CN106325154A - Tea rolling and frying machine control method based on mode recognition - Google Patents

Tea rolling and frying machine control method based on mode recognition Download PDF

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Publication number
CN106325154A
CN106325154A CN201610716271.6A CN201610716271A CN106325154A CN 106325154 A CN106325154 A CN 106325154A CN 201610716271 A CN201610716271 A CN 201610716271A CN 106325154 A CN106325154 A CN 106325154A
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quality
folium camelliae
camelliae sinensis
control parameter
data
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CN106325154B (en
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黄莉
丁琳
丁一琳
胡滨
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Hefeng Quanyi Tea Industry Co.,Ltd.
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Guizhou Tongren Hetai Tea Industry Co Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

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  • Automation & Control Theory (AREA)
  • Databases & Information Systems (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Feedback Control In General (AREA)
  • Tea And Coffee (AREA)

Abstract

The invention provides a tea rolling and frying machine control method based on mode recognition. The tea rolling and frying machine control method comprises the following steps that a. a model is established; b. data are acquired; c. the optimal possible results are compared; d. the optimal control parameters are acquired; e. the control parameters are compared; and f. the control parameters are adjusted. The tea rolling and frying process can be effectively adjusted, and the control parameters of a tea rolling and frying machine can be timely adjusted to the most appropriate result through real-time mode recognition and adjustment of the control parameters so that the quality of the finished product can be greatly enhanced.

Description

A kind of Folium Camelliae sinensis rolling heating stirring machine control method based on pattern recognition
Technical field
The present invention relates to a kind of Folium Camelliae sinensis rolling heating stirring machine control method based on pattern recognition.
Background technology
In prior art, the control of Folium Camelliae sinensis rolling heating stirring machine is artificial setup control parameter mostly, and this mode needs in a large number Attempting, and rule of thumb judge to adjust, on the one hand this mode adjusts and is easily caused defect rate height not in time, the most also Easily by good for quality raw material as more secondary materials processing.
Summary of the invention
For solving above-mentioned technical problem, the invention provides a kind of Folium Camelliae sinensis rolling heating stirring machine control method based on pattern recognition, Folium Camelliae sinensis rolling heating stirring machine control method based on pattern recognition by real-time pattern recognition and control parameter should be adjusted, can be the most right Folium Camelliae sinensis rolling stir-fry process is adjusted, and is most suitable result by the control parameter adjustment of Folium Camelliae sinensis rolling heating stirring machine in time, thus greatly Promote quality of finished.
The present invention is achieved by the following technical programs.
A kind of based on pattern recognition the Folium Camelliae sinensis rolling heating stirring machine control method that the present invention provides, comprises the steps:
A. set up model: by historical data, set up odour component → quality grading and control parameter → quality results Data model, and odour component possible under each quality grading is set up quality grading data base；
B. data are obtained: from the many groups gas sensor being arranged on Folium Camelliae sinensis rolling heating stirring machine, obtain current Folium Camelliae sinensis abnormal smells from the patient number According to, and attached current control parameter；
C. the optimum possible outcome of contrast: current Folium Camelliae sinensis odor data odour component → quality grading data model is carried out Pattern recognition, and the quality contrast quality grading data base that will identify that, it is judged that the high-quality classification most possibly reached；
D. optimization control parameter is obtained: according to controlling parameter → quality results model, inverse operation obtains corresponding above-mentioned possibility The control parameter of the high-quality classification reached, controls parameter and includes that speed is fried in the rolling of Folium Camelliae sinensis rolling heating stirring machine, temperature is fried in rolling and rolling is fried Time；
E. compared with control parameter: the control parameter that will obtain in step d, with current in step b control parameter carry out right Ratio；
F. adjust and control parameter: be controlled adjusting to Folium Camelliae sinensis rolling heating stirring machine according to comparing result, and reenter step b, Until manual-lock terminates.
Described step a is set up odour component → quality grading and control parameter → quality results data model, be Carry out on computer, and the data model write single-chip microcomputer step after completing that will establish.
The data model of described odour component → quality grading is instructed through 5 times by the BP neural network algorithm of three hidden layers Practice and set up.
Described control parameter → quality results model is polynary once linear equation.
The beneficial effects of the present invention is: by real-time pattern recognition and adjust control parameter, can be effectively to Folium Camelliae sinensis Rolling stir-fry process is adjusted, and is most suitable result by the control parameter adjustment of Folium Camelliae sinensis rolling heating stirring machine in time, thus promotes greatly Quality of finished.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the present invention.
Detailed description of the invention
Technical scheme is described further below, but claimed scope is not limited to described.
A kind of based on pattern recognition Folium Camelliae sinensis rolling heating stirring machine control method as shown in Figure 1, comprises the steps:
A. set up model: by historical data, set up odour component → quality grading and control parameter → quality results Data model, and odour component possible under each quality grading is set up quality grading data base；
B. data are obtained: from the many groups gas sensor being arranged on Folium Camelliae sinensis rolling heating stirring machine, obtain current Folium Camelliae sinensis abnormal smells from the patient number According to, and attached current control parameter；
C. the optimum possible outcome of contrast: current Folium Camelliae sinensis odor data odour component → quality grading data model is carried out Pattern recognition, and the quality contrast quality grading data base that will identify that, it is judged that the high-quality classification most possibly reached；
D. optimization control parameter is obtained: according to controlling parameter → quality results model, inverse operation obtains corresponding above-mentioned possibility The control parameter of the high-quality classification reached, controls parameter and includes that speed is fried in the rolling of Folium Camelliae sinensis rolling heating stirring machine, temperature is fried in rolling and rolling is fried Time；
E. compared with control parameter: the control parameter that will obtain in step d, with current in step b control parameter carry out right Ratio；
F. adjust and control parameter: be controlled adjusting to Folium Camelliae sinensis rolling heating stirring machine according to comparing result, and reenter step b, Until manual-lock terminates.
Thus, by the pattern recognition of gas data, and recognition result is contrasted, thus to controlling to carry out timely Optimize, finally can fast and effectively Folium Camelliae sinensis rolling stir-fry process be adjusted.
Described step a is set up odour component → quality grading and control parameter → quality results data model, be Carry out on computer, and the data model write single-chip microcomputer step after completing that will establish.
The data model of described odour component → quality grading is instructed through 5 times by the BP neural network algorithm of three hidden layers Practice and set up.
Described control parameter → quality results model is polynary once linear equation.

