A machine learning-based online training site control method

Abstract

The invention provides a machine learning-based on-line training field control method; obtain the instructions sent by the field controller and the data received, and use the received data as the input value and the sent instruction as the output value to perform multi-stage fitting on the insertion model ; Every time a segment of the kernel function is successfully fitted, the signal path is switched when the received data can fit the kernel function so that the kernel function is output as an instruction to be sent. The present invention can provide effective online learning through segment-by-segment fitting, which is convenient for enterprises to effectively reduce the investment of manpower and material resources, shorten the research and development cycle, and can smoothly complete the handover of control rights from the traditional field controller to the machine-learning plug-in controller , and it is convenient to remotely adjust parameters through another remote communication method.

Description

technical field [0001] The invention relates to an on-line training site control method based on machine learning. Background technique [0002] At present, the application of machine learning in industrial control is gradually increasing. However, the biggest problem that local enterprises generally encounter at present is the severe lack of data, which makes it difficult to complete the training of machine learning controllers. A compromise solution is to divide it into two phases , first collect data for a period of time, and complete the code of the machine learning controller at the same time, and then perform training based on the collected small amount of data to obtain a first-stage machine learning controller, put it into use, and continue to collect data for a long time during use. Then retrain the machine learning model based on all the collected data to obtain a second-stage machine learning controller, which is used for final control. However, with this method,...

AI Technical Summary

Problems solved by technology

However, using this method, firstly, the R&D cycle is too long, and secondly, the investment in manpower and material resources is extremely high. For enterprises, it is better to directly hire operators

[0003] In order to solve the above problems, the company designed the following figure 1 The on-site control system based on time-sharing control handover control shown in the figure can ensure that the handover of control power from the traditional on-site controller to the controller with machine learning as the control core can be a gradual replacement in terms of hardware. However, the existing technology does not provide technical enlightenment on how to complete the transfer of control

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