Quality evaluation system for preparing superconducting material and preparation process thereof

Abstract

The invention discloses a quality evaluation system for preparing a superconducting material and a preparation process thereof, which are used in the field of quality evaluation, and the quality evaluation system comprises a data collection module, a data analysis module, a multi-dimensional quality evaluation model construction module, a monitoring and adjusting module and a quality evaluation module. According to the method, various physical fields and various influence factors can be comprehensively considered, the performance of the superconducting material in a future environment is predicted by comprehensively considering the various influence factors and simulating different environmental conditions, optimization and adjustment are carried out according to an actual application result, different environmental conditions are simulated by utilizing simulation software, and the performance of the superconducting material in a future environment is predicted. The performance of the material under various conditions can be evaluated under the condition that the physical environment is not actually changed, the cost and time are saved, and the influence degree of different environmental factors on the performance of the superconducting material and the interaction among the different factors are analyzed through statistical methods such as variance analysis.

Description

Technical Field

[0001] The present invention relates to the field of quality assessment, and in particular to a quality assessment system for preparing superconducting materials and a preparation process thereof. Background Art

[0002] Superconducting materials are a special class of materials with superconductivity, that is, under certain low temperature conditions, their resistivity suddenly drops to zero, and electric current can flow without resistance in the material. Superconducting materials have broad application prospects, especially in the fields of electricity, medical care, transportation and IT industries.

[0003] The application of superconducting materials often requires them to have stable performance under specific conditions. Quality assessment can ensure the performance stability of materials in long-term use, which is particularly important for critical applications such as power transmission and medical devices. In many applications, such as superconducting magnets in MRI equipment, reliability is critical. Quality assessment can help identify potential defects or weaknesses that may lead to failures. High-quality superconducting materials can reduce maintenance costs and the risk of failure, thereby improving overall cost-effectiveness.

[0004] Different industries and fields have specific performance requirements for superconducting materials. Quality assessment ensures that the materials meet these industry standards to meet market demand. Through quality assessment, problems in the material preparation process can be discovered, thereby guiding R&D work and optimizing material formulations and production processes. In the fierce market competition, high-quality superconducting materials can enhance the competitiveness of products and attract more customers and market share.

[0005] Existing technologies are unable to respond to changes in the process in a timely manner, resulting in unstable product quality. The lack of preventive maintenance measures may lead to equipment failures and increased downtime, affecting production efficiency and product quality. Existing technologies may not be accurate enough in quality assessment and lack clear quality assessment indicators and judgment criteria, resulting in the inability to timely discover problems in the production process. At the same time, there is a lack of effective mechanisms to continuously improve the production process and product quality, resulting in an inability to adapt to market demand and technological advances. Existing technologies lack the ability to simulate different environmental conditions and are unable to predict the performance of superconducting materials in future environments, resulting in an inability to make adaptive adjustments in advance.

[0006] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0007] In order to overcome the above problems, the present invention aims to propose a quality assessment system and a preparation process for preparing superconducting materials, with the aim of solving the problem that the prior art may not be accurate enough in quality assessment, lacks clear quality assessment indicators and judgment criteria, resulting in the inability to timely discover problems in the production process, and lacks an effective mechanism to continuously improve the production process and product quality, resulting in the inability to adapt to market demand and technological progress.

[0008] To this end, the specific technical solution adopted by the present invention is as follows: According to one aspect of the present invention, there is provided a quality assessment system for preparing superconducting materials, the quality assessment system comprising: a data collection module, a data analysis module, a multi-dimensional quality assessment model construction module, a monitoring and adjustment module and a quality assessment module; The data collection module, the data analysis module, the multi-dimensional quality assessment model building module, the monitoring and adjustment module and the quality assessment module are connected in sequence; A data collection module, used to collect production data and environmental data of superconducting materials; Data analysis module, used to extract quality data of superconducting materials from production data and analyze the impact of environmental data on quality data; A multi-dimensional quality assessment model building module is used to build a multi-dimensional quality assessment model based on the analysis results to predict the performance of superconducting materials in different scenarios; A monitoring and adjustment module is used to monitor the preparation process of superconducting materials in real time and adjust the control parameters in the preparation process according to the prediction results of the multi-dimensional quality assessment model; The quality assessment module is used to set the quality assessment criteria, compare the quality results predicted by the model with the quality data in actual production, analyze the differences in the comparison results using the difference analysis recognition algorithm, and check the adjustment effect of the control parameters in the actual production process, while evaluating the quality of the superconducting material; The multi-dimensional quality assessment model building module builds a multi-dimensional quality assessment model based on the analysis results to predict the performance of superconducting materials in different scenarios, including: Obtain the impact of different environmental factors on various quality indicators of superconducting materials, and build a multi-dimensional quality assessment model based on the actual conditions of superconducting materials and application environments; Using the set multi-dimensional quality assessment model, different environmental conditions are simulated through simulation software to evaluate the performance changes of superconducting materials under different conditions; The data simulated from the multi-dimensional quality assessment model is combined with the preset quality indicators for analysis to evaluate the overall performance of superconducting materials in different scenarios and identify the impact of environmental factors on performance and the interaction between different environmental factors; Based on the results of the simulation analysis, the performance of superconducting materials under environmental conditions in a preset time period in the future is predicted, and the parameters of the multidimensional quality assessment model are adjusted according to the prediction results.

[0009] Optionally, the data analysis module includes, when extracting quality data of superconducting materials from production data and analyzing the influence of environmental data on the quality data: Extract quality indicators reflecting the quality of superconducting materials from production data; Use statistical analysis to identify features in quality data and analyze environmental data to determine the range of variation in environmental factors; Through correlation analysis, the impact of environmental factors on quality indicators in superconducting materials is identified.

