Method for detecting withering state of tea leaves and method for preparing wood-charm black tea by applying method for detecting withering state of tea leaves

Abstract

The invention relates to a method for detecting the withering state of tea leaves and a method for preparing wood-charm black tea by using the method. The method comprises the following technological processes: withering, rolling, fermenting, carrying out enzymolysis, carrying out primary drying, carrying out microwave-assisted scenting, carrying out secondary drying, packaging finished products and the like. Meanwhile, a microwave-assisted scenting technology is introduced during scenting, the heating effect and the penetrating effect of microwaves are fully utilized, aroma molecules are promoted to be released from the wood aroma raw materials, contact and adsorption of the aroma molecules and the tea leaves are accelerated, and therefore the aroma adsorption efficiency and quality are improved; in the withering treatment process, the withering state is monitored and judged in real time, so that the withering state monitoring and judging accuracy is improved, the preparation efficiency of the Mushu black tea is improved, and the quality stability of the Mushu black tea is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of Muyun black tea, and specifically, to a method for detecting the withering state of tea leaves and a method for preparing Muyun black tea using the same. Background Art

[0002] Muyun black tea is a tea product with unique flavor and aroma. The key steps in its production process include withering, rolling, fermentation, and drying. Among them, withering can effectively remove the moisture of tea leaves, and through withering treatment, the tea leaves can achieve better texture and taste in the subsequent rolling, fermentation, and drying processes.

[0003] In the preparation process of Muyun black tea, the judgment of the withering state is crucial for the quality and taste of Muyun black tea. However, the traditional judgment methods mainly rely on the experience of production personnel and manual observation. This method is easily affected by personal subjective consciousness and the experience level of supervisors. Different operators may have different judgment criteria, resulting in subjectivity in the evaluation of the withering state, which may cause inconsistencies in quality evaluation. Moreover, due to the interference of human factors, the traditional method has certain instability in judging the withering state. This is because human perception ability is affected by various factors such as environment and psychological state. Therefore, in different times and different environments, the judgment results of the withering state of the same batch of tea leaves may be different, leading to instability in quality evaluation, and there is also a problem of low efficiency.

[0004] Therefore, an optimized Muyun black tea and its preparation scheme are desired. Summary of the Invention

[0005] In view of this, the present invention provides a method for preparing Muyun black tea.

[0006] The technical solutions provided by the present invention are as follows:

[0007] In a first aspect, the present invention provides a method for preparing Muyun black tea, the method comprising the following steps:

[0008] Placing fresh tea leaf raw materials in a cool and ventilated place for withering treatment to obtain withered tea leaf raw materials with qualified withering state;

[0009] Performing rolling treatment on the withered tea leaf raw materials to obtain rolled tea leaves;

[0010] Performing fermentation and enzymatic hydrolysis reactions on the rolled tea leaves to obtain fermented tea leaves;

[0011] Performing primary drying treatment on the fermented tea leaves to obtain tea leaves after primary drying;

[0012] Select woody fragrance raw materials, where the woody fragrance raw materials include agarwood raw materials, costus root raw materials, clove raw materials, and sandalwood raw materials;

[0013] Grind the woody fragrance raw materials into powder to obtain woody fragrance raw material powder;

[0014] Place the tea leaves after primary drying and the woody fragrance raw material powder in different areas of a double - area connected container, and use microwave - assisted scenting technology to perform directional microwave treatment on the woody fragrance raw material powder to obtain tea leaves that absorb the woody fragrance;

[0015] Perform secondary drying treatment on the tea leaves that absorb the woody fragrance to obtain woody - flavored black tea;

[0016] Package the woody - flavored black tea to obtain the finished product of woody - flavored black tea.

[0017] In a second aspect, the present invention provides a method for detecting the withering state of tea leaf raw materials after withering, which is used to place the selected fresh tea leaf raw materials in a cool and ventilated place for withering treatment to obtain withered tea leaf raw materials with qualified withering states, including:

[0018] Obtain the image of the tea leaf withering state collected by a camera; extract the set of reference images of the tea leaf withering state marked as qualified from the database; respectively perform feature extraction on each of the reference images of the tea leaf withering state marked as qualified in the set of reference images of the tea leaf withering state marked as qualified through a tea leaf withering state feature extractor based on a deep neural network model to obtain a set of tea leaf withering state reference feature matrices; pass the set of tea leaf withering state reference feature matrices through a spatial attention module to obtain a set of spatially - explicit tea leaf withering state reference feature matrices; perform joint clustering analysis on the set of spatially - explicit tea leaf withering state reference feature matrices to obtain a reference tea leaf withering state clustering representation feature matrix; pass the tea leaf withering state image through the tea leaf withering state feature extractor based on the deep neural network model and the spatial attention module to obtain a tea leaf withering state detection feature matrix; calculate the hash similarity between the tea leaf withering state detection feature matrix and the reference tea leaf withering state clustering representation feature matrix, and determine whether the withering state is qualified.

[0019] Optionally, the deep neural network model is a convolutional neural network model.

[0020] Optionally, performing joint clustering analysis on the set of spatially - explicit tea leaf withering state reference feature matrices to obtain a reference tea leaf withering state clustering representation feature matrix includes: passing the set of spatially - explicit tea leaf withering state reference feature matrices through a joint clustering network to obtain the reference tea leaf withering state clustering representation feature matrix.

[0021] Optionally, obtaining the reference tea withering state clustering characterization feature matrix from the set of the spatially explicit tea withering state reference feature matrices through a joint clustering network includes: constructing an adjacency matrix and a degree matrix of the set of the spatially explicit tea withering state reference feature matrices; calculating a Laplacian matrix based on the adjacency matrix and the degree matrix; performing a normalization process on the Laplacian matrix to obtain a normalized Laplacian matrix; arranging the respective eigenvalues of the normalized Laplacian matrix from largest to smallest, and extracting the first K eigenvalues to calculate the eigenvectors of the first K eigenvalues; normalizing the eigenvectors of the first K eigenvalues and forming a feature vector matrix with the normalized eigenvectors of the first K eigenvalues to obtain the reference tea withering state clustering characterization feature matrix.

[0022] Optionally, constructing the adjacency matrix and the degree matrix of the set of the spatially explicit tea withering state reference feature matrices includes: calculating the association weight value between any two spatially explicit tea withering state reference feature matrices in the set of the spatially explicit tea withering state reference feature matrices with the following weight formula to obtain the adjacency matrix composed of multiple association weight values; wherein, the weight formula is:

[0023]

[0024] wherein, M i and M j are respectively the i-th and j-th spatially explicit tea withering state reference feature matrices in the set of the spatially explicit tea withering state reference feature matrices, σ is the variance between the i-th spatially explicit tea withering state reference feature matrix and the j-th spatially explicit tea withering state reference feature matrix, represents the square of the two-norm, exp(·) is the exponential operation, and W i,j is the eigenvalue at the (i, j) position in the adjacency matrix.

