Research on Empowering Urban Rail Transit Security with Intelligent Recognition of Individual Extreme Behavior

Abstract

Introduction: Individual extreme crime is one of the important risks of urban rail transit, and accurately identifying individual extreme behavior can effectively enhance the ability of police department to keep the safety of urban rail transit. At present, the early warning of individual extreme crimes generally adopts the integral method, which heavily relies on the experience of the police to assign values and cannot objectively and accurately describe the relevant factors that affect individual extreme behavior.

Objectives: This paper plans to propose a fuzzy reasoning warning method for identifying individual extreme behavior, providing a reference for public security organs to improve the security level of urban rail transit.

Methods: This studies a fuzzy processing method that conforms to the objective facts of individual extreme behavior, using a membership function to represent fuzziness and objectively describe the characteristics of individual extreme behavior and its influencing factors.

Results: This paper overcomes the limitation of relying heavily on individual police experience by extracting features from a large amount of case data to establish a membership function.

Conclusions: This paper's reasoning is based on accurate description of natural language, and the reasoning process overcomes subjectivity. The reasoning results contain more information, which can effectively improve the public security organs' ability to accurately identify individual extreme behavior and warning level.