Superconducting structure detection method, device and equipment and readable storage medium

Abstract

The embodiment of the invention relates to a superconducting structure detection method, device and equipment and a readable storage medium, and the method comprises the steps: carrying out the measurement of a superconducting structure, obtaining the mechanical data, and obtaining the current cycle number of an alternating load applied to the superconducting structure; calculating a damage factor of the superconducting structure according to the mechanical data; predicting the residual service life of the superconducting structure according to the damage factor and the current cycle index; and inputting the damage factor into a preset Weibull model to calculate and obtain a critical current value of the current cycle index. The residual service life of the superconducting structure under the fatigue load and the critical current after degradation can be detected in real time, the problem that in the prior art, gradual degradation of the performance of the superconducting material under the dynamic working condition is difficult to effectively restrain, and consequently detection data of the superconducting structure is inaccurate is solved, the accuracy of dynamic detection of the superconducting structure is improved, and the reliability of the superconducting structure is improved. And the stability and the safety of long-term operation of the superconducting magnet are ensured.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of optical fiber detection technology, and specifically to a superconducting structure detection method, device, equipment and readable storage medium. Background Art

[0002] With the rapid development of superconducting materials and magnet technology, superconducting magnets, due to their high current-carrying capacity and low loss, have been widely used in fields such as nuclear fusion devices, magnetic resonance imaging (MRI), particle accelerators, and new energy equipment. However, the current focus of superconducting magnet structural design and development is still on optimizing static mechanical properties under extreme operating conditions (such as extremely low temperatures and high magnetic field environments), such as the material's tensile strength, compressive stiffness, and static thermal stability. Although these static properties are the basis for ensuring the safe operation of magnets, in actual application scenarios, especially large-scale superconducting devices (such as tokamaks and superconducting energy storage systems), superconducting materials and magnets are often required to withstand the coupling effects of complex dynamic conditions for a long time, including large-scale rapid cooling and reheating cycles, frequent quenching and recovery processes, and periodic electromagnetic force shocks caused by strong alternating magnetic fields.

[0003] Existing research indicates that these dynamic conditions can significantly affect the microstructure and macroscopic properties of superconducting materials. For example, during thermal shock cycling, interfacial stress concentrations caused by differences in thermal expansion coefficients can lead to progressive damage within the material, including delamination, crack initiation, and interfacial debonding. Furthermore, under the influence of an alternating magnetic field, the alternating mechanical stresses generated by the combined effects of the Lorentz force and magnetostriction can induce cumulative fatigue in superconducting tapes or coils. Of particular note, even when a superconducting material is within the elastic deformation range (without plastic deformation), its current-carrying capacity can still degrade due to dynamic fatigue. Experimental data confirm that after tens of thousands of cycles of mechanical or thermal fatigue loading, the critical current (Ic) of high-temperature superconducting (HTS) tapes exhibits a significant decrease (by as much as 10% to 30%), accompanied by irreversible degradation of local superconducting properties and even localized overheating, leading to tape burnout. This phenomenon reveals the shortcomings of existing design methods in dynamic reliability assessment and life prediction, that is, traditional static strength design criteria are difficult to effectively suppress the progressive degradation of superconducting material performance under dynamic conditions, which in turn threatens the stability and safety of long-term operation of superconducting magnets.

[0004] Furthermore, existing superconducting magnet protection technologies (such as quench detection and protection systems) are primarily designed for sudden quench events, but lack effective means to suppress the cumulative material damage caused by repeated quench-recovery cycles. This dynamic degradation mechanism not only shortens the service life of superconducting magnets but also increases system maintenance costs and safety risks, becoming a bottleneck restricting the further large-scale application of superconducting technology. Therefore, there is an urgent need to develop a superconducting magnet structure design method for dynamic and complex operating conditions. By optimizing material interfaces, suppressing fatigue damage, and improving dynamic load-bearing capacity, high reliability and long-life operation of superconducting materials in a thermal-mechanical-electromagnetic multi-field coupled environment can be achieved. Summary of the Invention

[0005] In view of the above problems, the embodiments of the present invention provide a superconducting structure detection method, device, equipment and readable storage medium, which solve the shortcomings of existing design methods in dynamic reliability assessment and life prediction, that is, traditional static strength design criteria are difficult to effectively suppress the progressive degradation of superconducting material performance under dynamic working conditions, thereby threatening the stability and safety of long-term operation of superconducting magnets.

