Superconducting strip temperature monitoring method and apparatus, electronic device, and medium

Abstract

The present application discloses a superconducting strip temperature monitoring method and apparatus, an electronic device, and a medium. The method and apparatus are applied to an electronic device and are used for monitoring whether a working temperature of the superconducting strip of a superconducting magnet is abnormal. Specifically, the method comprises: acquiring a plurality of working parameters collected from a superconducting magnet and a real-time temperature value of a superconducting strip; inputting the plurality of working parameters into a pre-trained temperature prediction model for calculation to obtain a predicted temperature value of the superconducting strip; and on the basis of a matching degree of the predicted temperature value and the real-time temperature value, determining whether a working temperature is abnormal. The present solution can determine whether the working temperature of the superconducting strip is normal, and when an abnormality occurs, a system or operations and maintenance personnel can take disposal measures in time.

Description

A method, device, electronic device and medium for monitoring temperature of superconducting tape Technical Field

[0001] This application claims priority to a Chinese application filed with the Patent Office of China on December 14, 2023, with application number 202311727579.7 and invention name “A method, device, electronic device and medium for temperature monitoring of superconducting tape”, the entire contents of which are incorporated by reference into this application. Background Art

[0002] Maglev trains generate traction force through the interaction between superconducting magnets and traction coils on the ground, and generate suspension force and guiding force through interaction with figure-8 coils on the ground. Since superconducting magnets are required to generate extremely strong magnetism, and the key to maintaining magnetism of superconducting magnets is that they need to maintain a superconducting state, and since superconducting tapes need to maintain extremely low temperatures for superconducting magnets to work normally, it is necessary to monitor in real time whether the working temperature of the magnets of the superconducting tapes is abnormal, so that timely measures can be taken when the temperature is abnormal.

[0003] Summary of the Invention

[0004] In view of this, the present application provides a method, device, electronic equipment and medium for monitoring the temperature of a superconducting tape, which are used to monitor in real time whether the operating temperature of a superconducting tape of a superconducting magnet of a maglev train is abnormal.

[0005] In order to achieve the above objectives, the following solutions are proposed:

[0006] A temperature monitoring method for a superconducting tape is applied to electronic equipment and is used to monitor whether the operating temperature of the superconducting tape of the superconducting magnet is abnormal. The temperature monitoring method comprises the following steps:

[0007] Acquiring a plurality of operating parameters collected from the superconducting magnet and a real-time temperature value of the superconducting tape;

[0008] Inputting the plurality of operating parameters into a pre-trained temperature prediction model for calculation to obtain a predicted temperature value of the superconducting tape;

[0009] Whether the operating temperature is abnormal is determined based on a degree of matching between the predicted temperature value and the real-time temperature value.

[0010] Optionally, the multiple operating parameters include the vacuum degree in the cavity of the superconducting magnet and the temperature between the cold head and the connecting block, and also include the voltage and magnetic field strength of the superconducting tape.

[0011] Optionally, the determining whether the operating temperature is abnormal based on the degree of matching between the predicted temperature value and the real-time temperature value comprises the steps of:

[0012] Calculating the difference between the predicted temperature value and the real-time temperature value to obtain a temperature residual;

[0013] A judgment is made based on the temperature residual and a preset residual threshold range. If the temperature residual is within the preset residual threshold range, it is determined that the operating temperature is normal; otherwise, it is determined that the operating temperature is abnormal.

[0014] Optionally, before calculating the predicted temperature value, the method further includes the following steps:

[0015] Preprocessing is performed on the multiple working parameters, where the preprocessing includes part or all of deduplication processing, outlier removal processing, and interpolation processing.

[0016] Optionally, the temperature monitoring method further comprises the steps of:

[0017] Constructing a training sample set by acquiring historical data of the superconducting magnet;

[0018] The temperature prediction model is obtained by training a long short-term memory neural network based on the training sample set.

[0019] Optionally, the historical data includes a plurality of historical operating parameters of the superconducting magnet and historical real-time temperature values ​​of the superconducting tape, and the temperature monitoring method further includes the steps of:

[0020] Processing the plurality of historical operating parameters based on the temperature prediction model to obtain historical predicted temperature values;

[0021] The difference between the historical predicted temperature value and the historical real-time temperature value is calculated to obtain a historical residual, and the preset residual threshold range is determined based on the historical residual.

[0022] A temperature monitoring device for a superconducting tape is applied to electronic equipment and is used to monitor whether the operating temperature of the superconducting tape of the superconducting magnet is abnormal. The temperature monitoring device comprises:

[0023] a parameter acquisition module configured to acquire a plurality of operating parameters collected from the superconducting magnet and a real-time temperature value of the superconducting tape;

[0024] A first calculation module is configured to input the plurality of operating parameters into a pre-trained temperature prediction model for calculation to obtain a predicted temperature value of the superconducting tape;

[0025] The state judgment module is configured to determine whether the operating temperature is abnormal based on the matching degree between the predicted temperature value and the real-time temperature value.

[0026] Optionally, also include:

[0027] The preprocessing module is configured to preprocess the multiple operating parameters before the first calculation module calculates the predicted temperature value, and the preprocessing includes part or all of deduplication processing, outlier removal processing and interpolation processing.

[0028] Optionally, the temperature monitoring device further includes:

[0029] A sample set construction module is configured to construct a training sample set by acquiring historical data of the superconducting magnet;

[0030] The model training module is configured to perform training using a long short-term memory neural network based on the training sample set to obtain the temperature prediction model.

