An Adaptive Carbon Nanotube Growth Control Scheme and System Based on AI Deep Learning

Abstract

The present invention discloses an adaptive carbon nanotube growth control scheme and system based on AI deep learning, which relates to the technical field of nanomaterial manufacturing and regulation. It includes information acquisition: acquiring variable information of carbon nanotube growth and user requirements; reference library construction: generating a reference library by generating carbon nanotube growth parameters under different lithium battery performance indicators through a reference library generation method. The present invention generates a reference library storing basic growth parameters and basic battery performance through the set reference library generation method, judges the target relationship between a single variable of the basic growth parameters and the basic battery performance through the set identification method, and matches and adjusts the reference library according to the user requirements and the target relationship through the set matching and adjustment method to match target growth parameters that meet the user requirements for the user, and adaptively formulates target growth parameters that meet the user requirements for the user, so as to reduce the workload of the user and improve the production and preparation efficiency of carbon nanotubes.

Description

Technical Field

[0001] The present invention relates to the technical field of nanomaterial manufacturing and regulation, and specifically to an adaptive carbon nanotube growth control scheme and system based on AI deep learning. Background Art

[0002] Carbon nanotubes are nanoscale tubular structures composed of carbon atoms, and are widely used in the fields of materials science, electronic technology, drug delivery, etc. due to their unique physical, chemical, and mechanical properties. The growth process of carbon nanotubes usually adopts the chemical vapor deposition method. In the new energy field, lithium-ion batteries are widely used as efficient storage devices. Carbon nanotubes are regarded as ideal candidates for the negative electrode materials of lithium-ion batteries due to their excellent electrical conductivity and specific surface area.

[0003] A carbon nanotube manufacturing apparatus and a carbon nanotube manufacturing method with the patent publication number CN116600884A include a cylindrical reactor main body, in which there is an accommodation part, which is a space where reactions occur; and a distribution plate, which is located below the accommodation part of the reactor main body to distribute the reaction gas supplied to the accommodation part. Among them, the reactor main body includes: a lower reactor; an upper reactor, the diameter of which is formed to be larger than that of the lower reactor; and an expansion part, which connects the upper reactor to the lower reactor and has a gradually expanding diameter.

[0004] In the customized production scenario of negative electrode materials in high-performance lithium-ion batteries, in the existing technology for the preparation and growth of carbon nanotubes, the customization technology is relatively cumbersome, and it often requires staff to set parameters according to requirements, which leads to low efficiency in the preparation of customized carbon nanotubes. If the growth parameter settings cannot be customized, the diameter, length, and structure of the produced carbon nanotubes are all difficult to adjust, and cannot be optimized for specific applications. This may result in the produced carbon nanotubes not meeting specific performance requirements, reducing the market competitiveness of the products. At the same time, the existing technology lacks a real-time monitoring and optimization feedback mechanism, and the data of carbon nanotube products cannot be effectively used to adjust the growth parameters, resulting in low customization adaptability. Therefore, the present invention is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an adaptive carbon nanotube growth control scheme and system based on AI deep learning to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: An adaptive carbon nanotube growth control scheme based on AI deep learning, the scheme includes:

[0007] Information acquisition: Acquire the variable information of carbon nanotube growth and user requirements;

[0008] Reference construction: A reference library is generated by a reference library generation method to obtain carbon nanotube growth parameters under different lithium-ion battery performance indicators. The reference library includes basic battery performance and basic growth parameters.

[0009] Information processing: The relationship between variables in the basic growth parameters and performance in the basic battery performance is judged by an identification method to obtain the target relationship. The target relationship includes the first target relationship reflecting the relationship between a single variable and performance and the second target relationship reflecting the relationship between multiple variables and performance. The variables in the basic growth parameters are consistent with the variable information, and the corresponding relationship between the basic battery performance and the basic growth parameters is established.

[0010] Configuration selection: The basic battery performance corresponding to the user's needs is searched in the reference library by a matching adjustment method, and the basic growth parameters are extracted according to the corresponding relationship for optimization adjustment to obtain the target growth parameters and adjustment directions. The adjustment directions include the final relationship, the target difference, and the added value.

[0011] Information output: Carbon nanotubes are grown based on the target growth parameters.

[0012] Monitoring feedback: Based on the adjustment direction, it is judged by a judgment method whether the carbon nanotubes under the target growth parameters meet the user's needs, and the target growth parameters that do not meet the user's needs are continuously optimized to obtain the optimized growth parameters and the resulting performance. The resulting performance and the optimized growth parameters are stored in the reference library.

[0013] The reference library generation method includes: presetting benchmark growth parameters, producing carbon nanotubes based on the benchmark growth parameters and assembling them into a lithium-ion battery to obtain a benchmark lithium-ion battery, obtaining the performance of the benchmark lithium-ion battery to obtain the benchmark performance, adjusting single or multiple variables in the benchmark growth parameters by a numerical addition method to complete obtaining different growth parameters to obtain several derivative growth parameters, producing carbon nanotubes based on the several derivative growth parameters and assembling them into a lithium-ion battery to obtain a target lithium-ion battery, obtaining the performance of the target lithium-ion battery to obtain the target performance. The benchmark growth parameters and the derivative growth parameters are the basic growth parameters, and the benchmark performance and the target performance are the basic battery performance. A reference library is established to store the basic growth parameters and the basic battery performance.

