Anti-scale deposition method of self-anti-scale nano composite conductive ceramic electrode

Abstract

The invention provides an anti-scale deposition method of a self-anti-scale nano-composite conductive ceramic electrode, which comprises the following steps: carrying out a pressurization experiment in a high-pressure container aiming at an alloying electrode matrix sample to obtain the change rule of compressive strength and yield strength along with pressure, and calculating the anti-scale deposition of the self-anti-scale nano-composite conductive ceramic electrode according to the maximum pressure under the actual use working condition. Judging whether the sample meets a high-pressure-resistant requirement or not; the method comprises the following steps: carrying out a soaking corrosion experiment and an electrochemical impedance spectroscopy test on an alloyed electrode matrix sample, comparing with a pure matrix material to determine whether the corrosion resistance of the material is improved by alloying, and obtaining the maximum service time in different corrosive media; a full-process database of the alloying electrode matrix from preparation, characterization to use is established, a big data analysis and machine learning algorithm is adopted, internal relations among a preparation process, an organization structure and use performance are mined, material components and process parameters are optimized according to an analysis result, and the toughness and the conductivity of the electrode matrix are both improved.

Description

Technical Field

[0001] The invention relates to the field of information technology, and in particular to an anti-scaling deposition method for a self-anti-scaling nano-composite conductive ceramic electrode. Background Art

[0002] In the harsh environment of high temperature, high pressure and strong corrosion, how to develop a self-scaling nano-composite conductive ceramic electrode matrix material with excellent toughness and conductivity is a major challenge faced by the electrode manufacturing field. Traditional electrode matrix materials are prone to brittle fracture or reduced conductivity under extreme conditions, making it difficult to meet the requirements of long-term stable operation. Although alloying modification can improve the toughness of the material, it often reduces the conductivity. How to find a balance between microstructure regulation and macroscopic performance to achieve a synergistic improvement in toughness and conductivity is a key issue that needs to be solved urgently. At the same time, scale deposition is prone to occur during the use of the electrode, affecting its conductivity and service life. Therefore, how to construct a nano-composite coating with self-cleaning function on the surface of the electrode matrix to inhibit scale formation is also an important technical problem. In addition, the failure mechanism and life prediction model of the electrode matrix material in a complex environment are still imperfect, making it difficult to accurately evaluate its long-term performance. How to establish a quantitative relationship between material composition, preparation process, microstructure and performance to guide the optimal design of the electrode matrix material is also a technical bottleneck that needs to be broken through. Summary of the invention

[0003] The present invention provides a method for anti-scaling deposition of a self-scaling nano-composite conductive ceramic electrode, which mainly comprises:

[0004] Obtaining component ratio and process parameter data of the alloyed electrode substrate as a training sample set for the machine learning algorithm, using a support vector regression algorithm to establish a nonlinear mapping relationship model between component, process and performance for the training sample set, and determining the optimal component ratio and process parameter combination that takes both toughness and conductivity into account based on the mapping relationship model;

[0005] Obtaining an alloyed electrode substrate sample prepared by using an optimal combination of component ratio and process parameters, and obtaining microstructure parameters of the sample by using electron microscopy and X-ray diffraction, including grain size, precipitate phase distribution, and dislocation density, and judging whether the microstructure parameters meet a preset range. If so, determining that the alloying process obtains an ideal microstructure, and the preset range is determined according to target performance requirements and material design experience;

[0006] For the alloyed electrode substrate sample with an ideal microstructure, high temperature tensile and high temperature creep tests are used to characterize its high temperature toughness level, and the four-probe method is used to test its electrical conductivity to determine whether the high temperature toughness level and electrical conductivity meet the preset indicators. If so, it is determined that the alloyed electrode substrate can maintain stable comprehensive performance under high temperature environment, and the preset indicators include a yield strength of not less than 200 MPa at 600°C and a conductivity of not less than 40% IACS;

[0007] For the alloyed electrode matrix sample, a pressure test is carried out in a high-pressure container to obtain the variation law of compressive strength and yield strength with pressure, and according to the maximum pressure under actual use conditions, it is judged whether the sample meets the high-pressure resistance requirements;

[0008] For alloyed electrode substrate samples, immersion corrosion experiments and electrochemical impedance spectroscopy tests were conducted to determine whether alloying improves the corrosion resistance of the material by comparing it with the pure substrate material, and to obtain the maximum service life in different corrosive media;

[0009] In a simulated use environment with high temperature, high pressure, and strong corrosion, a long-term working test was conducted on the alloyed electrode matrix samples to obtain the evolution law of its organizational structure in a complex and harsh environment. A machine learning algorithm was used to establish a quantitative relationship model between organizational structure parameters and usage time to predict the service life of the electrode matrix.

[0010] A full-process database of alloyed electrode substrates from preparation and characterization to use is established. Big data analysis and machine learning algorithms are used to explore the intrinsic connection between preparation technology, organizational structure and performance. Based on the analysis results, the material composition and process parameters are optimized to achieve a balanced improvement in the toughness and conductivity of the electrode substrate.

[0011] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0012] The present invention discloses a method for developing high-performance electrode matrix materials. The method obtains the composition and process data of the alloyed electrode matrix, uses a machine learning algorithm to establish a composition-process-performance mapping model, and determines the optimal composition ratio and process parameters. The prepared sample is characterized in terms of microstructure and performance to verify whether it meets the preset indicators. Long-term tests are carried out in simulated extreme environments to establish a quantitative model of organizational evolution and usage time to predict the life of the electrode. By establishing a full-process database and conducting big data analysis, the intrinsic connection between process, structure and performance is explored to achieve a synergistic improvement in the toughness and conductivity of the electrode matrix. The present invention solves the problem that the electrode matrix material is prone to brittle fracture or decreased conductivity in a high-temperature, high-pressure and highly corrosive environment, and develops a self-scaling nano-composite conductive ceramic electrode matrix material with excellent toughness and conductivity, providing a reliable guarantee for the long-term stable operation of the electrode. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a flow chart of a method for preventing scale deposition of a self-scaling nano-composite conductive ceramic electrode.

[0014] Figure 2 It is a schematic diagram of a method for preventing scale deposition of a self-antiscaling nano-composite conductive ceramic electrode according to the present invention.

