A comprehensive performance analysis system and method for high-temperature superconducting magnets

Abstract

The present invention relates to the technical field of high-temperature superconducting magnets, and discloses a comprehensive performance analysis system and method for high-temperature superconducting magnets. A data testing module applies different applied test data to obtain test response data; a first construction module constructs a test response data chain according to the test response data; a first calculation module obtains a test response data set based on the test response data and standard response data, and calculates a test performance analysis coefficient; a second construction module constructs 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 based on the test performance analysis coefficient and the test performance analysis influence coefficient, determines whether the production requirements are met, and conducts comprehensive performance analysis on the high-temperature superconducting magnet from two aspects, avoiding one-sidedness of the comprehensive performance analysis results, ensuring the accuracy and efficiency of the comprehensive performance analysis, and ensuring the production quality and service performance of the high-temperature superconducting magnet.

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] A high-temperature superconducting magnet refers to a magnet made of materials that exhibit superconducting properties above the liquid nitrogen temperature (77K). Compared with traditional low-temperature superconducting magnets, high-temperature superconducting magnets have a higher operating temperature and lower operating costs. A high-temperature superconducting magnet generally consists of a superconducting coil, an insulating layer, a support structure, and a cooling system. The coil adopts a multi-stage winding design to enhance the magnetic field strength, while the insulating layer and the support structure ensure the stability and durability of the magnet. High-temperature superconducting magnets are widely used in medical imaging, particle accelerators, maglev transportation, and large-scale scientific experiments. Their high efficiency and low energy consumption characteristics make them an important part of many future high-tech fields.

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

[0004] Embodiments of the present invention provide a system and method for analyzing the comprehensive performance of high-temperature superconducting magnets, so as to solve the technical problems in the prior art that the comprehensive performance of high-temperature superconducting magnets cannot be analyzed, resulting in a relatively one-sided comprehensive performance analysis result and unable to ensure the accuracy and efficiency of the comprehensive performance analysis.

[0005] To achieve the above object, the present invention provides a system for analyzing the comprehensive performance of high-temperature superconducting magnets, including:

[0006] A data testing module, configured to 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;

[0007] A first construction module, configured to perform data analysis of the same type on all the test response data, and construct a test response data chain according to all the test response data of the same type;

[0008] The first calculation module is used to obtain the 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;

[0009] The second construction module is used to perform the same type of data analysis on the applied test data applied each time, and construct an applied test data chain according to all the applied test data of the same type;

[0010] The second calculation module is used to 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 results;

[0011] 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.

[0012] Further, the first calculation module is used for:

[0013] The first calculation module is used to randomly extract a test response data chain, compare the test response data on the extracted test response data chain with the corresponding standard response data. If the test response data is less than the standard response data, the test response data is divided into the low test response data set;

[0014] The first calculation module is used to divide the test response data into the equal test response data set if the test response data is equal to the standard response data;

[0015] The first calculation module is used to divide the test response data into the high test response data set if the test response data is greater than the standard response data;

[0016] The first calculation module is used to calculate the low mean and the low standard deviation of the low test response data set, calculate the sum value of the low mean and the low standard deviation, and use it as the low sum value;

[0017] 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;

[0018] 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 the test response data in the low test response data set that fall within the first data range, and construct a first subset;

[0019] The first calculation module is used to calculate the equal mean value and the equal standard deviation of the equal test response data set, calculate the sum value of the equal mean value and the equal standard deviation, and use it as the equal sum value;

[0020] The first calculation module is used to calculate the absolute value of the difference between the equal mean value and the equal standard deviation, and use it as the equal difference value;

[0021] 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 the test response data in the equal test response data set that fall within the second data range, and construct a second subset;

[0022] The first calculation module is used to calculate the high mean value and the high standard deviation of the high test response data set, calculate the sum value of the high mean value and the high standard deviation, and use it as the high sum value;

[0023] The first calculation module is used to calculate the absolute value of the difference between the high mean value and the high standard deviation, and use it as the high difference value;

[0024] 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 the test response data in the high test response data set that fall within the third data range, and construct a third subset;

[0025] The first calculation module is used to 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;

[0026] The first calculation module is used to calculate the sub-test performance analysis coefficient of the remaining test response data chain, and calculate the test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed according to all the sub-test performance analysis coefficients.

