CN114522199A - Preparation method of medlar granule - Google Patents

Preparation method of medlar granule Download PDF

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CN114522199A
CN114522199A CN202011218595.XA CN202011218595A CN114522199A CN 114522199 A CN114522199 A CN 114522199A CN 202011218595 A CN202011218595 A CN 202011218595A CN 114522199 A CN114522199 A CN 114522199A
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medlar
sample
image
wolfberry
saturation
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郭成哲
孙保伟
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Shanghai Haigeya Biotechnology Co ltd
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Shanghai Haigeya Biotechnology Co ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K36/00—Medicinal preparations of undetermined constitution containing material from algae, lichens, fungi or plants, or derivatives thereof, e.g. traditional herbal medicines
    • A61K36/18—Magnoliophyta (angiosperms)
    • A61K36/185—Magnoliopsida (dicotyledons)
    • A61K36/81—Solanaceae (Potato family), e.g. tobacco, nightshade, tomato, belladonna, capsicum or jimsonweed
    • A61K36/815—Lycium (desert-thorn)
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K35/00—Medicinal preparations containing materials or reaction products thereof with undetermined constitution
    • A61K35/56—Materials from animals other than mammals
    • A61K35/63—Arthropods
    • A61K35/64—Insects, e.g. bees, wasps or fleas
    • A61K35/644—Beeswax; Propolis; Royal jelly; Honey
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K36/00—Medicinal preparations of undetermined constitution containing material from algae, lichens, fungi or plants, or derivatives thereof, e.g. traditional herbal medicines
    • A61K36/18—Magnoliophyta (angiosperms)
    • A61K36/185—Magnoliopsida (dicotyledons)
    • A61K36/28—Asteraceae or Compositae (Aster or Sunflower family), e.g. chamomile, feverfew, yarrow or echinacea
    • A61K36/287—Chrysanthemum, e.g. daisy
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K36/00—Medicinal preparations of undetermined constitution containing material from algae, lichens, fungi or plants, or derivatives thereof, e.g. traditional herbal medicines
    • A61K36/18—Magnoliophyta (angiosperms)
    • A61K36/185—Magnoliopsida (dicotyledons)
    • A61K36/35—Caprifoliaceae (Honeysuckle family)
    • A61K36/355—Lonicera (honeysuckle)
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K36/00—Medicinal preparations of undetermined constitution containing material from algae, lichens, fungi or plants, or derivatives thereof, e.g. traditional herbal medicines
    • A61K36/18—Magnoliophyta (angiosperms)
    • A61K36/185—Magnoliopsida (dicotyledons)
    • A61K36/56—Loganiaceae (Logania family), e.g. trumpetflower or pinkroot
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K36/00—Medicinal preparations of undetermined constitution containing material from algae, lichens, fungi or plants, or derivatives thereof, e.g. traditional herbal medicines
    • A61K36/18—Magnoliophyta (angiosperms)
    • A61K36/185—Magnoliopsida (dicotyledons)
    • A61K36/73—Rosaceae (Rose family), e.g. strawberry, chokeberry, blackberry, pear or firethorn
    • A61K36/734—Crataegus (hawthorn)
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K9/00—Medicinal preparations characterised by special physical form
    • A61K9/14—Particulate form, e.g. powders, Processes for size reducing of pure drugs or the resulting products, Pure drug nanoparticles
    • A61K9/16—Agglomerates; Granulates; Microbeadlets ; Microspheres; Pellets; Solid products obtained by spray drying, spray freeze drying, spray congealing,(multiple) emulsion solvent evaporation or extraction
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P1/00—Drugs for disorders of the alimentary tract or the digestive system
    • A61P1/16—Drugs for disorders of the alimentary tract or the digestive system for liver or gallbladder disorders, e.g. hepatoprotective agents, cholagogues, litholytics
    • A—HUMAN NECESSITIES
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    • A61P13/12—Drugs for disorders of the urinary system of the kidneys
    • A—HUMAN NECESSITIES
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    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K2236/00—Isolation or extraction methods of medicinal preparations of undetermined constitution containing material from algae, lichens, fungi or plants, or derivatives thereof, e.g. traditional herbal medicine
    • A61K2236/30—Extraction of the material
    • A61K2236/33—Extraction of the material involving extraction with hydrophilic solvents, e.g. lower alcohols, esters or ketones
