Defect and magnetic memory signal relation model construction method and magnetic memory detection method

Abstract

The invention relates to the technical field of magnetic memory detection, particularly provides a construction method of a defect and magnetic memory signal relation model and a magnetic memory detection method, and aims to solve the problem that an existing magnetic memory detection method cannot perform quantitative analysis on defects. In order to achieve the purpose, the construction method of the defect and magnetic memory signal relation model comprises the steps that a magnetic memory characteristic quantity is obtained, and the magnetic memory characteristic quantity is used for reflecting the defect of the superconducting strip; determining a magnetic memory signal characteristic value according to the magnetic memory characteristic quantity, wherein the magnetic memory signal characteristic value is used for quantifying the structure of the defect; determining a magnetic memory signal characteristic value corresponding to the defect data of the preset structure by utilizing multi-physics field simulation; and constructing a defect and magnetic memory signal relation model according to the defect data and the magnetic memory signal characteristic value corresponding to the defect data. According to the application, the purpose of detecting and quantitatively analyzing the defects of the superconducting tape is achieved, faults of the superconducting tape in the application of an electric power system are prevented, and safe and stable operation of the electric power system is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of magnetic memory detection technology, and specifically to a method for constructing a defect and magnetic memory signal relationship model and a magnetic memory detection method. Background Art

[0002] Superconducting tape is a material with high conductivity, usually composed of a multilayer structure, which can achieve zero-resistance current transportation at ultra-low temperatures. Unlike traditional metal tapes, due to process limitations during preparation and post-processing, defects such as holes and cracks may exist; multilayer composite structures are also prone to interlayer delamination and cracking. These defects are subjected to complex stress after the superconducting tape carries current, causing serious damage to the performance and stability of the superconducting tape. Although the existing magnetic memory detection method can detect defects in superconducting tapes, there is no way to quantitatively analyze the defects.

[0003] Accordingly, the art needs a new solution for constructing a defect and magnetic memory signal relationship model to solve the above problems. Summary of the invention

[0004] In order to overcome the above-mentioned defects, the present application is proposed to solve or at least partially solve the technical problem that the existing magnetic memory detection method cannot perform quantitative analysis on defects.

[0005] In a first aspect, a method for constructing a defect-magnetic memory signal relationship model is provided, the method comprising: obtaining a magnetic memory characteristic quantity, the magnetic memory characteristic quantity being used to reflect the defect of a superconducting tape; determining a magnetic memory signal characteristic value according to the magnetic memory characteristic quantity, the magnetic memory signal characteristic value being used to quantify the structure of the defect; determining a magnetic memory signal characteristic value corresponding to defect data of a preset structure by using multi-physical field simulation; constructing a defect-magnetic memory signal relationship model according to the defect data and the magnetic memory signal characteristic value corresponding to the defect data

[0006] In a technical solution of the method for constructing the above-mentioned defect and magnetic memory signal relationship model, the magnetic memory characteristic quantity includes the magnetic field strength of the leakage magnetic field and the gradient of the magnetic field strength, and the determining of the magnetic memory signal characteristic value according to the magnetic memory characteristic quantity includes: determining the following magnetic memory signal characteristic values ​​based on the magnetic field strength and the gradient of the magnetic field strength:

[0007]

[0008] Where ΔHp represents the peak-to-peak value of the composite magnetic field intensity, ΔHp(x) represents the peak-to-peak value of the tangential magnetic field intensity, and ΔHp(y) represents the peak-to-peak value of the normal magnetic field intensity; ΔHp / Δx represents the rate of change of the composite magnetic field intensity, Δx 1 Indicates the difference between the maximum and minimum tangential magnetic field intensity measurement point coordinates, Δx2 Indicates the difference between the coordinates of the measuring point with the maximum and minimum normal magnetic field intensity; K a represents the composite average gradient, K a (x) represents the average gradient of the tangential magnetic field strength, K a (y) represents the average gradient of the normal magnetic field strength, K i (x) represents the gradient of the tangential magnetic field intensity measured for the i-th time, K i (y) represents the gradient of the normal magnetic field intensity measured for the i-th time, i∈[1,n], and n represents the total number of measurements.

