Method and process for extracting polysaccharide from orchid tea flowers

Abstract

The invention relates to the technical field of natural product extraction, and discloses an orchid tea flower polysaccharide extraction method and process, and the method comprises the following steps: obtaining experimental camellia pollen, and obtaining a spectrum-content mapping relation of the experimental camellia pollen; according to an enzyme extraction and alcohol precipitation method, obtaining a single-factor optimal value set of experimental camellia pollen, and constructing a camellia pollen polysaccharide purification control model; performing spectral analysis on the camellia pollen in the feeding area to obtain a fed camellia pollen spectral sequence; inquiring the difference between the feeding camellia pollen spectrum sequence and the experimental infrared spectrum of the experimental camellia pollen to obtain a spectrum distinguishing feature set, and further obtaining the camellia pollen content distinguishing distribution; performing configuration quantity prediction operation on each factor according to the feeding speed, the camellia pollen content differential distribution and the quality information to obtain a prediction control parameter sequence, and configuring a camellia pollen polysaccharide purification control model according to the prediction control parameter sequence to perform production control on a polysaccharide extraction production line. According to the method, the purity and efficiency of extracting the polysaccharide from the orchid and camellia pollen can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of natural product extraction, and in particular to a method and process for extracting polysaccharides from orchid tea flowers. Background Art

[0002] Orchid tea is a green tea variety produced in Shucheng, Anhui. With the development of the times, camellia pollen as a by-product of tea has gradually been accepted by the market. After the tea buds are picked, camellia pollen is also collected. The blooming of camellia usually occurs in autumn and winter or early spring. Camellia pollen contains rich amino acids, minerals, vitamins, polysaccharides and other ingredients.

[0003] As a bioactive substance, polysaccharides have been shown to have antioxidant, antibacterial, and anti-inflammatory functions in many studies, and are gradually emerging in the chemical, medical, and beauty industries. However, due to the special composition and complex structure of camellia pollen, the extraction process of polysaccharides is relatively difficult, and the extraction efficiency and purity of polysaccharides are low. Summary of the invention

[0004] The invention provides a method for extracting polysaccharides from orchid camellia flowers, the main purpose of which is to improve the purity and efficiency of extracting polysaccharides from orchid camellia flower pollen.

[0005] To achieve the above object, the present invention provides a method for extracting polysaccharides from orchid tea flowers, comprising:

[0006] Acquire experimental camellia pollen, perform spectral component analysis on the experimental camellia pollen to obtain an experimental infrared spectrum, and perform content determination on the experimental camellia pollen according to a pre-constructed component ratio determination method set to obtain a camellia pollen content sequence;

[0007] Machine learning is performed based on the experimental infrared spectrum and camellia pollen content sequence to obtain a spectrum-content mapping relationship;

[0008] According to the pre-constructed response surface test to optimize the enzyme extraction and alcohol precipitation method, a single factor comparative experiment is performed on the experimental camellia pollen to obtain an extraction rate experimental data sample set, and the optimal values ​​of each factor in the response surface test to optimize the enzyme extraction and alcohol precipitation method are obtained from the extraction rate experimental data sample set to obtain a single factor optimal value set;

[0009] According to the camellia pollen content sequence and the single factor optimal value set, a camellia pollen polysaccharide purification control model based on the response surface test to optimize the enzyme extraction and alcohol precipitation method is obtained, and the camellia pollen polysaccharide purification control model is used to control the production of the pre-constructed polysaccharide extraction production line;

[0010] According to a preset time frequency, a spectral analysis is performed on the camellia pollen in the feeding area of ​​the polysaccharide extraction production line to obtain a spectral sequence of the feeding camellia pollen;

[0011] Extracting a feed camellia pollen spectrum from the feed camellia pollen spectrum sequence in sequence, performing spectral feature extraction operations on the feed camellia pollen spectrum and the experimental infrared spectrum to obtain a feed camellia pollen spectral feature set and an experimental camellia pollen spectral feature set, and identifying distinguishing features of the feed camellia pollen spectral feature set and the experimental camellia pollen spectral feature set to obtain a spectral distinguishing feature set;

[0012] According to the spectrum-content mapping relationship, identifying the camellia pollen content distinguishing features corresponding to the spectral distinguishing feature set, obtaining a content distinguishing sequence, and obtaining a camellia pollen content distinguishing distribution according to the content distinguishing sequences corresponding to each feed camellia pollen spectrum in the feed camellia pollen spectrum sequence;

[0013] Obtaining the feed rate of the feeding zone, obtaining the quality information of the experimental camellia pollen, and performing a configuration amount prediction operation on each factor in the camellia pollen polysaccharide purification control model according to the feed rate, the difference distribution of the camellia pollen content and the quality information to obtain a prediction control parameter sequence;

[0014] The parameters of the camellia pollen polysaccharide purification control model are updated according to the prediction control parameter sequence to obtain an updated camellia pollen polysaccharide purification control model, and the updated camellia pollen polysaccharide purification control model is used to control the production of the polysaccharide extraction production line.

[0015] Optionally, the content of the experimental camellia pollen is determined according to a pre-constructed component ratio determination method set to obtain a camellia pollen content sequence, including:

[0016] According to a pre-constructed component ratio determination method set, the experimental camellia pollen is subjected to a drying operation based on a preset temperature value until the weight of the experimental camellia pollen remains stable, and the pollen water evaporation amount is obtained;

[0017] Using a pre-constructed Soxhlet extraction method, extracting lipids from the experimental camellia pollen to obtain lipid content;

[0018] Using a pre-constructed Kjeldahl nitrogen determination method, the protein in the experimental camellia pollen is measured to obtain the protein content;

[0019] Performing an ashing operation on the experimental camellia pollen based on a preset high temperature value to obtain an inorganic matter content;

[0020] Using a pre-constructed phenol-sulfuric acid method, the experimental camellia pollen is colorized to obtain a color development result, and the absorbance of the color development result is measured, and a pre-constructed standard curve is used to compare the absorbance to obtain the polysaccharide content;

[0021] The water evaporation amount, lipid content, protein content, inorganic matter content and polysaccharide content of the pollen are summarized to obtain a camellia pollen content sequence.

[0022] Optionally, the performing machine learning based on the experimental infrared spectrum and camellia pollen content sequence to obtain a spectrum-content mapping relationship includes:

[0023] Obtaining an initialization spectrum-content mapping relationship, and obtaining the mass information of the experimental camellia pollen, wherein the mass information of each portion of the experimental camellia pollen is the same;

[0024] Performing a curve feature extraction operation on the experimental infrared spectrum to obtain a spectrum image feature set;

[0025] Using the initialized spectrum-content mapping relationship, according to the quality information, performing a content prediction operation based on preset substance types on the spectral image feature set to obtain a predicted content sequence;

[0026] The loss value between the predicted content sequence and the camellia pollen content sequence is calculated and minimized to obtain the relationship mapping parameter when the loss value is minimum, and the initialized spectrum-content mapping relationship is updated according to the relationship mapping parameter to obtain the spectrum-content mapping relationship.

[0027] Optionally, the enzyme extraction and alcohol precipitation method is optimized according to the pre-constructed response surface test, and a single factor comparative experiment is performed on the experimental camellia pollen to obtain an extraction rate experimental data sample set, including:

[0028] Obtain the factors of each process in the enzyme extraction and alcohol precipitation method by pre-constructed response surface experiment optimization to obtain the factor set;

[0029] Acquire the experimental interval of each factor in the factor set, and perform an equal division operation on the experimental interval of each factor based on a preset division value to obtain an experimental value sequence of the experimental interval of each factor;

[0030] According to the experimental numerical sequence of the experimental interval of each factor, a control experiment based on the numerical variation of a single factor is performed on the response surface test to optimize the enzyme extraction and alcohol precipitation method to obtain a control experiment set, and the polysaccharide extraction rate corresponding to each control experiment in the control experiment set is obtained to obtain a polysaccharide extraction rate set;

[0031] According to the control experiment set and the polysaccharide extraction rate set, an extraction rate experiment data sample set is obtained.