[0015] Figure 3 It is another schematic diagram of the anti-scaling deposition method of a self-anti-scaling nano-composite conductive ceramic electrode of the present invention. DETAILED DESCRIPTION

[0016] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0017] like Figure 1-3 In this embodiment, a method for preventing scale deposition of a self-scaling nano-composite conductive ceramic electrode may specifically include:

[0018] Step S101, obtaining the composition ratio and process parameter data of the alloyed electrode substrate as a training sample set for the machine learning algorithm; for the training sample set, using a support vector regression algorithm to establish a nonlinear mapping relationship model between composition, process and performance; based on the mapping relationship model, determining the optimal composition ratio and process parameter combination that takes into account both toughness and conductivity.

[0019] Acquire historical production data of the alloyed electrode substrate, the historical production data including component ratio, process parameters and performance test results, and use the historical production data as a training sample set for the machine learning algorithm; preprocess the training sample set, remove outliers, and normalize the numerical features to obtain a preprocessed training sample set; adopt a support vector regression algorithm, use the component ratio and process parameters in the preprocessed training sample set as input features, and use toughness and conductivity as output targets to establish a nonlinear mapping relationship model of the alloyed electrode substrate performance; obtain a preset target value range of toughness and conductivity , taking the target value range as the constraint condition for optimizing the nonlinear mapping relationship model; using a grid search algorithm, under the constraint condition, searching for the optimal composition ratio and process parameter combination to obtain a candidate optimal parameter combination; inputting the candidate optimal parameter combination into the nonlinear mapping relationship model to predict the toughness and conductivity corresponding to the candidate optimal parameter combination; judging whether the predicted toughness and conductivity meet the target value range, if so, determining the candidate optimal parameter combination as the optimal formula for producing the alloyed electrode substrate, if not, returning to execute the grid search algorithm to continue searching for the optimal parameter combination.

[0020] Specifically, in the production process of alloyed electrode substrates, obtaining historical production data is a crucial first step. These data usually include composition ratios (such as the ratio of elements such as copper, nickel, and cobalt), process parameters (such as sintering temperature, pressing pressure, cooling rate, etc.), and performance test results (such as toughness, conductivity, etc.). For example, the composition ratio of a batch of electrode substrates may be 70% copper, 20% nickel, and 10% cobalt. The process parameters are sintering temperature 900℃, pressing pressure 200MPa, and cooling rate 10℃ / min. The performance test results show that the toughness is 120MPa and the conductivity is 98%IACS. Next, these historical production data are preprocessed. First, outliers are removed. For example, a test result that deviates significantly from other data may be caused by equipment failure or operational errors. Secondly, the numerical features are normalized so that data of different dimensions are on the same scale, which is convenient for subsequent machine learning algorithm processing. For example, the sintering temperature is normalized from 900℃ to 0.9, and the pressing pressure is normalized from 200MPa to 0.8. The support vector regression (SVR) algorithm is used to establish a nonlinear mapping relationship model with the preprocessed component ratio and process parameters as input features and toughness and conductivity as output targets. SVR maps the input features to a high-dimensional space through a kernel function (such as radial basis function RBF) to find an optimal hyperplane that minimizes the prediction error. For example, the input features are [0.7, 0.2, 0.1, 0.9, 0.8, 0.1] and the output target is [120, 98]. The model learns the complex relationship between these features and targets through training. The preset target value ranges of toughness and conductivity are obtained, such as toughness requirements between 100-150MPa and conductivity requirements between 95-100% IACS. These target value ranges are used as constraints for model optimization to ensure that the final parameter combination can meet the performance requirements. The grid search algorithm is used to search for the optimal component ratio and process parameter combination under the constraints. The grid search finds the optimal solution by traversing all possible parameter combinations. For example, the search range of the composition ratio is set to 60%-80% copper, 10%-30% nickel, and 5%-15% cobalt, and the search range of the process parameters is 850℃-950℃ sintering temperature, 150MPa-250MPa pressing pressure, and 5℃ / min-15℃ / min cooling rate. The grid search will try these combinations one by one to find the best parameters that meet the performance requirements. The candidate optimal parameter combination is input into the nonlinear mapping relationship model to predict its corresponding toughness and conductivity. For example, a candidate parameter combination is 75% copper, 20% nickel, and 5% cobalt, sintering temperature is 900℃, pressing pressure is 200MPa, and cooling rate is 10℃ / min. The model predicts that its toughness is 130MPa and conductivity is 97% IACS. Determine whether the predicted toughness and conductivity meet the target value range. If so, the parameter combination is determined as the optimal formula.For example, the above prediction results are all within the target range, so the combination is determined to be the optimal formula. If not, return to execute the grid search algorithm and continue to search for the optimal parameter combination until a combination that meets the requirements is found. In this way, the production formula of the alloyed electrode matrix can be systematically optimized to improve product quality and production efficiency. Preprocessing data ensures the accuracy of the model, the SVR model can capture complex nonlinear relationships, and the grid search ensures that the optimal solution is found under constraints. The whole process is closely linked and logically rigorous, and ultimately the production formula is optimized. This optimization method not only improves the performance of the alloyed electrode matrix, but also reduces production costs and enhances market competitiveness. Through a data-driven approach, the inefficiency and high cost of traditional trial and error methods are avoided, making the production process more scientific and controllable. In addition, the establishment and optimization process of the model also provides valuable experience and reference for subsequent production, which helps to continuously improve and enhance product quality. In practical applications, the model and search strategy can also be adjusted according to specific needs. For example, if the conductivity is required to be higher, the weight of the conductivity can be increased in the grid search, so that the search process is more inclined to find a parameter combination with better conductivity. This flexibility and adjustability enable the method to adapt to different production needs and environmental changes. In short, by obtaining historical production data, preprocessing data, establishing an SVR model, setting constraints, grid searching for the optimal parameter combination, predicting performance and judging whether the target value range is met, and finally determining the optimal production formula, this series of steps constitutes a complete and efficient production optimization process, providing strong technical support for the production of alloyed electrode substrates.

[0021] Step S102, obtaining an alloyed electrode substrate sample prepared by using an optimal combination of component ratios and process parameters; for the sample, using electron microscopy and X-ray diffraction to obtain microstructure parameters, including grain size, precipitate phase distribution, and dislocation density; judging whether the microstructure parameters meet a preset range, and if so, determining that the alloying process obtains an ideal microstructure, and the preset range is determined based on target performance requirements and material design experience.