[0027] Further, the first calculation module is used to:

[0028] 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:

[0029] ;

[0030] where, a1 is the sub-test 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 iis 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, c is the calculation adjustment factor of the sub-test performance analysis coefficient.

[0031] Further, the first calculation module is used to determine the calculation adjustment factor of the sub-test performance analysis coefficient according to the following steps:

[0032] The first calculation module is used to count the number of test response data n3 in the second subset;

[0033] The first calculation module is used to preset a first pre-calculation adjustment factor, a second pre-calculation adjustment factor, and a third pre-calculation adjustment factor;

[0034] The first calculation module is used to judge the numerical size relationship between n1 and n2, and determine a left boundary value and a right boundary value based on the numerical size relationship;

[0035] When the number of data n3 is less than the left boundary value, the first calculation module is used to select the first pre-calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient;

[0036] When the number of data 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 to select the second pre-calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient;

[0037] When the number of data n3 is greater than the right boundary value, the first calculation module is used to select the third pre-calculation adjustment factor as the calculation adjustment factor of the sub-test performance analysis coefficient.

[0038] Further, the first calculation module is used for:

[0039] The first calculation module is used to calculate the coefficient mean of all sub-test performance analysis coefficients;

[0040] The first calculation module is used to extract the same sub-test performance analysis coefficients from all sub-test performance analysis coefficients and obtain multiple test performance analysis coefficient sets;

[0041] The first calculation module is used to count the first set number of the test performance analysis coefficient sets;

[0042] The first calculation module is used to separately extract a test performance analysis coefficient from all the test performance analysis coefficient sets, and calculate the sum value of the first test performance analysis coefficients;

[0043] The first calculation module is used to eliminate all the test performance analysis coefficient sets smaller than the coefficient mean value, and count the number of the second set of the remaining test performance analysis coefficient sets;

[0044] The first calculation module is used to separately extract a test performance analysis coefficient from the remaining test performance analysis coefficient sets, and calculate the sum value of the second test performance analysis coefficients;

[0045] 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 number of the first set, the number of the second set, the sum value of the first test performance analysis coefficients, and the sum value of the second test performance analysis coefficients.

[0046] Further, the second calculation module is used for:

[0047] 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;

[0048] The second calculation module is used to calculate the second difference between each applied test data on the applied test data chain and the extreme difference value;

[0049] The second calculation module is used to calculate the difference variance of all the second differences, and generate a first difference sequence according to all the second differences less than or equal to the difference variance;

[0050] The second calculation module is used to generate a second difference sequence according to all the second differences greater than the difference variance;

[0051] 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 sequence and the second difference sequence;

[0052] The second calculation module is used to calculate the sub-test performance analysis influence coefficient of the remaining applied test data chain, and calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed according to all the sub-test performance analysis influence coefficients.

[0053] Further, the second calculation module is used for:

[0054] The second calculation module is used to calculate the sub-test performance analysis influence coefficient of the applied test data chain according to the following formula:

[0055] ;

[0056] Among them, p is the sub-test performance analysis influence coefficient for applying the test data chain, y is the difference variance, m1 is the number of second differences in the first difference sequence, m2 is the number of second differences in the second difference sequence, w1 is the mean corresponding to the first difference sequence, w2 is the mean corresponding to the second difference sequence, u t is the t-th second difference in the first difference sequence, s r is the r-th second difference in the second difference sequence.

[0057] Furthermore, the second calculation module is configured to:

[0058] The second calculation module is configured to randomly combine all the sub-test performance analysis influence coefficients in pairs to obtain multiple sub-test performance analysis influence coefficient combinations;

[0059] The second calculation module is configured to calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed according to multiple sub-test performance analysis influence coefficient combinations.