[0032] Optionally, the spectral analysis of camellia pollen in the feeding area of ​​the polysaccharide extraction production line to obtain a spectral sequence of the feeding camellia pollen includes:

[0033] Dividing the preset detection area in the feeding area of ​​the polysaccharide extraction production line into regions to obtain a sub-region set and a positional relationship between each sub-region in the sub-region set;

[0034] Using the probe device pre-built in the feeding area, spectral sampling is performed on each sub-area in the sub-area set to obtain a feed camellia pollen spectrum set;

[0035] According to the positional relationship, each feed camellia pollen spectrum sequence in the feed camellia pollen spectrum set is arranged in position to obtain a feed camellia pollen spectrum sequence.

[0036] Optionally, the step of obtaining a camellia pollen polysaccharide purification control model based on the response surface test to optimize the enzyme extraction and alcohol precipitation method according to the camellia pollen content sequence and the single factor optimal value set includes:

[0037] Normalizing the camellia pollen content sequence to obtain a content ratio sequence, and extracting numerical features from the camellia pollen content sequence and the content ratio sequence to obtain a content feature sequence;

[0038] Constructing a content-factor quantity mapping relationship between the content feature sequence and the single factor optimal value set;

[0039] According to the response surface experiment, the control order of various factors in the enzyme extraction and alcohol precipitation method is optimized to construct a polysaccharide purification process model;

[0040] Obtaining a default feed pollen mass, and configuring the values ​​of each factor in the polysaccharide purification process model according to the default feed pollen mass and content-factor quantity mapping relationship to obtain an updated polysaccharide purification process model;

[0041] The pre-constructed feed quality recognition network is used to numerically update the default feed pollen quality in the updated polysaccharide purification process model to obtain a camellia pollen polysaccharide purification control model.

[0042] Optionally, the configuration amount prediction operation is performed on each factor in the camellia pollen polysaccharide purification control model according to the feed rate, the distribution of camellia pollen content and the quality information to obtain a prediction control parameter sequence, including:

[0043] extracting a content distinction sequence from the camellia pollen content distinction distribution in sequence to obtain a target content distinction sequence;

[0044] Calculating the ratio of each content substance in the camellia pollen according to the target content distinction sequence and the camellia pollen content sequence to obtain a content change rate sequence;

[0045] According to the content change rate sequence, weighted calculation is performed on the proportion of each factor in the camellia pollen polysaccharide purification control model to obtain an optimized control parameter sequence;

[0046] Obtaining the optimized control parameter sequence of each content difference sequence in the camellia pollen content difference distribution to obtain an optimized control parameter sequence set, and performing mean calculation on the optimized control parameter sequence set to obtain a mean control parameter sequence;

[0047] The ratio of the feed rate to the mass information is calculated to obtain the feed share, and the mean control parameter sequence is multiplied according to the feed share to obtain the prediction control parameter sequence.

[0048] Optionally, after using the updated camellia pollen polysaccharide purification control model to control the polysaccharide extraction production line, the method further comprises:

[0049] Obtain the actual amount of polysaccharide produced;

[0050] According to the spectrum-content mapping relationship, the polysaccharide content of the feed share and the feed camellia pollen spectrum sequence is predicted to obtain the predicted production polysaccharide amount;

[0051] Calculating the error between the predicted polysaccharide production amount and the actual polysaccharide production amount to obtain a relative error value, and determining whether the relative error value is greater than a preset qualified threshold value;

[0052] When the relative error value is greater than the qualified threshold, the spectrum-content mapping relationship and the content-factor quantity mapping relationship are updated and trained to obtain an updated spectrum-content mapping relationship and an updated content-factor quantity mapping relationship.

[0053] To achieve the above object, the present invention also provides a process for extracting polysaccharides from orchid tea flowers, comprising:

[0054] A spectral analysis module is used to obtain experimental camellia pollen, perform spectral component analysis on the experimental camellia pollen to obtain an experimental infrared spectrum, perform content determination on the experimental camellia pollen according to a pre-constructed component ratio determination method set to obtain a camellia pollen content sequence, and perform machine learning based on the experimental infrared spectrum and the camellia pollen content sequence to obtain a spectrum-content mapping relationship;

[0055] A purification process analysis module, for optimizing the enzyme extraction and alcohol precipitation method according to a pre-constructed response surface test, performing a single factor comparative experiment on the experimental camellia pollen, obtaining an extraction rate experimental data sample set, and obtaining the optimal values ​​of each factor in the response surface test to optimize the enzyme extraction and alcohol precipitation method from the extraction rate experimental data sample set, obtaining a single factor optimal value set, and constructing a camellia pollen polysaccharide purification control model based on the response surface test to optimize the enzyme extraction and alcohol precipitation method according to the camellia pollen content sequence and the single factor optimal value set, and using the camellia pollen polysaccharide purification control model to perform production control on the pre-constructed polysaccharide extraction production line;

[0056] A camellia pollen identification module, for performing spectral analysis on the camellia pollen in the feeding area of ​​the polysaccharide extraction production line according to a preset time frequency to obtain a feed camellia pollen spectral sequence, and extracting a feed camellia pollen spectrum from the feed camellia pollen spectral sequence in turn, performing spectral feature extraction operations on the feed camellia pollen spectrum and the experimental infrared spectrum to obtain a feed camellia pollen spectral feature set and an experimental camellia pollen spectral feature set, and identifying distinguishing features of the feed camellia pollen spectral feature set and the experimental camellia pollen spectral feature set to obtain a spectral distinguishing feature set, and according to the spectrum-content mapping relationship, identifying the camellia pollen content distinguishing features corresponding to the spectral distinguishing feature set to obtain a content distinguishing sequence, and according to the content distinguishing sequence corresponding to each feed camellia pollen spectrum in the feed camellia pollen spectral sequence, obtaining a camellia pollen content distinguishing distribution;

[0057] A purification process parameter updating module is used to obtain the feed rate of the feeding area, obtain the quality information of the experimental camellia pollen, and perform configuration quantity prediction operations on various factors in the camellia pollen polysaccharide purification control model according to the feed rate, the difference distribution of camellia pollen content and the quality information to obtain a prediction control parameter sequence, and update the parameters of the camellia pollen polysaccharide purification control model according to the prediction control parameter sequence to obtain an updated camellia pollen polysaccharide purification control model, and use the updated camellia pollen polysaccharide purification control model to control the production of the polysaccharide extraction production line.

[0058] Optionally, the camellia pollen polysaccharide purification control model based on the response surface test optimization enzyme extraction and alcohol precipitation method is constructed according to the camellia pollen content sequence and the single factor optimal value set, including:

[0059] Normalizing the camellia pollen content sequence to obtain a content ratio sequence, and extracting numerical features from the camellia pollen content sequence and the content ratio sequence to obtain a content feature sequence;

[0060] Constructing a content-factor quantity mapping relationship between the content feature sequence and the single factor optimal value set;

[0061] According to the response surface experiment, the control order of various factors in the enzyme extraction and alcohol precipitation method is optimized to construct a polysaccharide purification process model;

[0062] Obtaining a default feed pollen mass, and configuring the values ​​of each factor in the polysaccharide purification process model according to the default feed pollen mass and content-factor quantity mapping relationship to obtain an updated polysaccharide purification process model;

[0063] The pre-constructed feed quality recognition network is used to numerically update the default feed pollen quality in the updated polysaccharide purification process model to obtain a camellia pollen polysaccharide purification control model.

[0064] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0065] A memory storing at least one instruction;

[0066] The processor executes the instructions stored in the memory to implement the above-mentioned method for extracting polysaccharides from orchid tea flowers.

[0067] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned orchid tea flower polysaccharide extraction method.

[0068] The present invention solves the problem described in the background technology. The present invention first obtains the mapping relationship between the spectrum and the content of each substance in the pollen through machine learning, so as to achieve a simple evaluation of the amount of polysaccharides that need to be extracted and the amount of other impurities that need to be filtered. Then, the present invention measures the optimal extraction conditions for the experimental camellia pollen through a large number of experiments, thereby constructing a camellia pollen polysaccharide purification control model with high efficiency, wherein the camellia pollen polysaccharide purification control model can control the process control on the polysaccharide extraction production line; the present invention monitors the camellia pollen in the feeding area to obtain the feeding camellia pollen spectral sequence, and by comparing the difference between the feeding camellia pollen spectral sequence and the experimental infrared spectrum, the difference in pollen content between the camellia pollen in the feeding area and the experimental camellia pollen is identified, and then according to the feeding speed of the feeding area, the parameters of each process in the camellia pollen polysaccharide purification control model are adjusted, thereby obtaining a camellia pollen polysaccharide extraction process that can be dynamically adjusted according to the pollen condition and the feeding speed. Therefore, the present invention can improve the purity and efficiency of extracting polysaccharides from orchid camellia pollen. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of a process for extracting polysaccharides from orchid tea flowers provided in one embodiment of the present invention;

[0070] Figure 2A functional module diagram of a polysaccharide extraction process for orchid tea flowers provided in one embodiment of the present invention;

[0071] Figure 3 A schematic diagram of the structure of an electronic device for implementing the method for extracting polysaccharides from orchid tea flowers provided in one embodiment of the present invention.

