A data analysis method for large-scale preparation of alloy and ceramic composite materials

Abstract

The present invention provides a large-scale preparation data analysis method for alloy and ceramic composite materials, belonging to the technical field of data analysis. The method utilizes the deviation of index data of index characteristics between different preparation batches and the optimal preparation batch and the deviation of control parameters at different stages to determine the correlation coefficients of index characteristics at different stages and different dimensions and the correlation stages of index characteristics at different dimensions, obtains the deviation of control parameters at different correlation stages of different preparation batches, determines the control difficulty of index characteristics at different dimensions based on the deviation of control parameters and the correlation coefficient, determines whether the current preparation scheme needs to be adjusted based on the control difficulty of index characteristics at different dimensions and the deviation of index data of index characteristics between different production batches, and ensures the efficiency and reliability of the preparation process.

Description

Technical Field

[0001] The invention belongs to the technical field of data analysis, and in particular relates to a method for analyzing data on the large-scale preparation of alloy and ceramic composite materials. Background Art

[0002] Alloy and ceramic composite materials have been widely used in the field of 3D printing due to their lightness and stability. In order to meet the material usage needs in the field of 3D printing, how to improve the preparation efficiency and material reliability based on the analysis and processing of preparation data has become a technical problem that needs to be solved urgently.

[0003] Specifically, in the invention patent application CN202311076577.6 "A digital twin system for ceramic sintering process", the details and changes of the actual sintering process are captured and detected in real time, and real-time control is performed according to the details and changes during the sintering process, thereby improving the production efficiency and sintering quality of ceramic materials. However, there are the following technical problems:

[0004] The existing technical solutions have neglected to generate differentiated preparation management strategies based on the analysis and processing results of the material preparation data. During the material preparation process, it is often necessary to dynamically adjust the preparation temperature, pressure and material doping ratio at different stages. Therefore, if the preparation plan is not optimized from two perspectives: the difficulty of controlling the parameters of the indicator characteristics in the associated preparation stage and the fluctuation of the indicator characteristics in the indicator data of different generation batches, it is impossible to improve the overall production efficiency and sintering quality while ensuring the control difficulty.

[0005] In view of the above technical problems, the present invention provides a method for analyzing data on large-scale preparation of alloy and ceramic composite materials. Summary of the invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a method for analyzing data on large-scale preparation of alloy and ceramic composite materials is provided.

[0008] A method for analyzing data of large-scale preparation of alloy and ceramic composite materials, specifically comprising:

[0009] S1 takes the alloy and ceramic composite material as the target preparation material, obtains the deviation of the index characteristics of the target preparation material between different preparation batches, and determines that the current preparation scheme meets the requirements based on the qualified conditions of the index characteristics of the target preparation material in different preparation batches, and then proceeds to the next step;

[0010] S2 determines the optimal preparation batch among the preparation batches using the analysis results of the indicator data of the indicator characteristics of different dimensions, and determines the correlation coefficients of the indicator characteristics of different stages and different dimensions and the correlation stages of the indicator characteristics of different dimensions using the deviations of the indicator data of the indicator characteristics of different preparation batches from the optimal preparation batch and the deviations of the control parameters at different stages;

[0011] S3 obtains the deviation of control parameters of different preparation batches at different correlation stages, and determines the control difficulty of indicator characteristics of different dimensions based on the deviation of control parameters and correlation coefficients;

[0012] S4 determines whether the current preparation plan needs to be adjusted based on the difficulty of controlling indicator characteristics in different dimensions and the deviation of indicator data of indicator characteristics between different production batches.

[0013] The beneficial effects of the present invention are:

[0014] 1. In the present invention, the control difficulty of indicator characteristics of different dimensions is determined by the deviation of control parameters and the correlation coefficient. This takes into account the differences in the deviation of control parameters associated with indicator characteristics of different dimensions in different preparation batches, resulting in differences in the difficulty of parameter control of control parameters. At the same time, the correlation between control parameters and indicator characteristics of different dimensions is also taken into account, thereby realizing the evaluation of the control difficulty of indicator characteristics from the control perspective of control parameters, and also laying the foundation for determining whether to adjust the preparation plan.

