CN113392851B - Intelligent discrimination method and device for black tea fermentation degree - Google Patents
Intelligent discrimination method and device for black tea fermentation degree Download PDFInfo
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- CN113392851B CN113392851B CN202110384291.9A CN202110384291A CN113392851B CN 113392851 B CN113392851 B CN 113392851B CN 202110384291 A CN202110384291 A CN 202110384291A CN 113392851 B CN113392851 B CN 113392851B
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Abstract
The invention discloses an intelligent judgment method and device for black tea fermentation degree, which are used for obtaining a first image of first identification tea; obtaining a first tender grade of the first marked tea; obtaining a second image of the first identification tea, and obtaining a first spread leaf thickness of the first identification tea according to the second image; obtaining a first matching degree of the first fresh and tender grade and the first spread leaf thickness and obtaining a first influence factor; obtaining a first central temperature of the first identified tea; obtaining a first leaf turning time of the first identification tea; obtaining a second matching degree of the first central temperature and the first leaf turning time, and obtaining a second influence factor; and obtaining a third image of the first marked tea, and obtaining a fermentation degree judgment result of the first marked tea according to the third image, the first influence factor and the second influence factor. The technical problem that the judgment on the black tea fermentation condition is not accurate enough and intelligent enough in the prior art is solved.
Description
Technical Field
The invention relates to the field related to judgment of black tea fermentation conditions, in particular to an intelligent judgment method and device for black tea fermentation degree.
Background
Black tea belongs to the whole fermentation tea, and is prepared by using proper fresh tea leaves as raw materials and refining the tea through a series of processes such as withering, rolling (cutting), fermentation, drying and the like. The fermentation of black tea is an important process influencing the quality of the black tea, and the judgment of the black tea fermentation condition in the prior art mostly depends on manpower.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the technical problem that judgment on the black tea fermentation condition is not accurate enough and intelligent enough exists in the prior art.
Disclosure of Invention
The embodiment of the application provides an intelligent judgment method and device for the fermentation degree of black tea, solves the technical problem that judgment on the fermentation condition of the black tea is not accurate enough and intelligent enough in the prior art, and achieves the technical effect of intelligently and accurately judging the fermentation condition of the black tea.
In view of the above, the present invention provides a method and an apparatus for intelligently determining the fermentation level of black tea.
In a first aspect, the present application further provides an intelligent determination method for black tea fermentation degree, where the method is applied to an intelligent determination system for black tea fermentation, and the intelligent determination system for black tea fermentation is in communication connection with a first camera device, a first identification device, and a first temperature sensor, and the method includes: identifying the first tea leaves through the first identification device to obtain first identification tea leaves; obtaining a first image of the first identification tea through the first camera device; performing image analysis on the first image to obtain a first tender grade of the first identification tea; obtaining a second image of the first identification tea through the first camera device, wherein the second image is an image of the first identification tea in the fermentation process; obtaining a first spread leaf thickness of the first identification tea according to the second image; obtaining a first matching degree of the first fresh and tender grade and the first spread leaf thickness, and obtaining a first influence factor according to the first matching degree; obtaining a first central temperature of the first identification tea through the first temperature sensor; obtaining first leaf turning time of the first identification tea according to the first camera device; obtaining a second matching degree of the first central temperature and the first leaf turning time, and obtaining a second influence factor according to the second matching degree; obtaining a third image of the first identification tea through the first camera device, wherein the third image is an image of the first identification tea after fermentation is completed; and obtaining a fermentation degree judgment result of the first identification tea according to the third image, the first influence factor and the second influence factor.
On the other hand, this application still provides an intelligent discriminating gear of black tea fermentation degree, the device includes: the first obtaining unit is used for identifying the first tea leaves through a first identification device to obtain first identification tea leaves; a second obtaining unit, configured to obtain a first image of the first identification tea leaf through a first camera device; a third obtaining unit, configured to perform image analysis on the first image to obtain a first tender grade of the first identified tea; a fourth obtaining unit, configured to obtain a second image of the first identified tea leaf through the first camera device, where the second image is an image of the first identified tea leaf in a fermentation process; a fifth obtaining unit, configured to obtain a first spread thickness of the first identified tea leaf according to the second image; a sixth obtaining unit, configured to obtain a first matching degree between the first tender grade and the first spread leaf thickness, and obtain a first influence factor according to the first matching degree; a seventh obtaining unit, configured to obtain a first central temperature of the first identification tea leaf through a first temperature sensor; an eighth obtaining unit, configured to obtain, according to the first imaging device, a first leaf turning time of the first identification tea leaf; a ninth obtaining unit, configured to obtain a second matching degree between the first center temperature and the first leaf turning time, and obtain a second influence factor according to the second matching degree; a tenth obtaining unit, configured to obtain, by using the first imaging device, a third image of the first identification tea, where the third image is an image of the first identification tea after fermentation is completed; an eleventh obtaining unit, configured to obtain a fermentation degree determination result of the first labeled tea leaf according to the third image, the first influence factor, and the second influence factor.