[0072] Description of reference numerals:

[0073] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0074] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0076] The embodiment of the present application provides a method for extracting polysaccharides from orchid tea flowers. The execution subject of the method for extracting polysaccharides from orchid tea flowers includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for extracting polysaccharides from orchid tea flowers can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0077] Reference Figure 1 FIG. 1 is a flow chart of a method for extracting polysaccharides from orchid tea flowers provided in one embodiment of the present invention. In this embodiment, the method for extracting polysaccharides from orchid tea flowers comprises:

[0078] S1. Acquire experimental camellia pollen, perform spectral component analysis on the experimental camellia pollen to obtain an experimental infrared spectrum, and perform content determination on the experimental camellia pollen according to a pre-constructed component ratio determination method set to obtain a camellia pollen content sequence.

[0079] The camellia pollen in the experimental camellia pollen refers to a powdery substance obtained by drying and grinding camellia flowers, including pollen, petals and other substances.

[0080] The spectral component analysis refers to obtaining characteristic spectral information by detecting the absorption, reflection, transmission or scattering behavior of a substance to light. Each substance has specific spectral characteristics within a certain wavelength range due to different molecular structures, chemical bonds and functional groups, so spectral technology can be used to identify the type and characteristics of a substance.

[0081] Specifically, in the embodiment of the present invention, spectral identification of experimental camellia pollen is performed by infrared light to obtain an experimental infrared spectrum, wherein the experimental camellia pollen can be Anhui Shucheng small orchid tea or camellia pollen similar to the local camellia pollen of the enterprise managed by the process of this scheme. By testing the experimental camellia pollen once, its effectiveness can be maintained for a long time.

[0082] In detail, in the embodiment of the present invention, the content of the experimental camellia pollen is determined according to the pre-constructed component ratio determination method set to obtain the camellia pollen content sequence, including:

[0083] According to a pre-constructed component ratio determination method set, the experimental camellia pollen is subjected to a drying operation based on a preset temperature value until the weight of the experimental camellia pollen remains stable, and the pollen water evaporation amount is obtained;

[0084] Using a pre-constructed Soxhlet extraction method, extracting lipids from the experimental camellia pollen to obtain lipid content;

[0085] Using a pre-constructed Kjeldahl nitrogen determination method, the protein in the experimental camellia pollen is measured to obtain the protein content;

[0086] Performing an ashing operation on the experimental camellia pollen based on a preset high temperature value to obtain an inorganic matter content;

[0087] Using a pre-constructed phenol-sulfuric acid method, the experimental camellia pollen is colorized to obtain a color development result, and the absorbance of the color development result is measured, and a pre-constructed standard curve is used to compare the absorbance to obtain the polysaccharide content;

[0088] The water evaporation amount, lipid content, protein content, inorganic matter content and polysaccharide content of the pollen are summarized to obtain a camellia pollen content sequence.

[0089] Among them, the component ratio determination method set refers to a method set for actually testing the main polysaccharide components in camellia pollen and other components that need to be filtered, including constant weight method, extraction method, Kjeldahl nitrogen determination method and other methods for determining various substances.

[0090] The preset temperature value is configured as 105° C. in the embodiment of the present invention.

[0091] The Soxhlet extraction method is a classic laboratory chemical extraction technique used to extract soluble compounds from solid samples, especially lipid (oil) extraction. Its principle is based on the principle of solvent reflux and continuous extraction. By recycling the purified solvent, the solvent is fully in contact with the sample, thereby extracting the lipid components in the sample.

[0092] Among them, the Kjeldahl method is a technology used to determine the total nitrogen content of nitrogen-containing compounds in a sample. The principle is to convert the organic nitrogen in the sample into inorganic nitrogen (i.e., ammonia nitrogen), and indirectly calculate the nitrogen content in the sample by quantitatively determining the ammonia content, and then measure the protein content.

[0093] The phenol-sulfuric acid method is used to determine the content of polysaccharides (such as glucose, sucrose, starch hydrolysate) or other carbohydrates (such as monosaccharides or oligosaccharides) in aqueous solution.

[0094] Wherein, the high temperature value is configured to be 550°C.

[0095] Specifically, in the embodiment of the present invention, the sun-dried experimental camellia pollen is first weighed, 5 grams of the experimental camellia pollen is extracted, and then placed in a drying furnace and dried to constant weight, and then the difference between the remaining mass and 5 grams is weighed to obtain the evaporation of pollen water, and then the dried experimental camellia powder is subjected to the material determination process of Soxhlet extraction, Kjeldahl nitrogen determination, high temperature ashing method and phenol-sulfuric acid method to obtain the camellia pollen content sequence. Wherein, the camellia pollen content sequence is based on the content of 5 grams of experimental camellia pollen.

[0096] S2. Perform machine learning based on the experimental infrared spectrum and camellia pollen content sequence to obtain a spectrum-content mapping relationship.

[0097] Among them, the machine learning refers to an important branch of artificial intelligence (AI), which refers to the development and application of algorithms to enable computers to learn from data and make predictions or decisions on new data based on the learned rules without explicit programming instructions.

[0098] In detail, in the embodiment of the present invention, the machine learning is performed based on the experimental infrared spectrum and camellia pollen content sequence to obtain the spectrum-content mapping relationship, including:

[0099] Obtaining an initialization spectrum-content mapping relationship, and obtaining the mass information of the experimental camellia pollen, wherein the mass information of each portion of the experimental camellia pollen is the same;

[0100] Performing a curve feature extraction operation on the experimental infrared spectrum to obtain a spectrum image feature set;

[0101] Using the initialized spectrum-content mapping relationship, according to the quality information, performing a content prediction operation based on preset substance types on the spectral image feature set to obtain a predicted content sequence;

[0102] The loss value between the predicted content sequence and the camellia pollen content sequence is calculated and minimized to obtain the relationship mapping parameter when the loss value is minimum, and the initialized spectrum-content mapping relationship is updated according to the relationship mapping parameter to obtain the spectrum-content mapping relationship.

[0103] The initialized spectrum-content mapping relationship is an initialized function formula, for example:

[0104] A i =α1K1+α2K2+α3K3+…+α j K j +b

[0105] In the formula, A i Represents the content value of the i-th substance, α1…α j Represents j network parameters to be trained, K1...k j represents the h spectral features in the spectrum, and b represents the bias term.

[0106] Among them, the quality information is the 5g set above.

[0107] The curve feature extraction operation refers to the process of extracting image information such as the position and slope of each line segment in the spectrogram, which can be achieved through an image-based neural network.

[0108] The preset substance types refer to some main components in pollen, such as polysaccharides, lipids, proteins, etc.

[0109] Among them, the content prediction operation refers to the operation of content identification through the fully connected layer of the neural network. Each neural node in each layer of the fully connected layer contains a weight coefficient. Through the connection of each weight coefficient, the mapping relationship obtained by machine learning is realized.

[0110] The loss value refers to a quantity that measures the difference between two numerical values, and the present invention uses a cross entropy loss algorithm to calculate the loss value.

[0111] The minimization process refers to the implementation of the gradient descent algorithm which is relatively common in the neural network training process. The present invention will not elaborate on the training execution content of the neural network.

[0112] Specifically, in an embodiment of the present invention, the spectral image feature set is used as a sample in the training process, and the camellia pollen content sequence is used as the true label of the sample. The network parameters and bias items to be trained in the initialized spectrum-content mapping relationship are continuously updated through the difference between the prediction result and the label, and finally tend to be stable, so that the spectrum-content mapping relationship is composed of each network parameter to be trained and the bias item.