[0022] Obtain a preset range of ideal microstructural parameters of the alloyed electrode substrate, wherein the preset range includes grain size, precipitated phase distribution and dislocation density; obtain microstructural parameters of the alloyed electrode substrate sample, and compare the microstructural parameters with the preset range; if the microstructural parameters meet the preset range, determine the optimized ratio and process parameters to prepare an alloyed electrode substrate with an ideal microstructural structure.

[0023] Specifically, obtaining the preset range of ideal microstructure parameters of the alloyed electrode matrix is ​​a key step in optimizing electrode performance. Ideal microstructure parameters generally include grain size, precipitate distribution and dislocation density. These parameters directly affect the mechanical properties and electrical conductivity of the electrode. First, grain size is an important factor affecting the mechanical properties of the material. Fine grains can improve the strength and toughness of the material, while larger grains may cause the material to become brittle. For example, the preset grain size range may be 10-30 microns to ensure that the electrode matrix has both sufficient strength and good toughness. Second, the precipitate distribution determines the uniformity and stability of the electrode matrix. Uniformly distributed precipitates can effectively improve the electrical conductivity and corrosion resistance of the material. For example, the preset precipitate distribution range may require that the precipitate particle diameter is between 1-5 nanometers and is evenly distributed. Finally, the dislocation density reflects the crystal defects inside the material. A moderate dislocation density can improve the strength of the material, but too high a dislocation density may cause the material to become brittle. For example, the preset dislocation density range may be 10^6-10^8 pieces / cm 2 In actual operation, the microstructure parameters of alloyed electrode matrix samples are usually obtained by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and other technical means. For example, the grain size of a batch of electrode matrix was 20 microns, the diameter of the precipitated phase particles was 3 nanometers and evenly distributed, and the dislocation density was 5×10^7 / cm 2. Compare these microstructure parameters with the preset range. If the preset range is met, the current composition ratio and process parameters can be determined to be the optimized ratio. For example, the above test results are all within the preset range, indicating that the composition ratio (such as copper 70%, nickel 20%, cobalt 10%) and process parameters (such as sintering temperature 900℃, pressing pressure 200MPa, cooling rate 10℃ / min) of this batch are ideal and can be used to prepare an alloyed electrode substrate with an ideal microstructure. If the microstructure parameters do not meet the preset range, the composition ratio and process parameters need to be adjusted. For example, if the grain size is too large (such as 40 microns), it may be necessary to reduce the sintering temperature or increase the pressing pressure to refine the grains; if the precipitated phase is unevenly distributed, it may be necessary to adjust the cooling rate to promote the uniform distribution of the precipitated phase; if the dislocation density is too high, it may be necessary to optimize the composition ratio and reduce crystal defects. In this way, the microstructure of the alloyed electrode substrate can be systematically optimized, thereby improving its comprehensive performance. The realization of an ideal microstructure not only depends on accurate composition ratios and process parameters, but also requires precise control and optimization of microstructure parameters. In the specific implementation process, the composition ratios and process parameters can be gradually adjusted and optimized through multiple experiments and data analysis. For example, the grain size obtained in the first experiment was 30 microns, close to the upper limit of the preset range. By lowering the sintering temperature to 850°C, the grain size obtained in the second experiment was 25 microns, which was closer to the median of the preset range, further improving the mechanical properties of the electrode matrix. In addition, the optimization of the distribution of precipitated phases can also be achieved by adjusting the cooling rate. For example, in the first experiment, the cooling rate was 10°C / min, and the distribution of precipitated phases was relatively concentrated. By increasing the cooling rate to 15°C / min, the distribution of precipitated phases obtained in the second experiment was more uniform, and the conductivity was significantly improved. The control of dislocation density is more complicated, and the influence of composition ratios and process parameters needs to be comprehensively considered. For example, by increasing the proportion of cobalt to 15% and adopting segmented heating during sintering, the dislocation density can be effectively reduced and the toughness of the material can be improved. In summary, by precisely controlling the grain size, precipitate distribution and dislocation density, the organizational structure of the alloyed electrode matrix can be optimized at the micro level, thereby improving its macroscopic performance. This method not only improves the comprehensive performance of the electrode matrix, but also reduces production costs and improves market competitiveness. Through data-driven and experimental verification, the inefficiency and high cost of the traditional trial and error method are avoided, making the production process more scientific and controllable. In practical applications, the optimization strategy can also be adjusted according to specific needs. For example, if higher electrical conductivity is required, the weight of the uniformity of the precipitate distribution can be increased during the optimization process, so that the optimization process is more inclined to find a parameter combination with better electrical conductivity. This flexibility and adjustability enable this method to adapt to different production needs and environmental changes.By obtaining the preset range of ideal microstructure parameters, testing the sample microstructure parameters, comparing and optimizing the component ratio and process parameters, an alloyed electrode matrix with an ideal microstructure is finally prepared. This series of steps constitutes a complete and efficient production optimization process, providing strong technical support for the production of alloyed electrode matrix.

[0024] Step S103, for the alloyed electrode substrate sample with an ideal microstructure, high-temperature tensile and high-temperature creep tests are used to characterize its high-temperature toughness level, and a four-probe method is used to test its electrical conductivity; it is determined whether the high-temperature toughness level and the electrical conductivity meet the preset indicators. If so, it is determined that the alloyed electrode substrate can maintain stable comprehensive performance in a high-temperature environment, and the preset indicators include a yield strength of not less than 200 MPa at 600°C and a conductivity of not less than 40% IACS.

[0025] The microstructure parameters of the alloyed electrode substrate sample are obtained, wherein the microstructure parameters include grain size and precipitated phase distribution; the microstructure parameters are analyzed by an image processing algorithm to obtain microstructure characteristics; a tensile test and a creep test are performed on the alloyed electrode substrate sample in a high temperature environment to obtain a stress-strain curve and a creep curve; the high temperature toughness index and the conductive performance index are input into a prediction model to determine whether a preset yield strength threshold and a conductivity threshold are met; if the preset threshold is met, it is determined that the alloyed electrode substrate meets the high temperature comprehensive performance requirements; if not, the composition and preparation process of the alloyed electrode substrate are adjusted, and the sample is re-obtained and the performance is evaluated.

