一种多量表心理危机预警方法及系统
By constructing a cross-scale knowledge graph and graph attention network, combined with temporal convolutional networks and information entropy calculation, the problem of the inability to identify respondents' concealment behavior in existing technologies is solved, and a highly accurate early warning of psychological crises is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ANHUI HUATU INFORMATION TECH CO LTD
- Filing Date
- 2026-05-08
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies cannot effectively identify respondents' concealment behavior, resulting in insufficient accuracy in early warning of psychological crises and an inability to perform dual-dimensional detection at the levels of behavioral dynamics and semantic logic.
We construct a cross-scale knowledge graph and use graph attention network for semantic consistency analysis. We combine temporal convolutional network and information entropy calculation to extract behavioral fluctuation and logical conflict features. We then conduct a comprehensive evaluation through a dual-path parallel feature extraction network and self-attention fusion mechanism to output the corrected crisis assessment results.
It improves the ability to detect highly concealing respondents. Through cross-scale semantic consistency analysis and behavioral dynamics detection, it identifies respondents' concealment strategies on the core crisis dimension, thereby improving the accuracy of psychological crisis early warning.
Smart Images
Figure CN122136007B_ABST