In a third aspect, the present invention provides an intelligent determination apparatus for black tea fermentation degree, comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
as the method comprises the steps of identifying first tea leaves, obtaining first identification tea leaves, obtaining a first image of the first identification tea leaves, obtaining a first tender grade of the first identification tea leaves through the first image, obtaining a first spread leaf thickness of the first identification tea leaves according to a second image, obtaining a first matching degree according to the first tender grade and the first spread leaf thickness, obtaining a first influence factor according to the first matching degree, obtaining a first central temperature of the first identification tea leaves through the first temperature sensor, obtaining a second matching degree according to the first central temperature and a first leaf turning time of the first identification tea leaves, obtaining a second influence factor according to the second matching degree, and judging the fermentation degree of the first identification tea leaves according to a third image of the first identification tea leaves, the first influence factor and the second influence factor, and through diversified judgment, the technical effect of more accurate judgment result of the fermentation condition is achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flow chart of a method for intelligently judging the fermentation degree of black tea in the embodiment of the present application;
FIG. 2 is a schematic structural diagram of an intelligent method for determining the fermentation degree of black tea in the embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a seventh obtaining unit 17, an eighth obtaining unit 18, a ninth obtaining unit 19, a tenth obtaining unit 20, an eleventh obtaining unit 21, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 305.
Detailed Description
The embodiment of the application solves the technical problem that the judgment on the fermentation condition of the black tea is not accurate enough and intelligent enough in the prior art by providing the intelligent judgment method and the intelligent judgment device for the fermentation degree of the black tea, and achieves the technical effect of intelligently and accurately judging the fermentation condition of the black tea. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
Black tea belongs to the whole fermentation tea, and is prepared by using proper fresh tea leaves as raw materials and refining the tea through a series of processes such as withering, rolling (cutting), fermentation, drying and the like. The fermentation of black tea is an important process influencing the quality of the black tea, and the judgment of the black tea fermentation condition in the prior art mostly depends on manpower. The technical problem that judgment on the black tea fermentation condition is not accurate enough and intelligent enough exists in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides an intelligent judgment method for black tea fermentation degree, which is applied to an intelligent judgment system for black tea fermentation, wherein the intelligent judgment system for black tea fermentation is in communication connection with a first camera device, a first identification device and a first temperature sensor, and the method comprises the following steps: marking the first tea leaves through the first marking device to obtain first marked tea leaves; obtaining a first image of the first identification tea through the first camera device; performing image analysis on the first image to obtain a first tender grade of the first identification tea; obtaining a second image of the first identification tea through the first camera device, wherein the second image is an image of the first identification tea in a fermentation process; obtaining a first spread thickness of the first identification tea according to the second image; obtaining a first matching degree of the first fresh and tender grade and the first spread leaf thickness, and obtaining a first influence factor according to the first matching degree; obtaining a first central temperature of the first identification tea through the first temperature sensor; obtaining first leaf turning time of the first identification tea according to the first camera device; obtaining a second matching degree of the first central temperature and the first leaf turning time, and obtaining a second influence factor according to the second matching degree; obtaining a third image of the first identification tea through the first camera device, wherein the third image is an image of the first identification tea after fermentation is completed; and obtaining a fermentation degree judgment result of the first identification tea according to the third image, the first influence factor and the second influence factor.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides an intelligent determination method for black tea fermentation degree, where the method is applied to an intelligent determination system for black tea fermentation, and the intelligent determination system for black tea fermentation is communicatively connected to a first camera device, a first identification device, and a first temperature sensor, and the method includes:
step S100: identifying the first tea leaves through the first identification device to obtain first identification tea leaves;
specifically, the intelligent black tea fermentation distinguishing system is a system for judging the fermentation condition of black tea, and the system is in communication connection with a first camera device, a first identification device and a first temperature sensor, wherein the first camera device is a device with an imaging function, the first identification device is a device capable of identifying, the identification can be a mark identification, a fluorescent identification and the like, the first temperature sensor is a sensor capable of measuring temperature, the first tea is tea to be processed after being picked, the first tea is identified through the first identification device, and the identified first tea is the first identification tea. For example, the mark may be a fluorescent substance mark, and the first mark device marks a fluorescent substance at a certain position of the first tea leaf to ensure uniqueness of the sample, so that the fermentation result judgment is more accurate.
