KNOWLEDGE GRAPH-DRIVEN DYNAMIC GENERATION TECHNOLOGY FOR FULL-SPECTRUM SCENARIO-BASED EMOTIONAL INTERVENTION PATHS: INNOVATION AND SOCIETAL VALUE

Abstract

The field of child emotional intervention faces core pain points such as insufficient full-spectrum coverage, rigid intervention paths, and poor scenario adaptability. Traditional intervention models struggle to meet the personalized needs of different groups with common emotional distress, mild emotional deficits, and severe emotional disorders associated with Autism Spectrum Disorder (ASD). To address this issue, this study, based on the invention patent "A Knowledge Graph-Based Dynamic Generation Device for Scenario-Based Emotional Intervention Paths", constructs an emotional intervention technology system integrating knowledge graphs, reinforcement learning, and intelligent optimization algorithms. The technology innovatively designs a knowledge graph architecture with five core ontologies, achieves precise classification of full-spectrum groups through K-means clustering, dynamically generates initial intervention paths using Q-learning reinforcement learning, and completes real-time optimization adjustments based on gradient descent algorithms. Meanwhile, it integrates poetic cultural elements and multi-scenario adaptation mechanisms, balancing the scientificity, interestingness, and safety of interventions. Breaking the "one-size-fits-all" limitation of traditional interventions, the technology realizes the whole-process intelligence from path generation and real-time adjustment to scenario migration. Its wide application will significantly improve the accuracy and accessibility of emotional interventions, promote the inclusive development of child emotional health services, and possess important technological innovation value and far-reaching social significance.