As a critical way of higher education quality assurance， the scientific and rational implementation of undergraduate teaching audit and evaluation impacts the level of talent cultivation in universities directly. However， traditional manual review modes are inefficient and subjective when faced with massive heterogeneous data， making it difficult to meet the demands for accuracy and standardization in undergraduate teaching evaluation. Therefore， an intelligent undergraduate teaching evaluation system based on Large Language Models （LLMs） and multi-agent architecture — SmartEval — was proposed. In the system， input contents were parsed through a semantic understanding module， the tasks were decomposed and scheduled using a planner， and a Retrieval-Augmented Generation （RAG） module was integrated with three types of agents： question-answering， summarization， and diagnostics to realize end-to-end automation of the entire process of “data collection-metric analysis-decision support”. Experimental results based on the “1+3+3” series reports on 2023 undergraduate teaching evaluation of selected universities demonstrate that SmartEval outperforms the existing mainstream LLMs， such as GLM-4 and Qwen2.5， in metrics such as question-answering accuracy， ROUGE-L （Recall-Oriented Understudy for Gisting Evaluation L） score for summarization， and F1-value for diagnostics significantly. Furthermore， consistency tests with expert groups validate the reliability of SmartEval results.

Aiming at the problems of poor robustness， low precision and success rate in the existing tiny object tracking algorithms， a tiny object tracking algorithm， SiamTrans， was proposed on the basis of Siamese network and Transformer. Firstly， a similarity response map calculation module was designed based on the Transformer mechanism. In the module， several layers of feature encoding-decoding structures were superimposed， and multi-head self-attention and multi-head cross-attention mechanisms were used to query template feature map information in feature maps of different levels of search regions， which avoided falling into local optimal solutions and obtained a high-quality similarity response map. Secondly， a Prediction Module （PM） based on Transformer mechanism was designed in the prediction subnetwork， and the self-attention mechanism was used to process redundant feature information in the prediction branch feature maps to improve the prediction precisions of different prediction branches. Experimental results on Small90 dataset show that， compared to the TransT （Transformer Tracking） algorithm， the tracking precision and tracking success rate of the proposed algorithm are 8.0 and 9.5 percentage points higher， respectively. It can be seen that the proposed algorithm has better tracking performance for tiny objects.

The Quality of Experience (QoE) of audio stream is significantly affected by packet loss in packet-switch network. To control the loss and analyze its effect on QoE, a simulated multimedia transport test bed was designed. And the mapping model between packet loss rate and QoE was established by regression analysis under specific encoding/decoding mode and Real-time Transport Protocol (RTP) packet interval. The model has low computational complexity and can predicate the impairment of packet loss on user experience in real-time.