Step S200: obtaining a first image of the first identification tea through the first camera device;
step S300: performing image analysis on the first image to obtain a first tender grade of the first identification tea;
specifically, a first image of the first identification tea is obtained through the first imaging device, for example, the first imaging device may be a camera or a monitoring camera, and the first imaging device captures an image of the first identification tea, where the first image is an image including the whole first identification tea, the image is processed and then color-analyzed, and a tenderness grade of the first identification tea is obtained according to a color condition of the first image.
Step S400: obtaining a second image of the first identification tea through the first camera device, wherein the second image is an image of the first identification tea in the fermentation process;
step S500: obtaining a first spread leaf thickness of the first identification tea according to the second image;
specifically, the total spread leaf thickness of the first identification tea leaves in the fermentation process is obtained through the first camera device, wherein the total spread leaf thickness is the spread leaf thickness information obtained through calculation according to the angle shot by the first camera device, the shot distance, the magnification factor, the shot image information and the like, and the spread leaf thickness information including the first identification tea leaves is obtained according to the second image.
Step S600: obtaining a first matching degree of the first fresh and tender grade and the first spread leaf thickness, and obtaining a first influence factor according to the first matching degree;
particularly, through the data of the tealeaves fermentation condition under the different stand leaf thickness of same tender grade in the big data of collection, construct tender grade and stand leaf thickness corresponding degree database, through the influence condition of the stand leaf thickness of the tealeaves of different tender grades to tealeaves fermentation effect, construct tender grade and the matching degree list of stand leaf thickness, will first tender grade with first stand leaf thickness input matching degree list obtains first tender grade with the first matching degree of first stand leaf thickness, and will first matching degree is as the first influence factor who influences tealeaves fermentation degree.
Step S700: obtaining a first central temperature of the first identification tea through the first temperature sensor;
step S800: obtaining first leaf turning time of the first identification tea according to the first camera device;
step S900: obtaining a second matching degree of the first central temperature and the first leaf turning time, and obtaining a second influence factor according to the second matching degree;
specifically, the first temperature sensor is a sensor capable of measuring temperature, and for example, the first temperature sensor may be a contact temperature sensor, such as a bimetal thermometer, a vapor pressure thermometer, a resistance thermometer, a pressure thermometer, or the like. The real-time central temperature information of the first identification tea leaves in the fermentation process is obtained through the first temperature sensor, the first identification tea leaves are subjected to image capture through the first camera device, and the time information of leaf turning of the first identification tea leaves is obtained according to the image capture result. Similarly, a relation between the leaf turning time and the central temperature is collected, an influence relation database of the temperature and the leaf turning time on the fermentation degree is constructed, a matching degree list of the central temperature and the leaf turning time is obtained based on the database, a second matching degree of the first central temperature and the first leaf turning time is obtained through the matching degree list, and the second matching degree is used as a second influence factor influencing the fermentation degree of the tea.
Step S1000: obtaining a third image of the first identification tea through the first camera device, wherein the third image is an image of the first identification tea after fermentation is completed;
step S1100: and obtaining a fermentation degree judgment result of the first identification tea according to the third image, the first influence factor and the second influence factor.
Particularly, the third image is the image after the fermentation of first sign tealeaves is accomplished, through first camera device is right carry out image shooting after the fermentation of first sign tealeaves is accomplished, obtain the third image information of first sign tealeaves, it is right the third image information carries out regional colour analysis, according to regional colour analysis result obtains the fermentation condition of first sign tealeaves, according to first influence factor, second influence factor are right the fermentation condition is adjusted, obtains the final judged result of the fermentation degree of first sign tealeaves, through pluralism, multi-angle to first tealeaves carries out analysis and judgement, reaches and makes the judged result of the fermentation degree of tealeaves is intelligent accurate technological effect more.
Further, black tea fermentation intelligence judgement system still with air component analytical equipment communication connection, this application embodiment still includes:
step 1210: obtaining a first accommodating space, wherein the first accommodating space is an air component analysis device space and is a closed space;
step S1220: placing the first marked tea into the first accommodating space, wherein the first marked tea is fermented tea;
step S1230: obtaining a first component analysis instruction;
step S1240: according to the first component analysis instruction, performing air component analysis on the first accommodating space through the air component analysis device;
step S1250: obtaining a first analysis result;
step S1260: and adjusting the fermentation degree judgment result according to the first analysis result.