[0010] Optionally, identifying the impact of environmental factors on quality indicators in superconducting materials through correlation analysis includes: Evaluate the support of various quality indicators of superconducting materials; Analyze the confidence between various quality indicators of superconducting materials; The support and confidence are used to evaluate the correlation between environmental factors and superconducting material quality indicators, and the support and confidence are combined in the evaluation process to evaluate the impact of environmental factors on the quality of superconducting materials.

[0011] Optionally, the support calculation formula is: ; The confidence calculation formula is: ; In the formula, Indicates the quality indicators of superconducting materials X support level; X Indicates quality indicators; represents the total number of observations; Indicates that when observing quality indicators X When the preset conditions are met, the quality index Y The confidence level that a certain condition is also met; Represents two quality indicators X and Y The probability of satisfying each preset condition at the same time; Indicates quality indicators X The probability of satisfying a pre-determined condition.

[0012] Optionally, using support and confidence to evaluate the correlation between environmental factors and superconducting material quality indicators, and combining support and confidence in the evaluation process to evaluate the impact of environmental factors on superconducting material quality includes: The Spearman rank correlation coefficient was used to analyze the correlation between the ranked environmental factor levels and the quality indicator levels; The calculated results of support and confidence are combined with the results of Spearman rank correlation coefficient to evaluate the impact of environmental factors on the quality indicators of superconducting materials; Based on the evaluation results, the environmental factors that have the greatest impact on the performance of superconducting materials are identified, and the impact of different environmental factors on various quality indicators in superconducting materials is obtained.

[0013] Optionally, combining the data simulated from the multi-dimensional quality assessment model with the preset quality indicators for analysis, evaluating the overall performance of the superconducting material in different scenarios, and identifying the impact of environmental factors on the performance and the interaction between different environmental factors includes the following steps: Collect data on the response of superconducting materials under various environmental conditions from simulation software for multi-dimensional quality assessment models; Variance analysis is used to evaluate various performance indicators, and the impact of different environmental factors on the performance of superconducting materials is analyzed based on the evaluation results; Identify and quantify the specific effects of different environmental factors on the properties of superconducting materials, while considering the interactions between different environmental factors.

[0014] Optionally, each performance index is evaluated by variance analysis, and the influence of different environmental factors on the performance of superconducting materials is analyzed based on the evaluation results, including: Group the data according to different environmental factors and determine the performance indicator values ​​within each group; Determine the environmental factors and performance indicators in the variance analysis model, and set the corresponding relationship between environmental factors and performance indicators; Calculate the between-group variance and within-group variance, and use the between-group variance and within-group variance to calculate statistics; According to a preset judgment standard, the preset judgment standard is compared with a critical value of the statistical quantity distribution to judge whether the environmental factor has an impact on the performance of the superconducting material; Compare the variance sizes between groups of different environmental factors, analyze the influence of different environmental factors on the performance of superconducting materials, and interpret the results of variance analysis as the specific influence of different environmental factors on the performance of superconducting materials.

[0015] Optionally, the quality assessment module sets the quality assessment criteria, compares the quality results predicted by the model with the quality data in actual production, analyzes the differences in the comparison results using a difference analysis recognition algorithm, and checks the adjustment effect of the control parameters in the actual production process. When evaluating the quality of the superconducting material, it includes: According to the characteristics of superconducting materials and production requirements, set quality assessment indicators and judgment standards including critical temperature and critical current; Collect quality data and measured values ​​of performance indicators of superconducting materials during the actual production process; Compare the quality results predicted by the model with the data from actual production in detail, and compare the differences between the predicted values ​​and the measured values ​​of each performance indicator; Analyze the differences in the comparison results using a difference analysis recognition algorithm; Based on the results of the difference analysis, identify the problems in the model prediction and optimize the model based on the identification results; By comparing the model prediction results and actual data before and after optimization, the effect of control parameter adjustment can be evaluated; Evaluate whether the optimized superconducting material meets the preset quality requirements; If the evaluation results show that the quality of the superconducting material exceeds the preset quality requirements, identifying factors for improving the quality; If the evaluation results show that the quality of the superconducting material does not meet the preset requirements, real-time improvement measures will be taken.

[0016] Optionally, analyzing the differences in the comparison results using a difference analysis recognition algorithm includes: Calculate the deviation between the predicted value and the actual value to quantify the difference in performance indicators; Based on the quantified performance indicator differences, analyze the statistical characteristics and distribution of the difference data and query the data rules; Based on the data patterns obtained from the query, statistical tests are used to verify the significance of the differences and determine the confidence intervals. At the same time, the key factors that cause the differences are identified and the sources of the differences are revealed. Detect abnormal difference values ​​at the source of the difference and analyze the causes of the abnormality.

[0017] According to another aspect of the present invention, a preparation process for preparing a superconducting material is also provided, the preparation process comprising the following steps: S1, preparing superconducting material powder into a single core rod; S2, drawing and assembling the single core rod multiple times to form a precursor wire; S3, coating the surface of the precursor wire with a high-strength material, and then coating the surface of the coated high-strength material with a layer of pure silver to obtain an enhanced precursor wire; S4. Heat-treating the enhanced precursor wire under pure oxygen conditions, cooling it to room temperature in a furnace to obtain a superconducting wire, and performing a performance test on the superconducting wire using the quality assessment system for preparing superconducting materials.

[0018] Compared with the prior art, this application has the following beneficial effects: 1. The support and confidence of the present invention are two intuitive metrics in the association rule analysis, which respectively measure the frequency and conditional probability of an event. It can be clearly seen that environmental factors are closely related to specific quality indicators, thereby providing a direct basis for operational decision-making. The result of the Spearman rank correlation coefficient is easy to interpret because it directly reflects the strength of the monotonic relationship between the two variables. The support, confidence and Spearman rank correlation coefficient are used in combination to analyze data from different angles, provide a more comprehensive perspective, and dynamically adjust analysis parameters, such as selecting different environmental factors or quality indicators for analysis, thereby optimizing the production process.