[0009] In a technical solution of the method for constructing the above-mentioned defect and magnetic memory signal relationship model, the method also includes: obtaining a preset defect type of the superconducting tape, parameters and dimensions corresponding to the preset defect type, and a preset load; combining the preset defect type, parameters and dimensions corresponding to the preset defect type, and a preset load to obtain defect data of a preset structure.

[0010] In a technical solution of the method for constructing the above-mentioned defect and magnetic memory signal relationship model, the defect and magnetic memory signal relationship model is constructed according to the defect data and the magnetic memory signal characteristic values ​​corresponding to the defect data, including: training a pre-acquired machine learning model according to the defect data and the magnetic memory signal characteristic values ​​corresponding to the defect data; and using the trained machine learning model as the defect and magnetic memory signal relationship model.

[0011] In a second aspect, a magnetic memory detection method is provided, the method comprising: obtaining a defect and magnetic memory signal relationship model constructed according to the first aspect or any corresponding technical solution thereof, and the magnetic field strength of the leakage magnetic field of the superconducting tape to be detected and the gradient of the magnetic field strength; performing defect detection on the superconducting tape to be detected using the defect and magnetic memory signal relationship model according to the magnetic field strength and the gradient of the magnetic field strength.

[0012] In a technical solution of the above-mentioned magnetic memory detection method, defect detection is performed on the superconducting tape to be detected according to the magnetic field strength and the gradient of the magnetic field strength using the defect and magnetic memory signal relationship model, including: processing the magnetic field strength and the gradient of the magnetic field strength to obtain corresponding magnetic memory signal characteristic values; determining the defect type of the superconducting tape to be detected and the parameters and dimensions corresponding to the defect type according to the magnetic memory signal characteristic values ​​using the defect and magnetic memory signal relationship model.

[0013] In a third aspect, a device for constructing a defect and magnetic memory signal relationship model is provided, the device comprising: a feature quantity acquisition module, used to obtain magnetic memory feature quantities, the magnetic memory feature quantities are used to reflect the defects of superconducting tapes; a feature value determination module, used to determine magnetic memory signal feature values ​​based on the magnetic memory feature quantities, the magnetic memory signal feature values ​​are used to quantify the structure of the defects; a simulation module, used to determine the magnetic memory signal feature values ​​corresponding to defect data of a preset structure using multi-physical field simulation; a model construction module, used to construct a defect and magnetic memory signal relationship model based on the defect data and the magnetic memory signal feature values ​​corresponding to the defect data.

[0014] In a fourth aspect, a magnetic memory detection device is provided, the device comprising: a model and data acquisition module, for acquiring a defect and magnetic memory signal relationship model constructed according to the first aspect or any corresponding technical solution thereof, and a magnetic field strength of a leakage magnetic field of a superconducting tape to be detected and a gradient of the magnetic field strength; a detection module, for performing defect detection on the superconducting tape to be detected using the defect and magnetic memory signal relationship model according to the magnetic field strength and the gradient of the magnetic field strength.

[0015] In a fifth aspect, a smart device is provided, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method for constructing a defect-magnetic memory signal relationship model in the above-mentioned first aspect or any corresponding technical solution thereof and the magnetic memory detection method in the above-mentioned second aspect or any corresponding technical solution thereof are implemented.

[0016] In the sixth aspect, a computer-readable storage medium is provided, which stores a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the method for constructing a defect and magnetic memory signal relationship model in the above-mentioned first aspect or any corresponding technical solution and the magnetic memory detection method in the above-mentioned second aspect or any corresponding technical solution.

