A graphite part preparation system and preparation method

Abstract

The present invention relates to the technical field of graphite parts preparation, and discloses a graphite parts preparation system and preparation method, the method comprising: obtaining historical preparation data of graphite parts and corresponding environmental data. Establishing a preparation model of graphite parts according to the historical preparation data and the environmental data during preparation. Obtaining real-time environmental data during graphite parts preparation, and substituting the real-time environmental data into the preparation model to obtain various preset conductivities of the graphite parts. Clustering each preset conductivity according to a clustering method, and obtaining the clustered preset conductivity. Using the preset preparation data corresponding to the preset conductivity as the preset preparation data of the graphite parts, and preparing the blank according to the preset preparation data. The present invention realizes an optimized graphite parts preparation process based on a precise model and data-driven by integrating historical preparation data, real-time environmental data and cluster analysis, thereby improving the stability of the preparation process and the quality consistency of the final product.

Description

Technical Field

[0001] The present invention relates to the technical field of graphite part preparation, and in particular to a graphite part preparation system and method. Background Art

[0002] With the rapid development of the electronics industry and the energy industry, graphite materials are widely used in the manufacture of electrodes, refractory materials, and high-performance composite materials due to their excellent conductivity, high temperature resistance, and corrosion resistance. However, the preparation process of graphite parts has an extremely important impact on the material properties, especially the precise control of its conductivity is directly related to the performance quality of the final product. Therefore, how to accurately control the preparation parameters of graphite parts through scientific methods and improve conductivity has become the key to technical research.

[0003] At present, the traditional preparation process of graphite parts usually relies on the rich experience of operators and a series of pre-set static parameters for production. This method often lacks flexibility and makes it difficult to adjust various environmental variables in real time during the production process. For example, fluctuations in key factors such as temperature, pressure, and humidity often have a significant impact on the conductivity of graphite parts. Since these environmental variables may change during the production process, it is difficult for traditional preparation methods to ensure the consistency and stability of the conductivity of graphite parts.

[0004] Therefore, it is urgent to invent a graphite part preparation technology to solve the problem of poor consistency in performance and conductivity of prepared graphite parts due to poor operational flexibility in the prior art. Summary of the invention

[0005] In view of this, the present invention proposes a graphite part preparation system and preparation method, aiming to solve the problem in the current technology that the performance and conductivity of the prepared graphite parts are poorly consistent due to poor operational flexibility.

[0006] The present invention provides a method for preparing a graphite part, comprising:

[0007] Obtain historical preparation data of graphite parts and corresponding environmental data;

[0008] Establishing a preparation model of the graphite part according to the historical preparation data and the environmental data during preparation;

[0009] Acquire real-time environmental data during graphite part preparation, and substitute the real-time environmental data into the preparation model to obtain various preset conductivities of the graphite part;

[0010] Clustering the preset conductivities according to a clustering method, and obtaining the clustered preset conductivities;

[0011] The preset preparation data corresponding to the preset conductivity is used as the preset preparation data of the graphite part, and the blank is prepared according to the preset preparation data.

[0012] Furthermore, when obtaining the historical preparation data of the graphite parts and the corresponding environmental data, it includes:

[0013] Obtaining the immersion time of the blank during the historical preparation of the graphite piece, the calcination time and temperature of the blank during calcination, the catalytic temperature and time during high-temperature catalysis, and the conductivity of the graphite piece after preparation;

[0014] The ambient temperature and humidity during the historical preparation of the graphite part are obtained.

[0015] Furthermore, when establishing a preparation model of a graphite part according to the historical preparation data and the environmental data during preparation, it includes:

[0016] Clearing duplicate data from the historical preparation data;

[0017] Establishing a preparation formula for each of the graphite pieces according to the impregnation time, roasting time, roasting temperature, catalytic temperature, catalytic time, ambient temperature, ambient humidity and conductivity of the historical preparation of each of the graphite pieces after removing the duplicate data;

[0018] The temperature linear relationship and humidity linear relationship between the preparation formulas are obtained, and a preparation model of the graphite part is established according to the relationship between the temperature linear relationship and humidity linear relationship and the preparation formulas.

