System and method for analyzing comprehensive performance of high-temperature superconducting magnet

Abstract

The invention relates to the technical field of high-temperature superconducting magnets, and discloses a comprehensive performance analysis system and method for a high-temperature superconducting magnet. A data test module applies different applied test data to obtain test response data; the first construction module constructs a test response data link according to the test response data; the first calculation module obtains a test response data set according to the test response data and the standard response data, and calculates a test performance analysis coefficient; the second construction module constructs an application test data chain according to the application test data; a second calculation module calculates a test performance analysis influence coefficient; the performance analysis module calculates a comprehensive test performance analysis coefficient according to the test performance analysis coefficient and the test performance analysis influence coefficient, judges whether the production requirement is met or not, and performs comprehensive performance analysis on the high-temperature superconducting magnet in two aspects, so that the one-sidedness of a comprehensive performance analysis result is avoided, and the comprehensive performance analysis precision and efficiency are ensured; and the production quality and the use performance of the high-temperature superconducting magnet are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of high-temperature superconducting magnets, and in particular to a system and method for analyzing the comprehensive performance of high-temperature superconducting magnets. Background Art

[0002] High-temperature superconducting magnets are magnets made of materials that exhibit superconducting properties above liquid nitrogen temperature (77K). Compared with traditional low-temperature superconducting magnets, high-temperature superconducting magnets have higher operating temperatures and lower operating costs. High-temperature superconducting magnets are usually composed of superconducting coils, insulation layers, support structures and cooling systems. The coils use a multi-level winding design to enhance the magnetic field strength, while the insulation layers and support structures ensure the stability and durability of the magnets. High-temperature superconducting magnets are widely used in medical imaging, particle accelerators, magnetic levitation transportation and large-scale scientific experiments. Their high efficiency and low energy consumption make them an important part of many high-tech fields in the future.

[0003] The comprehensive performance analysis of high-temperature superconducting magnets involves the interaction of multiple physical fields such as electromagnetic field, temperature field, stress field, etc. Traditional analysis methods can only consider the influence of a certain physical field separately, lacking comprehensive multi-field coupling analysis capabilities, resulting in a relatively one-sided comprehensive performance analysis of high-temperature superconducting magnets. In addition, the existing performance analysis methods are all processed and analyzed by relevant staff, which leads to large errors in comprehensive performance analysis and low efficiency. Summary of the invention

[0004] The embodiments of the present invention provide a system and method for comprehensive performance analysis of a high-temperature superconducting magnet, which are used to solve the technical problem that the high-temperature superconducting magnet cannot be comprehensively analyzed in the prior art, resulting in a relatively one-sided comprehensive performance analysis result and the inability to ensure the accuracy and efficiency of the comprehensive performance analysis.

[0005] In order to achieve the above object, the present invention provides a high temperature superconducting magnet comprehensive performance analysis system, comprising: A data testing module, used for determining a high temperature superconducting magnet to be analyzed, applying different applied test data to the high temperature superconducting magnet to be analyzed, and obtaining a plurality of test response data corresponding to the high temperature superconducting magnet to be analyzed, wherein the applied test data and the test response data correspond to each other; The first construction module is used to perform data analysis of the same type on all test response data and to construct a test response data chain based on all test response data of the same type; a first calculation module, configured to obtain standard response data corresponding to the high temperature superconducting magnet to be analyzed, divide the test response data chain into different test response data sets according to the relationship between the test response data and the standard response data, and calculate the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed according to the test response data sets; A second construction module is used to perform the same type of data analysis on each applied test data, and to construct an application test data chain according to all the application test data of the same type; a second calculation module, for determining the maximum applied test data and the minimum applied test data on the applied test data chain, analyzing the applied test data chain according to the maximum applied test data and the minimum applied test data, and calculating the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed based on the analysis result; The performance analysis module is used to calculate the comprehensive test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed according to the test performance analysis coefficient and the test performance analysis influence coefficient, and judge whether the high-temperature superconducting magnet to be analyzed meets the production requirements based on the comprehensive test performance analysis coefficient.

[0006] Furthermore, the first calculation module is used for: The first calculation module is used to randomly extract a test response data chain, and compare the test response data on the extracted test response data chain with the corresponding standard response data, and if the test response data is smaller than the standard response data, the test response data is classified into a low test response data set; The first calculation module is used for dividing the test response data into equal test response data sets if the test response data is equal to the standard response data; The first calculation module is used for dividing the test response data into a high test response data set if the test response data is greater than the standard response data; The first calculation module is used to calculate the low mean and the low standard deviation of the low test response data set, and calculate the sum of the low mean and the low standard deviation as the low sum; The first calculation module is used to calculate the absolute value of the difference between the low mean and the low standard deviation, and use it as the low difference; The first calculation module is used to determine a first data range according to the low sum value and the low difference value, extract all test response data in the low test response data set that are within the first data range, and construct a first subset; The first calculation module is used to calculate the equal mean and the equal standard deviation of the equal test response data set, and calculate the sum of the equal mean and the equal standard deviation as the equal sum value; The first calculation module is used to calculate the absolute value of the difference between the equal mean and the equal standard deviation, and use it as the equal difference value; The first calculation module is used to determine a second data range according to the equal sum value and the equal difference value, extract all test response data in the equal test response data set that are within the second data range, and construct a second subset; The first calculation module is used to calculate the high mean and the high standard deviation of the high test response data set, and calculate the sum of the high mean and the high standard deviation as the high sum; The first calculation module is used to calculate the absolute value of the difference between the high mean and the high standard deviation, and use it as the high difference; The first calculation module is used to determine a third data range according to the high sum value and the high difference value, extract all test response data in the high test response data set that are within the third data range, and construct a third subset; The first calculation module is used to calculate the subtest performance analysis coefficient of the test response data chain according to the first subset, the second subset and the third subset; The first calculation module is used to calculate the sub-test performance analysis coefficients of the remaining test response data chains, and calculate the test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed based on all the sub-test performance analysis coefficients.