    • A61K2236/331—Extraction of the material involving extraction with hydrophilic solvents, e.g. lower alcohols, esters or ketones using water, e.g. cold water, infusion, tea, steam distillation, decoction
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K2236/00—Isolation or extraction methods of medicinal preparations of undetermined constitution containing material from algae, lichens, fungi or plants, or derivatives thereof, e.g. traditional herbal medicine
    • A61K2236/30—Extraction of the material
    • A61K2236/39—Complex extraction schemes, e.g. fractionation or repeated extraction steps
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P60/00—Technologies relating to agriculture, livestock or agroalimentary industries
    • Y02P60/80—Food processing, e.g. use of renewable energies or variable speed drives in handling, conveying or stacking
    • Y02P60/85—Food storage or conservation, e.g. cooling or drying

Abstract

The embodiment of the disclosure discloses a preparation method of medlar granules. One embodiment of the method comprises: acquiring a medlar image of each medlar to obtain a medlar image set; for each image of lycium barbarum, the following processing steps are performed: inputting the medlar image into a medlar recognition model to obtain an identification result set corresponding to the medlar image; selecting a target Chinese wolfberry based on the identification result set; cleaning target medlar, placing in a drying oven, and generating dried medlar after 1.5 h; generating a first mixed solution based on the dried medlar; filtering the first mixed solution to generate a medlar clear solution; adding ingredients and pharmaceutic adjuvants into the clear wolfberry liquid to generate a second mixed solution; drying the second mixed solution in a fluidized bed for 2-3h to generate medlar granules; and filtering the medlar particles to generate target medlar particles. The embodiment can improve the sorting efficiency of the medlar, can keep the color and the fragrance of the medlar, and improves the using effect of the medlar granule electuary.

Description

Preparation method of medlar granule
Technical Field
The disclosed embodiment relates to the technical field of granules, in particular to a preparation method of medlar granules.
Background
The wolfberry fruit granule is prepared with wolfberry fruit, chrysanthemum, pale butterflybush flower and other Chinese medicinal materials and has the functions of nourishing liver, benefiting kidney, benefiting essence and improving eyesight. At present, during the preparation of medlar granule electuary, medlar is usually selected manually or automatically by a machine, and then the selected medlar is crushed, granulated and dried in the same closed container.
However, when the medlar is selected to prepare the medlar granule in the above way, the following technical problems often exist:
firstly, the conventional process for preparing the medlar granule can not well keep the original color and fragrance of medlar, so that the quality of the medlar granule is reduced, and the using effect of the medlar granule is reduced.
Secondly, the common automatic sorting technology for grading the medlar according to the saturation of the medlar has the problems of wrong sorting and missed sorting, so that secondary manual sorting is needed, and the efficiency of medlar sorting is reduced.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure provide a method for preparing fructus lycii granule, which solves one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a method for preparing a medlar granule, the method comprising: acquiring a medlar image of each medlar in the medlar group to obtain a medlar image set; for each medlar image in the medlar image set, executing the following processing steps: inputting the medlar image into a medlar identification model to obtain an identification result set corresponding to the medlar image; selecting a target Chinese wolfberry from the Chinese wolfberry group based on the generated identification result set; cleaning the target medlar with the preset weight, and placing the cleaned target medlar in an oven; controlling the temperature of the oven at 40-45 ℃ and setting the drying time to be 1.5h to generate dried medlar; generating a first mixed solution based on the dried medlar; filtering the first mixed solution by a 120-mesh screen to generate a medlar clear solution; adding ingredients and pharmaceutic adjuvant into the clear medlar liquid to generate a second mixed liquid; drying the second mixed solution in a fluidized bed for 2-3h to generate medlar granules; filtering the medlar granules by a 30-mesh screen to generate target medlar granules.
