In response to the contradiction between large-scale teaching and personalized needs in the digital transformation of ideological and political theory courses in universities, as well as the problems of lagging response and low resource scheduling efficiency in existing intelligent education systems in high concurrency scenarios, this paper designs and implements an AI empowered intelligent teaching system based on micro-service architecture. The system adopts a layered architecture design, deeply integrating natural language processing, knowledge graph and retrieval enhanced generation technologies, and realizing core functions such as intelligent content analysis, personalized learning recommendation, and intelligent question answering assistant. This study provides a complete technical solution from architecture design to algorithm optimization for intelligent teaching of ideological and political courses in universities, which has high promotional value.
The online health of mutual inductor directly affects the reliability of measurement and protection, and it is difficult for traditional offline detection to capture the performance drift. Therefore, this paper develops an intelligent mutual inductor quality monitoring platform that integrates cloud computing and multi-dimensional indicators, carries out testing based on four core parameters and nine characteristic indicators, combines lightweight Long Short-Term Memory to realize health state modeling and trend prediction, and relies on edge clouds to cooperate to complete early warning, life prediction, and evaluation. After three months' on-site verification of 68 10kV mutual inductor, the deviation between the comprehensive score and the laboratory calibration is less than 2.7%, the early detection rate of insulation deterioration is 94%, the overall detection rate is 96.3%, and the average relative error is 3.2%. Therefore, the abnormality can be warned in advance in extreme environment. The platform can provide a paradigm for the state perception of distribution networks.
This study proposes a computationally intensive and repeatable framework for large-scale thematic analysis of China literature texts from 1925 to 2024, aiming at supporting the realization of Qualcomm's text processing under the challenge of language complexity, including word segmentation ambiguity, traditional simplified text variants, and diachronic semantic drift. In order to realize large-scale time-aware topic modeling, the authors integrate Latent Dirichlet Assignment (LDA), Dynamic Topic Model (DTM), and Structural Topic Model (STM) into the high-performance computing pipeline, supplemented by word embedding alignment and loop verification. The workflow adopts closed-loop paradigm, quantitative analysis → close reading → model refining, and uses parallel preprocessing and distributed model training to efficiently process century-old text data. This paper presents an advanced computational text analysis based on domain-aware design and optimized performance, which provides a portable blueprint for large-scale cultural data analysis in a language environment with limited resources.
High-performance computing data centers demand reliable and energy-efficient power delivery to manage rapid workload variations and high power density. Traditional transformer systems often show limited efficiency and weak adaptability under high performance computing (HPC) conditions. This study designed a three-level solid-state transformer with intelligent fault-tolerant control and workload-aware optimization. The architecture employed a neutral-point-clamped topology to reduce switching stress, a residual-based mechanism to sustain operation during device faults, and adaptive voltage regulation that tracked real-time workload changes. Experiments on a 50 kW prototype using HPC-oriented traces achieved 95.8–97.1% median efficiency, a 21% improvement in mean time between failures, and notable reductions in tracking error. The results demonstrate that combining multilevel conversion with intelligent control enhances efficiency, resilience, and workload responsiveness, providing a practical route to more scalable and stable HPC data center power delivery.
This study presents AI-driven real-time load balancing (AI-RTLB), a framework for heterogeneous cloud–edge–Internet of Things (IoT) systems. AI-RTLB combines long short-term memory attention-based workload forecasting with adaptive multi-agent reinforcement learning (decentralized actors, shared critic) and a post-optimization layer that enforces energy, fairness, thermal, and power-budget constraints. This design anticipates demand surges, coordinates distributed decisions, and produces service-level agreement (SLA)-aware schedules. Across large data-center traces, edge workloads, and IoT demand logs, AI-RTLB reduces average latency by 16.3% and improves energy efficiency by 21.7% over strong baselines, while increasing throughput and lowering SLA violations (5.3%) with a lower fairness index (0.041). Convergence is faster and more stable than with single-agent deep reinforcement learning, and robustness is maintained under workload noise and mixed job types. An ablation study confirms complementary gains from prediction, multi-agent control, and fairness- and energy-aware optimization. AI-RTLB offers a practical path toward efficient, equitable, and sustainable computing.
