The equipment in railway station is complicated and diverse, and the health status assessment and prediction of equipment is crucial to the safe and stable operation of stations. Graph Neural Networks (GNNs) effectively combine graph data with deep learning technology, which has stronger data and knowledge representation capability and can efficiently handle some non-Euclidean spatial data problems with irregular station equipment associated network structure. Based on this, this paper takes the automatic gate machine and X-ray security checker as an example and proposes a health status assessment and prediction scheme for railway passenger station equipment based on Graph Long Short-Term Memory (G-LSTM) neural network. This paper first analyzes the main factors affecting the health status of passenger station equipment, as well as the correlation between the equipment. Then, the initial graph network structure of the passenger station equipment is constructed, and the G-LSTM model is used to evaluate and predict the health status of the passenger station equipment. Finally, this paper takes the automatic gate machine and X-ray security checker of a high-speed railway station in Beijing as an example to verify the proposed method. The experimental results show that all evaluation metrics perform well, indicating that the G-LSTM model has high accuracy in assessing and predicting the health status of automatic gate machine and X-ray security checker. This paper realizes the health status assessment and prediction of railway passenger station equipment, which can provide some reference for the Prognostics and Health Management (PHM) of equipment in railway stations.

Media coverage plays an important role in prevention and control the spread of COVID-19 during the pandemic. In this paper, an SIHRS model of COVID-19 pandemic with impulse and time delay under media coverage is established. The positive and negative emotions of public are considered by the impact of confirmed cases and medical resources. In order to restrain the negative information of public, the factor of policies and regulations with impulse and time delay is introduced. Furthermore, the system model is simulated and verified by the reported data of COVID-19 pandemic in Wuhan. The main results are as follows: (1) When the implementation rate of the negative information generated by the confirmed cases gradually reduced to 0.4 times, the cumulative confirmed cases will be significantly reduced to about 37000, indicating that the popularization of pandemic related media information should be broad; (2) When the implementation rate affected by the amount of policies and regulations information gradually increases to 3 times, the cumulative confirmed cases will be significantly reduced to about 28000, indicating that the policies and regulations information should be continuously and incrementally reported; (3) When the inhibition rate of policies and regulation information on negative information gradually increases to 3 times, the cumulative confirmed cases will also be significantly reduced to about 27000 cases, indicating that the targeted policies and regulations information has a significant impact on inhibiting the corresponding negative emotions.

Insulator defect detection in transmission lines has become increasingly important for the safe operation of power systems within Internet of Things (IoT)-enabled smart grid infrastructures. However, complex backgrounds, diverse defect characteristics, and limited computing resources at IoT edge nodes pose significant challenges to existing detection methods. In existing defect detection methods, the fixed receptive field of convolutional neural networks (CNNs) limits their ability to adapt to multiscale defects. Defect detection methods based on the Transformer architecture offer advantages in global modeling, but they are limited by their inability to accurately locate defect locations and high computational complexity, hindering their deployment on resource-constrained unmanned aerial vehicle (UAV) platforms as intelligent IoT sensing devices. Therefore, this article proposes an insulator defect monitoring framework based on dynamic adaptive perception and a local–global attention (LGA) enhancement fusion network, i.e., dynamic perception and precise localization detection Transformer (DPL-DETR), effectively addressing the core challenges of real-time intelligent insulator defect detection at the IoT edge. First, a dynamic perception scale-adaptive network (DSNet) is designed, which leverages a learnable dynamic convolution kernel mechanism to significantly enhance the model’s adaptive perception capability for defects of varying scales while maintaining lightweight characteristics essential for edge deployment. Second, a precise attention spatial enhancement network (PASEN) is constructed to achieve accurate localization and feature enhancement of defect regions in complex background environments while substantially reducing computational complexity to meet real-time IoT application requirements. In addition, an LGA fusion (LGAF) module is proposed, which employs a dual-branch collaborative mechanism to effectively fuse local detail features with global semantic information, thereby enhancing the model’s capability to recognize defects of varying morphologies under diverse environmental conditions. Experiments on the Transmission Line-ID and OPDL datasets show that our proposed defect detection method DPL-DETR achieves an effective balance between performance and efficiency, with mAP50 reaching 98.25% and mAP50–95 reaching 68.74%. Furthermore, the number of parameters is reduced by nearly 50%, and the computational complexity decreases to 79.1 GFLOPs, facilitating efficient deployment on UAV-mounted IoT edge devices. The results of all evaluation metrics surpass the best existing detection methods, demonstrating the potential of DPL-DETR as a reliable intelligent perception solution for IoT-based power infrastructure monitoring.

In the future, the Internet of things will reduce the cell radius and increase the number of low-power nodes to support thousands of times of traffic growth under 5G. As a virtual multiple input multiple output technology, cooperative communication technology can solve these problems effectively. According to the evolution characteristics of cooperative communication networks, a multi-domain cooperative communication network evolution model with preferential attachment and random attachment is constructed in this article. And then, the network properties and robustness are analyzed using the mean-field method and different attacks. Aiming at the resource constraints and resource allocation problems of communication nodes, a relay selection strategy based on the combination of maximum degree and minimum clustering coefficient is proposed. The simulation results show that the relay node selection strategy based on the combination of maximum degree and minimum clustering coefficient has significant advantages in selection steps and selection time, which greatly enhanced the performance of relay selection in multi-domain cooperative communication networks. Through real-time monitoring and updating of the performance and security indicators of the multi-domain cooperative communication networks, it provides a strong guarantee for the node deployment and security management of the Internet of things cooperative communication system.