[0017] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0018] In the technical solution of the present application, a magnetic memory signal characteristic value is constructed according to the magnetic memory characteristic quantity, the defect data of the preset structure is simulated, and the magnetic memory signal characteristic value corresponding to the defect of the preset structure is obtained, and then a defect and magnetic memory signal relationship model is constructed based on the relationship between the magnetic memory signal characteristic value and the defect structure. Thus, the purpose of detecting and quantitatively analyzing the defects of the superconducting tape is achieved, and the magnetic memory detection of the superconducting tape using the model is useful to ensure the performance and stability of the superconducting tape, prevent the superconducting tape from failing during the application of the power system, and ensure the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The disclosure of the present application will become easier to understand with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. Among them:

[0020] Figure 1 It is a schematic flow chart of the main steps of a method for constructing a defect and magnetic memory signal relationship model according to an embodiment of the present application;

[0021] Figure 2 It is a schematic flow chart of the main steps of a magnetic memory detection method according to an embodiment of the present application;

[0022] Figure 3 is a schematic diagram of a superconducting tape detection path according to an embodiment of the present application;

[0023] Figure 4 is a schematic diagram of a magnetic field intensity curve at a defect according to an embodiment of the present application;

[0024] Figure 5 It is a schematic diagram of the main structure block diagram of a device for constructing a defect and magnetic memory signal relationship model according to an embodiment of the present application;

[0025] Figure 6 It is a schematic diagram of the main structure block diagram of a magnetic memory detection device according to an embodiment of the present application;

[0026] Figure 7 The figure is a schematic diagram of the connection relationship between the processor and the memory of a smart device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0028] In the description of the present application, the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, it can be indirectly connected through an intermediate medium, it can also be the internal communication of two elements, it can be a wireless connection, or it can be a wired connection.

[0029] In addition, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as program codes, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Computer-readable storage media include any suitable media that can store program codes, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc.

[0030] In addition, if the meaning of "and / or" appears in this application, it includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or solutions that satisfy both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the protection scope required by this application.

[0031] Unlike traditional metal strips, superconducting tapes are limited by the process during preparation and post-processing, and defects such as holes and microcracks will exist in the iron-based superconducting core; and superconducting tapes are prepared into multi-layer composite structures, with uneven interlayer bonding strength, which is very easy to cause interlayer delamination or even cracking. These micro defects are very easy to cause dynamic damage when the superconducting tape is subjected to complex stress (tensile, compression and torsion composite) after carrying current, which brings serious hidden dangers to the functional realization and safety design of superconducting materials. Usually, superconducting tapes can be composed of multiple layers such as substrate layer, buffer layer, superconducting layer, and silver coating. In the preparation process of the tape, coating technology is often used, that is, as the temperature range decreases, different materials are coated on the substrate layer by layer, and finally a superconducting tape is made. In this process, the tape is subjected to external forces, which will produce residual strain; in addition, as the working temperature decreases, the expansion coefficients of each layer of material are different, resulting in thermal mismatch, and then thermal strain. Both residual strain and thermal strain will cause damage to the tape.

[0032] In order to detect defects that may occur in superconducting tape after long-term loading, the present embodiment provides a method for constructing a defect-magnetic memory signal relationship model. Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a method for constructing a defect and magnetic memory signal relationship model according to an embodiment of the present application. It should be noted that this embodiment is mainly aimed at superconducting tapes made of ferromagnetic materials, such as iron-based superconducting tapes. Figure 1 As shown, the method in the embodiment of the present application mainly includes the following steps S2 to S8:

[0033] Step S2, obtaining magnetic memory characteristic quantities, which are used to reflect defects in the superconducting tape.

[0034] In this embodiment, the magnetic memory characteristic quantity is, for example, the magnetic field intensity Hp of the leakage magnetic field and the gradient K of the magnetic field intensity. The leakage magnetic field refers to the magnetic field formed on the surface of the material after the ferromagnetic material is magnetized by the external magnetic field, which is caused by the distortion of the magnetic lines of force due to defects, stress or strain concentration on the surface or near the surface of the material, thereby escaping from the surface of the material. The magnetic field intensity at the defect is a sine wave, and the waveform amplitude gradually increases with the depth; the gradient of the magnetic field intensity reaches a maximum value at the defect. It should be noted that the magnetic field intensity Hp and the gradient K of the magnetic field intensity include components in the tangential (x direction) and normal (y direction) directions, and the gradient of the magnetic field intensity can be obtained by differentiating the magnetic field intensity.