[0026] Specifically, to obtain the microstructure parameters of the alloyed electrode matrix sample, it is first necessary to observe the sample in detail by high-resolution electron microscopy (HRTEM) and scanning electron microscopy (SEM). For example, the grain size can be determined by measuring the grain boundaries in the SEM image, assuming that the grain size distribution of a certain sample is between 50 nanometers and 200 nanometers. The distribution of the precipitated phase can be determined by analyzing the coherent diffraction spots in the HRTEM image, such as the precipitated phase in a certain sample is a nanoscale CuAl 2 phase, which is uniformly distributed in the matrix. The microstructure parameters are analyzed by an image processing algorithm. Specifically, an image processing library in MATLAB or Python, such as OpenCV, can be used to preprocess, edge detect, and segment the SEM and HRTEM images, thereby extracting characteristic parameters of grain size and precipitated phase distribution. For example, the grain boundaries are identified by an edge detection algorithm, and the average size and distribution range of the grains are calculated; the precipitated phase is identified by a regional segmentation algorithm, and its area fraction and distribution uniformity are counted. The alloyed electrode substrate sample is subjected to tensile testing and creep testing under high temperature environment, usually in a high temperature test furnace, with the temperature set to 600°C or higher. The tensile test is performed by a universal testing machine, and the stress-strain curve is recorded, from which indicators such as yield strength, tensile strength and elongation are extracted. For example, the yield strength of a sample at 600°C is 250MPa, the tensile strength is 300MPa, and the elongation is 20%. The creep test records the change of the strain of the sample under constant stress over time, draws a creep curve, and analyzes the creep rate and creep rupture time. For example, the creep rate of a sample under 100MPa stress is 10^-7s^-1, and the creep rupture time is 100 hours. The high temperature toughness index and the conductive performance index are input into the prediction model, and the prediction model can be a regression model based on machine learning, such as support vector regression (SVR) or neural network (NN), and the model is obtained by training historical data. The input indicators include yield strength, tensile strength, elongation, creep rate and conductivity, etc. The model output determines whether the preset yield strength threshold and conductivity threshold are met. For example, the preset yield strength threshold is 200MPa, and the conductivity threshold is 80%IACS (International Annealed Copper Standard). The model prediction results show that the yield strength of a certain sample is 250MPa and the conductivity is 85%IACS, which meets the preset threshold. If the preset threshold is met, it is determined that the alloyed electrode matrix meets the high-temperature comprehensive performance requirements and can be mass-produced and applied. For example, the alloyed electrode matrix can be used as an electrode material for high-temperature fuel cells, because of its excellent high-temperature toughness and electrical conductivity, it can significantly improve the efficiency and life of the battery. If not met, the composition and preparation process of the alloyed electrode matrix are adjusted, the sample is re-obtained and the performance is evaluated. Adjusting the composition may include increasing or decreasing the content of certain alloying elements, such as increasing the Cu element to improve conductivity and reducing the Fe element to reduce grain size.Adjustments to the preparation process may include changing the heat treatment temperature and time, such as increasing the annealing temperature from 500°C to 600°C and extending the holding time from 1 hour to 2 hours to optimize the microstructure. After re-obtaining the sample, repeat the above microstructure analysis and performance test steps until the preset performance requirements are met. The reason for this is that the microstructure of the alloyed electrode matrix directly determines its macroscopic performance. By precisely controlling the grain size, precipitate phase distribution and dislocation density, the high-temperature toughness and conductivity of the material can be significantly improved. High-temperature tensile and creep tests can truly reflect the performance of the material in the actual working environment, and the introduction of the prediction model greatly improves the efficiency and accuracy of performance evaluation. By continuously adjusting the composition and process parameters, an alloyed electrode matrix that meets the high-temperature comprehensive performance requirements is finally obtained to ensure its reliability and stability in high-temperature applications. Through the above-mentioned multi-faceted analysis and examples, it can be seen that the technical principles and implementation methods of each step, as well as the logical relationship and mutual support between them, jointly ensure the excellent performance of the alloyed electrode matrix in a high-temperature environment.

[0027] Step S104, for the alloyed electrode substrate sample, a pressurization test is carried out in a high-pressure container to obtain the variation law of compressive strength and yield strength with pressure; according to the maximum pressure under actual use conditions, it is judged whether the sample meets the high-pressure resistance requirement.

[0028] Obtain the material properties of the alloyed electrode matrix sample, determine the pressure range and pressurization step of the high-pressure container, and formulate a pressurization experiment plan. Place the sample in a high-pressure container, gradually increase the pressure, maintain the pressure at each pressure point, and record the compressive strength data after the sample deformation stabilizes; continue to increase the pressure until the sample yields and deforms, and record the yield strength data. According to the maximum pressure under actual working conditions, determine the critical values ​​of compressive strength and yield strength on the strength-pressure curve. Determine whether the compressive strength and yield strength of the sample are higher than the critical value. If so, it is determined to meet the high-pressure resistance requirements.

[0029] Specifically, obtaining the material properties of the alloyed electrode matrix sample is the basic part of the experiment. Material properties include but are not limited to density, hardness, elastic modulus, etc. For example, the density of an alloyed electrode matrix sample is 7.8g / cm 3, hardness is HB300, and elastic modulus is 210GPa. These parameters provide basic data for subsequent compressive performance tests, which helps to understand and predict the behavior of materials under different pressures. Determining the pressure range and pressurization step of the high-pressure vessel is the key to experimental design. Assuming that the maximum pressure required for the experiment is 500MPa, considering safety and the bearing capacity of the equipment, the pressure range can be set to 0-500MPa. The pressurization step is set according to the expected response of the material and the experimental accuracy requirements. For example, you can choose a step length of 10MPa, so that you can carefully observe the changes of the material under different pressures, while avoiding the loss of details caused by too large a step length. Place the sample in a high-pressure vessel, gradually increase the pressure, maintain the pressure at each pressure point, and record the compressive strength data after the sample deformation is stable. For example, when the pressure reaches 100MPa, maintain the pressure for 10 minutes, and record the deformation and stress-strain curve of the sample at this time. Continue to increase the pressure to 200MPa, 300MPa, etc., and repeat the above steps until it reaches 500MPa or the sample yields. According to the maximum pressure under actual working conditions, the critical values ​​of compressive strength and yield strength are determined on the strength-pressure curve. Assuming that the maximum pressure under actual working conditions is 300MPa, the corresponding compressive strength and yield strength values ​​are found on the strength-pressure curve. For example, if the compressive strength of the sample is 250MPa and the yield strength is 200MPa at 300MPa, these two values ​​are the critical values. Determine whether the compressive strength and yield strength of the sample are higher than the critical value. If the actual compressive strength of the sample at 300MPa is 260MPa and the yield strength is 210MPa, both of which are higher than the critical values ​​of 250MPa and 200MPa, it is determined to meet the high pressure resistance requirements. The specific implementation method is as follows: 1. **Material property test**: Use a densitometer to measure the sample density, a hardness tester to measure the hardness, and a universal testing machine to measure the elastic modulus. 2. **Preparation of high-pressure vessels**: Select a high-pressure resistant container to ensure that it can withstand a pressure of at least 500MPa. 3. **Pressure test**: Place the sample in a container and use a hydraulic pump to gradually increase the pressure. Maintain the pressure for 10 minutes for each increase of 10MPa and record the data. 4. **Data recording and analysis**: Use a stress-strain tester to record data, draw a strength-pressure curve, and determine the critical value. 5. **Result determination**: Compare the actual strength with the critical value to determine whether the sample meets the high-pressure resistance requirements. Through the above steps, the performance of alloyed electrode matrix samples in high-pressure environments can be systematically evaluated to ensure their reliability in practical applications. For example, a sample showed good compressive and yield strength in the experiment, proving that it can still maintain structural stability under high temperature and high pressure environments, and is suitable for the application of high-temperature electrode materials. The advantage of this method is that it can accurately control pressure changes and carefully observe the behavior of materials under different pressures, thereby providing reliable data support for material design and process optimization.In addition, through multi-step and multi-pressure point testing, the compressive performance of the material can be comprehensively evaluated to avoid misjudgment caused by a single test point. In short, through scientific and reasonable experimental design and rigorous data analysis, the high-pressure performance of the alloyed electrode matrix sample can be effectively evaluated to ensure its stability and reliability under high temperature and high pressure environments, providing strong guarantees for practical applications.

