The present application relates to the technical field of health management, in particular to an intelligent cloud
smoking control system and method based on a behavior science model, which comprises a behavior data generation module, a
coupling relationship extraction module, an abnormal action screening module, a relapse
signal identification module and an intervention behavior generation module. In the present application, the behavior amplitude, continuous intensity and action convergence information in the whole process of the smoking action are collected to construct a sequence of action fragments with sequential characteristics, the
coupling mode between high-frequency trigger scenes is established in combination with the
label connection relationship in the daily behavior intensive period, the non-stable behavior trajectory containing the shaking amplitude and posture
delay characteristics is identified on this basis, the repeated action sequence with abnormal
holding time and smoking interval is further extracted, the behavior combination with relapse tendency is identified therefrom, and various forms of intervention means are dynamically matched according to the feature type and frequency of the repeated mode, so as to improve the pertinence, timeliness and behavior correction efficiency of the prompt content.