Specifically, the air component analysis device is a device capable of analyzing the content of molecular composition components in air, the air component analysis device at least comprises a first accommodating space, the first accommodating space is a closed space, the fermented first identification tea leaves are placed in the first accommodating space of the air component analysis device, after standing for a predetermined time, the air component in the first accommodating space is analyzed by the air component analysis device according to the first component analysis instruction to obtain an air component analysis result in the first accommodating space, the component ratio analysis is performed on the emitted molecular condition of the first identification tea leaves in the predetermined time according to the analysis result, the fermentation degree judgment result is adjusted according to the analysis result, and the analysis of the air components is combined, the judgment on the fermentation result of the first identification tea leaves is more accurate.
Further, in the obtaining of the first analysis result, step S1250 of the embodiment of the present application further includes:
step S1251: obtaining a first measurement time node and a second measurement time node, wherein the second measurement time node is subsequent to the first measurement time node;
step S1252: according to the first component analysis instruction, performing air component analysis on the first accommodating space under the first measurement time node to obtain a second analysis result;
step S1253: according to the first component analysis instruction, performing air component analysis on the first accommodating space under the second measurement time node to obtain a third analysis result;
step S1254: distributing the weight ratio of the first measuring time node and the second measuring time node based on big data to obtain a first weight distribution result;
step S1255: and performing weighted calculation on the second analysis result and the third analysis result according to the first weight distribution result to obtain a first analysis result.
Specifically, the time for performing the component analysis on the accommodating space by the air component analysis device at least includes a first measurement time node and a second measurement time node, where the second measurement time node is after the first measurement time node, and according to the first component analysis instruction, the air component analysis device analyzes the air component of the first accommodating space at the first measurement time node to obtain a second analysis result at the first time node, and similarly, by the first component analysis instruction, the air component analysis device analyzes the air component in the first accommodating space at the second measurement time node to obtain a third analysis result at the second time node. According to the deviation condition of the big data to the measurement results of the first measurement time node and the second measurement time node and the true values of the fermentation degree results, obtaining weight distribution results under the first measurement time node and the second measurement time node, distributing the weight values of the second analysis result and the third analysis result according to the weight distribution results, and performing weighting calculation according to the distribution weights to obtain a first analysis result. And the analysis result is more objective and accurate by analyzing and calculating the air components under different time nodes.
Further, the performing image analysis on the first image to obtain the first tender grade of the first identified tea leaf further includes step S300 of the embodiment of the present application:
step S310: obtaining a first standard image adjustment parameter, and performing parameter adjustment on the first image according to the first standard image adjustment parameter to obtain a fourth image;
step S320: performing color analysis on the fourth image to obtain a first color set result;
step S330: obtaining a first color filtering instruction;
step S340: filtering the first color set result according to the first color filtering instruction to obtain a second color set result;
step S350: and obtaining a first tender grade of the first identification tea according to the second color set result.
Specifically, the first image adjustment parameter is a parameter for adjusting a standard image during color analysis of tea leaves, wherein data for adjusting the standard image adjustment parameter at least includes: exposure, resolution, contrast, saturation and the like, adjusting parameters of the first image through the first standard image adjustment parameters to obtain a fourth image, analyzing the color gamut of the colors of the fourth image to obtain a range in the color gamut set of the fourth image, obtaining a first color filtering instruction, performing color filtering on edge colors in the results in the first color set according to the color filtering instruction, and obtaining a color set result containing a main color, namely the second color set result. For example, the defined range of the edge color may use a color gamut of a center-most color in the first color concentration result as a center, set a concentration threshold, define a color that does not satisfy the concentration threshold as an edge color, filter the edge color to obtain a second color concentration result, obtain a first tenderness grade of the first identified tea according to the second color concentration result, and filter the edge color to make the determination of the tenderness grade of the first identified tea more accurate, so as to tamp a foundation for the subsequent accurate analysis of the fermentation degree.
Further, in the step S600 of obtaining the first matching degree between the first tender grade and the first spread leaf thickness, the present embodiment further includes:
step S610: inputting the first tender grade and the first spread leaf thickness into a first matching model, wherein the first matching model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: identification information of the first tender grade, the first spread leaf thickness and the identification matching degree result;
step S620: obtaining an output result of the first matching model, wherein the output result comprises a first degree of matching.
