The invention discloses an AI-driven dynamic decision and process linkage method and
system, and the method comprises the following steps: S1, receiving
business data of an
enterprise system through an API interface, and carrying out the preprocessing of the
business data; s2, inputting the preprocessed service data into a double-
algorithm model, wherein the double-
algorithm model outputs a
path switching value according to the switching times, the
switching time consumption and the switching deviation value of a historical process; s3, if the
path switching value reaches a first preset value, judging a
risk level according to the grading decision value to generate a flow adjustment decision instruction; and S4, generating a new flow through a flow adjustment decision instruction, calculating a flow matching degree, and feeding back the flow matching degree to the double-
algorithm model, so that the double-algorithm model adjusts a
path switching value according to the flow matching degree. According to the method, a double-algorithm model of the GBDT model and the RL model is adopted, and the advantages of the GBDT model in the aspects of
feature mining and regression prediction and the characteristics of the RL model in the aspects of dynamic decision optimization and feedback learning are exerted.