Claims (4)

1. a Folium Camelliae sinensis rolling heating stirring machine control method based on pattern recognition, it is characterised in that: comprise the steps:
A. set up model: by historical data, set up odour component → quality grading and control the data of parameter → quality results Model, and odour component possible under each quality grading is set up quality grading data base；
B. data are obtained: from the many groups gas sensor being arranged on Folium Camelliae sinensis rolling heating stirring machine, obtain current Folium Camelliae sinensis odor data, and Attached current control parameter；
C. the optimum possible outcome of contrast: current Folium Camelliae sinensis odor data odour component → quality grading data model is carried out pattern Identify, and the quality contrast quality grading data base that will identify that, it is judged that the high-quality classification most possibly reached；
D. obtaining optimization control parameter: according to controlling parameter → quality results model, inverse operation obtains corresponding above-mentioned be likely to be breached The control parameter of high-quality classification, control parameter and include that speed is fried in the rolling of Folium Camelliae sinensis rolling heating stirring machine, temperature is fried in rolling and the rolling stir-fry time；
E. compared with control parameter: the control parameter that will obtain in step d, contrasts with the current parameter that controls in step b；
F. adjust and control parameter: be controlled adjusting to Folium Camelliae sinensis rolling heating stirring machine according to comparing result, and reenter step b, until Manual-lock terminates.
2. Folium Camelliae sinensis rolling heating stirring machine control method based on pattern recognition as claimed in claim 1, it is characterised in that: described step a The middle data model set up odour component → quality grading and control parameter → quality results, is to carry out on computers, and will build The data model write single-chip microcomputer step after completing stood.
3. Folium Camelliae sinensis rolling heating stirring machine control method based on pattern recognition as claimed in claim 1, it is characterised in that: described abnormal smells from the patient becomes Point → data model of quality grading set up through 5 training by the BP neural network algorithm of three hidden layers.
4. Folium Camelliae sinensis rolling heating stirring machine control method based on pattern recognition as claimed in claim 1, it is characterised in that: described control is joined Number → quality results model is polynary once linear equation.
CN201610716271.6A 2016-08-24 2016-08-24 A kind of tealeaves rolling heating stirring machine control method based on pattern-recognition Active CN106325154B (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110609523A (en) * 2019-07-18 2019-12-24 浙江工业大学 Cooperative control method for units in primary tea leaf making process

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001327249A (en) * 2000-05-19 2001-11-27 Kagoshima Prefecture Method for evaluating tea leaf and system therefor
CN101419166A (en) * 2008-11-18 2009-04-29 江苏大学 Tea quality nondestructive detecting method and device based on near-infrared spectrum and machine vision technology
CN102722201A (en) * 2012-06-29 2012-10-10 重庆大学 Automatic baking monitoring system and method
CN202956625U (en) * 2012-11-27 2013-05-29 王思远 Automatic control system of tea frying machine
CN103487558A (en) * 2013-07-30 2014-01-01 中国标准化研究院 Detection method for abnormal samples in mode identification and analysis of tea quality through intelligent sensory signals
CN204305975U (en) * 2014-09-24 2015-05-06 新昌县博驰电子有限公司 Automatic tea frying machine control system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001327249A (en) * 2000-05-19 2001-11-27 Kagoshima Prefecture Method for evaluating tea leaf and system therefor
CN101419166A (en) * 2008-11-18 2009-04-29 江苏大学 Tea quality nondestructive detecting method and device based on near-infrared spectrum and machine vision technology
CN102722201A (en) * 2012-06-29 2012-10-10 重庆大学 Automatic baking monitoring system and method
CN202956625U (en) * 2012-11-27 2013-05-29 王思远 Automatic control system of tea frying machine
CN103487558A (en) * 2013-07-30 2014-01-01 中国标准化研究院 Detection method for abnormal samples in mode identification and analysis of tea quality through intelligent sensory signals
CN204305975U (en) * 2014-09-24 2015-05-06 新昌县博驰电子有限公司 Automatic tea frying machine control system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110609523A (en) * 2019-07-18 2019-12-24 浙江工业大学 Cooperative control method for units in primary tea leaf making process

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