In the context of the booming Internet of Vehicles, predicting vehicle trajectories is crucial for intelligent transportation systems. Existing methods, reliant on sensor data and behavior models, struggle with intricate relationships between vehicles and dynamic road networks. To overcome these challenges, we propose the Vehicle Interaction-based Dynamic Graph Neural Network (VI-DGNN) model. This model constructs a vehicle interaction graph to capture temporal and spatial dependencies among vehicles. A spatiotemporal attention network is employed to discern patterns in vehicle movements, addressing high-speed changes. Our model introduces a vehicle interaction mechanism for dynamic movement, leveraging proximity timestamp graph structures. By incorporating vehicle behavioral features and road network topology, our model minimizes distribution prediction variance, enhancing stability. Experimental results on real datasets demonstrate superior long-term prediction performance compared to state-of-the-art baselines.

Consumer electronics products are widely used in the agricultural field, but traditional consumer electronics products are limited to specific environmental applications and are susceptible to external attacks. The next generation of imaging technology is driving the widespread application of electronic consumer products. We propose a novel multi-source visual data fusion insect recognition algorithm called SE-SANet, which has high anti-interference capability and robustness to cope with various attacks in practical applications. Specifically, first, a deep residual contraction network is used to set different thresholds for different samples. Second, a spatial attention mechanism is introduced to assist the model in recognizing image features. When the feature data is input into the attention mechanism, the model filters the features based on their contribution values in spatial locations and ultimately produces the recognition result. The model shows excellent anti-interference ability on our collected dataset of 30 different insects, with a recognition correctness of 93.25%, which is higher than that of the traditional methods Inception-V4, Vgg16, Googlenet, Alexnet, 3.68%, 4.97%, 3.22% and 3.69%, respectively. We propose that the SE-SA model has important research implications in improving the robustness of insect recognition techniques and enhancing the model’s anti-interference capability.

Centralized heating is an energy-saving and environmentally friendly way that is strongly promoted by the state. It can improve energy utilization and reduce carbon emissions. However, Centralized heating depends on accurate heat demand forecasting. On the one hand, it is impossible to save energy if over producing, while on the other hand, it is impossible to meet the heat demand of enterprises if there is not enough capacity. Therefore, it is necessary to forecast the future trend of heat consumption, so as to provide a reliable basis for enterprises to reasonably deploy fuel stocks and boiler power. At the same time, it is also necessary to analyze and monitor the steam consumption of enterprises for abnormalities in order to monitor pipeline leakage and enterprise gas theft. Due to the nonlinear characteristics of heat load, it is difficult for traditional forecasting methods to capture data trend. Therefore, it is necessary to study the characteristics of heat loads and explore suitable heat load prediction models. In this paper, industrial steam consumption of a paper manufacturer is used as an example, and steam consumption data are periodically analyzed to study its time series characteristics; then steam consumption prediction models are established based on ARIMA model and LSTM neural network, respectively. The prediction work was carried out in minutes and hours, respectively. The experimental results show that the LSTM neural network has greater advantages in this steam consumption load prediction and can meet the needs of heat load prediction.