The above embodiments of the present disclosure have the following advantages: firstly, acquiring a medlar image of each medlar in a medlar group to obtain a medlar image set. For each medlar image in the medlar image set, executing the following processing steps: and inputting the medlar image into a medlar identification model to obtain an identification result set corresponding to the medlar image. And obtaining an identification result set by utilizing a pre-trained medlar identification model. The time spent on selecting the medlar is reduced, and the medlar selection efficiency is improved. Next, based on the generated discrimination result set, a target lycium barbarum is selected from the group of lycium barbarum. According to the identification result output by the medlar identification model, the medlar used for preparing medlar granule electuary is selected, so that the problem of low efficiency caused by manual selection again after automatic sorting by a machine can be effectively solved. Then, the target medlar with the preset weight is taken, cleaned and placed in an oven. And the temperature of the oven is controlled to be 40-45 ℃. The drying time was set to 1.5 h. Thereby producing dried lycium barbarum. The medlar is dried, so that the medlar can be effectively prevented from mildewing and rotting. Next, a first mixed solution is generated based on the dried lycium barbarum. And filtering the first mixed solution by a 120-mesh screen to generate a medlar clear solution. Decocting dried fructus Lycii for several times, and purifying the fructus Lycii clear liquid. Then, adding ingredients and pharmaceutic adjuvants into the clear medlar liquid to generate a second mixed liquid. And placing the second mixed solution in a fluidized bed for drying for 2-3h to generate the medlar granules. Adding the ingredients and pharmaceutic adjuvants used for preparing the medlar, and drying in a fluidized state to obtain the medlar granules. Drying under the fluidization state, so that the medlar particles are heated uniformly, and the drying speed is improved. And finally, filtering the medlar particles by a 30-mesh screen to generate target medlar particles. And removing fine powder and large particles by using a screen to obtain the target medlar particles. The separation of the medlar is realized by improving the common automatic separation technology, and the target medlar is subjected to the technical processes of drying, decocting, adding ingredients, drying, granulating and the like. The efficiency of matrimony vine is selected separately is improved, simultaneously, remains the original color and luster and the fragrant smell of matrimony vine for the quality of matrimony vine granule dissolved medicine improves, and then, has improved the result of use of matrimony vine granule dissolved medicine.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
Fig. 1 is a schematic diagram of an application scenario of a method of making a wolfberry granule according to some embodiments of the present disclosure;
fig. 2 is a flow diagram of some embodiments of a method of making a wolfberry granule according to the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of an application scenario of a method of making a wolfberry granule according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may receive a wolfberry image of each wolfberry in the wolfberry group sent by the terminal device 102, resulting in a wolfberry image set 103. Then, the computing device 101 may perform the following processing steps for each of the lycium barbarum images 1031 in the set of lycium barbarum images: and inputting the medlar image into a medlar identification model to obtain an identification result set 104 corresponding to the medlar image. The computing device 101 may then select a target wolfberry 105 from the set of wolfberries based on the generated set of authentication results 104. Next, the computing device 101 may take the target lycium barbarum 105 of the preset weight, wash it, and place it in the oven. Then, the computing device 101 may control the temperature of the oven at 40-45 ℃ and set the drying time to 1.5h to generate the dried medlar 106; next, the computing device 101 may generate a first mixed liquor 107 based on the dried wolfberry 106 described above. Thereafter, the computing device 101 may filter the first mixture 107 through a 120-mesh screen to generate the medlar clear solution 108. Next, the computing device 101 may add the ingredients and pharmaceutical excipients to the clear lycium barbarum liquid 108 to generate a second mixed solution 109. The computing device 101 may then dry the second mixture 109 in the fluidized bed for 2-3 hours to produce the wolfberry particles 110. Finally, the computing device 101 may filter the wolfberry particles 110 through a 30-mesh screen to generate target wolfberry particles 111.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to fig. 2, a flow 200 of some embodiments of a method of making a wolfberry granule according to the present disclosure is shown. The preparation method of the medlar granule comprises the following steps:
step 201, obtaining a wolfberry image of each wolfberry in a wolfberry group to obtain a wolfberry image set.
In some embodiments, the execution subject (e.g., 101 described in fig. 1) of the method for preparing wolfberry granules may obtain, through a wired connection or a wireless connection, a wolfberry image of each wolfberry in a group of wolfberries sent by a user to a computing device, to obtain a set of wolfberry images. The image of the Chinese wolfberry may be an image that does not include images other than the Chinese wolfberry.
As an example, the image of the chinese wolfberry may be: wolfberry image 001, wolfberry image 002, wolfberry image 003, wolfberry image 004, and wolfberry image 005. The above-mentioned wolfberry image set may be [ wolfberry image 001, wolfberry image 002, wolfberry image 003, wolfberry image 004, wolfberry image 005 ].
Step 202, for each image of Chinese wolfberry in the Chinese wolfberry image set, executing the following processing steps: and inputting the medlar image into a medlar recognition model to obtain an identification result set corresponding to the medlar image.
