数智化背景下高职院校心理健康监测预警系统的优化与实践路径

Optimization and Practical Pathways of Mental Health Monitoring and Early Warning System in Higher Vocational Colleges under the Context of Digital Intelligence

摘要: 在数智化技术全面渗透教育领域的时代背景下，高职院校心理健康监测预警工作迎来系统性转型与智能化升级的重要契机。高职院校作为培养高素质技术技能人才的关键阵地，其学生心理健康状况直接关系人才培养质量、校园安全稳定与社会长远发展。当前我国高职院校心理健康监测预警体系已基本实现测评常态化、预警网络化与技术初步应用，但在实际运行中仍然面临数据采集维度单一、多源信息整合不足、家校社医协同机制薄弱、智能算法应用浅层化等现实困境，难以适配高职学生心理问题突发性、情境性、隐蔽性、复杂性的现实特征，也无法满足精准化、动态化、全周期式心理健康服务的现实需求。依托数智化技术所具备的多模态感知、大数据分析、动态建模、智能交互等核心能力，本研究立足高职院校办学特点与学生心理发展规律，从平台标准化建设、多模态数据融合、智能算法优化、家校社医协同四个维度，系统提出心理健康监测预警系统的优化方向与实践路径，通过统一技术规范、打通数据壁垒、提升预警精度、完善联动机制，全面增强高职院校心理健康服务的精准性、时效性与系统性，为新时代技术技能人才身心健康发展提供坚实保障。

Abstract: Against the background of the comprehensive penetration of digital intelligence technology into the education field, mental health monitoring and early warning in vocational colleges has ushered in an important opportunity for systematic transformation and intelligent upgrading. As a key position for cultivating high-quality technical and skilled talents, the mental health of students in vocational colleges is directly related to the quality of talent training, campus safety and stability, and long-term social development. At present, the mental health monitoring and early warning system in vocational colleges in China has basically realized the normalization of assessment, the networking of early warning and the initial application of technology. However, in actual operation, it still faces practical dilemmas such as single data collection dimension, insufficient multi-source information integration, weak home-school-community-medical cooperation mechanism, and shallow application of intelligent algorithms. It is difficult to adapt to the sudden, situational, hidden and complex characteristics of psychological problems of vocational college students, and cannot meet the practical needs of precise, dynamic and full-cycle mental health services. Relying on the core capabilities of digital intelligence technology such as multimodal perception, big data analysis, dynamic modeling and intelligent interaction, based on the school-running characteristics of vocational colleges and the psychological development laws of students, this study systematically puts forward the optimization direction and practical path of the mental health monitoring and early warning system from four dimensions: platform standardization construction, multimodal data fusion, intelligent algorithm optimization, and home-school-community-medical cooperation. By unifying technical specifications, breaking data barriers, improving early warning accuracy, and improving the linkage mechanism, we will comprehensively enhance the accuracy, timeliness and systematicness of mental health services in vocational colleges, so as to provide a solid guarantee for the physical and mental health development of technical and skilled talents in the new era.