[0006] According to one aspect of an embodiment of the present invention, a superconducting structure detection method is provided, the method comprising: Acquiring mechanical data by measuring the superconducting structure and obtaining the current number of cycles of the alternating load applied to the superconducting structure; Calculating the damage factor of the superconducting structure according to the mechanical data; predicting the remaining service life of the superconducting structure based on the damage factor and the current number of cycles; The damage factor is input into a preset Weibull model to calculate and obtain the critical current value of the current cycle number.

[0007] In an optional manner, obtaining mechanical data by measuring the superconducting structure specifically includes: Mechanical data of the superconducting structure is obtained by any one of distributed optical fiber sensing, strain gauges, and DIC technology, wherein the mechanical data includes at least one or more of the maximum strain value of the superconducting structure under the initial alternating load, the maximum strain value of the superconducting structure under the Nth alternating load, and the maximum strain value of the superconducting structure before failure under the initial alternating load.

[0008] In an optional manner, calculating the damage factor of the superconducting structure according to the mechanical data specifically includes: The difference between the maximum strain value of the superconducting structure under the Nth alternating load and the maximum strain value of the superconducting structure under the initial alternating load is recorded as the first strain difference; The difference between the maximum strain value of the superconducting structure before failure under the initial alternating load and the maximum strain value of the superconducting structure under the initial alternating load is recorded as the second strain difference; The damage factor is obtained by calculating the ratio of the first strain difference to the second strain difference.

[0009] In an optional manner, the value of the damage factor is between [0, 1]; when the damage factor is equal to 0, it indicates that the superconducting material is not damaged; when the damage factor is equal to 1, it indicates that the superconducting material is completely failed.

[0010] In an optional manner, predicting the remaining service life of the superconducting structure according to the damage factor and the current number of cycles specifically includes: Obtaining a cycle ratio according to the ratio of the current number of cycles to the fatigue life; The remaining service life is obtained based on the damage-cycle relationship function of the damage factor and the cycle ratio.

[0011] In an optional manner, the damage-cycle relationship function is , where N is the current cycle number, N f is the fatigue life, D is the damage factor, and a, b, c, and d are all preset fitting parameters.

[0012] In an optional manner, when the damage factor is input into a preset Weibull model to calculate the critical current value of the current cycle number, the Weibull model is ,in, is the scale parameter, is the shape parameter, is the critical current after fatigue degradation, is the initial critical current.

[0013] According to another aspect of an example of the present invention, there is provided a superconducting structure detection device, the device comprising: a data acquisition module, configured to acquire mechanical data by measuring the superconducting structure and to acquire the current number of cycles of the alternating load applied to the superconducting structure; a damage calculation module, configured to calculate a damage factor of the superconducting structure based on the mechanical data; a remaining life calculation module, configured to predict the remaining service life of the superconducting structure according to the damage factor and the current number of cycles; The critical current calculation module is used to input the damage factor into a preset Weibull model to calculate and obtain the critical current value of the current cycle number.

[0014] According to another aspect of an example of the present invention, there is provided a superconducting structure detection device, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the operations of the superconducting structure detection method as described above.

[0015] According to another aspect of an example of the present invention, a readable storage medium is provided, wherein the storage medium stores at least one executable instruction. When the executable instruction is executed on a superconducting structure detection device as described above, the superconducting structure detection device performs the operation of the superconducting structure detection method as described above.