[0019] Furthermore, when establishing the preparation model of the graphite part, it includes:

[0020] Obtaining the actual conductivity of the graphite piece in any of the historical preparation data and the corresponding preparation formula for the graphite piece;

[0021] Substituting the preparation formula of the graphite part into the preparation model, and obtaining a preset conductivity of the preparation model;

[0022] According to the relationship between the preset conductivity and the actual conductivity, it is determined whether to modify the preparation model of the graphite part, wherein:

[0023] When the preset conductivity is consistent with the actual conductivity, it is determined that there is no need to modify the preparation model of the graphite part;

[0024] When the preset conductivity is inconsistent with the actual conductivity, iterative correction is performed according to the preparation model of the graphite part until the preset conductivity is consistent with the actual conductivity.

[0025] Furthermore, obtaining real-time environmental data during the preparation of the graphite part, and substituting the real-time environmental data into the preparation model to obtain each preset conductivity of the graphite part includes:

[0026] Acquire the real-time temperature and real-time humidity in the real-time environmental data;

[0027] According to the matching between the real-time temperature and the real-time humidity and the temperature linear relationship and the humidity linear relationship in the preparation model, the preset conductivity of each preparation formula that matches the real-time temperature and the real-time humidity is obtained.

[0028] Furthermore, clustering the preset conductivities according to a clustering method and obtaining the clustered preset conductivities comprises:

[0029] Matching is performed according to each of the preset conductivity and the configured preset conductivity range, wherein:

[0030] If the preset conductivity is within the preset conductivity range, retaining the preset conductivity;

[0031] If the preset conductivity is not within the preset conductivity range, the preset conductivity is eliminated.

[0032] Furthermore, clustering the preset conductivities according to a clustering method and obtaining the clustered preset conductivities comprises:

[0033] Acquire the retained preset conductivity values, and remove duplicate data from the preset conductivity values;

[0034] Density clustering is performed on the preset conductivities after removing duplicate data, and the clustered preset conductivities are obtained.

[0035] Furthermore, when performing density clustering on the preset conductivity after removing duplicate data and obtaining the clustered preset conductivity, it includes:

[0036] Obtaining the Euclidean distance between the preset conductivities;

[0037] Density clustering is performed according to the Euclidean distances between the preset conductivities, and the preset conductivities after the density clustering are obtained.

[0038] Compared with the prior art, the beneficial effect of the present invention is that by introducing a data-driven preparation model, the method can monitor and analyze key variables (such as temperature, humidity, pressure, etc.) in the current environment in real time during the actual production process, and substitute these data into the preparation model to optimize the conductivity. This dynamic control mechanism not only improves the flexibility of the preparation process, but also greatly reduces the manual intervention that relies on experience, thereby reducing the human error in the production process. In addition, by clustering different preset conductivity, the regularity of the conductive properties of graphite parts can be identified, and the optimal conductivity group can be effectively screened out. This process replaces the traditional practice of relying on repeated experiments or empirical inference through data analysis, significantly shortening the research and development and adjustment cycle. The optimal conductivity after clustering can not only better meet the performance requirements in actual applications, but also improve the quality consistency of the product, thereby improving the reliability of graphite parts in high-demand application scenarios. Next, by combining the preparation model with real-time data, the method can quickly adjust the process parameters in each production, avoid unnecessary repetitive experiments, and significantly improve production efficiency. This model-driven intelligent preparation method also lays the foundation for large-scale production, making the preparation of graphite parts more efficient and standardized. Finally, by systematically analyzing the preparation process of graphite parts, the multiple process steps that were originally dispersed (such as impregnation, roasting, and high-temperature catalysis) were incorporated into an integrated model for unified optimization. This integrated approach makes the preparation process more controllable and reduces the uncertainty in the process. Especially for complex environmental variables, the real-time feedback mechanism can effectively avoid their negative impact on the quality of graphite parts and ensure that the produced graphite parts always maintain high performance.