[0030] Step S105, conducting an immersion corrosion test and an electrochemical impedance spectroscopy test on the alloyed electrode substrate sample, and determining whether alloying improves the corrosion resistance of the material by comparing with the pure substrate material, and obtaining the maximum service life in different corrosive media.

[0031] According to the material properties of the electrode substrate, an alloying method is selected to prepare alloyed electrode substrate samples and pure substrate samples. An electrochemical workstation is used to obtain the electrochemical impedance data of the samples after immersion corrosion. If the corrosion resistance of the alloyed electrode substrate is better than that of the pure substrate, the maximum service life of the material in different corrosive media is determined based on the mass loss rate. If the corrosion resistance of the alloyed electrode substrate is lower than that of the pure substrate, the reason for the decrease in corrosion resistance caused by alloying is determined by analyzing the electrochemical impedance spectrum data, and the alloying process parameters are optimized. Combining the results of the immersion corrosion experiment and the electrochemical impedance spectrum test, the maximum service life of the alloyed electrode substrate in different corrosive media is obtained.

[0032] Specifically, according to the material properties of the electrode matrix, selecting a suitable alloying method is a key step to improve the performance of the electrode matrix. Assuming that the main component of an electrode matrix is ​​nickel, in order to improve its corrosion resistance and mechanical properties, chromium and molybdenum elements are added for alloying. When preparing the alloyed electrode matrix sample, a powder metallurgy method is used. The specific steps include mixing nickel powder, chromium powder and molybdenum powder, pressing the blank, and sintering in a high-temperature vacuum furnace to obtain a uniform alloyed electrode matrix sample. At the same time, a pure nickel matrix sample is prepared as a control group. An immersion corrosion experiment is carried out using an electrochemical workstation. First, the alloyed electrode matrix sample and the pure matrix sample are immersed in a simulated corrosive medium, such as a 3.5% sodium chloride solution, to simulate the seawater corrosion environment. The electrochemical impedance spectroscopy (EIS) test is used to obtain the electrochemical impedance data of the sample at different immersion times. Assume that after immersion for 24 hours, the impedance value of the alloyed electrode matrix is ​​5000Ω, while the impedance value of the pure matrix sample is 3000Ω, which indicates that the alloyed electrode matrix has higher corrosion resistance. If the corrosion resistance of the alloyed electrode substrate is better than that of the pure substrate, the maximum service life of the material in different corrosive media can be further determined by the mass loss rate. Assuming that the mass loss rate of the alloyed electrode substrate in sodium chloride solution is 0.1 mg / cm 2 ·days, while the mass loss rate of pure matrix is ​​0.2mg / cm 2·days. Based on this data, it can be inferred that in a specific corrosion environment, the maximum service life of the alloyed electrode substrate is 1000 days, while the maximum service life of the pure substrate is 500 days. If the corrosion resistance of the alloyed electrode substrate is lower than that of the pure substrate, it is necessary to analyze the electrochemical impedance spectroscopy data to determine the reason for the decrease in corrosion resistance caused by alloying. Assuming that in the EIS test, the impedance value of the alloyed electrode substrate is lower than that of the pure substrate, and multiple time constants appear in the impedance spectrum, indicating that an uneven corrosion product film may have been formed during the corrosion process. Scanning electron microscopy (SEM) and energy spectrum analysis (EDS) revealed that there are chromium and molybdenum enriched areas in the corrosion product film, resulting in uneven electrochemical activity, which in turn reduces the corrosion resistance. To address this problem, the alloying process parameters are optimized, such as adjusting the addition ratio of chromium and molybdenum, or using a more uniform sintering process to improve the uniformity and stability of the corrosion product film. Combining the results of the immersion corrosion experiment and the electrochemical impedance spectroscopy test, the maximum service life of the alloyed electrode substrate in different corrosive media can be obtained. For example, in sodium chloride solution, the mass loss rate of the optimized alloyed electrode matrix was reduced to 0.05 mg / cm 2 · days, and it is calculated that its maximum service life can be extended to 2000 days. This result not only provides an important reference for the selection and application of materials, but also shows that the corrosion resistance of the electrode substrate can be effectively improved through reasonable optimization of the alloying process. When selecting the alloying method, it is necessary to consider the type and addition ratio of the alloying elements, as well as the influence of the preparation process on the microstructure and performance of the material. For example, adding chromium can form a dense oxide film and improve corrosion resistance; while adding molybdenum can enhance the mechanical strength and high temperature resistance of the material. The alloyed electrode substrate prepared by powder metallurgy can ensure the uniform distribution of alloying elements and avoid uneven performance caused by component segregation. Electrochemical impedance spectroscopy is an important means to evaluate the corrosion resistance of materials. By measuring the impedance value of the sample at different frequencies, the impedance spectrum of the material can be obtained. The Nyquist diagram composed of the real and imaginary parts of the impedance spectrum and the Bode diagram of the phase angle changing with frequency can intuitively reflect the electrochemical behavior and corrosion mechanism of the material. For example, the size and shape of the capacitive arc in the Nyquist diagram can reflect the properties and thickness of the corrosion product film; the phase angle peak in the Bode diagram can characterize the charge transfer resistance of the corrosion process. Calculating the maximum service life of a material in different corrosive media by mass loss rate is a quantitative evaluation method based on corrosion rate. Assuming that in a certain corrosive medium, the corrosion rate of the material is v (unit: mg / cm 2 · days), the maximum service life T (unit: day) can be calculated by the formula T = δ / v, where δ is the allowable corrosion depth of the material (unit: mg / cm 2). This method is simple and intuitive, and is suitable for quickly evaluating the corrosion resistance and service life of materials. When optimizing the alloying process parameters, it is necessary to comprehensively consider the interaction of alloying elements, the influence of the preparation process on the microstructure, and the needs of the actual application environment. For example, by adjusting the sintering temperature and time, the grain size and phase composition of the alloyed electrode substrate can be controlled, thereby affecting its corrosion resistance. Through SEM and EDS analysis, the morphology and composition of the corrosion product film can be intuitively observed, providing a basis for process optimization. In short, through systematic experimental design and data analysis, the corrosion resistance of the alloyed electrode substrate can be comprehensively evaluated, the alloying process parameters can be optimized, and the reliability and service life of the material in practical applications can be improved. This method not only provides reliable data support for material design and process optimization, but also provides an important reference for material selection and use in practical applications.