[0025] Optionally, calculating the hash similarity between the tea withering state detection feature matrix and the reference tea withering state clustering characterization feature matrix, and determining whether the withering state is qualified includes: performing image semantic feature expression discrimination enhancement on the tea withering state detection feature matrix to obtain an optimized tea withering state detection feature matrix; calculating the hash similarity between the optimized tea withering state detection feature matrix and the reference tea withering state clustering characterization feature matrix; determining whether the withering state is qualified based on the comparison between the hash similarity and a predetermined threshold.

[0026] Optionally, in response to the hash similarity being greater than the predetermined threshold, determining that the withering state is qualified.

[0027] In a third aspect, the present invention provides a Mu Yun black tea, which is prepared by the preparation method of the Mu Yun black tea as described above.

[0028] Adopting the above technical solution, it is prepared through technological processes such as withering, rolling, fermenting, enzymatic hydrolysis, primary drying, microwave-assisted scenting, secondary drying, and finished product packaging; meanwhile, when scenting, by introducing the microwave-assisted scenting technology, the heating effect and penetration effect of microwave are fully utilized to promote the release of aroma molecules from the woody fragrance raw materials and accelerate the contact and adsorption between the aroma molecules and the tea leaves, thereby improving the aroma adsorption efficiency and quality; during the withering process, the real-time monitoring and judgment of the withering state improve the accuracy of withering state monitoring and judgment, and contribute to improving the preparation efficiency of the Mu Yun black tea and ensuring the stable quality of the Mu Yun black tea.

[0029] Other features and advantages of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objectives, and advantages of the present invention will become more apparent:

[0031] Figure 1 is a flowchart of a preparation method of a Mu Yun black tea shown according to an exemplary embodiment.

[0032] Figure 2 is according to Figure 1 shown in the embodiment, a flowchart of step 102 of a preparation method of a Mu Yun black tea.

[0033] Figure 3 is a block diagram of a preparation system of a Mu Yun black tea shown according to an exemplary embodiment.

[0034] Figure 4 is a block diagram of an electronic device shown according to an exemplary embodiment.

[0035] Figure 5 is an application scenario diagram of a preparation method of a Mu Yun black tea shown according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The present invention will be described in detail below with reference to the embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several adjustments and improvements can still be made. These all belong to the protection scope of the present invention.

[0037] In order to solve the above problems, the present invention provides a Muyun black tea and a preparation method thereof, which is prepared by adopting process flows such as withering, rolling, fermentation, enzymolysis, primary drying, microwave-assisted scenting, secondary drying, and finished product packaging; at the same time, by introducing microwave-assisted scenting technology during scenting, the heating effect and penetration effect of microwaves are fully utilized to promote the release of aroma molecules from woody aroma raw materials, and accelerate the contact and adsorption of aroma molecules with tea leaves, thereby improving the aroma adsorption efficiency and quality; in the process of withering treatment, the withering state is monitored and judged in real time, which improves the accuracy of withering state monitoring and judgment, and helps to improve the preparation efficiency of Muyun black tea and ensure the stable quality of Muyun black tea.

[0038] The specific implementation modes of the present invention are described in detail below with reference to the accompanying drawings.

[0039] Black tea is a fully fermented tea with the characteristics of red leaves, red soup and red leaves. The production process of black tea mainly includes withering, rolling, fermentation and drying. Among them, withering and fermentation are two key steps. Withering can remove moisture from tea leaves, and fermentation can oxidize tea polyphenols in tea leaves, thus forming the unique color, aroma and taste of black tea.

[0040] Agarwood, costus, cloves and sandalwood are all woody fragrances, and their common aromas are: calm, heavy, natural, fresh, lasting and long. According to traditional Chinese medicine, agarwood is spicy, bitter, slightly warm in nature, and belongs to the spleen meridian, stomach meridian and kidney meridian. It is good at regulating qi and treating symptoms of qi stagnation in the middle and lower jiao, such as cold pain in the abdomen, vomiting, asthma, and constipation. Compared with the spicy and fragrant aromas such as costus, cloves, and sandalwood, which are used to warm, regulate and relieve pain, agarwood is specially used to absorb true qi and treat diseases of kidney deficiency, spleen deficiency and cold, and is the best among all fragrances. Modern pharmacological studies have found that agarwood has a significant spasm-relieving effect on gastrointestinal smooth muscle, can also inhibit the central nervous system, and play an asthma-relieving effect. In addition, it has sedative, analgesic, antihypertensive and antibacterial effects.

[0041] Enzymolysis is a process that uses the catalytic effect of enzymes to decompose substrates. Specifically, the effects of enzymolysis are mainly in the following aspects: ① Enzymolysis can destroy the cell walls of tea leaves, making it easier to release the aromatic substances in tea leaves, thereby improving the aroma absorption capacity of tea leaves. ② Enzymolysis can produce a variety of enzymes, which can catalyze the transformation of aroma components in tea leaves, thereby increasing the aroma components of tea leaves. ③ Improve the taste of tea leaves: Enzymolysis can produce a variety of flavor substances, which can improve the taste of tea leaves and make them more mellow and sweet.

[0042] The microwave-assisted scenting technology utilizes the heating effect and penetration effect of microwaves to promote the release of aroma molecules from the woody fragrance raw materials and accelerate the contact and adsorption of aroma molecules with black tea leaves, thereby improving the aroma adsorption efficiency and quality. The advantages of the microwave-assisted scenting technology include: ① Microwaves can rapidly release the aroma components in the spices, thus shortening the scenting time. ② Microwaves can evenly distribute the aroma components in the spices in the tea leaves, thereby improving the scenting efficiency. ③ The aroma is rich and long-lasting, enabling the black tea leaves to have a more persistent aroma. ④ By combining the woody fragrance with black tea, a woody rhyme black tea with unique aroma and efficacy can be produced. The woody rhyme black tea not only has the color, aroma, and taste of black tea but also has the aroma and efficacy of sandalwood.

[0043] Therefore, the present invention provides a woody rhyme black tea and a preparation method thereof. Based on the traditional black tea manufacturing process, this method introduces an enzymolysis process. Enzymolysis can further damage the tea leaf tissue and increase the aroma absorption surface. Meanwhile, during scenting, by introducing the microwave-assisted scenting technology, the heating effect and penetration effect of microwaves are fully utilized to promote the release of aroma molecules from the woody fragrance raw materials and accelerate the contact and adsorption of aroma molecules with the tea leaves, thereby improving the aroma adsorption efficiency and quality. The woody rhyme black tea of the present invention has the following characteristics: ① The aroma is rich and long-lasting, with the aroma of agarwood rhyme. ② The color is ruddy and translucent, and the soup color is bright red and shiny. ③ The taste is mellow, sweet, and has a long aftertaste. Meanwhile, the woody rhyme black tea also has health benefits, meets the standards of a new type of healthy beverage, and has broad market prospects.

[0044] In view of the above technical problems, in the technical solution of the present invention, a woody black tea and a preparation method thereof are proposed.

