Research on terminal situational awareness model integrating user behavior portraits and graph matching

The demand for precise monitoring and analysis of user behavior in power grid terminals has surged dramatically. Traditional monitoring methods struggle to adapt to complex behavior patterns. this paper proposes a method based on constructing behavior portraits using statistical and behavioral features, aimed at uncovering the underlying connections in user behavior to effectively preempt and manage potential risks in the power grid system. Firstly, statistical features are extracted from user behavior sequences using data mining techniques and transformed into statistical feature labels. Then, through neural network training, these sequences are deeply modeled to generate behavioral feature tags. Finally, by combining these two types of tags, a behavioral profile of terminal users is constructed, and graph matching technology is used for terminal situational awareness, effectively identifying and addressing potential security threats. Experimental results validate the effectiveness of this method, which not only precisely captures and analyzes user behavior features but also significantly enhances the security management capabilities of power grid enterprises.