Reference value cloud computing system and method based on composite material test database and medium

Abstract

The invention discloses a reference value cloud computing system, a reference value cloud computing method and a reference value cloud computing system based on a composite material test database, a data import module provides a standardized data template for a user to report data, and a test data work package is formed. The data storage module performs hierarchical structure storage on the test data work package, the hierarchical structure comprises task single layers, test item layers and test piece layers, each task single layer corresponds to a plurality of test item layers, and each test item layer corresponds to a plurality of test piece layers. And the cloud computing module performs regularization processing, abnormal data processing and reference value calculation on the test data work packet stored in the layered structure to obtain a cloud computing result. And the data display module performs data preview and graphical display on the cloud computing result according to requirements. According to the method, data heterogeneous storage and full-text retrieval can be realized, after abnormal value processing and regularization processing are carried out on the test data, reference value online calculation is carried out in a multi-condition matching optimal mode, and the efficiency and accuracy of reference value calculation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of composite materials, and in particular to a reference value cloud computing system, method and medium based on a composite material test database. Background Art

[0002] Composite materials and their structures are formed at the same time. Slight differences in raw materials and forming processes will lead to increased variability in performance data. In order to ensure the safety of composite material structures, it is necessary to conduct multiple batches and multiple heats of experiments on the material-level benchmark values ​​and the laminate-level design benchmark values. These test data need to be regularized, analyzed for abnormal data, and statistically analyzed to determine performance characterization values ​​and design benchmark values ​​with a certain degree of confidence and reliability. Composite materials generally use B benchmark values ​​(95% confidence and 90% probability). The material value is determined by the test data of the laminate or single layer on a probabilistic basis (such as benchmark values ​​with 99% and 95% confidence, 90% probability and 95% confidence, respectively).

[0003] The composite material test site has accumulated tens of thousands of test data in material identification, process verification, mechanical testing, etc. The existing composite material test data lacks effective and standardized management, lacks procedural, standardized, and rapid data processing tools, and has relatively weak statistical computing capabilities, which cannot meet the requirements of rapid and accurate calculation of test data benchmark values, and cannot generate stress-strain curves in real time. It is urgent to establish a structured test library for composite materials, including test tasks, test items, test pieces, and test data, to realize cloud computing of composite material benchmark values, and ensure data consistency and traceability.

[0004] Based on the above problems, the applicant proposed the technical solution of this application. Summary of the invention

[0005] In order to achieve the above object, the present invention discloses a benchmark value cloud computing system based on a composite material test database, comprising the following modules:

[0006] The data import module is used to provide a standardized data template for users to report data, and to package the reported data to form a test data work package, which includes an attribute type file and a data type file;

[0007] A data storage module, used for storing the test data work package in a hierarchical structure, wherein the hierarchical structure includes a task layer, a test project layer and a test piece layer, each task layer corresponds to a plurality of test project layers, and each test project layer corresponds to a plurality of test piece layers;

[0008] The cloud computing module is used to perform regularization processing, abnormal data processing and benchmark value calculation on the test data work packages stored in the hierarchical structure to obtain cloud computing results;

[0009] The data display module is used to preview and graphically display the cloud computing results according to needs.

[0010] Preferably, the attribute type file includes at least material information, test piece manufacturing information, test piece testing information and test results, and the data type file includes at least a destruction model photo, a test process video and an original point file.

[0011] Preferably, the task attributes in the task single layer include at least the test task order number, task order type, project type, test level, test type, test method and test purpose; different project attribute sets are provided in the test project layer according to different project types, and the project attribute sets are stored in the form of project attribute templates; different test attribute sets are provided in the test piece layer according to different project types, and the test attribute sets are stored in the form of test attribute templates.

[0012] Preferably, before the regularization processing, it is determined whether the project type is in the regularization processing set. If the project type is in the regularization processing set, regularization processing is performed; otherwise, regularization processing is not performed.

[0013] Preferably, after the regularization processing, whether the discrete coefficient needs to be modified is determined based on the number of data batches in the test data work package. When the number of data batches is less than a first preset value, the discrete coefficient needs to be modified, otherwise, the discrete coefficient does not need to be modified.

[0014] Preferably, based on the number of samples in the test data work package, the test data work package is confirmed as small sample data. If there is no obvious difference between the batches of data in the small sample data, the minimum sample method is used to calculate the benchmark value. If there is an obvious difference between the batches of data in the small sample data, the minimum batch method is used to calculate the benchmark value.