[0039] On the other hand, the present application also provides a graphite part preparation system, comprising:

[0040] A data acquisition module, electrically connected to the database, for acquiring historical preparation data of the graphite parts and corresponding environmental data;

[0041] A model generation module, electrically connected to the data acquisition module, and used for establishing a preparation model of the graphite part according to the historical preparation data and the environmental data during preparation;

[0042] An analysis module, electrically connected to the environment detection module and the model generation module, respectively, the analysis module is used to obtain real-time environmental data during the preparation of the graphite part, and substitute the real-time environmental data into the preparation model to obtain each preset conductivity of the graphite part; the analysis module is also used to cluster each preset conductivity according to a clustering method, and obtain the clustered preset conductivity;

[0043] A central control module is electrically connected to the analysis module, and is used to use the preset preparation data corresponding to the preset conductivity as the preset preparation data of the graphite part, and prepare the blank according to the preset preparation data.

[0044] Furthermore, when the central control module is used to prepare the blank according to the preset preparation data, it includes:

[0045] The central control module is also used to obtain the real-time preparation data of the graphite part, and determine whether to modify the real-time preparation data according to the relationship between the real-time preparation data and the preset preparation data:

[0046] When the real-time preparation data is consistent with the preset preparation data, the central control module determines not to modify the real-time preparation data;

[0047] When the real-time preparation data is inconsistent with the preset preparation data, the central control module determines the real-time preparation data correction coefficient according to the difference between the real-time preparation data and the preset preparation data, and corrects the real-time preparation data according to the correction coefficient.

[0048] It can be understood that the graphite part preparation system and preparation method in the above-mentioned embodiments of the present invention have the same beneficial effects, which will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0050] Figure 1 A flowchart of a method for preparing a graphite part provided by an embodiment of the present invention;

[0051] Figure 2 A functional block diagram of a graphite part preparation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0053] With the rapid development of the electronics industry and the energy industry, graphite materials are widely used in the manufacture of electrodes, refractory materials, and high-performance composite materials due to their excellent conductivity, high temperature resistance, and corrosion resistance. However, the preparation process of graphite parts has an extremely important impact on the material properties, especially the precise control of its conductivity is directly related to the performance quality of the final product. Therefore, how to accurately control the preparation parameters of graphite parts through scientific methods and improve conductivity has become the key to technical research.

[0054] At present, the traditional preparation process of graphite parts usually relies on the rich experience of operators and a series of pre-set static parameters for production. This method often lacks flexibility and makes it difficult to adjust various environmental variables in real time during the production process. For example, fluctuations in key factors such as temperature, pressure, and humidity often have a significant impact on the conductivity of graphite parts. Since these environmental variables may change during the production process, it is difficult for traditional preparation methods to ensure the consistency and stability of the conductivity of graphite parts.

[0055] In view of this, the present invention proposes a graphite part preparation system and preparation method, aiming to solve the problem in the current technology that the performance and conductivity of the prepared graphite parts are poorly consistent due to poor operational flexibility.

[0056] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a method for preparing a graphite part, comprising:

[0057] Step S100: Acquire historical preparation data of graphite parts and corresponding environmental data.

[0058] Specifically, when obtaining the historical preparation data of the graphite parts and the corresponding environmental data, it includes: obtaining the immersion time of the blank during the historical preparation of the graphite parts, the calcination time and calcination temperature of the blank during the calcination, the catalytic temperature and catalytic time during the high-temperature catalysis, and the conductivity of the graphite parts after preparation. Obtain the environmental temperature and humidity during the historical preparation of the graphite parts.