[0015] 2. In the present invention, whether the current preparation scheme needs to be adjusted is determined based on the difficulty of controlling indicator features in different dimensions and the deviation of indicator data of indicator features between different production batches. Not only the deviation of the stability of the indicator data under the current preparation scheme caused by the deviation of the indicator data of the indicator features between different production batches is taken into consideration, but also the difficulty of controlling the indicator data of the indicator features is taken into consideration. This realizes the screening of indicator features that need to adjust the indicator data from the two perspectives of control difficulty and stability, thereby ensuring the reliability of production.

[0016] A further technical solution is that the index characteristics include high temperature resistance, corrosion resistance, mechanical properties, wear resistance, and fracture toughness.

[0017] A further technical solution is that the deviation of the indicator characteristic includes the performance deviation of the indicator characteristic between different preparation batches.

[0018] A further technical solution is that the qualified status of the indicator characteristics includes the qualified rates of the indicator characteristics in different dimensions and the qualified rates of the target preparation materials.

[0019] A further technical solution is to determine whether the current preparation scheme needs to be adjusted, specifically including:

[0020] Determine the deviation amount of the indicator data of the indicator characteristics between different production batches by using the deviation of the indicator data of the indicator characteristics between different production batches;

[0021] Determine the index characteristic fluctuation coefficient of the index characteristics of different dimensions based on the deviation of the index data of the index characteristics between different production batches, and determine the characteristic change demand coefficient of the index characteristics of different dimensions by the product of the index characteristic fluctuation coefficient and the control difficulty;

[0022] The characteristic change demand coefficients of indicator characteristics in different dimensions are used to determine whether the current preparation plan needs to be adjusted.

[0023] A further technical solution is that when there is an indicator feature with a characteristic change demand coefficient greater than a preset demand coefficient threshold, it is determined that the current preparation plan needs to be adjusted, and the indicator feature with the characteristic change demand coefficient greater than the preset demand coefficient threshold is adjusted.

[0024] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0027] Figure 1 is a flow chart of a data analysis method for large-scale preparation of alloy and ceramic composites;

[0028] Figure 2 is a flow chart of a method for determining an optimal preparation batch among preparation batches;

[0029] Figure 3 is a flow chart of a method for determining a correlation coefficient;

[0030] Figure 4 It is a flow chart of a method for determining the control difficulty of an indicator characteristic. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0032] During the material preparation process, it is often necessary to dynamically adjust the preparation temperature, pressure and material doping ratio at different stages. Therefore, it is necessary to optimize the preparation plan by controlling the difficulty of the parameters of the indicator characteristics in the related preparation stages, so as to improve the overall production efficiency and sintering quality.

[0033] Determine whether the current preparation scheme meets the requirements: Determine based on the qualified indicator characteristics of the target preparation material between different preparation batches. Specifically, determine whether the current preparation scheme meets the requirements by the proportion of qualified preparation times. Specifically, when the proportion of qualified preparation times is less than 0.8, it is determined that the current preparation scheme does not meet the requirements.

[0034] Optimal preparation batch: Determined by using the deviation between the indicator characteristics and the indicator data, the preparation batch with the smallest deviation between the indicator characteristics and the indicator data is taken as the optimal preparation number.

[0035] The correlation coefficient between different stages and indicator characteristics is determined by multiplying the deviation ratio of the indicator data of the indicator characteristics of different preparation times and the optimal preparation batch by the deviation ratio of the control parameters at different stages. Specifically, when the deviation ratio of the indicator data is 0.2 and the deviation ratio of the control parameter is 0.05, the correlation coefficient is 0.1, and the stage with a correlation coefficient greater than 0.03 is regarded as the correlation stage of the indicator characteristic.

