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Procedure parameter-based result feedback method and device

A technology of process parameters and feedback requests, applied in the field of process knowledge systems, can solve problems affecting manufacturing efficiency and quality, low system early warning efficiency, and inaccurate analysis results, and achieve the effect of improving classification accuracy

Active Publication Date: 2018-01-12
JIANGSU KANION PHARMA CO LTD +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, KPS can collect data in the manufacturing process and perform parameter analysis and feedback based on empirical values. The feedback surface is relatively narrow, and the analysis results are often not accurate enough to accurately and comprehensively reflect the problems existing in the system, resulting in low early warning efficiency of the system. , affecting manufacturing efficiency and quality

Method used

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  • Procedure parameter-based result feedback method and device
  • Procedure parameter-based result feedback method and device
  • Procedure parameter-based result feedback method and device

Examples

Experimental program
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Effect test

Embodiment 1

[0059] In one embodiment of the present invention, a kind of result feedback method based on process parameter is provided, see figure 1 , the method includes the following processes:

[0060] S1. Receive a result feedback request, where the feedback request includes an intermediate result type.

[0061] Specifically, the intermediate result type is the intermediate result that needs to be controlled in a certain section of the process manufacturing process, and the specific type should be determined according to the actual section, such as: in the extraction section, the component content is the main CQA; in the concentration section, The specific gravity of solid content and extract is CQA; in the alcohol precipitation and extraction process, the transfer rate of index components and the removal rate of impurities are CQA, etc.

[0062] S2. Obtain a process parameter corresponding to the feedback request, where the process parameter is a multidimensional parameter and forms...

Embodiment 2

[0069] In one embodiment of the present invention, a kind of training method of result feedback neural network model is provided, see figure 2 , the method process includes:

[0070] S21. Obtain process parameter sample data to be trained, where the process parameter sample includes multiple process parameter sets and corresponding known target values;

[0071] S22. Establish an initial network model, the initial network model including an input layer, a hidden layer, an output layer, an initial weight, and an initial bias;

[0072] S23. Using the backpropagation method, update the initial weight and the initial bias;

[0073] S24. Determine whether weight convergence is achieved, and if yes, obtain the result and feed it back to the neural network model; otherwise, repeat S23-S24.

[0074] The process of error backpropagation is to apportion the error to all units in each layer, so as to obtain the error signal of each layer unit, and then correct the weight of each unit, ...

Embodiment 3

[0103] In one embodiment of the present invention, a kind of result feedback device based on process parameter is provided, see Figure 5 , the device includes the following modules:

[0104] A request module 310, configured to receive a result feedback request, where the feedback request includes an intermediate result type;

[0105] A parameter module 320, configured to acquire a process parameter corresponding to the feedback request, where the process parameter is a multidimensional parameter and forms a process parameter set;

[0106] The input module 330 is used to input the process parameter set into the result feedback neural network model, and the result feedback neural network model is obtained by training the process parameter samples;

[0107] The output module 340 is configured to obtain the output result of the result feedback neural network model.

[0108] Further, the device also includes a training module 350, and the training module 350 includes:

[0109] ...

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Abstract

The invention discloses a procedure parameter-based result feedback method and device. The method comprises receiving a result feedback request which comprises an intermediate result type; acquiring procedure parameters corresponding to the result feedback request, wherein the procedure parameters are multidimensional parameters and compose a procedure parameter set; inputting the procedure parameter set into a result feedback neural network model which is acquired through sample training the procedure parameters; acquiring the output result of the result feedback neural network model. By establishing the result feedback neural network model, the procedure parameter-based result feedback method can classify input parameters during a PKS (process knowledge system) manufacturing process to determine whether reactants are quality and to achieve intelligent feedback.

Description

technical field [0001] The invention relates to the field of process knowledge systems, in particular to a process parameter-based result feedback method and device. Background technique [0002] In the Process Knowledge System (PKS for short), the intelligent feedback of parameters mainly includes real-time monitoring of parameter data, and if the alarm exceeds the range, a warning is issued. That is to say, in the prior art, only parameters are fed back and monitoring without feedback on its corresponding results. [0003] In the existing technology, KPS can collect data in the manufacturing process and perform parameter analysis and feedback based on empirical values. The feedback surface is relatively narrow, and the analysis results are often not accurate enough to accurately and comprehensively reflect the problems existing in the system, resulting in low early warning efficiency of the system. , affecting manufacturing efficiency and quality. Contents of the invent...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06K9/62G06N5/02G07C3/00G07C3/14G06Q50/04
CPCG06N3/08G06N5/02G06Q50/04G07C3/00G07C3/14Y02P90/30
Inventor 萧伟刘雪松凌娅陈勇王振中姜晓红毕宇安李页瑞包乐伟章晨峰王磊陈永杰杜定益
Owner JIANGSU KANION PHARMA CO LTD
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