[0015] Preferably, if the number of samples in the test data work package is greater than the second preset value, the merging method is used preferentially to calculate the benchmark value. For the test data work package that does not meet the processing requirements of the merging method, an ADK check is performed. The ADK check is to determine whether each batch of data comes from a unified matrix. If the ADK check passes, a single-point method is selected to calculate the benchmark value. If the ADK check fails but passes the manual review, a single-point method is selected to calculate the benchmark value.

[0016] Preferably, if the ADK test fails and the manual review fails, it is determined whether the number of data batches in the test data work package is greater than or equal to the third preset value. If the number of data batches in the test data work package is greater than or equal to the third preset value, the minimum batch method is used to calculate the benchmark value; otherwise, the variance analysis ANOVA method is used to calculate the benchmark value.

[0017] The present invention also discloses a benchmark value cloud computing method based on a composite material test database, comprising the following steps:

[0018] Provide standardized data templates for users to report data, and package the reported data to form a test data work package, which includes attribute type files and data type files;

[0019] The test data work package is stored in a hierarchical structure, wherein the hierarchical structure includes a task layer, a test project layer, and a test piece layer, each task layer corresponds to a plurality of test project layers, and each test project layer corresponds to a plurality of test piece layers;

[0020] Regularization, abnormal data processing and benchmark value calculation are performed on the test data work packages stored in the hierarchical structure to obtain cloud computing results;

[0021] Perform data preview and graphical display of the cloud computing results as required.

[0022] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is processed and executed, the above-mentioned benchmark value cloud computing method based on the composite material test database is implemented.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention is based on hierarchical storage of composite material test site business data, stores and manages test data from three dimensions: test task list, test project, and test piece, realizes data heterogeneous storage and full-text retrieval of composite material test database, and after outlier processing and regularization processing of test data, performs online calculation of benchmark values ​​by matching multiple conditions in the best way, thereby improving the efficiency and accuracy of benchmark value calculation.

[0025] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the framework of the benchmark value cloud computing system based on the composite material test database of the present invention.

[0027] Figure 2 It is a flow chart of a benchmark value cloud computing method based on a composite material test database of the present invention. DETAILED DESCRIPTION

[0028] In order to make the technical means, creative features, objectives and effects of the invention easier to understand, the invention is further described below with reference to specific diagrams. However, the invention is not limited to the following implementation cases.

[0029] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0030] At present, when calculating the benchmark value through composite material test data, engineers mainly use local software to import composite material test data at the test site to calculate the benchmark value to ensure the consistency and traceability between the data. Due to the characteristics of complex test items, numerous test specifications, many test pieces, many test batches, and large data volume in composite material tests, the statistical calculation function is relatively weak, and it is impossible to realize the joint calculation of multiple test piece data, and it is impossible to realize the rapid calculation of the benchmark values ​​of different test material specifications, different test items, different plies, and different test environments. The performance of the user's local computer affects the efficiency of data analysis, and the drawing function is relatively weak, and it is necessary to generate stress-strain curves and test piece load strength curves in real time. The present invention provides a new composite material test database architecture design, which can realize cloud computing of benchmark values ​​by processing and structured storage of test data, according to the structured management of test items, test specifications, test pieces, test batches, and test data, and based on the database structure, compiles evaluation criteria such as ADK test for compatibility of different groups of data samples, matching of data distribution functions, and calculation logic of normal distribution, Weibull distribution, lognormal distribution, and non-parametric distribution.

[0031] like Figure 1As shown, the present invention provides a benchmark value cloud computing system based on a composite material test database, including a data import module, a data storage module, a cloud computing module and a data display module. The data import module is used to provide a standardized data template for users to report data, and to package the reported data to form a test data work package, wherein the test data work package includes an attribute type file and a data type file. The data storage module is used to store the test data work package in a hierarchical structure, wherein the hierarchical structure includes a task layer, a test project layer and a test piece layer, each task layer corresponds to a plurality of test project layers, and each test project layer corresponds to a plurality of test piece layers. The cloud computing module is used to perform regularization processing, abnormal data processing and benchmark value calculation on the test data work package stored in the hierarchical structure to obtain cloud computing results. The data display module is used to perform data preview and graphical display of the cloud computing results according to requirements.