[0035] In one implementation, the magnetic memory characteristic quantity may also be selected from other characteristic quantities that can reflect the defects of the superconducting tape, such as the total gradient modulus of the magnetic memory gradient tensor, the horizontal gradient modulus of the magnetic memory gradient tensor, etc. This embodiment does not impose specific restrictions on this.

[0036] Step S4, determining a magnetic memory signal characteristic value according to the magnetic memory characteristic quantity, and the magnetic memory signal characteristic value is used to quantify the structure of the defect.

[0037] In this embodiment, the magnetic memory characteristic quantity is processed to obtain a magnetic memory signal characteristic value that can quantify the defect structure. As an example, the depth of the crack can be quantitatively evaluated by fitting the peak-to-valley difference data of the magnetic field intensity; the width of the crack can be quantitatively evaluated by fitting the gradient of the magnetic field intensity.

[0038] Step S6, using multi-physics field simulation to determine the magnetic memory signal characteristic value corresponding to the defect data of the preset structure.

[0039] In this embodiment, multi-physics simulation is used to simulate the interaction of superconducting tape in multi-physics fields (such as electromagnetic field, thermal field, mechanical field, etc.), and predict the characteristic value of magnetic memory signal of superconducting tape under the action of multi-physics field. It should be noted that defect data of preset structure can be added to superconducting tape in simulation, such as cracks of preset size, holes, and loads of preset size, to predict the characteristic value of magnetic memory signal corresponding to the above defects.

[0040] Step S8, constructing a defect-magnetic memory signal relationship model based on the defect data and the magnetic memory signal characteristic value corresponding to the defect data.

[0041] In this embodiment, a model representing the relationship between the defect structure and the magnetic memory signal is fitted using defect data and characteristic values ​​of the magnetic memory signal obtained by performing multi-physical field simulation based on the defect data.

[0042] In one embodiment, the defect-magnetic memory signal relationship model can be obtained by training a machine learning model using simulation data. Specifically, the length, width, load size, etc. of the defect can be used as input features, and the characteristic value of the magnetic field memory signal can be used as an output feature to fit a regression model that can represent the relationship between the defect and the magnetic memory signal.

[0043] Based on the method described in steps S2 to S8 above, a magnetic memory signal characteristic value is constructed according to the magnetic memory characteristic quantity, the defect data of the preset structure is simulated, and the magnetic memory signal characteristic value corresponding to the defect of the preset structure is obtained, and then a defect and magnetic memory signal relationship model is constructed based on the relationship between the magnetic memory signal characteristic value and the defect structure. Thus, the purpose of detecting and quantitatively analyzing the defects of the superconducting tape is achieved, and the magnetic memory detection of the superconducting tape using the model is used to ensure the performance and stability of the superconducting tape, prevent the superconducting tape from failing during the application of the power system, and ensure the safe and stable operation of the power system.

[0044] In one implementation of the embodiment of the present application, the magnetic memory characteristic quantity includes the magnetic field intensity of the leakage magnetic field and the gradient of the magnetic field intensity, and the above step S4 may further include the following step S42:

[0045] Step S42, determining the following magnetic memory signal characteristic values ​​based on the magnetic field strength and the gradient of the magnetic field strength:

[0046]

[0047] Where ΔHp represents the peak-to-peak value of the composite magnetic field intensity, ΔHp(x) represents the peak-to-peak value of the tangential magnetic field intensity, and ΔHp(y) represents the peak-to-peak value of the normal magnetic field intensity; ΔHp / Δx represents the rate of change of the composite magnetic field intensity, Δx 1 Indicates the difference between the maximum and minimum tangential magnetic field intensity measurement point coordinates, Δx 2 Indicates the difference between the coordinates of the measuring point with the maximum and minimum normal magnetic field intensity; K a represents the composite average gradient, K a (x) represents the average gradient of the tangential magnetic field strength, K a (y) represents the average gradient of the normal magnetic field strength, K i (x) represents the gradient of the tangential magnetic field intensity measured for the i-th time, K i (y) represents the gradient of the normal magnetic field intensity measured for the i-th time, i∈[1,n], and n represents the total number of measurements.

