Knowledge graph-based training resource allocation method, apparatus and device, and medium

Abstract

The invention relates to the field of big data, and provides a knowledge graph-based training resource allocation method, apparatus and device, and a medium, which can realize automatic planning of apath based on a knowledge graph and multiple dimensions so as to facilitate subsequent training resource recommendation according to the searched path and generate training resource recommendation information according to the target path. Finally, training resource recommendation based on the knowledge graph and path planning is realized so as to assist configuration of training resources. In addition, the invention also relates to a block chain technology, and the training resource knowledge graph can be stored in the block chain node.

Description

technical field [0001] The present invention relates to the field of big data technology, in particular to a training resource allocation method, device, equipment and medium based on a knowledge map. Background technique [0002] At present, due to the small number of audiences, most of the trainings for departmental courses of the sales department cannot be held as scheduled, such as departmental-course bridging training, departmental-course early training, and departmental-course newcomer training. Issues such as the department's staff increase rate and the low rate of on-the-job appointments have also prevented the sales department from undertaking semi-standard training courses for new recruits. In addition, there is a shortage of tutor resources in the sales department, and a shortage of high-quality tutors. However, while the supply is insufficient, newcomer training, as an important boost to effective activities, is still a rigidly needed task of training and counse...

AI Technical Summary

Problems solved by technology

[0002] At present, due to the small number of audiences, most of the trainings for departmental courses of the sales department cannot be held as scheduled, such as the connection training of departmental courses, early training of departmental courses and intensive training for newcomers of departmental courses, etc.

Issues such as the department's staff increase rate and the low rate of on-the-job appointments have also prevented the sales department from undertaking semi-standard training courses for newcomers.

In addition, there is a shortage of tutor resources in the sales department, and a shortage of high-quality tutors

However, while the supply is insufficient, the training of newcomers, as an important boost to effective activities, is still a rigidly required task of training and counseling in the sales department, resulting in long-term imbalances between supply and demand and resource misallocation.

[0003] In view of the above situation, it is possible to solve the problems of difficulty in starting courses, lack of instructors, and lack of resources in the sales department by means of overall planning by the business area and rotation of the sales department. However, the factors covered by the existing solutions are still relatively one-sided. When covering at the same time When there are multiple factors, multiple algorithm models will reduce computing efficiency, so it is difficult to strike a balance between model coverage and computing efficiency

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