[0113] S3. According to the pre-constructed response surface experiment to optimize the enzyme extraction and alcohol precipitation method, a single factor comparative experiment is carried out on the experimental camellia pollen to obtain a set of extraction rate experimental data samples, and the optimal values ​​of each factor in the response surface experiment to optimize the enzyme extraction and alcohol precipitation method are obtained from the extraction rate experimental data sample set to obtain a single factor optimal value set.

[0114] Among them, the response surface test optimization enzyme extraction and alcohol precipitation method is a widely recognized polysaccharide extraction technology, which is used to improve the extraction rate and purity of polysaccharides through enzyme purification and alcohol precipitation, while reducing reagent consumption and time cost. Its main process includes weighing, petroleum ether defatting, drying, ethanol decolorization, drying, adding appropriate amount of enzyme, uniform mixing, centrifugation, collecting supernatant, alcohol precipitation, centrifugation, collecting precipitation, and storage. Among them, the appropriate amount of enzyme includes protease, pectinase, cellulase, etc.

[0115] From the main process of optimizing the enzyme extraction and alcohol precipitation method by response surface experiment, it can be seen that factors such as the amount of various enzymes added, the solid-liquid ratio, and the extraction temperature will interfere with the final polysaccharide collection results. Therefore, it is necessary to conduct a flux experiment to first obtain process information with high extraction efficiency for local orchid camellia pollen.

[0116] In detail, in the embodiment of the present invention, the enzyme extraction and alcohol precipitation method is optimized according to the pre-constructed response surface test, and a single factor comparative experiment is performed on the experimental camellia pollen to obtain an extraction rate experimental data sample set, including:

[0117] Obtain the factors of each process in the enzyme extraction and alcohol precipitation method by pre-constructed response surface experiment optimization to obtain the factor set;

[0118] Acquire the experimental interval of each factor in the factor set, and perform an equal division operation on the experimental interval of each factor based on a preset division value to obtain an experimental value sequence of the experimental interval of each factor;

[0119] According to the experimental numerical sequence of the experimental interval of each factor, a control experiment based on the numerical variation of a single factor is performed on the response surface test to optimize the enzyme extraction and alcohol precipitation method to obtain a control experiment set, and the polysaccharide extraction rate corresponding to each control experiment in the control experiment set is obtained to obtain a polysaccharide extraction rate set;

[0120] According to the control experiment set and the polysaccharide extraction rate set, an extraction rate experiment data sample set is obtained.

[0121] The factors include the amount of protease added, the amount of pectinase added, the amount of cellulase added, the mass ratio of the complex enzyme, the extraction pH value, the material-liquid ratio, the extraction time, the extraction temperature and the ethanol volume fraction.

[0122] The experimental range refers to the commonly used ranges corresponding to various factors, such as the amount of protease added is about 0.8% to 1.6%, the material-liquid ratio is 5 times to 25 times, the extraction time is 6 to 12 hours, etc.

[0123] Among them, the preset division value is set to 5, which means that 5 equally spaced values ​​are extracted from each interval. For example, if the amount of protease added is about 0.8% to 1.6%, the experimental value sequence converted is (0.8%, 1.0%, 1.2%, 1.4%, 1.6%); if the amount of pectinase added is about 0.01% to 0.05%, the experimental value sequence of pectinase addition is (0.01%, 0.02%, 0.03%, 0.04%, 0.05%).

[0124] The control experiment based on the change of the value of a single factor refers to a test method in which one factor is changed while the other factors remain unchanged. For example, when the single factor is protease, the experimental data 1 is [protease 0.8%, pectinase 0.03%, cellulase 0.04%, material-liquid ratio 1:5, etc.], and the experimental data 2 is [protease 1.0%, pectinase 0.03%, cellulase 0.04%, material-liquid ratio 1:5, etc.]. Each experimental data can be used as a control experiment to form a control experiment set.

[0125] Specifically, in the embodiment of the present invention, each of the above-mentioned experimental data will have a polysaccharide extraction rate, and therefore, a polysaccharide extraction rate set will be obtained. Through the corresponding relationship between the experimental data and the polysaccharide extraction rate, an extraction rate experimental data sample set can be generated.

[0126] Furthermore, in the embodiment of the present invention, each factor corresponding to the experimental data with the highest extraction rate is found, thereby obtaining the optimal value set of a single factor.

[0127] S4. According to the camellia pollen content sequence and the single factor optimal numerical set, a camellia pollen polysaccharide purification control model based on the response surface experiment to optimize the enzyme extraction and alcohol precipitation method is obtained, and the camellia pollen polysaccharide purification control model is used to control the production of the pre-constructed polysaccharide extraction production line.

[0128] Among them, the camellia pollen polysaccharide purification control model is an intelligent auxiliary control model, which can automatically adjust various parameters in the polysaccharide extraction production line, such as enzyme content, temperature and time at each stage.

[0129] Among them, the polysaccharide extraction production line divides the polysaccharide extraction steps from the hardware equipment, including the feeding area, the extraction area and the product output area. The feeding area transports the camellia pollen to the extraction area for processing through technologies such as conveyor belts. Finally, impurities, waste liquid or polysaccharides can be discharged from different outlets in the product output area.

[0130] In detail, in the embodiment of the present invention, the control model for the purification of camellia pollen polysaccharides by the enzyme extraction and alcohol precipitation method optimized by the response surface test is obtained according to the camellia pollen content sequence and the single factor optimal value set, including:

[0131] Normalizing the camellia pollen content sequence to obtain a content ratio sequence, and extracting numerical features from the camellia pollen content sequence and the content ratio sequence to obtain a content feature sequence;

[0132] Constructing a content-factor quantity mapping relationship between the content feature sequence and the single factor optimal value set;

[0133] According to the response surface experiment, the control order of various factors in the enzyme extraction and alcohol precipitation method is optimized to construct a polysaccharide purification process model;

[0134] Obtaining a default feed pollen mass, and configuring the values ​​of each factor in the polysaccharide purification process model according to the default feed pollen mass and content-factor quantity mapping relationship to obtain an updated polysaccharide purification process model;

[0135] The pre-constructed feed quality recognition network is used to numerically update the default feed pollen quality in the updated polysaccharide purification process model to obtain a camellia pollen polysaccharide purification control model.

[0136] Among them, the normalization operation refers to converting the numerical value of each content into a percentage of the overall mass, so as to better understand the proportional relationship between the various contents.

[0137] The numerical feature extraction refers to converting the corresponding relationship between the contents of each numerical value and the size relationship between the numerical values ​​into a binary character form that can be understood by a machine.

[0138] Among them, the calculation process of the content-factor quantity mapping relationship is similar to the above-mentioned spectrum-content mapping relationship. The difference is that the output result is converted into the specific value of each factor, each content feature is used as a variable in the formula, and each network parameter and bias item to be trained are re-obtained.

[0139] The default feed pollen mass is an updateable parameter, which can be initially configured as 5 g and then modified according to the specific camellia pollen feeding speed in the feeding area.

[0140] The feed quality recognition network is a neural network that estimates the amount of camellia pollen input, and is used to estimate the total pollen mass based on the feed rate and the feed camellia pollen spectral sequence, thereby adjusting the parameters of the consumable factors in the extraction area exponentially.

[0141] Specifically, in an embodiment of the present invention, a polysaccharide purification process model is constructed through a content-factor quantity mapping relationship, and then the output port of a pre-constructed feed quality recognition network is connected to the input port of the polysaccharide purification process model to obtain a complete camellia pollen polysaccharide purification control model.

[0142] S5. According to a preset time frequency, spectral analysis is performed on the camellia pollen in the feeding area of ​​the polysaccharide extraction production line to obtain a spectral sequence of the feeding camellia pollen.

[0143] In the embodiment of the present invention, the camellia pollen raw material may change the total amount of various substances in the input camellia pollen at any time depending on the origin of the purchaser, the tea variety, the date, or the uneven distribution of the camellia pollen during the feeding process on the conveyor belt. Therefore, detection can be performed according to a preset time frequency, for example, once per minute.

[0144] In detail, in the embodiment of the present invention, the spectral analysis of the camellia pollen in the feeding area of ​​the polysaccharide extraction production line to obtain the spectral sequence of the feeding camellia pollen includes:

[0145] Dividing the preset detection area in the feeding area of ​​the polysaccharide extraction production line into regions to obtain a sub-region set and a positional relationship between each sub-region in the sub-region set;

[0146] Using the probe device pre-built in the feeding area, spectral sampling is performed on each sub-area in the sub-area set to obtain a feed camellia pollen spectrum set;

[0147] According to the positional relationship, each feed camellia pollen spectrum sequence in the feed camellia pollen spectrum set is arranged in position to obtain a feed camellia pollen spectrum sequence.