Specifically, the first matching model is a neural network model in machine learning, and a neural network is a complex neural network system formed by widely connecting a large number of simple processing units (called neurons), reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial neural networks are a description of the first-order characteristics of the human brain system. Briefly, it is a mathematical model. And through training of a large amount of training data, inputting the first fresh and tender grade and the first spread leaf thickness into a first matching model, and obtaining the matching condition of the first fresh and tender grade and the first spread leaf thickness.
Furthermore, the training process also comprises a supervised learning process, each group of supervised data comprises a first tender grade, a first spreading leaf thickness and identification information for identifying a matching degree result, the first tender grade and the first spreading leaf thickness are input into the neural network model, the neural network model is continuously self-corrected and adjusted according to the identification information for identifying the matching degree result, and the group of supervised learning is ended until an obtained output result is consistent with the identification information, and the next group of data supervised learning is carried out; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through supervised learning of the neural network model, the neural network model can process the input information more accurately, so that a more accurate matching degree result is obtained, and a basis is provided for more accurate evaluation and tamping of the black tea fermentation degree in the follow-up process.
Further, in step S1100 of the embodiment of the present application, the obtaining a result of determining the fermentation degree of the first labeled tea according to the third image, the first influencing factor and the second influencing factor further includes:
step S1110: obtaining a first segmentation instruction;
step S1120: performing color region segmentation on the third image according to the first segmentation instruction to obtain a first color segmentation result;
step S1130: according to the first color segmentation result, performing regional color evaluation on the third image to obtain a first fermentation degree estimation result;
step S1140: and adjusting the first fermentation degree estimation result according to the first influence factor and the second influence factor to obtain a fermentation degree judgment result of the first marked tea.
Further, the embodiment of the present application further includes:
step S1131: obtaining a first fermentation color dataset;
step S1132: performing color matching on the first color region according to the first fermentation color data set to obtain a first fermentation degree result of the first color region;
step S1133: performing color matching on the second color region according to the first fermentation color data set to obtain a second fermentation degree result of the second color region;
step S1134: obtaining the area proportional relation of the first color region and the second color region;
step S1135: and calculating the weight value of the first fermentation degree result and the second fermentation degree result according to the area proportion relation to obtain a first fermentation degree pre-estimation result.
Specifically, after a third image is obtained, color area segmentation processing is performed on the third image according to the first segmentation instruction to obtain a first color segmentation result, wherein the first color segmentation result at least comprises a first color area and a second color area, a color data set of the fermentation condition of the first tea is obtained through big data, the first color area is color-matched through the first fermentation color data set to obtain a first fermentation degree result of the first color area, the second color area is color-matched according to the first fermentation color data set to obtain a second fermentation degree result of the second color area, and the weight values of the first fermentation degree result and the second fermentation degree result are obtained according to the area proportion condition of the first color area and the second color area, and calculating the weight value of the first fermentation degree result and the second fermentation degree result according to the weight value to obtain a first fermentation degree estimation result, and adjusting the first fermentation degree estimation result according to the first influence factor and the second influence factor to obtain a fermentation degree judgment result of the first identification tea.
To sum up, the intelligent determination method and device for black tea fermentation degree provided by the embodiment of the application have the following technical effects:
1. as the method comprises the steps of identifying first tea leaves, obtaining first identification tea leaves, obtaining a first image of the first identification tea leaves, obtaining a first tender grade of the first identification tea leaves through the first image, obtaining a first spread leaf thickness of the first identification tea leaves according to a second image, obtaining a first matching degree according to the first tender grade and the first spread leaf thickness, obtaining a first influence factor according to the first matching degree, obtaining a first central temperature of the first identification tea leaves through the first temperature sensor, obtaining a second matching degree according to the first central temperature and a first leaf turning time of the first identification tea leaves, obtaining a second influence factor according to the second matching degree, and judging the fermentation degree of the first identification tea leaves according to a third image of the first identification tea leaves, the first influence factor and the second influence factor, and through diversified judgment, the technical effect of more accurate judgment result of the fermentation condition is achieved.
2. Due to the adoption of the mode of filtering the edge color, the determination of the tenderness grade of the first identification tea is more accurate, and the foundation is tamped for the subsequent accurate analysis of the fermentation degree.