[0014] Furthermore, the numerical addition method includes: presetting a fixed value and an adjustment factor. The fixed value includes positive and negative values. Based on the fixed value, the adjustment factor, and the benchmark growth parameters, a single variable to be adjusted in the benchmark growth parameters is adjusted by a numerical addition formula to obtain several adjustment variables. The single variable or multiple variables in the benchmark growth parameters are correspondingly replaced with the adjustment variables to obtain the derivative growth parameters. The numerical addition formula is: , is the adjustment variable, is the single variable to be adjusted in the benchmark growth parameters, is a regulatory factor, is a fixed value.

[0015] Furthermore, the recognition method includes: taking the reference growth parameters and the basic battery performance as a reference, extracting the basic growth parameters for single-variable adjustment in the reference library to obtain the first analyzed growth parameters, extracting the basic battery performance corresponding to the first analyzed growth parameters in the reference library based on the corresponding relationship to obtain the first analyzed battery performance, comparing the first analyzed battery performance with the reference battery performance to obtain the first difference value, comparing the reference growth parameters with the first analyzed growth parameters to obtain the first variable value, integrating the first difference value and the first variable value to obtain the first target relationship, extracting the basic growth parameters for multi-variable adjustment in the reference library to obtain the second analyzed growth parameters, extracting the basic battery performance corresponding to the second analyzed growth parameters in the reference library based on the corresponding relationship to obtain the second analyzed battery performance, comparing the second analyzed battery performance with the reference battery performance to obtain the second difference value, comparing the reference growth parameters with the second analyzed growth parameters to obtain the second variable value, integrating the second difference value and the second variable value to obtain the second target relationship, and integrating the first target relationship and the second target relationship to obtain the target relationship.

[0016] Furthermore, the matching and adjustment method includes: determining the required battery performance or required growth parameters based on user requirements. When the user requirement is the required growth parameters, the target growth parameters are the required growth parameters. When the user requirement is the required battery performance, traversing the basic battery performance in the reference library based on the required battery performance to obtain a matching result. The matching result is either successful or failed. When the matching result is successful, extracting the basic battery performance consistent with the required battery performance in the reference library to obtain the target battery performance, and extracting the basic growth parameters corresponding to the target battery performance in the reference library based on the corresponding relationship to obtain the target growth parameters. When the matching result is failed, extracting two basic battery performances in the reference library that include the required battery performance to obtain two endpoint battery performances, extracting the basic growth parameters corresponding to the two endpoint battery performances in the reference library based on the corresponding relationship to obtain two endpoint growth parameters, and optimizing and adjusting the two endpoint growth parameters through the adjustment method based on the target relationship to obtain the target growth parameters.

[0017] Furthermore, the adjustment method includes: presetting a growth parameter threshold, subtracting the performance of two end-point batteries from the required battery performance to obtain two differences, selecting the difference with the smallest value among the two differences to obtain a target difference, determining the number of sub-performances with non-zero performance values in the target difference to obtain a target number, locking the performance relationship between one or more variables and the target number in the target relationship based on the target number to obtain a number of sub-target relationships, extracting the sub-target relationship that matches the sub-performance in the target difference from the number of sub-target relationships to obtain a final relationship, obtaining an added value by calculating a proportional relationship based on the final relationship and the target difference, adding the added value to the end-point growth parameter corresponding to the target difference to obtain a target growth parameter, and recording the final relationship, the target difference, and the added value to obtain an adjustment direction.

[0018] Furthermore, the judgment method includes: monitoring the performance of a finished lithium-ion battery composed of carbon nanotubes to obtain a result performance, determining whether the result performance is consistent with the user's requirements. When the result performance is inconsistent with the user's requirements, optimizing the target growth parameter through an optimization method to obtain an optimized growth parameter, growing carbon nanotubes based on the optimized growth parameter, and continuing to monitor the performance of the finished lithium-ion battery composed of the optimized carbon nanotubes to obtain a final performance, determining whether the final performance is consistent with the user's requirements. When the final performance is inconsistent with the user's requirements, the optimized growth parameter is optimized through the optimization method until the performance of the finished lithium-ion battery composed of the optimized carbon nanotubes is consistent with the user's requirements.