[0033] Step S106, conduct a long-term working test on the alloyed electrode substrate sample in a simulated use environment of high temperature, high pressure, and strong corrosion, so as to obtain the evolution law of its organizational structure in a complex and harsh environment; use a machine learning algorithm to establish a quantitative relationship model between organizational structure parameters and usage time, so as to predict the service life of the electrode substrate.

[0034] The data preprocessing technology is used to clean and standardize the organizational structure evolution data to remove outliers and noise data, so as to obtain high-quality machine learning model training data; based on the training data, a support vector machine or a random forest machine learning algorithm is used to establish a quantitative model of organizational structure parameters and usage time, and the prediction accuracy of the quantitative model is improved through model training and optimization; the optimized alloy electrode substrate is put into practical application to obtain its tracking monitoring data in the actual use environment, and the monitoring data is used to further optimize the quantitative model to form a model optimization closed loop, so as to continuously improve the performance and life of the alloy electrode substrate.

[0035] Specifically, for the organizational structure evolution data, data preprocessing technology is first required to clean and standardize it. Data preprocessing is a key step to ensure the quality of machine learning model training data. For example, suppose there is a set of organizational structure evolution data of alloy electrode substrate under different temperature and time conditions. These data may contain experimental errors, outliers caused by equipment failures, and environmental noise. Through data cleaning, statistical methods, such as box plot analysis, can be used to identify and eliminate outliers that deviate significantly from the normal range. Furthermore, standardization can convert data of different dimensions to the same scale. Commonly used methods such as Z-score standardization make the mean of the data 0 and the standard deviation 1, thereby eliminating the dimensional effect and facilitating subsequent model training. After obtaining high-quality training data, support vector machine (SVM) or random forest (RF) machine learning algorithm is used to establish a quantitative model of organizational structure parameters and usage time. Support vector machine maximizes the classification interval of samples by finding the optimal hyperplane, which is suitable for classification and regression problems of small samples and high-dimensional data. For example, the radial basis function (RBF) kernel in SVM can be selected, the optimal parameters can be determined through cross-validation, and a regression model between organizational structure parameters (such as grain size, phase composition, etc.) and usage time can be established. Random forest improves the generalization ability of the model by constructing multiple decision trees, voting or averaging. Assume that the random forest algorithm is used, and the same quantitative model is established by grid search optimization parameters such as the number of trees and the maximum depth. Model training and optimization are key links to improve prediction accuracy. For example, through the cross-validation method, the data set is divided into a training set and a validation set, and training and validation are repeated to adjust the model parameters until the best model parameter combination is found. Furthermore, the model performance can be evaluated using indicators such as confusion matrix and mean square error (MSE) to ensure that the prediction accuracy of the model meets the requirements of practical applications. The optimized alloy electrode matrix is ​​put into practical application to obtain its tracking and monitoring data in the actual use environment. For example, suppose that the alloy electrode matrix is ​​applied to the electrolytic cell of a chemical equipment, and its key parameters such as temperature, current density, and corrosion rate are monitored in real time by installing sensors. These monitoring data not only reflect the performance of the alloy electrode matrix in the actual environment, but also provide valuable data support for the further optimization of the model. The quantitative model is further optimized using the monitoring data to form a closed loop of model optimization. For example, by comparing and analyzing the actual monitoring data with the model prediction data, it is found that the prediction deviation of the model under certain specific conditions is large. By analyzing the causes of these deviations, such as changes in environmental factors, material aging, etc., the model is iteratively optimized. New characteristic variables can be introduced, the model structure can be adjusted, and even more suitable algorithms can be replaced to continuously improve the prediction accuracy and generalization ability of the model. This closed-loop optimization process not only improves the performance and life of the alloy electrode matrix, but also provides a strong guarantee for its reliability in practical applications.For example, by continuously optimizing the model, the maximum service life of the alloy electrode matrix in different corrosive media can be predicted more accurately, so as to formulate more scientific maintenance and replacement strategies, reduce equipment failures and downtime, and improve production efficiency. Through the close cooperation and iterative optimization of the above-mentioned links, an efficient and reliable alloy electrode matrix performance prediction and optimization system is finally formed. Data preprocessing ensures the quality of model training data, machine learning algorithms establish scientific quantitative models, and tracking and monitoring data in actual applications provide real-time feedback for model optimization. The closed-loop optimization mechanism continuously improves the prediction accuracy and actual application effect of the model. This systematic approach not only improves the performance and life of the alloy electrode matrix, but also provides strong technical support for material research and application in related fields.

[0036] Step S107, establish a full-process database of the alloyed electrode substrate from preparation, characterization to use, use big data analysis and machine learning algorithms to explore the intrinsic relationship between preparation process, organizational structure and performance; according to the analysis results, optimize the material composition and process parameters to achieve a balanced improvement in the toughness and conductivity of the electrode substrate.