[0019] 2. The present invention can comprehensively consider a variety of physical fields and a variety of influencing factors to provide a comprehensive performance evaluation. By comprehensively considering a variety of influencing factors, the model can provide a more accurate performance evaluation. By simulating different environmental conditions, the model can predict the performance of superconducting materials in future environments, and optimize and adjust according to actual application results to improve the accuracy and applicability of the model. By simulating different environmental conditions using simulation software, the performance of materials under various conditions can be evaluated without actually changing the physical environment, saving cost and time. Through statistical methods such as variance analysis, the degree of influence of different environmental factors on the performance of superconducting materials and the interaction between different factors can be deeply analyzed.

[0020] 3. The present invention can respond quickly to changes in the process by real-time monitoring of the preparation process of superconducting materials, ensuring the stability of the preparation process and product quality. By monitoring equipment status and environmental conditions, preventive maintenance can be performed before problems occur, reducing equipment failures and downtime. Parameters can be adjusted based on real-time data and model predictions to ensure that decisions are based on actual data and scientific analysis.

[0021] 4. The present invention sets clear quality assessment indicators and discrimination criteria to ensure that the production process has clear quality goals. By collecting production data in real time, it is possible to promptly discover the difference between prediction and reality and take prompt action. By analyzing the differences, the quality assessment model can be continuously optimized to improve the prediction accuracy. By evaluating the quality of the optimized product, it is ensured that the product meets or exceeds the preset quality requirements. By identifying factors that improve quality, the production process can be continuously improved to promote the improvement of product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above characteristics, features and advantages of the present invention and their implementation methods and methods will become more clearly understood in conjunction with the following description of the embodiments, which are described in detail in conjunction with the accompanying drawings. Herein, a schematic diagram is shown: Figure 1 is a principle block diagram of a quality assessment system for preparing superconducting materials according to an embodiment of the present invention; Figure 2 The present invention is a flowchart of a process for preparing a superconducting material according to an embodiment of the present invention.

[0023] In the figure: 1. Data collection module; 2. Data analysis module; 3. Multi-dimensional quality assessment model construction module; 4. Monitoring and adjustment module; 5. Quality assessment module. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] According to an embodiment of the present invention, a quality assessment system for preparing a superconducting material and a preparation process thereof are provided.

[0026] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a quality assessment system for preparing superconducting materials is provided, the quality assessment system comprising: a data collection module 1, a data analysis module 2, a multi-dimensional quality assessment model building module 3, a monitoring and adjustment module 4 and a quality assessment module 5; The data collection module 1, the data analysis module 2, the multi-dimensional quality assessment model building module 3, the monitoring and adjustment module 4 and the quality assessment module 5 are connected in sequence; The data collection module 1 is used to collect production data and environmental data of superconducting materials.

[0027] It needs to be explained that, first of all, it is necessary to clarify the production data and environmental data that need to be collected. The production data includes the production parameters of superconducting materials (such as temperature, pressure, humidity, etc.); the environmental data includes the surrounding environment data that may affect production (such as temperature, humidity, radiation level, etc.).

[0028] Deploy appropriate sensors to collect the required data. For example, temperature sensors, humidity sensors, pressure sensors, etc., deploy these sensors in the production environment to ensure that they can detect the required data accurately and in real time.

[0029] The sensors will monitor the production process and environment in real time, and send the detected data to the data collection module, which will perform preliminary cleaning and screening on the collected data, remove outliers and irrelevant data, and format and standardize the data for subsequent analysis and model processing.

[0030] The data analysis module 2 is used to extract the quality data of the superconducting material from the production data and analyze the influence of the environmental data on the quality data.

[0031] Preferably, the data analysis module 2 includes: extracting quality data of superconducting materials from production data and analyzing the influence of environmental data on the quality data; Extract quality indicators reflecting the quality of superconducting materials from production data; Use statistical analysis to identify features in quality data and analyze environmental data to determine the range of variation in environmental factors; Through correlation analysis, the impact of environmental factors on quality indicators in superconducting materials is identified.

[0032] It needs to be explained that key indicators reflecting the quality of superconducting materials, such as critical temperature, critical current density, etc., are screened out from the production data; data query and processing techniques (such as SQL query, data cleaning tools) are used to isolate these indicators; statistical analysis is performed on the extracted quality data, including calculation of mean, standard deviation, skewness, kurtosis, etc.; exploratory data analysis techniques (such as box plots, histograms) are used to identify data distribution and outliers; statistical analysis helps identify the characteristics and distribution of quality data, so as to better understand the variability and trends of the data; environmental data at the same time as the production process, such as temperature, humidity, etc., are collected, and the environmental data are pre-processed, including data cleaning and standardization, to ensure data quality; association analysis techniques, such as correlation coefficient analysis and regression analysis, are used to explore the relationship between environmental factors and quality indicators. Through association and regression analysis, it is possible to identify which environmental factors are key variables affecting the quality of superconducting materials, which is crucial for optimizing production conditions and improving product quality. The analysis results are summarized, and an analysis report is written in the form of charts and text to explain the analysis results and propose possible improvement measures and suggestions.

[0033] Using statistical analysis to identify features in quality data and analyze environmental data to determine the range of environmental factors includes the following steps: Ensure that all relevant quality data and environmental data have been collected and preprocessed as necessary, such as data cleaning and missing value processing; Apply descriptive statistical analysis to quality data, such as calculating mean, median, standard deviation, skewness, and kurtosis; Descriptive statistical analysis was also performed on the environmental data to determine its range of variation and typical distribution; Create time series graphs and box plots of environmental variables to visualize their changing trends and anomalies; Statistical methods such as Pearson correlation coefficient and Spearman rank correlation were used to analyze the correlation between environmental factors and quality data; Use multivariate analysis methods, such as principal component analysis or factor analysis, to explore the complex relationships between multiple environmental variables and quality indicators.