[0059] It is understandable that by obtaining historical preparation data, including impregnation time, calcination time and temperature, high-temperature catalytic conditions (catalytic temperature and time), and the conductivity of the prepared graphite parts, detailed input data can be provided for establishing the preparation model. In addition, collecting variables related to the external environment during the preparation process, such as ambient temperature and humidity, helps to further analyze the impact of environmental factors on the quality of graphite parts. This systematic data collection provides a basis for the subsequent construction of the preparation model, ensuring that the model can accurately simulate and optimize the different variables in the preparation process of graphite parts, thereby improving the conductivity and quality stability of graphite parts.

[0060] Step S200: establishing a graphite part preparation model according to historical preparation data and environmental data during preparation.

[0061] Specifically, when establishing a preparation model for graphite parts based on historical preparation data and environmental data during preparation, it includes: removing duplicate data in historical preparation data. Establishing a preparation formula for each graphite part based on the immersion time, roasting time, roasting temperature, catalytic temperature, catalytic time, ambient temperature, ambient humidity and conductivity of each graphite part during historical preparation after removing duplicate data. Obtaining the temperature linear relationship and humidity linear relationship between each preparation formula, and establishing a preparation model for graphite parts based on the relationship between the temperature linear relationship, the humidity linear relationship and each preparation formula.

[0062] Specifically, when establishing a preparation model for a graphite part, it includes: obtaining the actual conductivity of the graphite part in any historical preparation data and the corresponding preparation formula for the preparation of the graphite part. Substituting the preparation formula of the graphite part into the preparation model, and obtaining the preset conductivity of the preparation model. According to the relationship between the preset conductivity and the actual conductivity, it is determined whether to modify the preparation model of the graphite part, wherein: when the preset conductivity is consistent with the actual conductivity, it is determined that the preparation model of the graphite part does not need to be modified. When the preset conductivity is inconsistent with the actual conductivity, it is iteratively corrected according to the preparation model of the graphite part until the preset conductivity is consistent with the actual conductivity.

[0063] It can be seen that the historical preparation data is cleaned to eliminate duplicate data. This step ensures the accuracy and reliability of the data used and prevents analytical deviations caused by data redundancy. The cleaned data includes key preparation parameters such as impregnation time, calcination time and temperature, catalytic temperature and time, and related environmental factors such as ambient temperature and humidity, and the corresponding conductivity of the graphite parts is recorded. These data lay the foundation for establishing an effective preparation model. Secondly, in the process of establishing the preparation model, by substituting the cleaned data into the model, a mathematical expression (preparation formula) for the conductivity of the graphite parts under each preparation condition can be established. These preparation formulas reflect the effects of different process parameters and environmental conditions on conductivity. Specifically, by analyzing the linear relationship between each preparation formula and temperature and humidity, the main factors affecting conductivity can be identified, and a preparation model that comprehensively considers environmental and process parameters can be constructed based on this. This model can not only reflect the predicted conductivity under each preparation condition, but also reveal how process parameters and environmental factors interact to affect the performance of the final graphite parts. Finally, the prediction accuracy of the model can be further evaluated by comparing the actual measured conductivity in the historical data with the preset conductivity predicted by the model. If the preset conductivity is consistent with the actual conductivity, it means that the model can accurately reflect the key factors in the preparation process and no correction is required. However, if there is a difference between the two, the model needs to be optimized through iterative correction. This iterative process involves adjusting the parameters in the model so that the model prediction value gradually approaches the actual measured value. Through multiple iterative optimizations, the prediction accuracy of the model can be significantly improved, making it better adapted to actual production conditions.