In some embodiments, the executing subject may input each of the images of the lycium barbarum set to a lycium barbarum recognition model trained in advance, so as to obtain a set of identification results of each image of the lycium barbarum. The pre-trained Chinese wolfberry identification model can comprise an input layer, a hidden layer and an output layer. The node number of the input layer and the node number of the output layer are fixed, and the proper number of layers and the node number of the hidden layer are selected, so that the performance of the neural network is greatly influenced. Wherein, the identification result can be the grade of the medlar. Wherein, the medlar grade can be: first-level medlar, second-level medlar, third-level medlar and fourth-level medlar. Wherein, the first-level medlar has the highest grade and the highest saturation. The four-level medlar has the lowest grade and the lowest saturation. The image of the Chinese wolfberry can be: the image comprises a medlar image 001, a medlar image 002, a medlar image 003, a medlar image 004 and a medlar image 005. Wherein, the identification result corresponding to the medlar image 001 is first-level medlar. The identification result corresponding to the medlar image 002 is the third-level medlar. The corresponding identification result of the medlar image 003 is second-level medlar. The identification result corresponding to the medlar image 004 is four-level medlar. The identification result corresponding to the medlar image 005 is first-level medlar. The set of authentication results may be: { [ wolfberry image 001: primary medlar ], [ medlar image 002: tertiary wolfberry ], [ wolfberry image 003: secondary lycium barbarum ], [ lycium barbarum image 004: four-level wolfberry ], [ wolfberry image 005: first-level medlar ] }.
For example, the execution subject may input the wolfberry image 001 to a wolfberry recognition model trained in advance, and obtain the discrimination result of the wolfberry image 001 [ wolfberry image 001: primary medlar ]. The execution subject may input the image 002 to a pre-trained wolfberry recognition model to obtain an identification result of the image 002 [ image 002: three-level medlar ]. The execution subject may input the wolfberry image 003 to a wolfberry recognition model trained in advance, and obtain an identification result of the wolfberry image 003 [ wolfberry image 003: second level medlar ]. The execution subject may input the medlar image 004 to a medlar recognition model trained in advance, and obtain an identification result of the medlar image 004 [ medlar image 004: four-level medlar ]. The execution subject may input the wolfberry image 005 to a wolfberry recognition model trained in advance, and obtain an identification result of the wolfberry image 005 [ wolfberry image 005: primary medlar ]. So as to obtain a set of identification results corresponding to each medlar image { [ medlar image 001: primary medlar ], [ medlar image 002: tertiary medlar ], [ medlar image 003: secondary lycium barbarum ], [ lycium barbarum image 004: four-level wolfberry ], [ wolfberry image 005: first-level medlar ] }.
In some optional implementations of some embodiments, the above-mentioned wolfberry recognition model may be trained by:
the method comprises the steps of firstly, obtaining a sample set, wherein the sample comprises a sample medlar image and sample saturation information corresponding to the sample medlar image.
As an example, the sample may be [ sample matrimony vine 1, 50% ]][ sample matrimony vine 2, 45%][ sample matrimony vine 3, 47% ]][ sample matrimony vine 4, 43%][ sample matrimony vine 5, 49% ]2][ sample matrimony vine 6, 40%][ sample matrimony vine 7, 46%]The sample set can be { [ sample matrimony vine 1, 50%][ sample matrimony vine 2, 45%][ sample matrimony vine 3, 47% ]][ sample matrimony vine 4, 43%][ sample matrimony vine 5, 49% ]][ sample matrimony vine 6, 40%][ sample matrimony vine 7, 46%]}。
In a second step, the following training steps may be performed based on the sample set: first, the sample medlar image of at least one sample in the sample set may be respectively input to an initial neural network, so as to obtain saturation information corresponding to each sample in the at least one sample. The initial neural network may be various neural networks capable of obtaining the wolfberry saturation information according to the wolfberry image, for example, a convolutional neural network, a deep neural network, and the like. Secondly, determining a loss value of the saturation information corresponding to each sample in the at least one sample based on the saturation information corresponding to each sample in the at least one sample and the sample saturation information corresponding to each sample medlar image in the at least one sample; and then, in response to the fact that the loss value is determined to be converged to a preset threshold value, taking the initial neural network as a trained Chinese wolfberry recognition model. Wherein the predetermined threshold may be 0.01. In addition, in response to determining that the loss value does not converge to the predetermined threshold, network parameters of the initial neural network are adjusted, and a training sample set is composed using unused samples, the adjusted initial neural network is used as the initial neural network, and the training step is performed again.
In some optional implementations of some embodiments, the performing subject may determine a loss value of the saturation information corresponding to each of the at least one sample, and may include:
the method comprises the steps of firstly, generating a first saturation score and a second saturation score based on saturation information corresponding to each sample in at least one sample and sample saturation information corresponding to a sample Chinese wolfberry image in the at least one sample, and obtaining a first saturation score group and a second saturation score group.