[0034] Preferably, identifying the influence of environmental factors on quality indicators in superconducting materials through correlation analysis includes: Evaluate the support of various quality indicators of superconducting materials; Analyze the confidence between various quality indicators of superconducting materials; The support and confidence are used to evaluate the correlation between environmental factors and superconducting material quality indicators, and the support and confidence are combined in the evaluation process to evaluate the impact of environmental factors on the quality of superconducting materials.

[0035] Preferably, the support calculation formula is: ; The confidence calculation formula is: ; In the formula, Indicates the quality indicators of superconducting materials X support level; X Indicates quality indicators; represents the total number of observations; Indicates that when observing quality indicators X When the preset conditions are met, the quality index Y The confidence level that a certain condition is also met; Represents two quality indicators X and Y The probability of satisfying each preset condition at the same time; Indicates quality indicators X The probability of satisfying a pre-determined condition.

[0036] It needs to be explained that key quality indicators, such as superconducting critical temperature, critical current, etc., are determined, relevant environmental variables, such as temperature, humidity, pressure, etc., are selected, historical data related to these indicators and factors are collected, and data cleaning is performed, including missing value processing and outlier processing, to ensure data quality; support is defined as the frequency of occurrence of a certain quality indicator, and the support of each quality indicator is calculated using the number of records in the data set. Confidence refers to the conditional probability of the occurrence of another quality indicator when a certain quality indicator is known. The confidence between all pairs of quality indicators is calculated to evaluate the dependency between them. Confidence helps understand the degree of association between different quality indicators, which is very important for understanding the interactions in the production process. Association rule mining technology is used to explore the relationship between environmental factors and quality indicators, and the support and confidence between each environmental factor and a specific quality indicator are evaluated. Combined with the results of support and confidence, the impact of environmental factors on quality is comprehensively evaluated, and a detailed analysis report is prepared, including data charts, key findings and recommended improvement measures.

[0037] Preferably, using support and confidence to evaluate the correlation between environmental factors and superconducting material quality indicators, and combining support and confidence to evaluate the impact of environmental factors on superconducting material quality during the evaluation process includes: The Spearman rank correlation coefficient was used to analyze the correlation between the ranked environmental factor levels and the quality indicator levels; The calculated results of support and confidence are combined with the results of Spearman rank correlation coefficient to evaluate the impact of environmental factors on the quality indicators of superconducting materials; Based on the evaluation results, the environmental factors that have the greatest impact on the performance of superconducting materials are identified, and the impact of different environmental factors on various quality indicators in superconducting materials is obtained.

[0038] It should be explained that the environmental factors and quality indicator data were sorted, each data point was converted into its rank in the data set, and the correlation between the rankings was calculated using the Spearman rank correlation coefficient formula. The Spearman rank correlation coefficient is applicable to non-parametric data and can effectively measure the monotonic relationship between environmental factors and quality indicators, that is, whether an increase or decrease in one variable is consistent with an increase or decrease in another variable.

[0039] Calculate the support of each environmental factor, that is, the frequency of occurrence of a specific environmental condition.

[0040] Calculate the confidence level, that is, the probability of a certain quality indicator occurring under the premise of certain known environmental factors.

[0041] Combined with the results of Spearman's rank correlation coefficient, the statistical significance and practical correlation between these environmental factors and quality indicators were analyzed. Support shows the prevalence of environmental factors, and confidence reflects the degree of direct correlation between these factors and quality indicators. Combined with the Spearman correlation coefficient, the complex relationship between environmental factors and quality indicators can be fully understood.

[0042] Based on the above analysis results, the environmental factors that have the greatest impact on quality indicators are identified, and multivariate statistical methods, such as regression analysis, are used to further verify the intensity and direction of the impact of these key factors on quality indicators.

[0043] Spearman's rank correlation coefficient, also often referred to as Spearman's rank correlation or Spearman's correlation coefficient, is a nonparametric rank correlation measure. It is used to measure the strength of the monotonic relationship between two variables, that is, whether an increase or decrease in one variable is consistent with an increase or decrease in the other variable, regardless of the specific values ​​of the variables. The value of the Spearman rank correlation coefficient is between -1 and 1, where: ρ = 1 indicates a perfect positive correlation, that is, the rankings of the two variables are exactly the same.

[0044] ρ = -1 indicates a perfect negative correlation, that is, when the rank of one variable increases, the rank of the other variable decreases.

[0045] ρ = 0 means there is no correlation, that is, there is no relationship between the rankings of the two variables.

[0046] The multi-dimensional quality assessment model construction module 3 is used to construct a multi-dimensional quality assessment model based on the analysis results to predict the performance of superconducting materials in different scenarios.

[0047] The multi-dimensional quality assessment model building module 3 includes: building a multi-dimensional quality assessment model based on the analysis results to predict the performance of superconducting materials in different scenarios; Obtain the impact of different environmental factors on various quality indicators of superconducting materials, and build a multi-dimensional quality assessment model based on the actual conditions of superconducting materials and application environments; Using the set multi-dimensional quality assessment model, different environmental conditions are simulated through simulation software to evaluate the performance changes of superconducting materials under different conditions; The data simulated from the multi-dimensional quality assessment model is combined with the preset quality indicators for analysis to evaluate the overall performance of superconducting materials in different scenarios and identify the impact of environmental factors on performance and the interaction between different environmental factors; Based on the results of the simulation analysis, the performance of superconducting materials under environmental conditions in a preset time period in the future is predicted, and the parameters of the multidimensional quality assessment model are adjusted according to the prediction results.

[0048] It needs to be explained that the multidimensional quality assessment model is a model that integrates multiple physical fields (such as electric field, magnetic field, temperature field, etc.) and multiple influencing factors (such as environmental conditions, material properties, etc.). It is used to evaluate the performance of superconducting materials in different scenarios, and comprehensively considers various complex situations that superconducting materials may encounter in practical applications, providing more accurate and comprehensive performance evaluation.

[0049] Features of multiphysics coupling models: 1) Comprehensiveness: The model takes into account multiple physical fields and multiple influencing factors, and can fully reflect the performance of superconducting materials.

