A method for predicting capacitor performance based on machine learning
Through the capacitor performance prediction method based on machine learning, the problem of inaccurate performance prediction of biochar supercapacitor material is solved. By optimizing model and feature selection, the prediction accuracy of capacitor performance is improved, and the preparation of high-performance biochar supercapacitors is promoted. .
Patent Information
- Application Number
- CN202411455783.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-18
- Publication Date
- 2024-12-20
- Estimated Expiration
- 2044-10-18
AI Technical Summary
When designing and screening high-performance supercapacitor materials, it is difficult for the prior art to accurately capture the impact of the physical and chemical properties of the material on capacitance performance, resulting in inaccurate material performance prediction.
Using a capacitor performance prediction method based on machine learning, the target data of the biochar supercapacitor is collected, preprocessed and feature extraction is performed, the performance prediction model is established, the hyperparameters is optimized, and the optimal model is determined through Bayesian optimization and random search algorithms. , determine the standard range of value of target characteristics, and thus improve the prediction accuracy of capacitor performance.
Through optimization of model and feature selection, the prediction accuracy of capacitor performance is significantly improved, the mechanism between material structure and capacitor performance is revealed, and the preparation of high-performance biochar supercapacitors is promoted.
Smart Images
Figure CN118981630B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of capacitor performance prediction, and in particular to a capacitor performance prediction method based on machine learning. Background Art
[0002] Integrated supercapacitors to prepare self-powered systems have been widely used in various portable microelectronic devices and sensing fields. In this case, how to design and screen high-performance supercapacitor materials is one of the current and future challenges.
[0003] Biochar is a carbon-rich material produced by pyrolysis of organic raw materials under oxygen-limited conditions. Modified biochar after special treatment has a high specific surface area and excellent conductivity, and is widely used as supercapacitor electrode material.
[0004] The capacitance performance of biochar supercapacitors is usually determined by the physical and chemical properties of the material, such as the pore structure of the material, and the detection conditions. From a synthetic perspective, the physical and chemical parameters of the material can be controlled by controlling the synthesis parameters. Therefore, accurately capturing the impact of the physical and chemical properties of the material on the capacitance performance is very helpful for the design and screening of high-performance biochar material supercapacitors.
[0005] As an emerging new material development strategy, machine learning can effectively explore the relationship between material structure and performance, accelerate the chemical synthesis of high-performance device materials, and develop a capacitor performance prediction method based on machine learning, which will help reveal the hidden mechanism between material structure and capacitor performance and promote the preparation of high-performance biochar supercapacitors due to its powerful ability to explore complex potential relationships. Summary of the invention
[0006] The purpose of the present invention is to provide a capacitor performance prediction method based on machine learning to solve the above technical problems.
[0007] The purpose of the present invention can be achieved through the following technical solutions:
[0008] A capacitor performance prediction method based on machine learning comprises the following steps:
[0009] S1: collecting target data of biochar supercapacitors, wherein the target data includes target factors and specific capacitance, and the target factors are factors that affect specific capacitance, preprocessing the target factors to obtain target features, and determining a training set and a prediction set based on the target features;
[0010] S2: Establish a supercapacitor performance prediction model based on a machine learning algorithm, use it as an initial model, train the initial model based on a cross-validation method and the training set, and determine the evaluation index of the initial model after training, the evaluation index includes the determination coefficient ZBfir , root mean square error ZB sec And the mean absolute error ZB thi , calculate the judgment value P = (ε-ZB fir )*(ZB sec +ZB thi ), ε is the preset correction constant and ε>1;
[0011] The hyperparameters of the initial model are optimized based on a random search algorithm, and the judgment value after optimizing the hyperparameters is determined to generate coordinate points (a, P a ), P a represents the judgment value after the a-th optimization of the hyperparameter, and the coordinate points are fitted to obtain a fitting curve f(P);
[0012] Calculate the judgment value difference PCZ a =|P a -P a-1 |, get the maximum judgment value difference PCZ max , calculate the selected value , where η is a preset correction coefficient, and N represents the total number of times the hyperparameter is optimized;
[0013] S3: Sort the selected values by size. The larger the selected value, the lower the ranking. The initial model corresponding to the top 30% of the selected values in the sorting is used as the target model. The optimal hyperparameters of the target model are determined based on Bayesian optimization. The selected value of the target model at this time is determined, and the target model corresponding to the minimum selected value is used as the accurate model.
