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Efficient energy data management forms a critical foundation for unlocking the carbon reduction potential of subway systems, holding significant importance in advancing urban processes towards lowcarbon and clean environments. Low-precision sampling is difficult to reflect the actual energy consumption of the station, and high-precision sampling has high requirements for the data storage and transmission capacity of the monitoring system. In order to determine the appropriate sampling accuracy, this study analyses the power load fluctuation characteristics of stations on a subway line in the North China Plain and optimizes the sampling granularity for achieving minimal data storage requirements while effectively capturing energy consumption fluctuation information. The findings indicate that a higher sampling granularity for power load monitoring is advisable during the summer to capture the frequent fluctuation characteristics of ventilation and air-conditioning system energy consumption. For a typical underground station, it is recommended to use a sampling interval of 5 min in summer and 15 min or longer in other seasons. For a typical elevated station, a sampling interval of 10 min is recommended in summer, and 20 min or longer in other seasons.

Experience knowledge is extremely important in maintenance domain. However, it is difficult to express and extract this kind of knowledge. Conceptual Graph is a new and powerful visual knowledge representation method. This paper proposes one technique based on conceptual graph to extract knowledge from experiences accumulated in maintenance actions. This technique introduces conceptual graph to maintenance domain. With VE distribution pump as an example, the method of conceptual graph is introduced first, and then it is applied to expressing knowledge extracted from experiences accumulated in maintenance actions. Finally, the similarity between graphs of new fault case and base cases is computed, through which maintenance staff could find out the most similar case when new fault appears. The causes and solutions of the most similar case could assist maintenance staff to resolve new faults.