[0050] 2) Complexity: Since it involves the interaction of multiple physical fields, the model is relatively complex and requires precise mathematical description and effective calculation methods.

[0051] 3) Accuracy: By comprehensively considering multiple factors, the model can provide more accurate performance evaluation and provide a stronger scientific basis for practical applications.

[0052] Steps to construct a multidimensional quality assessment model: 1) Data collection and preprocessing: Collect multi-physics field data and environmental data related to the performance of superconducting materials, and perform necessary preprocessing, such as data cleaning and missing value processing.

[0053] 2) Feature selection and extraction: Select features that have a significant impact on the performance of superconducting materials from the collected data and perform feature extraction.

[0054] 3) Model selection and construction: According to actual needs and data characteristics, select the appropriate model framework, such as regression model, machine learning model, etc., and build a multi-physics field coupling model.

[0055] 4) Model training and validation: Use part of the data to train the model and use another part of the data to validate it to ensure the accuracy and generalization ability of the model.

[0056] 5) Model application and optimization: Apply the model to actual scenarios, evaluate the performance of superconducting materials under different environmental conditions, and optimize and adjust the model based on the application results to improve the accuracy and applicability of the model.

[0057] Preferably, the data simulated from the multi-dimensional quality assessment model is combined with the preset quality indicators for analysis to evaluate the overall performance of the superconducting material in different scenarios, and to identify the impact of environmental factors on the performance and the interaction between different environmental factors, including the following steps: Collect data on the response of superconducting materials under various environmental conditions from simulation software for multi-dimensional quality assessment models; Variance analysis is used to evaluate various performance indicators, and the impact of different environmental factors on the performance of superconducting materials is analyzed based on the evaluation results; Identify and quantify the specific effects of different environmental factors on the properties of superconducting materials, while considering the interactions between different environmental factors.

[0058] It needs to be explained that the data previously collected and analyzed are analyzed and collated, especially the data on the impact of environmental factors on the quality indicators of superconducting materials.

[0059] According to the actual conditions of superconducting materials and application environments, the multidimensional quality assessment model provides a comprehensive and accurate tool for the performance evaluation of superconducting materials in different scenarios by comprehensively considering multiple physical fields and multiple influencing factors. It is of great significance in the research, development and practical application of superconducting materials.

[0060] Use simulation software (such as MATLAB, ANSYS, etc.) to set different environmental condition parameters, such as temperature, pressure, humidity, etc. Through simulation, the performance of materials under various conditions can be evaluated without actually changing the physical environment, saving cost and time. Analyze the data obtained from the simulation, compare it with the preset quality indicators, evaluate the impact of different environmental factors on the performance of superconducting materials and their interactions, and predict the performance of superconducting materials under various environmental conditions in a certain period of time in the future based on the current simulation analysis results. Adjust the model parameters according to the prediction results to optimize the accuracy and applicability of the model.

[0061] Preferably, each performance index is evaluated by variance analysis, and the influence of different environmental factors on the performance of superconducting materials is analyzed based on the evaluation results, including: Group the data according to different environmental factors and determine the performance indicator values ​​within each group; Determine the environmental factors and performance indicators in the variance analysis model, and set the corresponding relationship between environmental factors and performance indicators; Calculate the between-group variance and within-group variance, and use the between-group variance and within-group variance to calculate statistics; According to a preset judgment standard, the preset judgment standard is compared with a critical value of the statistical quantity distribution to judge whether the environmental factor has an impact on the performance of the superconducting material; Compare the variance sizes between groups of different environmental factors, analyze the influence of different environmental factors on the performance of superconducting materials, and interpret the results of variance analysis as the specific influence of different environmental factors on the performance of superconducting materials.

[0062] It needs to be explained that the collected data are grouped according to different environmental factors, such as temperature, humidity, magnetic field strength, etc.; the performance indicator values ​​within each group are determined, such as the critical current and critical temperature of superconducting materials; the dependent variable (performance indicator) and independent variable (environmental factor) in the variance analysis model are determined; the corresponding relationship between environmental factors and performance indicators is set, that is, which performance indicators are affected by which environmental factors; the dependent variable (performance indicator) and independent variable (environmental factor) in the variance analysis model are determined; the corresponding relationship between environmental factors and performance indicators is set, that is, which performance indicators are affected by which environmental factors, and the intra-group variance (the variability of the performance indicators under each environmental condition) and the inter-group variance (the variability between the average values ​​of the performance indicators under different environmental conditions) are calculated.

[0063] Use statistics to compare the ratio of the between-group variance to the within-group variance.

[0064] Among them, F = within-group variance / between-group variance; F represents the statistic.

[0065] Determine the significance level (usually 0.05 or 0.01), and find the corresponding critical value based on the F distribution table, compare the calculated F value with the critical value, determine whether to reject the null hypothesis (that is, environmental factors have no effect), compare the inter-group variance of different environmental factors, analyze which environmental factors have a more significant impact on the performance of superconducting materials, and interpret the results of variance analysis as the specific impact of different environmental factors on the performance of superconducting materials.

[0066] The intra-group variance measures the deviation between each data point in the group and its group mean. Specifically, the intra-group variance reflects the consistency or variability of the performance indicators under the same environmental conditions. The calculation steps are as follows: 1) Calculate the average value of each group: For each environment group, first calculate the average value of all performance indicator values ​​of the group.

[0067] 2) Calculate the variance of each group: For each data point in each group, calculate the square of the difference between its value and the mean of the group, then sum these squared differences and divide them by the number of data points in the group minus one to get the variance of each group.

[0068] 3) Combine into total within-group variance: Take the weighted average of the variances of all groups, with the weight being the degrees of freedom of each group (i.e. the number of data points in each group minus one), and get the total within-group variance.

[0069] Between-Group Variance: Between-Group Variance measures the degree of variation in the average values ​​of performance indicators between different environmental groups, that is, the difference between the mean of each group and the total mean. The calculation steps are as follows: 1) Calculate the overall mean: Calculate the overall mean of all data, which is the sum of all data points in all groups divided by the total number of data points.