And secondly, generating a loss value by the following formula based on the saturation information corresponding to each sample in the at least one sample, the sample saturation information corresponding to the sample medlar image in the at least one sample, the first saturation score group and the second saturation score group:
Figure BDA0002761273320000071
wherein C represents a loss value. N represents the number of training samples included in at least one sample in the sample set. i represents a serial number. p represents a preset boundary value. m represents a first saturation score in the first saturation score group. m isiIs shown aboveThe ith first saturation score in the first saturation score group. n represents a second saturation score in the second saturation score group. n isiAnd represents the ith second saturation score in the second saturation score group. max [0 ] (p- (m)i×ln mi-ni×ln ni))]Represents the calculation of the sum of 0 and (p- (m)i×ln mi-ni×ln ni) Maximum value of).
The formula is used as an invention point of the embodiment of the disclosure, and the technical problem mentioned in the background art is solved, namely, the problem that the conventional automatic sorting technology for grading the medlar according to the saturation of the medlar has wrong sorting and missed sorting, so that secondary manual sorting is needed, and the efficiency of medlar sorting is reduced. Factors that lead to a reduction in the efficiency of wolfberry picking are often as follows: because the automatic sorting technology has the problems of wrong sorting and missed sorting, secondary manual sorting needs to be carried out on sorting results. If the factors are solved, the separation efficiency of the medlar can be improved, and the preparation requirement of medlar particles is met. To achieve this effect, embodiments of the present disclosure propose an initial neural network as a lycium barbarum recognition model when the loss value converges to a predetermined threshold. The above formula is introduced to calculate the loss value. And calculating the difference value between the first saturation score group and the second saturation score group generated according to the saturation information corresponding to each sample in the at least one sample and the sample saturation information corresponding to the sample medlar image in the at least one sample, and introducing a boundary value for testing a boundary to obtain a loss value. And judging whether the initial neural network is trained or not according to the loss value. And then, taking the trained initial neural network as a Chinese wolfberry recognition model. The wolfberry is sorted by utilizing the wolfberry identification model, so that the sorting efficiency of the wolfberry is improved, and the preparation requirement of wolfberry granule electuary is met.
And step 203, selecting a target Chinese wolfberry from the Chinese wolfberry group based on the identification result set.
In some embodiments, the performing agent may select a target wolfberry from the group of wolfberries based on the generated set of authentication results. Wherein, the target medlar can be medlar used for preparing medlar granule electuary.
As an example, the target lycium barbarum may be lycium barbarum 1 corresponding to the lycium barbarum image 001, and lycium barbarum 5 corresponding to the lycium barbarum image 005.
In some optional implementations of some embodiments, the executing entity selecting the target lycium barbarum from the group of lycium barbarum based on the generated set of identification results may include the following steps:
in a first step, an authentication result is selected from the generated authentication result set as a target authentication result.
As an example, inputting each lycium barbarum image to a lycium barbarum recognition model generates a set of authentication results { [ lycium barbarum image 001: primary medlar ], [ medlar image 002: tertiary wolfberry ], [ wolfberry image 003: secondary lycium barbarum ], [ lycium barbarum image 004: four-level wolfberry ], [ wolfberry image 005: first-level medlar ] }. From the set of identification results { [ lycium barbarum image 001: primary medlar ], [ medlar image 002: tertiary wolfberry ], [ wolfberry image 003: secondary lycium barbarum ], [ lycium barbarum image 004: quaternary medlar ], [ medlar images 005: the first-level medlar is selected from [ medlar image 001: primary wolfberry ] and [ wolfberry image 005: primary medlar ] as the target identification result.
And secondly, determining the medlar corresponding to the target identification result as the target medlar.
As an example, the target discrimination result [ lycium barbarum image 001: primary wolfberry fruit ] and target discrimination result [ wolfberry image 005: the medlar 1 and medlar 5 corresponding to the first-level medlar ] are determined as target medlar.
And 204, taking target medlar with preset weight, cleaning and placing in an oven.
In some embodiments, after the execution subject selects the target lycium barbarum, the target lycium barbarum with a preset weight is taken, cleaned and placed in the oven.
As an example, the target lycium barbarum may be lycium barbarum 1 and lycium barbarum 5 (primary lycium barbarum) described above. The predetermined weight may be 800 g. Taking 800g of the medlar 1 and the medlar 5. Cleaning and placing in an oven.
And step 205, controlling the temperature of the oven to be 40-45 ℃, and setting the drying time to be 1.5h to generate the dried medlar.
In some embodiments, the execution body controls the temperature of the oven to be between 40 and 45 ℃, the drying time is set to be 1.5h, and after 1.5h, the moisture of the medlar 1 and the medlar 5 is evaporated to generate the dried medlar.