[0019] Furthermore, the optimization method includes: presetting a difference range and a reduction number, obtaining feedback address information, dynamically accumulating and reducing the added value based on the reduction number for the optimized number of times to obtain a dynamic value, obtaining the difference between the result performance and the required battery performance to obtain a finished product difference, determining whether the finished product difference is within the difference range. When the finished product difference is within the difference range, the finished product difference is fed back to the user based on the feedback address information, and the user decides whether to optimize. When the finished product difference is outside the difference range or the user decides to optimize, the relationship between the finished product difference and the target difference is determined. When the finished product difference is greater than the target difference, the endpoint growth parameter corresponding to the target difference is increased based on the dynamic value to obtain an optimized growth parameter. When the finished product difference is less than the target difference, the endpoint growth parameter corresponding to the target difference is reduced based on the dynamic value to obtain an optimized growth parameter. The performance of the lithium-ion battery finished product composed of the optimized growth parameter carbon nanotubes is obtained to obtain the final performance, and the difference between the final performance and the required battery performance is obtained to obtain the final difference. Determine whether the final difference is within the difference range. When the final difference is within the difference range, the final difference is fed back to the user based on the feedback address information, and the user decides whether to optimize. When the final difference is outside the difference range or the user decides to optimize, the relationship between the final difference and the target difference is determined. When the final difference is greater than the target difference, the optimized growth parameter corresponding to the finished product difference is reduced based on the dynamic value to obtain the final growth parameter. When the final difference is less than the target difference, the target growth parameter corresponding to the finished product difference is increased based on the dynamic value to obtain the final growth parameter. When the performance of the lithium-ion battery finished product composed of the optimized growth parameter is consistent with the user's requirements, the optimization is stopped.

[0020] An AI deep learning-based adaptive carbon nanotube growth control system uses the above-mentioned AI deep learning-based adaptive carbon nanotube growth control scheme.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] The AI deep learning-based adaptive carbon nanotube growth control scheme and system generate a reference library storing basic growth parameters and basic battery performance through the set reference library generation method, determine the target relationship between the single variable of the basic growth parameter and the basic battery performance through the set identification method, and match and adjust the method to cooperate with the reference library to match the target growth parameter that meets the user's needs for the user according to the user's needs and the target relationship, and adaptively formulate the target growth parameter that meets the user's needs for the user, so as to reduce the user's workload and improve the production and preparation efficiency of carbon nanotubes.

[0023] Meanwhile, through the set judgment method, a feedback interface can be added to obtain the performance of the finished lithium-ion battery composed of carbon nanotubes under the target growth parameter conditions to obtain the result performance. The process of obtaining the result performance can be obtained through the user's own feedback to facilitate monitoring whether the result performance is consistent with the user's requirements. When it is inconsistent, the target growth parameters are continuously optimized through the optimization method until the performance of the finished lithium-ion battery composed of carbon nanotubes is consistent with the user's requirements, and the optimized growth parameters and their resulting result performance are stored in the reference library to expand the reference range of the reference library.

[0024] Meanwhile, through the setting of the numerical addition method, different target growth parameters can be regularly obtained, which is convenient for users to judge the relationship between the change of the target growth parameters and the battery performance. The rule is formulated by the user according to the actual use situation. Through the setting of dynamic values, the quantity adjusted each time can be continuously reduced during the optimization process to increase the probability of accurately obtaining the final growth parameters consistent with the user's requirements. By setting the difference range, users can select whether to optimize by themselves to improve the preparation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the reference construction and information processing structure of the present invention;

[0026] Figure 2 It is a schematic diagram of the configuration selection, information output and listening feedback structure of the present invention;

[0027] Figure 3 It is a schematic diagram of the matching adjustment method structure of the present invention;

[0028] Figure 4 It is a schematic diagram of the process of matching the required battery performance of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0029] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] In the customized production scenario of the anode material in high-performance lithium-ion batteries, by precisely controlling the growth conditions of carbon nanotubes, carbon nanotubes with different diameters, shapes and structures can be customized to improve the battery capacity, charging speed and cycle life. The present invention adaptively provides the growth parameters of carbon nanotubes corresponding to the user's needs according to the user's needs, so as to facilitate the production of carbon nanotubes required, and then facilitate the customization of the anode material of lithium-ion batteries.

[0031] As Figures 1 - 4 shown, the present invention provides a technical solution: an adaptive carbon nanotube growth control solution based on AI deep learning, and the solution includes:

[0032] Information acquisition: acquiring variable information of carbon nanotube growth and user requirements;

[0033] Reference construction: generating a reference library by generating carbon nanotube growth parameters under different lithium battery performance indicators through a reference library generation method, and the reference library includes basic battery performance and basic growth parameters;

[0034] Information processing: judging the relationship between variables in the basic growth parameters and performance in the basic battery performance through an identification method to obtain a target relationship, and the target relationship includes a first target relationship reflecting the relationship between a single variable and performance and a second target relationship reflecting the relationship between multiple variables and performance. The variables in the basic growth parameters are consistent with the variable information, and the corresponding relationship between the basic battery performance and the basic growth parameters is established;

[0035] Configuration selection: searching for the basic battery performance corresponding to the user requirements in the reference library through a matching adjustment method and extracting the basic growth parameters according to the corresponding relationship for optimization adjustment to obtain target growth parameters and adjustment directions, and the adjustment directions include final relationship, target difference and added value;