[0014] The standard value range of the target feature is determined based on the precise model and the prediction set. When the value of the target feature falls within the standard value range, the specific capacitance of the biochar supercapacitor is the highest.
[0015] As a further solution of the present invention: in the step S3, the process of determining the standard range of the value of the target feature based on the accurate model and the prediction set specifically includes:
[0016] Step 1: Determine the evaluation index of the accurate model based on the cross-validation method and the prediction set, determine the judgment value Pcur at this time, set the judgment value threshold Pzq, and when the judgment value Pcur>Pzq, execute step 2;
[0017] Step 2: Determine the average absolute contribution rate of the target feature based on the precise model, and sort the target features according to the average absolute contribution rate. The larger the average absolute contribution rate, the lower the corresponding target feature is ranked.
[0018] Step 3: removing the last target feature in the ranking from the prediction set, and repeating steps 1 to 2 until the corresponding judgment value is less than the judgment value threshold after a target feature is removed from the prediction set;
[0019] Determine the average absolute contribution rate of the target features that have not been removed at this time, and sort the target features that have not been removed at this time according to the size of the average absolute contribution rate. The larger the average absolute contribution rate, the higher the corresponding target feature is ranked. The first C target features in the sorting are taken as the main features, and C is a preset number.
[0020] Step 4: Determine the standard range of values of the main features based on the partial dependence diagram, the individual conditional expectation diagram and the precise model.
[0021] As a further solution of the present invention: in the step three, when the number of target features that have not been removed is C and the judgment value of the precise model is still greater than the judgment value threshold, a warning message is sent to the preset management personnel.
[0022] As a further solution of the present invention: the step S3 further includes the following steps:
[0023] Determining the SHAP value of the main feature;
[0024] When the SHAP value of the main feature is positive, the main feature is positively correlated with the specific capacitance of the biochar supercapacitor;
[0025] When the SHAP value of the main feature is negative, the main feature is negatively correlated with the specific capacitance of the biochar supercapacitor.
[0026] As a further solution of the present invention: in step S1, the process of preprocessing the target factor to obtain the target feature specifically includes:
[0027] Standardize the numerical target factors to eliminate the dimension;
[0028] Encode non-numeric target factors through one-hot encoding;
[0029] The outliers in the target factor are removed by the isolation forest algorithm.
[0030] As a further solution of the present invention: in step S1, the process of determining the training set and the prediction set based on the target feature specifically includes:
[0031] Obtain the specific capacitance D corresponding to different target features, establish a target mapping between target features and specific capacitance D, and establish a sample set YJH=(Y 1 , Y 2 , …, Y m ), Ym represents the mth target mapping, and divides the sample set into a training set and a prediction set according to a preset ratio.
[0032] As a further solution of the present invention: in the step S2, in the process of training the initial model based on the cross-validation method and the training set and determining the evaluation index of the trained initial model, a five-fold cross-validation is adopted.
[0033] As a further solution of the present invention: in the step S1, in the process of establishing the supercapacitor performance prediction model based on the machine learning algorithm, the machine learning algorithm includes random forest, decision tree, artificial neural network, and support vector machine.