Example two
Based on the same inventive concept as the intelligent judgment method for the fermentation degree of black tea in the previous embodiment, the invention also provides an intelligent judgment device for the fermentation degree of black tea, as shown in fig. 2, the device comprises:
the first obtaining unit 11 is configured to identify a first tea leaf by a first identification device to obtain a first identified tea leaf;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain a first image of the first identified tea leaf through a first camera;
a third obtaining unit 13, where the third obtaining unit 13 is configured to perform image analysis on the first image to obtain a first tender grade of the first identified tea;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to obtain a second image of the first marked tea leaf through the first camera device, where the second image is an image of the first marked tea leaf in a fermentation process;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to obtain a first spread thickness of the first identified tea leaf according to the second image;
a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to obtain a first matching degree between the first tender grade and the first spread leaf thickness, and obtain a first influence factor according to the first matching degree;
a seventh obtaining unit 17, wherein the seventh obtaining unit 17 is configured to obtain a first central temperature of the first identified tea leaf through a first temperature sensor;
an eighth obtaining unit 18, where the eighth obtaining unit 18 is configured to obtain a first leaf turning time of the first identified tea leaf according to the first camera device;
a ninth obtaining unit 19, where the ninth obtaining unit 19 is configured to obtain a second matching degree between the first center temperature and the first leaf turning time, and obtain a second influence factor according to the second matching degree;
a tenth obtaining unit 20, where the tenth obtaining unit 20 is configured to obtain a third image of the first identified tea leaf through the first camera, where the third image is an image of the first identified tea leaf after fermentation is completed;
an eleventh obtaining unit 21, where the eleventh obtaining unit 21 is configured to obtain a fermentation degree judgment result of the first labeled tea leaf according to the third image, the first influence factor, and the second influence factor.
Further, the apparatus further comprises:
a twelfth obtaining unit, configured to obtain a first accommodating space, where the first accommodating space is an air component analysis device space and is a closed space;
the first placing unit is used for placing the first identification tea into the first accommodating space, wherein the first identification tea is fermented tea;
a thirteenth obtaining unit configured to obtain a first component analysis instruction;
a fourteenth obtaining unit, configured to perform air component analysis on the first accommodating space through the air component analysis device according to the first component analysis instruction;
a fifteenth obtaining unit for obtaining a first analysis result;
a first adjusting unit, configured to adjust the fermentation degree determination result according to the first analysis result.
Further, the apparatus further comprises:
a sixteenth obtaining unit, configured to obtain a first measurement time node and a second measurement time node, where the second measurement time node is subsequent to the first measurement time node;
a seventeenth obtaining unit, configured to perform air component analysis on the first accommodating space under the first measurement time node according to the first component analysis instruction, and obtain a second analysis result;
an eighteenth obtaining unit, configured to perform air component analysis on the first accommodating space under the second measurement time node according to the first component analysis instruction, and obtain a third analysis result;
a nineteenth obtaining unit, configured to allocate weight ratios of the first measurement time node and the second measurement time node based on big data, and obtain a first weight allocation result;
a twentieth obtaining unit, configured to perform weighted calculation on the second analysis result and the third analysis result according to the first weight distribution result, so as to obtain a first analysis result.
Further, the apparatus further comprises:
a twenty-first obtaining unit, configured to obtain a first standard image adjustment parameter, and perform parameter adjustment on the first image according to the first standard image adjustment parameter to obtain a fourth image;
a twenty-second obtaining unit, configured to perform color analysis on the fourth image to obtain a first color set result;
a twenty-third obtaining unit to obtain a first color filtering instruction;
a twenty-fourth obtaining unit, configured to filter the first color set result according to the first color filtering instruction, and obtain a second color set result;
a twenty-fifth obtaining unit, configured to obtain, according to the second color set result, a first tender grade of the first identified tea leaf.
Further, the apparatus further comprises:
a first input unit, configured to input the first tender grade and the first spread leaf thickness into a first matching model, where the first matching model is obtained through training of multiple sets of training data, and each set of the multiple sets of training data includes: identification information of the first tender grade, the first spread leaf thickness and the identification matching degree result;
a twenty-sixth obtaining unit configured to obtain an output result of the first matching model, where the output result includes a first matching degree.
Further, the apparatus further comprises:
a twenty-seventh obtaining unit configured to obtain a first division instruction;
a twenty-eighth obtaining unit, configured to perform color region segmentation on the third image according to the first segmentation instruction, and obtain a first color segmentation result;
a twenty-ninth obtaining unit, configured to perform regional color evaluation on the third image according to the first color segmentation result, and obtain a first fermentation degree estimation result;
and the second adjusting unit is used for adjusting the first fermentation degree estimation result according to the first influence factor and the second influence factor to obtain a fermentation degree judgment result of the first identification tea.
