MWO and five public datasets, our model shows high accuracy in recognizing power sector entities, outperforming comparative models on public datasets.