[0034] Beneficial effects of the present invention: In the present invention, first, target data is collected, and target factors in the target data are preprocessed to obtain target features, and a training set and a prediction set are obtained according to the target features; it is understandable that the target data can be obtained from published literature, and the target factors include but are not limited to the porous structure characteristics and chemical composition characteristics of biochar supercapacitor electrode materials, the porous structure characteristics include but are not limited to specific surface area, micropore specific surface area and pore size, and the chemical composition characteristics include but are not limited to carbon content and nitrogen content, which will not be repeated here; then, an initial model is established based on machine learning, the evaluation index of the initial model is determined, and the judgment value is calculated; it is worth noting that the judgment value is used to comprehensively measure the performance of the initial model, the smaller the judgment value, the better the performance of the model, specifically, the closer the determination coefficient is to 1, the better the performance of the initial model, the smaller the root mean square error and the mean absolute error, which also indicates that the performance of the initial model is better; then, the hyperparameters of the initial model are optimized based on a random search algorithm, the judgment value is re-determined after each optimization, and a target image is drawn; it is understandable that the judgment value of the initial model when the hyperparameters are not optimized is P 0 , the corresponding coordinate point is (0, P 0 ), considering that hyperparameters will affect the performance of the model, it is possible that the P 0The higher the value is, the lower the judgment value will be (the better the model's performance will be) after optimizing the hyperparameters. Therefore, the hyperparameters need to be optimized first. Bayesian optimization can explore the hyperparameter space more effectively by constructing a probabilistic model of the objective function. Compared with methods such as random search, it can usually find the optimal hyperparameters faster and reduce the number of evaluations. However, Bayesian optimization requires the construction of a proxy model (such as a Gaussian process or a tree structure). This model needs to be updated after each new hyperparameter evaluation. The computational cost of training the model increases significantly when the dimension of the parameter space is high. Therefore, in this scheme, random search is first used to optimize the hyperparameters and determine the change in the judgment value of the initial model (i.e., the change in performance). The lower the judgment value, the better the model's performance. The selected value is used as the standard for measuring the change in the judgment value. The larger the maximum judgment value difference is, the better the performance of the model will be. After optimizing the hyperparameters, the performance of the model is improved higher, and the selection value should be smaller (because the initial model with a smaller selection value is used as the target model later), and the relationship between the remaining parameters and the selection value can refer to the above ideas; after that, after determining the target model, the optimal hyperparameters of the target model are determined based on Bayesian optimization, the selection value of the target model at this time is determined, and the target model corresponding to the minimum selection value is used as the precise model; it is worth noting that the initial model with better performance after optimizing the hyperparameters N times is selected from the initial model, thereby reducing the computational cost of using Bayesian optimization for all initial models; random search does not need to build a complex model or update the proxy model, it only evaluates by randomly sampling hyperparameter combinations, so the computational overhead of each iteration is small; finally, the standard range of the value of the target feature is determined based on the precise model and the prediction set. The present invention can improve the prediction accuracy of capacitor performance by optimizing the model and feature selection, and reveal the hidden mechanism between material structure and capacitor performance through machine learning, so as to promote the preparation of high-performance biochar supercapacitors. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] The present invention will be further described below in conjunction with the accompanying drawings.
[0036] Figure 1 It is a flow chart of a capacitor performance prediction method based on machine learning in the present invention. DETAILED DESCRIPTION
[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.
[0038] See also Figure 1As shown, the present invention is a capacitor performance prediction method based on machine learning, comprising the following steps:
[0039] S1: collecting target data of biochar supercapacitors, wherein the target data includes target factors and specific capacitance, and the target factors are factors that affect specific capacitance, preprocessing the target factors to obtain target features, and determining a training set and a prediction set based on the target features;
[0040] S2: Establish a supercapacitor performance prediction model based on a machine learning algorithm, use it as an initial model, train the initial model based on a cross-validation method and the training set, and determine the evaluation index of the trained initial model, wherein the evaluation index includes a determination coefficient ZB fir , root mean square error ZB sec And the mean absolute error ZB thi , calculate the judgment value P = (ε-ZB fir )*(ZB sec +ZB thi ), ε is the preset correction constant and ε>1;
[0041] The hyperparameters of the initial model are optimized based on a random search algorithm, and the judgment value after optimizing the hyperparameters is determined to generate coordinate points (a, P a ), P a represents the judgment value after the a-th optimization of the hyperparameter, and the coordinate points are fitted to obtain a fitting curve f(P);
[0042] Calculate the judgment value difference PCZ a =|P a -P a-1 |, get the maximum judgment value difference PCZ max , calculate the selected value , where η is a preset correction coefficient, and N represents the total number of times the hyperparameter is optimized;
[0043] S3: Sort the selected values by size. The larger the selected value, the lower the ranking. The initial model corresponding to the top 30% of the selected values in the sorting is used as the target model. The optimal hyperparameters of the target model are determined based on Bayesian optimization. The selected value of the target model at this time is determined, and the target model corresponding to the minimum selected value is used as the accurate model.
[0044] The standard value range of the target feature is determined based on the precise model and the prediction set. When the value of the target feature falls within the standard value range, the specific capacitance of the biochar supercapacitor is the highest.
