As a core task of computer vision perception, 3D scene understanding has received widespread attention. However, the current research mainly focuses on the semantic understanding task at the level of entity objects and often neglects the semantic relationships between objects in the scene. This paper proposes a 3D scene graph prediction model based on deep learning methods for scanned point cloud data of indoor scenes to predict the semantic graph about the class of entity objects and their relationships. The model uses a multi-scale pyramidal feature extraction network, MP-DGCNN, to fuse features with the learned category-related unbiased meta-embedding vectors, and the relationship inference of the scene graph uses an ENA-GNN network incorporating node and edge cross-attention; in addition, considering the long-tail distribution effect, a category grouping re-weighting scheme is used in the embedded prior knowledge and loss function. For the 3D scene graph prediction task, experiments on the indoor point cloud 3DSSG dataset show that the model proposed in this paper performs well compared with the latest baseline model, and the prediction effectiveness and accuracy are substantially improved.

To overcome the limitations of long-term prediction of PM_2.5 concentration, a multi-factor information flow causality analysis method is used to screen suitable meteorological and air pollutant-related factors and concatenate them with a PM_2.5 sequence as the dataset. A modal decomposition algorithm is used as a module to be integrated into the autoformer (transformer improved with autocorrelation mechanism) model to improve it, and the modal autoformer (empirical modal decomposition combined with autoformer) is proposed. The constructed model decomposes the sequence into several components by using the modal decomposition module and uses the self-correlation mechanism and decomposition structure to decompose and extract features of different components at the time-feature level. Based on the matching method, the model is adjusted for different component features to improve the long-term prediction effect. The model is applied to three cities in Henan Province, Zhengzhou, Luoyang, and Zhumadian, as examples for experiments, and gated neural unit (GRU), informer, autoformer, and modal GRU (empirical modal decomposition combined with GRU model) are constructed for comparative verification. The results show that the modal autoformer can better cope with the complex characteristics of long-term prediction of the PM_2.5 time series, has strong spatial adaptability and that its various indicators are optimal for the three cities, with R² values being all above 0.96, where the highest is 0.987 in Zhengzhou; MAPE (Mean absolute percentage error) values all being less than 10, where the best is 7.602 in Zhumadian; and MAE (Mean absolute error) values all being less than 4. The prediction effect is stable enough, showing its feasibility and adaptability in long-term prediction.

Foreign trade is an important part of the national economy. Promoting the development of foreign trade can regulate the optimal allocation of resources, raise the level of domestic productivity, and accelerate economic development. As a traditional inland agricultural province, Henan Province has inherent disadvantages in developing foreign trade due to its geographical location. However, it has characteristic advantages in terms of population and transportation, so it is necessary to study the pattern of foreign trade and the factors affecting it in this region. In this research study, statistical data were assessed with methods such as the foreign trade dependence, geographical detector, and gravity models to analyze the trade scale, pattern, spatio-temporal variation characteristics, and foreign trade mechanisms in Henan Province. The results show that the trade pattern of Henan Province from 2002 to 2021 has evident spatial and temporal heterogeneity, with a relatively homogeneous overall commodity structure, weak competitive advantages, and a high degree of dependence on US trade. Innovation and transportation are essential internal factors, while the external factors are positively affected by the GDP of both Henan Province and the trading countries, FTAs, trade openness, and the population in the long run and are negatively impacted by distance. This study provides suggestions and decision support for formulating foreign trade policies for Henan Province. It also provides a research basis for related corresponding studies of other regions with similar characteristics.