[0070] 2) Calculate the between-group sum of squares: For each group, calculate the square of the difference between the group mean and the total mean, and then multiply this square of the difference by the number of data points in the group to reflect the effect of group size. Sum these values ​​for all groups to get the between-group sum of squares.

[0071] 3) Calculate the between-group variance: Divide the between-group sum of squares by the between-group degrees of freedom (the total number of groups minus one) to obtain the between-group variance.

[0072] By calculating these two variances, we can use the F statistic (the ratio of the between-group variance to the within-group variance) to test whether different environmental factors have a significant statistical impact on performance indicators. If the between-group variance is significantly greater than the within-group variance, it means that different environmental factors have a significant impact on performance indicators.

[0073] The monitoring and adjustment module 4 is used to monitor the preparation process of the superconducting material in real time and adjust the control parameters in the preparation process according to the prediction results of the multi-dimensional quality evaluation model.

[0074] It needs to be explained that sensors and measuring equipment are installed: various sensors, such as temperature sensors, pressure sensors, humidity sensors, etc., are installed during the preparation process to monitor environmental conditions and equipment status in real time.

[0075] Data collection and transmission: Sensors collect data in real time and transmit the data to the monitoring center or central processing system through the data acquisition system.

[0076] Data processing and analysis: The monitoring center processes and analyzes the received data in real time to monitor key parameters and potential problems in the preparation process.

[0077] Predicting performance: Using previously established multi-dimensional quality assessment models, the performance of superconducting materials under current preparation conditions is predicted based on real-time monitoring data.

[0078] Compare forecasts to standards: Compare forecast performance to predetermined quality standards to determine if adjustments are needed.

[0079] Parameter adjustment: If the predicted performance does not meet the standard, adjust the corresponding control parameters such as temperature, pressure, reaction time, etc. according to the model suggestions.

[0080] Integrated data management: Integrate monitoring data, model prediction results, and adjustment records into a unified management system for easy tracking and management.

[0081] Automation and Optimization: Based on historical data and model feedback, the automatic adjustment logic is continuously optimized to achieve more precise and efficient control.

[0082] Alarm and Report: When abnormal conditions are detected or manual intervention is required, the system will trigger an alarm and send a report to the operator.

[0083] The quality assessment module 5 is used to set the quality assessment criteria, compare the quality results predicted by the model with the quality data in actual production, analyze the differences in the comparison results using the difference analysis recognition algorithm, and check the adjustment effect of the control parameters in the actual production process, and evaluate the quality of the superconducting material; Preferably, the quality assessment module 5 sets the quality assessment criteria, compares the quality results predicted by the model with the quality data in actual production, analyzes the differences in the comparison results using a difference analysis recognition algorithm, and checks the adjustment effect of the control parameters in the actual production process. When evaluating the quality of the superconducting material, it includes: According to the characteristics of superconducting materials and production requirements, set quality assessment indicators and judgment standards including critical temperature and critical current; Collect quality data and measured values ​​of performance indicators of superconducting materials during the actual production process; Compare the quality results predicted by the model with the data from actual production in detail, and compare the differences between the predicted values ​​and the measured values ​​of each performance indicator; Analyze the differences in the comparison results using a difference analysis recognition algorithm; Based on the results of the difference analysis, identify the problems in the model prediction and optimize the model based on the identification results; By comparing the model prediction results and actual data before and after optimization, the effect of control parameter adjustment can be evaluated; Evaluate whether the optimized superconducting material meets the preset quality requirements; If the evaluation results show that the quality of the superconducting material exceeds the preset quality requirements, identifying factors for improving the quality; If the evaluation results show that the quality of the superconducting material does not meet the preset requirements, real-time improvement measures will be taken.

[0084] It should be explained that the quality assessment indicators are determined according to the key performance requirements of superconducting materials (such as critical temperature and critical current); the judgment criteria for each indicator are set, and the judgment criteria are formulated based on product design requirements and application scenarios. In the production process, sensors and detection equipment are used to collect the actual performance data of superconducting materials to ensure the accuracy and completeness of the data, laying the foundation for subsequent data analysis, and the prediction results of the multi-dimensional quality assessment model are compared with the actual production data in detail, and the differences between the predicted values ​​and the measured values ​​of each performance indicator are analyzed. Using statistical methods and data analysis techniques, the reasons for the differences between the predicted values ​​and the measured values ​​are deeply studied, and possible factors causing the differences are identified and recorded, such as inaccurate model assumptions, data quality problems or variations in the production process; according to the results of the difference analysis, the multi-dimensional quality assessment model is adjusted, the model parameters and algorithms are optimized, the prediction results of the model before and after optimization are compared, the actual effect of the control parameter adjustment is evaluated, and the optimization is analyzed to help achieve or exceed the set quality standards. Finally, it is evaluated whether the optimized superconducting material meets the preset quality requirements. If the results show that the material quality does not meet the preset requirements, improvement measures are implemented immediately.

[0085] Preferably, analyzing the differences in the comparison results using a difference analysis and recognition algorithm includes: Calculate the deviation between the predicted value and the actual value to quantify the difference in performance indicators; Based on the quantified performance indicator differences, analyze the statistical characteristics and distribution of the difference data and query the data rules; Based on the data patterns obtained from the query, statistical tests are used to verify the significance of the differences and determine the confidence intervals. At the same time, the key factors that cause the differences are identified to reveal the sources of the differences. Detect abnormal difference values ​​of the difference sources and analyze the abnormal reasons.