[0048] Since the gradient of the magnetic field strength is calculated by the differential of the magnetic field strength, this embodiment measures the gradient of the tangential / normal magnetic field strength multiple times to calculate the average gradient of the magnetic field strength, thereby improving the accuracy of the gradient calculation result; in addition, the rate of change of the magnetic field strength can simplify the calculation. When the accuracy requirement is not high or the defect size is large, the rate of change of the magnetic field strength can be used instead of the gradient, thereby improving the calculation efficiency.

[0049] In one implementation of the embodiment of the present application, step S5 may be further included before step S6, specifically including the following steps S52 and S54:

[0050] Step S52, obtaining a preset defect type of the superconducting tape, parameters and dimensions corresponding to the preset defect type, and a preset load.

[0051] Step S54, combining the preset defect type, the parameters and dimensions corresponding to the preset defect type, and the preset load to obtain defect data of the preset structure.

[0052] In this embodiment, Tables 1 to 3 are preset defect data tables:

[0053] Table 1. Preset crack defect table

[0054] Table 2. Preset hole defect table

[0055] Table 3. Preset load table

[0056] It should be noted that the load in Table 3 refers to the external torque acting on the superconducting tape; the number of groups is the number obtained by combining the multi-dimensional sizes of the defects. For example, in Table 1, there are 5 crack sizes of transverse crack length and 3 widths. By combining the length and width, 15 groups of different crack sizes can be obtained; in addition, in order to simplify the experiment, it is assumed that the circular holes, rectangular holes and triangular holes in the hole types are all regular polygons. It can be understood that the above Tables 1 and 3 are just some examples for reference, and the defect types may include others. The corresponding parameters and sizes and preset loads can also be set according to actual conditions.

[0057] In one implementation, based on the preset defect data in Tables 1 to 3 above, multiple groups of simulation experiments are performed to determine the magnetic memory signal characteristic values ​​corresponding to the defect data of the preset structure.

[0058] In one implementation of the embodiment of the present application, the above step S8 may further include the following steps S82 and S84:

[0059] Step S82: training the pre-acquired machine learning model according to the defect data and the magnetic memory signal characteristic values ​​corresponding to the defect data.

[0060] In this embodiment, the above-mentioned defect data is processed by data standardization and normalization as the input feature of the machine learning model, and the magnetic memory signal characteristic value corresponding to the defect data is processed by data standardization and normalization as the output feature of the machine learning model. The model is trained according to the input features and output features, and the degree of fit between the input data and the output data is observed. For example, the R2 value is used as a standard for measuring the degree of fit between the input and output. Specifically, the closer R2 is to 1, the higher the degree of fit and the better the fitting effect. The R2 value is improved by adjusting parameters such as the number of model iterations, learning efficiency, and learning factor.

[0061] Step S84, using the trained machine learning model as a defect and magnetic memory signal relationship model.

[0062] In this embodiment, when the R2 value of the model is greater than a preset threshold, it is considered that the model meets the engineering requirements and the model is used as a defect-magnetic memory signal relationship model.

[0063] Another aspect of the present application also provides a magnetic memory detection method, see the attached Figure 2 , Figure 2 FIG. 1 is a flow chart of the main steps of a magnetic memory detection method according to an embodiment of the present application. Figure 2 As shown, the method mainly includes the following steps S10 and S12:

[0064] Step S10, obtaining the defect-magnetic memory signal relationship model constructed by the first aspect or any corresponding technical solution and the magnetic field intensity and the gradient of the leakage magnetic field of the superconducting tape to be detected.

