Abstract: The present invention provides a multi-modal data prediction method based on a causal Markov model, belonging to the technical field of intelligent traffic technology; the method of the present invention includes: collecting regional data and multi-modal traffic data of a research region; taking the time position, the regional point of interest and the weather information as conditional feature variables; taking the regional attraction factor, the bicycle demand factor, the taxi demand factor, the bus demand factor and the traffic speed factor as physical concept variables; taking the bicycle traffic flow, the taxi traffic flow, the bus traffic flow and the regional speed as multi-modal traffic data observation variables, and describing the generation process of the multi-modal traffic flow by using a causal Markov process; solving the causal Markov process by using a neural network, and training a built neural network for the multi-modal traffic data observation.

Abstract: Provided are a search request processing method, an apparatus, a computer device, a storage medium, a computer product, and a computer program. The method includes: in response to a search instruction, sending a search request comprising a target question; acquiring a plurality of sets of answer information corresponding to the target question, wherein each set of answer information includes answer summary information and a multimedia content supporting the answer summary information; the genre of the multimedia content is determined on the basis of a category of the target question, and the answer summary information is extracted on the basis of the multimedia content; and displaying the answer summary information in each set of answer information on a search result page, and displaying, when any piece of answer summary information is selected, at least part of the multimedia content supporting the selected answer summary information.

Abstract: An urban data prediction method based on a generative causal interpretation model is provided. The generative causal interpretation model includes exogenous variables, spatio-temporal conditional parent variables, controlled causal transition functions, and spatio-temporal mixing functions. By inferring the model's exogenous variables, causal descriptors, spatio-temporal conditional parent variables, and other causal latent variables from the observation data and fitting the corresponding functions such as the controlled causal transfer function and the spatio-temporal mixing function, the invention can predict the spatio-temporal data in city level based on the model. The observation data of the urban complex system can be decomposed into causal descriptors with physical meanings. Under the influence of stable causal structure, the robustness and applicability of the model can be improved, so that the prediction results are more in line with the operation of urban complex systems.