An optimization method for extracting active ingredients from dandelion based on I-WO-SVR model

Abstract

The present invention relates to a method for optimizing the extraction of active ingredients from dandelions based on an I-WO-SVR model, comprising: obtaining factors to be predicted, including ethanol concentration, ultrasonic time, ultrasonic temperature, and ultrasonic power; inputting the factors to be predicted into the I-WO-SVR model to obtain the chlorogenic acid extraction rate of dandelion stems; the I-WO-SVR model is obtained by training a support vector regression (SVR) prediction model using a training set and optimizing the penalty parameters and kernel parameters of the SVR prediction model using an improved walrus optimizer (I-WO); wherein the training set includes the original factors and the chlorogenic acid extraction rates corresponding to the original factors. This method combines modern artificial intelligence technology with traditional Chinese medicine to design an optimization method for extracting active ingredients from Chinese medicinal materials. It has broad application prospects and can provide new ideas for Chinese medicine extraction and its intelligent industrial production.

Description

Technical Field

[0001] The present invention relates to the technical field of modern extraction of traditional Chinese medicine, and in particular to a method for optimizing the extraction process of effective components of dandelion based on an I-WO-SVR model. Background Art

[0002] Dandelion, commonly known as dandelion and yellow flowered dandelion, is a perennial herb in the Asteraceae family. Both the dried and whole herb can be used medicinally, earning it the reputation of "Queen of Herbs" and "Natural Penicillin." Chlorogenic acid, the primary component of dandelion leaves, is internationally recognized as "plant gold" for its broad lipid-lowering, antibacterial, antioxidant, anti-tumor, and hepatoprotective and choleretic properties. Therefore, the extraction of chlorogenic acid from dandelion has become a hot topic in functional food research and development.

[0003] Traditional extraction process optimization methods typically use partial experiments instead of comprehensive ones. Consequently, they cannot analyze experimental effects and their interactions individually, as would be required for full experiments. Machine learning methods, which can effectively solve nonlinear problems and perform global optimization, can reduce experimental costs and time. Consequently, they can be applied to extraction process optimization with promising results.

[0004] Support vector regression (SVR) is a classic algorithm in machine learning that provides technical support for optimizing the extraction of active ingredients from traditional Chinese medicines. By introducing a kernel function, SVR maps data into a high-dimensional space, where regression analysis can be performed, effectively handling nonlinear problems. However, SVR's performance is highly dependent on the selection of its hyperparameters, and the walrus optimization algorithm is a promising candidate for optimizing SVR model parameters.

[0005] Therefore, there is an urgent need for an optimization method for the extraction process of dandelion active ingredients based on the I-WO-SVR model. By constructing a prediction model that integrates the improved walrus optimizer and SVR, the optimization method for the extraction process of dandelion active ingredients can be determined, which can improve the efficiency of Chinese medicine ingredient extraction. Summary of the Invention

[0006] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a dandelion active ingredient extraction process optimization method based on the I-WO-SVR model. This method can combine artificial intelligence algorithms with traditional Chinese medicine extraction processes, and by constructing a prediction model for process parameters, optimize the model based on the model to obtain the optimal parameters of the extraction process.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A method for optimizing the extraction process of effective components from dandelion based on an I-WO-SVR model, comprising:

[0009] Obtaining factors to be predicted, wherein the factors to be predicted include: ethanol concentration, ultrasound time, ultrasound temperature, and ultrasound power;

[0010] The factors to be predicted are input into the I-WO-SVR model to obtain the chlorogenic acid extraction rate of dandelion stems; the I-WO-SVR model uses a training set to train an SVR prediction model, and uses the improved walrus optimizer I-WO to optimize the penalty parameters and kernel parameters of the SVR prediction model; wherein, the training set includes: extraction influencing factors and corresponding chlorogenic acid extraction rates.

[0011] Optionally, optimizing the penalty parameters and kernel parameters of the SVR prediction model using the improved walrus optimizer I-WO includes:

[0012] Determine the SVR objective function, select the radial basis function as the kernel function of the SVR prediction model, and determine the penalty factor C and kernel function parameter g of the SVR prediction model through the improved walrus optimizer I-WO:

[0013] The SVR objective function expression is:

[0014]

[0015]

[0016] Among them, ω and b are SVR model parameters, x h 、y h Represent the input variables and output variables of the model respectively, n is the number of support vectors, and ε represents the tolerance deviation;

[0017] The kernel function The expression is:

[0018]

[0019] in, is the support vector, x is the original sample set, and g is the kernel function parameter.

