Abstract: This invention discloses a method for comprehensively apportioning the source contribution to fine particular matter (PM2.5) based on the receptor model and the chemical transport model, which comprises the receptor model calculation steps, chemical transport model calculation steps and comprehensive source apportionment steps; the said comprehensive source apportionment step comprises the following sub-steps: according to the principle of inverse proportionality between uncertainty and weight coefficient, the first (receptor model) uncertainty and the second (chemical transport model) uncertainty are normalized to obtain their respective weight coefficients; the comprehensive source apportionment results are calculated based on the apportionment results of the receptor model and the chemical transport model, and their respective weight coefficient.

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: 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.