As an example, the temperature of the oven may be set to 42 ℃. The execution main body takes 800g of the medlar 1 and the medlar 5, cleans the medlar 1 and the medlar 5, puts the medlar into an oven, sets the temperature of the oven at 42 ℃, dries the medlar 1 and the medlar 5 for 1.5h, and then takes out the medlar 1 and the medlar 5, thereby generating the dried medlar.
Step 206, generating a first mixed solution based on the dried lycium barbarum.
In some embodiments, the executive body is processed to prepare the first mixed solution according to the dried lycium barbarum.
As an example, the dried lycium barbarum can be lycium barbarum dried by an oven. The first mixed solution can be a mixed solution obtained by decocting medlar and water. The execution main body can be processed and decocted according to the dried medlar to generate a first mixed solution.
In some optional implementations of some embodiments, the performing the step of generating a first mixture based on the dried lycium barbarum may include the steps of:
firstly, crushing the dried medlar into medlar powder. Adding 500-. The time is 2-2.5 h. A primary mixed solution is generated.
For example, the above-mentioned powder of lycium barbarum may be obtained by pulverizing the powder with a mill or a pulverizer. The amount of the water added to the reaction mixture of 500ml and 700ml may be 700 ml. The above time may be 2.5 h. Adding 700ml of water into the Chinese wolfberry powder by the executive body, and decocting for 2.5 hours to generate the primary mixed liquid.
And step two, cooling the temperature of the primary mixed solution to 20-30 ℃, and adding 500-700ml of water for secondary decoction. The time is 1-1.2h, and secondary mixed liquor is generated.
As an example, the water addition amount of 500-700ml may be 650 ml. The above time may be 1.5 h. And adding 650ml of water into the primary mixed solution when the temperature of the primary mixed solution is cooled to 25-26 ℃, and decocting for 1.5h to generate the secondary mixed solution.
Thirdly, cooling the temperature of the secondary mixed liquid to 20-30 ℃, and adding 500-700ml of water for carrying out third decoction. The time is 0.5-1h, and a first mixed solution is generated.
As an example, the water adding amount of 500-700ml may be 500 ml. The above time may be 0.7 h. And adding 650ml of water into the secondary mixed solution when the temperature of the secondary mixed solution is cooled to 20-21 ℃, and decocting for 1.5 h. Thereby, the first mixed solution is produced.
And step 207, filtering the first mixed solution through a 120-mesh screen to generate a medlar clear solution.
In some embodiments, the performing body may filter the first mixture through a 120-mesh sieve to remove pulp and peel, thereby producing a clear wolfberry solution.
As an example, a 120 mesh screen may be used for filtration. The aperture of the screen can filter pulp and peel residues in the mixed liquid. Thereby, the medlar clear solution is generated.
And 208, adding the ingredients and the pharmaceutic adjuvant into the clear wolfberry liquid to generate a second mixed solution.
In some embodiments, the execution subject may add ingredients and pharmaceutical excipients to the clear solution of lycium barbarum to generate a second mixed solution.
In some optional implementations of some embodiments, the ingredients may include, but are not limited to, the following: 30-50g of chrysanthemum, 20-35g of butterflybush flower, 30-40g of honey, 20-30g of hawthorn and 30-60g of honeysuckle.
In some optional implementations of some embodiments, the pharmaceutical excipients may include, but are not limited to, the following: 20-30% of colorant, 40-80% of adhesive, 70-80% of stabilizer and 3-10% of flavoring agent. Wherein the colorant may be: carotene, cocoa butter, caramel butter, or mixtures thereof. The binder may be: bean gum, starch gum, gum arabic, hide gum, or mixtures thereof. The stabilizer may be: gums, dextrins, sugar esters or other sugar derivatives. The flavoring agent may be: sucrose, aromatic syrup, glycerin, sorbitol, aspartame or a mixture thereof. The execution main body can add the ingredients and the pharmaceutic adjuvant into the clear wolfberry liquid to generate a second mixed solution.
And 209, drying the second mixed solution in a fluidized bed for 2-3h to generate the medlar granules.
In some embodiments, the performing body may dry the second mixture in a fluidized bed for 2-3 hours to produce the granules of lycium barbarum.
In some optional implementations of some embodiments, the drying the second mixed solution in the fluidized bed for 2 to 3 hours to generate the wolfberry particles may include the following steps:
the first step, the second mixed liquid is placed in a fluidized bed, the temperature of the fluidized bed is controlled to be 40-50 ℃, the wind pressure is set to be 0.25Mpa, and the first particles are obtained after 1.5-2 hours.