[0032] When importing data, the user downloads a standardized data template and imports the completed data template for data reporting. The attribute type file in the test data work package includes material information, test piece manufacturing information, test piece test information and test results. The data type file includes damage model photos, test process videos and original point files.

[0033] The three-layer hierarchical structure design adopted for data storage can ensure that the test data is clearly organized, easy to expand and manage. The three-layer hierarchical structure is specifically divided into a task order layer, a test project layer and a test piece layer. The task order layer is the starting point of the entire data storage, in which the task attributes of each task order include the test task order number, task order type, project type, test level, test type, test method and test purpose. Different project attribute sets are provided in the test project layer according to different project types, and the project attribute sets are stored in the form of project attribute templates. The storage form of the project attribute template simplifies the management and definition of test projects. For example, the "process part test project template" stores all relevant attributes of the "process part" type test project, including the test project number, test project name, structure type, test start date, test end date, tooling material, tooling number, main material specification, material specification, layering sequence, etc. The test piece layer is the smallest data storage unit. Multiple test pieces are protected under each test item. Different test attribute sets are set in the test piece layer according to different project types. The test attribute sets are stored in the form of test attribute templates. For example, the "fatigue threshold value test piece template" is used to store the attributes of the "fatigue threshold value" type test pieces, such as specimen number, specimen width, specimen thickness, aperture, stress level, stress ratio, maximum load, number of cycles, failure mode, etc.

[0034] The storage, reading, loading, preprocessing and analysis of composite test data in the present invention are all processed on the cloud platform, and the analysis results and curve graphs are returned to the client. Users do not need to calculate, analyze, and draw on the local computer. The effect of calculation and analysis is high, and the reliability and safety are further improved. Composite test data includes test test data and process test data. When importing test test data and process test data, different standardized data templates can be selected. The test personnel upload the test data package, which supports the storage of test data files, including test pictures, videos and files. The test piece information can also be manually entered to form a test data work package. After uploading, the test data work package will go through a manual approval process. If it is not approved, the test data work package will be returned to the test personnel to ensure data integrity, traceability and accuracy. If approved, the test data work package will be stored in a hierarchical structure. After storage, the data can be viewed, added, deleted, queried, imported, exported and other database operation functions.

[0035] Specifically, given a set of test data, it is necessary to determine which method to choose for calculating the benchmark value. There are many methods for calculating the benchmark value, and the present invention involves a minimum sample method, a minimum batch method, a single point method, a variance analysis method, and a sample merging method. In the cloud computing module, based on the test matrix, the benchmark value calculation process is compiled using Python in the test database storing the test data work package, and the above calculation method is automatically called according to the data characteristics to realize the online automatic calculation of the benchmark value.

[0036] According to the project type, first determine whether the test data work package needs to be regularized, that is, determine whether the project type in the test data work package is in the regularization processing set. If the project type is in the regularization processing set, regularization processing is performed, otherwise, regularization processing is not performed. For example, the "laminate filling hole tensile strength" project requires regularization of the data, while the "laminate post-impact compression strength" project does not require regularization of the data. A regularization processing set is stored in the database, and the regularization processing set stores all project types that require regularization. Regularization processing is to directly compare the results of mechanical tests by adjusting the original data to the specified fiber volume content. Common projects that require regularization processing include warp and weft tensile modulus, warp and weft compression modulus, and laminate opening tensile strength.

[0037] After the regularization process, it is determined whether the discrete coefficient needs to be modified according to the number of data batches in the test data work package. When the number of data batches is less than a first preset value, the discrete coefficient needs to be modified, otherwise, the discrete coefficient does not need to be modified. In this embodiment, the first preset value is a positive integer of 8.

[0038] For situations where the discrete coefficient does not need to be modified, the test data work package will be confirmed as small sample data based on the number of samples in the test data work package. If there is no obvious difference between the batches of data in the small sample data, the minimum sample method is used to calculate the benchmark value. If there is an obvious difference between the batches of data in the small sample data, the minimum batch method is used to calculate the benchmark value. In general, if the number of samples in the test data work package is less than 3 batches or the number of valid data is less than 18 pieces, it is considered that the sample is small and is determined to be small sample data. If the batch differences between the small sample data are relatively obvious, the minimum batch method is used for calculation, otherwise the minimum sample method is used for calculation. In judging whether the batch differences are relatively obvious, the judgment method used is manual judgment or threshold judgment, or a combination of the two.

