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Battery-powered electric vehicles (EVs) have a limited on-board energy storage and present the problem of driving mileage anxiety. Moreover, battery energy storage density cannot be effectively improved in a short time, which is a technical bottleneck of EVs. By considering the impact of traffic information on energy consumption forecasting, an energy-saving path planning method for EVs that takes traffic information into account is proposed. The modeling process of the EV model and the construction process of the traffic simulation model are expounded. In addition, the long-term, short-term memory neural network (LSTM) model is selected to predict the energy consumption of EVs, and the sequence to sequence technology is used in the model to integrate the driving condition data of EVs with traffic information. In order to apply the predicted energy consumption to travel guidance, a road planning method with the optimal coupling of energy consumption and distance is proposed. The experimental results show that the energy-based economic path uses 9.9% lower energy consumption and 40.2% shorter travel time than the distance-based path, and a 1.5% lower energy consumption and 18.6% longer travel time than the time-based path.

Chang 22-19 belongs to the banshen 51 fault block of changlu oilfield, which is located 3km north of group 10 of changlu salt field.  Production fell to 74% by July 2021. Through the analysis of formation potential, a perforating software is used to optimize reasonable perforating and lifting process parameters to make the well produce efficiently and develop efficiently.