[0045] Figure 1 It is a flowchart of a preparation method of a woody black tea shown according to an exemplary embodiment, as Figure 1 shown, and the method includes:

[0046] Step 101: Screen complete fresh tea leaf raw materials from the fresh tea leaf raw materials to obtain the screened fresh tea leaf raw materials;

[0047] Step 102: Place the screened fresh tea leaf raw materials in a cool and ventilated place for withering treatment to obtain withered tea leaf raw materials with qualified withering state;

[0048] Step 103: Knead the withered tea leaf raw materials to obtain kneaded tea leaves;

[0049] Step 104: Conduct fermentation and enzymolysis reactions on the kneaded tea leaves to obtain fermented tea leaves;

[0050] Step 105: Conduct primary drying treatment on the fermented tea leaves to obtain the tea leaves after primary drying;

[0051] Step 106: Select woody fragrance raw materials, where the woody fragrance raw materials include agarwood raw materials, costus root raw materials, clove raw materials, and sandalwood raw materials;

[0052] Step 107: Grind the woody fragrance raw materials into powder to obtain woody fragrance raw material powder;

[0053] Step 108: Place the tea leaves after primary drying and the woody fragrance raw material powder in different areas of a double - area connected container, and use microwave - assisted scenting technology to perform directional microwave treatment on the woody fragrance raw material powder to obtain tea leaves that absorb the woody fragrance;

[0054] Step 109: Perform secondary drying treatment on the tea leaves that absorb the woody fragrance to obtain wood - flavored black tea;

[0055] Step 1010: Package the wood - flavored black tea to obtain the finished product of wood - flavored black tea.

[0056] Among them, in an embodiment of the present invention, a method for making wood - flavored fragrant black tea is prepared by a technological process of withering, rolling, fermenting, enzymatic hydrolysis, primary drying, microwave - assisted scenting, secondary drying, and finished product packaging.

[0057] The specific preparation method includes the following steps:

[0058] (1) Raw material selection: Spread the fresh tea leaf raw materials flat for airing, with a thickness of 3 - 5 cm; Select finished woody fragrance raw materials with pure taste and flavor, mainly including one, two, or more of agarwood, costus root, clove, and sandalwood, etc. After humidifying it by 10% - 15% by weight before scenting, reserve it for use.

[0059] (2) Withering: Spread the above - mentioned fresh tea leaves in a cool and ventilated place and wither until the leaves become soft. The withering parameters are: thickness 12 - 15 cm, withering temperature 20 - 25 °C, withering time 12 - 24 hours, and the specific time depends on the weather conditions and the moisture content of the tea leaves.

[0060] (3) Rolling: Roll the withered tea leaves until the leaves break and tea juice seeps out. The rolling parameters are: rolling temperature 25 - 30 °C, rolling time 30 - 60 minutes, and the specific time depends on the tenderness and aroma of the tea leaves.

[0061] (4) Fermentation: Place the rolled tea leaves in a warm and humid environment for fermentation to oxidize the tea polyphenols in the tea leaves and form the unique color, aroma, and taste of black tea. The fermentation parameters are: fermentation temperature 25 - 30 °C, fermentation time 2 - 4 hours, and the specific time depends on the tenderness and aroma of the tea leaves.

[0062] (5) Enzymatic hydrolysis: Add biological enzymes to the fermented tea leaves, including one, two or more of 0.5%-1% pectinase, 1.0%-2.0% cellulase, 1.0%-2.0% hemicellulase, etc. The enzymatic hydrolysis parameters are: enzymatic hydrolysis temperature 25-30°C, hydrolysis time 10-30 minutes.

[0063] (6) Primary drying: Dry the fermented tea leaves at 40-50°C until the water content is 10%-15%, and the specific time depends on the state of the tea leaves.

[0064] (7) Microwave-assisted scenting: Place the dried tea leaves and the powder of the woody fragrance raw material in two different areas of the same sealed container, and there is a connection in the middle for the aroma to circulate. Use the microwave-assisted scenting technology to conduct directional microwave treatment on the woody raw material. The microwave parameters are: microwave frequency 2.45 GHz, microwave power 100-200 W, microwave treatment time 10-30 minutes, the microwave operation frequency is 2-4 times a day, and the woody fragrance raw material is humidified by 10%-15% before each microwave; scent for a period of time to allow the tea leaves to absorb the aroma of the woody fragrance raw material. The scenting parameters are: scenting temperature 20-25°C, scenting time 1-2 weeks, and the specific time depends on the aroma concentration of the woody fragrance raw material and the aroma absorption ability of the black tea leaves; the dosage of the woody fragrance raw material should be determined according to the aroma concentration and aroma absorption ability of the tea leaves, and is 1%-5%; during the scenting process, the tea leaves should be frequently turned over to make the tea leaves evenly absorb the aroma of the woody fragrance raw material.

[0065] (8) Secondary drying: Dry the scented tea leaves until the water content is 6%-8%.

[0066] (9) Finished product packaging: Pack the tea leaves after secondary drying with aluminum foil composite packaging to prevent the aroma from dissipating. The packaging parameters are: aluminum foil thickness: 100-200 μm and good sealing performance.

[0067] Correspondingly, in the above preparation method of the woody flavor black tea, during the withering process, the real-time monitoring and judgment of the withering state are crucial factors affecting the subsequent preparation efficiency and taste of the woody flavor black tea. However, the traditional method relies on experience and manual observation when judging the withering state, which has subjectivity and instability and may lead to inconsistent quality.

[0068] In an embodiment of the present invention, Figure 2 is according to Figure 1 The flowchart of step 102 of a preparation method of a woody flavor black tea shown in the illustrated embodiment. Step 102, place the selected fresh tea leaf raw materials in a cool and ventilated place for withering treatment to obtain withered tea leaf raw materials with qualified withering state, including:

[0069] Step 1021, obtain the withering state image of the tea leaves collected by the camera;

[0070] Step 1022: Extract a set of reference images of tea withering states marked as qualified from the database;

[0071] Step 1023: Respectively perform feature extraction on each reference image of tea withering state marked as qualified in the set of reference images of tea withering states marked as qualified through a tea withering state feature extractor based on a deep neural network model to obtain a set of reference feature matrices of tea withering states;

[0072] Step 1024: Pass the set of reference feature matrices of tea withering states through a spatial attention module to obtain a set of spatially explicit reference feature matrices of tea withering states;

[0073] Step 1025: Perform joint clustering analysis on the set of spatially explicit reference feature matrices of tea withering states to obtain a clustering characterization feature matrix of the reference tea withering state;

[0074] Step 1026: Pass the tea withering state image through the tea withering state feature extractor based on the deep neural network model and the spatial attention module to obtain a detection feature matrix of the tea withering state;

[0075] Step 1027: Calculate the hash similarity between the detection feature matrix of the tea withering state and the clustering characterization feature matrix of the reference tea withering state, and determine whether the withering state is qualified.

[0076] Among them, the deep neural network model is a convolutional neural network model.

[0077] In view of the above technical problems, the technical concept of the present invention is to monitor and collect images of tea withering states through a camera, and automatically analyze the images of tea withering states by using image processing and analysis algorithms based on machine vision and artificial intelligence at the backend, so as to capture the semantic similarity between the actually collected features of tea withering states and the reference state features that meet the requirements of tea withering states, and determine whether the actual withering state of the tea is qualified based on this. In this way, by introducing an automated judgment method based on image processing and deep learning technologies, the accuracy of withering state monitoring and judgment can be improved, and it is helpful to improve the preparation efficiency of Mu Yun black tea and ensure the stable quality of Mu Yun black tea.

