Artificial intelligence-based resource allocation method, device, electronic device, and medium

Abstract

The present invention relates to the field of artificial intelligence technology, and provides an artificial intelligence-based resource allocation method, device, electronic equipment, and medium. When extracting the labeler's ability label from the labeler's portrait and determining the allocation of multiple resources to be marked , by classifying each resource to be labeled to obtain the category category, and then predicting the difficulty of each resource to be labeled based on the category category to obtain the labeling difficulty, which improves the prediction accuracy of labeling difficulty, and then based on multiple capability labels and multiple Create a labeling model with a labeling difficulty, solve the labeling model with convex optimization, and obtain a labeling model with known labeling parameters. Finally, based on the labeling model with known labeling parameters, you can calculate the labeling accuracy of each labeler for labeling resources, so that The resources to be labeled are allocated based on the labeling accuracy. The present invention matches the labeler's ability label with the labeling difficulty of the resources to be marked, thereby improving the accuracy of labeling the resources to be marked.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based resource allocation method, device, electronic equipment and medium. Background technique [0002] Crowdsourcing refers to the practice that a company or organization outsources the work tasks performed by employees in the past to a non-specific public network in a free and voluntary manner. The tasks of crowdsourcing are usually undertaken by individuals, but the distribution of crowdsourcing tasks is urgently needed. solved problem. [0003] In the process of implementing the present invention, the inventors found that in common crowdsourcing tagging systems, the taggers' acquisition tasks are often randomly generated. However, the random generation method does not take into account the annotator's preference and professional level differences, and the difficulty of the resources to be annotated is also uneven. It is eas...

AI Technical Summary

Problems solved by technology

However, the random generation method does not take into account the annotator's preference and professional level differences, and the difficulty of the resources to be annotated is also uneven. It is easy to push resources that the annotator is not good at or not interested in to the annotator, resulting in poor annotation quality

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