[0016] The present invention provides a superconducting structure detection method, apparatus, device, and readable storage medium. The beneficial effects of the present invention are as follows: the present invention obtains mechanical data by measuring the superconducting structure and obtains the current number of cycles of alternating loads applied to the superconducting structure; calculates the damage factor of the superconducting structure based on the mechanical data; predicts the remaining service life of the superconducting structure based on the damage factor and the current number of cycles; and inputs the damage factor into a preset Weibull model to calculate the critical current value for the current number of cycles. The present invention can detect the remaining service life and post-degradation critical current of a superconducting structure under fatigue load in real time, resolving the problem in the prior art of the difficulty in effectively suppressing the progressive degradation of superconducting material performance under dynamic conditions, resulting in inaccurate superconducting structure detection data. This improves the accuracy of dynamic detection of superconducting structures and ensures the long-term stability and safety of superconducting magnets.

[0017] The above description is only an overview of the technical solutions of the embodiments of the present invention. In order to more clearly understand the technical means of the embodiments of the present invention, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiments of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the drawings. In the drawings: Figure 1 A schematic flow chart of a superconducting structure detection method according to embodiment 1 of the present invention is shown; Figure 2 A schematic diagram of a process for calculating the damage factor of a superconducting structure based on mechanical data according to Example 1 of the present invention is shown; Figure 3The figure shows the relationship between the damage factor D and the maximum strain of the REBCO high-temperature superconducting tape established by using the maximum strain measured by the distributed optical fiber according to an embodiment of the present invention; Figure 4 A schematic diagram of a process for predicting the remaining service life of a superconducting structure according to a damage factor and a current number of cycles in accordance with Embodiment 1 of the present invention is shown; Figure 5 The figure shows the relationship between the damage factor D and the cycle ratio of the REBCO high-temperature superconducting tape established by using the maximum strain measured by the distributed optical fiber according to an embodiment of the present invention; Figure 6 The Weibull model of the damage factor D and critical current of a REBCO high-temperature superconducting tape established using distributed optical fiber according to an embodiment of the present invention is shown; Figure 7 FIG2 shows a schematic structural diagram of a superconducting structure detection device according to Example 2 of the present invention; Figure 8 A schematic structural diagram of a superconducting structure detection device according to embodiment 3 of the present invention is shown. DETAILED DESCRIPTION

[0019] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0020] Example 1, Figure 1-6 An embodiment of a superconducting structure detection method of the present invention is shown. The method is applied to the remaining service life detection and critical current detection of the superconducting structure. Specifically, the method includes: 110. Obtaining mechanical data by measuring the superconducting structure and obtaining the current number of cycles of the alternating load applied to the superconducting structure. In step 110, obtaining mechanical data by measuring the superconducting structure specifically includes obtaining mechanical data of the superconducting structure by using any one of distributed optical fiber sensing, strain gauges, and DIC technology, wherein the mechanical data includes at least one or more of the maximum strain value of the superconducting structure under the initial alternating load, the maximum strain value of the superconducting structure under the Nth alternating load, and the maximum strain value of the superconducting structure before failure under the initial alternating load. The current number of cycles can be obtained by capturing load changes in real time using sensors such as strain, vibration, and temperature, combined with signal processing algorithms such as Rainflow to count and calculate the number of cycles. The current number of cycles can also be predicted using operating condition parameters based on a cumulative damage model or machine learning.

[0021] 120. Calculate the damage factor of the superconducting structure based on the mechanical data. In step 120, the damage factor of the superconducting structure is calculated based on the mechanical data, specifically including: recording the difference between the maximum strain value of the superconducting structure under the Nth alternating load and the maximum strain value of the superconducting structure under the initial alternating load as a first strain difference; recording the difference between the maximum strain value of the superconducting structure before failure under the initial alternating load and the maximum strain value of the superconducting structure under the initial alternating load as a second strain difference; and obtaining the damage factor by calculating the ratio of the first strain difference to the second strain difference.