[0064] It can be understood that by cleaning up the duplicates in the historical preparation data, the accuracy and completeness of the data used are ensured. This data preprocessing step avoids the interference of redundant information on the analysis results and provides a reliable basis for the establishment of subsequent models. Accurate data input is the prerequisite for establishing an efficient preparation model, which helps to reduce the error in the model and improve the predictive ability of the model. Secondly, in the process of establishing the preparation model, a detailed preparation formula was established by analyzing the relationship between the impregnation time, calcination time and temperature, catalytic conditions, and environmental factors (such as temperature and humidity) and conductivity. These preparation formulas accurately reflect the effect of each combination of process parameters on the conductivity of graphite parts. By obtaining the linear relationship between temperature and humidity and integrating these relationships into the preparation model, the effect of environmental factors on the preparation process can be better captured. This modeling method provides a scientific basis for the optimization of process parameters, so that the preparation process of graphite parts can maintain consistent performance under different environmental conditions. Finally, by comparing the actual conductivity with the preset conductivity predicted by the model, the dynamic optimization of the preparation model is achieved. After each preparation process, the difference between the actual conductivity and the model prediction value is used to determine whether the model needs to be corrected. If there is an inconsistency between the preset conductivity and the actual conductivity, the model parameters are iteratively corrected to make the model prediction value closer to the actual measured value. This iterative optimization mechanism ensures that the model can adapt to various changes in actual production and further improves the accuracy and stability of the preparation process.

[0065] Step S300: acquiring real-time environmental data during the preparation of the graphite part, and substituting the real-time environmental data into the preparation model to acquire various preset conductivities of the graphite part.

[0066] Specifically, the real-time environmental data during the preparation of the graphite parts is obtained, and the real-time environmental data is substituted into the preparation model to obtain each preset conductivity of the graphite parts, including: obtaining the real-time temperature and real-time humidity in the real-time environmental data. According to the real-time temperature and real-time humidity, the linear relationship between the temperature and the linear relationship between the humidity in the preparation model is matched, and the preset conductivity of each preparation formula that matches the real-time temperature and the real-time humidity is obtained.

[0067] It can be seen that by obtaining real-time temperature and humidity data during the preparation of graphite parts, changes in the current production environment can be reflected in real time. These environmental factors have a direct impact on the final performance of graphite parts, so accurately capturing and analyzing these data is the key to ensuring the quality of preparation. In actual operation, real-time temperature and humidity data will be input into the established graphite parts preparation model. The model establishes a linear relationship between temperature and humidity and the conductivity of graphite parts by integrating historical preparation data and environmental factors. These linear relationships show how process parameters affect the conductivity of graphite parts under different environmental conditions. By substituting real-time data into these linear relationships, the conductivity under current environmental conditions can be predicted, thereby achieving precise control of the preparation process. Specifically, the real-time environmental data is matched with the linear relationship between temperature and humidity in the preparation model to determine the preset conductivity of each preparation formula under the current environment. This process not only takes into account the impact of ambient temperature and humidity on conductivity, but also calculates the corresponding conductivity prediction value through the preset linear relationship in the model. This dynamic matching method enables the preparation process of graphite parts to be adjusted in real time to cope with changes in environmental conditions, thereby improving the accuracy and stability of preparation.

[0068] Understandably, through this real-time data-driven optimization strategy, each step in the production process can be adjusted according to current environmental conditions. This approach not only improves the performance and consistency of graphite parts, but also enhances the flexibility and adaptability of the production process. Real-time prediction and adjustment can effectively reduce quality fluctuations caused by environmental factors, improving the overall quality of products and production efficiency.

[0069] Step S400: clustering the preset conductivities according to a clustering method, and obtaining the clustered preset conductivities.

[0070] Specifically, clustering each preset conductivity according to the clustering method, and obtaining the clustered preset conductivity, includes: matching each preset conductivity with a configured preset conductivity range, wherein: if the preset conductivity is within the preset conductivity range, the preset conductivity is retained. If the preset conductivity is not within the preset conductivity range, the preset conductivity is eliminated.

[0071] Specifically, clustering the preset conductivities according to the clustering method and obtaining the clustered preset conductivities includes: obtaining the retained preset conductivities and removing duplicate data from the preset conductivities. Density clustering is performed on the preset conductivities from which the duplicate data has been removed, and the clustered preset conductivities are obtained.