[0078] Specifically, in the technical solution of the present invention, first, an image of the withering state of tea leaves collected by a camera is obtained, and a set of reference images of the withering state of tea leaves marked as qualified is extracted from the database. Then, considering that in the set of reference images of the withering state of tea leaves marked as qualified, there are semantic features regarding the qualified withering state of tea leaves in each of the reference images of the withering state of tea leaves marked as qualified, and such semantic features may have different manifestation forms and there are slight differences. Therefore, in order to be able to capture respectively the semantic features and manifestation states of the qualified withering state of tea leaves in each of the reference images of the withering state of tea leaves marked as qualified, so as to perform a more comprehensive semantic representation of the qualified withering state, in the technical solution of the present invention, it is necessary to respectively perform feature mining on each of the reference images of the withering state of tea leaves marked as qualified in the set of reference images of the withering state of tea leaves marked as qualified through a feature extractor for the withering state of tea leaves based on a convolutional neural network model, so as to respectively extract the reference semantic feature information regarding the withering state of tea leaves in each reference image of the withering state of tea leaves, thereby obtaining a set of reference feature matrices of the withering state of tea leaves.

[0079] It should be understood that during the withering process of tea leaves, some regions in the image of the withering state of tea leaves may have a greater impact on the judgment result of the withering state. For example, in the region of the tea leaves in the image, it is possible to judge whether the leaves are soft to determine the withering state of the tea leaves. Therefore, it is necessary to focus the attention on these key regions when capturing the features of the withering state of tea leaves. Based on this, in the technical solution of the present invention, the set of reference feature matrices of the withering state of tea leaves is further passed through a spatial attention module to obtain a set of spatially explicit reference feature matrices of the withering state of tea leaves. Through the spatial attention mechanism, the model can dynamically adjust the importance of different regions according to the content of the image of the withering state of tea leaves, making the model more focused on the key parts that affect the judgment of the withering state, thereby improving the accuracy of the detection and judgment of the withering state of tea leaves.

[0080] Furthermore, considering that in the actual process of detecting the withering state of tea leaves, each image in the set of reference images of the withering state of tea leaves contains the semantic features of qualified withering states. And there is an implicit correlation relationship between these different qualified withering state performance features. Therefore, in order to discover and capture the potential connections between these semantic features of qualified withering states, help understand the comprehensive semantics of qualified withering states of tea leaves, and thus more accurately detect the qualification of the withering state, in the technical solution of the present invention, the set of spatially explicit reference feature matrices of the withering state of tea leaves is further passed through a joint clustering network to obtain a reference clustering characterization feature matrix of the withering state of tea leaves. It should be understood that the joint clustering network can perform clustering analysis on the spatially explicit qualified semantic features of the withering state of tea leaves, so as to discover potential data structures and patterns, help understand the relationship between the qualified withering state features of tea leaves, and thus better understand the feature representation of the qualified withering state of tea leaves. This helps to identify different qualified withering state patterns and features, providing a basis for subsequent classification and analysis.

[0081] In an embodiment of the present invention, performing joint clustering analysis on the set of spatially explicit reference feature matrices of the withering state of tea leaves to obtain a reference clustering characterization feature matrix of the withering state of tea leaves includes: passing the set of spatially explicit reference feature matrices of the withering state of tea leaves through a joint clustering network to obtain the reference clustering characterization feature matrix of the withering state of tea leaves.

[0082] Furthermore, passing the set of spatially explicit reference feature matrices of the withering state of tea leaves through a joint clustering network to obtain the reference clustering characterization feature matrix of the withering state of tea leaves includes: constructing an adjacency matrix and a degree matrix of the set of spatially explicit reference feature matrices of the withering state of tea leaves; calculating a Laplacian matrix based on the adjacency matrix and the degree matrix; performing normalization processing on the Laplacian matrix to obtain a normalized Laplacian matrix; arranging the eigenvalues of the normalized Laplacian matrix from largest to smallest, and extracting the first K eigenvalues to calculate the eigenvectors of the first K eigenvalues; normalizing the eigenvectors of the first K eigenvalues and forming a matrix of eigenvectors with the normalized eigenvectors of the first K eigenvalues to obtain the reference clustering characterization feature matrix of the withering state of tea leaves.

[0083] Furthermore, constructing the adjacency matrix and the degree matrix of the set of spatially explicit reference feature matrices of the withering state of tea leaves includes: calculating the association weight value between any two spatially explicit reference feature matrices in the set of spatially explicit reference feature matrices of the withering state of tea leaves according to the following weight formula to obtain the adjacency matrix composed of a plurality of association weight values; where the weight formula is:

[0084]

[0085] where M i and M j are the i-th and j-th spatially explicit tea withering state reference feature matrices in the set of the spatially explicit tea withering state reference feature matrices respectively, σ is the variance between the i-th spatially explicit tea withering state reference feature matrix and the j-th spatially explicit tea withering state reference feature matrix, represents the square of the two-norm, exp(·) is the exponential operation, and W i,j is the eigenvalue at the (i, j) position in the adjacency matrix.

[0086] Then, in order to detect and analyze the features of the actual tea withering state and compare it with the clustering semantic features of the qualified tea withering state, the same processing needs to be performed on the tea withering state image. Based on this, in the technical solution of the present invention, the tea withering state image is further passed through the tea withering state feature extractor based on the convolutional neural network model and the spatial attention module to obtain the tea withering state detection feature matrix. Through such a processing method, the actual semantic feature information about the tea withering state in the tea withering state image can be captured.

[0087] Subsequently, the hash similarity between the tea withering state detection feature matrix and the reference tea withering state clustering characterization feature matrix is calculated. It should be understood that by calculating the hash similarity, the similarity between the combined clustering semantic features of the qualified tea withering state and the semantic features of the actually detected tea withering state can be quickly compared, so as to determine whether the tea withering state detection feature matrix has a similar feature representation to the reference tea withering state clustering characterization feature matrix, which helps to judge whether the withering state of the tea meets the expectations.

[0088] In an embodiment of the present invention, calculating the hash similarity between the tea withering state detection feature matrix and the reference tea withering state clustering characterization feature matrix and determining whether the withering state is qualified includes: enhancing the distinguishability of the image semantic feature expression of the tea withering state detection feature matrix to obtain an optimized tea withering state detection feature matrix; calculating the hash similarity between the optimized tea withering state detection feature matrix and the reference tea withering state clustering characterization feature matrix; and determining whether the withering state is qualified based on the comparison between the hash similarity and a predetermined threshold.

[0089] In the technical solution described above, each tea withering state reference feature matrix in the set of tea withering state reference feature matrices expresses the image semantic features of the corresponding tea withering state reference image. Thus, after passing the set of spatially explicit tea withering state reference feature matrices through the joint clustering network, the reference tea withering state clustering characterization feature matrix can obtain the set image semantic feature representation of the tea withering state reference image set based on the joint clustering distribution of the image semantic features of each tea withering state reference image. Therefore, there is a distinction in image semantic feature expression from the image semantic feature representation of the tea withering state image of the tea withering state detection feature matrix.

