## 5 Conclusions  
This study focuses on constructing dynamic datasets for vertical domain models, proposing an innovative engineering-oriented approach based on multi-role collaborative workflows and intelligent annotation techniques. The proposed methodology effectively addresses the challenges encountered by traditional dataset construction methods, particularly regarding efficiency, consistency, and dynamic adaptability.  
Through a carefully designed multi-role collaboration framework, the responsibilities and collaborative processes of data augmentation personnel, automatic annotation personnel, and reviewers are explicitly defined. This structured workflow enables seamless progression from data processing to annotation review, significantly enhancing annotation efficiency and data quality. Additionally, the developed intelligent annotation algorithms--specifically the annotation box correction algorithm based on rotation matrices--successfully resolve annotation deformation errors arising from rotation scenarios, greatly improving annotation accuracy and the level of automation.  
The construction of the WATER-DET dataset in the water conservancy engineering domain, along with comparative experiments involving Labellmg, LabelMe, and Make Sense, demonstrates the effectiveness of the proposed method. Results show that the response time for dynamic classification adjustments is reduced to the minute level. Moreover, the proposed approach achieves a 100% improvement in annotation efficiency, maintains high annotation consistency following data augmentation, and provides a user-friendly environment conducive to efficient review processes. These advantages become even more pronounced with larger-scale datasets, highlighting the method's superiority and practical utility in dynamic dataset construction for vertical domain models.  
While this research has made substantial progress, integration with traditional annotation tools remains necessary for foundational tasks. Future work will focus on embedding the proposed methodology into manual annotation tools such as LabelImg and LabelMe, thereby enhancing annotation intelligence levels and enabling distributed data storage and collaborative annotation workflows. Furthermore, to address current limitations in processing diverse annotation formats and more complex image characteristics--such as varying resolutions, irregular object shapes, dense object distributions, or high-noise environments--future enhancements will expand the augmentation framework to support broader annotation types and improve adaptability under challenging conditions.  
The findings of this study, which integrate multi-role collaboration, process reengineering, and advanced automatic annotation tools, provide an effective solution for improving dataset construction efficiency. Moreover, the proposed approach shows promising potential for application in other fields. For instance, future exploration could investigate constructing dynamic datasets for agricultural pest monitoring or geological disaster early warning, further advancing the innovative application of artificial intelligence technologies across diverse industries.  
Conceptualization, X.S. and J.X.; methodology, G.Z.; software, X.W.; validation, X.S., X.W., G.Z. and J.X.; formal analysis, X.W.; investigation, J.X.; resources, X.S.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
I would like to express my gratitude to all those who have helped me during the writing of this thesis.  
The authors declare no conflicts of interest.