[0020] Optionally, optimizing the SVR prediction model using the improved walrus optimizer I-WO includes:

[0021] Step 1: Initialize the walrus population using infinite folding iterative chaotic mapping;

[0022] Step 2: When the danger signal of the walrus environment is too high, the walrus population is migrated, and adaptive weights are introduced for the first time to improve the walrus population migration process;

[0023] Step 3: When the walrus is attacked by a natural predator while foraging underwater, it sends out the danger signal and flees the current activity area. The adaptive weight is introduced for the second time to improve the walrus escape process.

[0024] Step 4: Repeat steps 2 to 3 until the maximum number of iterations is reached, and output the optimal penalty parameters and kernel parameters.

[0025] Alternatively, the formula for initializing the walrus population using infinite folding iterative chaotic mapping is:

[0026]

[0027] Among them, δ is the control parameter, X i and X i+1 They represent the values of the i-th walrus and the (i+1)-th walrus in the sequence generated by the infinite folding iterative chaotic map.

[0028] Alternatively, the formula for improving the walrus population migration process by introducing adaptive weights for the first time is:

[0029]

[0030] in, represents the new position of the i-th walrus in the j-th dimension at the d+1-th iteration during the migration process, It represents the current position of the i-th walrus on the j-th dimension at the d-th iteration during the migration process. Two patrol walruses are set during the migration process, and their positions are and α1 represents the adaptive weight introduced for the first time.

[0031] Optionally, the formula for obtaining the adaptive weight is:

[0032]

[0033] Where D is the maximum number of iterations, d is the current number of iterations, and e is a natural constant.

[0034] Optionally, the formula for improving the walrus escape process by introducing adaptive weights for the second time is:

[0035]

[0036] in, represents the distance between the current walrus and the best walrus, Λ is the risk factor, and α2 is the adaptive weight introduced for the second time.

[0037] Optionally, obtaining the training set includes:

[0038] Drawing a standard curve, using the standard curve, calculating the concentration of chlorogenic acid in dandelion flower stems, and then calculating the chlorogenic acid extraction rate;

[0039] Based on the single factor experiment, a response surface experiment with 4 factors and 3 levels was conducted with chlorogenic acid extraction rate as the evaluation index to obtain experimental sample data.

[0040] According to the experimental results, a sample set is constructed, the sample set is screened and data enhanced, the expanded sample set is standardized, and the sample data is set as a training set.

[0041] The beneficial effects of the present invention are:

[0042] The present invention can determine the optimal process parameters for extracting the effective ingredients of dandelion, thereby improving production efficiency while ensuring product quality, and providing a new method for traditional Chinese medicine extraction and its intelligent industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 This is a schematic diagram of a method for optimizing the extraction process of dandelion active ingredients based on the I-WO-SVR model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] like Figure 1As shown, this embodiment discloses a method for optimizing the extraction process of effective components from dandelion based on the I-WO-SVR model, comprising: obtaining factors to be predicted, which include: ethanol concentration, ultrasonic time, ultrasonic temperature, and ultrasonic power; inputting the factors to be predicted into the I-WO-SVR model to obtain the chlorogenic acid extraction rate of dandelion; the I-WO-SVR model is obtained by training an SVR prediction model using a training set, and optimizing the penalty parameters and kernel parameters of the SVR prediction model using an improved walrus optimizer I-WO; wherein the training set includes: original factors and the chlorogenic acid extraction rates corresponding to the original factors.

[0048] Furthermore, obtaining a training set includes:

[0049] Draw a standard curve and use it to calculate the concentration of chlorogenic acid in dandelion flower stems, and then calculate the chlorogenic acid extraction rate;

[0050] Based on the single factor experiment, a response surface experiment with 4 factors and 3 levels was conducted with chlorogenic acid extraction rate as the evaluation index to obtain experimental sample data.

[0051] According to the experimental results, a sample set is constructed, the sample set is screened and data enhanced, the expanded sample set is standardized, and the sample data is divided into a training set.

