Gel solid electrolyte prediction method and device, electronic equipment and storage medium

Abstract

The invention discloses a gel solid electrolyte prediction method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a plurality of features of a gel solid electrolyte; determining a target hyper-parameter combination of the initial regression analysis model by taking the conductivity and capacitance of the gel solid electrolyte as regression targets according to each feature; training a target regression analysis model by taking the features as training samples and taking the conductivity and capacitance of the gel solid electrolyte as training labels; using the initial regression analysis model of the target hyper-parameter combination as a target regression analysis model; using the trained target regression analysis model to determine a numerical value, corresponding to the first target value, of each feature as a second target value; the first target value is the target value of the conductivity and capacitance of the gel solid electrolyte. According to the method, the conductivity and capacitance values of the gel solid electrolyte can be quickly and accurately predicted under the condition of no physical test, and each obtained characteristic value can be reversely designed.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a gel solid electrolyte prediction method, device, electronic equipment and storage medium. Background Art

[0002] Gel solid electrolytes have been widely used in biomedical engineering, drug delivery, flexible electronics, etc. due to their unique physical and chemical properties. Conductivity and capacitance are important performance indicators that directly affect their application effects.

[0003] The related technologies have the following problems: 1) The measurement process of the conductivity and capacitance of the gel solid electrolyte is complicated, time-consuming and costly; 2) It is difficult to manufacture a gel solid electrolyte with target conductivity and capacitance. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a gel solid electrolyte prediction method, device, electronic device and storage medium to improve the measurement efficiency of the conductivity and capacitance of the gel solid electrolyte and to improve the efficiency of manufacturing the gel solid electrolyte with the conductivity and capacitance as the target values.

[0005] To achieve the above object, one aspect of an embodiment of the present application provides a gel solid electrolyte prediction method, the method comprising the following steps:

[0006] Obtain multiple characteristics of gel solid electrolytes;

[0007] Determine a target hyperparameter combination of an initial regression analysis model by using each of the features and taking the conductivity and capacitance of the gel solid electrolyte as regression targets;

[0008] The target regression analysis model is trained using the features as training samples and the conductivity and capacitance of the gel solid electrolyte as training labels; wherein the initial regression analysis model using the target hyperparameter combination is used as the target regression analysis model;

[0009] The trained target regression analysis model is used to determine the values ​​corresponding to the first target values ​​of each of the features as the second target value; wherein the first target value is the target value of the conductivity and capacitance of the gel solid electrolyte.

[0010] In some embodiments, the step of obtaining the multiple characteristics of the gel solid electrolyte comprises the following steps:

[0011] Acquiring characteristic data of the gel solid electrolyte;

[0012] The various features of the gel solid electrolyte are constructed according to the characteristic data.

[0013] In some embodiments, the step of using each of the features to determine the target hyperparameter combination of the initial regression analysis model with the conductivity and capacitance of the gel solid electrolyte as regression targets comprises the following steps:

[0014] Determine each first hyperparameter combination of the initial regression analysis model using grid search;

[0015] Based on the regression target, a k-fold cross validation method is performed on each of the features to screen out a second hyperparameter combination from each of the first hyperparameter combinations; wherein k is a positive integer;

[0016] Outputting an importance score of each of the features based on the initial regression analysis model using the second hyperparameter;

[0017] Eliminate the features whose importance scores are lower than a preset score threshold to obtain the remaining features;

[0018] Returning to the first hyperparameter combinations of the initial regression analysis model determined by using the grid search, to obtain the current first hyperparameter combinations;

[0019] Based on the currently remaining features and the current first hyperparameter combinations, the k-fold cross-validation method is performed on each of the features based on the regression target to screen out a second hyperparameter combination from each of the first hyperparameter combinations; the currently obtained second hyperparameter combination is used as the target parameter combination.

[0020] In some embodiments, performing a k-fold cross validation method on each of the features based on the regression target to screen out a second hyperparameter combination from each of the first hyperparameter combinations comprises the following steps:

[0021] Randomly divide each of the current features into k parts as k data subsets;

[0022] Using one of the data subsets as a validation set and the remaining data subsets as training sets, performing fitting calculations using each of the training sets based on the regression target and the initial regression analysis model, and calculating an average determination coefficient corresponding to the validation set;

[0023] The data subset for which the average determination coefficient has not been calculated is used as the current validation set, and the remaining data subsets are used as the current training set, returning to the step of using one data subset as the validation set and the remaining data subsets as the training set, performing fitting calculations using each training set based on the regression target and the initial regression analysis model, and calculating the average determination coefficient corresponding to the validation set, until the average determination coefficients corresponding to all the data subsets are calculated;

[0024] The first hyperparameter combination in the initial regression analysis model when the average determination coefficient with a maximum value is calculated is determined as the second hyperparameter combination.