[0007] Furthermore, the first calculation module is used for: The first calculation module is used to calculate the sub-test performance analysis coefficient of the test response data chain according to the following formula: ; Where a1 is the subtest performance analysis coefficient of the test response data chain, e1 is the calculation coefficient corresponding to the first subset, n1 is the number of test response data in the first subset, g i is the i-th test response data in the first subset, f1 is the low sum value, f2 is the low difference value, e2 is the calculation coefficient corresponding to the third subset, n2 is the number of test response data in the third subset, h j is the j-th test response data in the third subset, f3 is the high sum value, f4 is the high difference value, e1+e2=3, and e1>e2, and c is the calculation adjustment factor of the sub-test performance analysis coefficient.

[0008] Furthermore, the first calculation module is used to determine the calculation adjustment factor of the sub-test performance analysis coefficient according to the following steps: The first calculation module is used to count the number n3 of test response data in the second subset; The first calculation module is used to preset a first preset calculation adjustment factor, a second preset calculation adjustment factor and a third preset calculation adjustment factor; The first calculation module is used to determine the numerical value relationship between n1 and n2, and determine a left boundary value and a right boundary value based on the numerical value relationship; The first calculation module is used for selecting the first preset calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient when the data quantity n3 is less than the left boundary value; The first calculation module is used for selecting the second preset calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient when the data quantity n3 is greater than or equal to the left boundary value and less than or equal to the right boundary value; The first calculation module is used for selecting the third preset calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient when the data quantity n3 is greater than the right boundary value.

[0009] Furthermore, the first calculation module is used for: The first calculation module is used to calculate the coefficient mean of all sub-test performance analysis coefficients; The first calculation module is used to extract the same sub-test performance analysis coefficient from all sub-test performance analysis coefficients and obtain multiple test performance analysis coefficient sets; The first calculation module is used to count the first set quantity of the test performance analysis coefficient set; The first calculation module is used to extract a test performance analysis coefficient from all test performance analysis coefficient sets respectively, and calculate a first test performance analysis coefficient and value; The first calculation module is used to eliminate all test performance analysis coefficient sets that are smaller than the coefficient mean, and count the number of second sets of remaining test performance analysis coefficient sets; The first calculation module is used to extract a test performance analysis coefficient from the remaining test performance analysis coefficient set, and calculate the second test performance analysis coefficient and value; The first calculation module is used to calculate the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed according to the first set number, the second set number, the first test performance analysis coefficient and value, and the second test performance analysis coefficient and value.

[0010] Furthermore, the second calculation module is used for: The second calculation module is used to calculate the difference between the maximum applied test data and the minimum applied test data, and use it as the extreme difference value of the applied test data chain; The second calculation module is used to calculate the second difference between each applied test data in the applied test data chain and the extreme value; The second calculation module is used to calculate the difference variance of all second difference values, and generate a first difference value sequence according to all second difference values ​​that are less than or equal to the difference variance; The second calculation module is used to generate a second difference value sequence according to all second difference values ​​greater than the difference variance; The second calculation module is used to calculate the sub-test performance analysis influence coefficient of the applied test data chain according to the first difference series and the second difference series; The second calculation module is used to calculate the sub-test performance analysis influence coefficients of the remaining applied test data chains, and calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed based on all the sub-test performance analysis influence coefficients.

[0011] Furthermore, the second calculation module is used for: The second calculation module is used to calculate the sub-test performance analysis impact coefficient of the applied test data chain according to the following formula: ; Where p is the subtest performance analysis impact coefficient of the applied test data chain, y is the difference variance, m1 is the number of second differences in the first difference series, m2 is the number of second differences in the second difference series, w1 is the mean corresponding to the first difference series, w2 is the mean corresponding to the second difference series, u t is the tth second difference in the first difference sequence, s r is the rth second difference in the second difference sequence.

[0012] Furthermore, the second calculation module is used for: The second calculation module is used to randomly combine all sub-test performance analysis influence coefficients in pairs to obtain multiple sub-test performance analysis influence coefficient combinations; The second calculation module is used to calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed according to a combination of multiple sub-test performance analysis influence coefficients.