[0036] Control difficulty of indicator characteristics: The control difficulty is determined by the product of the deviation ratio of control parameters and the correlation coefficient in different correlation stages. Specifically, when the deviation ratios are 0.1, 0.2, and 0.1 respectively, and the correlation coefficients are 0.2, 0.3, and 0.1, the control difficulty is 0.09.

[0037] Determine whether the current preparation plan needs to be adjusted: utilize the deviation of the indicator data of the indicator characteristics between different production batches to determine the deviation amount of the indicator data of the indicator characteristics between different production batches, determine the indicator characteristic fluctuation coefficient of the indicator characteristics of different dimensions based on the deviation amount of the indicator data of the indicator characteristics between different production batches, determine the characteristic change demand coefficient of the indicator characteristics of different dimensions by the product of the indicator characteristic fluctuation coefficient and the control difficulty, and utilize the characteristic change demand coefficient of the indicator characteristics of different dimensions to determine whether the current preparation plan needs to be adjusted.

[0038] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a method for analyzing data of large-scale preparation of alloy and ceramic composite materials is provided, which specifically includes:

[0039] S1 takes the alloy and ceramic composite material as the target preparation material, obtains the deviation of the index characteristics of the target preparation material between different preparation batches, and determines that the current preparation scheme meets the requirements based on the qualified conditions of the index characteristics of the target preparation material in different preparation batches, and then proceeds to the next step;

[0040] S2 determines the optimal preparation batch among the preparation batches using the analysis results of the indicator data of the indicator characteristics of different dimensions, and determines the correlation coefficients of the indicator characteristics of different stages and different dimensions and the correlation stages of the indicator characteristics of different dimensions using the deviations of the indicator data of the indicator characteristics of different preparation batches from the optimal preparation batch and the deviations of the control parameters at different stages;

[0041] S3 obtains the deviation of control parameters of different preparation batches at different correlation stages, and determines the control difficulty of indicator characteristics of different dimensions based on the deviation of control parameters and correlation coefficients;

[0042] S4 determines whether the current preparation plan needs to be adjusted based on the difficulty of controlling indicator characteristics in different dimensions and the deviation of indicator data of indicator characteristics between different production batches.

[0043] Furthermore, the index characteristics include high temperature resistance, corrosion resistance, mechanical properties, wear resistance, and fracture toughness.

[0044] It can be understood that the deviation of the indicator characteristic includes the performance deviation of the indicator characteristic between different preparation batches.

[0045] It should also be noted that the qualification of the indicator characteristics includes the qualification rate of the indicator characteristics in different dimensions and the qualification rate of the target preparation material.

[0046] Specifically, determine whether the current preparation scheme meets the requirements, including:

[0047] Determine the benchmark index data of the index characteristics of the target preparation materials in different dimensions between different preparation batches by taking the median of the index data of the index characteristics of the target preparation materials in different dimensions from different preparation batches;

[0048] Determine the deviation of the index characteristics between different preparation batches by using the benchmark index data, and determine the batch consistency coefficient of the target preparation material by using the average value of the deviation of the index characteristics between different preparation batches;

[0049] Determine the qualified rate of the index characteristics of different dimensions by using the qualified conditions of the index characteristics of the target index material at different preparation times, and determine the qualified rate evaluation amount of different preparation times in combination with the qualified rate of the target preparation material at different preparation times;

[0050] The pass rate of the target preparation material is determined by using the average value of the pass rate evaluation values ​​of different preparation times, and the preparation reliability coefficient of the current preparation scheme is determined by multiplying the pass rate and the batch consistency coefficient. The preparation reliability coefficient is used to determine whether the current preparation scheme meets the requirements.

[0051] Furthermore, when the current preparation scheme does not meet the requirements, it is determined that the current preparation scheme needs to be adjusted.