As an example, the temperature may be 45 ℃. The first particles may be particles in which the second mixed solution is in the form of particles. The execution main body can place the second mixed liquid in the fluidized bed, and simultaneously, the temperature of the fluidized bed is controlled to be 45 ℃ and the wind pressure is set to be 0.25 Mpa. The above-mentioned preformed body may obtain the above-mentioned first granules after 1.8 h.
And secondly, maintaining the temperature of the fluidized bed in which the first particles are positioned at 40-50 ℃, adjusting the air pressure to 0.15Mpa, and generating the medlar particles after 0.5-1 h.
As an example, the temperature may be 40 ℃. The performing body may set a temperature of the fluidized bed in which the first particles are positioned to 45 ℃. The air pressure was set to 0.25 Mpa. The medlar granules are produced after 0.8 h.
And step 210, filtering the medlar particles through a 30-mesh screen to generate target medlar particles.
In some embodiments, the performing body can filter the lycium barbarum particles through a 30-mesh screen to remove fine powder and large particles, and generate the target lycium barbarum particles.
Optionally, the generated target medlar granules are weighed, bagged according to the specification of 15g, sterilized and sealed to obtain medlar granule electuary.
As an example, the execution body may weigh the target lycium barbarum particles produced by filtering through a 30-mesh screen. Bagging with the specification of 15g, sterilizing and sealing to obtain the medlar granule electuary.
In some alternative implementations of some embodiments, the sterilizing may include sterilizing using pasteurization. The sterilization conditions may be: sterilizing at 60-80 deg.C for 15-30 min. And sealing the target medlar granules subjected to pasteurization by the execution main body to obtain medlar granule electuary.
The above embodiments of the present disclosure have the following advantages: firstly, acquiring a medlar image of each medlar in a medlar group to obtain a medlar image set. For each medlar image in the medlar image set, executing the following processing steps: and inputting the medlar image into a medlar identification model to obtain an identification result set corresponding to the medlar image. And obtaining an identification result set by utilizing a pre-trained medlar identification model. The time spent on selecting the medlar is reduced, and the medlar selection efficiency is improved. Next, based on the generated discrimination result set, a target lycium barbarum is selected from the group of lycium barbarum. According to the identification result output by the medlar identification model, the medlar used for preparing medlar granule electuary is selected, so that the problem of low efficiency caused by manual selection again after automatic sorting by a machine can be effectively solved. Then, the target medlar with the preset weight is taken, cleaned and placed in an oven. And the temperature of the oven is controlled to be 40-45 ℃. The drying time was set to 1.5 h. Thereby producing dried lycium barbarum. The medlar is dried, so that the medlar can be effectively prevented from mildewing and rotting. Next, a first mixed solution is generated based on the dried lycium barbarum. And filtering the first mixed solution by a 120-mesh screen to generate medlar clear solution. Decocting dried fructus Lycii for several times, and purifying the fructus Lycii clear liquid. Then, adding ingredients and pharmaceutic adjuvants into the clear medlar liquid to generate a second mixed liquid. And placing the second mixed solution in a fluidized bed for drying for 2-3h to generate the medlar granules. Adding the ingredients and medicinal adjuvants for preparing fructus Lycii, and drying in fluidized state to obtain fructus Lycii granule. Drying under the fluidization state, so that the medlar particles are heated uniformly, and the drying speed is improved. And finally, filtering the medlar particles by a 30-mesh screen to generate target medlar particles. And removing fine powder and large particles by using a screen to obtain the target medlar particles. The separation of the medlar is realized by improving the common automatic separation technology, and the target medlar is subjected to the technical processes of drying, decocting, adding ingredients, drying, granulating and the like. The efficiency of matrimony vine is selected separately is improved, simultaneously, remains the original color and luster and the fragrant smell of matrimony vine for the quality of matrimony vine granule dissolved medicine improves, and then, has improved the result of use of matrimony vine granule dissolved medicine.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The functions described herein above 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 (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) the features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims (10)

1. A preparation method of medlar granules comprises the following steps:
acquiring a medlar image of each medlar in the medlar group to obtain a medlar image set;
for each image of the set of images of Lycium barbarum, performing the following processing steps:
inputting the medlar image into a medlar identification model to obtain an identification result set corresponding to the medlar image;
selecting a target wolfberry from the group of wolfberries based on the generated set of discrimination results;
cleaning the target medlar with the preset weight, and placing the cleaned target medlar in an oven;
controlling the temperature of the oven at 40-45 ℃ and setting the drying time to be 1.5h to generate dried medlar;
generating a first mixed solution based on the dried medlar;
filtering the first mixed solution by a 120-mesh screen to generate a medlar clear solution;
adding ingredients and pharmaceutic adjuvants into the clear wolfberry liquid to generate a second mixed solution;
drying the second mixed solution in a fluidized bed for 2-3h to generate medlar granules;
and filtering the medlar particles by a 30-mesh screen to generate target medlar particles.
