Intelligent charging service recommendation method and system based on user portrait

Abstract

The invention provides an intelligent charging service recommendation method and a system based on a user portrait, and the method comprises the steps: obtaining an initial selectable charging stationbased on the state of an electric vehicle, the current coordinate of the electric vehicle and the resource condition of the charging station; determining a pushable charging station from the initialselectable charging stations based on a pre-obtained user portrait label weight; wherein the user portrait label weight is determined by training feature data and user behavior data in the user orderdata based on a multi-task deep neural network. Through implementation of the method, the charging demand under the user travel scene determined by the current electric vehicle state and the user portrait can be accurately predicted, so that an optimal charging station selection scheme is provided for the user, and through implementation of the scheme, the user experience is improved, and the charging station operation income is also improved.

Description

technical field [0001] The invention relates to the field of electric vehicle charging, in particular to an intelligent recommendation system for electric vehicle charging stations. Background technique [0002] The recommendation system is a personalized information push system that recommends the information and products that the user is interested in to the user according to the user's information needs and interests. A good recommendation system can not only provide users with personalized services, but also establish a close relationship with users, making users rely on recommendations. In recent years, the development of fields such as machine learning and deep learning has provided method guidance for recommendation systems, making them widely used in many fields, among which the most typical field with good development and application prospects is the field of e-commerce. [0003] However, in the field of charging services in the electric vehicle after-sales service...

AI Technical Summary

Problems solved by technology

At the same time, unlike gasoline, the properties of the electric energy provided by the charging station are also much more complicated, including: 1) Similar to the quality of gasoline that affects the service life of gasoline pumps, the charging power also affects the use of power batteries Life; 2) Because of the long charging time and the small number of charging stations, the queuing time at charging stations is much longer than that at gas stations; 3) Unlike refueling prices that are priced by region, charging costs are mainly determined by electricity prices, charging service fees and parking fees , now the electricity price generally adopts the peak and valley electricity price, which will fluctuate greatly at different times of the day. With the introduction of the dynamic charging service fee pricing mechanism, the randomness of the charging fee will become more and more random; 4) Although the electricity on the demand side There is no difference in quality, but from the perspective of the supply side, the power supplied by users may come from traditional thermal power plants or renewable energy. With the pilot of carbon emission rights in some cities, whether the charging energy is "green power" is also potential One of the influencing factors that may affect the user's decision in the future

To sum up, the "commodity" attribute of charging service has great randomness and uncertainty. Therefore, how to improve the matching between the charging recommendation scheme in the charging station recommendation system and the real needs of users is the focus of those skilled in the art. question

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