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公开(公告)号:US20210233405A1
公开(公告)日:2021-07-29
申请号:US17024421
申请日:2020-09-17
Inventor: Weijia ZHANG , Hao LIU , Hui XIONG
Abstract: The present disclosure provides a parking lot free parking space predicting method and apparatus etc., and relates to the field of artificial intelligence. The method comprises: building a parking lot association graph and an information propagation graph for parking lots in a region to be processed, each junction in the graphs representing a parking lot, and connecting parking lots meeting a predetermined condition through edges; as for any parking lot i without a real-time sensor, determining local space correlation information of parking lot i according to environment context features of the parking lot i and neighboring parking lots which are in the parking lot association graph and connected to the parking lot i through edges; determining free parking space estimation information of the parking lot i according to free parking space information of neighboring parking lots with real-time sensors in the information propagation graph; determining time correlation information of the parking lot i according to the determined two kinds of information, and predicting future free parking space information of the parking lot i according to the information. The solution of the present disclosure may be applied to improve the accuracy of the prediction result.
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公开(公告)号:US20210383279A1
公开(公告)日:2021-12-09
申请号:US17445905
申请日:2021-08-25
Inventor: Weijia ZHANG , Hao LIU , Dejing DOU , Hui XIONG
Abstract: Provided are an intelligent recommendation method and apparatus, a model training method and apparatus, an electronic device, and a storage medium, which relate to artificial intelligence technologies, and are applicable to the intelligent recommendation and the intelligent transportation technologies. The intelligent recommendation method includes: determining an object recommendation request; determining, according to a multi-agent strategy model and the object recommendation request, object execution actions of at least two agent objects matching the object recommendation request; determining a target object execution action according to the object execution actions; and recommending the object recommendation request to a target agent object corresponding to the target object execution action.
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