[0036] Information output: growing carbon nanotubes based on the target growth parameters;

[0037] Monitoring feedback: judging whether the carbon nanotubes under the target growth parameters meet the user requirements based on the adjustment direction through a judgment method and continuously optimizing the target growth parameters that do not meet the user requirements to obtain optimized growth parameters and result performance, and storing the result performance and the optimized growth parameters in the reference library;

[0038] It should be noted that in the information acquisition stage, the variable information for carbon nanotube growth can be obtained through online retrieval. For example, when preparing carbon nanotubes using chemical vapor deposition, the variable information for carbon nanotube growth includes the concentration ratio of reaction gases, temperature, catalyst, reaction time, pressure, oxygen concentration, etc. The variable information for carbon nanotube growth is the variable information in the carbon nanotube growth parameters, and the user's requirement is the preparation requirement of the user, such as the need to prepare a lithium-ion battery with specific performance indicators or the need to prepare carbon nanotubes under specific growth parameter conditions. Referring to the generation method of the reference library set in the construction stage, the reference library of carbon nanotube growth parameters under different lithium-ion battery performance indicators can be generated. The reference library stores the basic battery performance and basic growth parameters. Through the set reference library generation method, the basic battery performance of different battery performances and the corresponding basic growth parameters can be collected and obtained, so as to directly refer to the basic growth parameters through the reference library according to the user's requirement in the follow-up to obtain the target growth parameters and play a reference role. In the information processing stage, the relationship between the variables in the basic growth parameters and the performance in the basic battery performance is judged through the set identification method, so as to adjust the growth parameters required by the user in combination with the reference library according to the target relationship in the follow-up. By establishing a corresponding relationship, it is convenient to extract the corresponding basic growth parameters according to the basic battery performance or extract the corresponding basic battery performance according to the basic growth parameters in the follow-up. In the configuration selection stage, through the set matching and adjustment method, the corresponding basic battery performance can be found in the reference library according to the user's requirement, and the basic growth parameters can be optimized and adjusted according to the target relationship to obtain the target growth parameters required by the user. By generating the adjustment direction, it is convenient to optimize the target growth parameters in the follow-up. In the information output stage, carbon nanotubes are prepared according to the provided target growth parameters. In the monitoring and feedback stage, it is detected through the judgment method whether the carbon nanotubes produced under the target growth parameters meet the user's requirement, and the target growth parameters of the carbon nanotubes that do not meet the user's requirement are optimized to obtain the optimized growth parameters. During the production of carbon nanotubes, single or several sample carbon nanotubes can be produced under the target growth parameters. By analyzing the sample carbon nanotubes, it is judged whether they meet the user's requirement. If they meet, no optimization is performed; if they do not meet, optimization is performed. At the same time, the optimized growth parameters and result performance are stored in the reference library to expand the reference range of the reference library and facilitate use.

[0039] The method for generating a reference library includes: presetting reference growth parameters, producing carbon nanotubes based on the reference growth parameters and assembling them into a lithium-ion battery to obtain a reference lithium-ion battery, obtaining the performance of the reference lithium-ion battery to obtain reference performance, adjusting one or more variables in the reference growth parameters through a numerical addition method to obtain a number of target growth parameters, producing carbon nanotubes based on the number of derived growth parameters and assembling them into a lithium-ion battery to obtain a target lithium-ion battery, obtaining the performance of the reference lithium-ion battery to obtain target performance, where the reference growth parameters and the derived growth parameters are basic growth parameters, and the reference performance and the target performance are basic battery performances, and establishing a reference library for storing the basic growth parameters and the basic battery performances.

[0040] It should be noted that the reference growth parameters are set by the user himself. The specific function is to serve as the carrier of the most basic growth parameters. Usually, the intermediate value growth parameter between the maximum growth parameter and the minimum growth parameter is selected. Through the set numerical addition method, one or more variables in the reference growth parameters are adjusted to obtain target growth parameters different from the reference growth parameters, and the target performance of the carbon nanotubes under the target growth parameters is obtained and assembled into a lithium-ion battery to obtain a target lithium-ion battery. Through multiple adjustments and multiple performance acquisitions, the reference range of the reference library can be expanded. The wider the data in the reference library, the more accurate the subsequent matching process according to the user's needs. At the same time, the growth parameters and performances of past lithium-ion batteries can also be obtained as basic growth parameters and basic battery performances and stored in the reference library.

[0041] The numerical addition method includes: presetting a fixed value and an adjustment factor. The fixed value includes positive and negative values. Based on the fixed value, the adjustment factor and the reference growth parameters, a number of adjustment variables are obtained by adjusting a single variable to be adjusted in the reference growth parameters through a numerical addition formula. The single variable or multiple variables in the reference growth parameters are correspondingly replaced with the adjustment variables to obtain the derived growth parameters. The numerical addition formula is: , is the adjustment variable, is a single variable to be adjusted in the reference growth parameters, is the adjustment factor, is the fixed value.

