The complexity of data and business content in enterprise software design makes it difficult to grasp, which leads to problems such as low team coding efficiency and easy errors. The key to solving this problem is to take the business data of the enterprise as the center and drive the code development with the characteristics of the data itself. Therefore, this study proposes an automatic front-end code generation method based on data and template integration (GC-DT). This method utilizes the enterprise business table structure and templates to form the process of automatic code generation, and realizes the automatic generation of VUE front-end code. Practical application shows that compared with the traditional method, this method can effectively reduce the workload of software development, shorten the software development cycle, and still effectively maintain the consistency and correctness of the team code in the face of complex content structure. Therefore, this method has high application value in enterprise software development.

Low-latency violent event detection is essential for intelligent edge surveillance. Current violent recognition models suffer three critical deployment drawbacks: prohibitive computational overhead from full fine‑tuning, inadequate temporal modeling for continuous video streams, and severe performance degradation when handling unseen violent categories in open surveillance scenes. We propose a lightweight open-set vision-language framework based on pre-trained CLIP. Its dual‑branch structure achieves cross‑modal alignment to support cross‑scene generalization toward novel violent patterns, and we further conduct preliminary explorations for open‑set violent identification for long monitoring footage. Evaluations on five surveillance datasets confirm competitive accuracy. Our parameter-efficient fine-tuning(PEFT) alleviates the inherent conflict between generalization and edge computing overhead, with strong adaptability across low-power embedded terminals. The framework establishes an extensible paradigm for real-time public safety early warning on edge devices, providing generalizable technical references for deploying multi-modal foundation models in urban security governance and broader edge vision perception tasks.

Large cross-modal models such as contrastive language-image pre-training suffer from heavy computation costs and weak anti-interference ability, which brings obstacles to lightweight deployment on edge devices with limited computing resources, a common requirement in industrial edge devices. Traditional spatial visual prompt learning cannot mine discriminative frequency texture features, resulting in poor generalization under scarce labeled data and noisy industrial scenes. This paper proposes frequency-domain adaptive visual prompt learning (FD-AVPL). The framework decomposes images into multi-band frequency features, introduces weighted attention fusion, and generates frequency-aware prompts to fine-tune pre-trained models with tiny parameter overhead. Tests on five public datasets show FD-AVPL outperforms state-of-the-art prompt methods by 2.0%–9.0% accuracy in full-shot and few-shot scenarios. It maintains strong robustness to noise, occlusion and illumination shifts, adding only 1.1 M/1.3 M trainable parameters for RN50/ViT-B/32. The proposed method provides a lightweight parameter-efficient tuning scheme for large vision-language models, which is well-suited to the deployment demands of lightweight tuning scheme for deploying large vision-language models on resource-limited embedded terminals including industrial terminals.