We propose a spatial-temporal inference model, which uses a large amount of spatialtemporal data in the city to help governmentsenterprises predict future long-short-term important urban indicator values, such as traffic flow, human mobility, pollution level, number of criminal caseseven commercial profitability. The SIM model exploits IoT to integrate multiple real-time geospatial big data, including population, the flow of people, geographical data, Traffic,real-time sensor values. SIM can make effective predictionsprovide explainability for making decisions.

Our technologies are able to analyze customer behaviorsprovide personalized automatic services. We take two directions: (1) Establish a customer behavior predictionrecommendation system by analyzing consumption records, exploring behavior features,strengthening the link between marketing strategiesbehavior analysis (2) Collaborative EDM subjects generation by analyzing the relationship between customers’ click recordsconsumption for understanding the relationship between customer intentionsfinancial products,for generating personalized marketing strategies.