[0052] Specifically:

[0053] Step (1) Construct a data sample set:

[0054] Step (1.1) Drawing of chlorogenic acid standard curve:

[0055] Accurately weigh 5 mg of chlorogenic acid standard into a small beaker, add an appropriate amount of anhydrous ethanol, and dissolve completely with ultrasonication. Transfer to a 100 mL volumetric flask and dilute to the mark with anhydrous ethanol to prepare the chlorogenic acid standard solution. Transfer 1, 3, 5, 7, and 9 mL of the chlorogenic acid standard solution to a 10 mL volumetric flask, dilute to the mark with anhydrous ethanol, and let stand for 15 minutes. Measure the absorbance at 329 nm using anhydrous ethanol as a blank control. Perform three replicate measurements for each group, and calculate the average value. Draw a standard curve with the absorbance of the chlorogenic acid standard solution as the ordinate and the concentration of the chlorogenic acid standard solution as the abscissa.

[0056] Step (1.2) Extraction of chlorogenic acid from dandelion flower stems:

[0057] Collect fresh dandelion flower stems, wash them, put them in a 60℃ oven to dry to constant weight, crush them with a grinder, pass them through a 100-mesh sieve to obtain dandelion flower stem powder, and put them into a brown ground-mouth bottle for later use. Accurately weigh 1.00g of dandelion flower stem powder in a conical flask, add a 30% ethanol aqueous solution at a solid-liquid ratio of 1:20 (g / mL), shake well, and place it in a 300W ultrasonic cleaner for ultrasonic-assisted extraction at 50℃ for 1h, cool to room temperature, centrifuge at 4000r / min for 10min, absorb the supernatant volume, dilute, and measure the absorbance of the solution at 329nm. Calculate the chlorogenic acid concentration in the extract according to the standard curve, and calculate the chlorogenic acid extraction rate using the following formula:

[0058]

[0059] Where: η is the chlorogenic acid extraction rate; ρ is the chlorogenic acid concentration (μg / mL) calculated from the standard curve; V is the volume of the supernatant (mL); F is the dilution factor of the solution; m is the mass of dandelion dry powder (g).

[0060] Step (1.3) Single factor experiment:

[0061] A single-factor experiment was conducted using ethanol concentration, ultrasonic temperature, ultrasonic time, and ultrasonic power as the experimental factors, with chlorogenic acid extraction rate as the experimental indicator. Extractions were repeated twice using ethanol concentrations of 20%, 30%, 40%, 50%, and 60%; ultrasonic times of 30, 45, 60, 75, and 90 minutes; ultrasonic temperatures of 30°C, 40°C, 50°C, 60°C, and 70°C; and ultrasonic powers of 240W, 300W, 360W, 420W, and 480W. Chlorogenic acid content in the extracts was determined. Except for the modified single factors, the appropriate conditions for each factor in the pre-experimental experiment were used: ethanol concentration of 30%, ultrasonic time of 60 minutes, ultrasonic temperature of 50°C, and ultrasonic power of 300W.

[0062] Step (1.4) Response surface design of 4 factors and 3 levels:

[0063] On the basis of the single-factor experiment, a 4-factor 3-level response surface experimental design was conducted with chlorogenic acid extraction rate as the evaluation index to obtain the experimental sample data, as shown in Table 1. Table 1 shows the experimental factors, levels and coding.

[0064] Table 1

[0065]

[0066] Step (1.5) Data preprocessing:

[0067] Due to the attribute differences between various input parameters in the sample data, the prediction accuracy of the extraction rate may be inaccurate. Standardization processing can handle the differences between various input parameters and ensure the accuracy of SVR model prediction.

[0068] Step (1.5.1) Constructing a sample set: Screen the data obtained from the experiment, delete the sample data with outlier characteristics, and obtain the experimental sample data. Use data enhancement technology to expand the data sample set.

[0069] Step (1.5.2) Standardization: Standardize the expanded sample data set using the following formula to map the sample data to the range [0, 1]:

[0070]

[0071] In the above formula, x o is the sample set after standardization, x is the original sample set, x min is the minimum sample in the sample set, x max is the largest sample in the sample set.