[0052] It can be understood that the qualified rate evaluation amount of the different preparation times is determined according to the product of the qualified rate of the indicator characteristics of different dimensions of different preparation times and the qualified rate of the target preparation material.

[0053] It should also be noted that the current preparation scheme is determined to meet the requirements, including:

[0054] Determine the benchmark index data of the index characteristics of the target preparation materials in different dimensions between different preparation batches by taking the median of the index data of the index characteristics of the target preparation materials in different dimensions between different preparation batches;

[0055] Determine the deviation of the index characteristics between different preparation batches by using the benchmark index data, and determine the deviation preparation batch of the target preparation material by using the deviation of the index characteristics;

[0056] Determine the qualified rate of the index characteristics of different dimensions by using the qualified conditions of the index characteristics of the target index material at different preparation times, and determine the deviation preparation characteristics by the average qualified rate of the index characteristics of different dimensions at different preparation times;

[0057] The production reliability coefficient of the current production plan is determined by multiplying the quantity ratio of the deviation production features and the quantity ratio of the deviation production batches, and whether the current production plan meets the requirements is determined by the production reliability coefficient.

[0058] It should also be noted that the current preparation scheme is determined to meet the requirements, including:

[0059] Determine the qualified rate of the target preparation material at different preparation times by using the qualified conditions of the index characteristics of the target index material at different preparation times, and judge whether the proportion of the preparation times at which the qualified rate of the target preparation material does not meet the requirements is within a preset interval. If so, determine that the current preparation scheme does not meet the requirements. If not, proceed to the next step.

[0060] The qualified conditions of the index characteristics of the target index material at different preparation times are used to determine the qualified rates of the index characteristics of different dimensions, and the deviation preparation characteristics are determined by the average qualified rates of the index characteristics of different dimensions at different preparation times, and it is judged whether the quantity proportion of the deviation preparation characteristics meets the requirements. If not, it is determined that the current preparation scheme does not meet the requirements. If so, proceed to the next step;

[0061] Determine the benchmark index data of the index characteristics of the target preparation materials in different dimensions between different preparation batches by taking the median of the index data of the index characteristics of the target preparation materials in different dimensions between different preparation batches, determine the deviation of the index characteristics between different preparation batches by taking the benchmark index data, and determine the deviation preparation batch of the target preparation material by taking the deviation of the index characteristics, judge whether the deviation preparation times meet the requirements, if so, proceed to the next step, if not, determine that the current preparation scheme does not meet the requirements;

[0062] The production reliability coefficient of the current production plan is determined by multiplying the quantity ratio of the deviation production features and the quantity ratio of the deviation production batches, and whether the current production plan meets the requirements is determined by the production reliability coefficient.

[0063] Specifically, Figure 2 As shown, the method for determining the optimal preparation batch among the preparation batches is:

[0064] Determine the qualified rates of the indicator features of different dimensions by analyzing the indicator data of the indicator features of different dimensions, and determine the qualified rates of the preparation of different preparation batches by the average values ​​of the qualified rates of the indicator features of different dimensions;

[0065] The optimal preparation batch among the preparation batches is determined based on the preparation qualification rates of different preparation batches.

[0066] Furthermore, when the qualified preparation rate of the preparation batch is the largest, the preparation batch is determined to be the optimal preparation batch.

[0067] It should also be noted that the method for determining the optimal preparation batch among the preparation batches is:

[0068] S21 determines the qualified rate of the indicator features of different dimensions based on the analysis results of the indicator data of the indicator features of different dimensions, and judges whether there are indicator features of which the qualified rate does not meet the requirements in the preparation batch. If so, it is determined that the preparation batch does not belong to the optimal preparation batch. If not, proceed to the next step;

[0069] S22 determines the qualified rate of different preparation batches by the average value of the qualified rate of the indicator characteristics of different dimensions, and judges whether the qualified rate of the preparation batch is less than the preset qualified rate. If so, it is determined that the preparation batch does not belong to the optimal preparation batch. If not, proceed to the next step;

