A working condition-based machine learning progress control method and related device

Abstract

The embodiments of the present application provide a working condition-based machine learning progress control method and related apparatus, the method includes: acquiring a progress query request from the electronic device; determining the target working condition according to the target working condition code the current learning situation; determine whether there is a preparatory learning stage in the target operating condition according to the current learning situation; if so, send the current learning situation and prompt information to the electronic device, and the prompt information is used to prompt the user Create the task of the target working condition in the preparatory learning stage; if it does not exist, send the current learning condition to the electronic device. In this way, the training stage is automatically identified through the working conditions, and the user is assisted to complete the whole process of machine learning by displaying information and sending prompt messages, which greatly improves the efficiency of machine learning, and the user can quickly understand the current learning progress. The user's decision-making difficulty.

Description

technical field [0001] The present application belongs to the general data processing field of the Internet industry, and specifically relates to a working condition-based machine learning progress control method and related devices. Background technique [0002] At present, it is an increasingly popular trend to train production through machine learning to help optimize process parameters, but traditional machine learning training is to generate learning process parameter data through the planning and arrangement of personnel, and then arrange it into the production process, relying on manual management and testing. At the same time, there are multiple stages of training, such as training, learning, and verification, which all require personnel to make decisions and coordinate management and control. On the one hand, the efficiency is relatively low, and it also depends on the skills and experience of personnel, and it is also prone to errors. SUMMARY OF THE INVENTION [...

AI Technical Summary

Problems solved by technology

[0002] At present, it is an increasingly popular trend to use machine learning to train production and help optimize process parameters. However, traditional machine learning training is to generate learning process parameter data through personnel planning and arrangement, and then arrange it in the production process. The progress and quality of the production training process. At the same time, there are multiple stages of training, such as training, learning, and verification, which require personnel decision-making, coordination and control. On the one hand, the efficiency is relatively low, and it also depends on the skills and experience of personnel, and it is also prone to errors.

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