[0090] In this way, when calculating the hash similarity between the tea withering state detection feature matrix and the reference tea withering state clustering characterization feature matrix, and thus mapping the tea withering state detection feature matrix and the reference tea withering state clustering characterization feature matrix into the hash similarity probability space respectively, it is expected to enhance the distinction of the image semantic feature expression of the tea withering state detection feature matrix, thereby improving the calculation accuracy of the hash similarity.

[0091] Based on this, before calculating the hash similarity between the tea withering state detection feature matrix and the reference tea withering state clustering characterization feature matrix, the image semantic feature expression distinction of the tea withering state detection feature matrix is strengthened to obtain an optimized tea withering state detection feature matrix. This process specifically includes:

[0092] Determine the number of zero eigenvalues in the tea withering state detection feature matrix, and calculate the reciprocal of the logarithm to the base 2 of the number of zero eigenvalues and the exponential value to the base of the natural constant of the reciprocal of the number of zero eigenvalues respectively to obtain the first tea withering state representation value and the second tea withering state representation value;

[0093] Expand the tea withering state detection feature matrix into a tea withering state detection feature vector;

[0094] Calculate the power function of each eigenvalue of the tea withering state detection feature vector with the reciprocal of the number of zero eigenvalues as the exponent to obtain the tea withering state detection structure state vector;

[0095] Dot-multiply the tea withering state detection structure state vector with the first tea withering state detection representation value to obtain the tea withering state detection information representation vector;

[0096] Dot-multiply the autocorrelation matrix of the tea withering state detection structure state vector with the second tea withering state detection representation value and the weight hyperparameter respectively to obtain the tea withering state detection regression understanding matrix;

[0097] Multiply the tea withering state detection information representation vector and the tea withering state detection regression understanding matrix to obtain an optimized tea withering state detection feature vector;

[0098] Reshape the features of the optimized tea withering state detection feature vector to obtain the optimized tea withering state detection feature matrix.

[0099] That is, the first tea withering state detection representation value and the second tea withering state detection representation value are expressed as:

[0100] α = [log2(n)] -1

[0101] β = exp[(n) -1

[0102] where n is the number of zero eigenvalues in the tea withering state detection feature matrix, α is the first tea withering state representation value, β is the second tea withering state representation value, and exp(·) is the exponential function with the natural constant as the base.

[0103] And the optimized tea withering state detection feature vector is expressed as:

[0104]

[0105] where V is the tea withering state detection feature vector, w is the weight hyperparameter, M is the tea withering state detection regression understanding matrix, V1 is the tea withering state detection information representation vector, n is the number of zero eigenvalues in the tea withering state detection feature matrix, α is the first tea withering state representation value, β is the second tea withering state representation value, represents calculating the power function of each eigenvalue of the tea withering state detection feature vector with the reciprocal of the number of zero eigenvalues as the exponent, T represents the transpose of the vector, and ⊙ is the element-wise multiplication, is the matrix multiplication, and V' is the optimized tea withering state detection feature vector.

[0106] ​Based on this, in the above preferred example, by modeling the zero dimension of the vector field of the tea withering state detection feature vector after expanding the tea withering state detection feature matrix as the global field redundancy dependence, the effective long-range modeling of the tea withering state detection feature map under the field linear complexity is realized. In this way, during the feature regression process of the tea withering state detection feature map, in the selective feature enhancement state space based on the feature distribution, the structured correlation self-distillation between information representation and decoding regression understanding is carried out, so as to promote the remote dynamic regression understanding balance of its feature convergence while maintaining the overall information complexity of the feature distribution of the tea withering state detection feature map, so as to improve the discriminability of the image semantic features of the tea withering state detection feature matrix, thereby improving the calculation accuracy of the hash similarity. In this way, it helps to improve the accuracy of withering state monitoring and judgment, and realizes the intelligent detection of the tea withering state.

[0107] Furthermore, based on the comparison between the hash similarity and a predetermined threshold, it is determined whether the withering state is qualified, so as to realize the intelligent detection and judgment of the tea withering state. In this way, by introducing an automated judgment method based on image processing and deep learning technologies, the accuracy of withering state monitoring and judgment can be improved, and it helps to improve the preparation efficiency of the woody rhyme black tea and ensure the stable quality of the woody rhyme black tea.

[0108] In an embodiment of the present invention, in response to the hash similarity being greater than the predetermined threshold, it is determined that the withering state is qualified.

[0109] To sum up, adopting the above solution, the withering state image of the tea is monitored and collected by a camera, and the image processing and analysis algorithm based on machine vision and artificial intelligence is used at the backend to automatically analyze the withering state image of the tea, so as to capture the semantic similarity between the actually collected withering state features of the tea and the reference state features that meet the requirements of the tea withering state, and thereby determine whether the actual withering state of the tea is qualified. In this way, by introducing an automated judgment method based on image processing and deep learning technologies, the accuracy of withering state monitoring and judgment can be improved, and it helps to improve the preparation efficiency of the woody rhyme black tea and ensure the stable quality of the woody rhyme black tea.

[0110] In an embodiment of the present invention, a woody rhyme black tea is provided, and the woody rhyme black tea is prepared by the preparation method of the woody rhyme black tea as described above.

[0111] Figure 3 is a block diagram of a preparation system for a woody rhyme black tea shown according to an exemplary embodiment. As Figure 3 shown, the preparation system 200 includes:

[0112] A complete fresh tea leaf raw material screening module 201, which is used to screen complete fresh tea leaf raw materials from the fresh tea leaf raw materials to obtain the screened fresh tea leaf raw materials;

[0113] The withering treatment module 202 is configured to place the screened fresh tea leaf raw materials in a cool and ventilated place for withering treatment to obtain withered tea leaf raw materials with qualified withering state;

[0114] The rolling treatment module 203 is configured to perform rolling treatment on the withered tea leaf raw materials to obtain rolled tea leaves;

[0115] The fermentation and enzymolysis reaction module 204 is configured to perform fermentation and enzymolysis reactions on the rolled tea leaves to obtain fermented tea leaves;

[0116] The primary drying treatment module 205 is configured to perform primary drying treatment on the fermented tea leaves to obtain tea leaves after primary drying;

[0117] The woody fragrance raw material selection module 206 is configured to select woody fragrance raw materials, wherein the woody fragrance raw materials include agarwood raw materials, costus root raw materials, clove raw materials, and sandalwood raw materials;

[0118] The grinding module 207 is configured to grind the woody fragrance raw materials into powder form to obtain woody fragrance raw material powder;

[0119] The directional microwave treatment module 208 is configured to place the tea leaves after primary drying and the woody fragrance raw material powder in different regions of a double-region connected container, and perform directional microwave treatment on the woody fragrance raw material powder by using microwave-assisted scenting technology to obtain tea leaves that absorb the woody fragrance;

[0120] The secondary drying treatment module 209 is configured to perform secondary drying treatment on the tea leaves that absorb the woody fragrance to obtain woody rhyme black tea;

[0121] The packaging module 2010 is configured to package the woody rhyme black tea to obtain the finished product of woody rhyme black tea.

