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In the proposed method, the requirements of different customer connected to the distribution system are determined and measured by customer satisfaction degree, where, the direct and indirect economic losses caused by the power quality disturbances occurring in the distribution system can be determined by the customer satisfaction degree. By the help of customer losses, a high supply quality system programming model and method are developed. Considering the compatibility between power quality disturbances caused by distribution system fault and customer requirements, maximum entropy method is used. The proposed method is validated by a test system. The results have shown that the proposed method can insure the power supply quality with appropriate cost.

Process mining, as a high-level field in data mining, plays a crucial role in enhancing operational efficiency and decision-making across organizations. In this survey paper, we delve into the growing significance and ongoing trends in the field of process mining, advocating a specific viewpoint on its contents, application, and development in modern businesses and process management, particularly in cross-organizational settings. We first summarize the framework of process mining, common industrial applications, and the latest advances combined with artificial intelligence, such as workflow optimization, compliance checking, and performance analysis. Then, we propose a holistic framework for intelligent process analysis and outline initial methodologies in cross-organizational settings, highlighting both challenges and opportunities. This particular perspective aims to revolutionize process mining by leveraging artificial intelligence to offer sophisticated solutions for complex, multi-organizational data analysis. By integrating advanced machine learning techniques, we can enhance predictive capabilities, streamline processes, and facilitate real-time decision-making. Furthermore, we pinpoint avenues for future investigations within the research community, encouraging the exploration of innovative algorithms, data integration strategies, and privacy-preserving methods to fully harness the potential of process mining in diverse, interconnected business environments.

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