[0072] Step (1.5.3) uses the K-fold cross-validation method to divide the sample data into a training set and a validation set:

[0073] The original sample set N is divided into K equal parts, one of which is used as the model validation set, and the remaining K-1 parts are used as the model training set. This process of dividing the validation set needs to be repeated K times. The sample data of each subset will be selected in sequence. After the validation set data is selected, the remaining data is used as the training set. In each round, the training set data is imported into the SVR model constructed in step (2) to train the model, and the results are applied to the validation set.

[0074] Furthermore, the improved walrus optimizer is used to optimize the SVR prediction model, including:

[0075] Step 1: Initialize the walrus population using infinite folding iterative chaotic mapping;

[0076] Step 2: When the danger signal of the walrus environment is too high, the walrus population is migrated and adaptive weights are introduced to improve the walrus population migration process;

[0077] Step 3: When the walrus is attacked by a natural predator while foraging underwater, it sends out a danger signal and flees the current activity area. Adaptive weights are introduced again to improve the walrus escape process.

[0078] Step 4: Repeat steps 2 to 3 until the maximum number of iterations is reached, and output the optimal penalty parameters and kernel parameters.

[0079] Specifically:

[0080] Step (2) Construct the I-WO-SVR model:

[0081] The SVR model is a branch of the support vector machine (SVM) and can be used to solve nonlinear regression problems. The SVR model has high prediction accuracy, can effectively avoid overfitting problems, improve the generalization ability of the model, and can also obtain good statistical laws for small sample data. Compared with the traditional regression model, the SVR model has a certain fault tolerance. Even if there is a certain deviation in the predicted value, it can be considered that the predicted value is reasonable. The present invention solves the nonlinear relationship between different ethanol concentrations, ultrasonic time, ultrasonic temperature, ultrasonic power and the extraction rate of dandelion chlorogenic acid by constructing an SVR model.

[0082] Step (2.1) builds the SVR prediction model:

[0083] Step (2.1.1) establishes the regression equation and objective function: if the given training sample A={(x1,y1),(x2,y2),...,(x h ,y h ),...,(x N ,y N )}, x and y represent the input variables (ethanol concentration, ultrasonic time, ultrasonic temperature, and ultrasonic power) and output variable (chlorogenic acid extraction rate of dandelion) of the model, respectively. For the training samples, the regression equation F(x) can be established as follows:

[0084]

[0085] Among them, ω and b are SVR model parameters, ω T is the transpose operation of ω, is the eigenvector after mapping x.

[0086] The SVR objective function can be expressed as:

[0087]

[0088]

[0089] Where C is the penalty factor, which indicates the tolerance to error. n is the number of support vectors, and ε indicates the tolerance deviation.

[0090] Step (2.1.2) Determine the kernel function of the SVR model: When establishing a support vector machine model, the most important thing is to select the kernel function of the model.

[0091] The kernel function has an important influence on the performance of the support vector machine model. It can map the input samples into a high-dimensional space, thus making the problem easier to handle. Common kernel functions include linear kernel function, polynomial kernel function, radial basis kernel function (RBF) and sigmoid kernel function, and RBF kernel function is the first choice because of its simplicity and powerful nonlinear modeling ability. Therefore, the present invention selects radial basis function (RBF) as the kernel function of the SVR prediction model, and its specific expression is as follows:

[0092]

[0093] in, is the support vector, and g is the parameter of the RBF kernel function.

[0094] In step (2.2), the infinite folding iterative chaotic map is used to initialize the population, and the adaptive distribution is used to improve the migration and escape processes of the walrus population. The improvements to WO include:

[0095] WO is a swarm intelligence optimization method designed to simulate walruses' migration, reproduction, habitat, foraging, collection, and escape behaviors after receiving key signals (danger and safety signals). However, WO still has room for improvement in terms of convergence accuracy and solution speed. To this end, the present invention improves WO through the following steps to solve the optimal C and g parameters in the SVR model:

[0096] Step (2.2.1) Initialize the population using an infinite folding iterative chaotic map: Using chaotic variables for optimization search can evenly distribute the initial population in the search space, which is more advantageous than using random variables for random search. The ergodic and random nature of chaos can prevent the search process from falling into local optimality. Through chaotic mapping, the diversity of the initial state of the walrus population is increased, effectively preventing falling into local optimality and premature convergence, and improving global convergence and accuracy. The expression of the infinite folding iterative chaotic map is as follows:

[0097]

[0098] Among them, δ is the control parameter, which takes values between (0,1). i and X i+1 They represent the values of the i-th walrus and the (i+1)-th walrus in the sequence generated by the infinite folding iterative chaotic map.