[0072] Specifically, when performing density clustering on the preset conductivity after removing duplicate data and obtaining the clustered preset conductivity, it includes: obtaining the Euclidean distance between the preset conductivity. According to the Euclidean distance between the preset conductivity, density clustering is performed, and the preset conductivity after density clustering is obtained.

[0073] It can be seen that in the data preprocessing stage, each preset conductivity is matched with the configured preset conductivity range. This operation ensures that only the conductivity that meets the preset range is retained, thereby avoiding the interference of abnormal data beyond the expected range on subsequent analysis. This step effectively filters out the data that does not meet the conditions, lays the foundation for further clustering analysis, and ensures the validity and accuracy of the data. Secondly, in the cleaning process of the preset conductivity, the data set is further purified by removing duplicate data. Duplicate data may cause deviations in the analysis results, so removing duplicate data helps to improve the accuracy of the analysis. Next, the density clustering method is applied to the preset conductivity that has been cleared of duplicates. Density clustering is an unsupervised learning technique that identifies the natural structure of data by analyzing the density distribution between data points. This method clusters similar data points together to form multiple clusters by calculating the Euclidean distance between each preset conductivity. This method can effectively identify patterns and groups in the conductivity data, providing a basis for further decision-making. Specifically, the density clustering method uses the Euclidean distance to measure the similarity between each data point. By setting appropriate parameters (such as neighborhood radius and minimum number of points), the conductivity data can be divided into different clusters according to their density distribution. Each cluster represents a group of data points with similar characteristics in conductivity, and the center values ​​of these clusters can be used as the preset conductivity after clustering. In this way, the most representative conductivity can be extracted, thereby simplifying the data processing and analysis process.

[0074] Specifically, by obtaining any two preset conductivities and the characteristics between the two preset conductivities, through the formula, Get the Euclidean distance between any two preset conductivities, where x and y are two preset conductivities, xi and yi are the i-th features of the two preset conductivities, and n is the dimension of the feature, and then according to the formula: The centroid value of each cluster is obtained during clustering, where C is the centroid, S is the set of data points in the cluster, and |S| is the number of points in the cluster. In this way, the conductivity data of graphite parts can be systematically analyzed and optimized. The Euclidean distance ensures the similarity measurement of data points, the density clustering algorithm realizes the effective grouping of data, and the centroid calculation provides representative data for each cluster. These steps work together to effectively improve the data analysis capabilities in the graphite parts preparation process.

[0075] Step S500: using the preset preparation data corresponding to the preset conductivity as the preset preparation data of the graphite part, and preparing the blank according to the preset preparation data.

[0076] In the above embodiment, by introducing a data-driven preparation model, the method can monitor and analyze key variables (such as temperature, humidity, pressure, etc.) in the current environment in real time during the actual production process, and substitute these data into the preparation model to optimize the conductivity. This dynamic control mechanism not only improves the flexibility of the preparation process, but also greatly reduces the manual intervention that relies on experience, thereby reducing human errors in the production process. In addition, by clustering different preset conductivity, the regularity of the conductive properties of graphite parts can be identified, and the optimal conductivity group can be effectively screened out. This process replaces the traditional practice of relying on repeated experiments or empirical inference through data analysis, significantly shortening the research and development and adjustment cycle. The optimal conductivity after clustering can not only better meet the performance requirements in actual applications, but also improve the quality consistency of the product, thereby improving the reliability of graphite parts in high-demand application scenarios. Next, by combining the preparation model with real-time data, the method can quickly adjust the process parameters in each production, avoid unnecessary repetitive experiments, and significantly improve production efficiency. This model-driven intelligent preparation method also lays the foundation for large-scale production, making the preparation of graphite parts more efficient and standardized. Finally, by systematically analyzing the preparation process of graphite parts, the multiple process steps that were originally dispersed (such as impregnation, roasting, and high-temperature catalysis) were incorporated into an integrated model for unified optimization. This integrated approach makes the preparation process more controllable and reduces the uncertainty in the process. Especially for complex environmental variables, the real-time feedback mechanism can effectively avoid their negative impact on the quality of graphite parts and ensure that the produced graphite parts always maintain high performance.