[0060] Furthermore, the performance analysis module is configured to:

[0061] The performance analysis module is configured 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;

[0062] The performance analysis module is configured to, when the comprehensive test performance analysis coefficient is less than the preset comprehensive test performance analysis coefficient, determine that the high-temperature superconducting magnet to be analyzed does not meet the production requirements;

[0063] The performance analysis module is configured to, when the comprehensive test performance analysis coefficient is greater than or equal to the preset comprehensive test performance analysis coefficient, determine that the high-temperature superconducting magnet to be analyzed meets the production requirements.

[0064] To achieve the above object, the present invention also provides a method for comprehensive performance analysis of a high-temperature superconducting magnet, including:

[0065] Determine the 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;

[0066] Perform data analysis of the same type on all the test response data, and construct a test response data chain according to all the test response data of the same type;

[0067] Obtain the standard response data corresponding to the high-temperature superconducting magnet to be analyzed. According to the relationship between the test response data and the standard response data, divide the test response data chain into different test response data sets, and calculate the test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed based on the test response data sets;

[0068] Perform the same type of data analysis on each applied test data, and construct an applied test data chain based on all the applied test data of the same type;

[0069] 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 results;

[0070] 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.

[0071] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0072] The present invention discloses a high-temperature superconducting magnet comprehensive performance analysis system and method. The data testing module applies different applied test data to obtain test response data; the first construction module constructs a test response data chain 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 the test performance analysis coefficient; the second construction module constructs an applied test data chain according to the applied test data; the second calculation module calculates the test performance analysis influence coefficient; the performance analysis module calculates the comprehensive test performance analysis coefficient according to the test performance analysis coefficient and the test performance analysis influence coefficient, judges whether it meets the production requirements, conducts comprehensive performance analysis on the high-temperature superconducting magnet from two aspects, avoids the one-sidedness of the comprehensive performance analysis result, ensures the accuracy and efficiency of the comprehensive performance analysis, and ensures the production quality and service performance of the high-temperature superconducting magnet. Description of the Drawings

[0073] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0074] Figure 1 Shows the structural schematic diagram of the high-temperature superconducting magnet comprehensive performance analysis system in the embodiment of the present invention;

[0075] Figure 2 The flowchart of the comprehensive performance analysis method for high-temperature superconducting magnets in the embodiments of the present invention is shown. Detailed implementation manners

[0076] The following further describes in detail the specific implementation manners of the present invention in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0077] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying 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 construed as a limitation to the present application.

[0078] The terms "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0079] In the description of the present 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 may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0080] The following is a description of the preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0081] As Figure 1 shown, the embodiments of the present invention disclose a comprehensive performance analysis system for high-temperature superconducting magnets, including:

[0082] A data testing module, configured to 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;

[0083] The first construction module is used to perform the same type of data analysis on all test response data and construct a test response data chain based on all test response data of the same type;

[0084] The first calculation module is used to obtain the 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 set;

[0085] The second construction module is used to perform the same type of data analysis on each applied test data and construct an applied test data chain based on all applied test data of the same type;

[0086] The second calculation module is used to 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;

[0087] 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.

[0088] In this embodiment, the applied test data includes, but is not limited to, magnetic field strength, temperature, mechanical stress, etc.

[0089] In this embodiment, the test response data includes, but is not limited to, resistivity, critical current, flux pinning force, etc.

[0090] 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.

[0091] In this embodiment, the test response data includes resistivity, critical current, flux pinning force, etc. Constructing a test response data chain according to all the resistivities also corresponds to constructing a test response data chain according to all test response data of the same type. A corresponding test response data chain can also be constructed according to all the critical currents.

[0092] In this embodiment, the applied test data includes magnetic field strength, temperature, mechanical stress, etc. An applied test data chain is constructed based on all the magnetic field strengths, that is, an applied test data chain is constructed corresponding to all the applied test data of the same type. A corresponding test response data chain can also be constructed based on all the temperatures.

[0093] The beneficial effects of the above technical solution are as follows: The present invention conducts comprehensive performance analysis on high-temperature superconducting magnets from two aspects, avoids one-sidedness of the comprehensive performance analysis results, ensures the accuracy and efficiency of the comprehensive performance analysis, and guarantees the production quality and service performance of high-temperature superconducting magnets.