[0025] In some embodiments, the method of using the trained target regression analysis model to determine the value corresponding to the first target value of each feature as the second target value includes the following steps:

[0026] Repeat the process of adjusting the values ​​of each feature and inputting each feature into the trained target regression analysis model until the values ​​of conductivity and capacitance output by the trained target regression analysis model match the first target value, thereby obtaining the current values ​​of each feature as the second target value.

[0027] In some embodiments, the repeatedly adjusting the values ​​of each of the features and inputting each of the features into the trained target regression analysis model until the values ​​of conductivity and capacitance output by the trained target regression analysis model match the first target value, and obtaining the current values ​​of each of the features as the second target value, comprises the following steps:

[0028] Creating a population, wherein the population includes a plurality of particles; wherein one particle represents a numerical combination of each of the features, and the numerical value of each of the features serves as the position of the corresponding particle;

[0029] Initializing the position of each of the particles;

[0030] Input each particle into the trained target regression analysis model respectively, and then calculate the sum of squares of the difference between the output value of the trained target regression analysis model and the target value as the fitness of the corresponding particle;

[0031] If the current fitness of the particle is lower than its individual historical minimum fitness, its individual historical minimum fitness is updated to the current fitness, and its individual historical optimal position is updated to the current position;

[0032] If the current fitness of the particle is lower than the historical minimum fitness of the population, the historical minimum fitness of the population is updated to the current minimum fitness, and the historical optimal position of the population is updated to the current optimal position;

[0033] Updating the position of each particle within the velocity and position limit values;

[0034] Return to the step of inputting each particle into the trained target regression analysis model, and then calculating the sum of the squares of the differences between the output value of the trained target regression analysis model and the target value as the fitness of the corresponding particle, until a preset end condition is reached, and determining the current historical optimal position of the population as the second target value.

[0035] In some embodiments, the method further comprises the following steps:

[0036] Measuring the actual values ​​of conductivity and capacitance of the gel solid electrolyte sample; wherein the gel solid electrolyte sample is manufactured according to the second target value of each of the characteristics;

[0037] Comparing the actual value with the first target value to obtain a comparison result;

[0038] If the comparison result does not meet the expected condition, then return to the step of repeatedly adjusting the value of each feature and inputting each feature into the trained target regression analysis model until the current comparison result meets the expected condition.

[0039] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a gel solid electrolyte prediction device, the device comprising:

[0040] A feature acquisition unit, used to acquire multiple features of the gel solid electrolyte;

[0041] A hyperparameter determination unit, used to determine a target hyperparameter combination of an initial regression analysis model using the characteristics and taking the conductivity and capacitance of the gel solid electrolyte as regression targets;

[0042] A model training unit, used to train a target regression analysis model using the features as training samples and the conductivity and capacitance of the gel solid electrolyte as training labels; wherein the initial regression analysis model using the target hyperparameter combination is used as the target regression analysis model;

[0043] The inverse design unit is used to use the trained target regression analysis model to determine the value corresponding to each of the features and the first target value as the second target value; wherein the first target value is the target value of the conductivity and capacitance of the gel solid electrolyte.

[0044] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned gel solid electrolyte prediction method when executing the computer program.

[0045] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application 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, the above-mentioned method for predicting a gel solid electrolyte is implemented.