[0042] It should be noted that the fixed value and the adjustment factor are determined according to the actual usage. The fixed value is a specific number, and the adjustment factor is a variable number, such as 1, 2, 3, etc. By changing the adjustment factor, more adjustment variables can be obtained, so as to expand the reference range of the reference library. For example, when adjusting the temperature variable, the adjustment variable is T. When the single variable to be adjusted in the reference growth parameter is 900 °C, the fixed values are 10 °C and -10 °C, and the adjustment factors are 1, 2, 3. At this time, there are multiple adjustment variables, specifically T = 900 - 1×10 and T = 900 - 1×(-10), T = 900 - 2×10 and T = 900 - 2×(-10), and T = 900 - 3×10 and T = 900 - 3×(-10). By setting the numerical addition method, different target growth parameters can be obtained regularly, and then it is convenient for users to judge the relationship between the change of the target growth parameter and the battery performance. And the rule is formulated by the user according to the actual usage situation.

[0043] As Figure 1 shown, the recognition method includes: using the reference growth parameter and the basic battery performance as the reference, extracting the basic growth parameter for single variable adjustment in the reference library to obtain the first analyzed growth parameter, extracting the basic battery performance corresponding to the first analyzed growth parameter in the reference library based on the corresponding relationship to obtain the first analyzed battery performance, comparing the first analyzed battery performance with the reference battery performance to obtain the first difference value, comparing the reference growth parameter with the first analyzed growth parameter to obtain the first variable value, integrating the first difference value and the first variable value to obtain the first target relationship, extracting the basic growth parameter for multiple variable adjustment in the reference library to obtain the second analyzed growth parameter, extracting the basic battery performance corresponding to the second analyzed growth parameter in the reference library based on the corresponding relationship to obtain the second analyzed battery performance, comparing the second analyzed battery performance with the reference battery performance to obtain the second difference value, comparing the reference growth parameter with the second analyzed growth parameter to obtain the second variable value, integrating the second difference value and the second variable value to obtain the second target relationship, and integrating the first target relationship and the second target relationship to obtain the target relationship.

[0044] It should be noted that through the set identification method, the first target relationship reflecting the influence of a single variable adjustment on battery performance and the second target relationship reflecting the influence of multi-variable adjustment on battery performance can be obtained. The first difference value is obtained by comparing the first analyzed battery performance with the reference battery performance, and the second difference value is obtained by comparing the second analyzed battery performance with the reference battery performance, that is, the first difference value and the second difference value are obtained by subtracting the reference battery information from the first analyzed battery performance and the second analyzed battery performance. The specific process is to subtract the corresponding variable data items. Similarly, the first variable value is obtained by comparing the reference growth parameter with the first analyzed growth parameter, and the second variable value is obtained by comparing the reference growth parameter with the second analyzed growth parameter. And by obtaining the difference values and variable values, it is convenient to subsequently adjust the growth parameter to the target growth parameter required by the user according to the target relationship.

[0045] As Figures 2 - 4 shown, the matching adjustment method includes: determining the required battery performance or required growth parameter based on the user's needs. When the user's need is the required growth parameter, the target growth parameter is the required growth parameter. When the user's need is the required battery performance, the reference library is traversed based on the required battery performance to obtain a matching result. The matching result is either successful or failed. When the matching result is successful, the basic battery performance consistent with the required battery performance in the reference library is extracted to obtain the target battery performance, and the basic growth parameter corresponding to the target battery performance is extracted from the reference library based on the corresponding relationship to obtain the target growth parameter. When the matching result is failed, the two basic battery performances including the required battery performance in the reference library are extracted to obtain two end-point battery performances, and the basic growth parameters corresponding to the two end-point battery performances are extracted from the reference library based on the corresponding relationship to obtain two end-point growth parameters. The target growth parameter is obtained by optimizing and adjusting the two end-point growth parameters through the adjustment method based on the target relationship. Figure 4 The size of the middle circle represents the comprehensive size of the battery performance.

[0046] It should be noted that user requirements are divided into multiple types, specifically, battery performance requirements or growth parameter requirements. When the user requirement is for growth parameters, that is, the user provides the target growth parameters themselves. In this case, the required growth parameters are the target growth parameters. When the user requirement is for battery performance, the matching result is obtained by matching the basic battery performance in the reference library, and the next instruction is given according to the matching result. When the matching result feedback is successful, it means that there is a basic battery performance in the reference library that is consistent with the required battery performance. At this time, the basic growth parameters of the consistent basic battery performance are the target growth parameters. When the matching result feedback is failed, it means that there is no basic battery performance in the reference library that is consistent with the required battery performance. At this time, two basic battery performances including the required battery performance are extracted, that is, the required battery performance is between the battery performance ranges of the combination of the two basic battery performances, and the target growth parameters can be obtained by optimizing and adjusting the endpoint growth parameters of the endpoint battery performance through the adjustment method. Through the set matching and adjustment method, the user's requirements can be judged and analyzed, and different target growth parameters can be provided according to the user's requirements. Through the set adjustment method, the target growth parameters that meet the user's requirements can be adaptively formulated for the user to reduce the user's workload and improve the production and preparation efficiency of carbon nanotubes.

