Data processing method and device

Abstract

Embodiments of the invention provide a data processing method and apparatus. The method comprises the steps of: performing statistics on first service data of a first type of personnel to form a forward feature library; performing statistics on the second business data of the second type of personnel to form a negative feature library; constructing a naive Bayes classifier according to the positive feature library and the negative feature library; inputting the feature data of the to-be-tested person into a naive Bayesian classifier to obtain a classification result; and if the feature data belongs to the negative category, obtaining difference information between the feature data and the data in the positive feature library and sending the difference information to a preset terminal. Theembodiment of the invention provides a data processing method and device. The naive Bayesian classifier is constructed according to the positive feature library and the negative feature library to identify the performance condition of the to-be-tested person and send the corresponding difference information for reminding, so that the automatic acquisition of the personnel ability information and the automatic reminding about the ability difference condition are achieved; meanwhile, the real-time monitoring of the personnel ability is achieved.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a data processing method and device. Background technique [0002] As the scale of the enterprise continues to grow, more and more newcomers join in. Many companies do not have a complete newcomer training and guidance system, and more often use the "teacher-apprentice" system to train and guide newcomers into the industry. Although the skill level of "masters" is generally high, it is limited to the difference in teaching ability. Some "masters" bring out "apprentices" with high professional skills, while some "masters" bring out "apprentices" with low professional skills. . [0003] In addition, in an enterprise, employees have different ability levels, and it is usually difficult for employees to realize the gap between themselves and other employees, and it is also difficult for employees to know what shortcomings they have. Therefore, how to automatically obtain...

AI Technical Summary

Problems solved by technology

Although the skill level of "masters" is generally high, it is limited to the difference in teaching ability. Some "masters" bring out "apprentices" with high professional skills, while some "masters" bring out "apprentices" with low professional skills.

[0003] In addition, in an enterprise, employees have different ability levels, and it is usually difficult for employees to recognize the gap between themselves and other employees, and it is not clear what their own deficiencies are.

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