Abstract: Various embodiments include a data fusion method based on a knowledge graph. The method may include: mapping an original data structure to a standard data structure; acquiring standard entities and determining attributes contained in the standard entities, wherein the attributes are related to data fields in the standard data structure; establishing associative relationships between the standard entities and generating a knowledge graph with the standard entities as nodes and the associative relationships between the standard entities as edges; and loading the knowledge graph and generating a query mode containing multiple queryable instances, wherein the queryable instances correspond to the edges and/or nodes in the knowledge graph.

Abstract: A system and method are for determining a vehicle speed. In an embodiment, the system includes a first collecting module, adapted to collect traffic information; and a deep learning module, including a control model and a neural network. The control model is generated after the neural network autonomously deeply learns based on inputted traffic information. The control model is adapted to receive current traffic information including a speed and a location of a vehicle, traffic light status at a junction ahead, and traffic condition information in a road segment ahead currently collected by the first collecting module; to compute a recommended vehicle speed for the vehicle based on the current traffic information; and to output the recommended vehicle speed. Such a system may reduce the stopping times at the intersection.

Abstract: Implementations of embodiments disclose an information processing method, apparatus, and system. In an embodiment, the information processing method includes: monitoring at least one microservice request sent by each of at least two microservices, a microservice request being used by one microservice to call another microservice; determining a call relationship between a first microservice and a second microservice from each detected microservice request, the first microservice being a microservice sending the microservice request, and the second microservice being a microservice that the microservice request requests to call; and generating call information between the at least two microservices based on all determined call relationships.

Abstract: A system and method are for determining a vehicle speed. In an embodiment, the system includes a first collecting module, adapted to collect traffic information; and a deep learning module, including a control model and a neural network. The control model is generated after the neural network autonomously deeply learns based on inputted traffic information. The control model is adapted to receive current traffic information including a speed and a location of a vehicle, traffic light status at a junction ahead, and traffic condition information in a road segment ahead currently collected by the first collecting module; to compute a recommended vehicle speed for the vehicle based on the current traffic information; and to output the recommended vehicle speed. Such a system may reduce the stopping times at the intersection.

Abstract: Implementations of embodiments disclose an information processing method, apparatus, and system. In an embodiment, the information processing method includes: monitoring at least one microservice request sent by each of at least two microservices, a microservice request being used by one microservice to call another microservice; determining a call relationship between a first microservice and a second microservice from each detected microservice request, the first microservice being a microservice sending the microservice request, and the second microservice being a microservice that the microservice request requests to call; and generating call information between the at least two microservices based on all determined call relationships.