## 1 Introduction  
In the digital era, computer vision technology, a cornerstone of the artificial intelligence domain, is being rapidly integrated into a wide range of industries. This technology provides innovative and efficient solutions to complex problems, as clearly demonstrated by its applications across multiple sectors [1]. In the medical field, it facilitates accurate disease diagnosis, such as the precise detection of lung nodules [2, 3, 4, 5], thereby increasing early detection rates and enhancing treatment outcomes. In industrial production, it supports rigorous quality control by identifying product defects in real time [6, 7, 8], reducing waste and improving product standards. Within the security sector, it enables advanced monitoring systems to swiftly recognize potential threats, thereby strengthening public safety. In the transportation industry, computer vision serves as a foundational compo nent for autonomous driving technologies [9; 10; 11; 12; 13], which aim to deliver safer and more efficient mobility. Central to these applications is the development of efficient and precise vertical domain models. The effectiveness of these models depends fundamentally on the availability of high-quality datasets [14; 15; 16]. Such datasets allow models to acquire a comprehensive understanding of diverse features and patterns, equipping them to produce reliable predictions and decisions. Nevertheless, as computer vision technology becomes more deeply embedded in specialized fields, the demands on datasets are intensifying. Owing to the dynamic and evolving nature of real-world environments, datasets must exhibit both timeliness and diversity. In the medical domain, for instance, the continuous mutation of diseases and the emergence of new variants necessitate regular updates to datasets to include novel cases and symptoms [17; 18]. This ongoing refinement ensures that models retain their accuracy in diagnosis and prediction. In industrial production, the introduction of new products and the optimization of manufacturing processes require datasets to adapt promptly to changing production conditions [19; 20; 21]. Failure to update datasets may lead to diminished performance in defect detection and process optimization. In the engineering sector, such as in water conservancy projects [1], data reflect rapid and continuous variations. For example, river water levels and quality fluctuate in response to seasonal patterns, climatic conditions, and anthropogenic influences. The surface water extent of the Haihe River Basin, for instance, changes with precipitation levels and water usage. Constructing a dynamic dataset capable of capturing these shifts in a timely manner is essential for maintaining the accuracy and relevance of data used in decision-making. From a research standpoint, as investigations advance, datasets require ongoing refinement. This process encompasses adding new data, updating existing records, and removing outdated entries. Moreover, the tasks associated with datasets and their categorical structures often demand repeated adjustments. Consequently, it is essential to improve both the efficiency and quality of dataset annotation and construction from a dynamic and adaptable perspective. In practical applications, as data continue to accumulate, integrating the most recent information into existing datasets becomes vital for enhancing model recognition capabilities. A dynamic dataset capable of accommodating temporal and spatial variations not only strengthens model performance but also serves as a critical driver for the expanded implementation of artificial intelligence in specialized domains. Therefore, the effective development of such dynamic datasets has emerged as a prominent research focus. Nevertheless, most current research efforts predominantly emphasize optimizing model accuracy and recall rates. Although these metrics are important, this line of research tends to offer limited practical value and faces challenges in achieving widespread adoption in the near term [1].