Further, the apparatus further comprises:
a thirtieth obtaining unit configured to obtain a first color region and a second color region of the third image according to the first color segmentation result;
a thirty-first obtaining unit for obtaining a first fermentation color dataset;
a thirty-second obtaining unit, configured to perform color matching on the first color region according to the first fermentation color data set, and obtain a first fermentation degree result of the first color region;
a thirty-third obtaining unit, configured to perform color matching on the second color region according to the first fermentation color data set, and obtain a second fermentation degree result of the second color region;
a thirty-fourth obtaining unit configured to obtain an area proportional relationship between the first color region and the second color region;
a thirty-fifth obtaining unit, configured to perform weight value calculation on the first fermentation degree result and the second fermentation degree result according to the area proportional relationship, so as to obtain a first fermentation degree estimation result.
Various changes and specific examples of the method for intelligently determining the fermentation degree of black tea in the first embodiment of fig. 1 are also applicable to the apparatus for intelligently determining the fermentation degree of black tea of the present embodiment, and a person skilled in the art can clearly know the method for implementing the apparatus for intelligently determining the fermentation degree of black tea in the present embodiment through the detailed description of the method for intelligently determining the fermentation degree of black tea, so for the sake of brevity of the description, detailed descriptions thereof are omitted here.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the intelligent determination method for the fermentation degree of black tea in the foregoing embodiments, the present invention further provides an intelligent determination device for the fermentation degree of black tea, on which a computer program is stored, which when executed by a processor implements the steps of any one of the foregoing intelligent determination methods for the fermentation degree of black tea.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the invention provides an intelligent judgment method for black tea fermentation degree, which is applied to an intelligent judgment system for black tea fermentation, wherein the intelligent judgment system for black tea fermentation is in communication connection with a first camera device, a first identification device and a first temperature sensor, and the method comprises the following steps: identifying the first tea leaves through the first identification device to obtain first identification tea leaves; obtaining a first image of the first identification tea through the first camera device; performing image analysis on the first image to obtain a first tender grade of the first identification tea; obtaining a second image of the first identification tea through the first camera device, wherein the second image is an image of the first identification tea in a fermentation process; obtaining a first spread thickness of the first identification tea according to the second image; obtaining a first matching degree of the first fresh and tender grade and the first spread leaf thickness, and obtaining a first influence factor according to the first matching degree; obtaining a first central temperature of the first identification tea through the first temperature sensor; obtaining first leaf turning time of the first identification tea according to the first camera device; obtaining a second matching degree of the first central temperature and the first leaf turning time, and obtaining a second influence factor according to the second matching degree; obtaining a third image of the first identification tea through the first camera device, wherein the third image is an image of the first identification tea after fermentation is completed; and obtaining a fermentation degree judgment result of the first identification tea according to the third image, the first influence factor and the second influence factor. The technical problem that the judgment on the fermentation condition of the black tea is not accurate enough and intelligent enough in the prior art is solved, and the technical effect of intelligently and accurately judging the fermentation condition of the black tea is achieved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. An intelligent discrimination method for black tea fermentation degree is applied to an intelligent discrimination system for black tea fermentation, wherein the intelligent discrimination system for black tea fermentation is in communication connection with a first camera device, a first identification device and a first temperature sensor, and the method comprises the following steps:
identifying the first tea leaves through the first identification device to obtain first identification tea leaves;
obtaining a first image of the first identification tea through the first camera device;
performing image analysis on the first image to obtain a first freshness grade of the first identification tea;
obtaining a second image of the first identification tea through the first camera device, wherein the second image is an image of the first identification tea in a fermentation process;
obtaining a first spread leaf thickness of the first identification tea according to the second image;
obtaining a first matching degree of the first fresh and tender grade and the first spread leaf thickness, and obtaining a first influence factor according to the first matching degree;
obtaining a first central temperature of the first identification tea through the first temperature sensor;
obtaining first leaf turning time of the first identification tea according to the first camera device;
obtaining a second matching degree of the first central temperature and the first leaf turning time, and obtaining a second influence factor according to the second matching degree;
obtaining a third image of the first identification tea through the first camera device, wherein the third image is an image of the first identification tea after fermentation is completed;
and obtaining a fermentation degree judgment result of the first identification tea according to the third image, the first influence factor and the second influence factor.