[0045] It should be noted that first, the target data is collected, and the target factors in the target data are preprocessed to obtain the target features, and the training set and the prediction set are obtained according to the target features; it is understandable that the target data can be obtained from published literature, and the target factors include but are not limited to the porous structure characteristics and chemical composition characteristics of the biochar supercapacitor electrode material, the porous structure characteristics include but are not limited to the specific surface area, micropore specific surface area and pore size, and the chemical composition characteristics include but are not limited to the carbon content and nitrogen content, which will not be repeated here; then, the initial model is established based on machine learning, the evaluation index of the initial model is determined, and the judgment value is calculated; it is worth noting that the judgment value is used to comprehensively measure the performance of the initial model. The smaller the judgment value, the better the performance of the model. Specifically, the closer the determination coefficient is to 1, the better the performance of the initial model, the smaller the root mean square error and the mean absolute error, which also indicates that the performance of the initial model is better; then, the hyperparameters of the initial model are optimized based on the random search algorithm, the judgment value is re-determined after each optimization, and the target image is drawn; it is understandable that the judgment value of the initial model when the hyperparameters are not optimized is P 0 , the corresponding coordinate point is (0, P 0 ), considering that hyperparameters will affect the performance of the model, it is possible that the P 0Higher, but after optimizing the hyperparameters, the judgment value will decrease (the model's performance is better), so it is necessary to optimize the hyperparameters first; Bayesian optimization can more effectively explore the hyperparameter space by constructing a probabilistic model of the objective function. Compared with methods such as random search, it can usually find the optimal hyperparameters faster and reduce the number of evaluations. However, Bayesian optimization requires the construction of a proxy model (such as a Gaussian process or a tree structure). This model needs to be updated after each new hyperparameter evaluation. The computational cost of training the model increases significantly when the dimension of the parameter space is high. Therefore, in this scheme, random search is first used to optimize the hyperparameters, and the change in the judgment value of the initial model (that is, the change in performance) is determined. The lower the judgment value, the better the performance of the model. The selection value is used as the standard for measuring the change in the judgment value. The larger the maximum judgment value difference, the higher the performance of the model after a certain hyperparameter optimization, and the selection value It should be smaller (because the initial model with a smaller selection value will be used as the target model later), and the relationship between the remaining parameters and the selection value can refer to the above ideas. f(N) represents the function value obtained by substituting N into the fitting curve f(P); after that, after the target model is determined, the optimal hyperparameters of the target model are determined based on Bayesian optimization, the selection value of the target model at this time is determined, and the target model corresponding to the minimum selection value is used as the accurate model; it is worth noting that the initial model (i.e., the target model) with better performance after N times of hyperparameter optimization using random search is selected from the initial model, thereby reducing the computational cost of using Bayesian optimization for all initial models; random search does not need to build complex models or update proxy models, it only evaluates by randomly sampling hyperparameter combinations, so the computational overhead of each iteration is small; finally, the standard range of values of the target feature is determined based on the accurate model and the prediction set.
[0046] In another preferred embodiment of the present invention, in step S3, the process of determining the standard range of values of the target feature based on the precise model and the prediction set specifically includes:
[0047] Step 1: Determine the evaluation index of the accurate model based on the cross-validation method and the prediction set, determine the judgment value Pcur at this time, set the judgment value threshold Pzq, and when the judgment value Pcur>Pzq, execute step 2;
[0048] Step 2: Determine the average absolute contribution rate of the target feature based on the precise model, and sort the target features according to the average absolute contribution rate. The larger the average absolute contribution rate, the lower the corresponding target feature is ranked.
[0049] Step 3: removing the last target feature in the ranking from the prediction set, and repeating steps 1 to 2 until the corresponding judgment value is less than the judgment value threshold after a target feature is removed from the prediction set;
[0050] Determine the average absolute contribution rate of the target features that have not been removed at this time, and sort the target features that have not been removed at this time according to the size of the average absolute contribution rate. The larger the average absolute contribution rate, the higher the corresponding target feature is ranked. The first C target features in the sorting are taken as the main features, and C is a preset number.
[0051] Step 4: Determine the standard range of values of the main features based on the partial dependence diagram, the individual conditional expectation diagram and the precise model.