[0039] If the sample size is sufficient, that is, the number of samples in the test data work package is greater than the second preset value, the merging method is preferentially used to calculate the benchmark value. In this embodiment, the second preset value is determined according to the actual situation. For test data work packages that do not meet the processing requirements of the merging method, an ADK check is performed. The ADK check is to determine whether each batch of data comes from a unified matrix. If the ADK check passes, a single-point method is selected to calculate the benchmark value. If the ADK check fails but the manual review passes, a single-point method is selected to calculate the benchmark value. If the ADK check fails and the manual review fails, it is determined whether the number of data batches in the test data work package is greater than or equal to the third preset value. In this embodiment, the third preset value is a positive integer of 5. If the number of data batches in the test data work package is greater than or equal to the third preset value, the minimum batch method is used to calculate the benchmark value. Otherwise, the variance analysis ANOVA method is used to calculate the benchmark value.

[0040] In this embodiment, during the calculation of the composite material design reference value, since the test piece will generate abnormal data due to human factors, it is necessary to automatically complete the abnormal data preprocessing, screen the abnormal data, and correct and judge the abnormal data before statistical analysis. The quantitative screening of abnormal test data of composite materials adopts the maximum normed residual method (MNR) test. This method detects an abnormal data from a data set each time. Each time an abnormal data is detected, the abnormal data must be processed before the next abnormal data test is performed.

[0041] The MNR statistic is:

[0042]

[0043] Where i = 1, 2, 3...n, n is a positive integer, X i represents the i-th sample data, represents the mean of the sample data, and s represents the sample variance.

[0044] In some examples, in addition to the MNR test method, according to the relevant principles of statistics, there are also Grubbs test method, Dixon test method, etc., the principle of which is to treat non-abnormal data as a normal distribution to screen abnormal data overall. The Grubbs test method determines whether there are outliers by calculating the degree of deviation of each data point from the mean. If the degree of deviation of a data point exceeds a certain threshold, the data point is considered to be an outlier and can be eliminated. The Dixon test method determines whether there are outliers in the sample by calculating the difference between the maximum and minimum values ​​in the sample.

[0045] In this embodiment, during the test, the MNR statistic is compared with the critical value corresponding to the sample size n, and the critical value can be obtained by looking up a table or calculating the formula:

[0046]

[0047] Where: C represents the critical value, t represents the [1-α / (2n)] quantile of the t distribution with n-2 degrees of freedom, α represents the significance level, and α=0.05 is recommended.

[0048] If the test shows that MNR < C, there is no outlier, otherwise the corresponding x i If there are outliers, you need to remove them and repeat this process until no outliers are found.

[0049] It should be noted that when calculating the benchmark value, the merging method is to obtain the common statistics of the test results in all environments with a specific failure mode by merging environmental samples, and the single-point method is used to perform benchmark value statistics for different test environment conditions. If there is obvious batch variability in the sample data, the sample data is processed as structured data, and the variance analysis method can be used for calculation. Due to manufacturing and testing, the data group with less than 3 batches of valid data or less than 18 pieces of valid data can be calculated using a small sample method. Through the pretreatment of test data outliers and regularization, the online calculation of the benchmark value is realized by the sample merging method, the single-point method, the variance analysis method, the statistical method based on the small sample, and the minimum batch statistical method, providing the dynamic scalability of the benchmark value calculation. The present invention processes data analysis through a cloud platform, and then returns the analysis results to the client, so that the user does not need to perform calculations and plots on the local computer, which not only improves the efficiency of calculation and analysis, but also saves user computer resources and has strong scalability.

[0050] like Figure 2 As shown, the second embodiment of the present invention discloses a benchmark value cloud computing method based on a composite material test database, comprising the following steps:

[0051] Step S1, providing a standardized data template for users to report data, and packaging the reported data to form a test data work package, wherein the test data work package includes an attribute type file and a data type file.

[0052] Step S2, storing the test data work package in a hierarchical structure, wherein the hierarchical structure includes a task layer, a test project layer and a test piece layer, each task layer corresponds to a plurality of test project layers, and each test project layer corresponds to a plurality of test piece layers.

[0053] Step S3, performing regularization processing, abnormal data processing and benchmark value calculation on the test data work package stored in the hierarchical structure to obtain cloud computing results.