[0065] In this embodiment, the magnetic field strength and the gradient of the leakage magnetic field obtained after the superconducting tape to be detected is weakly magnetized are obtained, wherein the gradient value can be calculated by averaging multiple measurements, and the magnetic field strength can be collected using a magnetoresistive sensor. Specifically, Figure 3 is a schematic diagram of a superconducting tape detection path according to an embodiment of the present application, such as Figure 3 As shown, the dotted line is the detection path of the superconducting tape, x and y are the coordinate axes to be established, and the magnetic field intensity curve at the defect is as follows Figure 4 As shown. Among them, H(x) and H(y) represent the magnetic field strength curves in the x direction (tangential) and y direction (normal), respectively. The synthetic magnetic field strength can be synthesized by H(x) and H(y) using trigonometric functions, which is used to represent the scalar ΔHp of the magnetic field strength in the x and y planes; ΔHp(x) and ΔHp(y) represent the peak-to-peak value of the local (defect) magnetic field strength in the x direction and the peak-to-peak value of the magnetic field strength in the y direction, respectively; Δx 1 Indicates the difference between the maximum and minimum tangential magnetic field intensity measurement point coordinates, Δx 2 Indicates the difference between the coordinates of the measurement point with the maximum and minimum normal magnetic field intensity.

[0066] Furthermore, the local x-direction and y-direction magnetic field gradient values ​​K(x) and K(y) of the magnetic field intensity can be determined by differentiating the magnetic field intensity:

[0067]

[0068] Step S12, performing defect detection on the superconducting tape to be detected by using a defect-magnetic memory signal relationship model according to the magnetic field intensity and the gradient of the magnetic field intensity.

[0069] In this embodiment, the data such as the magnetic field intensity and the gradient of the magnetic field intensity obtained in step S10 are input into the defect and magnetic memory signal relationship model, and the defect of the superconducting tape to be detected is output using the relationship model.

[0070] In one implementation of the embodiment of the present application, the above step S12 may further include the following steps S122 and S124:

[0071] Step S122, processing the magnetic field intensity and the gradient of the magnetic field intensity to obtain corresponding magnetic memory signal characteristic values.

[0072] In this embodiment, the magnetic field strength and the gradient of the magnetic field strength obtained in step S10 are converted into corresponding magnetic memory signal characteristic values. For the specific calculation method of the magnetic memory signal characteristic value, please refer to Figure 1 Step S4 of the illustrated embodiment will not be described in detail here.

[0073] Step S124, determining the defect type of the superconducting tape to be detected and the parameters and dimensions corresponding to the defect type by using the defect-magnetic memory signal relationship model according to the magnetic memory signal characteristic value.

[0074] In this embodiment, the defect type and the parameters and sizes corresponding to the defect type are inferred based on the processed magnetic memory signal characteristic values ​​using the defect-magnetic memory signal relationship model.

[0075] In one implementation, the stress (load) magnitude can be obtained based on the characteristic value of the magnetic signal, and combined with the stress-strain curve and fatigue life curve of the ferromagnetic material, the life of the superconducting tape can be further predicted.

[0076] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted schemes are equivalent to the technical schemes described in this application, and therefore will also fall within the scope of protection of this application.

[0077] Another aspect of the present application also provides a device for constructing a defect and magnetic memory signal relationship model, including: a feature quantity acquisition module 2, used to obtain magnetic memory feature quantities, which are used to reflect the defects of superconducting tapes; a feature value determination module 4, used to determine the magnetic memory signal feature value based on the magnetic memory feature quantity, which is used to quantify the structure of the defect; a simulation module 6, used to determine the magnetic memory signal feature value corresponding to the defect data of a preset structure using multi-physical field simulation; a model construction module 8, used to construct a defect and magnetic memory signal relationship model based on the defect data and the magnetic memory signal feature value corresponding to the defect data.