[0122] Next, refer to Figure 4 , which shows a schematic structural diagram of an electronic device 600 suitable for implementing the embodiments of the present invention. The terminal devices in the embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0123] As Figure 4As shown, the electronic device 600 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 601, which may perform various appropriate actions and processes according to the programs stored in the read-only memory (ROM) 602 or the programs loaded from the storage device 608 into the random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.

[0124] Generally, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or wirelessly to exchange data. Although Figure 4 the electronic device 600 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices may be implemented or had.

[0125] Specifically, according to an embodiment of the present invention, the process described above with reference to the flowchart may be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes program codes for executing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above functions defined in the method of the embodiment of the present invention are executed.

[0126] It should be noted that the above-mentioned computer-readable medium of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present invention, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable signal medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0127] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (for example, a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet (for example, the Internet), and end-to-end networks (for example, ad hoc end-to-end networks), as well as any currently known or future-developed network.

[0128] The above-mentioned computer-readable medium can be included in the above-mentioned electronic device; it can also exist separately without being assembled into the electronic device.

[0129] Computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The foregoing programming languages include, but are not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or alternatively, may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0130] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0131] The modules described in the embodiments of the present invention may be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the module itself in some cases. For example, the test parameter acquisition module may also be described as "the module for acquiring device test parameters corresponding to the target device".

[0132] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), system on a chip (SOC), complex programmable logic devices (CPLD), and the like.

[0133] In the context of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0134] Figure 5 is an application scenario diagram of a method for preparing Mu Yun black tea shown according to an exemplary embodiment. As Figure 5 shown, in this application scenario, first, obtain an image of the withering state of tea leaves collected by a camera (for example, C1 as illustrated in Figure 5 ); extract a set of reference images of the withering state of tea leaves marked as qualified from a database (for example, C2 as illustrated in Figure 5 ); then, input the obtained image of the withering state of tea leaves and the reference image of the withering state of tea leaves into a server (for example, S as illustrated in Figure 5 ) deployed with an algorithm for preparing Mu Yun black tea, where the server can process the image of the withering state of tea leaves and the reference image of the withering state of tea leaves based on the algorithm for preparing Mu Yun black tea to determine whether the withering state is qualified.

[0135] The present invention will be further described in detail below in conjunction with specific embodiments:

[0136] Example 1:

[0137] A method for making agarwood-flavored black tea is prepared by a technological process of withering, rolling, fermenting, drying, scenting, and finished product packaging. The specific preparation method includes the following steps: (1) Raw material selection: The fresh tea leaves are spread out flat to air-dry, with a thickness of 3-5 cm; Select finished woody agarwood raw materials with pure taste and flavor, and humidify them by 15% by weight before scenting and then set aside. (2) Withering: Spread the above fresh tea leaves in a cool and ventilated place until the leaves become soft. The withering parameters are: thickness 12 cm, withering temperature 25 °C, withering time 12 hours. (3) Rolling: Roll the withered tea leaves until the leaves break and tea juice oozes out. The rolling parameters are: rolling temperature 30 °C, and the specific time depends on the tenderness and aroma of the tea leaves. In this embodiment, the rolling time is 30 minutes. (4) Fermentation: Place the rolled tea leaves in a warm and humid environment for fermentation to oxidize the tea polyphenols in the tea leaves to form the unique color, aroma, and taste of black tea. The fermentation parameters are: fermentation temperature 30 °C, fermentation time 2 hours. (5) Primary drying: Dry the fermented tea leaves at 50 °C until the water content is 10%-15%. (6) Scenting: Place the dried tea leaves and the powder of woody agarwood raw materials in two different areas in the same sealed container, and there is a connection in the middle for the aroma to circulate. Scent for a period of time to make the tea leaves absorb the aroma of the woody agarwood raw materials. The scenting parameters are: scenting temperature 25 °C, scenting time 2 weeks; The dosage of the woody agarwood raw materials should depend on the aroma concentration and aroma absorption ability of the tea leaves. In this embodiment, the mass ratio of the woody agarwood raw materials to the fresh tea leaves is 3:100; During the scenting process, the tea leaves should be frequently turned over (once every 12 hours) to make the tea leaves evenly absorb the aroma of the woody agarwood raw materials. (7) Secondary drying: Dry the scented tea leaves until the water content is 6%-8%. (8) Finished product packaging: Package the secondarily dried tea leaves with aluminum foil composite packaging to prevent the aroma from dissipating. The packaging parameters are: aluminum foil thickness: 200 μm and good sealing performance.

[0138] For the agarwood-flavored black tea in this embodiment, on the basis of the conventional black tea manufacturing process, only the conventional scenting process is used, and the enzymolysis process and microwave-assisted scenting technology are not introduced. The obtained agarwood-flavored black tea has a clear and lasting aroma, but the agarwood aroma is not obvious, and at the same time, it lacks a sense of hierarchy; it is mellow, sweet, but the aftertaste is weak; the soup color is bright red; the leaf bottom is reddish-brown, complete and uniform.

[0139] Example 2:

[0140] A method for making agarwood-scented black tea is prepared by withering, rolling, fermentation, drying, scenting, and finished product packaging. The specific preparation method comprises the following steps: (1) Raw material selection: fresh tea leaves are spread out to air with a thickness of 3-5 cm; finished woody agarwood raw materials with pure taste and flavor are selected, and they are moistened by 15% by weight before scenting for later use. (2) Withering: the fresh tea leaves are spread out in a cool and ventilated place and withered until the leaves become soft. The withering parameters are: thickness 12 cm, withering temperature 25° C., and withering time 12 hours. (3) Rolling: the withered tea leaves are rolled until the leaves are broken and the tea juice is precipitated. The rolling parameters are: rolling temperature 30° C., and the specific time is determined according to the tenderness and aroma of the tea leaves. In this embodiment, the rolling time is 30 minutes. (4) Fermentation: the rolled tea leaves are placed in a warm and humid environment for fermentation to oxidize the tea polyphenols in the tea leaves and form the unique color, aroma, and taste of black tea. Fermentation parameters are: fermentation temperature 30°C, fermentation time 2 hours. (5) Enzyme hydrolysis: add biological enzymes to the fermented tea leaves, including 1.0% pectinase, 1.0% cellulase, and 2.0% hemicellulase (calculated by the mass fraction of fresh tea leaves). Enzyme hydrolysis parameters are: enzymolysis temperature 30°C, fermentation time 20 minutes. (6) Initial drying: dry the enzymolyzed tea leaves at 50°C to a moisture content of 10%-15%. (7) Scenting: place the dried tea leaves and woody aroma raw material powder in two different areas of the same sealed container, and connect the middle to allow the aroma to circulate. Scenting for a period of time allows the tea leaves to absorb the aroma of the woody aroma raw materials. The scenting parameters are: scenting temperature 25°C, scenting time 2 weeks; the amount of woody aroma raw material should be determined according to the aroma concentration and aroma absorption capacity of the tea leaves, and in this embodiment, it is 3% based on the mass fraction of fresh tea leaves; the tea leaves should be turned frequently during the scenting process (turned once every 12 hours) to allow the tea leaves to evenly absorb the aroma of the woody aroma raw material. (8) Secondary drying: Dry the scented tea leaves to a moisture content of 6%-8%. (9) Finished product packaging: The tea leaves after secondary drying are packaged in aluminum foil composite packaging to prevent the aroma from being lost. Packaging parameters are: aluminum foil thickness: 200μm with good sealing performance.