[0047] The adjustment method includes: presetting a growth parameter threshold, subtracting the two endpoint battery performances from the required battery performance to obtain two differences, selecting the difference with the smallest value among the two differences to obtain the target difference, judging the number of sub-performances with non-zero performance values in the target difference to obtain the target number, locking the performance relationship between one or more variables and the target number in the target relationship based on the target number to obtain several sub-target relationships, extracting the sub-target relationship that matches the sub-performance in the target difference from the several sub-target relationships to obtain the final relationship, obtaining the added value by calculating the proportional relationship based on the final relationship and the target difference, adding the added value to the endpoint growth parameter corresponding to the target difference to obtain the target growth parameter, and recording the final relationship, the target difference, and the added value to obtain the adjustment direction.

[0048] It should be noted that the growth parameter threshold is determined according to the actual usage situation, specifically the maximum growth parameter threshold. That is, when the target growth parameter exceeds the growth parameter threshold, the produced carbon nanotubes cannot be put into use or there are a large number of defects, etc. By comparing the target growth parameter with the preset growth parameter threshold, a barrier can be provided for adjusting the target growth parameter, so that the target growth parameter is within the normal range. The target difference is obtained by subtracting the demand battery performance from the smallest value among the battery performances at the two endpoints. When there is an item with a value of 0 in the target difference, it means that a certain sub-performance in the endpoint battery performance is the same as a certain sub-performance in the demand battery performance. Then, this sub-performance is not considered to reduce the system occupancy. The target quantity is obtained by judging the number of sub-performances with non-zero performance values in the target difference. Based on the target quantity, single or multiple variables and the performance relationship with the target quantity are locked in the target relationship to obtain several sub-target relationships, narrowing the scope in advance for subsequent accurate search and reducing the system occupancy during the search process. The final relationship is obtained by extracting the sub-target relationship that matches the sub-performance in the target difference from several sub-target relationships, that is, finding the target relationship with the same sub-performance as that in the target difference among several sub-target relationships to obtain the final relationship. Through this final relationship and the target difference, the added value is calculated by calculating the proportional relationship. The final relationship includes variable values and difference values. By combining the target difference, the specific value for variable adjustment, that is, the added value, can be calculated.

[0049] As Figure 2 shown, the judgment method includes: monitoring the performance of the finished lithium-ion battery composed of carbon nanotubes to obtain the result performance, and judging whether the result performance is consistent with the user's requirements. When the result performance is inconsistent with the user's requirements, the target growth parameter is optimized through the optimization method to obtain the optimized growth parameter. Based on the optimized growth parameter, the carbon nanotubes are grown, and the performance of the finished lithium-ion battery composed of the optimized carbon nanotubes is continuously monitored to obtain the final performance. Then, it is judged whether the final performance is consistent with the user's requirements. When the final performance is inconsistent with the user's requirements, the optimized growth parameter is optimized through the optimization method until the performance of the finished lithium-ion battery composed of the optimized carbon nanotubes is consistent with the user's requirements.

[0050] It should be noted that through the set judgment method, a feedback interface can be added to obtain the performance of the finished lithium-ion battery composed of carbon nanotubes under the target growth parameter condition to obtain the result performance. The process of obtaining the result performance can be obtained through the user's self-feedback, so as to facilitate monitoring whether the result performance is consistent with the user's requirements. When they are inconsistent, the target growth parameter is continuously optimized through the optimization method until the performance of the finished lithium-ion battery composed of carbon nanotubes is consistent with the user's requirements.

[0051] The optimization method includes: presetting a difference range and a reduction number, obtaining feedback address information, dynamically accumulating and reducing the added value based on the reduction number for the number of optimization times to obtain a dynamic value, obtaining the difference between the result performance and the required battery performance to obtain a finished product difference, determining whether the finished product difference is within the difference range. When the finished product difference is within the difference range, the finished product difference is fed back to the user based on the feedback address information, and the user decides whether to optimize. When the finished product difference is outside the difference range or the user decides to optimize, the relationship between the finished product difference and the target difference is determined. When the finished product difference is greater than the target difference, the endpoint growth parameter corresponding to the target difference is increased based on the dynamic value to obtain an optimized growth parameter. When the finished product difference is less than the target difference, the endpoint growth parameter corresponding to the target difference is reduced based on the dynamic value to obtain an optimized growth parameter. Obtaining the performance of the lithium-ion battery finished product composed of the optimized growth parameter carbon nanotubes to obtain the final performance, obtaining the difference between the final performance and the required battery performance to obtain the final difference, determining whether the final difference is within the difference range. When the final difference is within the difference range, the final difference is fed back to the user based on the feedback address information, and the user decides whether to optimize. When the final difference is outside the difference range or the user decides to optimize, the relationship between the final difference and the target difference is determined. When the final difference is greater than the target difference, the optimized growth parameter corresponding to the finished product difference is reduced based on the dynamic value to obtain the final growth parameter. When the final difference is less than the target difference, the target growth parameter corresponding to the finished product difference is increased based on the dynamic value to obtain the final growth parameter. When the performance of the lithium-ion battery finished product composed of the optimized growth parameter is consistent with the user's requirements, the optimization is stopped.