[0013] Furthermore, the performance analysis module is used to: The performance analysis module is used to determine whether the high-temperature superconducting magnet to be analyzed meets the production requirements according to the relationship between the comprehensive test performance analysis coefficient and the preset comprehensive test performance analysis coefficient; The performance analysis module is used to determine that the high-temperature superconducting magnet to be analyzed does not meet production requirements when the comprehensive test performance analysis coefficient is less than the preset comprehensive test performance analysis coefficient; The performance analysis module is used to determine that the high-temperature superconducting magnet to be analyzed meets production requirements when the comprehensive test performance analysis coefficient is greater than or equal to the preset comprehensive test performance analysis coefficient.

[0014] In order to achieve the above object, the present invention also provides a method for analyzing the comprehensive performance of a high-temperature superconducting magnet, comprising: Determine a high temperature superconducting magnet to be analyzed, apply different applied test data to the high temperature superconducting magnet to be analyzed, and obtain a plurality of test response data corresponding to the high temperature superconducting magnet to be analyzed, wherein the applied test data and the test response data correspond to each other; Performing data analysis of the same type on all test response data, and constructing a test response data chain based on all test response data of the same type; Acquire standard response data corresponding to the high temperature superconducting magnet to be analyzed, divide the test response data chain into different test response data sets according to the relationship between the test response data and the standard response data, and calculate the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed according to the test response data sets; Performing the same type of data analysis on each applied test data, and constructing an application test data chain based on all the application test data of the same type; Determine the maximum applied test data and the minimum applied test data on the applied test data chain, analyze the applied test data chain according to the maximum applied test data and the minimum applied test data, and calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed based on the analysis result; A comprehensive test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed is calculated according to the test performance analysis coefficient and the test performance analysis influence coefficient, and based on the comprehensive test performance analysis coefficient, it is determined whether the high-temperature superconducting magnet to be analyzed meets production requirements.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The invention discloses a system and method for comprehensive performance analysis of a high-temperature superconducting magnet. A data testing module applies different applied test data to obtain test response data; a first building module builds a test response data chain according to the test response data; a first calculation module obtains a test response data set according to the test response data and standard response data, and calculates a test performance analysis coefficient; a second building module builds an applied test data chain according to the applied test data; a second calculation module calculates a test performance analysis influence coefficient; a performance analysis module calculates a comprehensive test performance analysis coefficient according to the test performance analysis coefficient and the test performance analysis influence coefficient, determines whether production requirements are met, and performs comprehensive performance analysis of the high-temperature superconducting magnet in two aspects, thereby avoiding the one-sidedness of the comprehensive performance analysis result, ensuring the accuracy and efficiency of the comprehensive performance analysis, and ensuring the production quality and use performance of the high-temperature superconducting magnet. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A schematic diagram of the structure of a high-temperature superconducting magnet comprehensive performance analysis system according to an embodiment of the present invention is shown; Figure 2 A schematic flow chart of a method for analyzing the comprehensive performance of a high-temperature superconducting magnet in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0017] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0018] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses a high-temperature superconducting magnet comprehensive performance analysis system, comprising: A data testing module, used for determining a high temperature superconducting magnet to be analyzed, applying different applied test data to the high temperature superconducting magnet to be analyzed, and obtaining a plurality of test response data corresponding to the high temperature superconducting magnet to be analyzed, wherein the applied test data and the test response data correspond to each other; The first construction module is used to perform data analysis of the same type on all test response data and to construct a test response data chain based on all test response data of the same type; a first calculation module, configured to obtain standard response data corresponding to the high temperature superconducting magnet to be analyzed, divide the test response data chain into different test response data sets according to the relationship between the test response data and the standard response data, and calculate the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed according to the test response data sets; A second construction module is used to perform the same type of data analysis on each applied test data, and to construct an application test data chain according to all the application test data of the same type; a second calculation module, for determining the maximum applied test data and the minimum applied test data on the applied test data chain, analyzing the applied test data chain according to the maximum applied test data and the minimum applied test data, and calculating the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed based on the analysis result; The performance analysis module is used to calculate the comprehensive test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed according to the test performance analysis coefficient and the test performance analysis influence coefficient, and judge whether the high-temperature superconducting magnet to be analyzed meets the production requirements based on the comprehensive test performance analysis coefficient.

[0023] In this embodiment, the applied test data includes but is not limited to magnetic field strength, temperature, mechanical stress, etc.

[0024] In this embodiment, the test response data includes but is not limited to resistivity, critical current, magnetic flux pinning force, etc.

[0025] In this embodiment, when the applied test data is applied to the high-temperature superconducting magnet to be analyzed, a set of test response data will be generated. When the applied test data is changed, a second set of test response data will be generated. Therefore, each set of applied test data corresponds to each set of test response data.

[0026] In this embodiment, the test response data includes resistivity, critical current, flux pinning force, etc. A test response data chain is constructed based on all resistivities, which corresponds to constructing a test response data chain based on all test response data of the same type. A corresponding test response data chain can also be constructed based on all critical currents.