[0070] S23 determines whether the preparation qualification rate of the preparation batch is within the preset preparation qualification rate range, if yes, proceeds to the next step, if no, proceeds to step S26;

[0071] S24 determines the distribution of the index data of the index characteristics of the preparation batch in different dimensions, and determines the characteristic reliability coefficients of the index characteristics of the preparation batch in different dimensions based on the distribution, and judges whether the characteristic reliability coefficients of the index characteristics of the preparation batch in different dimensions are all greater than the preset coefficient threshold value, if so, proceeds to the next step, if not, determines that the preparation batch does not belong to the optimal preparation batch;

[0072] S25: determining the comprehensive reliability coefficient of the preparation batch based on the characteristic reliability coefficients of the indicator characteristics of the preparation batch in different dimensions, and judging whether the comprehensive reliability coefficient of the preparation batch is greater than a preset reliability coefficient threshold value; if so, proceeding to the next step; if not, determining that the preparation batch does not belong to the optimal preparation batch;

[0073] S26 determines the reliability of the preparation results of different preparation batches based on the preparation qualification rates and comprehensive reliability coefficients of the different preparation batches, and determines the optimal preparation batch among the preparation batches using the reliability of the preparation results.

[0074] Specifically, Figure 3 As shown, the method for determining the correlation coefficient is:

[0075] Obtain the deviations of the control parameters of different preparation batches and the optimal preparation times at different stages, and determine the control parameter deviations at different stages according to the deviations of the control parameters at different stages, and use the control parameter deviations to determine the preparation batches whose control parameter deviations at only a specific stage are greater than a preset deviation threshold, and use them as the screening deviation batches;

[0076] Determine the deviation index characteristics of different screening deviation batches and the deviation amount of the deviation index characteristics according to the deviation of the index data of the index characteristics of different screening deviation batches and the optimal preparation batch, determine the deviation amount of the deviation index characteristics in different screening deviation batches, and determine the basic correlation coefficient between the deviation index characteristics and the specific stage by using the proportion of the number of screening deviation batches whose deviation amounts do not meet the requirements;

[0077] The batch correlation coefficients of different screening deviation batches are determined by the deviation amounts of the deviation indicator characteristics in different screening deviation batches and the deviation amounts of the control parameters in a specific stage, and the correlation coefficient between the deviation indicator characteristics and the specific stage is determined by the sum of the batch correlation coefficients of different screening deviation batches and the basic correlation coefficient.

[0078] Furthermore, the value range of the correlation coefficient between the deviation index feature and the specific stage is between 0 and 1. When the correlation coefficient between the deviation index feature and the specific stage is greater than a preset correlation coefficient, the specific stage is determined to be the correlation stage of the deviation index feature.

[0079] It should also be noted that the method for determining the correlation coefficient is:

[0080] Obtain the deviations of the control parameters of different preparation batches and the optimal preparation times at different stages, and determine the control parameter deviations at different stages according to the deviations of the control parameters at different stages, and use the control parameter deviations to determine the preparation batches whose control parameter deviations at only a specific stage are greater than a preset deviation threshold, and use them as the screening deviation batches;

[0081] Determining the deviation amounts of the index characteristics of different screening deviation batches according to the deviations of the index data of the index characteristics of different screening deviation batches and the optimal preparation batch;

[0082] The batch correlation coefficients of different screening deviation batches are determined by the ratio of the deviation amount of the indicator characteristics in different screening deviation batches to the deviation amount of the control parameter in a specific stage, and the correlation coefficient between the indicator characteristics and the specific stage is determined by the average value of the batch correlation coefficients of different screening deviation batches.