[0148] The detection area refers to the area within the one-second movement range of the conveyor belt on the cross-sectional area of ​​the conveyor belt, for example:

[0149]

[0150] The number of rows in the matrix is ​​3, the number of columns is 2, → represents the conveying direction of the conveyor belt, then the number of rows 3 is the width of the conveyor belt, and the number of columns 2 is the displacement distance of the conveyor belt in one second.

[0151] Among them, the area division refers to dividing the detection area into 6 sub-areas 1 to 6, and recording [sub-area 1 is on the left of sub-area 2 and above sub-area 3], [sub-area 2 is on the right of sub-area 1 and above sub-area 4]..., thereby obtaining the positional relationship.

[0152] Specifically, in an embodiment of the present invention, a pre-constructed probe is used to perform non-destructive detection on camellia pollen in each sub-region to obtain a feed camellia pollen spectrum set, which is then arranged according to positional relationships to obtain a feed camellia pollen spectrum sequence.

[0153] S6. Extract a feed camellia pollen spectrum from the feed camellia pollen spectrum sequence in turn, perform spectral feature extraction operations on the feed camellia pollen spectrum and the experimental infrared spectrum to obtain a feed camellia pollen spectrum feature set and an experimental camellia pollen spectrum feature set, and identify distinguishing features of the feed camellia pollen spectrum feature set and the experimental camellia pollen spectrum feature set to obtain a spectral distinguishing feature set.

[0154] Since the conveyor belt is relatively wide, even the same batch of camellia pollen may have differences in quality. Therefore, by identifying the feed camellia pollen spectrum in each sub-region of the feed camellia pollen spectrum sequence, it can be determined whether the camellia pollen at each sub-region position is different from the experimental camellia pollen.

[0155] The spectral feature extraction operation is similar to the curve feature extraction operation in S2. Here, two spectral objects are identified in parallel to obtain a feed camellia pollen spectral feature set and an experimental camellia pollen spectral feature set.

[0156] The distinguishing feature refers to a feature with a large difference in the numerical value of a certain content. For example, if the difference is greater than 10%, it can be indicated that a certain content is a distinguishing feature.

[0157] Furthermore, classification and identification are performed through a neural network to find the corresponding content substances in the feed camellia pollen spectral feature set and the experimental camellia pollen spectral feature set, and then the parameter difference of each content substance in the two sets is calculated to obtain a spectral distinction feature set.

[0158] S7. According to the spectrum-content mapping relationship, identify the camellia pollen content distinguishing features corresponding to the spectral distinguishing feature set to obtain a content distinguishing sequence, and obtain the camellia pollen content distinguishing distribution according to the content distinguishing sequences corresponding to each feed camellia pollen spectrum in the feed camellia pollen spectral sequence.

[0159] Specifically, in the embodiment of the present invention, there is a correspondence between the spectrum and the content of each substance in the spectrum-content mapping relationship. Therefore, the content distinction sequence can be identified from the spectral distinction feature set, indicating the difference between the camellia pollen in the feeding area and the experimental camellia pollen.

[0160] Then, according to the content distinction sequence corresponding to each feed camellia pollen spectrum in the feed camellia pollen spectrum sequence, the camellia pollen content distinction distribution is obtained.

[0161] S8. Obtain the feed rate of the feeding zone, obtain the quality information of the experimental camellia pollen, and perform configuration amount prediction operation on each factor in the camellia pollen polysaccharide purification control model according to the feed rate, the difference distribution of camellia pollen content and the quality information to obtain a prediction control parameter sequence.

[0162] The feed rate is related to the scale of the polysaccharide extraction production line, and is configured to be 60 g / s in the embodiment of the present invention.

[0163] Among them, the quality information is 5g.

[0164] In detail, in the embodiment of the present invention, the configuration amount prediction operation is performed on each factor in the camellia pollen polysaccharide purification control model according to the feed rate, the distribution of camellia pollen content and the quality information to obtain a prediction control parameter sequence, including:

[0165] extracting a content distinction sequence from the camellia pollen content distinction distribution in sequence to obtain a target content distinction sequence;

[0166] Calculating the ratio of each content substance in the camellia pollen according to the target content distinction sequence and the camellia pollen content sequence to obtain a content change rate sequence;

[0167] According to the content change rate sequence, weighted calculation is performed on the proportion of each factor in the camellia pollen polysaccharide purification control model to obtain an optimized control parameter sequence;

[0168] Obtaining the optimized control parameter sequence of each content difference sequence in the camellia pollen content difference distribution to obtain an optimized control parameter sequence set, and performing mean calculation on the optimized control parameter sequence set to obtain a mean control parameter sequence;

[0169] The ratio of the feed rate to the mass information is calculated to obtain the feed share, and the mean control parameter sequence is multiplied according to the feed share to obtain the prediction control parameter sequence.

[0170] It should be known that the order of magnitude of the contents of various substances corresponding to the different distribution of camellia pollen content is relatively large, and the camellia pollen content sequence corresponds to 5g of experimental camellia pollen. Therefore, it is necessary to first distinguish the pollen types, fine-tune the control parameters in the extraction zone, and then adjust them again according to the weight ratio.

[0171] Specifically, in the embodiment of the present invention, it is first necessary to obtain a content change rate sequence based on the ratio of the target content difference sequence to the camellia pollen content sequence. In the content change rate sequence, if the substance content is zero or less than 0.1%, it can be assumed that the camellia pollen does not change in a certain substance content. If the substance content is greater than 0.1%, such as content A, it indicates that the camellia pollen in the feeding area has changed in content A compared to the experimental camellia pollen. Therefore, the content change rate sequence is weighted to calculate the proportion of each factor in the camellia pollen polysaccharide purification control model to obtain an optimized control parameter sequence.

[0172] Among them, the weighted calculation process varies according to the different effects of various factors. For example, values ​​that play a consumption role can be weighted directly, values ​​that play a catalytic role can be weighted exponentially, and constant values ​​such as temperature can remain unchanged. The specific weights can be fine-tuned according to experimental data.

[0173] Specifically, in the embodiment of the present invention, the camellia pollen content difference distribution has multiple content difference sequences, so the mean of each optimization control parameter sequence can be calculated to obtain the mean control parameter sequence of the entire detection area. At this time, the mean control parameter sequence represents the parameters corresponding to the camellia pollen in the 5g feeding area.

[0174] At this time, there are 60g of camellia pollen to be fed into the feeding area, and the ratio of the feeding speed to the mass information is calculated to obtain the feeding share, that is, 12 portions. Then, the mean control parameter sequence is multiplied according to the feeding share to obtain the prediction control parameter sequence. Among them, the weighting process is similar to the above weighting process.

[0175] S9. Update the parameters of the camellia pollen polysaccharide purification control model according to the predicted control parameter sequence to obtain an updated camellia pollen polysaccharide purification control model, and use the updated camellia pollen polysaccharide purification control model to control the production line of the polysaccharide extraction.

[0176] In the embodiment of the present invention, the amount of each factor in the prediction control parameter sequence is an amount capable of processing 12 portions of camellia pollen.

[0177] Specifically, the parameters of the camellia pollen polysaccharide purification control model are updated according to the prediction control parameter sequence to obtain an updated camellia pollen polysaccharide purification control model, thereby controlling the polysaccharide extraction production line for production.

[0178] In detail, in the embodiment of the present invention, after the updated camellia pollen polysaccharide purification control model is used to control the production line of the polysaccharide extraction, the method further includes:

[0179] Obtain the actual amount of polysaccharide produced;

[0180] According to the spectrum-content mapping relationship, the polysaccharide content of the feed share and the feed camellia pollen spectrum sequence is predicted to obtain the predicted production polysaccharide amount;

[0181] Calculating the error between the predicted polysaccharide production amount and the actual polysaccharide production amount to obtain a relative error value, and determining whether the relative error value is greater than a preset qualified threshold value;

[0182] When the relative error value is greater than the qualified threshold, the spectrum-content mapping relationship and the content-factor quantity mapping relationship are updated and trained to obtain an updated spectrum-content mapping relationship and an updated content-factor quantity mapping relationship.