2. The method of claim 1, wherein the method further comprises:
weighing the generated target medlar granules, bagging according to the specification of 15g, sterilizing and sealing to obtain the medlar granule electuary.
3. The method of claim 2, wherein the generating a first mixed liquor based on the dried wolfberry comprises:
crushing the dried medlar into medlar powder, adding 500ml of water and 700ml of water for decoction for 2-2.5h to generate primary mixed liquid;
cooling the primary mixed solution to 20-30 ℃, adding 500-;
and cooling the temperature of the secondary mixed solution to 20-30 ℃, adding 500-.
4. The method of claim 3, wherein drying the second mixture in a fluidized bed for 2-3 hours to produce the fructus Lycii granules comprises:
placing the second mixed solution in a fluidized bed, controlling the temperature of the fluidized bed at 40-50 ℃, setting the air pressure at 0.25Mpa, and obtaining first particles after 1.5-2 h;
and (3) keeping the temperature of the fluidized bed in which the first particles are positioned at 40-50 ℃, adjusting the air pressure to 0.15Mpa, and generating the medlar particles after 0.5-1 h.
5. The method of claim 4, wherein the ingredients comprise at least one of: 30-50g of chrysanthemum, 20-35g of butterflybush flower, 30-40g of honey, 20-30g of hawthorn and 30-60g of honeysuckle.
6. The method of claim 5, wherein the pharmaceutical excipient comprises at least one of: 20-30% of colorant, 40-80% of adhesive, 70-80% of stabilizer and 3-10% of flavoring agent.
7. The method of claim 6, wherein the sterilizing comprises pasteurizing under the following conditions: sterilizing at 60-80 deg.C for 15-30 min.
8. The method of claim 7, wherein the selecting a target wolfberry from the group of wolfberries based on the generated set of authentication results comprises:
selecting an authentication result from the generated authentication result set as a target authentication result;
and determining the medlar corresponding to the target identification result as the target medlar.
9. The method of claim 8, wherein the wolfberry recognition model is trained by:
obtaining a sample set, wherein the sample comprises a sample medlar image and sample saturation information corresponding to the sample medlar image;
based on the sample set, performing the following training steps:
respectively inputting the sample medlar image of at least one sample in the sample set to an initial neural network to obtain saturation information corresponding to each sample in the at least one sample;
determining a loss value of saturation information corresponding to each of the at least one sample based on the saturation information corresponding to each of the at least one sample and the sample saturation information corresponding to each of the at least one sample for the sample wolfberry image;
in response to determining that the loss value converges to a predetermined threshold, treating the initial neural network as a trained wolfberry recognition model;
in response to determining that the loss value does not converge on a predetermined threshold, adjusting network parameters of the initial neural network, and forming a training sample set using unused samples, performing the training step again with the adjusted initial neural network as the initial neural network.
10. The method of claim 9, wherein the determining a loss value of saturation information for each of the at least one sample comprises:
generating a first saturation score and a second saturation score based on the saturation information corresponding to each sample in the at least one sample and the sample saturation information corresponding to each sample medlar image in the at least one sample, and obtaining a first saturation score group and a second saturation score group;
generating a loss value based on saturation information corresponding to each of the at least one sample, sample saturation information corresponding to each of the at least one sample for a sample lycium barbarum image, the first saturation score group and the second saturation score group by:
Figure FDA0002761273310000031
wherein C represents a loss value, N represents the number of training samples included in at least one sample in the sample set, i represents a sequence number, p represents a preset boundary value, m represents a first saturation score in the first saturation score group, and m represents a second saturation score in the first saturation score groupiRepresenting the ith first saturation score in the first saturation score group, n representing the second saturation score in the second saturation score group, niRepresents the ith second saturation score in the second saturation score group, max [0, (p- (m)i×ln mi-ni×ln ni))]Represents the calculation of 0 and p- (m)i×ln mi-ni×ln ni) Maximum value of).
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104367704A (en) * 2014-08-18 2015-02-25 瓜州昊泰生物科技有限公司 Method for extraction of matrimony vine

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104367704A (en) * 2014-08-18 2015-02-25 瓜州昊泰生物科技有限公司 Method for extraction of matrimony vine

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