[0027] In this embodiment, the applied test data includes magnetic field strength, temperature, mechanical stress, etc., and an applied test data chain is constructed according to all magnetic field strengths, that is, an applied test data chain is constructed according to all applied test data of the same type, and a corresponding test response data chain can also be constructed according to all temperatures.

[0028] The beneficial effect of the above technical solution is that the present invention performs comprehensive performance analysis on high-temperature superconducting magnets in two aspects, avoids the one-sidedness of the comprehensive performance analysis results, ensures the accuracy and efficiency of the comprehensive performance analysis, and ensures the production quality and performance of the high-temperature superconducting magnets.

[0029] In some embodiments of the present application, the first computing module is used to: The first calculation module is used to randomly extract a test response data chain, and compare the test response data on the extracted test response data chain with the corresponding standard response data, and if the test response data is smaller than the standard response data, the test response data is classified into a low test response data set; The first calculation module is used for dividing the test response data into equal test response data sets if the test response data is equal to the standard response data; The first calculation module is used for dividing the test response data into a high test response data set if the test response data is greater than the standard response data; The first calculation module is used to calculate the low mean and the low standard deviation of the low test response data set, and calculate the sum of the low mean and the low standard deviation as the low sum; The first calculation module is used to calculate the absolute value of the difference between the low mean and the low standard deviation, and use it as the low difference; The first calculation module is used to determine a first data range according to the low sum value and the low difference value, extract all test response data in the low test response data set that are within the first data range, and construct a first subset; The first calculation module is used to calculate the equal mean and the equal standard deviation of the equal test response data set, and calculate the sum of the equal mean and the equal standard deviation as the equal sum value; The first calculation module is used to calculate the absolute value of the difference between the equal mean and the equal standard deviation, and use it as the equal difference value; The first calculation module is used to determine a second data range according to the equal sum value and the equal difference value, extract all test response data in the equal test response data set that are within the second data range, and construct a second subset; The first calculation module is used to calculate the high mean and the high standard deviation of the high test response data set, and calculate the sum of the high mean and the high standard deviation as the high sum; The first calculation module is used to calculate the absolute value of the difference between the high mean and the high standard deviation, and use it as the high difference; The first calculation module is used to determine a third data range according to the high sum value and the high difference value, extract all test response data in the high test response data set that are within the third data range, and construct a third subset; The first calculation module is used to calculate the subtest performance analysis coefficient of the test response data chain according to the first subset, the second subset and the third subset; The first calculation module is used to calculate the sub-test performance analysis coefficients of the remaining test response data chains, and calculate the test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed based on all the sub-test performance analysis coefficients.

[0030] In this embodiment, the standard response data is set in advance. The standard response data can reflect the performance of the high-temperature superconducting magnet to be analyzed. For example, the smaller the resistivity, the stronger the performance of the high-temperature superconducting magnet to be analyzed. The specific resistivity can be set according to actual production requirements and is not specifically limited here.

[0031] In this embodiment, if all the test response data are equal to the corresponding standard response data, it is judged that the high-temperature superconducting magnet to be analyzed meets the production requirements; if all the test response data are not equal to the corresponding standard response data, it is judged that the high-temperature superconducting magnet to be analyzed does not meet the production requirements; if one or more test response data are equal to the corresponding standard response data and the remaining test response data are not equal to the corresponding standard response data, a test response data chain is randomly selected; if one or more test response data are not equal to the corresponding standard response data and the remaining test response data are equal to the corresponding standard response data, a test response data chain is randomly selected.

[0032] In this embodiment, a test response data chain is randomly extracted. If the extracted test response data chain is composed of resistivity, the standard response data is resistivity.

[0033] In this embodiment, the absolute value of the difference means that the difference is calculated first and then the absolute value is taken.

[0034] In this embodiment, when the test response data is greater than or equal to the low difference value and less than or equal to the low sum value, it is determined that the corresponding test response data is within the first data range, and the rest are not given one by one by examples.

[0035] The beneficial effect of the above technical solution is that the present invention can accurately divide the test response data chain into a first subset, a second subset and a third subset, and calculate the sub-test performance analysis coefficient of the test response data chain according to the first subset, the second subset and the third subset, thereby ensuring the calculation accuracy of the sub-test performance analysis coefficient and laying the foundation for the calculation of the test performance analysis coefficient.

[0036] In some embodiments of the present application, the first computing module is used to: The first calculation module is used to calculate the sub-test performance analysis coefficient of the test response data chain according to the following formula: ; Where a1 is the subtest performance analysis coefficient of the test response data chain, e1 is the calculation coefficient corresponding to the first subset, n1 is the number of test response data in the first subset, g i is the i-th test response data in the first subset, f1 is the low sum value, f2 is the low difference value, e2 is the calculation coefficient corresponding to the third subset, n2 is the number of test response data in the third subset, h j is the j-th test response data in the third subset, f3 is the high sum value, f4 is the high difference value, e1+e2=3, and e1>e2, and c is the calculation adjustment factor of the sub-test performance analysis coefficient.