[0086] It should be explained that for each performance indicator, the difference between the predicted value and the actual value is calculated: deviation = actual value − predicted value; Perform statistical analysis on all deviations, including calculation of mean deviation, standard deviation, etc.; Use statistical methods (such as histograms, box plots, etc.) to analyze the distribution characteristics of deviations; Calculate statistical indicators of deviation data, such as mean, variance, skewness, and kurtosis; Use statistical hypothesis tests (such as t-tests or ANOVA) to determine whether the deviation is statistically significant; Determine the confidence interval of the bias to assess the reliability of the bias range; Identify and quantify key factors that influence bias through regression analysis, decision trees, or other machine learning techniques; Conduct detailed analysis of the impact of key factors to understand how they lead to differences between forecasts and actual results; Identify outliers in statistical distributions (e.g. using Z-scores, IQR methods, etc.); Conduct in-depth analysis of each outlier to determine possible causes such as operator error, equipment failure, or data entry error.

[0087] According to another embodiment of the present invention, Figure 2 As shown, a preparation process for preparing a superconducting material is also provided, and the preparation process comprises the following steps: S1, preparing superconducting material powder into a single core rod; S2, drawing and assembling the single core rod multiple times to form a precursor wire; S3, coating the surface of the precursor wire with a high-strength material, and then coating the surface of the coated high-strength material with a layer of pure silver to obtain an enhanced precursor wire; S4. Heat-treating the enhanced precursor wire under pure oxygen conditions, cooling it to room temperature in a furnace to obtain a superconducting wire, and performing a performance test on the superconducting wire using the quality assessment system for preparing superconducting materials.

[0088] In summary, with the help of the above technical scheme of the present invention, the support and confidence of the present invention are two intuitive metrics in the association rule analysis, which measure the frequency and conditional probability of event occurrence respectively, and clearly see that environmental factors are closely related to specific quality indicators, thereby providing a direct basis for operational decision-making. The result of the Spearman rank correlation coefficient is easy to interpret because it directly reflects the strength of the monotonic relationship between the two variables. The support, confidence and Spearman rank correlation coefficient are used in combination to analyze data from different angles, provide a more comprehensive perspective, and dynamically adjust analysis parameters, such as selecting different environmental factors or quality indicators for analysis, thereby optimizing the production process; the present invention can comprehensively consider a variety of physical fields and a variety of influencing factors to provide a comprehensive performance evaluation. By comprehensively considering a variety of influencing factors, the model can provide a more accurate performance evaluation. By simulating different environmental conditions, the model can predict the performance of superconducting materials in future environments, and optimize and adjust according to actual application results to improve the accuracy and applicability of the model. By simulating different environmental conditions using simulation software, it can be used without actually changing the environmental conditions. The performance of materials under various conditions can be evaluated in a changing physical environment, saving costs and time. Through statistical methods such as variance analysis, the degree of influence of different environmental factors on the performance of superconducting materials and the interaction between different factors can be deeply analyzed. The present invention can quickly respond to changes in the process by real-time monitoring of the preparation process of superconducting materials to ensure the stability of the preparation process and product quality. By monitoring the equipment status and environmental conditions, preventive maintenance can be performed before problems occur to reduce equipment failures and downtime. Parameters can be adjusted based on real-time data and model predictions to ensure that decisions are based on actual data and scientific analysis. The present invention sets clear quality assessment indicators and discrimination criteria to ensure that the production process has clear quality goals. By collecting production data in real time, the difference between prediction and reality can be discovered in time and action can be taken quickly. By analyzing the difference, the quality assessment model can be continuously optimized to improve the accuracy of prediction. By evaluating the quality of the optimized product, it is ensured that the product meets or exceeds the preset quality requirements. By identifying factors that improve quality, the production process can be continuously improved to promote the improvement of product quality.

[0089] Although the present invention has been disclosed as above with preferred embodiments, the embodiments are merely examples for the convenience of description and are not intended to limit the present invention. Those skilled in the art may make several changes and modifications without departing from the spirit and scope of the present invention. The scope of protection claimed by the present invention shall be based on the claims.

Claims

1. A quality assessment system for preparing superconducting materials, characterized in that: The quality assessment system includes: a data collection module, a data analysis module, a multi-dimensional quality assessment model building module, a monitoring and adjustment module and a quality assessment module; The data collection module, the data analysis module, the multi-dimensional quality assessment model building module, the monitoring and adjustment module and the quality assessment module are connected in sequence; A data collection module, used to collect production data and environmental data of superconducting materials; Data analysis module, used to extract quality data of superconducting materials from production data and analyze the impact of environmental data on quality data; A multi-dimensional quality assessment model building module is used to build a multi-dimensional quality assessment model based on the analysis results to predict the performance of superconducting materials in different scenarios; A monitoring and adjustment module is used to monitor the preparation process of superconducting materials in real time and adjust the control parameters in the preparation process according to the prediction results of the multi-dimensional quality assessment model; The quality assessment module is used to set the quality assessment criteria, compare the quality results predicted by the model with the quality data in actual production, analyze the differences in the comparison results using the difference analysis recognition algorithm, and check the adjustment effect of the control parameters in the actual production process, while evaluating the quality of the superconducting material; The multi-dimensional quality assessment model building module builds a multi-dimensional quality assessment model based on the analysis results to predict the performance of superconducting materials in different scenarios, including: Obtain the impact of different environmental factors on various quality indicators of superconducting materials, and build a multi-dimensional quality assessment model based on the actual conditions of superconducting materials and application environments; Using the set multi-dimensional quality assessment model, different environmental conditions are simulated through simulation software to evaluate the performance changes of superconducting materials under different conditions; The data simulated from the multi-dimensional quality assessment model is combined with the preset quality indicators for analysis to evaluate the overall performance of superconducting materials in different scenarios and identify the impact of environmental factors on performance and the interaction between different environmental factors; Based on the results of the simulation analysis, the performance of superconducting materials under environmental conditions in a preset time period in the future is predicted, and the parameters of the multidimensional quality assessment model are adjusted according to the prediction results.

2. A quality assessment system for preparing superconducting materials according to claim 1, characterized in that: The data analysis module includes: extracting quality data of superconducting materials from production data and analyzing the influence of environmental data on quality data; Extract quality indicators reflecting the quality of superconducting materials from production data; Use statistical analysis to identify features in quality data and analyze environmental data to determine the range of variation in environmental factors; Through correlation analysis, the impact of environmental factors on quality indicators in superconducting materials is identified.