[0094] In some embodiments of the present application, the first calculation module is used for:

[0095] The first calculation module is used to randomly select a test response data chain, compare the test response data on the selected test response data chain with the corresponding standard response data. If the test response data is less than the standard response data, the test response data is classified into the low test response data set;

[0096] The first calculation module is used to, if the test response data is equal to the standard response data, classify the test response data into the equal test response data set;

[0097] The first calculation module is used to, if the test response data is greater than the standard response data, classify the test response data into the high test response data set;

[0098] The first calculation module is used to calculate the low mean and low standard deviation of the low test response data set, calculate the sum value of the low mean and the low standard deviation, and use it as the low sum value;

[0099] 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;

[0100] The first calculation module is used to determine the first data range according to the low sum value and the low difference, extract all the test response data in the low test response data set that are within the first data range, and construct the first subset;

[0101] The first calculation module is used to calculate the equal mean and equal standard deviation of the equal test response data set, calculate the sum value of the equal mean and the equal standard deviation, and use it as the equal sum value;

[0102] 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;

[0103] 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 the test response data in the equal-test response data set that fall within the second data range, and construct a second subset;

[0104] The first calculation module is used to calculate the high mean value and the high standard deviation of the high-test response data set, calculate the sum value of the high mean value and the high standard deviation, and use it as the high-sum value;

[0105] The first calculation module is used to calculate the absolute value of the difference between the high mean value and the high standard deviation, and use it as the high-difference value;

[0106] 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 the test response data in the high-test response data set that fall within the third data range, and construct a third subset;

[0107] The first calculation module is used to 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;

[0108] The first calculation module is used to calculate the sub-test performance analysis coefficient of the remaining test response data chains, and calculate the test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed according to all the sub-test performance analysis coefficients.

[0109] 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.

[0110] In this embodiment, if all the test response data are equal to the corresponding standard response data, it is determined 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 determined that the high-temperature superconducting magnet to be analyzed does not meet the production requirements. If there is one or more test response data 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 there is one or more test response data 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.

[0111] In this embodiment, a test response data chain is randomly selected. For example, if the selected test response data chain is composed of resistivity, the standard response data is resistivity.

[0112] In this embodiment, the absolute value of the difference refers to first calculating the difference and then taking the absolute value.

[0113] 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. The rest will not be exemplified one by one.

[0114] The beneficial effects of the above technical solution are as follows: 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, ensuring the calculation accuracy of the sub-test performance analysis coefficient and laying a foundation for the calculation of the test performance analysis coefficient.

[0115] In some embodiments of the present application, the first calculation module is configured to:

[0116] The first calculation module is configured to calculate the sub-test performance analysis coefficient of the test response data chain according to the following formula:

[0117] ;

[0118] where a1 is the sub-test 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.

[0119] In some embodiments of the present application, the first calculation module is configured to determine the calculation adjustment factor of the sub-test performance analysis coefficient according to the following steps:

[0120] The first calculation module is configured to count the number of data n3 of the test response data in the second subset;

[0121] The first calculation module is configured to preset a first pre-calculation adjustment factor, a second pre-calculation adjustment factor, and a third pre-calculation adjustment factor;

[0122] The first calculation module is configured to judge the numerical size relationship between n1 and n2, and determine a left boundary value and a right boundary value based on the numerical size relationship;

[0123] The first calculation module is used to select the first pre-designed 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;

[0124] The first calculation module is used to select the second pre-designed 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;

[0125] The first calculation module is used to select the third pre-designed 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.

[0126] In this embodiment, the first pre-designed calculation adjustment factor is preferably 0.95 here, the second pre-designed calculation adjustment factor is preferably 1.15 here, and the third pre-designed calculation adjustment factor is preferably 1.25 here.

[0127] 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.

[0128] 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 pre-designed 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 pre-designed 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 pre-designed calculation adjustment factor is selected as the calculation adjustment factor of the sub-test performance analysis coefficient.

[0129] The beneficial effects of the above technical solutions are: The present invention selects the corresponding pre-designed 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.