[0078] It is understandable that the above defect and magnetic memory signal relationship model construction device is used to perform Figure 1The embodiment of the method for constructing a defect and magnetic memory signal relationship model shown in the figure has similar technical principles, technical problems solved and technical effects produced. Technicians in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and relevant instructions of the device for constructing a defect and magnetic memory signal relationship model can refer to the contents described in the embodiment of the method for constructing a defect and magnetic memory signal relationship model, which will not be repeated here.

[0079] Another aspect of the present application also provides a magnetic memory detection device, including: a model and data acquisition module 10, used to obtain the defect and magnetic memory signal relationship model constructed by the above-mentioned first aspect or any corresponding technical solution, and the magnetic field strength and the gradient of the magnetic field strength of the leakage magnetic field of the superconducting tape to be detected; a detection module 12, used to perform defect detection on the superconducting tape to be detected based on the magnetic field strength and the gradient of the magnetic field strength using the defect and magnetic memory signal relationship model.

[0080] It is understandable that the magnetic memory detection device is used to perform Figure 2 The magnetic memory detection method embodiment shown in the figure has similar technical principles, technical problems solved and technical effects produced. Technicians in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the magnetic memory detection device can refer to the contents described in the embodiment of the magnetic memory detection method, which will not be repeated here.

[0081] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0082] Another aspect of the present application also provides a computer-readable storage medium.

[0083] In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium may be configured to store a program for executing the method for constructing a defect and magnetic memory signal relationship model and a magnetic memory detection method of the above-mentioned method embodiment, and the program may be loaded and run by a processor to implement the method for constructing the above-mentioned defect and magnetic memory signal relationship model and the magnetic memory detection method. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method section of the embodiment of the present application. The computer-readable storage medium may be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

[0084] Another aspect of the present application also provides a smart device.

[0085] In an embodiment of an intelligent device according to the present application, the intelligent device may include at least one processor; and a memory connected to the at least one processor in communication; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any of the above embodiments is implemented. Figure 7 , Figure 7 FIG. 4 exemplarily shows that the memory 11 and the processor 12 are communicatively connected via a bus.

[0086] In some embodiments of the present application, the smart device may further include at least one sensor for sensing information. The sensor is connected to any type of processor mentioned in the present application. Optionally, the smart device described in the present application may be, but is not limited to, a mobile phone, a tablet computer, a desktop, a laptop, a handheld computer, a notebook computer, a vehicle-mounted device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook, a personal digital assistant (personal digital assistant, PDA), an augmented reality (augmented reality, AR)\virtual reality (virtual reality, VR) device, etc., and the embodiments of the present application are not limited to this.

[0087] So far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.

Claims

1. A method for constructing a defect and magnetic memory signal relationship model, characterized in that: The method comprises: Acquiring a magnetic memory characteristic quantity, wherein the magnetic memory characteristic quantity is used to reflect defects of the superconducting tape; Determine a magnetic memory signal characteristic value according to the magnetic memory characteristic quantity, wherein the magnetic memory signal characteristic value is used to quantify the structure of the defect; Determine the characteristic value of the magnetic memory signal corresponding to the defect data of the preset structure by using multi-physics field simulation; A defect-magnetic memory signal relationship model is constructed according to the defect data and the magnetic memory signal characteristic value corresponding to the defect data.