2. A method according to claim 1 wherein the intelligent black tea fermentation identification system is further communicatively connected to an air composition analysis device, the method further comprising:
obtaining a first accommodating space, wherein the first accommodating space is an inner space of an air composition analysis device, and the first accommodating space is a closed space;
placing the first marked tea into the first accommodating space, wherein the first marked tea is fermented tea;
obtaining a first component analysis instruction;
according to the first component analysis instruction, performing air component analysis on the first accommodating space through the air component analysis device;
obtaining a first analysis result;
and adjusting the fermentation degree judgment result according to the first analysis result.
3. The method of claim 2, wherein the obtaining a first analysis result further comprises:
obtaining a first measurement time node and a second measurement time node, wherein the second measurement time node is subsequent to the first measurement time node;
according to the first component analysis instruction, performing air component analysis on the first accommodating space under the first measurement time node to obtain a second analysis result;
according to the first component analysis instruction, performing air component analysis on the first accommodating space under the second measurement time node to obtain a third analysis result;
distributing the weight ratio of the first measuring time node and the second measuring time node based on big data to obtain a first weight distribution result;
and performing weighted calculation on the second analysis result and the third analysis result according to the first weight distribution result to obtain a first analysis result.
4. The method of claim 1, wherein said image analyzing said first image to obtain a first tender grade of said first identified leaf, said method further comprising:
obtaining a first standard image adjustment parameter, and performing parameter adjustment on the first image according to the first standard image adjustment parameter to obtain a fourth image;
performing color analysis on the fourth image to obtain a first color set result;
obtaining a first color filtering instruction;
filtering the first color set result according to the first color filtering instruction to obtain a second color set result;
and obtaining a first tender grade of the first identification tea according to the second color set result.
5. The method of claim 4, wherein said obtaining a first degree of match of said first tender grade to said first spread leaf thickness, further comprises:
inputting the first tender grade and the first spread leaf thickness into a first matching model, wherein the first matching model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: identification information of the first tender grade, the first spread leaf thickness and the identification matching degree result;
obtaining an output result of the first matching model, wherein the output result comprises a first degree of matching.
6. The method according to claim 5, wherein the obtaining of the fermentation degree judgment result of the first identified tea leaf according to the third image, the first influencing factor and the second influencing factor further comprises:
obtaining a first segmentation instruction;
performing color region segmentation on the third image according to the first segmentation instruction to obtain a first color segmentation result;
according to the first color segmentation result, performing regional color evaluation on the third image to obtain a first fermentation degree estimation result;
and adjusting the first fermentation degree estimation result according to the first influence factor and the second influence factor to obtain a fermentation degree judgment result of the first marked tea.
7. The method of claim 6, wherein the method further comprises:
obtaining a first color region and a second color region of the third image according to the first color segmentation result;
obtaining a first fermentation color dataset;
performing color matching on the first color region according to the first fermentation color data set to obtain a first fermentation degree result of the first color region;
performing color matching on the second color region according to the first fermentation color data set to obtain a second fermentation degree result of the second color region;
obtaining the area proportional relation of the first color region and the second color region;
and calculating the weight value of the first fermentation degree result and the second fermentation degree result according to the area proportion relation to obtain a first fermentation degree pre-estimation result.
8. An intelligent discrimination device for black tea fermentation degree, wherein the device comprises:
the first obtaining unit is used for identifying the first tea leaves through a first identification device to obtain first identification tea leaves;
a second obtaining unit, configured to obtain a first image of the first identification tea leaf through a first camera device;
a third obtaining unit, configured to perform image analysis on the first image to obtain a first tender grade of the first identified tea;
a fourth obtaining unit, configured to obtain a second image of the first identified tea leaf through the first camera device, where the second image is an image of the first identified tea leaf in a fermentation process;
a fifth obtaining unit, configured to obtain a first spread thickness of the first identified tea leaf according to the second image;
a sixth obtaining unit, configured to obtain a first matching degree between the first tender grade and the first spread leaf thickness, and obtain a first influence factor according to the first matching degree;
a seventh obtaining unit, configured to obtain a first central temperature of the first identification tea leaf through a first temperature sensor;
an eighth obtaining unit, configured to obtain, according to the first imaging device, a first leaf turning time of the first identification tea leaf;
a ninth obtaining unit, configured to obtain a second matching degree between the first center temperature and the first leaf turning time, and obtain a second influence factor according to the second matching degree;
a tenth obtaining unit, configured to obtain, by using the first imaging device, a third image of the first identification tea, where the third image is an image of the first identification tea after fermentation is completed;
an eleventh obtaining unit, configured to obtain a fermentation degree determination result of the first labeled tea leaf according to the third image, the first influence factor, and the second influence factor.
9. An intelligent discrimination apparatus for the degree of fermentation of black tea comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the program.
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