[0046] The embodiments of the present application include at least the following beneficial effects:

[0047] The present application can obtain multiple features of gel solid electrolytes; use each feature to determine the target hyperparameter combination of the initial regression analysis model with the conductivity and capacitance of the gel solid electrolyte as regression targets; use the features as training samples, and use the conductivity and capacitance of the gel solid electrolyte as training labels to train the target regression analysis model; wherein, the initial regression analysis model of the target hyperparameter combination is used as the target regression analysis model; use the trained target regression analysis model to determine the values ​​corresponding to the first target value of each feature as the second target value; wherein the first target value is the target value of the conductivity and capacitance of the gel solid electrolyte. Based on multiple features of the gel solid electrolyte, the present application can quickly and accurately predict the specific values ​​of the conductivity and capacitance of the gel solid electrolyte through the trained target regression analysis model without physical testing, thereby improving measurement efficiency and reducing costs; in addition, the present application can also reversely design the specific values ​​of each feature in the gel solid electrolyte according to the target values ​​of conductivity and capacitance, and the various feature values ​​obtained based on the reverse design can efficiently manufacture gel solid electrolytes with conductivity and capacitance as target values. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A schematic diagram of a gel solid electrolyte prediction method provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the structure of a gel solid electrolyte prediction device provided in an embodiment of the present application;

[0051] Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0053] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0054] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0056] The embodiment of the present application provides a gel solid electrolyte prediction method, device, electronic device and storage medium. The technical solution of the present application includes: obtaining multiple features of the gel solid electrolyte; using each feature to determine the target hyperparameter combination of the initial regression analysis model with the conductivity and capacitance of the gel solid electrolyte as regression targets; using the features as training samples, and using the conductivity and capacitance of the gel solid electrolyte as training labels, training the target regression analysis model; wherein, the initial regression analysis model of the target hyperparameter combination is used as the target regression analysis model; using the trained target regression analysis model to determine the values ​​corresponding to the first target value of each feature as the second target value; wherein the first target value is the target value of the conductivity and capacitance of the gel solid electrolyte. Based on the multiple features of the gel solid electrolyte, the present application can quickly and accurately predict the specific values ​​of the conductivity and capacitance of the gel solid electrolyte through the trained target regression analysis model without physical testing, thereby improving measurement efficiency and reducing costs; in addition, the present application can also reversely design the specific values ​​of each feature in the gel solid electrolyte according to the target values ​​of conductivity and capacitance, and the various feature values ​​obtained based on the reverse design can efficiently manufacture gel solid electrolytes with conductivity and capacitance as target values.

[0057] The embodiment of the present application provides a gel solid electrolyte prediction method, which relates to the field of data processing technology. The gel solid electrolyte prediction method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The cloud server, the server can also be a node server in the blockchain network; the software can be an application that implements the gel solid electrolyte prediction method, etc., but is not limited to the above form.

[0058] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0059] Reference Figure 1 , the embodiment of the present application provides a gel solid electrolyte prediction method, the method may include but is not limited to including S100 to S130, specifically as follows:

[0060] S100: Obtain multiple characteristics of gel solid electrolyte.

[0061] Specifically, parameters such as polymer, cross-linking agent, initiator, solution, solute and manufacturing environment conditions required for manufacturing the gel solid electrolyte can be used as the characteristics of the gel solid electrolyte.

[0062] Furthermore, S100 may include S101-S102:

[0063] S101: Acquire characteristic data of the gel solid electrolyte.

[0064] Exemplarily, the characteristic data of the gel solid electrolyte in this embodiment may include:

[0065] Polymer composition: The type of polymer in the gel (e.g., polyacrylamide, polyvinyl alcohol).

[0066] Polymer ratio and dosage: the mass percentage of each polymer in the gel, the mass of each polymer, and the total mass of the polymers.

[0067] Cross-linking agent: cross-linking agent mass percentage, cross-linking agent mass.

[0068] Initiator: initiator mass percentage, initiator mass.

[0069] The solute types and mass percentages of the electrolyte solution.

[0070] The type and mass percentage of the solvent of the electrolyte solution.

[0071] The molar concentration of the electrolyte solution.

[0072] Reaction time: The length of time the reaction takes during gel synthesis.

[0073] Reaction conditions: including reaction temperature and whether defoaming treatment is performed.

[0074] Ambient temperature and length of drying time.

[0075] Conductivity and capacitance of gel solid electrolytes.

[0076] S102: constructing the various characteristics of the gel solid electrolyte according to the characteristic data.

[0077] Optionally, before constructing the feature, this embodiment may pre-process the characteristic data, for example, data cleaning, removing duplicate records in the characteristic data; and filling missing values, where the missing data of the ambient temperature is filled with 25°C.

[0078] Then, the features related to conductivity and capacitance are extracted and constructed based on the characteristic data:

[0079] Create a separate column of features, with a value of 0 representing a single network and a value of 1 representing a dual network.