[0183] In the embodiment of the present invention, the polysaccharide produced per unit time, or the polysaccharide extraction production line has only 60g of camellia pollen, when 60g of camellia pollen is completely extracted, the polysaccharide obtained is used as the actual production amount of polysaccharide. For example, the actual production amount of polysaccharide is 11.5g

[0184] In the embodiment of the present invention, through the spectrum-content mapping relationship, the predicted production amount of polysaccharides, for example, 12 g, can be calculated based on the spectral sequence of 60 g and the feed camellia pollen.

[0185] The qualified threshold may be 0.05.

[0186] Specifically, in the embodiment of the present invention, the error between the predicted polysaccharide production amount and the actual polysaccharide production amount is calculated to be 0.5g, and the relative error is 0.04. If it is less than the qualified threshold value of 0.05, it indicates that it is qualified and the polysaccharide extraction purity is high. However, when the relative error is greater than the qualified threshold value, it indicates that the polysaccharide extraction purity is low and there is more waste. The spectrum-content mapping relationship and the content-factor mapping relationship can be optimized by training network parameters, thereby ensuring that the updated camellia pollen polysaccharide purification control model can have a longer timeliness.

[0187] The present invention solves the problem described in the background technology. The present invention first obtains the mapping relationship between the spectrum and the content of each substance in the pollen through machine learning, so as to achieve a simple evaluation of the amount of polysaccharides that need to be extracted and the amount of other impurities that need to be filtered. Then, the present invention measures the optimal extraction conditions for the experimental camellia pollen through a large number of experiments, thereby constructing a camellia pollen polysaccharide purification control model with high efficiency, wherein the camellia pollen polysaccharide purification control model can control the process control on the polysaccharide extraction production line; the present invention monitors the camellia pollen in the feeding area to obtain the feeding camellia pollen spectral sequence, and by comparing the difference between the feeding camellia pollen spectral sequence and the experimental infrared spectrum, the difference in pollen content between the camellia pollen in the feeding area and the experimental camellia pollen is identified, and then according to the feeding speed of the feeding area, the parameters of each process in the camellia pollen polysaccharide purification control model are adjusted, thereby obtaining a camellia pollen polysaccharide extraction process that can be dynamically adjusted according to the pollen condition and the feeding speed. Therefore, the present invention can improve the purity and efficiency of extracting polysaccharides from orchid camellia pollen.

[0188] like Figure 2 1 is a functional module diagram of an orchid tea flower polysaccharide extraction process provided by an embodiment of the present invention.

[0189] The orchid tea flower polysaccharide extraction process 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the orchid tea flower polysaccharide extraction process 100 may include a spectral analysis module 101, a purification process analysis module 102, a camellia pollen identification module 103, and a purification process parameter update module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0190] The spectral analysis module 101 is used to obtain experimental camellia pollen, perform spectral component analysis on the experimental camellia pollen to obtain an experimental infrared spectrum, and perform content determination on the experimental camellia pollen according to a pre-constructed component ratio determination method set to obtain a camellia pollen content sequence, and perform machine learning based on the experimental infrared spectrum and the camellia pollen content sequence to obtain a spectrum-content mapping relationship;

[0191] The purification process analysis module 102 is used to optimize the enzyme extraction and alcohol precipitation method according to the pre-constructed response surface test, perform a single factor comparative experiment on the experimental camellia pollen, obtain an extraction rate experimental data sample set, and obtain the optimal values ​​of each factor in the response surface test to optimize the enzyme extraction and alcohol precipitation method from the extraction rate experimental data sample set to obtain a single factor optimal value set, and construct a camellia pollen polysaccharide purification control model based on the response surface test to optimize the enzyme extraction and alcohol precipitation method according to the camellia pollen content sequence and the single factor optimal value set, and use the camellia pollen polysaccharide purification control model to control the production of the pre-constructed polysaccharide extraction production line;

[0192] The camellia pollen identification module 103 is used to perform spectral analysis on the camellia pollen in the feeding area of ​​the polysaccharide extraction production line according to a preset time frequency to obtain a feed camellia pollen spectral sequence, and extract a feed camellia pollen spectrum from the feed camellia pollen spectral sequence in turn, perform spectral feature extraction operations on the feed camellia pollen spectrum and the experimental infrared spectrum to obtain a feed camellia pollen spectral feature set and an experimental camellia pollen spectral feature set, and identify the distinguishing features of the feed camellia pollen spectral feature set and the experimental camellia pollen spectral feature set to obtain a spectral distinguishing feature set, and identify the camellia pollen content distinguishing features corresponding to the spectral distinguishing feature set according to the spectrum-content mapping relationship to obtain a content distinguishing sequence, and obtain a camellia pollen content distinguishing distribution according to the content distinguishing sequence corresponding to each feed camellia pollen spectrum in the feed camellia pollen spectral sequence;

[0193] The purification process parameter updating module 104 is used to obtain the feed rate of the feeding area, obtain the quality information of the experimental camellia pollen, and perform configuration amount prediction operations on various factors in the camellia pollen polysaccharide purification control model according to the feed rate, the difference distribution of camellia pollen content and the quality information to obtain a prediction control parameter sequence, and update the parameters of the camellia pollen polysaccharide purification control model according to the prediction control parameter sequence to obtain an updated camellia pollen polysaccharide purification control model, and use the updated camellia pollen polysaccharide purification control model to control the polysaccharide extraction production line.

[0194] In detail, the modules in the orchid tea flower polysaccharide extraction process 100 of the embodiment of the present invention are used in the same manner as described above. Figure 1 The same technical means are used as the orchid tea flower polysaccharide extraction method described in, and can produce the same technical effects, so I will not go into details here.

[0195] like Figure 3 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a method for extracting polysaccharides from orchid tea flowers provided by an embodiment of the present invention.

[0196] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method for extracting polysaccharides from orchid tea flowers.

[0197] Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the orchid tea flower polysaccharide extraction method program, etc., but can also be used to temporarily store data that has been output or is to be output.

[0198] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as orchid tea flower polysaccharide extraction method programs, etc.) stored in the memory 11, and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0199] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0200] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0201] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0202] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0203] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0204] The orchid tea flower polysaccharide extraction method program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0205] Acquire experimental camellia pollen, perform spectral component analysis on the experimental camellia pollen to obtain an experimental infrared spectrum, and perform content determination on the experimental camellia pollen according to a pre-constructed component ratio determination method set to obtain a camellia pollen content sequence;

[0206] Machine learning is performed based on the experimental infrared spectrum and camellia pollen content sequence to obtain a spectrum-content mapping relationship;

[0207] According to the pre-constructed response surface test to optimize the enzyme extraction and alcohol precipitation method, a single factor comparative experiment is performed on the experimental camellia pollen to obtain an extraction rate experimental data sample set, and the optimal values ​​of each factor in the response surface test to optimize the enzyme extraction and alcohol precipitation method are obtained from the extraction rate experimental data sample set to obtain a single factor optimal value set;

[0208] According to the camellia pollen content sequence and the single factor optimal value set, a camellia pollen polysaccharide purification control model based on the response surface test to optimize the enzyme extraction and alcohol precipitation method is obtained, and the camellia pollen polysaccharide purification control model is used to control the production of the pre-constructed polysaccharide extraction production line;

[0209] According to a preset time frequency, a spectral analysis is performed on the camellia pollen in the feeding area of ​​the polysaccharide extraction production line to obtain a spectral sequence of the feeding camellia pollen;

[0210] Extracting a feed camellia pollen spectrum from the feed camellia pollen spectrum sequence in sequence, performing spectral feature extraction operations on the feed camellia pollen spectrum and the experimental infrared spectrum to obtain a feed camellia pollen spectral feature set and an experimental camellia pollen spectral feature set, and identifying distinguishing features of the feed camellia pollen spectral feature set and the experimental camellia pollen spectral feature set to obtain a spectral distinguishing feature set;

[0211] According to the spectrum-content mapping relationship, identifying the camellia pollen content distinguishing features corresponding to the spectral distinguishing feature set, obtaining a content distinguishing sequence, and obtaining a camellia pollen content distinguishing distribution according to the content distinguishing sequences corresponding to each feed camellia pollen spectrum in the feed camellia pollen spectrum sequence;

[0212] Obtaining the feed rate of the feeding zone, obtaining the quality information of the experimental camellia pollen, and performing a configuration amount prediction operation on each factor in the camellia pollen polysaccharide purification control model according to the feed rate, the difference distribution of the camellia pollen content and the quality information to obtain a prediction control parameter sequence;

[0213] The parameters of the camellia pollen polysaccharide purification control model are updated according to the prediction control parameter sequence to obtain an updated camellia pollen polysaccharide purification control model, and the updated camellia pollen polysaccharide purification control model is used to control the production of the polysaccharide extraction production line.