[0083] It should also be noted that the method for determining the correlation coefficient is:

[0084] Obtain the deviations of the control parameters of different preparation batches and the optimal preparation times at different stages, and determine the control parameter deviations at different stages according to the deviations of the control parameters at different stages, and use the control parameter deviations to determine the preparation batches whose control parameter deviations at only a specific stage are greater than a preset deviation threshold, and use them as the screening deviation batches;

[0085] Determine whether the number of screening deviation batches is greater than the preset batch number threshold. If so, proceed to the next step. If not, use the preset correlation coefficient to determine the correlation coefficient between the indicator characteristics of the specific stage and different dimensions.

[0086] Determine the deviation index characteristics of different screening deviation batches according to the deviation of the index data of the index characteristics of different screening deviation batches and the optimal preparation batch, and judge whether the proportion of the number of screening deviation batches whose deviation amount of the deviation preparation characteristics does not meet the requirements is less than the preset proportion, if not, proceed to the next step, if yes, determine the correlation coefficient between the specific stage and the deviation index characteristic by using the preset correlation coefficient;

[0087] Determine the basic correlation coefficient between the deviation index characteristic and the specific stage by using the proportion of the number of screening deviation batches whose deviation amount does not meet the requirements, and determine the batch correlation coefficients of different screening deviation batches by using the deviation amount of the deviation index characteristic in different screening deviation batches and the control parameter deviation amount in the specific stage;

[0088] Determine whether the deviations between the batch correlation coefficients of different screening deviation batches meet the requirements. If so, proceed to the next step. If not, use the basic correlation coefficient to determine the correlation coefficient between the specific stage and the deviation indicator feature.

[0089] The correlation coefficient between the deviation indicator feature and the specific stage is determined by summing the batch correlation coefficients of different screening deviation batches and the basic correlation coefficient.

[0090] Specifically, Figure 4 As shown, the method for determining the control difficulty of the indicator feature is:

[0091] Obtaining a basic control amount of the control parameter, and determining a deviation amount of the control parameter in different preparation batches based on the deviation between the parameter data of the control parameter in different preparation batches and the reference control amount;

[0092] Determine the parameter control difficulty of different control parameters based on the deviations in different preparation batches, and determine the product of the parameter control difficulty of different control parameters and the correlation coefficient in combination with the correlation coefficient of the different control parameters and the indicator characteristics, and use the product as the control difficulty correction coefficient of the different control parameters;

[0093] The control difficulty of the indicator feature is determined by the sum of the control difficulty correction coefficients of different control parameters.

[0094] Further, determine whether the current preparation scheme needs to be adjusted, including:

[0095] Determine the deviation amount of the indicator data of the indicator characteristics between different production batches by using the deviation of the indicator data of the indicator characteristics between different production batches;

[0096] Determine the index characteristic fluctuation coefficient of the index characteristics of different dimensions based on the deviation of the index data of the index characteristics between different production batches, and determine the characteristic change demand coefficient of the index characteristics of different dimensions by the product of the index characteristic fluctuation coefficient and the control difficulty;

[0097] The characteristic change demand coefficients of indicator characteristics in different dimensions are used to determine whether the current preparation plan needs to be adjusted.

[0098] It can be understood that when there is an indicator feature with a characteristic change demand coefficient greater than the preset demand coefficient threshold, it is determined that the current preparation plan needs to be adjusted, and the indicator feature with the characteristic change demand coefficient greater than the preset demand coefficient threshold is adjusted.