[0130] In some embodiments of the present application, the first calculation module is used for:

[0131] The first calculation module is used to calculate the coefficient mean of all sub-test performance analysis coefficients;

[0132] The first calculation module is used to extract the same sub-test performance analysis coefficients from all sub-test performance analysis coefficients and obtain multiple test performance analysis coefficient sets;

[0133] The first calculation module is used to count the first set quantity of the test performance analysis coefficient sets;

[0134] The first calculation module is used to separately extract a test performance analysis coefficient from all the test performance analysis coefficient sets, and calculate the sum value of the first test performance analysis coefficients;

[0135] The first calculation module is used to eliminate all the test performance analysis coefficient sets that are less than the coefficient mean value, and count the number of the second set of the remaining test performance analysis coefficient sets;

[0136] The first calculation module is used to separately extract a test performance analysis coefficient from the remaining test performance analysis coefficient sets, and calculate the sum value of the second test performance analysis coefficients;

[0137] 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 number of the first set, the number of the second set, the sum value of the first test performance analysis coefficients, and the sum value of the second test performance analysis coefficients.

[0138] 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 separately extracted, that is, 2, 5, 8, 9. If the obtained coefficient mean value is 6, then the remaining test performance analysis coefficient sets are {8, 8}, {9, 9}, and the number of the second set is 2.

[0139] In this embodiment, the test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed is calculated according to the following formula:

[0140] ;

[0141] wherein, c1 is the test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed, k1 is the number of the first set, k2 is the number of the second set, k3 is the sum value of the second test performance analysis coefficients, and k4 is the sum value of the first test performance analysis coefficients.

[0142] The beneficial effects of the above technical solution are as follows: The present invention calculates the test performance analysis coefficient of the high-temperature superconducting magnet to be analyzed according to the number of the first set, the number of the second set, the sum value of the first test performance analysis coefficients, and the sum value of the second test performance analysis coefficients, 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, ensuring the comprehensiveness of the comprehensive performance analysis of the high-temperature superconducting magnet.

[0143] In some embodiments of the present application, the second calculation module is used for:

[0144] 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 value of the applied test data chain;

[0145] The second calculation module is used to calculate the second difference between each of the applied test data on the applied test data chain and the extreme difference;

[0146] The second calculation module is used to calculate the difference variance of all the second differences, and generate a first difference sequence according to all the second differences less than or equal to the difference variance;

[0147] The second calculation module is used to generate a second difference sequence according to all the second differences greater than the difference variance;

[0148] 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 sequence and the second difference sequence;

[0149] The second calculation module is used to calculate the sub-test performance analysis influence coefficient of the remaining applied test data chain, and calculate the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed according to all the sub-test performance analysis influence coefficients.

[0150] The beneficial effects of the above technical solutions are as follows: The present invention calculates the sub-test performance analysis influence coefficient of the applied test data chain according to the first difference sequence and the second difference sequence, further laying a foundation for the calculation of the test performance analysis influence coefficient and providing reliable data support.

[0151] In some embodiments of the present application, the second calculation module is used for:

[0152] The second calculation module is used to calculate the sub-test performance analysis influence coefficient of the applied test data chain according to the following formula:

[0153] ;

[0154] where p is the sub-test performance analysis influence coefficient of the applied test data chain, y is the difference variance, m1 is the number of second differences in the first difference sequence, m2 is the number of second differences in the second difference sequence, w1 is the mean value corresponding to the first difference sequence, w2 is the mean value corresponding to the second difference sequence, u t is the t-th second difference in the first difference sequence, s r is the r-th second difference in the second difference sequence.

[0155] In some embodiments of the present application, the second calculation module is used for:

[0156] The second calculation module is used to randomly combine all the sub-test performance analysis influence coefficients in pairs to obtain a plurality of sub-test performance analysis influence coefficient combinations;

[0157] 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 the combination of multiple sub-test performance analysis influence coefficients.

[0158] In this embodiment, if there is an unmatched sub-test performance analysis influence coefficient, this sub-test performance analysis influence coefficient is deleted.

