基于深度学习的可视化仪表板生成技术研究

Study on Visual Dashboard Generation Technology Based on Deep Learning

仪表板是支持制造企业开展数据可视化分析和经营决策的重要手段。为解决可视化仪表板设计与实现过程中用户对专业知识依赖性强及流程迭代繁杂的问题,提出了一种基于深度学习技术YOLOv5s算法的可视化仪表板自动识别与生成方法。首先,基于YOLOv5s算法对仪表板图像及手绘草图中包含的可视化图表组件进行检测,并针对在检测过程中出现的手绘草图中不规则线条对识别图表的干扰及误检等问题,引入CA注意力机制来增强模型对重要特征的关注及目标精确定位能力,从而提高模型的识别精度;其次,将图表检测模型部署在Web中,服务器根据模型检测结果调用封装好的可视化图表组件代码,生成多组件组合的初始仪表板;最后,基于此Web设计,开发了一款数据可视化仪表板构建平台,为用户提供可修改配置仪表板样式及数据的详细选项,以方便用户快速构建完整的仪表板。通过收集Tableau,Power BI等可视化工具产生的仪表板图像及企业应用过程中手绘仪表板草图形成数据集,基于该数据集进行实验验证,改进的模型识别精度比原YOLOv5s模型提升了2.1%,mAP为98.4%,并通过系统部署应用验证了图表识别方法及开发的平台可有效识别及生成相应图表组件,满足用户的基本需求。

Dashboard is an important tool to support manufacturing enterprises in data visualization analysis and business decision-making.In order to solve the problems of users’strong dependence on professional knowledge and complicated process iteration in the design and implementation of visual dashboards,a method for automatic recognition and generation of visual dashboards based on the YOLOv5s algorithm in deep learning technology is proposed.Firstly,based on the YOLOv5s algorithm,the visual chart components contained in the dashboard images and hand-drawn sketches are detected,and in order to address the problems of interference and false detection caused by irregular lines in hand-drawn sketches during the detection process,the CA attention mechanism is introduced to enhance the ability of the model to focus on important features and accurately locate the target,so as to improve the recognition accuracy of the model.Secondly,deploy the chart detection model in the web,and the server calls the encapsulated visual chart component code according to the model detection results to generate the initial dashboard of multi-component combination.Finally,a data visualization dashboard building platform is developed based on this web design,which provides users with detailed options to modify and configure the dashboard style and data,so that users can quickly build a complete dashboard.Through the collection of dashboard images generated by visualization tools such as Tableau and Power BI and hand-drawn dashboard sketches during the enterprise application process to form a dataset for experimental validation,the improved model increases the recognition accuracy by 2.1%compared to the original YOLOv5s model,and the mAP is 98.4%.The system deployment application verifies that the chart recognition method and the developed platform can effectively identify and generate the corresponding chart components to meet the basic needs of users.