[0022] 130, predicting the remaining service life of the superconducting structure based on the damage factor and the current number of cycles; In step 130, predicting the remaining service life of the superconducting structure based on the damage factor and the current number of cycles specifically includes: obtaining a cycle ratio based on the ratio of the current number of cycles to the fatigue life; obtaining the remaining service life based on a damage-cycle relationship function between the damage factor and the cycle ratio. Wherein, the damage-cycle relationship function is , where N is the current cycle number, N f is the fatigue life, D is the damage factor, and a, b, c, and d are all preset fitting parameters.

[0023] 140, input the damage factor into the preset Weibull model to calculate the critical current value of the current number of cycles. In step 140, when inputting the damage factor into the preset Weibull model to calculate the critical current value of the current number of cycles, the Weibull model is ,in, is the scale parameter, is the shape parameter, is the critical current after fatigue degradation, is the initial critical current.

[0024] The present invention measures a superconducting structure to obtain mechanical data and the current number of cycles of alternating loads applied to the superconducting structure; calculates the damage factor of the superconducting structure based on the mechanical data; predicts the remaining useful life of the superconducting structure based on the damage factor and the current number of cycles; and inputs the damage factor into a preset Weibull model to calculate the critical current value for the current number of cycles. The present invention enables real-time detection of the remaining useful life and post-degradation critical current of a superconducting structure under fatigue load, resolving the prior art issue of inaccurate superconducting structure detection data due to the difficulty in effectively suppressing the progressive degradation of superconducting material performance under dynamic conditions. This improves the accuracy of dynamic detection of superconducting structures and ensures the long-term stability and safety of superconducting magnets.

[0025] In one optional approach, mechanical data is obtained by measuring the superconducting structure, specifically including obtaining mechanical data of the superconducting structure using any one of distributed fiber optic sensing, strain gauges, and DIC technology. The mechanical data includes at least one or more of the maximum strain value of the superconducting structure under the initial alternating load, the maximum strain value of the superconducting structure under the Nth alternating load, and the maximum strain value of the superconducting structure before failure under the initial alternating load. In this embodiment, mechanical data is acquired using a distributed fiber optic sensing device. Specifically, the distributed fiber optic sensing device includes a distributed fiber optic interrogator, a distributed optical fiber, a fatigue testing machine, a cryogenic dewar, REBCO high-temperature superconducting tape, a quench detection voltage lead for measuring critical current, and a current lead device for applying current. The fatigue testing machine is equipped with an electro-hydraulic servo system and a cryogenic dewar to provide a low-temperature environment for testing superconducting materials. It is connected to a computer via a controller for automatic loading. The two end clamps of the fatigue testing machine are made of oxygen-free copper and are used for clamping the test sample and conducting power testing. Distributed optical fibers are welded to the surface of REBCO high-temperature superconducting tape using cost-effective welding technology, primarily measuring strain during fatigue testing. A fiber optic interrogator is directly connected to the distributed optical fiber sensors on the REBCO CC tape surface for real-time acquisition and storage of measured values. Voltage leads for quench detection are welded to both ends of the REBCO high-temperature superconducting tape surface. These lead leads are used for critical current measurement during testing, with a quench determined based on a voltage standard of 1µV / cm.

[0026] exist Figure 2 In the illustrated embodiment, the damage factor of the superconducting structure is calculated based on the mechanical data, specifically including: 210, recording the difference between the maximum strain value of the superconducting structure under the Nth alternating load and the maximum strain value of the superconducting structure under the initial alternating load as a first strain difference; 220, recording the difference between the maximum strain value of the superconducting structure before failure under the initial alternating load and the maximum strain value of the superconducting structure under the initial alternating load as a second strain difference; 230, the damage factor is obtained by calculating the ratio of the first strain difference to the second strain difference.