[0080] For each polymer, a corresponding column is created. The Boolean value indicates whether the polymer exists or not. A Boolean value of 0 in the column represents the absence of the polymer, and a Boolean value of 1 in the column represents the presence of the polymer.

[0081] The total mass percentage of polymers in the gel, the mass percentage of each polymer in the gel, and the total mass of the polymers are used as features.

[0082] The mass percentage and mass of crosslinker and initiator in the gel were characterized.

[0083] The solutes are characterized by ionic radius, ionic charge, relative atomic mass of the atom to which the ion corresponds, molar concentration of the ion, and mass percent in the gel.

[0084] The solvent is characterized using the solvent dielectric constant, solvent molecular weight, solvent boiling point, and mass percent of the solvent in the gel.

[0085] The molar concentration of the electrolyte solution is used as a characteristic.

[0086] The reaction time length and reaction temperature during gel synthesis are used as features, and whether defoaming treatment is performed is indicated by a Boolean value.

[0087] Ambient temperature and length of drying time are also considered as characteristics.

[0088] Standardization processing is performed, and the features constructed for the solvent and solute parts are scaled Min-Max in the range of 0 to 1. The solute ion radius and the relative atomic mass of the ion corresponding to the atom are scaled. The solvent dielectric constant, solvent molecular weight, and solvent boiling point are scaled.

[0089] S110: Determine a target hyperparameter combination of an initial regression analysis model by using each of the features and taking the conductivity and capacitance of the gel solid electrolyte as regression targets.

[0090] It can be understood that, in this embodiment, the conductivity and capacitance of the gel solid electrolyte are used as regression targets, and the initial regression analysis model is used to analyze various features, thereby determining the target hyperparameter combination of the initial regression analysis model.

[0091] The regression analysis model without determined hyperparameters is used as the initial regression analysis model, and the target hyperparameter combination is a number of hyperparameters that make the prediction performance of the initial regression analysis model optimal.

[0092] Furthermore, S110 may include S111 to S116:

[0093] S111: using grid search to determine each first hyperparameter combination of the initial regression analysis model;

[0094] S112: performing a k-fold cross validation method on each of the features based on the regression target, so as to screen out a second hyperparameter combination from each of the first hyperparameter combinations; wherein k is a positive integer;

[0095] S113: outputting the importance score of each of the features based on the initial regression analysis model using the second hyperparameter;

[0096] S114: Eliminate the features whose importance scores are lower than a preset score threshold, and obtain the remaining features;

[0097] S115: Returning to the method of using the grid search to determine each first hyperparameter combination of the initial regression analysis model, and obtaining each current first hyperparameter combination;

[0098] S116: Based on the currently remaining features and the current first hyperparameter combinations, perform the k-fold cross-validation method on each of the features based on the regression target to screen out a second hyperparameter combination from each of the first hyperparameter combinations; the currently obtained second hyperparameter combination is used as the target parameter combination.

[0099] Furthermore, S112 may include S1121 to S1124:

[0100] S1121: Randomly divide each of the current features into k parts as k data subsets;

[0101] S1122: using one of the data subsets as a validation set and the remaining data subsets as training sets, performing fitting calculations using each of the training sets based on the regression target and the initial regression analysis model, and calculating an average determination coefficient corresponding to the validation set;

[0102] S1123: taking the data subset for which the average determination coefficient has not been calculated as the current validation set, and the remaining data subsets as the current training set, returning to taking one of the data subsets as the validation set and the remaining data subsets as the training set, performing fitting calculations using each of the training sets based on the regression target and the initial regression analysis model, and calculating the average determination coefficient corresponding to the validation set, until the average determination coefficients corresponding to all the data subsets are calculated;

[0103] S1124: Determine the first hyperparameter combination in the initial regression analysis model when the average determination coefficient with the maximum value is calculated as the second hyperparameter combination.

[0104] Exemplarily, a description is given below with reference to specific embodiments.

[0105] Optionally, this embodiment uses a random forest model as an initial regression analysis model, and divides each feature into an 80% training set and a 20% validation set. The features of the validation set can be used to verify the accuracy of the initial regression analysis model derived from the training set.

[0106] In some embodiments, the k-fold cross validation method can be a 10-fold cross validation method, that is, k is 10, and k can also be any positive integer less than 10 (for example, 5, 7, 8, etc.) or greater than 10 (for example, 11, 12, 13, etc.).