[0099] Step (2.2.2) Adaptive migration: When the danger signal of the walrus environment is too high, the walrus population will migrate to an area more suitable for survival. In this stage, adaptive weights are introduced to improve the walrus population migration process. The new walrus position is updated as follows:

[0100]

[0101] in, represents the new position of the i-th walrus in the j-th dimension at the d+1-th iteration during the migration process, Indicates the current position of the i-th walrus in the j-th dimension at the d-th iteration during the migration process. Two patrol walruses are set during the migration process, and their positions are and α1 represents the adaptive weight introduced for the first time, which is determined by the following formula:

[0102]

[0103] Where D is the maximum number of iterations and d is the current number of iterations.

[0104] Step (2.2.3) Adaptive Escape: Walruses are also attacked by natural predators while foraging underwater. They will flee their current activity area based on danger signals from their companions. This behavior occurs in the late iteration of WO. A certain degree of disturbance to the population helps the walruses conduct global exploration. The present invention chooses to use adaptive weights again to adjust the escape process, effectively coordinating the relationship between global search and local search. The replaced escape process is:

[0105]

[0106] in represents the distance between the current walrus and the best walrus, Λ is the risk factor, and α2 is the adaptive weight introduced for the second time.

[0107] Step (2.3) Establish the I-WO-SVR model: Use the I-WO algorithm to optimize the penalty parameters and kernel parameters of SVR.

[0108] The penalty factor C and kernel function parameter g in the SVR model are two key indicators that affect the model prediction results. The larger C is, the less tolerant the model is to errors, and it is easy to produce overfitting; the smaller C is, the worse the generalization ability is, which will cause underfitting. The size of the kernel function parameter g will affect the number of support vectors. The larger g is, the fewer support vectors there are. Conversely, the more support vectors there are, and C and g that are too large or too small will affect the speed and accuracy of the estimation. At the same time, different combinations of parameters will also affect the performance of the model. Therefore, determining the optimal parameters C and g is particularly important for the prediction effect of the model. In order to find the optimal parameters of the model more conveniently and quickly, the present invention uses an improved walrus optimizer to find the optimal C and g parameters of the model, further establishes an I-WO-SVR model, and improves the accuracy of model prediction.

[0109] Step (3) determines the optimal extraction scheme for the active ingredients of dandelion: input the training set into the I-WO-SVR model for training, and apply the trained model to the validation set. 2 The accuracy of the SVR model was measured by calculating the difference between the predicted value (chlorogenic acid extraction rate predicted by the I-WO-SVR model) and the true value (chlorogenic acid extraction rate in the training set) using three indicators: root mean square error (RMSE) and mean absolute error (MAE). The calculation formulas for the three indicators are as follows:

[0110]

[0111]

[0112]

[0113] Where y is the predicted value, E[y] is the mean of the predicted values; z is the true value, and E[z] is the mean of the true values.

[0114] In general, R 2 The larger the value of is, the better, and the smaller the RMSE and MAE values are, the better. 2 The value infinitely approaches 1 and the error infinitely approaches 0, which means that the closer the model prediction value is to the true value, the better the model fit is.

[0115] When R 2 When the value is close to 1 and both the RMSE and MAE are small, the I-WO-SVR model has high prediction accuracy and can effectively predict the extraction rate of chlorogenic acid from dandelion stems. After the I-WO-SVR model is trained, the corresponding extraction rate can be output by adjusting the input factors to be predicted (ethanol concentration, ultrasonic time, ultrasonic temperature, and ultrasonic power). When the extraction rate is the highest, the obtained process parameter combination is the optimal extraction solution.

[0116] This embodiment can couple the relationship between extraction process parameters and extraction rate through the I-WO-SVR model, thereby determining the optimal extraction process parameters, for example:

[0117] The extraction rate was the highest when the ethanol concentration was 30%, the ultrasonic time was 60 min, the ultrasonic temperature was 50℃ and the ultrasonic power was 300w, indicating that the optimal extraction process was 30% ethanol concentration, 60 min ultrasonic time, 50℃ ultrasonic temperature and 300w ultrasonic power.