[0027] In steps 210-230, the damage factor is calculated as follows: , is the maximum strain value of the superconducting structure under the initial alternating load; represents the maximum strain value of the superconducting structure under the Nth alternating load, Indicates the maximum strain before failure of the superconducting structure under the initial alternating load. The damage factor is between [0,1], indicating the degree of damage to the superconducting structure; when the damage factor is equal to 0, it means that the superconducting material is not damaged; when the damage factor is equal to 1, it means that the superconducting material has completely failed. Figure 3 , Figure 3 The relationship between the damage factor D and the maximum strain of the REBCO high-temperature superconducting tape established by the maximum strain measured by distributed optical fiber in the embodiment of the present invention is as follows: the maximum strain value of the REBCO high-temperature superconducting tape at different cycle times is measured by distributed optical fiber, and the equation is used to calculate the maximum strain value of the REBCO high-temperature superconducting tape at different cycle times. The fatigue damage factor calculation model of REBCO high-temperature superconducting tape was constructed, and the strain sensitivity coefficient was obtained by fitting. The linear equation can be directly applied to the fatigue damage assessment of high-temperature superconducting tapes.

[0028] exist Figure 4 In the illustrated embodiment, the remaining service life of the superconducting structure is predicted based on the damage factor and the current number of cycles, specifically including: 410, obtaining a cycle ratio according to the ratio of the current number of cycles to the fatigue life; in step 410, the cycle ratio A=N / Nf, wherein Nf is the fatigue life and N is the current number of cycles.

[0029] 420, according to the damage-cycle relationship function of the damage factor and the cycle ratio, the remaining service life is obtained. In step 420, the damage-cycle relationship function is , where N is the current cycle number, N f is the fatigue life, D is the damage factor, and a, b, c, and d are preset fitting parameters. a, b, c, and d can be obtained by performing multiple tests on a distributed optical fiber sensing device on a superconducting structure.

[0030] See also Figure 5 , Figure 5 The relationship between the damage factor D and the cycle ratio of REBCO high-temperature superconducting tape, established using the maximum strain measured by distributed optical fiber in an embodiment of the present invention, is presented. A polynomial equation for the damage factor and cycle ratio of REBCO high-temperature superconducting tape under different loads and different cycle numbers is constructed using distributed optical fiber: By recording the cycle number N, this equation can be directly applied to the prediction of the remaining useful life of REBCO high-temperature superconducting tape.

[0031] In step 140, the damage factor is input into the preset Weibull model to calculate the critical current value of the current cycle number. The Weibull model is ,in, is the scale parameter, is the shape parameter, is the critical current after fatigue degradation, is the initial critical current. In a specific example, The characteristic life under the damage factor D can be characterized, such as the number of cycles at which Ic drops to 36.8% of the initial value; Reflects the dispersion of the degradation rate, such as when β>1, the degradation is accelerated. Figure 6 , Figure 6 The Weibull model of the damage factor D and critical current of REBCO high-temperature superconducting tape established using distributed optical fiber in the embodiment of the present invention; after using optical fiber to construct the damage factor and test critical current of REBCO high-temperature superconducting tape, a Weibull model of fatigue critical current degradation of REBCO high-temperature superconducting tape was established, and the parameters in the model were calibrated. , , which can be used as a general model parameter to evaluate the critical current degradation of REBCO high-temperature superconducting tapes.

[0032] Example 2 Figure 7 FIG. 1 shows an embodiment of a superconducting structure detection device 700 of the present invention. The superconducting structure detection device 700 includes a data acquisition module 710, a damage calculation module 720, a remaining life calculation module 730, and a critical current calculation module 740. Data acquisition module 710, used in step 110 of Example 1, specifically comprises: obtaining mechanical data by measuring the superconducting structure, specifically comprising: obtaining mechanical data of the superconducting structure by using any one of distributed optical fiber sensing, strain gauges, and DIC technology, wherein the mechanical data includes at least one or more of the maximum strain value of the superconducting structure under the initial alternating load, the maximum strain value of the superconducting structure under the Nth alternating load, and the maximum strain value of the superconducting structure before failure under the initial alternating load. The current cycle number can be obtained by using strain, vibration, temperature, and other sensors to capture load changes in real time, combined with signal processing algorithms such as Rainflow to count the number of cycles. The current cycle number can also be predicted using operating condition parameters based on a cumulative damage model or machine learning.

