The spatiotemporal evolution analysis of social media event themes provides important support for network public opinion monitoring and emergency management. However, the cross modal, interactive, and time sensitive features of social media data pose significant challenges to existing topic mining methods. Traditional methods have shortcomings in cross modal semantic representation, information influence modeling, time decay mechanisms, and large-scale data processing, which limit the accuracy and stability of topic evolution analysis. Therefore, this article proposes a distributed cross modal dynamic topic evolution analysis framework, Cross-Modal Enhanced BERTopic (CME-BERTopic). This framework is based on Chinese-CLIP with Self Attention and Cross Attention (CN-CLIP-SA-CA) to extract deep cross modal semantic features, and enhances the HDBSCAN clustering algorithm with information interaction influence to optimize event topic representation. Meanwhile, Top-K Compression with Influence Decay Dynamic Topic Model (TCI-DTM) was designed to depict the dynamic evolution of topics over time. Finally, integrate each module into a distributed computing framework to support efficient processing of large-scale social media data. The experimental results based on the Flickr and DeepSeek datasets show that Distribute CME-BERTopic (DCME-BERTopic) and TCI-BERTopic is significantly better than traditional BERTopic in topic recognition and evolution tracking, with stronger adaptability, robustness, and computational efficiency.

The steel plate is one of the main products in steel industries, and its surface quality directly affects the final product performance. How to detect surface defects of steel plates in real time during the production process is a challenging problem. The single or fixed model compression method cannot be directly applied to the detection of steel surface defects, because it is difficult to consider the diversity of production tasks, the uncertainty caused by environmental factors, such as communication networks, and the influence of process and working conditions in steel plate production. In this paper, we propose an adaptive model compression method for steel surface defect online detection based on expert knowledge and working conditions. First, we establish an expert system to give lightweight model parameters based on the correlation between defect types and manufacturing processes. Then, lightweight model parameters are adaptively adjusted according to working conditions, which improves detection accuracy while ensuring real-time performance. The experimental results show that compared with the detection method of constant lightweight parameter model, the proposed method makes the total detection time cut down by 23.1%, and the deadline satisfaction ratio increased by 36.5%, while upgrading the accuracy by 4.2% and reducing the false detection rate by 4.3%.

Managing massive electric power data is a typical big data application because electric power systems generate millions or billions of status, debugging, and error records every single day. To guarantee the safety and sustainability of electric power systems, massive electric power data need to be processed and analyzed quickly to make real-time decisions. Traditional solutions typically use relational databases to manage electric power data. However, relational databases cannot efficiently process and analyze massive electric power data when the data size increases significantly. In this paper, we show how electric power data can be managed by using HBase, a distributed database maintained by Apache. Our system consists of clients, HBase database, status monitors, data migration modules, and data fragmentation modules. We evaluate the performance of our system through a series of experiments. We also show how HBase’s parameters can be tuned to improve the efficiency of our system.