一种面向任务的数据动态重组与匹配推荐方法

A Task-Oriented Data Dynamic Recombination and Matching Recommendation Method

围绕任务活动中提出的关键信息需求,该文提出了一种面向任务的数据动态重组方法,辅助生成关键信息需求成果结论。首先通过知识图谱技术对任务关联的各类活动过程中的状态数据进行初步预处理,得到相应关键信息整编素材;再通过建立数据分析模型,对数据进行分类处理,获取不同种类的推荐素材;为增强各类核心数据的敏捷性,提出了任务智能优化算法推荐功能,该算法将用户信息、权限信息、各类活动数据信息进行重组融合,推荐生成任务关联程度高的关键信息需求成果结论。同时,为提高数据的全面性,该文采用了活动状态数据协同处理方法,对同一任务关联的活动状态数据进行共享和协同处理,将数据融合重组并形成相应结构化成果呈递给使用人员,从数据处理层面为任务中活动状态数据的动态组织及掌握提供高效的支撑能力。

This paper proposes a task-oriented dynamic data recombination method to help generate the results of key information requirements.Firstly,the state data of various activities with the task were preprocessed by the knowledge graph technology to get the corresponding key information reorganization materials.Through the establishment of data analysis model,the data are classified and processed to obtain different kinds of recommendation materials;In order to enhance the agility of all kinds of core data,the recommendation function of task intelligent optimization algorithm is proposed.The algorithm recombines user information,authority in formation and all kinds of activity data information,and recommends the results and conclusions of key infor mation needs with high degree of task correlation.At the same time,in order to improve the comprehensive data,this paper adopts the active collaborative data processing method,for the same tasks associated with the active data sharing and coordination treatment,should be able to restructure and form corresponding structured data fusion results presented to use personnel,from the aspect of data processing for the task of dynamic active data organization and the master to provide effective support ability.