[0033] The damage calculation module 720, used in step 120 of Example 1, specifically includes: calculating the damage factor of the superconducting structure based on the mechanical data, specifically including: recording the difference between the maximum strain value of the superconducting structure under the Nth alternating load and the maximum strain value of the superconducting structure under the initial alternating load as a first strain difference; recording the difference between the maximum strain value of the superconducting structure before failure under the initial alternating load and the maximum strain value of the superconducting structure under the initial alternating load as a second strain difference; and obtaining the damage factor by calculating the ratio of the first strain difference to the second strain difference.

[0034] The remaining service life calculation module 730 is used in step 130 of embodiment 1, specifically including: predicting the remaining service life of the superconducting structure based on the damage factor and the current number of cycles, specifically including: obtaining the cycle ratio based on the ratio of the current number of cycles to the fatigue life; obtaining the remaining service life based on the damage-cycle relationship function of the damage factor and the cycle ratio. Wherein, the damage-cycle relationship function is , where N is the current cycle number, N f is the fatigue life, D is the damage factor, and a, b, c, and d are all preset fitting parameters.

[0035] The critical current calculation module 740 is used in step 140 of embodiment 1, specifically comprising: inputting the damage factor into the preset Weibull model to calculate the critical current value of the current cycle number, the Weibull model is ,in, is the scale parameter, is the shape parameter, is the critical current after fatigue degradation, is the initial critical current.

[0036] Example 3: Figure 5 A schematic structural diagram of an embodiment of a superconducting structure detection device of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the superconducting structure detection device.

[0037] like Figure 5 As shown, the superconducting structure detection device may include: a processor (processor) 602 , a communications interface (Communications Interface) 604 , a memory (memory) 606 , and a communication bus 608 .

[0038] Processor 602, communication interface 604, and memory 606 communicate with each other via communication bus 608. Communication interface 604 is used to communicate with other devices, such as client devices or other server network elements. Processor 602 is used to execute program 610, which may specifically perform the steps described in the aforementioned embodiment of the substructure-based container structure simulation method.

[0039] Specifically, the program 610 may include program code including computer-executable instructions.

[0040] Processor 602 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the substructure-based container structure simulation device may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0041] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, or may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0042] The program 610 can be specifically called by the processor 602 to enable the superconducting structure detection device to perform the operations of step 110 to step 140 of embodiment 1.

[0043] Example 4: An embodiment of the present invention provides a readable storage medium storing at least one executable instruction. When the executable instruction is executed on a substructure-based superconducting structure detection device, the substructure-based superconducting structure detection device executes the superconducting structure detection method of embodiment 1.

[0044] The executable instructions may be specifically used to enable the substructure-based superconducting structure detection device to perform the following operations: 110, obtaining mechanical data by measuring the superconducting structure, and obtaining the current number of cycles of the alternating load applied to the superconducting structure; 120. Calculate the damage factor of superconducting structures based on mechanical data; 130, predicting the remaining service life of a superconducting structure based on the damage factor and the current number of cycles; 140, input the damage factor into the preset Weibull model to calculate and obtain the critical current value of the current cycle number.

[0045] The present invention measures a superconducting structure to obtain mechanical data and the current number of cycles of alternating loads applied to the superconducting structure; calculates the damage factor of the superconducting structure based on the mechanical data; predicts the remaining useful life of the superconducting structure based on the damage factor and the current number of cycles; and inputs the damage factor into a preset Weibull model to calculate the critical current value for the current number of cycles. The present invention enables real-time detection of the remaining useful life and post-degradation critical current of a superconducting structure under fatigue load, resolving the prior art issue of inaccurate superconducting structure detection data due to the difficulty in effectively suppressing the progressive degradation of superconducting material performance under dynamic conditions. This improves the accuracy of dynamic detection of superconducting structures and ensures the long-term stability and safety of superconducting magnets.

[0046] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system or other device. In addition, the embodiments of the present invention are not directed to any particular programming language.

[0047] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present invention may be practiced without these specific details. Similarly, in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. The claims that follow the detailed description are hereby expressly incorporated into that detailed description, with each claim itself serving as a separate embodiment of the present invention.

