This paper proposes a digital cloud platform for rural revitalization projects in the tobacco industry to address chal-lenges such as low efficiency and high risks in project management.The digital cloud platform adopts a"one project,one file"management mode,ensuring real-time synchronization and verification of key information such as project initiation,fund disburse-ment,and completion acceptance.It puts forward a comprehensive management method for project data review,fund utilization,and project process implementation.Built on a microservices architecture,the digital cloud platform integrates technologies such as big data analysis,cloud service middleware,cloud storage services,and big data algorithms to achieve full-process digitized man-agement including project application,decision-making,execution,dynamic tracking evaluation,and supervision inspection.The development of this digital cloud platform will greatly improve project management efficiency and reduce financial risks,thus effec-tively serving rural revitalization.
With the rapid development of China's agriculture industry,especially in the management and application of aqua-culture industry,there are risks of increased information integration and imbalanced management patterns.The main challenges faced include the synchronization of technology and project management throughout the entire project lifecycle,as well as the paral-lelism between physical entity information and visualized data.In order to address these challenges,an analysis of the business and information transmission processes of the aquaculture farm information management system is conducted,and based on the business requirements of each module and the technical difficulties that need to be overcome,the system is designed using React+Spring Boot+Cesium.js technology as the foundation.This system aims to improve decision-making assistance and support through spatial data visualization simulations,and also to develop feasible aquaculture plans and workforce scheduling through visualization.
Abstract The field of face recognition has recently become a quite popular area of research, which is of great significance to the development of technology. It introduced the definition of face recognition technology and its development process, as well as the technical advantages and application scenarios of the technology. with the development of deep learning, face recognition technology based on deep learning is gradually realized. firstly, the limitations of traditional face recognition technology are pointed out. Then, several popular face recognition methods based on depth learning are analyzed. Finally, this paper introduces the application of deep learning technology in face recognition, summarizes a new deep learning model based on big data, and summarizes and prospects the development of face recognition technology in the future.
智慧图书馆管理中,馆藏剔旧是一个至关重要的环节.为了提高图书剔旧效率并减轻馆员工作负担,提出了一种基于OpenCV技术和卷积神经网络CNN的图书即时智能剔旧系统.采用OpenCV技术对图书条形码图像进行分割,然后运用卷积神经网络CNN进行图像识别,将图书条形码与数据库进行比对,并结合层次分析法计算得到图书的剔旧权重值,最后根据设定的阈值将结果反馈给前端.设计和实现有效地提高了图书剔旧的效率,为高校图书馆的智能化服务提供了创新思路.
针对磁瓦缺陷提取难,且基于传统卷积神经网络的磁瓦缺陷检测算法对于全局特征表述差、上下文交互不充分等缺点,提出一种基于Swin-Transformer的磁瓦检测算法,其通过在Swin-Transformer的基础中结合YOLOv3模型的neck模块构建出磁瓦缺陷检测算法,并且为了使网络能够获取更加丰富的梯度组合,在neck部分优化设计了CSPA模块.同时为了使网络学习更多有用的特征,通过锐化和同态滤波对收集到的磁瓦图像进行增强.实验结果表明,改进算法对磁瓦缺陷检测的mAP为87.1％,相对于YOLOv3和YOLOv5,分别提高了5.3和1.5个百分点.