[0107] A plurality of first hyperparameter combinations are obtained by using grid search, and a second hyperparameter combination is obtained by screening the first hyperparameter combinations through a 10-fold cross validation method.

[0108] For each first hyperparameter combination in the grid search, the training set data is randomly divided into 10 (k) subsets of equal size. Each subset will be used as a validation set, and the remaining 9 subsets are combined as training sets. The initial regression analysis model is trained using the current first hyperparameter combination and the training set. The validation set data is predicted using the trained initial regression analysis model, and the determination coefficient of the k-th fold is calculated. The calculation formula is:

[0109]

[0110] Among them, y i is the label value of the i-th feature of the validation set, is the label prediction value of the initial regression analysis model for the i-th feature of the validation set, is the average value of the labels corresponding to all features in the validation set, and n is the number of features in the validation set. A random forest model is trained for each of the two labels, and the methods of selecting the optimal hyperparameters and selecting features are the same.

[0111] Optionally, k can be any positive integer from 1 to 10. When k is 10, the average determination coefficient R 2 The calculation formula is:

[0112]

[0113] When k takes other values, 10 in the calculation formula of the average determination coefficient is replaced by the value of k.

[0114] The fitting calculation is performed to return the average determination coefficient corresponding to each validation set, and the first hyperparameter combination with the highest average determination coefficient of the 10 validation sets is used as the second hyperparameter combination.

[0115] In the case of the second hyperparameter combination, the importance score calculated by the random forest model for each feature is output, and the features with importance scores lower than the preset score threshold (for example, one percent) are eliminated. After elimination, grid search and 10-fold cross-validation are performed again, and the hyperparameter combination with the highest average determination coefficient of the 10 validation subsets in the current cross-validation is used as the target hyperparameter combination.

[0116] Retrain the random forest model using the target hyperparameter combination and verify the results on the 20% validation set. If the average determination coefficients of the validation set and the training set are similar, save the trained random forest model.

[0117] S120: Using the features as training samples and the conductivity and capacitance of the gel solid electrolyte as training labels, training a target regression analysis model; wherein the initial regression analysis model of the target hyperparameter combination is used as the target regression analysis model.

[0118] It can be understood that this embodiment can train an initial regression analysis model set with a target hyperparameter combination (i.e., a target regression analysis model), wherein each feature marked with specific values ​​of conductivity and capacitance is used as training data, that is, each feature and its specific value are input into the target regression analysis model, and the target regression analysis model outputs the corresponding specific values ​​of conductivity and capacitance.

[0119] S130: Determine, by using the trained target regression analysis model, the numerical value corresponding to each of the features and the first target value as the second target value; wherein the first target value is the target numerical value of the conductivity and capacitance of the gel solid electrolyte.

[0120] Specifically, in practical applications, a gel solid electrolyte with conductivity and capacitance of certain specific data may be required. In this embodiment, the target values ​​of conductivity and capacitance can be defined first, and then the trained target regression analysis model can be used to reversely infer the characteristic values ​​required to manufacture the gel solid electrolyte with the target values.

[0121] It should be noted that the first target value includes a target value of conductivity and a target value of capacitance, and the second target value includes target values ​​of various characteristics.

[0122] Further, S130 may include:

[0123] S131: Repeat the process of adjusting the values ​​of each feature and inputting each feature into the trained target regression analysis model until the values ​​of conductivity and capacitance output by the trained target regression analysis model match the first target value, thereby obtaining the current values ​​of each feature as the second target value.

[0124] Specifically, if the difference between the value output by the target regression analysis model and the first target value is within a preset range, the two can be considered to match. At this time, the current value of each feature is the second target value, that is, the gel solid electrolyte manufactured with the current value of each feature, its conductivity and capacitance values ​​match the first target value.