[0037] In some embodiments of the present application, the first calculation module is used to determine the calculation adjustment factor of the sub-test performance analysis coefficient according to the following steps: The first calculation module is used to count the number n3 of test response data in the second subset; The first calculation module is used to preset a first preset calculation adjustment factor, a second preset calculation adjustment factor and a third preset calculation adjustment factor; The first calculation module is used to determine the numerical value relationship between n1 and n2, and determine a left boundary value and a right boundary value based on the numerical value relationship; The first calculation module is used for selecting the first preset calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient when the data quantity n3 is less than the left boundary value; The first calculation module is used for selecting the second preset calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient when the data quantity n3 is greater than or equal to the left boundary value and less than or equal to the right boundary value; The first calculation module is used for selecting the third preset calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient when the data quantity n3 is greater than the right boundary value.

[0038] In this embodiment, the first preset calculation adjustment factor is preferably 0.95, the second preset calculation adjustment factor is preferably 1.15, and the third preset calculation adjustment factor is preferably 1.25.

[0039] In this embodiment, the smaller value of n1 and n2 is used as the left boundary value, and the larger value is used as the right boundary value.

[0040] In this embodiment, when n1=n2, a value is randomly selected from n1 and n2 as the boundary value. When the data quantity n3 is less than the boundary value, the first preset calculation adjustment factor is selected as the calculation adjustment factor of the sub-test performance analysis coefficient; when the data quantity n3 is equal to the boundary value, the second preset calculation adjustment factor is selected as the calculation adjustment factor of the sub-test performance analysis coefficient; when the data quantity n3 is greater than the boundary value, the third preset calculation adjustment factor is selected as the calculation adjustment factor of the sub-test performance analysis coefficient.

[0041] The beneficial effect of the above technical solution is that the present invention selects the corresponding preset calculation adjustment factor according to the data quantity n3, realizes the dynamic adjustment of the sub-test performance analysis coefficient, makes the calculation result more accurate, and ensures the comprehensiveness of the calculation.

[0042] In some embodiments of the present application, the first computing module is used to: The first calculation module is used to calculate the coefficient mean of all sub-test performance analysis coefficients; The first calculation module is used to extract the same sub-test performance analysis coefficient from all sub-test performance analysis coefficients and obtain multiple test performance analysis coefficient sets; The first calculation module is used to count the first set quantity of the test performance analysis coefficient set; The first calculation module is used to extract a test performance analysis coefficient from all test performance analysis coefficient sets respectively, and calculate a first test performance analysis coefficient and value; The first calculation module is used to eliminate all test performance analysis coefficient sets that are smaller than the coefficient mean, and count the number of second sets of remaining test performance analysis coefficient sets; The first calculation module is used to extract a test performance analysis coefficient from the remaining test performance analysis coefficient set, and calculate the second test performance analysis coefficient and value; The first calculation module is used to calculate the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed according to the first set number, the second set number, the first test performance analysis coefficient and value, and the second test performance analysis coefficient and value.

[0043] In this embodiment, if multiple test performance analysis coefficient sets are {2, 2}, {5, 5, 5}, {8, 8}, {9, 9}, then the number of the first set is 4, and a test performance analysis coefficient is extracted respectively, that is, 2, 5, 8, 9. If the average value of the coefficient obtained above is 6, the remaining test performance analysis coefficient sets are {8, 8}, {9, 9}, and the number of the second set is 2.

[0044] In this embodiment, the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed is calculated according to the following formula: ; Wherein, c1 is the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed, k1 is the first set number, k2 is the second set number, k3 is the second test performance analysis coefficient and value, and k4 is the first test performance analysis coefficient and value.

[0045] The beneficial effect of the above technical solution is that the present invention calculates the test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed according to the first set number, the second set number, the first test performance analysis coefficient and value, and the second test performance analysis coefficient and value, which not only ensures the calculation accuracy of the test performance analysis coefficient and avoids errors, but also provides a basis for the comprehensive performance analysis of the high-temperature superconducting magnet, thereby ensuring the comprehensiveness of the comprehensive performance analysis of the high-temperature superconducting magnet.

[0046] In some embodiments of the present application, the second computing module is used to: The second calculation module is used to calculate the difference between the maximum applied test data and the minimum applied test data, and use it as the extreme difference value of the applied test data chain; The second calculation module is used to calculate the second difference between each applied test data in the applied test data chain and the extreme value; The second calculation module is used to calculate the difference variance of all second difference values, and generate a first difference value sequence according to all second difference values ​​that are less than or equal to the difference variance; The second calculation module is used to generate a second difference value sequence according to all second difference values ​​greater than the difference variance; The second calculation module is used to calculate the sub-test performance analysis influence coefficient of the applied test data chain according to the first difference series and the second difference series; The second calculation module is used to calculate the sub-test performance analysis influence coefficients of the remaining applied test data chains, and calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed based on all the sub-test performance analysis influence coefficients.