[0052] It should be noted that the difference range and the reduction number are set by the user according to the actual usage situation. The size of the reduction number should be as small as possible to facilitate more precise optimization to obtain the final growth parameter consistent with the user's requirements. The dynamic value is obtained by reducing the added value by the reduction number for the number of optimization times. Specifically, when the number of optimization times is the first time, the dynamic value is the added value minus the reduction number. When the number of optimization times is the second time, the dynamic value is the added value minus 2×the reduction number, and so on. By setting the dynamic value, the amount of each adjustment can be continuously reduced during the optimization process to increase the probability of accurately obtaining the final growth parameter consistent with the user's requirements, which is beneficial for use. The feedback address information is the user's address information, specifically the user's email, IP address, telephone number, etc. By setting the difference range, the user can select whether to optimize by himself, so as to improve the preparation efficiency and be beneficial for use.

[0053] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended embodiments and their equivalents.

Claims

1. An adaptive carbon nanotube growth control method based on AI deep learning, characterized in that: The method includes: Information acquisition: acquiring variable information on carbon nanotube growth and user requirements; Reference construction: generating a reference library by the reference library generation method to obtain carbon nanotube growth parameters under different lithium-ion battery performance indicators, where the reference library includes basic battery performance and basic growth parameters; Information processing: establishing the correspondence between the basic battery performance and the basic growth parameters, and based on the correspondence, determining the relationship between the variables in the basic growth parameters and the performance in the basic battery performance through an identification method to obtain the target relationship, where the target relationship includes a first target relationship reflecting the relationship between a single variable and the performance and a second target relationship reflecting the relationship between multiple variables and the performance, and the variables in the basic growth parameters are consistent with the variable information; Configuration selection: searching in the reference library for the basic battery performance corresponding to the user requirements through a matching adjustment method and extracting the basic growth parameters according to the correspondence, and optimizing and adjusting the basic growth parameters based on the target relationship through a matching adjustment method to obtain the target growth parameters and the adjustment direction, where the adjustment direction includes the final relationship, the target difference, and the added value; Information output: growing carbon nanotubes based on the target growth parameters; Monitoring feedback: judging whether the carbon nanotubes under the target growth parameters meet the user requirements through a judgment method based on the adjustment direction and continuously optimizing the target growth parameters that do not meet the user requirements to obtain the optimized growth parameters and the resulting performance, and storing the resulting performance and the optimized growth parameters in the reference library; The reference library generation method includes: presetting benchmark growth parameters, producing carbon nanotubes based on the benchmark growth parameters and assembling them into a lithium-ion battery to obtain a benchmark lithium-ion battery, acquiring the performance of the benchmark lithium-ion battery to obtain the benchmark performance, adjusting one or more variables in the benchmark growth parameters through a numerical addition method to complete obtaining several derivative growth parameters, producing carbon nanotubes based on the several derivative growth parameters and assembling them into a lithium-ion battery to obtain a target lithium-ion battery, acquiring the performance of the target lithium-ion battery to obtain the target performance, where the benchmark growth parameters and the derivative growth parameters are the basic growth parameters, and the benchmark performance and the target performance are the basic battery performance, and establishing a reference library for storing the basic growth parameters and the basic battery performance; The recognition method includes: taking the reference growth parameters and reference performance as the benchmark, extracting the basic growth parameters with a single variable adjusted in the reference library to obtain the first analyzed growth parameters, extracting the basic battery performance corresponding to the first analyzed growth parameters in the reference library based on the corresponding relationship to obtain the first analyzed battery performance, comparing the first analyzed battery performance with the reference battery performance to obtain the first difference value, comparing the reference growth parameters with the first analyzed growth parameters to obtain the first variable value, integrating the first difference value and the first variable value to obtain the first target relationship, extracting the basic growth parameters with multiple variables adjusted in the reference library to obtain the second analyzed growth parameters, extracting the basic battery performance corresponding to the second analyzed growth parameters in the reference library based on the corresponding relationship to obtain the second analyzed battery performance, comparing the second analyzed battery performance with the reference performance to obtain the second difference value, comparing the reference growth parameters with the second analyzed growth parameters to obtain the second variable value, integrating the second difference value and the second variable value to obtain the second target relationship, and integrating the first target relationship and the second target relationship to obtain the target relationship; The matching and adjustment method includes: determining the required battery performance or required growth parameters based on user requirements. When the user requirement is the required growth parameters, the target growth parameters are the required growth parameters. When the user requirement is the required battery performance, traversing the basic battery performance in the reference library based on the required battery performance to obtain a matching result, and the matching result is success or failure. When the matching result is success, extracting the basic battery performance consistent with the required battery performance in the reference library to obtain the target battery performance, and extracting the basic growth parameters corresponding to the target battery performance in the reference library based on the corresponding relationship to obtain the target growth parameters. When the matching result is failure, extracting two basic battery performances including the required battery performance in the reference library to obtain two end-point battery performances, and the required battery performance is between the battery performance ranges combined by the two basic battery performances. Extracting the basic growth parameters corresponding to the two end-point battery performances in the reference library based on the corresponding relationship to obtain two end-point growth parameters, and optimizing and adjusting the two end-point growth parameters through the adjustment method based on the target relationship to obtain the target growth parameters. The adjustment method includes: presetting a growth parameter threshold, subtracting the two end-point battery performances from the required battery performance to obtain two differences, selecting the difference with the smallest numerical value among the two differences to obtain the target difference, judging the number of sub-performances with non-zero performance values in the target difference to obtain the target number, locking the performance relationships with the same number of performances as the target number in the single or multiple variables in the target relationship based on the target number to obtain several sub-target relationships, extracting the sub-target relationship matching the sub-performance in the target difference from the several sub-target relationships to obtain the final relationship, obtaining the added value by calculating the proportional relationship based on the final relationship and the target difference, adding the added value to the end-point growth parameter corresponding to the target difference to obtain the target growth parameter, and recording the final relationship, the target difference, and the added value to obtain the adjustment direction.