[0077] In another preferred embodiment based on the above embodiment, Figure 2The present embodiment shown provides a graphite parts preparation system, including: a data acquisition module, a model generation module, an analysis module and a central control module. The data acquisition module is electrically connected to the database, and the data acquisition module is used to obtain the historical preparation data of the graphite parts and the corresponding environmental data. The model generation module is electrically connected to the data acquisition module, and the model generation module is used to establish a preparation model of the graphite parts according to the historical preparation data and the environmental data during preparation. The analysis module is electrically connected to the environmental detection and model generation modules respectively, and the analysis module is used to obtain the real-time environmental data during the preparation of the graphite parts, and substitute the real-time environmental data into the preparation model to obtain each preset conductivity of the graphite parts. The analysis module is also used to cluster each preset conductivity according to the clustering method, and obtain the clustered preset conductivity. The central control module is electrically connected to the analysis module, and the central control module is used to use the preset preparation data corresponding to the preset conductivity as the preset preparation data of the graphite parts, and prepare the blank according to the preset preparation data.

[0078] Specifically, when the central control module is used to prepare the blank according to the preset preparation data, it includes: the central control module is also used to obtain the real-time preparation data of the graphite part, and determine whether to correct the real-time preparation data according to the relationship between the real-time preparation data and the preset preparation data: when the real-time preparation data is consistent with the preset preparation data, the central control module determines not to correct the real-time preparation data. When the real-time preparation data is inconsistent with the preset preparation data, the central control module determines the real-time preparation data correction coefficient according to the difference between the real-time preparation data and the preset preparation data, and corrects the real-time preparation data according to the correction coefficient.

[0079] It can be seen that the central control module first obtains the real-time preparation data of the graphite parts and compares it with the preset preparation data to ensure that the preparation process meets the established standards. When the real-time data is consistent with the preset data, the central control module does not need to be adjusted, ensuring the stability and accuracy of the preparation process. However, when the real-time data is inconsistent with the preset data, the central control module will calculate the difference between the two, determine the correction coefficient, and adjust the real-time preparation data. This process dynamically optimizes the preparation parameters through the calculation and application of the correction coefficient to ensure that the quality of the final graphite parts meets the expected standards, thereby improving the accuracy and consistency of the preparation process.

[0080] It can be understood that the graphite part preparation system and preparation method in the above-mentioned embodiments of the present invention have the same beneficial effects, which will not be described in detail.

[0081] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0082] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for preparing a graphite part, characterized in that: include: Obtain historical preparation data of graphite parts and corresponding environmental data; Establishing a preparation model of the graphite part according to the historical preparation data and the environmental data during preparation; Acquire real-time environmental data during graphite part preparation, and substitute the real-time environmental data into the preparation model to obtain various preset conductivities of the graphite part; Clustering the preset conductivities according to a clustering method, and obtaining the clustered preset conductivities; Using the preset preparation data corresponding to the preset conductivity as the preset preparation data of the graphite part, and preparing the blank according to the preset preparation data; When obtaining historical preparation data of graphite parts and corresponding environmental data, include: Obtaining the immersion time of the blank during the historical preparation of the graphite piece, the calcination time and temperature of the blank during calcination, the catalytic temperature and time during high-temperature catalysis, and the conductivity of the graphite piece after preparation; Obtaining the ambient temperature and humidity during the historical preparation of the graphite part; When establishing a graphite part preparation model according to the historical preparation data and the environmental data during preparation, the method includes: Clearing duplicate data from the historical preparation data; Establishing a preparation formula for each of the graphite pieces according to the impregnation time, roasting time, roasting temperature, catalytic temperature, catalytic time, ambient temperature, ambient humidity and conductivity of the historical preparation of each of the graphite pieces after removing the duplicate data; Obtaining a temperature linear relationship and a humidity linear relationship between the preparation formulas, and establishing a preparation model for the graphite part according to the relationship between the temperature linear relationship, the humidity linear relationship and the preparation formulas; When establishing the preparation model of the graphite part, it includes: Obtaining the actual conductivity of the graphite piece in any of the historical preparation data and the corresponding preparation formula for the graphite piece; Substituting the preparation formula of the graphite part into the preparation model, and obtaining a preset conductivity of the preparation model; According to the relationship between the preset conductivity and the actual conductivity, it is determined whether to modify the preparation model of the graphite part, wherein: When the preset conductivity is consistent with the actual conductivity, it is determined that there is no need to modify the preparation model of the graphite part; When the preset conductivity is inconsistent with the actual conductivity, iterative correction is performed according to the preparation model of the graphite part until the preset conductivity is consistent with the actual conductivity; Acquiring real-time environmental data during graphite part preparation, and substituting the real-time environmental data into the preparation model to obtain each preset conductivity of the graphite part, including: Acquire the real-time temperature and real-time humidity in the real-time environmental data; According to the matching between the real-time temperature and the real-time humidity and the temperature linear relationship and the humidity linear relationship in the preparation model, the preset conductivity of each preparation formula that matches the real-time temperature and the real-time humidity is obtained.