[0052] It is worth noting that when Pcur>Pzq, it means that the performance of the model has not met expectations. Therefore, we choose to remove the target features with small average absolute contribution rates. These features have limited contribution to the predictive performance of the model and may introduce noise, thus affecting the overall performance of the model. By removing low-contribution features, we can simplify the model, reduce the risk of overfitting, and improve the generalization ability of the model. After the performance of the model meets expectations, we select the main features with the average absolute contribution rate and determine the standard range of the main features based on the partial dependence diagram, individual conditional expectation diagram, and precise model. The partial dependence diagram and individual conditional expectation diagram are important tools for visualizing and analyzing the impact of features in machine learning models. Their main function is to help understand the relationship between features and prediction results. By combining the analysis of partial dependence diagrams and individual conditional expectation diagrams, we can more comprehensively understand the role of the main features, thereby providing a reliable standard value range for the performance optimization of biochar supercapacitors, which will not be elaborated here. Interpretability tools are used to explain the prediction results, understand the input feature attributes that have a greater impact on the target attribute (specific capacitance), and how they affect the energy storage performance of supercapacitors, and obtain the controllable biochar supercapacitor electrode material synthesis knowledge system learned during the model establishment process, which will help accelerate the R&D cycle of high-performance supercapacitors and promote innovative development in the application field of biochar supercapacitors.
[0053] In another preferred embodiment of the present invention, in step three, when the number of target features that have not been removed is C and the judgment value of the precise model is still greater than the judgment value threshold, a warning message is sent to a preset manager.
[0054] In another preferred embodiment of the present invention, the step S3 further includes the following steps:
[0055] Determining the SHAP value of the main feature;
[0056] When the SHAP value of the main feature is positive, the main feature is positively correlated with the specific capacitance of the biochar supercapacitor;
[0057] When the SHAP value of the main feature is negative, the main feature is negatively correlated with the specific capacitance of the biochar supercapacitor.
[0058] It should be noted that the SHAP value considers all possible feature combinations and evaluates the impact of each feature on the prediction results under different combinations, thereby accurately calculating the actual contribution of a single feature to the final output; when the SHAP value is positive, it means that an increase in the feature will lead to an increase in the model's predicted results, indicating a positive correlation. For example, an increase in the content of a certain chemical component may increase the specific capacitance; when the SHAP value is negative, it means that an increase in the feature will lead to a decrease in the model's predicted results, indicating a negative correlation; the introduction of the SHAP value enhances the interpretability of the model, allowing researchers to clearly understand how each major feature specifically affects the performance of the capacitor, thereby providing more precise guidance on optimizing design and material selection.
[0059] In another preferred embodiment of the present invention, in step S1, the process of preprocessing the target factor to obtain the target feature specifically includes:
[0060] Standardize the numerical target factors to eliminate the dimension;
[0061] Encode non-numeric target factors through one-hot encoding;
[0062] The outliers in the target factor are removed by the isolation forest algorithm.
[0063] It should be noted that for numerical target factors, a standardization method is used to eliminate the dimension effect. This process unifies the values of different scales into a standard range (specifically set based on experience), thereby improving the efficiency and stability of model training; for non-numerical target factors, one-hot encoding is used to convert them into numerical form, thereby converting each category feature into a binary vector to ensure that the model can effectively utilize these category information without introducing false order relationships; the isolation forest algorithm is used to identify and remove outliers in the target factor. Isolation forest is a tree-based algorithm that can effectively identify outliers in the data, thereby improving the robustness of the model and reducing the negative impact on model performance.
[0064] In another preferred embodiment of the present invention, in step S1, the process of determining the training set and the prediction set based on the target feature specifically includes:
[0065] Obtain the specific capacitance D corresponding to different target features, establish a target mapping between target features and specific capacitance D, and establish a sample set YJH=(Y 1 , Y 2 , …, Y m), Y m represents the mth target mapping, and divides the sample set into a training set and a prediction set according to a preset ratio.
[0066] It can be understood that the ratio of the training set to the prediction set is 7:3.
[0067] In another preferred embodiment of the present invention, in step S2, in the process of training the initial model based on the cross-validation method and the training set and determining the evaluation index of the trained initial model, five-fold cross-validation is used.
[0068] In another preferred embodiment of the present invention, in step S1, in the process of establishing the supercapacitor performance prediction model based on a machine learning algorithm, the machine learning algorithm includes a random forest, a decision tree, an artificial neural network, and a support vector machine.
[0069] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.