[0125] Furthermore, S131 may include:

[0126] S1311: Create a population, the population including a plurality of particles; wherein one particle represents a numerical combination of each of the features, and the numerical value of each of the features serves as the position of the corresponding particle;

[0127] S1312: Initializing the position of each particle;

[0128] S1313: inputting each particle into the trained target regression analysis model respectively, and then calculating the sum of squares of the difference between the output value of the trained target regression analysis model and the target value as the fitness of the corresponding particle;

[0129] S1314: If the current fitness of the particle is lower than its individual historical minimum fitness, its individual historical minimum fitness is updated to the current fitness, and its individual historical optimal position is updated to the current position;

[0130] S1315: If the current fitness of the particle is lower than the historical minimum fitness of the population, the historical minimum fitness of the population is updated to the current minimum fitness, and the historical optimal position of the population is updated to the current optimal position;

[0131] S1316: updating the position of each particle within the limit value range of speed and position;

[0132] S1317: Return to the step of inputting each particle into the trained target regression analysis model, and then calculating the sum of the squares of the differences between the output value of the trained target regression analysis model and the target value as the fitness of the corresponding particle, until the preset end condition is reached, and determining the current historical optimal position of the population as the second target value.

[0133] Specifically, the historical minimum fitness of the population is the lowest historical minimum fitness among all individual particles, and the historical optimal position of the population is the best historical optimal position among all individual particles.

[0134] The following is a description of the specific embodiments.

[0135] First, target values ​​of conductivity and capacitance are defined as the starting point for reverse design.

[0136] Adjust the value of each feature in the target regression analysis model after input training, such as adjusting the mass percentage of the polymer in the gel, the mass and mass percentage of the cross-linking agent and the initiator, the molar concentration of the solution and the mass percentage of the solute solvent, the length of the reaction time, the reaction temperature, etc., to determine a combination of feature values ​​that can enable the output conductivity and capacitance of the target regression analysis model to reach the target value.

[0137] A population is created, which includes multiple particles; wherein one particle represents a numerical combination of various features, and the numerical value of each feature is used as the position of the corresponding particle.

[0138] Particle initialization: Particles are uniformly distributed to a fixed combination of polymer solution solutes, and variable features are randomly initialized.

[0139] Polymer type: fixed polymer type, fixed single and double networks.

[0140] Solution type: Fixed solution types, such as solute type and solvent type.

[0141] Randomly initialize variable features: mass percentage of polymer in gel, polymer mass, mass of cross-linker and initiator, molar concentration of solution, mass percentage of solute, mass percentage of solvent, reaction time, reaction temperature, drying and standing time, etc.

[0142] Using the defined values ​​of conductivity and capacitance as targets, the fitness is the sum of squares of the differences between the conductivity and capacitance predicted by the target regression analysis model of the particle's input features and the target values. The smaller the sum of squares, the closer the input feature combination corresponding to the particle is to the required value.

[0143] If the fitness of the current particle S J Lower than its individual historical minimum fitness S J,best , then update the individual's historical minimum fitness S J,best and the individual's historical optimal position X J,best ; If the fitness of the current particle S J Lower than the population's historical minimum fitness S all,best , then update the population's historical minimum fitness S all,best and the population's historical optimal position X all,best .

[0144] Update the position of each particle. For particle speed and particle position that exceed the maximum value range, limit them to the maximum value. For particle position and particle speed that exceed the minimum value range, limit them to the minimum value.

[0145] Until the preset end condition is reached, the preset end condition can be that the sum of the squares of the differences between the conductivity and capacitance predicted by the target regression analysis model and the target value is less than a threshold or the maximum number of iterations is reached, the calculation is stopped and the current historical optimal position of the population is output as the target value of each feature.

[0146] In addition, in order to further improve the accuracy of the actual values ​​of conductivity and capacitance, the embodiment of the present application may further include S141 to S143:

[0147] S141: measuring actual values ​​of conductivity and capacitance of a gel solid electrolyte sample; wherein the gel solid electrolyte sample is manufactured according to the second target value of each of the characteristics;

[0148] S142: Compare the actual value with the first target value to obtain a comparison result;

[0149] S143: If the comparison result does not meet the expected condition, return to the step of repeatedly adjusting the value of each feature and inputting each feature into the trained target regression analysis model until the current comparison result meets the expected condition.

[0150] Specifically, this embodiment can design an experiment based on the target values ​​of various characteristics, and manufacture a gel solid electrolyte sample accordingly, and then measure the actual conductivity and capacitance of the manufactured gel solid electrolyte sample, and compare them with the expected results (first target value) to obtain a comparison result.

[0151] If the actual conductivity and capacitance measured do not match the first target value, that is, the comparison result does not meet the expected conditions, the values ​​of each feature are adjusted until the comparison result meets the expected conditions. Optionally, this embodiment can repeatedly perform S131 to adjust the values ​​of each feature until the comparison result meets the expected conditions.

