Wind Power Business Process Remaining Time Prediction and System Application
Aiming at the complexity of industrial scene event logs and the small amount of data in industrial scenarios,a remaining time pre-diction method based on time convolutional network is proposed.Firstly,causal convolution is used to model process instance data.Then,the model is trained on track prefixes of different lengths to improve the pertinence of the model.Finally,the remaining time prediction model is used in the actual wind power operation and maintenance business scenario to develop the wind power operation and maintenance business sys-tem to realize the visual display of work ticket form data and event log data.Compared with the traditional remaining time prediction method,the prediction effect of the proposed method is significantly improved,and the average absolute error is reduced by an average of 5%.The de-veloped wind power wind power operation and maintenance business system can predict the remaining time in the process of invoicing unfin-ished work tickets,and realize business process overtime alarms.
business process analysisremaining time predictiontemporal convolution networkwind power business processprocess vi-sualization
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