A risk behavior monitoring and early warning method and system suitable for special crowds

Abstract

The invention provides a risk behavior monitoring and early warning method and system suitable for special crowds. The invention discloses a risk behavior monitoring and early warning method suitablefor special populations. The method comprises the steps of obtaining individual facial activity monitoring data of the special populations; Inputting the recorded facial activity characteristics of the special crowd individuals into a pre-trained emotion classifier for emotion classification; And determining whether to carry out early warning according to the emotion type of the special crowd individual. According to the embodiment, the emotion state is recognized and analyzed through the face image, and therefore the early warning result can be more accurate. Moreover, the emotion of the individual can be dynamically monitored in real time, the real-time performance of monitoring can be achieved, and the monitoring efficiency can be improved.

Description

technical field [0001] The invention relates to the technical field of early warning, in particular to a risk behavior monitoring and early warning method and system suitable for special groups of people. Background technique [0002] At present, early warning for special groups of people can include the following methods: For example, managers conduct regular behavioral observations and individual interviews with users, and then judge whether there are abnormal behaviors among special groups of people based on their own experience. For another example, managers can design qualitative / quantitative questions based on psychological theories, use reliability and validity and revise the questionnaire, and infer whether there is a possibility of abnormal behavior based on the results of questionnaires filled out by special groups of people. [0003] However, the existing early warning methods have the following disadvantages: [0004] First, the observation and interview methods...

AI Technical Summary

Problems solved by technology

[0004] First, the observation and interview methods are highly subjective and imprecise, and it is difficult to unify the measurement standards, so it is difficult to accurately judge and identify

[0005] Second, it takes a lot of time to fill out the questionnaire, resulting in relatively low evaluation efficiency

[0006] Third, the evaluation cycle is relatively long, and real-time monitoring cannot be achieved

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