Objective Log repair is one of the main research topics in business process management. When business process models include resource constraints control-flow-based process modeling techniques and traditional control-flow repair methods struggle to detect issues from a resource perspective in the model. Therefore a resource-aware log repair method based on consistency detection was proposed. Methods Firstly the process was modeled using Petri nets and then the resource perspective was integrated into the existing Petri net to form a resource-aware Petri net of the process. The association between activities and resources in the process was established. Then deviations between the control flow and data flow of the generated event logs were analyzed using alignment methods and finally event logs with resource data anomalies were classified and repaired. Results The feasibility of the method was verified by integrating the daily operation process of the fully automated urban rail driving system with the fault handling process and the composite fitness was extended based on trace fitness. This fitness can calculate the comprehensive impact of the control flow perspective and the resource perspective on model fitting. Compared with traditional repair methods limited to control-flow alignment the proposed method achieves higher composite fitness and better results in log repair. Conclusion By using process modeling techniques with added resource constraints and extending traditional log repair methods which focus only on control flow to the data flow aspect potential data anomalies from a resource perspective in the process are no longer ignored expanding the scope of log repair and helping to address potential resource-related issues in logs.