[0141] The agarwood fragrance black tea of ​​this implementation case introduced an enzymatic hydrolysis process on the basis of the conventional black tea production process, but did not introduce microwave-assisted scenting technology. The obtained agarwood fragrance black tea has a richer and lasting aroma, a slightly pronounced agarwood aroma, but a slightly worse sense of layering; the taste is more mellow and sweet, with a stronger aftertaste; the soup color is more red and bright; the leaf bottom is reddish brown, and the completeness is not uniform.

[0142] Embodiment 3:

[0143] A method for making agarwood-flavored black tea is prepared by a technological process including withering, rolling, fermenting, drying, scenting, and finished product packaging. The specific preparation method includes the following steps: (1) Raw material selection: The fresh tea leaves are spread out flat for airing, with a thickness of 3 - 5 cm; select finished woody agarwood raw materials with pure taste and flavor, and humidify it by 15% by weight before scenting and then set aside. (2) Withering: Spread the above-mentioned fresh tea leaves in a cool and ventilated place until the leaves become soft. The withering parameters are: thickness 12 cm, withering temperature 25°C, withering time 12 hours. (3) Rolling: Roll the withered tea leaves until the leaves break and tea juice oozes out. The rolling parameters are: rolling temperature 30°C, and the specific time depends on the tenderness and aroma of the tea leaves. In this embodiment, the rolling time is 30 minutes. (4) Fermenting: Place the rolled tea leaves in a warm and humid environment for fermentation to oxidize the tea polyphenols in the tea leaves to form the unique color, aroma, and taste of black tea. The fermentation parameters are: fermentation temperature 30°C, fermentation time 2 hours. (5) Enzymolysis: Add biological enzymes to the fermented tea leaves, including 1.0% pectinase, 1.0% cellulase, 2.0% hemicellulase (calculated by the mass fraction of fresh tea leaves). The enzymolysis parameters are: enzymolysis temperature 30°C, enzymolysis time 20 minutes. (6) Primary drying: Dry the enzymolyzed tea leaves at 50°C until the water content is 10% - 15%. (7) Microwave-assisted scenting: Place the dried tea leaves and the woody agarwood raw material powder in two different areas of the same sealed container, and there is a connection in the middle for the aroma to flow through. Use microwave-assisted scenting technology to conduct directional microwave treatment on the woody raw material. The microwave parameters are: microwave frequency 2.45 GHz, microwave power 200 W, microwave treatment time 20 minutes, the microwave operation frequency is 2 times a day, and the woody agarwood raw material is humidified by 10% - 15% before each microwave; scent for a period of time to make the tea leaves absorb the aroma of the woody agarwood raw material. The scenting parameters are: scenting temperature 25°C, scenting time 2 weeks, and the specific time depends on the aroma concentration of the woody agarwood raw material and the aroma absorption ability of the black tea leaves; the dosage of the woody agarwood raw material should depend on the aroma concentration of the tea leaves and the aroma absorption ability. In this embodiment, calculated by the mass fraction of fresh tea leaves, it is 1%; during the scenting process, the tea leaves should be frequently turned over (once every 12 hours) to make the tea leaves evenly absorb the aroma of the woody agarwood raw material.

[0144] (8) Secondary drying: Dry the scented tea leaves until the water content is 6% - 8%. (9) Finished product packaging: Package the secondarily dried tea leaves with aluminum foil composite packaging to prevent the aroma from dissipating. The packaging parameters are: aluminum foil thickness: 200 μm and good sealing performance.

[0145] The agarwood fragrant black tea of ​​this implementation case has introduced an enzymatic hydrolysis process and a microwave-assisted scenting technology on the basis of the conventional black tea production process. The aroma of this agarwood fragrant black tea is more intense and lasting, and the agarwood aroma and the tea aroma are harmoniously blended with rich layers; the taste is more mellow and sweet, and the aftertaste is more lasting; the soup color is more bright red, clearer and more transparent; the leaf bottom is reddish brown, and the whole is uneven.

[0146] Embodiment 4:

[0147] A method for making sandalwood fragrant black tea is prepared by a process flow of withering, rolling, fermentation, drying, scenting and finished product packaging. The specific preparation method comprises the following steps: (1) raw material selection: fresh tea leaves are spread out to air with a thickness of 3-5 cm; finished woody fragrant sandalwood raw materials with pure taste and flavor are selected, and they are moistened by 15% by weight before scenting for later use. (2) withering: the fresh tea leaves are spread out in a cool and ventilated place and withered until the leaves become soft. The withering parameters are: thickness 12 cm, withering temperature 25°C, and withering time 12 hours. (3) rolling: the withered tea leaves are rolled until the leaves are broken and the tea juice is precipitated. The rolling parameters are: rolling temperature 30°C, and the specific time is determined according to the tenderness and aroma of the tea leaves. In this embodiment, the rolling time is 30 minutes. (4) fermentation: the rolled tea leaves are placed in a warm and humid environment for fermentation to oxidize the tea polyphenols in the tea leaves and form the unique color, aroma and taste of black tea. Fermentation parameters are: fermentation temperature 30°C, fermentation time 2 hours. (5) Enzymatic hydrolysis: add biological enzymes to the fermented tea leaves, including 1.0% pectinase, 1.0% cellulase, and 2.0% hemicellulase (calculated by the mass fraction of fresh tea leaves). Enzymatic hydrolysis parameters are: enzymatic hydrolysis temperature 30°C, fermentation time 20 minutes. (6) Primary drying: dry the enzymatically hydrolyzed tea leaves at 50°C to a moisture content of 10%-15%. (7) Microwave-assisted scenting: place the dried tea leaves and woody aroma raw material powder in two different areas of the same sealed container, and connect the middle to allow the aroma to flow. Use microwave-assisted scenting technology to perform directional microwave treatment on the woody raw materials. Microwave parameters are: microwave frequency 2.45GHz, microwave power 200W, microwave treatment time 20 minutes, microwave operation frequency twice a day, and the woody aroma raw materials are humidified by 10%-15% before each microwave; scenting for a period of time to allow the tea leaves to absorb the aroma of the woody aroma raw materials. The scenting parameters are: scenting temperature 25°C, scenting time 2 weeks, the specific time is determined according to the aroma concentration of the woody aroma raw material and the aroma absorption capacity of the black tea leaves; the amount of the woody aroma raw material should be determined according to the aroma concentration and aroma absorption capacity of the tea leaves, and in this embodiment, it is 5% based on the mass fraction of fresh tea leaves; the tea leaves should be turned frequently during the scenting process (once every 12 hours) to allow the tea leaves to evenly absorb the aroma of the woody aroma raw material.

[0148] (8) Secondary drying: Dry the tea after scenting to a water content of 6%-8%. (9) Finished product packaging: Package the tea after secondary drying with aluminum foil composite packaging to prevent the loss of aroma. The packaging parameters are: aluminum foil thickness: 200 μm and good sealing performance.