[0048] Those skilled in the art will appreciate that the modules in the devices of the embodiments can be adaptively changed and installed in one or more devices different from the embodiments. The modules, units, or components in the embodiments can be combined into one module, unit, or component, and furthermore, they can be divided into multiple submodules, subunits, or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive.

[0049] It should be noted that the above embodiments illustrate rather than limit the invention, and that alternative embodiments may be devised by a person skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments should not be understood as limiting the order of execution unless otherwise specified.

Claims

1. A superconducting structure detection method, characterized in that: The method comprises: Acquiring mechanical data by measuring the superconducting structure and obtaining the current number of cycles of the alternating load applied to the superconducting structure; Calculating the damage factor of the superconducting structure according to the mechanical data; predicting the remaining service life of the superconducting structure based on the damage factor and the current number of cycles; The damage factor is input into a preset Weibull model to calculate and obtain the critical current value of the current cycle number.

2. The superconducting structure detection method according to claim 1, characterized in that: The obtaining of mechanical data by measuring the superconducting structure specifically includes: Mechanical data of the superconducting structure is obtained by any one of distributed optical fiber sensing, strain gauges, and DIC technology, wherein the mechanical data includes at least one or more of the maximum strain value of the superconducting structure under the initial alternating load, the maximum strain value of the superconducting structure under the Nth alternating load, and the maximum strain value of the superconducting structure before failure under the initial alternating load.

3. The superconducting structure detection method according to claim 2, characterized in that: Calculating the damage factor of the superconducting structure according to the mechanical data specifically includes: The difference between the maximum strain value of the superconducting structure under the Nth alternating load and the maximum strain value of the superconducting structure under the initial alternating load is recorded as the first strain difference; The difference between the maximum strain value of the superconducting structure before failure under the initial alternating load and the maximum strain value of the superconducting structure under the initial alternating load is recorded as the second strain difference; The damage factor is obtained by calculating the ratio of the first strain difference to the second strain difference.

4. The superconducting structure detection method according to claim 3, characterized in that: The value of the damage factor is between [0, 1]. When the damage factor is equal to 0, it indicates that the superconducting material is not damaged. When the damage factor is equal to 1, it indicates that the superconducting material is completely failed.

5. The superconducting structure detection method according to claim 3, characterized in that: Predicting the remaining service life of the superconducting structure according to the damage factor and the current number of cycles specifically includes: Obtaining a cycle ratio according to the ratio of the current number of cycles to the fatigue life; The remaining service life is obtained based on the damage-cycle relationship function of the damage factor and the cycle ratio.

6. The superconducting structure detection method according to claim 5, characterized in that: The damage-cycle relationship function is: , where N is the current cycle number, N f is the fatigue life, D is the damage factor, and a, b, c, and d are all preset fitting parameters.

7. The superconducting structure detection method according to claim 1, characterized in that: When the damage factor is input into the preset Weibull model to calculate the critical current value of the current cycle number, the Weibull model is: ,in, is the scale parameter, is the shape parameter, is the critical current after fatigue degradation, is the initial critical current.

8. A superconducting structure detection device, characterized in that: The device comprises: a data acquisition module, configured to acquire mechanical data by measuring the superconducting structure and to acquire the current number of cycles of the alternating load applied to the superconducting structure; a damage calculation module, configured to calculate a damage factor of the superconducting structure based on the mechanical data; a remaining life calculation module, configured to predict the remaining service life of the superconducting structure according to the damage factor and the current number of cycles; The critical current calculation module is used to input the damage factor into a preset Weibull model to calculate and obtain the critical current value of the current cycle number.

9. A superconducting structure detection device, characterized in that: include: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the operation of the superconducting structure detection method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The storage medium stores at least one executable instruction. When the executable instruction is executed on the superconducting structure detection device according to claim 9, the superconducting structure detection device performs the operation of the superconducting structure detection method according to any one of claims 1 to 7.

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