[0152] The beneficial effects of the embodiments of the present application may include:

[0153] A method is provided to predict the conductivity and capacitance of gel solid electrolytes without physical measurements, which greatly reduces experimental time and cost. In addition, the method can continuously improve the prediction accuracy by continuously updating the training data, and can reversely design the specific values ​​of each feature of the gel solid electrolyte based on the target values ​​of conductivity and capacitance.

[0154] Reference Figure 2 The embodiment of the present application also provides a gel solid electrolyte prediction device, which can implement the above-mentioned gel solid electrolyte prediction method, and the device includes:

[0155] A feature acquisition unit, used to acquire multiple features of the gel solid electrolyte;

[0156] A hyperparameter determination unit, used to determine a target hyperparameter combination of an initial regression analysis model using the characteristics and taking the conductivity and capacitance of the gel solid electrolyte as regression targets;

[0157] A model training unit, used to train a target regression analysis model using the features as training samples and the conductivity and capacitance of the gel solid electrolyte as training labels; wherein the initial regression analysis model using the target hyperparameter combination is used as the target regression analysis model;

[0158] The inverse design unit is used to use the trained target regression analysis model to determine the value corresponding to each of the features and the first target value as the second target value; wherein the first target value is the target value of the conductivity and capacitance of the gel solid electrolyte.

[0159] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0160] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned gel solid electrolyte prediction method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, a car computer, etc.

[0161] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0162] See also Figure 3 , Figure 3 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0163] The processor 301 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0164] The memory 302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 302, and the processor 301 is used to call and execute the gel solid electrolyte prediction method of the embodiment of this application;

[0165] Input / output interface 303, used to implement information input and output;

[0166] The communication interface 304 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0167] A bus 305 that transmits information between the various components of the device (e.g., the processor 301, the memory 302, the input / output interface 303, and the communication interface 304);

[0168] The processor 301 , the memory 302 , the input / output interface 303 and the communication interface 304 are connected to each other in communication within the device via the bus 305 .

[0169] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned gel solid electrolyte prediction method is implemented.

[0170] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0171] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0172] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0173] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0174] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, 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.

[0175] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0176] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0177] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0178] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0179] The units described above as separate components may or may not be physically separated, and the components shown as units 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0180] In addition, each functional unit in each embodiment of the present application 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 software functional units.

[0181] If the integrated unit 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. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0182] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A gel solid electrolyte prediction method, characterized in that: The method comprises the following steps: Obtain multiple characteristics of gel solid electrolytes; Determine a target hyperparameter combination of an initial regression analysis model by using each of the features and taking the conductivity and capacitance of the gel solid electrolyte as regression targets; The target regression analysis model is trained using the features as training samples and the conductivity and capacitance of the gel solid electrolyte as training labels; wherein the initial regression analysis model using the target hyperparameter combination is used as the target regression analysis model; The trained target regression analysis model is used to determine the values ​​corresponding to the first target values ​​of each of the features as the second target value; wherein the first target value is the target value of the conductivity and capacitance of the gel solid electrolyte.

2. A gel solid electrolyte prediction method according to claim 1, characterized in that: The method of obtaining multiple characteristics of the gel solid electrolyte comprises the following steps: Acquiring characteristic data of the gel solid electrolyte; The various features of the gel solid electrolyte are constructed according to the characteristic data.

3. A gel solid electrolyte prediction method according to claim 1, characterized in that: The method of using each of the features to determine the target hyperparameter combination of the initial regression analysis model with the conductivity and capacitance of the gel solid electrolyte as regression targets comprises the following steps: Determine each first hyperparameter combination of the initial regression analysis model using grid search; Based on the regression target, a k-fold cross validation method is performed on each of the features to screen out a second hyperparameter combination from each of the first hyperparameter combinations; wherein k is a positive integer; Outputting an importance score of each of the features based on the initial regression analysis model using the second hyperparameter; Eliminate the features whose importance scores are lower than a preset score threshold to obtain the remaining features; Returning to the first hyperparameter combinations of the initial regression analysis model determined by using the grid search, to obtain the current first hyperparameter combinations; Based on the currently remaining respective features and the current respective first hyperparameter combinations, the k-fold cross validation method is performed on the respective features based on the regression target to screen out a second hyperparameter combination from the respective first hyperparameter combinations; the currently obtained second hyperparameter combination is used as the target parameter combination.

