基于概念漂移检测的数字孪生流程预测模型OA北大核心CSTPCD
Digital twin process prediction model based on concept drift detection
预测性流程监控可以在业务流程运行过程中提供及时的信息,以便采取措施来应对潜在风险,如何提高流程预测的准确度一直受到高度关注.现有的研究方法大部分都在静态环境下引入,很少有结合数字孪生技术用于动态环境的流程预测.为此,提出了一个基于概念漂移检测的方法,并构建数字孪生流程预测模型(digital twin based on concept drift,DTBCD)预测下一个活动.首先利用事件流行为关系和权重散度将流程中的活动进行特征提取,得到数据流的特征集,其次进行漂移检测,动态选择特征集输入人工智能模型中训练并预测下一个活动,然后运用物联网和云计算等先进技术创建数字孪生虚拟环境,最后得到基于概念漂移的数字孪生模型.通过公开可用的数据集进行评估分析,实验结果表明,提出的方法能够有效提高预测的准确性.
Predictive process monitoring can provide timely information during the operation of business processes,in order to take measures to address potential risks.How to improve the accuracy of process prediction has always been highly concerned.Most of the existing research methods focus on process prediction in static environments,with few combining digital twin tech-nology for process prediction in dynamic environments.To this end,this paper proposed a method based on concept drift de-tection and constructed a digital twin process prediction model to predict the next activity.Firstly,this method used behavioral relationship between event streams and weight divergence to extract features from activities in the process and obtained the fea-ture sets of data flows.Secondly,this method performed drift detection.It dynamically selected feature sets and input them in-to the artificial intelligence model for training and predicting the next activity.Then,it used advanced technologies such as the Internet of Things and cloud computing to create a digital twin virtual environment.Finally,this paper obtained a digital twin model based on concept drift.It carried out evaluation and analysis on publicly available datasets,and the experimental results show that the proposed method can improve the effectiveness of prediction.
熊正云;方贤文
安徽理工大学数学与大数据学院,安徽淮南 232001安徽理工大学数学与大数据学院,安徽淮南 232001||安徽省煤矿安全大数据分析与预警技术工程实验室,安徽淮南 232001
计算机与自动化
预测性流程监控活动预测漂移检测数字孪生
predictive process monitoringactivity predictiondrift detectiondigital twin
《计算机应用研究》 2024 (007)
2039-2045 / 7
国家自然科学基金资助项目(61572035);安徽省重点研究与开发计划资助项目(2022a05020005);安徽省自然科学基金资助项目(水科学联合基金)(2308085US11);安徽理工大学研究生创新基金资助项目(2022CX2137)
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