[0047] The beneficial effect of the above technical solution is: the present invention calculates the sub-test performance analysis influence coefficient of the applied test data chain according to the first difference series and the second difference series, further lays the foundation for the calculation of the test performance analysis influence coefficient and provides reliable data support.

[0048] In some embodiments of the present application, the second computing module is used to: The second calculation module is used to calculate the sub-test performance analysis impact coefficient of the applied test data chain according to the following formula: ; Where p is the subtest performance analysis impact coefficient of the applied test data chain, y is the difference variance, m1 is the number of second differences in the first difference series, m2 is the number of second differences in the second difference series, w1 is the mean corresponding to the first difference series, w2 is the mean corresponding to the second difference series, u t is the tth second difference in the first difference sequence, s r is the rth second difference in the second difference sequence.

[0049] In some embodiments of the present application, the second computing module is used to: The second calculation module is used to randomly combine all sub-test performance analysis influence coefficients in pairs to obtain multiple sub-test performance analysis influence coefficient combinations; The second calculation module is used to calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed according to a combination of multiple sub-test performance analysis influence coefficients.

[0050] In this embodiment, if there is an unmatched sub-test performance analysis impact coefficient, the sub-test performance analysis impact coefficient is deleted.

[0051] In this embodiment, the test performance analysis influence coefficient of the high temperature superconducting magnet to be analyzed is calculated according to the following formula: ; Where c2 is the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed, n4 is the number of sub-test performance analysis influence coefficient combinations, q1 z is the larger sub-test performance analysis impact coefficient in the z-th sub-test performance analysis impact coefficient combination, q2 z is the smaller sub-test performance analysis impact coefficient in the z-th sub-test performance analysis impact coefficient combination, For all The minimum value in For all The maximum value in v 2 For all The variance of .

[0052] In this embodiment, when the two sub-test performance analysis influence coefficients in the sub-test performance analysis influence coefficient combination are equal, then =0.

[0053] The beneficial effect of the above technical solution is that the present invention calculates the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed based on a combination of multiple sub-test performance analysis influence coefficients, which lays a foundation for the comprehensive performance analysis of the high-temperature superconducting magnet, avoids the subjectivity of manual participation, and provides another basis for the comprehensive performance analysis of the high-temperature superconducting magnet.

[0054] In some embodiments of the present application, the performance analysis module is used to: The performance analysis module is used to determine whether the high-temperature superconducting magnet to be analyzed meets the production requirements according to the relationship between the comprehensive test performance analysis coefficient and the preset comprehensive test performance analysis coefficient; The performance analysis module is used to determine that the high-temperature superconducting magnet to be analyzed does not meet production requirements when the comprehensive test performance analysis coefficient is less than the preset comprehensive test performance analysis coefficient; The performance analysis module is used to determine that the high-temperature superconducting magnet to be analyzed meets production requirements when the comprehensive test performance analysis coefficient is greater than or equal to the preset comprehensive test performance analysis coefficient.

[0055] In this embodiment, a first calculation coefficient is configured for the test performance analysis coefficient, the first calculation coefficient=0.7, a second calculation coefficient is configured for the test performance analysis influence coefficient, the second calculation coefficient=0.3, and the comprehensive test performance analysis coefficient=0.7c1+0.3c2.

[0056] In this embodiment, the preset comprehensive test performance analysis coefficient is preferably 8, and can be adjusted according to actual conditions. The preset comprehensive test performance analysis coefficient is used to determine whether the performance of the high-temperature superconducting magnet to be analyzed meets production requirements.

[0057] The beneficial effect of the above technical solution is that the present invention performs two-aspect comprehensive performance analysis on the high-temperature superconducting magnet by testing the performance analysis coefficient and the test performance analysis influence coefficient, thereby avoiding the one-sidedness of the comprehensive performance analysis result, ensuring the accuracy and efficiency of the comprehensive performance analysis, and ensuring the production quality and use performance of the high-temperature superconducting magnet.

[0058] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0059] Correspondingly, such as Figure 2 As shown, the present application also provides a method for analyzing the comprehensive performance of a high-temperature superconducting magnet, comprising: S110: determining a high temperature superconducting magnet to be analyzed, applying different applied test data to the high temperature superconducting magnet to be analyzed, and acquiring a plurality of test response data corresponding to the high temperature superconducting magnet to be analyzed, wherein the applied test data and the test response data correspond to each other; S120: performing data analysis of the same type on all test response data, and constructing a test response data chain according to all test response data of the same type; S130: acquiring standard response data corresponding to the high temperature superconducting magnet to be analyzed, dividing the test response data chain into different test response data sets according to the relationship between the test response data and the standard response data, and calculating the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed according to the test response data sets; S140: performing data analysis of the same type on each applied test data, and constructing an application test data chain according to all the application test data of the same type; S150: determining the maximum applied test data and the minimum applied test data on the applied test data chain, analyzing the applied test data chain according to the maximum applied test data and the minimum applied test data, and calculating the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed based on the analysis result; S160: Calculating a comprehensive test performance analysis coefficient of the high temperature superconducting magnet to be analyzed according to the test performance analysis coefficient and the test performance analysis influence coefficient, and judging whether the high temperature superconducting magnet to be analyzed meets production requirements based on the comprehensive test performance analysis coefficient.

