多模态数据赋能青少年心理健康风险预警的实践路径研究

A Study on Practical Pathways of Multimodal Data-Empowered Mental-Health Risk Early Warning for Adolescents

摘要: 在数字化与社会转型的双重背景下，青少年心理危机呈现低龄化、隐蔽化、复杂化趋势，传统依靠人工观察、阶段性测评的工作模式难以满足新时代校园心理健康治理需求。研究以杭州12~18岁青少年为对象，系统梳理当前青少年心理危机预警与干预的现实困境、技术瓶颈与机制短板。其次，构建“数据层–特征层–决策层”三级智能预警架构，形成“低风险常态关注、中风险精准支持、高风险紧急处置”的分级干预体系，并制定配套的数据安全规范、预警报告单模板与家校社医协同流程。研究表明，多模态数据融合可显著提升青少年心理危机早期识别准确率与响应速度，凭借多模态数据识别能力和个体、群体全维画像分析等核心功能，可对青少年进行全面深度分析，为校园心理危机提供超前研判和精准决策服务，其打造的“无感评估分析智能体”能实现危机的精准预警干预。同时，分级干预模式能够优化资源配置、压实工作责任、降低危机恶化风险，正如基于多模态大数据构建的大学生心理危机预警体系，会针对不同严重程度设计相应的心理辅导方案，确保危机情况得到及时处置。

Abstract: Against the dual backdrop of digitalization and social transformation, adolescent psychological crises are trending toward younger onset age, concealment, and growing complexity. Traditional work models relying on manual observation and periodic assessments can hardly meet the demands of campus mental-health governance in the new era. Targeting adolescents aged 12~18 in Hangzhou, this study systematically sorts out the practical dilemmas, technical bottlenecks, and institutional shortcomings in current early warning and intervention practices for adolescent psychological crises. This research constructs a three-tier intelligent early-warning framework consisting of “data layer - feature layer - decision-making layer”. It establishes a tiered intervention system “featuring routine attention for low-risk cases, targeted support for medium-risk cases, and emergency response for high-risk cases”. Supporting data-safety specifications, early-warning report templates, and collaborative workflows for families, schools, communities, and medical institutions are also formulated. The findings indicate that multimodal data fusion can markedly improve the accuracy and response speed of early identification for adolescent psychological crises. With core capabilities including multimodal data recognition and comprehensive profiling of individuals and groups, the approach enables thorough in-depth analysis of adolescents and delivers forward-looking judgment and precise decision-making support for campus psychological crises. The built-in “intelligent agent for non-perceptual assessment and analysis” achieves accurate early warning and intervention for crises. Meanwhile, the tiered-intervention model optimizes resource allocation, clarifies work responsibilities, and mitigates risks of crisis deterioration. Analogous to multimodal big-data-driven early-warning systems for university student psychological crises, corresponding psychological-counseling protocols are devised for different severity levels to guarantee timely handling of crisis events.