2. The adaptive carbon nanotube growth control method based on AI deep learning according to claim 1, characterized in that: The numerical addition method includes: presetting a fixed value and an adjustment factor, where the fixed value includes positive and negative values, and adjusting a single variable to be adjusted in the reference growth parameter through a numerical addition formula based on the fixed value, the adjustment factor, and the reference growth parameter to obtain a number of adjusted variables, and replacing the variables in the reference growth parameter with the adjusted variables correspondingly to obtain a derivative growth parameter. The numerical addition formula is: , is the adjusted variable, is a single variable to be adjusted in the reference growth parameter, is the adjustment factor, is the fixed value.

3. The adaptive carbon nanotube growth control method based on AI deep learning according to claim 1, characterized in that: The judgment method includes: monitoring the performance of the finished lithium-ion battery composed of carbon nanotubes to obtain the result performance, judging whether the result performance is consistent with the user requirements. When the result performance is inconsistent with the user requirements, optimizing the target growth parameters through an optimization method to obtain the optimized growth parameters, growing carbon nanotubes based on the optimized growth parameters, and continuing to monitor the performance of the finished lithium-ion battery composed of the optimized carbon nanotubes to obtain the final performance, judging whether the final performance is consistent with the user requirements. When the final performance is inconsistent with the user requirements, then optimizing the optimized growth parameters through the optimization method until the performance of the finished lithium-ion battery composed of the optimized carbon nanotubes is consistent with the user requirements.

4. An adaptive carbon nanotube growth control method based on AI deep learning according to claim 3, characterized in that: The optimization method includes: presetting a difference range and a reduction number, obtaining feedback address information, dynamically accumulating and reducing the added value based on the reduction number after the optimization times to obtain a dynamic value, obtaining the difference between the result performance and the required battery performance to obtain the finished product difference, judging whether the finished product difference is within the difference range. When the finished product difference is within the difference range, then feedback the finished product difference to the user based on the feedback address information, and let the user decide whether to optimize. When the finished product difference is outside the difference range or the user decides to optimize, judge the relationship between the finished product difference and the target difference. When the finished product difference is greater than the target difference, increase the endpoint growth parameter corresponding to the target difference based on the dynamic value to obtain the optimized growth parameters. When the finished product difference is less than the target difference, reduce the endpoint growth parameter corresponding to the target difference based on the dynamic value to obtain the optimized growth parameters. Obtain the performance of the finished lithium-ion battery composed of the optimized growth parameters to obtain the final performance, obtain the difference between the final performance and the required battery performance to obtain the final difference, judge whether the final difference is within the difference range. When the final difference is within the difference range, then feedback the final difference to the user based on the feedback address information, and let the user decide whether to optimize. When the final difference is outside the difference range or the user decides to optimize, judge the relationship between the final difference and the target difference. When the final difference is greater than the target difference, reduce the optimized growth parameter corresponding to the finished product difference based on the dynamic value to obtain the final growth parameters. When the final difference is less than the target difference, increase the target growth parameter corresponding to the finished product difference based on the dynamic value to obtain the final growth parameters. When the performance of the finished lithium-ion battery composed of the optimized growth parameters is consistent with the user requirements, then stop the optimization.

5. An adaptive carbon nanotube growth control system based on AI deep learning, characterized in that: An adaptive carbon nanotube growth control method based on AI deep learning according to any one of claims 1-4 is used.

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