[0099] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0100] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for analyzing data on large-scale preparation of alloy and ceramic composite materials, characterized in that: Specifically include: Taking alloy and ceramic composite materials as target preparation materials, obtaining the deviation of index characteristics of the target preparation materials between different preparation batches, and determining that the current preparation scheme meets the requirements in combination with the qualified conditions of the index characteristics of the target preparation materials in different preparation batches, then proceeding to the next step; Determine the optimal preparation batch among the preparation batches using the analysis results of the indicator data of the indicator characteristics of different dimensions, and determine the correlation coefficients of the indicator characteristics of different stages and different dimensions and the correlation stages of the indicator characteristics of different dimensions using the deviations of the indicator data of the indicator characteristics of different preparation batches and the optimal preparation batch and the deviations of the control parameters at different stages; Obtain the deviation of control parameters of different preparation batches at different correlation stages, and determine the control difficulty of indicator characteristics of different dimensions based on the deviation of control parameters and correlation coefficients; Determine whether the current preparation plan needs to be adjusted based on the control difficulty of indicator characteristics in different dimensions and the deviation of indicator data of indicator characteristics between different production batches; Confirm that the current preparation plan meets the requirements, including: Determine the benchmark index data of the index characteristics of the target preparation materials in different dimensions between different preparation batches by taking the median of the index data of the index characteristics of the target preparation materials in different dimensions from different preparation batches; Determine the deviation of the index characteristics between different preparation batches by using the benchmark index data, and determine the batch consistency coefficient of the target preparation material by using the average value of the deviation of the index characteristics between different preparation batches; Determine the qualified rate of the index characteristics of different dimensions by using the qualified conditions of the index characteristics of the target preparation material at different preparation times, and determine the qualified rate evaluation amount of different preparation times in combination with the qualified rate of the target preparation material at different preparation times; The pass rate of the target preparation material is determined by using the average value of the pass rate evaluation values ​​of different preparation times, and the preparation reliability coefficient of the current preparation scheme is determined by multiplying the pass rate and the batch consistency coefficient. The preparation reliability coefficient is used to determine whether the current preparation scheme meets the requirements.

2. The method for analyzing data for large-scale preparation of alloy and ceramic composite materials according to claim 1, characterized in that: The index characteristics include high temperature resistance, corrosion resistance, mechanical properties, wear resistance, and fracture toughness.

3. The method for analyzing data for large-scale preparation of alloy and ceramic composite materials according to claim 1, characterized in that: The deviation of the indicator feature includes the performance deviation of the indicator feature between different preparation batches.

4. The method for analyzing data for large-scale preparation of alloy and ceramic composite materials according to claim 1, characterized in that: When the current preparation scheme does not meet the requirements, it is determined that the current preparation scheme needs to be adjusted.

5. The method for analyzing data for large-scale preparation of alloy and ceramic composite materials according to claim 1, characterized in that: The method for determining the optimal preparation batch among the preparation batches is: Determine the qualified rates of the indicator features of different dimensions by analyzing the indicator data of the indicator features of different dimensions, and determine the qualified rates of the preparation of different preparation batches by the average values ​​of the qualified rates of the indicator features of different dimensions; The optimal preparation batch among the preparation batches is determined based on the preparation qualification rates of different preparation batches.

6. The method for analyzing data for large-scale preparation of alloy and ceramic composite materials according to claim 5, characterized in that: When the qualified preparation rate of the preparation batch is the highest, the preparation batch is determined to be the optimal preparation batch.

7. The method for analyzing data for large-scale preparation of alloy and ceramic composite materials according to claim 1, characterized in that: Determine whether the current preparation plan needs to be adjusted, including: Determine the deviation amount of the indicator data of the indicator characteristics between different production batches by using the deviation of the indicator data of the indicator characteristics between different production batches; Determine the index characteristic fluctuation coefficient of the index characteristics of different dimensions based on the deviation of the index data of the index characteristics between different production batches, and determine the characteristic change demand coefficient of the index characteristics of different dimensions by the product of the index characteristic fluctuation coefficient and the control difficulty; The characteristic change demand coefficients of indicator characteristics in different dimensions are used to determine whether the current preparation plan needs to be adjusted.

8. The method for analyzing data for large-scale preparation of alloy and ceramic composite materials according to claim 7, characterized in that: When there is an indicator feature whose characteristic change demand coefficient is greater than the preset demand coefficient threshold, it is determined that the current preparation plan needs to be adjusted, and the indicator feature whose characteristic change demand coefficient is greater than the preset demand coefficient threshold is adjusted.

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