2. The method according to claim 1, characterized in that The magnetic memory characteristic quantity includes the magnetic field intensity of the leakage magnetic field and the gradient of the magnetic field intensity, and the determining of the magnetic memory signal characteristic value according to the magnetic memory characteristic quantity includes: The following magnetic memory signal characteristic values ​​are determined based on the magnetic field strength and the gradient of the magnetic field strength: Wherein, ΔHp represents the peak-to-peak value of the composite magnetic field intensity, ΔHp(x) represents the peak-to-peak value of the tangential magnetic field intensity, and ΔHp(y) represents the peak-to-peak value of the normal magnetic field intensity; ΔHp / Δx represents the rate of change of the composite magnetic field intensity, Δx1 represents the difference between the measurement point coordinates of the maximum and minimum values ​​of the tangential magnetic field intensity, and Δx2 represents the difference between the measurement point coordinates of the maximum and minimum values ​​of the normal magnetic field intensity; K a represents the composite average gradient, K a (x) represents the average gradient of the tangential magnetic field strength, K a (y) represents the average gradient of the normal magnetic field strength, K i (x) represents the gradient of the tangential magnetic field intensity measured for the i-th time, K i (y) represents the gradient of the normal magnetic field intensity measured for the i-th time, i∈[1,n], and n represents the total number of measurements.

3. The method according to claim 1, characterized in that The method further comprises: Obtaining a preset defect type of the superconducting tape, parameters and dimensions corresponding to the preset defect type, and a preset load; The preset defect type, the parameters and dimensions corresponding to the preset defect type, and the preset load are combined to obtain defect data of the preset structure.

4. The method according to claim 1 or 3, characterized in that: The step of constructing a defect-magnetic memory signal relationship model according to the defect data and the magnetic memory signal characteristic value corresponding to the defect data includes: Training a pre-acquired machine learning model according to the defect data and the magnetic memory signal characteristic values ​​corresponding to the defect data; The trained machine learning model is used as the relationship model between the defect and the magnetic memory signal.

5. A magnetic memory detection method, characterized in that: The method comprises: Obtaining a defect-magnetic memory signal relationship model constructed by the method of any one of claims 1 to 4, and the magnetic field strength of the leakage magnetic field of the superconducting tape to be detected and the gradient of the magnetic field strength; Defect detection is performed on the superconducting tape to be detected according to the magnetic field intensity and the gradient of the magnetic field intensity using the defect and magnetic memory signal relationship model.

6. The method according to claim 5, characterized in that The method of performing defect detection on the superconducting tape to be detected by using the defect-magnetic memory signal relationship model according to the magnetic field intensity and the gradient of the magnetic field intensity includes: Processing the magnetic field strength and the gradient of the magnetic field strength to obtain corresponding magnetic memory signal characteristic values; The defect type of the superconducting tape to be detected and the parameters and dimensions corresponding to the defect type are determined according to the magnetic memory signal characteristic value using the defect and magnetic memory signal relationship model.

7. A device for constructing a defect and magnetic memory signal relationship model, characterized in that: The device comprises: A feature quantity acquisition module, used to acquire magnetic memory feature quantities, wherein the magnetic memory feature quantities are used to reflect defects of the superconducting tape; A characteristic value determination module, used to determine a magnetic memory signal characteristic value according to the magnetic memory characteristic quantity, wherein the magnetic memory signal characteristic value is used to quantify the structure of the defect; A simulation module, used to determine the characteristic value of the magnetic memory signal corresponding to the defect data of the preset structure by using multi-physics field simulation; The model building module is used to build a defect and magnetic memory signal relationship model according to the defect data and the magnetic memory signal characteristic value corresponding to the defect data.

8. A magnetic memory detection device, characterized in that: The device comprises: A model and data acquisition module, used to obtain the defect and magnetic memory signal relationship model constructed by any one of the methods of claims 1 to 4 and the magnetic field strength of the leakage magnetic field of the superconducting tape to be detected and the gradient of the magnetic field strength; The detection module is used to perform defect detection on the superconducting tape to be detected according to the magnetic field strength and the gradient of the magnetic field strength by using the defect and magnetic memory signal relationship model.

9. A smart device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, it implements the method for constructing a defect and magnetic memory signal relationship model as described in any one of claims 1 to 4 and the magnetic memory detection method as described in claim 5 or 6.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the method for constructing a defect and magnetic memory signal relationship model described in any one of claims 1 to 4 and the magnetic memory detection method described in claim 5 or 6.

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