[0118] The present invention can improve production efficiency while ensuring product quality, and provides a new method for traditional Chinese medicine extraction and its intelligent industrial production.

[0119] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for optimizing the extraction process of effective components from dandelion based on the I-WO-SVR model, characterized in that: include: Obtaining factors to be predicted, wherein the factors to be predicted include: ethanol concentration, ultrasound time, ultrasound temperature, and ultrasound power; The factors to be predicted are input into the I-WO-SVR model to obtain the chlorogenic acid extraction rate of dandelion stems; the I-WO-SVR model uses a training set to train an SVR prediction model, and uses an improved walrus optimizer I-WO to optimize the penalty parameters and kernel parameters of the SVR prediction model; wherein the training set includes: extraction influencing factors and corresponding chlorogenic acid extraction rates; The SVR prediction model is optimized using the improved walrus optimizer I-WO, including: Step 1: Initialize the walrus population using infinite folding iterative chaotic mapping; Step 2: When the danger signal of the walrus environment is too high, the walrus population is migrated, and adaptive weights are introduced for the first time to improve the walrus population migration process; Step 3: When the walrus is attacked by a natural predator while foraging underwater, it sends out the danger signal and flees the current activity area. The adaptive weight is introduced for the second time to improve the walrus escape process. Step 4: Repeat steps 2 to 3 until the maximum number of iterations is reached, and output the optimal penalty parameters and kernel parameters.

2. The method for optimizing the extraction process of effective components from dandelion based on the I-WO-SVR model according to claim 1, characterized in that: The penalty parameters and kernel parameters of the SVR prediction model optimized by the improved walrus optimizer I-WO include: Determine the SVR objective function, select the radial basis function as the kernel function of the SVR prediction model, and determine the penalty factor C and kernel function parameter g of the SVR prediction model through the improved walrus optimizer I-WO: The SVR objective function expression is: Among them, ω and b are SVR model parameters, x h 、y h They represent the input variables and output variables of the model respectively, n is the number of support vectors, and ε represents the tolerance deviation; The kernel function The expression is: in, is the support vector, x is the original sample set, and g is the kernel function parameter.

3. The method for optimizing the extraction process of effective components from dandelion based on the I-WO-SVR model according to claim 1, characterized in that: The formula for initializing the walrus population using infinite folding iterative chaotic mapping is: Among them, δ is the control parameter, X i and X i+1 They represent the values of the i-th walrus and the (i+1)-th walrus in the sequence generated by the infinite folding iterative chaotic map.

4. The method for optimizing the extraction process of effective components from dandelion based on the I-WO-SVR model according to claim 1, characterized in that: The formula for improving the migration process of walrus populations by introducing adaptive weights for the first time is: in, represents the new position of the i-th walrus in the j-th dimension at the d+1-th iteration during the migration process, It represents the current position of the i-th walrus on the j-th dimension at the d-th iteration during the migration process. Two patrol walruses are set during the migration process, and their positions are and α1 represents the adaptive weight introduced for the first time.

5. The method for optimizing the extraction process of effective components from dandelion based on the I-WO-SVR model according to claim 4, characterized in that: The formula for obtaining the adaptive weight is: Where D is the maximum number of iterations, d is the current number of iterations, and e is a natural constant.

6. The method for optimizing the extraction process of effective components from dandelion based on the I-WO-SVR model according to claim 1, characterized in that: The formula for improving the walrus escape process by introducing adaptive weights for the second time is: in, represents the distance between the current walrus and the best walrus, Λ is the risk factor, and α2 is the adaptive weight introduced for the second time.

7. The method for optimizing the extraction process of effective components from dandelion based on the I-WO-SVR model according to claim 1, characterized in that: Obtaining the training set includes: Drawing a standard curve, using the standard curve, calculating the concentration of chlorogenic acid in dandelion flower stems, and then calculating the chlorogenic acid extraction rate; Based on the single factor experiment, a response surface experiment with 4 factors and 3 levels was conducted with chlorogenic acid extraction rate as the evaluation index to obtain experimental sample data. According to the experimental results, a sample set is constructed, the sample set is screened and data augmented, the augmented sample set is standardized, and the sample data is set as a training set.

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