2. The method for preparing a graphite part according to claim 1, characterized in that: Before clustering the preset conductivities according to the clustering method and obtaining the clustered preset conductivities, the method includes: Matching is performed according to each of the preset conductivity and the configured preset conductivity range, wherein: If the preset conductivity is within the preset conductivity range, retaining the preset conductivity; If the preset conductivity is not within the preset conductivity range, the preset conductivity is eliminated.

3. The method for preparing a graphite part according to claim 2, characterized in that: When clustering the preset conductivity according to the clustering method and obtaining the clustered preset conductivity, it includes: Acquire the retained preset conductivity values, and remove duplicate data from the preset conductivity values; Density clustering is performed on the preset conductivities after removing duplicate data, and the clustered preset conductivities are obtained.

4. The method for preparing a graphite part according to claim 3, characterized in that: When density clustering is performed on the preset conductivity after removing duplicate data, and the clustered preset conductivity is obtained, the method includes: Obtaining the Euclidean distance between the preset conductivities; Density clustering is performed according to the Euclidean distances between the preset conductivities, and the preset conductivities after the density clustering are obtained.

5. A graphite part preparation system, applicable to a graphite part preparation method as claimed in any one of claims 1 to 4, characterized in that: include: A data acquisition module, electrically connected to the database, for acquiring historical preparation data of the graphite parts and corresponding environmental data; A model generation module, electrically connected to the data acquisition module, and used for establishing a preparation model of the graphite part according to the historical preparation data and the environmental data during preparation; An analysis module, electrically connected to the environment detection module and the model generation module, respectively, the analysis module is used to obtain real-time environmental data during the preparation of the graphite part, and substitute the real-time environmental data into the preparation model to obtain each preset conductivity of the graphite part; the analysis module is also used to cluster each preset conductivity according to a clustering method, and obtain the clustered preset conductivity; A central control module is electrically connected to the analysis module, and is used to use the preset preparation data corresponding to the preset conductivity as the preset preparation data of the graphite part, and prepare the blank according to the preset preparation data.

6. A graphite part preparation system as claimed in claim 5, characterized in that: When the central control module is used to prepare the blank according to the preset preparation data, it includes: The central control module is also used to obtain the real-time preparation data of the graphite part, and determine whether to modify the real-time preparation data according to the relationship between the real-time preparation data and the preset preparation data: When the real-time preparation data is consistent with the preset preparation data, the central control module determines not to modify the real-time preparation data; When the real-time preparation data is inconsistent with the preset preparation data, the central control module determines the real-time preparation data correction coefficient according to the difference between the real-time preparation data and the preset preparation data, and corrects the real-time preparation data according to the correction coefficient.

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