[0037] The preparation process, characterization data and performance of the alloyed electrode substrate are obtained, and a full-process database is established; the data in the database are preprocessed, and a prediction model between the preparation process, organizational structure and performance is established through a machine learning algorithm; according to the prediction model, the key influencing factors and the optimal parameter range are determined, and the material composition and process parameters are optimized; the alloyed electrode substrate samples are prepared using the optimized material composition and process parameters, and the test and characterization data are fed back to the database, and the prediction model is iteratively updated until both the toughness and the conductivity of the electrode substrate are improved.

[0038] Specifically, obtaining the preparation process, characterization data and performance of the alloyed electrode matrix and establishing a full-process database is to fully grasp the entire process of the electrode matrix from raw material selection to final application. Taking nickel cobalt manganese oxide (NMC) ternary positive electrode material as an example, its preparation process includes ball milling, spray drying, high-temperature sintering and other steps. Characterization data include particle size distribution, crystal structure, specific surface area, etc., and performance includes electrochemical capacity, cycle stability, rate performance, etc. Integrating these data into the database can facilitate data analysis and model building. Preprocessing the data in the database is to ensure the accuracy and consistency of the data. Taking the particle size distribution data as an example, there may be outliers caused by measurement errors. Through box plot analysis, data points that exceed the upper and lower quartiles 1.5 times the IQR range are removed, and then standardized to make the data conform to the normal distribution, which is convenient for the input of subsequent machine learning models. Through machine learning algorithms, a prediction model between preparation process, organizational structure and performance can be established. Support vector machine (SVM) or random forest (RF) algorithm can be selected. Assuming the use of random forest algorithm, the input features include ball milling time, sintering temperature, particle size distribution, crystal structure parameters, etc., and the output targets are electrochemical capacity and cycle life. The model parameters, such as the number of trees and maximum depth, are optimized through cross-validation and grid search to obtain the best prediction model. According to the prediction model, the key influencing factors and the optimal parameter range are determined, and the material composition and process parameters can be further optimized. For example, the model shows that the electrochemical capacity is the highest when the sintering temperature is 800℃, but the cycle life is the best at 750℃. Through multi-objective optimization algorithms, such as NSGA-II, the optimal temperature range that takes into account both capacity and life is found, such as 760-780℃. The alloyed electrode matrix samples prepared with optimized material composition and process parameters can significantly improve the material performance. Taking NMC as an example, after adjusting the cobalt content and sintering temperature, the prepared electrode matrix showed higher initial capacity and better cycle stability in laboratory tests. The test and characterization data are fed back to the database to form a data closed loop, and the prediction model can be continuously updated iteratively. In the specific implementation process, the material composition is optimized first. Assuming that the original formula is Ni:Co:Mn=8:1:1, it is found through model analysis that when the cobalt content is increased to Ni:Co:Mn=7:2:1, the conductivity and cycle performance of the electrode matrix are improved. Then, the ball milling time and sintering temperature are optimized. The ball milling time is extended from 10 hours to 12 hours, and the sintering temperature is adjusted from 750°C to 770°C to further refine the particle size and improve the crystallinity. Feedback from test and characterization data is crucial. Assuming that the capacity retention rate of the newly prepared electrode matrix is ​​90% after 500 cycles, which is 5% higher than before optimization, these data are entered into the database, and the model will adjust the parameter weights according to the new data to improve the prediction accuracy. Through multiple iterations, the prediction accuracy and performance of the model are gradually improved.The establishment of this full-process database and prediction model can not only effectively guide material preparation and process optimization, but also significantly shorten the R&D cycle and reduce experimental costs. Through data-driven methods, the optimal combination of materials and process parameters can be quickly found based on a large amount of experimental data, avoiding the waste of resources caused by the traditional trial and error method. In practical applications, this optimization method can be widely used in the development of various alloyed electrode matrices. For example, in the field of lithium-ion batteries, the energy density and cycle life of the battery can be significantly improved by optimizing the composition and preparation process of the positive electrode material; in the field of fuel cells, the electrocatalytic activity and stability can be improved by optimizing the composition and preparation process of the catalyst. The progress of these technologies will provide strong support for the development of the new energy industry. Through this systematic data analysis and model prediction method, not only can the toughness and conductivity of the electrode matrix be improved, but also reference can be provided for the development and optimization of other material systems. The core of this method lies in the comprehensiveness of the data and the accuracy of the model. Only on the basis of high-quality data can a reliable prediction model be constructed to guide actual production and application. In actual operation, attention should also be paid to the timeliness of the data and the adaptability of the model. With the continuous emergence of new materials and new processes, databases and models also need to be continuously updated and maintained to ensure the accuracy and practicality of their prediction results. Through continuous optimization and iteration, the comprehensive performance of the alloyed electrode matrix can be continuously improved to meet the growing industrial needs.

[0039] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for anti-scaling deposition of a self-anti-scaling nanocomposite conductive ceramic electrode, characterized in that: The method comprises: Obtaining component ratio and process parameter data of the alloyed electrode substrate as a training sample set for the machine learning algorithm, using a support vector regression algorithm to establish a nonlinear mapping relationship model between component, process and performance for the training sample set, and determining the optimal component ratio and process parameter combination that takes both toughness and conductivity into account based on the mapping relationship model; Obtaining an alloyed electrode substrate sample prepared by using an optimal combination of component ratio and process parameters, and obtaining microstructure parameters of the sample by using electron microscopy and X-ray diffraction, including grain size, precipitate phase distribution, and dislocation density, and judging whether the microstructure parameters meet a preset range. If so, determining that the alloying process obtains an ideal microstructure, and the preset range is determined according to target performance requirements and material design experience; For the alloyed electrode substrate sample with an ideal microstructure, high temperature tensile and high temperature creep tests are used to characterize its high temperature toughness level, and the four-probe method is used to test its electrical conductivity to determine whether the high temperature toughness level and electrical conductivity meet the preset indicators. If so, it is determined that the alloyed electrode substrate can maintain stable comprehensive performance under high temperature environment, and the preset indicators include a yield strength of not less than 200 MPa at 600°C and a conductivity of not less than 40% IACS; For the alloyed electrode matrix sample, a pressure test is carried out in a high-pressure container to obtain the variation law of compressive strength and yield strength with pressure, and according to the maximum pressure under actual use conditions, it is judged whether the sample meets the high-pressure resistance requirements; For alloyed electrode substrate samples, immersion corrosion experiments and electrochemical impedance spectroscopy tests were conducted to determine whether alloying improves the corrosion resistance of the material by comparing it with the pure substrate material, and to obtain the maximum service life in different corrosive media; In a simulated use environment with high temperature, high pressure, and strong corrosion, a long-term working test was conducted on the alloyed electrode matrix samples to obtain the evolution law of its organizational structure in a complex and harsh environment. A machine learning algorithm was used to establish a quantitative relationship model between organizational structure parameters and usage time to predict the service life of the electrode matrix. A full-process database of alloyed electrode substrates from preparation and characterization to use is established. Big data analysis and machine learning algorithms are used to explore the intrinsic connection between preparation technology, organizational structure and performance. Based on the analysis results, the material composition and process parameters are optimized to achieve a balanced improvement in the toughness and conductivity of the electrode substrate.