[0060] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0061] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0062] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-temperature superconducting magnet comprehensive performance analysis system, characterized in that: include: A data testing module, used for determining a high temperature superconducting magnet to be analyzed, applying different applied test data to the high temperature superconducting magnet to be analyzed, and obtaining a plurality of test response data corresponding to the high temperature superconducting magnet to be analyzed, wherein the applied test data and the test response data correspond to each other; The first construction module is used to perform data analysis of the same type on all test response data and to construct a test response data chain based on all test response data of the same type; a first calculation module, configured to obtain standard response data corresponding to the high temperature superconducting magnet to be analyzed, divide the test response data chain into different test response data sets according to the relationship between the test response data and the standard response data, and calculate the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed according to the test response data sets; A second construction module is used to perform the same type of data analysis on each applied test data, and to construct an application test data chain according to all the application test data of the same type; a second calculation module, for determining the maximum applied test data and the minimum applied test data on the applied test data chain, analyzing the applied test data chain according to the maximum applied test data and the minimum applied test data, and calculating the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed based on the analysis result; The performance analysis module is used to calculate the comprehensive test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed according to the test performance analysis coefficient and the test performance analysis influence coefficient, and judge whether the high-temperature superconducting magnet to be analyzed meets the production requirements based on the comprehensive test performance analysis coefficient.

2. The high temperature superconducting magnet comprehensive performance analysis system according to claim 1, characterized in that: The first calculation module is used for: The first calculation module is used to randomly extract a test response data chain, and compare the test response data on the extracted test response data chain with the corresponding standard response data, and if the test response data is smaller than the standard response data, the test response data is classified into a low test response data set; The first calculation module is used for dividing the test response data into equal test response data sets if the test response data is equal to the standard response data; The first calculation module is used for dividing the test response data into a high test response data set if the test response data is greater than the standard response data; The first calculation module is used to calculate the low mean and the low standard deviation of the low test response data set, and calculate the sum of the low mean and the low standard deviation as the low sum; The first calculation module is used to calculate the absolute value of the difference between the low mean and the low standard deviation, and use it as the low difference; The first calculation module is used to determine a first data range according to the low sum value and the low difference value, extract all test response data in the low test response data set that are within the first data range, and construct a first subset; The first calculation module is used to calculate the equal mean and the equal standard deviation of the equal test response data set, and calculate the sum of the equal mean and the equal standard deviation as the equal sum value; The first calculation module is used to calculate the absolute value of the difference between the equal mean and the equal standard deviation, and use it as the equal difference value; The first calculation module is used to determine a second data range according to the equal sum value and the equal difference value, extract all test response data in the equal test response data set that are within the second data range, and construct a second subset; The first calculation module is used to calculate the high mean and the high standard deviation of the high test response data set, and calculate the sum of the high mean and the high standard deviation as the high sum; The first calculation module is used to calculate the absolute value of the difference between the high mean and the high standard deviation, and use it as the high difference; The first calculation module is used to determine a third data range according to the high sum value and the high difference value, extract all test response data in the high test response data set that are within the third data range, and construct a third subset; The first calculation module is used to calculate the subtest performance analysis coefficient of the test response data chain according to the first subset, the second subset and the third subset; The first calculation module is used to calculate the sub-test performance analysis coefficients of the remaining test response data chains, and calculate the test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed based on all the sub-test performance analysis coefficients.

3. The high temperature superconducting magnet comprehensive performance analysis system according to claim 2, characterized in that: The first calculation module is used for: The first calculation module is used to calculate the sub-test performance analysis coefficient of the test response data chain according to the following formula: ； Where a1 is the subtest performance analysis coefficient of the test response data chain, e1 is the calculation coefficient corresponding to the first subset, n1 is the number of test response data in the first subset, g i is the i-th test response data in the first subset, f1 is the low sum value, f2 is the low difference value, e2 is the calculation coefficient corresponding to the third subset, n2 is the number of test response data in the third subset, h j is the j-th test response data in the third subset, f3 is the high sum value, f4 is the high difference value, e1+e2=3, and e1>e2, and c is the calculation adjustment factor of the sub-test performance analysis coefficient.

4. The high temperature superconducting magnet comprehensive performance analysis system according to claim 3, characterized in that: The first calculation module is used to determine the calculation adjustment factor of the sub-test performance analysis coefficient according to the following steps: The first calculation module is used to count the number n3 of test response data in the second subset; The first calculation module is used to preset a first preset calculation adjustment factor, a second preset calculation adjustment factor and a third preset calculation adjustment factor; The first calculation module is used to determine the numerical value relationship between n1 and n2, and determine a left boundary value and a right boundary value based on the numerical value relationship; The first calculation module is used for selecting the first preset calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient when the data quantity n3 is less than the left boundary value; The first calculation module is used for selecting the second preset calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient when the data quantity n3 is greater than or equal to the left boundary value and less than or equal to the right boundary value; The first calculation module is used for selecting the third preset calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient when the data quantity n3 is greater than the right boundary value.