3. A quality assessment system for preparing superconducting materials according to claim 2, characterized in that: The identification of the influence of environmental factors on the quality indicators of superconducting materials through correlation analysis includes: Evaluate the support of various quality indicators of superconducting materials; Analyze the confidence between various quality indicators of superconducting materials; The support and confidence are used to evaluate the correlation between environmental factors and superconducting material quality indicators, and the support and confidence are combined in the evaluation process to evaluate the impact of environmental factors on the quality of superconducting materials.

4. A quality assessment system for preparing superconducting materials according to claim 3, characterized in that: The support calculation formula is: ； The confidence calculation formula is: ； In the formula, Indicates the quality indicators of superconducting materials X support level; X Indicates quality indicators; represents the total number of observations; Indicates that when observing quality indicators X When the preset conditions are met, the quality index Y The confidence level that a certain condition is also met; Represents two quality indicators X and Y The probability of satisfying each preset condition at the same time; Indicates quality indicators X The probability of satisfying a pre-determined condition.

5. A quality assessment system for preparing superconducting materials according to claim 4, characterized in that: The use of support and confidence to evaluate the correlation between environmental factors and superconducting material quality indicators, and combining support and confidence in the evaluation process to evaluate the impact of environmental factors on superconducting material quality include: The Spearman rank correlation coefficient was used to analyze the correlation between the ranked environmental factor levels and the quality indicator levels; The calculated results of support and confidence are combined with the results of Spearman rank correlation coefficient to evaluate the impact of environmental factors on the quality indicators of superconducting materials; Based on the evaluation results, the environmental factors that have the greatest impact on the performance of superconducting materials are identified, and the impact of different environmental factors on various quality indicators in superconducting materials is obtained.

6. A quality assessment system for preparing superconducting materials according to claim 1, characterized in that: The method of analyzing the data simulated from the multi-dimensional quality assessment model in combination with the preset quality indicators to evaluate the overall performance of the superconducting material in different scenarios and identify the impact of environmental factors on the performance and the interaction between different environmental factors includes the following steps: Collect data on the response of superconducting materials under various environmental conditions from simulation software for multi-dimensional quality assessment models; Variance analysis is used to evaluate various performance indicators, and the impact of different environmental factors on the performance of superconducting materials is analyzed based on the evaluation results; Identify and quantify the specific effects of different environmental factors on the properties of superconducting materials, while considering the interactions between different environmental factors.

7. A quality assessment system for preparing superconducting materials according to claim 6, characterized in that: The use of variance analysis to evaluate various performance indicators and the analysis of the influence of different environmental factors on the performance of superconducting materials based on the evaluation results include: Group the data according to different environmental factors and determine the performance indicator values ​​within each group; Determine the environmental factors and performance indicators in the variance analysis model, and set the corresponding relationship between environmental factors and performance indicators; Calculate the between-group variance and within-group variance, and use the between-group variance and within-group variance to calculate statistics; According to a preset judgment standard, the preset judgment standard is compared with a critical value of the statistical quantity distribution to judge whether the environmental factor has an impact on the performance of the superconducting material; Compare the variance sizes between groups of different environmental factors, analyze the influence of different environmental factors on the performance of superconducting materials, and interpret the results of variance analysis as the specific influence of different environmental factors on the performance of superconducting materials.

8. A quality assessment system for preparing superconducting materials according to claim 1, characterized in that: The quality assessment module sets the quality assessment criteria, compares the quality results predicted by the model with the quality data in actual production, analyzes the differences in the comparison results using a difference analysis and recognition algorithm, and checks the adjustment effect of the control parameters in the actual production process. When evaluating the quality of the superconducting material, it includes: Set quality assessment indicators and criteria based on superconducting material characteristics and production requirements; Collect quality data and measured values ​​of performance indicators of superconducting materials during the actual production process; Compare the quality results predicted by the model with the data from actual production in detail, and compare the differences between the predicted values ​​and the measured values ​​of each performance indicator; Analyze the differences in the comparison results using a difference analysis recognition algorithm; Based on the results of the difference analysis, identify the problems in the model prediction and optimize the model based on the identification results; By comparing the model prediction results and actual data before and after optimization, the effect of control parameter adjustment can be evaluated; Evaluate whether the optimized superconducting material meets the preset quality requirements; If the evaluation results show that the quality of the superconducting material exceeds the preset quality requirements, identifying factors for improving the quality; If the evaluation results show that the quality of the superconducting material does not meet the preset requirements, real-time improvement measures will be taken.

9. A quality assessment system for preparing superconducting materials according to claim 8, characterized in that: The analyzing the differences in the comparison results using the difference analysis and recognition algorithm comprises: Calculate the deviation between the predicted value and the actual value to quantify the difference in performance indicators; Based on the quantified performance indicator differences, analyze the statistical characteristics and distribution of the difference data and query the data rules; Based on the data patterns obtained from the query, statistical tests are used to verify the significance of the differences and determine the confidence intervals. At the same time, the key factors that cause the differences are identified to reveal the sources of the differences. Detect abnormal difference values ​​of the difference sources and analyze the abnormal reasons.

10. A process for preparing a superconducting material, used to prepare the superconducting material according to claims 1-9, characterized in that: The preparation process comprises the following steps: S1, preparing superconducting material powder into a single core rod; S2, drawing and assembling the single core rod multiple times to form a precursor wire; S3, coating the surface of the precursor wire with a high-strength material, and then coating the surface of the coated high-strength material with a layer of pure silver to obtain an enhanced precursor wire; S4. Heat-treating the enhanced precursor wire under pure oxygen conditions, cooling it to room temperature in a furnace to obtain a superconducting wire, and performing a performance test on the superconducting wire using the quality assessment system for preparing superconducting materials.

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