2. The anti-scaling deposition method of the self-anti-scaling nano-composite conductive ceramic electrode according to claim 1, characterized in that: include: Acquire historical production data of the alloyed electrode substrate, the historical production data including component ratios, process parameters and performance test results, and use the historical production data as a training sample set for a machine learning algorithm; Preprocessing the training sample set, removing outliers, and normalizing the numerical features to obtain a preprocessed training sample set; A support vector regression algorithm is used, the composition ratio and process parameters in the pre-processed training sample set are used as input features, and toughness and electrical conductivity are used as output targets to establish a nonlinear mapping relationship model of the alloyed electrode matrix performance; Obtaining a preset target value range of toughness and electrical conductivity, and using the target value range as a constraint condition for optimizing the nonlinear mapping relationship model; Using a grid search algorithm, under the constraints, the optimal combination of component ratio and process parameters is searched to obtain a candidate optimal parameter combination; Inputting the candidate optimal parameter combination into the nonlinear mapping relationship model to predict the toughness and electrical conductivity corresponding to the candidate optimal parameter combination; Determine whether the predicted toughness and conductivity meet the target value range. If so, determine the candidate optimal parameter combination as the optimal formula for producing the alloyed electrode matrix. If not, return to execute the grid search algorithm to continue searching for the optimal parameter combination.

3. The anti-scaling deposition method of the self-anti-scaling nano-composite conductive ceramic electrode according to claim 1, characterized in that: include: Obtaining a preset range of ideal microstructure parameters of the alloyed electrode substrate, wherein the preset range includes grain size, precipitate phase distribution, and dislocation density; Acquiring microstructural parameters of the alloyed electrode substrate sample, and comparing the microstructural parameters with the preset range; If the microstructure parameters meet the preset range, the optimized ratio and process parameters are determined to prepare an alloyed electrode substrate with an ideal microstructure.

4. The anti-scaling deposition method of the self-anti-scaling nano-composite conductive ceramic electrode according to claim 1, characterized in that: include: Acquiring microstructural parameters of the alloyed electrode substrate sample, wherein the microstructural parameters include grain size and precipitated phase distribution; Analyzing the microstructure parameters using an image processing algorithm to obtain microstructure characteristics; Performing a tensile test and a creep test on the alloyed electrode substrate sample in a high temperature environment to obtain a stress-strain curve and a creep curve; Inputting the high temperature toughness index and the electrical conductivity index into the prediction model to determine whether the preset yield strength threshold and electrical conductivity threshold are met; If the preset threshold is met, it is determined that the alloyed electrode substrate meets the high temperature comprehensive performance requirements; If not, adjust the composition and preparation process of the alloyed electrode substrate, re-obtain samples and evaluate the performance.

5. The anti-scaling deposition method of the self-anti-scaling nano-composite conductive ceramic electrode according to claim 1, characterized in that: include: Obtain the material properties of the alloyed electrode matrix sample, determine the pressure range and pressurization step of the high-pressure vessel, and formulate a pressurization experiment plan; The sample is placed in a high-pressure container, the pressure is gradually increased, the pressure is maintained at each pressure point, and after the deformation of the sample is stable, the compressive strength data is recorded; Continue to increase the pressure until the sample undergoes yield deformation, and record the yield strength data; According to the maximum pressure under actual working conditions, the critical values ​​of compressive strength and yield strength are determined on the strength-pressure curve; Determine whether the compressive strength and yield strength of the sample are higher than the critical value. If so, it is determined to meet the high pressure resistance requirements.

6. The anti-scaling deposition method of the self-anti-scaling nano-composite conductive ceramic electrode according to claim 1, characterized in that: include: According to the material properties of the electrode substrate, an alloying method is selected to prepare an alloyed electrode substrate sample and a pure substrate sample; An electrochemical workstation was used to obtain electrochemical impedance data of samples after immersion corrosion; If the corrosion resistance of the alloyed electrode substrate is better than that of the pure substrate, the maximum service life of the material in different corrosive media shall be determined according to the mass loss rate; If the corrosion resistance of the alloyed electrode substrate is lower than that of the pure substrate, the cause of the decrease in corrosion resistance caused by alloying can be determined by analyzing the electrochemical impedance spectroscopy data, and the alloying process parameters can be optimized; Based on the results of immersion corrosion experiments and electrochemical impedance spectroscopy tests, the maximum service life of the alloyed electrode substrate in different corrosive media was obtained.

7. The anti-scaling deposition method of the self-anti-scaling nano-composite conductive ceramic electrode according to claim 1, characterized in that: include: For the organizational structure evolution data, data preprocessing technology is used to clean and standardize it, remove outliers and noise data, and obtain high-quality machine learning model training data; Based on the training data, a support vector machine or a random forest machine learning algorithm is used to establish a quantitative model of tissue structure parameters and usage time, and the prediction accuracy of the quantitative model is improved through model training and optimization; The optimized alloy electrode substrate is put into practical application to obtain tracking monitoring data in the actual use environment, and the quantitative model is further optimized using the monitoring data to form a model optimization closed loop, thereby continuously improving the performance and life of the alloy electrode substrate.

8. The anti-scaling deposition method of the self-anti-scaling nano-composite conductive ceramic electrode according to claim 1, characterized in that: include: Obtain the preparation process, characterization data and performance of the alloyed electrode substrate and establish a full-process database; Preprocessing the data in the database, and establishing a prediction model between the preparation process and the organizational structure and the performance through a machine learning algorithm; According to the prediction model, key influencing factors and optimal parameter ranges are determined to optimize material composition and process parameters; The alloyed electrode substrate samples are prepared using the optimized material composition and process parameters, the test and characterization data are fed back to the database, and the prediction model is iteratively updated until both the toughness and the conductivity of the electrode substrate are improved.

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