4. A gel solid electrolyte prediction method according to claim 3, characterized in that: The performing of the k-fold cross validation method on each of the features based on the regression target to screen out a second hyperparameter combination from each of the first hyperparameter combinations comprises the following steps: Randomly divide each of the current features into k parts as k data subsets; Using one of the data subsets as a validation set and the remaining data subsets as training sets, performing fitting calculations using each of the training sets based on the regression target and the initial regression analysis model, and calculating an average determination coefficient corresponding to the validation set; The data subset for which the average determination coefficient has not been calculated is used as the current validation set, and the remaining data subsets are used as the current training set, returning to the step of using one data subset as the validation set and the remaining data subsets as the training set, performing fitting calculations using each training set based on the regression target and the initial regression analysis model, and calculating the average determination coefficient corresponding to the validation set, until the average determination coefficients corresponding to all the data subsets are calculated; The first hyperparameter combination in the initial regression analysis model when the average determination coefficient with a maximum value is calculated is determined as the second hyperparameter combination.

5. A gel solid electrolyte prediction method according to claim 1, characterized in that: The method of using the trained target regression analysis model to determine the value corresponding to each of the features and the first target value as the second target value comprises the following steps: Repeat the process of adjusting the values ​​of each feature and inputting each feature into the trained target regression analysis model until the values ​​of conductivity and capacitance output by the trained target regression analysis model match the first target value, thereby obtaining the current values ​​of each feature as the second target value.

6. A gel solid electrolyte prediction method according to claim 5, characterized in that: The repeatedly adjusting the value of each feature and inputting each feature into the trained target regression analysis model until the values ​​of conductivity and capacitance output by the trained target regression analysis model match the first target value, and obtaining the current value of each feature as the second target value, comprises the following steps: Creating a population, wherein the population includes a plurality of particles; wherein one particle represents a numerical combination of each of the features, and the numerical value of each of the features serves as the position of the corresponding particle; Initializing the position of each of the particles; Input each particle into the trained target regression analysis model respectively, and then calculate the sum of squares of the difference between the output value of the trained target regression analysis model and the target value as the fitness of the corresponding particle; If the current fitness of the particle is lower than its individual historical minimum fitness, its individual historical minimum fitness is updated to the current fitness, and its individual historical optimal position is updated to the current position; If the current fitness of the particle is lower than the historical minimum fitness of the population, the historical minimum fitness of the population is updated to the current minimum fitness, and the historical optimal position of the population is updated to the current optimal position; Updating the position of each particle within the velocity and position limit values; Return to the step of inputting each particle into the trained target regression analysis model, and then calculating the sum of the squares of the differences between the output value of the trained target regression analysis model and the target value as the fitness of the corresponding particle, until a preset end condition is reached, and determining the current historical optimal position of the population as the second target value.

7. A gel solid electrolyte prediction method according to claim 5, characterized in that: The method further comprises the following steps: Measuring the actual values ​​of conductivity and capacitance of the gel solid electrolyte sample; wherein the gel solid electrolyte sample is manufactured according to the second target value of each of the characteristics; Comparing the actual value with the first target value to obtain a comparison result; If the comparison result does not meet the expected condition, then return to the step of repeatedly adjusting the value of each feature and inputting each feature into the trained target regression analysis model until the current comparison result meets the expected condition.

8. A gel solid electrolyte prediction device, characterized in that: The device comprises: A feature acquisition unit, used to acquire multiple features of the gel solid electrolyte; A hyperparameter determination unit, used to determine a target hyperparameter combination of an initial regression analysis model using the characteristics and taking the conductivity and capacitance of the gel solid electrolyte as regression targets; A model training unit, used to train a target regression analysis model using the features as training samples and the conductivity and capacitance of the gel solid electrolyte as training labels; wherein the initial regression analysis model using the target hyperparameter combination is used as the target regression analysis model; The inverse design unit is used to use the trained target regression analysis model to determine the value corresponding to each of the features and the first target value as the second target value; wherein the first target value is the target value of the conductivity and capacitance of the gel solid electrolyte.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements a gel solid electrolyte prediction method as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a gel solid electrolyte prediction method according to any one of claims 1 to 7 is implemented.

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