[0149] In the sandalwood-scented black tea of this embodiment, on the basis of the conventional black tea manufacturing process, an enzymatic hydrolysis process and a microwave-assisted scenting technology are introduced. This sandalwood-scented black tea has a rich and long-lasting aroma, and the sandalwood aroma and the tea aroma are harmoniously integrated with rich layers; the taste is more mellow, sweet, and has a long-lasting aftertaste; the soup color is bright red, clear and transparent; the leaf bottom is reddish-brown, complete but uneven.

[0150] The wood-scented black tea of the present invention has the following beneficial effects: It has the characteristics of a strong and long-lasting wood aroma, a rosy and bright color, a bright red soup color, a mellow, sweet taste, and a long aftertaste. At the same time, it also contains beneficial aromas such as agarwood, wood fragrance, clove, and sandalwood, and has the effects of calming the nerves, sedation, and regulating qi. In addition, the preparation process of the wood-scented black tea of the present invention is simple, the cost is low, it is easy to promote and mass-produce, and it has a high market value.

[0151] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles applied. Those skilled in the art should understand that the disclosed scope in the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions in the present invention.

[0152] In addition, although the operations are depicted in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present invention. Certain features described in the context of a single embodiment can also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0153] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various deformations or modifications within the scope of the claims, which does not affect the essence of the present invention.

Claims

1. A method for detecting the withering state of tea leaves, characterized in that, A detection method for determining whether the withered state of the tea leaves obtained after placing fresh tea leaf raw materials in a cool and ventilated place for withering treatment is qualified, including: Obtaining an image of the withered state of the tea leaves collected by a camera; Extracting from a database a set of reference images of the withered state of the tea leaves marked as qualified; Respectively extracting features from each of the reference images of the withered state of the tea leaves marked as qualified in the set of reference images of the withered state of the tea leaves marked as qualified through a tea withered state feature extractor based on a deep neural network model to obtain a set of reference feature matrices of the withered state of the tea leaves; Passing the set of reference feature matrices of the withered state of the tea leaves through a spatial attention module to obtain a set of spatially explicit reference feature matrices of the withered state of the tea leaves; Performing joint clustering analysis on the set of spatially explicit reference feature matrices of the withered state of the tea leaves to obtain a reference clustering characterization feature matrix of the withered state of the tea leaves; Passing the image of the withered state of the tea leaves through the tea withered state feature extractor based on the deep neural network model and the spatial attention module to obtain a detection feature matrix of the withered state of the tea leaves; Calculating the hash similarity between the detection feature matrix of the withered state of the tea leaves and the reference clustering characterization feature matrix of the withered state of the tea leaves, and determining whether the withered state is qualified.

2. The tea withering state detection method according to claim 1, wherein The deep neural network model is a convolutional neural network model.

3. The tea withering state detection method according to claim 2, characterized in that, Performing joint clustering analysis on the set of spatially explicit reference feature matrices of the withered state of the tea leaves to obtain a reference clustering characterization feature matrix of the withered state of the tea leaves, including: passing the set of spatially explicit reference feature matrices of the withered state of the tea leaves through a joint clustering network to obtain the reference clustering characterization feature matrix of the withered state of the tea leaves.

4. The tea withering state detection method according to claim 3, wherein, Passing the set of spatially explicit reference feature matrices of the withered state of the tea leaves through a joint clustering network to obtain the reference clustering characterization feature matrix of the withered state of the tea leaves, including: Constructing an adjacency matrix and a degree matrix of the set of spatially explicit reference feature matrices of the withered state of the tea leaves; Calculating a Laplacian matrix based on the adjacency matrix and the degree matrix; Performing normalization processing on the Laplacian matrix to obtain a normalized Laplacian matrix; Arranging the respective eigenvalues of the normalized Laplacian matrix from largest to smallest, and extracting the first K eigenvalues to calculate the eigenvectors of the first K eigenvalues; Normalizing the eigenvectors of the first K eigenvalues and forming a feature vector matrix with the normalized eigenvectors of the first K eigenvalues to obtain the reference clustering characterization feature matrix of the withered state of the tea leaves.

5. The tea withering state detection method according to claim 4, characterized in that, Constructing an adjacency matrix and a degree matrix of the set of spatially explicit reference feature matrices of the withered state of the tea leaves, including: Calculating the association weight value between any two spatially explicit reference feature matrices in the set of spatially explicit reference feature matrices of the withered state of the tea leaves according to the following weight formula to obtain the adjacency matrix composed of a plurality of association weight values; wherein, the weight formula is: Among them, M i and M j are the i-th and j-th spatially manifested tea withering state reference feature matrices in the set of the spatially manifested tea withering state reference feature matrices respectively, σ is the variance between the i-th spatially manifested tea withering state reference feature matrix and the j-th spatially manifested tea withering state reference feature matrix, ‖·‖2 2 represents the square of the two-norm, exp(·) is the exponential operation, and W i,j is the eigenvalue at the (i, j) position in the adjacency matrix.

6. The tea withering state detection method according to claim 5, characterized in that Calculating the hash similarity between the detection feature matrix of the withered state of the tea leaves and the reference clustering characterization feature matrix of the withered state of the tea leaves, and determining whether the withered state is qualified, including: Perform image semantic feature expression discrimination enhancement on the tea withering state detection feature matrix to obtain an optimized tea withering state detection feature matrix; Calculate the hash similarity between the optimized tea withering state detection feature matrix and the reference tea withering state clustering characterization feature matrix; Based on the comparison between the hash similarity and a predetermined threshold, determine whether the withering state is qualified.

7. The tea withering state detection method according to claim 6, characterized in that, In response to the hash similarity being greater than the predetermined threshold, determine that the withering state is qualified.

8. A method for preparing woody rhyme black tea, characterized in that, It includes the following steps: Place the fresh tea leaf raw materials in a cool and ventilated place for withering treatment to obtain withered tea leaf raw materials with qualified withering state; Perform rolling treatment on the withered tea leaf raw materials to obtain rolled tea leaves; Perform fermentation and enzymatic hydrolysis reactions on the rolled tea leaves to obtain fermented tea leaves; Perform primary drying treatment on the fermented tea leaves to obtain tea leaves after primary drying; Select woody fragrance raw materials, where the woody fragrance raw materials include one or several of agarwood raw materials, costus root raw materials, clove raw materials, and sandalwood raw materials; Grind the woody fragrance raw materials into powder form to obtain woody fragrance raw material powder; Place the tea leaves after primary drying and the woody fragrance raw material powder in different regions of a double-region connected container, and use microwave-assisted scenting technology to perform directional microwave treatment on the woody fragrance raw material powder to obtain tea leaves that absorb the woody fragrance; Perform secondary drying treatment on the tea leaves that absorb the woody fragrance to obtain woody rhyme black tea; Package the woody rhyme black tea to obtain the finished product of woody rhyme black tea.

9. The preparation method of the woody rhyme black tea according to claim 8, characterized in that, The withering state of the withered tea leaf raw materials is detected according to claims 1 to 7.

10. A woody rhyme black tea prepared by the preparation method of the woody rhyme black tea according to any one of claims 8 to 9.

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