Industrial control method, device and system based on reinforcement learning and electronic equipment

A kind of industrial equipment and reinforcement learning technology, applied in the computer field, can solve problems such as difficult to solve, long modeling time, high cost, etc., to achieve the effect of ensuring accuracy, short modeling time, and improving efficiency

Inactive Publication Date: 2022-07-08
POLIXIR TECH LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, the prediction model in the existing MPC method is obtained by manual modeling based on human experience, and its model accuracy is heavily dependent on human experience, and the modeling time is long and the cost is high
Moreover, the existing optimizer takes a long time to solve the process, and it is difficult to solve the nonlinear situation with complex constraints
It can be seen that the existing industrial control methods cannot effectively guarantee the accuracy and efficiency of industrial control

Method used

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  • Industrial control method, device and system based on reinforcement learning and electronic equipment
  • Industrial control method, device and system based on reinforcement learning and electronic equipment
  • Industrial control method, device and system based on reinforcement learning and electronic equipment

Examples

Experimental program
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Embodiment 1

[0030] figure 1 This is a flowchart of an industrial control method based on reinforcement learning provided in Embodiment 1 of the present invention, and this embodiment is applicable to the case of industrial control of industrial equipment. The method may be performed by an industrial control apparatus based on reinforcement learning, the apparatus may be implemented in the form of hardware and / or software, and the apparatus may be configured in an electronic device, such as an industrial controller. like figure 1 As shown, the method specifically includes the following steps:

[0031] S110. Acquire current operation data of the industrial equipment.

[0032] Wherein, the industrial equipment can be any equipment that needs industrial control. The current operation data may refer to the operation data of the industrial equipment at the current moment, which may be used to characterize the current environmental state of the industrial equipment. The current operating dat...

Embodiment 2

[0051] figure 2 This is a flowchart of an industrial control method based on reinforcement learning provided in the second embodiment of the present invention. On the basis of the above-mentioned embodiments, this embodiment describes in detail the process of abnormal detection of operating data, and on the basis, The processing process after data anomaly is detected is also described in detail. The explanations of the terms that are the same as or corresponding to the above-mentioned embodiments are not repeated here. see figure 2 , the industrial control method based on reinforcement learning provided by this embodiment specifically includes the following steps:

[0052] S210. Acquire current operation data of the industrial equipment.

[0053] S220. Determine target control information based on the target control decision model corresponding to the industrial equipment and the current operation data.

[0054] S230. Send the target control information to the industrial...

Embodiment 3

[0069] image 3 This is a schematic structural diagram of an industrial control device based on reinforcement learning provided in Embodiment 3 of the present invention. like image 3 As shown, the device specifically includes: a current operation data acquisition module 310 , a target control information determination module 320 and a target control information sending module 330 .

[0070] Among them, the current operation data acquisition module 310 is used to acquire the current operation data of the industrial equipment; the target control information determination module 320 is used to determine the target control information based on the target control decision model corresponding to the industrial equipment and the current operation data , wherein the target control decision model is obtained by performing reinforcement learning on a preset control decision model based on the target virtual environment model corresponding to the industrial equipment in advance, and th...

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Abstract

The embodiment of the invention discloses an industrial control method, device and system based on reinforcement learning and electronic equipment. The method comprises the steps of obtaining current operation data of industrial equipment; target control information is determined based on a target control decision model corresponding to the industrial equipment and the current operation data, and the target control decision model is obtained by performing reinforcement learning on a preset control decision model based on a target virtual environment model corresponding to the industrial equipment in advance; the target virtual environment model corresponding to the industrial equipment is obtained by performing environment modeling based on historical operation data of the industrial equipment; and sending the target control information to the industrial equipment, so that the industrial equipment operates based on the target control information. According to the technical scheme of the embodiment of the invention, the accuracy and efficiency of industrial control can be effectively ensured.

Description

technical field [0001] The embodiments of the present invention relate to computer technology, and in particular, to an industrial control method, apparatus, system and electronic device based on reinforcement learning. Background technique [0002] Industrial controllers can be used to control industrial equipment in industrial production processes to ensure the normal operation of industrial equipment. Generally, industrial controllers can perform industrial control based on Model Predictive Control (MPC) (Model Predictive Control). The MPC method consists of two parts, one part is the prediction model used to predict the future state, and the other part is the optimizer that solves the optimal control based on the future state. [0003] At present, the prediction model in the existing MPC method is obtained by manual modeling based on human experience, and the model accuracy is heavily dependent on human experience, and the modeling time is long and the cost is high. Mo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418G06N3/04G06N3/08
CPCG05B19/41885G06N3/08G05B2219/32339G06N3/045Y02P90/02
Inventor 薛飞邹晓川
Owner POLIXIR TECH LTD
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