基于深度学习的突发事件多源异构情报融合及推荐研究

Deep Learning Based Fusion and Recommendation of Heterogeneous Multi-Source Intelligence for Emergency Events

【目的/意义】突发事件中情报复杂多样、规律难预测,对社会公共安全至关重要。如何快速有效获取和分析突发事件中情报,进行安全预警与危机情报推荐,是保障社会安全的重要课题。【方法/过程】本文构建了一个基于深度学习的突发事件多源异构情报融合与危机情报推荐系统。该系统通过深度学习算法提取事件数据特征,实现对多源异构情报的高效融合,并根据数据分析结果进行危机情报生成和策略推荐。【结果/结论】基于深度学习构建出安全保障的体系框架,通过实证分析,实现对复杂突发事件中情报的智能处理与推荐。有效地提高了对事件中危机情报的获取和利用效率,为相关部门快速制定科学的应急方案提供鼎力支持,具有重要的实践应用价值。【创新/局限】实现了基于深度学习的多源异构情报融合框架在突发事件分析中的创新应用,可以高效获取情报并自动生成推荐。框架需要大量标注数据进行模型训练,标注工作量大,样本标注的一致性和正确性需要进一步提高。

【Purpose/significance】Emergency events feature complex and diverse information with unpredictable patterns,which is crucial to social and public security.How to quickly and effectively obtain and analyze intelligence in emergencies and carry out safety warnings and crisis intelligence recommendations is an important issue in ensuring social security.【Method/process】This paper constructs a deep learning based fusion and recommendation system for heterogeneous multi-source intelligence on emergency events.The system utilizes deep learning algorithms to extract features from event data,achieving efficient fusion of heterogeneous multisource intelligence,and generates and recommends crisis intelligence strategies based on data analytics results.【Result/conclusion】Based on deep learning,a security assurance system framework is constructed,and through empirical analysis,intelligent processing and recommendation of intelligence in complex emergencies are achieved.It effectively improves the efficiency of obtaining and utilizing crisis intelligence in incidents,provides strong support for relevant departments to quickly formulate scientific emergency plans,and has important practical application value.【Innovation/limitation】The study realized the innovative application of deep learning based multi-source heterogeneous information fusion framework in emergency event analysis,which can efficiently acquire intelligence and automatically generate recommendations.However,the framework requires a large amount of labeled data for model training.The workload of manual annotation is extensive,and the consistency and correctness of sample labeling need further improvement.