[0054] Step S4: Preview and graphically display the cloud computing results according to needs.

[0055] Since the first embodiment corresponds to this embodiment, this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and the technical effects that can be achieved in the first embodiment can also be achieved in this embodiment. In order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the first embodiment.

[0056] The third embodiment of the present invention relates to a computer-readable storage medium having a computer program / instruction stored thereon, wherein the computer program / instruction implements the steps of the method in the first embodiment when executed by a processor.

[0057] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A benchmark value cloud computing system based on a composite material test database, characterized in that: Includes the following modules: The data import module is used to provide a standardized data template for users to report data, and to package the reported data to form a test data work package, which includes an attribute type file and a data type file; A data storage module, used for storing the test data work package in a hierarchical structure, wherein the hierarchical structure includes a task layer, a test project layer and a test piece layer, each task layer corresponds to a plurality of test project layers, and each test project layer corresponds to a plurality of test piece layers; The cloud computing module is used to perform regularization processing, abnormal data processing and benchmark value calculation on the test data work packages stored in the hierarchical structure to obtain cloud computing results; The data display module is used to preview and graphically display the cloud computing results according to needs.

2. According to the reference value cloud computing system based on the composite material test database according to claim 1, it is characterized in that: The attribute type file includes at least material information, test piece manufacturing information, test piece testing information and test results, and the data type file includes at least a destruction model photo, a test process video and an original point file.

3. The reference value cloud computing system based on composite material test database according to claim 1 is characterized in that: The task attributes in the task order layer include at least the test task order number, task order type, project type, test level, test type, test method and test purpose; different project attribute sets are provided in the test project layer according to different project types, and the project attribute sets are stored in the form of project attribute templates; different test attribute sets are provided in the test piece layer according to different project types, and the test attribute sets are stored in the form of test attribute templates.

4. The reference value cloud computing system based on the composite material test database according to claim 3 is characterized in that: Before the regularization processing, it is determined whether the project type is in the regularization processing set. If the project type is in the regularization processing set, regularization processing is performed; otherwise, regularization processing is not performed.

5. The benchmark value cloud computing system based on composite material test database according to claim 1 is characterized in that: After the regularization processing, whether the discrete coefficient needs to be modified is determined according to the number of data batches in the test data work package. When the number of data batches is less than a first preset value, the discrete coefficient needs to be modified; otherwise, the discrete coefficient does not need to be modified.

6. The benchmark value cloud computing system based on composite material test database according to claim 1 is characterized in that: According to the number of samples in the test data work package, the test data work package is confirmed as small sample data. If there is no obvious difference between the batches of data in the small sample data, the minimum sample method is used to calculate the benchmark value. If there is an obvious difference between the batches of data in the small sample data, the minimum batch method is used to calculate the benchmark value.

7. The reference value cloud computing system based on composite material test database according to claim 6 is characterized in that: If the number of samples in the test data work package is greater than the second preset value, the merging method is used preferentially to calculate the benchmark value. For the test data work package that does not meet the processing requirements of the merging method, an ADK check is performed. The ADK check is to determine whether each batch of data comes from a unified matrix. If the ADK check passes, a single-point method is selected to calculate the benchmark value. If the ADK check fails but passes the manual review, a single-point method is selected to calculate the benchmark value.

8. The reference value cloud computing system based on composite material test database according to claim 7 is characterized in that: If the ADK test fails and the manual review fails, determine whether the number of data batches in the test data work package is greater than or equal to the third preset value. If the number of data batches in the test data work package is greater than or equal to the third preset value, use the minimum batch method to calculate the benchmark value; otherwise, use the variance analysis method to calculate the benchmark value.

9. A benchmark value cloud computing method based on a composite material test database, characterized in that: The steps include: Provide standardized data templates for users to report data, and package the reported data to form a test data work package, which includes attribute type files and data type files; The test data work package is stored in a hierarchical structure, wherein the hierarchical structure includes a task layer, a test project layer, and a test piece layer, each task layer corresponds to a plurality of test project layers, and each test project layer corresponds to a plurality of test piece layers; Regularization, abnormal data processing and benchmark value calculation are performed on the test data work packages stored in the hierarchical structure to obtain cloud computing results; Perform data preview and graphical display of the cloud computing results as required.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is processed and executed, the reference value cloud computing method based on the composite material test database as claimed in claim 9 is implemented.

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