[0214] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0215] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0216] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0217] Acquire experimental camellia pollen, perform spectral component analysis on the experimental camellia pollen to obtain an experimental infrared spectrum, and perform content determination on the experimental camellia pollen according to a pre-constructed component ratio determination method set to obtain a camellia pollen content sequence;

[0218] Machine learning is performed based on the experimental infrared spectrum and camellia pollen content sequence to obtain a spectrum-content mapping relationship;

[0219] According to the pre-constructed response surface test to optimize the enzyme extraction and alcohol precipitation method, a single factor comparative experiment is performed on the experimental camellia pollen to obtain an extraction rate experimental data sample set, and the optimal values ​​of each factor in the response surface test to optimize the enzyme extraction and alcohol precipitation method are obtained from the extraction rate experimental data sample set to obtain a single factor optimal value set;

[0220] According to the camellia pollen content sequence and the single factor optimal value set, a camellia pollen polysaccharide purification control model based on the response surface test to optimize the enzyme extraction and alcohol precipitation method is obtained, and the camellia pollen polysaccharide purification control model is used to control the production of the pre-constructed polysaccharide extraction production line;

[0221] According to a preset time frequency, a spectral analysis is performed on the camellia pollen in the feeding area of ​​the polysaccharide extraction production line to obtain a spectral sequence of the feeding camellia pollen;

[0222] Extracting a feed camellia pollen spectrum from the feed camellia pollen spectrum sequence in sequence, performing spectral feature extraction operations on the feed camellia pollen spectrum and the experimental infrared spectrum to obtain a feed camellia pollen spectral feature set and an experimental camellia pollen spectral feature set, and identifying distinguishing features of the feed camellia pollen spectral feature set and the experimental camellia pollen spectral feature set to obtain a spectral distinguishing feature set;

[0223] According to the spectrum-content mapping relationship, identifying the camellia pollen content distinguishing features corresponding to the spectral distinguishing feature set, obtaining a content distinguishing sequence, and obtaining a camellia pollen content distinguishing distribution according to the content distinguishing sequences corresponding to each feed camellia pollen spectrum in the feed camellia pollen spectrum sequence;

[0224] Obtaining the feed rate of the feeding zone, obtaining the quality information of the experimental camellia pollen, and performing a configuration amount prediction operation on each factor in the camellia pollen polysaccharide purification control model according to the feed rate, the difference distribution of the camellia pollen content and the quality information to obtain a prediction control parameter sequence;

[0225] The parameters of the camellia pollen polysaccharide purification control model are updated according to the prediction control parameter sequence to obtain an updated camellia pollen polysaccharide purification control model, and the updated camellia pollen polysaccharide purification control model is used to control the production of the polysaccharide extraction production line.

[0226] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, processes and methods can be implemented in other ways. For example, the process embodiments described above are only illustrative, and actual implementation may have other division methods.

[0227] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0228] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0229] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0230] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for extracting polysaccharides from orchid tea flowers, characterized in that: The method comprises: Acquire experimental camellia pollen, perform spectral component analysis on the experimental camellia pollen to obtain an experimental infrared spectrum, and perform content determination on the experimental camellia pollen according to a pre-constructed component ratio determination method set to obtain a camellia pollen content sequence; Machine learning is performed based on the experimental infrared spectrum and camellia pollen content sequence to obtain a spectrum-content mapping relationship; According to the pre-constructed response surface test to optimize the enzyme extraction and alcohol precipitation method, a single factor comparative experiment is performed on the experimental camellia pollen to obtain an extraction rate experimental data sample set, and the optimal values ​​of each factor in the response surface test to optimize the enzyme extraction and alcohol precipitation method are obtained from the extraction rate experimental data sample set to obtain a single factor optimal value set; According to the camellia pollen content sequence and the single factor optimal value set, a camellia pollen polysaccharide purification control model based on the response surface test to optimize the enzyme extraction and alcohol precipitation method is obtained, and the camellia pollen polysaccharide purification control model is used to control the production of the pre-constructed polysaccharide extraction production line; According to a preset time frequency, a spectral analysis is performed on the camellia pollen in the feeding area of ​​the polysaccharide extraction production line to obtain a spectral sequence of the feeding camellia pollen; Extracting a feed camellia pollen spectrum from the feed camellia pollen spectrum sequence in sequence, performing spectral feature extraction operations on the feed camellia pollen spectrum and the experimental infrared spectrum to obtain a feed camellia pollen spectral feature set and an experimental camellia pollen spectral feature set, and identifying distinguishing features of the feed camellia pollen spectral feature set and the experimental camellia pollen spectral feature set to obtain a spectral distinguishing feature set; According to the spectrum-content mapping relationship, identifying the camellia pollen content distinguishing features corresponding to the spectral distinguishing feature set, obtaining a content distinguishing sequence, and obtaining a camellia pollen content distinguishing distribution according to the content distinguishing sequences corresponding to each feed camellia pollen spectrum in the feed camellia pollen spectrum sequence; Obtaining the feed rate of the feeding zone, obtaining the quality information of the experimental camellia pollen, and performing a configuration amount prediction operation on each factor in the camellia pollen polysaccharide purification control model according to the feed rate, the difference distribution of the camellia pollen content and the quality information to obtain a prediction control parameter sequence; The parameters of the camellia pollen polysaccharide purification control model are updated according to the prediction control parameter sequence to obtain an updated camellia pollen polysaccharide purification control model, and the updated camellia pollen polysaccharide purification control model is used to control the production of the polysaccharide extraction production line.

2. The method for extracting polysaccharides from orchid tea flowers according to claim 1, characterized in that: The method of measuring the content of the experimental camellia pollen according to the pre-constructed component ratio determination method set to obtain a camellia pollen content sequence includes: According to a pre-constructed component ratio determination method set, the experimental camellia pollen is subjected to a drying operation based on a preset temperature value until the weight of the experimental camellia pollen remains stable, and the pollen water evaporation amount is obtained; Using a pre-constructed Soxhlet extraction method, extracting lipids from the experimental camellia pollen to obtain lipid content; Using a pre-constructed Kjeldahl nitrogen determination method, the protein in the experimental camellia pollen is measured to obtain the protein content; Performing an ashing operation on the experimental camellia pollen based on a preset high temperature value to obtain an inorganic matter content; Using a pre-constructed phenol-sulfuric acid method, the experimental camellia pollen is colorized to obtain a color development result, and the absorbance of the color development result is measured, and a pre-constructed standard curve is used to compare the absorbance to obtain the polysaccharide content; The water evaporation amount, lipid content, protein content, inorganic matter content and polysaccharide content of the pollen are summarized to obtain a camellia pollen content sequence.

3. The method for extracting polysaccharides from orchid tea flowers according to claim 2, characterized in that: The method of performing machine learning based on the experimental infrared spectrum and camellia pollen content sequence to obtain a spectrum-content mapping relationship includes: Obtaining an initialization spectrum-content mapping relationship, and obtaining the mass information of the experimental camellia pollen, wherein the mass information of each portion of the experimental camellia pollen is the same; Performing a curve feature extraction operation on the experimental infrared spectrum to obtain a spectrum image feature set; Using the initialized spectrum-content mapping relationship, according to the quality information, performing a content prediction operation based on preset substance types on the spectral image feature set to obtain a predicted content sequence; The loss value between the predicted content sequence and the camellia pollen content sequence is calculated and minimized to obtain the relationship mapping parameter when the loss value is minimum, and the initialized spectrum-content mapping relationship is updated according to the relationship mapping parameter to obtain the spectrum-content mapping relationship.