Claims
1. A capacitor performance prediction method based on machine learning, characterized in that: The following steps are involved: S1: collecting target data of a biochar supercapacitor, wherein the target data includes a target factor and specific capacitance, wherein the target factor is a factor affecting the specific capacitance, preprocessing the target factor to obtain a target feature, and determining a training set and a prediction set based on the target feature; S2: Establish a supercapacitor performance prediction model based on a machine learning algorithm, use it as an initial model, train the initial model based on a cross-validation method and the training set, and determine the evaluation index of the trained initial model, the evaluation index includes the determination coefficient ZB fir , root mean square error ZB sec and mean absolute error ZB thi , calculate the judgment value P=(ε-ZB fir )*(ZB sec +ZB thi ), ε is the preset correction constant and ε>1; Based on the random search algorithm, the hyperparameters of the initial model are optimized, the judgment value after optimizing the hyperparameters is determined, and the coordinate points (a, P a ), P a represents the judgment value after the a-th optimization of the hyperparameter, and the coordinate points are fitted to obtain the fitting curve f(P); Calculate the judgment value difference PCZ a =|P a -P a-1 |, get the maximum judgment value difference PCZ max , calculate the selected value , where η is a preset correction coefficient, and N represents the total number of times the hyperparameter is optimized; S3: Sort the selected values by size, with larger selected values ranking lower. Use the initial models corresponding to the top 30% of the selected values in the sorting as the target model. Determine the optimal hyperparameters of the target model based on Bayesian optimization, determine the selected value of the target model at this time, and use the target model corresponding to the minimum selected value as the accurate model. The standard value range of the target feature is determined based on the precise model and the prediction set. When the value of the target feature falls within the standard value range, the specific capacitance of the biochar supercapacitor is the highest.
2. The capacitor performance prediction method based on machine learning according to claim 1, characterized in that: In step S3, the process of determining the standard value range of the target feature based on the precise model and the prediction set specifically includes: Step 1: Determine the evaluation index of the precision model based on the cross-validation method and the prediction set, determine the judgment value Pcur at this time, set the judgment value threshold Pzq, and when the judgment value Pcur>Pzq, execute step 2; Step 2: Determine the average absolute contribution rate of the target feature based on the precise model, and sort the target features according to the average absolute contribution rate. The larger the average absolute contribution rate, the lower the corresponding target feature is ranked. Step 3: removing the last target feature in the ranking from the prediction set, and repeating steps 1 to 2 until the corresponding judgment value after removing a target feature from the prediction set is less than the judgment value threshold; Determine the average absolute contribution rate of the target features that have not been removed at this time, and sort the target features that have not been removed at this time according to the size of the average absolute contribution rate. The larger the average absolute contribution rate, the higher the corresponding target feature is ranked. The top C target features in the sorting are taken as the main features, where C is a preset number; Step 4: Determine the standard range of values for the main features based on the partial dependence graph, the individual conditional expectation graph, and the precise model.
3. The capacitor performance prediction method based on machine learning according to claim 2, characterized in that: In the step three, when the number of target features that have not been removed is C and the judgment value of the precise model is still greater than the judgment value threshold, an early warning message is sent to the preset management personnel.
4. The method for predicting capacitor performance based on machine learning according to claim 2, characterized in that: The step S3 further includes the following steps: Determining the SHAP value of the main feature; When the SHAP value of the main feature is positive, the main feature is positively correlated with the specific capacitance of the biochar supercapacitor; When the SHAP value of the main feature is negative, the main feature is negatively correlated with the specific capacitance of the biochar supercapacitor.
5. The capacitor performance prediction method based on machine learning according to claim 1, characterized in that: In step S1, the process of preprocessing the target factors to obtain target features specifically includes: Standardize the numerical target factors to eliminate the dimension; Encode non-numeric target factors using one-hot encoding; The outliers in the target factor are removed by the isolation forest algorithm.
6. The capacitor performance prediction method based on machine learning according to claim 1, characterized in that: In step S1, the process of determining the training set and the prediction set based on the target feature specifically includes: Obtain the specific capacitance D corresponding to different target features, establish a target mapping between target features and specific capacitance D, and establish a sample set YJH=(Y1,Y2,…,Y m ), Y m represents the mth target mapping, and divides the sample set into a training set and a prediction set according to a preset ratio.
7. The capacitor performance prediction method based on machine learning according to claim 1, characterized in that: In step S2, five-fold cross validation is used in the process of training the initial model based on the cross validation method and the training set and determining the evaluation index of the trained initial model.
8. The method for predicting capacitor performance based on machine learning according to claim 1, characterized in that: In step S1, in the process of establishing a supercapacitor performance prediction model based on a machine learning algorithm, the machine learning algorithm includes random forest, decision tree, artificial neural network, and support vector machine.