Research On Task Allocation Strategies Of Knowledge Point Labeling Based On Collective Intelligence

The rapid development of information technology has led to the explosive growth of large educational resources.The knowledge-based tagging of learning resources based on the collective intelligence is conducive to the construction of a reasonable knowledge system,the solution of the disorderly organization of large educational resources,and the use of intelligence of everyone.The method is efficient and convenient,but the attributes of the workers are different.How to arrange the tasks reasonably and assign them based on the attributes of the workers to achieve higher efficiency in task completion and better quality of results is the core of this study.This paper studies the current situation of task allocation based on collective intelligence in China and abroad,and combines the knowledge point labeling task characteristics of learning resources,and proposes a task allocation strategy based on knowledge labeling.Through theoretical analysis and experimental research,the effectiveness of this strategy is verified,and specific research is carried out in the following areas:(1)Study the common influencing factors of task allocation based on collective intelligence,and combine the characteristics of knowledge point labeling tasks with learning resources,and obtain the influencing factors of task allocation based on knowledge,marking the weight of influencing factors through AHP analytic hierarchy process.Construct a reasonable evaluation system to provide support for the arrangement of follow-up tasks and the selection of attribute weights for task workers.(2)Based on the constructed knowledge point labeling task allocation evaluation system based on collective intelligence,design a reasonable task form,cluster the design of learning resources of the same topic through the topic word extraction algorithm,so as to facilitate the assignment of tasks of the same topic to those skilled in the field workers,according to the task arrangement,design an integral reward mechanism to promote the enthusiasm of the user to complete the task,vectorize the representation of the worker attribute,and combine the concept of entropy to propose a quantitative representation of the activity in the worker attribute.The value of a controls the quantitative representation of confidence in the attributes of workers.(3)Based on the user attributes,the task allocation strategy is studied.First,users who are good in the field related to the task topic are found.Based on different allocation purposes,different optimal workers are selected.According to the Euclidean distance similarity calculation formula,the calculation and Workers in the neighborhood of the best worker perform task assignment to achieve the purpose of task assignment.Three different allocation strategies are designed,which are task allocation that considers task completion quality,task allocation that considers task completion efficiency,and task allocation that considers task completion quality and task completion efficiency.Based on the above mechanism,we implement the task allocation system based on the collective intelligence of knowledge point labeling,from the functional design,architecture design,database design and logic among the pages,mainly including task allocation module,user labeling module,user task viewing module,labeling results Audit module and user attribute visualization module.In order to verify the stability of the system,the function and performance were tested,and the results met the requirements.