4. The method for extracting polysaccharides from orchid tea flowers according to claim 3, characterized in that: The enzyme extraction and alcohol precipitation method is optimized according to the pre-constructed response surface test, and a single factor comparative experiment is performed on the experimental camellia pollen to obtain an extraction rate experimental data sample set, including: Obtain the factors of each process in the enzyme extraction and alcohol precipitation method by pre-constructed response surface experiment optimization to obtain the factor set; Acquire the experimental interval of each factor in the factor set, and perform an equal division operation on the experimental interval of each factor based on a preset division value to obtain an experimental value sequence of the experimental interval of each factor; According to the experimental numerical sequence of the experimental interval of each factor, a control experiment based on the numerical variation of a single factor is performed on the response surface test to optimize the enzyme extraction and alcohol precipitation method to obtain a control experiment set, and the polysaccharide extraction rate corresponding to each control experiment in the control experiment set is obtained to obtain a polysaccharide extraction rate set; According to the control experiment set and the polysaccharide extraction rate set, an extraction rate experiment data sample set is obtained.

5. The method for extracting polysaccharides from orchid tea flowers according to claim 4, characterized in that: The step of performing spectral analysis on the camellia pollen in the feeding area of ​​the polysaccharide extraction production line to obtain a spectral sequence of the feeding camellia pollen comprises: Dividing the preset detection area in the feeding area of ​​the polysaccharide extraction production line into regions to obtain a sub-region set and a positional relationship between each sub-region in the sub-region set; Using the probe device pre-built in the feeding area, spectral sampling is performed on each sub-area in the sub-area set to obtain a feed camellia pollen spectrum set; According to the positional relationship, each feed camellia pollen spectrum sequence in the feed camellia pollen spectrum set is arranged in position to obtain a feed camellia pollen spectrum sequence.

6. The method for extracting polysaccharides from orchid tea flowers according to claim 5, characterized in that: The method of obtaining a camellia pollen polysaccharide purification control model based on the response surface test optimization enzyme extraction and alcohol precipitation method according to the camellia pollen content sequence and the single factor optimal value set includes: Normalizing the camellia pollen content sequence to obtain a content ratio sequence, and extracting numerical features from the camellia pollen content sequence and the content ratio sequence to obtain a content feature sequence; Constructing a content-factor quantity mapping relationship between the content feature sequence and the single factor optimal value set; According to the response surface experiment, the control order of various factors in the enzyme extraction and alcohol precipitation method is optimized to construct a polysaccharide purification process model; Obtaining a default feed pollen mass, and configuring the values ​​of each factor in the polysaccharide purification process model according to the default feed pollen mass and content-factor quantity mapping relationship to obtain an updated polysaccharide purification process model; The pre-constructed feed quality recognition network is used to numerically update the default feed pollen quality in the updated polysaccharide purification process model to obtain a camellia pollen polysaccharide purification control model.

7. The method for extracting polysaccharides from orchid tea flowers according to claim 6, characterized in that: According to the feed rate, the distribution of camellia pollen content and the quality information, the configuration amount prediction operation is performed on each factor in the camellia pollen polysaccharide purification control model to obtain a prediction control parameter sequence, including: extracting a content distinction sequence from the camellia pollen content distinction distribution in sequence to obtain a target content distinction sequence; Calculating the ratio of each content substance in the camellia pollen according to the target content difference sequence and the camellia pollen content sequence to obtain a content change rate sequence; According to the content change rate sequence, weighted calculation is performed on the proportion of each factor in the camellia pollen polysaccharide purification control model to obtain an optimized control parameter sequence; Obtaining the optimized control parameter sequence of each content difference sequence in the camellia pollen content difference distribution to obtain an optimized control parameter sequence set, and performing mean calculation on the optimized control parameter sequence set to obtain a mean control parameter sequence; The ratio of the feed rate to the mass information is calculated to obtain the feed share, and the mean control parameter sequence is multiplied according to the feed share to obtain the prediction control parameter sequence.

8. The method for extracting polysaccharides from orchid tea flowers according to claim 7, characterized in that: After the updated camellia pollen polysaccharide purification control model is used to control the polysaccharide extraction production line, the method further includes: Obtain the actual amount of polysaccharide produced; According to the spectrum-content mapping relationship, the polysaccharide content of the feed share and the feed camellia pollen spectrum sequence is predicted to obtain the predicted production polysaccharide amount; Calculating the error between the predicted polysaccharide production amount and the actual polysaccharide production amount to obtain a relative error value, and determining whether the relative error value is greater than a preset qualified threshold value; When the relative error value is greater than the qualified threshold, the spectrum-content mapping relationship and the content-factor quantity mapping relationship are updated and trained to obtain an updated spectrum-content mapping relationship and an updated content-factor quantity mapping relationship.

9. A process for extracting polysaccharides from orchid tea flowers, characterized in that: The process comprises: A spectral analysis module is used to obtain experimental camellia pollen, perform spectral component analysis on the experimental camellia pollen to obtain an experimental infrared spectrum, perform content determination on the experimental camellia pollen according to a pre-constructed component ratio determination method set to obtain a camellia pollen content sequence, and perform machine learning based on the experimental infrared spectrum and the camellia pollen content sequence to obtain a spectrum-content mapping relationship; A purification process analysis module, for optimizing the enzyme extraction and alcohol precipitation method according to a pre-constructed response surface test, performing a single factor comparative experiment on the experimental camellia pollen, obtaining an extraction rate experimental data sample set, and obtaining the optimal values ​​of each factor in the response surface test to optimize the enzyme extraction and alcohol precipitation method from the extraction rate experimental data sample set, obtaining a single factor optimal value set, and constructing a camellia pollen polysaccharide purification control model based on the response surface test to optimize the enzyme extraction and alcohol precipitation method according to the camellia pollen content sequence and the single factor optimal value set, and using the camellia pollen polysaccharide purification control model to perform production control on the pre-constructed polysaccharide extraction production line; A camellia pollen identification module, for performing spectral analysis on the camellia pollen in the feeding area of ​​the polysaccharide extraction production line according to a preset time frequency to obtain a feed camellia pollen spectral sequence, and extracting a feed camellia pollen spectrum from the feed camellia pollen spectral sequence in turn, performing spectral feature extraction operations on the feed camellia pollen spectrum and the experimental infrared spectrum to obtain a feed camellia pollen spectral feature set and an experimental camellia pollen spectral feature set, and identifying distinguishing features of the feed camellia pollen spectral feature set and the experimental camellia pollen spectral feature set to obtain a spectral distinguishing feature set, and according to the spectrum-content mapping relationship, identifying the camellia pollen content distinguishing features corresponding to the spectral distinguishing feature set to obtain a content distinguishing sequence, and according to the content distinguishing sequence corresponding to each feed camellia pollen spectrum in the feed camellia pollen spectral sequence, obtaining a camellia pollen content distinguishing distribution; A purification process parameter updating module is used to obtain the feed rate of the feeding area, obtain the quality information of the experimental camellia pollen, and perform configuration quantity prediction operations on various factors in the camellia pollen polysaccharide purification control model according to the feed rate, the difference distribution of camellia pollen content and the quality information to obtain a prediction control parameter sequence, and update the parameters of the camellia pollen polysaccharide purification control model according to the prediction control parameter sequence to obtain an updated camellia pollen polysaccharide purification control model, and use the updated camellia pollen polysaccharide purification control model to control the production of the polysaccharide extraction production line.

10. The process for extracting polysaccharides from orchid tea flowers according to claim 9, characterized in that: According to the camellia pollen content sequence and the single factor optimal value set, a camellia pollen polysaccharide purification control model based on the response surface test optimization enzyme extraction and alcohol precipitation method is constructed, comprising: Normalizing the camellia pollen content sequence to obtain a content ratio sequence, and extracting numerical features from the camellia pollen content sequence and the content ratio sequence to obtain a content feature sequence; Constructing a content-factor quantity mapping relationship between the content feature sequence and the single factor optimal value set; According to the response surface experiment, the control order of various factors in the enzyme extraction and alcohol precipitation method is optimized to construct a polysaccharide purification process model; Obtaining a default feed pollen mass, and configuring the values ​​of each factor in the polysaccharide purification process model according to the default feed pollen mass and content-factor quantity mapping relationship to obtain an updated polysaccharide purification process model; The pre-constructed feed quality recognition network is used to numerically update the default feed pollen quality in the updated polysaccharide purification process model to obtain a camellia pollen polysaccharide purification control model.

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