5. The high temperature superconducting magnet comprehensive performance analysis system according to claim 3, characterized in that: The first calculation module is used for: The first calculation module is used to calculate the coefficient mean of all sub-test performance analysis coefficients; The first calculation module is used to extract the same sub-test performance analysis coefficient from all sub-test performance analysis coefficients and obtain multiple test performance analysis coefficient sets; The first calculation module is used to count the first set quantity of the test performance analysis coefficient set; The first calculation module is used to extract a test performance analysis coefficient from all test performance analysis coefficient sets respectively, and calculate a first test performance analysis coefficient and value; The first calculation module is used to eliminate all test performance analysis coefficient sets that are smaller than the coefficient mean, and count the number of second sets of remaining test performance analysis coefficient sets; The first calculation module is used to extract a test performance analysis coefficient from the remaining test performance analysis coefficient set, and calculate the second test performance analysis coefficient and value; The first calculation module is used to calculate the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed according to the first set number, the second set number, the first test performance analysis coefficient and value, and the second test performance analysis coefficient and value.

6. The high temperature superconducting magnet comprehensive performance analysis system according to claim 1, characterized in that: The second calculation module is used for: The second calculation module is used to calculate the difference between the maximum applied test data and the minimum applied test data, and use it as the extreme difference value of the applied test data chain; The second calculation module is used to calculate the second difference between each applied test data in the applied test data chain and the extreme value; The second calculation module is used to calculate the difference variance of all second difference values, and generate a first difference value sequence according to all second difference values ​​that are less than or equal to the difference variance; The second calculation module is used to generate a second difference value sequence according to all second difference values ​​greater than the difference variance; The second calculation module is used to calculate the sub-test performance analysis influence coefficient of the applied test data chain according to the first difference series and the second difference series; The second calculation module is used to calculate the sub-test performance analysis influence coefficients of the remaining applied test data chains, and calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed based on all the sub-test performance analysis influence coefficients.

7. The high temperature superconducting magnet comprehensive performance analysis system according to claim 6, characterized in that: The second calculation module is used for: The second calculation module is used to calculate the sub-test performance analysis impact coefficient of the applied test data chain according to the following formula: ； Where p is the subtest performance analysis impact coefficient of the applied test data chain, y is the difference variance, m1 is the number of second differences in the first difference series, m2 is the number of second differences in the second difference series, w1 is the mean corresponding to the first difference series, w2 is the mean corresponding to the second difference series, u t is the tth second difference in the first difference sequence, s r is the rth second difference in the second difference sequence.

8. The high temperature superconducting magnet comprehensive performance analysis system according to claim 1, characterized in that: The second calculation module is used for: The second calculation module is used to randomly combine all sub-test performance analysis influence coefficients in pairs to obtain multiple sub-test performance analysis influence coefficient combinations; The second calculation module is used to calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed according to a combination of multiple sub-test performance analysis influence coefficients.

9. The high temperature superconducting magnet comprehensive performance analysis system according to claim 1, characterized in that: The performance analysis module is used to: The performance analysis module is used to determine whether the high-temperature superconducting magnet to be analyzed meets the production requirements according to the relationship between the comprehensive test performance analysis coefficient and the preset comprehensive test performance analysis coefficient; The performance analysis module is used to determine that the high-temperature superconducting magnet to be analyzed does not meet production requirements when the comprehensive test performance analysis coefficient is less than the preset comprehensive test performance analysis coefficient; The performance analysis module is used to determine that the high-temperature superconducting magnet to be analyzed meets production requirements when the comprehensive test performance analysis coefficient is greater than or equal to the preset comprehensive test performance analysis coefficient.

10. A method for analyzing the comprehensive performance of a high-temperature superconducting magnet, characterized in that: include: Determine a high temperature superconducting magnet to be analyzed, apply different applied test data to the high temperature superconducting magnet to be analyzed, and obtain a plurality of test response data corresponding to the high temperature superconducting magnet to be analyzed, wherein the applied test data and the test response data correspond to each other; Performing data analysis of the same type on all test response data, and constructing a test response data chain based on all test response data of the same type; Acquire standard response data corresponding to the high temperature superconducting magnet to be analyzed, divide the test response data chain into different test response data sets according to the relationship between the test response data and the standard response data, and calculate the test performance analysis coefficient of the high temperature superconducting magnet to be analyzed according to the test response data sets; Performing the same type of data analysis on each applied test data, and constructing an application test data chain based on all the application test data of the same type; Determine the maximum applied test data and the minimum applied test data on the applied test data chain, analyze the applied test data chain according to the maximum applied test data and the minimum applied test data, and calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed based on the analysis result; A comprehensive test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed is calculated according to the test performance analysis coefficient and the test performance analysis influence coefficient, and based on the comprehensive test performance analysis coefficient, it is determined whether the high-temperature superconducting magnet to be analyzed meets production requirements.

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