[0159] 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:

[0160] ;

[0161] where c2 is the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed, n4 is the number of combinations of sub-test performance analysis influence coefficients, q1 z is the larger sub-test performance analysis influence coefficient in the z-th combination of sub-test performance analysis influence coefficients, q2 z is the smaller sub-test performance analysis influence coefficient in the z-th combination of sub-test performance analysis influence coefficients, is the minimum value of all ; is the maximum value of all ; v 2 is the variance of all .

[0162] In this embodiment, when the two sub-test performance analysis influence coefficients in the combination of sub-test performance analysis influence coefficients are equal, then = 0.

[0163] The beneficial effects of the above technical solution are: The present invention calculates the test performance analysis influence coefficient of the high-temperature superconducting magnet to be analyzed according to the combination of multiple sub-test performance analysis influence coefficients, laying a foundation for the comprehensive performance analysis of the high-temperature superconducting magnet, avoiding the subjectivity of manual participation, and providing another aspect of basis for the comprehensive performance analysis of the high-temperature superconducting magnet.

[0164] In some embodiments of the present application, the performance analysis module is used for:

[0165] The performance analysis module is used to judge 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;

[0166] The performance analysis module is used to judge that the high-temperature superconducting magnet to be analyzed does not meet the production requirements when the comprehensive test performance analysis coefficient is less than the preset comprehensive test performance analysis coefficient;

[0167] The performance analysis module is used to determine that the high-temperature superconducting magnet to be analyzed meets the production requirements when the comprehensive test performance analysis coefficient is greater than or equal to the preset comprehensive test performance analysis coefficient.

[0168] 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.

[0169] In this embodiment, the preset comprehensive test performance analysis coefficient is preferably 8 here, and can be specifically adjusted according to the actual situation. The preset comprehensive test performance analysis coefficient is used to determine whether the performance of the high-temperature superconducting magnet to be analyzed meets the production requirements.

[0170] The beneficial effects of the above technical solution are as follows: The present invention conducts comprehensive performance analysis on the high-temperature superconducting magnet from two aspects through the test performance analysis coefficient and the test performance analysis influence coefficient, 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 service performance of the high-temperature superconducting magnet.

[0171] In order to further elaborate the technical idea of the present invention, the technical solution of the present invention will be described in combination with a specific application scenario.

[0172] Correspondingly, as Figure 2 shown, the present application also provides a method for comprehensive performance analysis of a high-temperature superconducting magnet, including:

[0173] S110: Determine the 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, where the applied test data and the test response data correspond to each other;

[0174] S120: Perform data analysis of the same type on all the test response data, and construct a test response data chain based on all the test response data of the same type;

[0175] S130: Obtain the 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 set;

[0176] S140: Perform data analysis of the same type on each applied test data, and construct an applied test data chain based on all the applied test data of the same type;

[0177] S150: Determine the maximum and minimum applied test data on the applied test data chain, analyze the applied test data chain based on the maximum and 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 results;

[0178] S160: 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 determine whether the high-temperature superconducting magnet to be analyzed meets the production requirements based on the comprehensive test performance analysis coefficient.

[0179] 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.

[0180] Although the present invention has been described above with reference to the embodiments, various improvements can be made to it and components therein can 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 in the present invention can be combined with each other in any way, and the situations of these combinations are not all described in this specification only for the consideration of saving space and resources.

[0181] Those of ordinary skill in the art can understand that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features. 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; a performance analysis module, configured to calculate 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 to determine whether the high-temperature superconducting magnet to be analyzed meets production requirements based on the comprehensive test performance analysis coefficient; 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; 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.

2. The high temperature superconducting magnet comprehensive performance analysis system according to claim 1, 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.

3. 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 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.

4. 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.

5. The high temperature superconducting magnet comprehensive performance analysis system according to claim 4, 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.

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 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.

7. 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.

8. A method for analyzing the comprehensive performance of a high-temperature superconducting magnet, applied to the system for analyzing the comprehensive performance of a high-temperature superconducting magnet as claimed in any one of claims 1 to 7, 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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