[0031] Optionally, the historical data includes a plurality of historical operating parameters of the superconducting magnet and historical real-time temperature values ​​of the superconducting tape, and the temperature monitoring device further includes:

[0032] a prediction processing module, configured to process the plurality of historical operating parameters based on the temperature prediction model to obtain a historical predicted temperature value;

[0033] The second calculation module is configured to calculate the difference between the historical predicted temperature value and the historical real-time temperature value to obtain a historical residual, and determine the preset residual threshold range based on the historical residual.

[0034] An electronic device comprising at least one processor and a memory connected to the processor, wherein:

[0035] The memory is used to store computer programs or instructions;

[0036] The processor is configured to execute the computer program or instruction to enable the electronic device to implement the temperature monitoring method for a superconducting tape as described above.

[0037] A medium is applied to an electronic device, wherein the medium carries one or more computer programs. The computer programs can be executed by the electronic device, thereby enabling the electronic device to implement the temperature monitoring method of a superconducting tape as described above.

[0038] As can be seen from the above technical solutions, this application discloses a method, device, electronic device, and medium for monitoring the temperature of a superconducting tape. This method and device, applied to electronic equipment, are used to monitor whether the operating temperature of a superconducting tape in a superconducting magnet is abnormal. Specifically, the method and device obtain multiple operating parameters collected from the superconducting magnet and the real-time temperature value of the superconducting tape; input the multiple operating parameters into a pre-trained temperature prediction model for calculation to obtain a predicted temperature value for the superconducting tape; and determine whether the operating temperature is abnormal based on the degree of match between the predicted temperature value and the real-time temperature value. This solution can determine whether the operating temperature of the superconducting tape is normal, allowing the system or maintenance personnel to take timely remedial measures when an abnormality occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0040] FIG1 is a flow chart of a method for monitoring the temperature of a superconducting tape according to an embodiment of the present application;

[0041] FIG2 is a flow chart of another method for monitoring the temperature of a superconducting tape according to an embodiment of the present application;

[0042] FIG3 is a flow chart of a method for training a temperature prediction model according to an embodiment of the present application;

[0043] FIG4 is a flow chart of another method for training a temperature prediction model according to an embodiment of the present application;

[0044] FIG5 is a block diagram of a temperature monitoring device for a superconducting tape according to an embodiment of the present application;

[0045] FIG6 is a block diagram of another temperature monitoring device for a superconducting tape according to an embodiment of the present application;

[0046] FIG7 is a block diagram of another temperature monitoring device for a superconducting tape according to an embodiment of the present application;

[0047] FIG8 is a block diagram of another temperature monitoring device for a superconducting tape according to an embodiment of the present application;

[0048] FIG9 is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] The superconducting tape temperature monitoring method and device provided in this application are applied to electronic equipment for monitoring the temperature of superconducting tape in a superconducting magnet to determine whether the superconducting tape's operating temperature is abnormal. The electronic equipment can be understood as a computer, server, cloud platform, or even an embedded device with data computing and information processing capabilities, and the temperature monitoring device can be understood as the electronic equipment itself or a functional module of the electronic equipment. Based on the above premise, this application provides the following specific embodiments.

[0051] FIG1 is a flow chart of a method for monitoring the temperature of a superconducting tape according to an embodiment of the present application.

[0052] As shown in FIG1 , the temperature monitoring method provided in this embodiment is used to monitor the temperature of a superconducting tape of a superconducting magnet. The temperature monitoring method comprises the following steps:

[0053] S101. Acquire multiple operating parameters of a superconducting magnet and a real-time temperature value of a superconducting tape.

[0054] The multiple operating parameters include the vacuum level within the superconducting magnet cavity and the temperature between the cold head and the connecting block, which are key components of the superconducting magnet. They also include the voltage and magnetic field strength of the superconducting tape. These operating parameters and real-time temperature values ​​are detected by sensors located in corresponding locations within the superconducting magnet.

[0055] S103: Input multiple operating parameters into the temperature prediction model for calculation.

[0056] This temperature prediction model extracts a trained neural network model. When implementing the present invention, the temperature prediction model calculates these operating parameters based on the aforementioned operating parameters to obtain a predicted temperature value for the superconducting tape. This predicted temperature value refers to the ideal operating temperature of the superconducting tape under the operating conditions determined by the aforementioned operating parameters. This serves as a benchmark for comparing the operating temperature of the superconducting tape. Theoretically, the closer the actual temperature of the superconducting tape is to the predicted temperature value, the more normal its operating conditions are.

[0057] S105: Determine whether the operating temperature is normal based on the predicted temperature value and the real-time temperature value.

[0058] After obtaining the ideal operating temperature of the superconducting tape, i.e., the predicted temperature value, the predicted temperature value is compared with the real-time temperature value to determine whether the operating temperature of the superconducting tape is normal. This is specifically implemented by the following means:

[0059] First, the difference between the predicted temperature value and the real-time temperature value is calculated to obtain the temperature residual between the two.

[0060] Then, a determination is made as to whether the temperature residual is within a preset residual threshold range [-3σ, 3σ]. If so, the operating temperature of the superconducting tape is determined to be normal; if not, the operating temperature is determined to be abnormal. The preset residual threshold range can be obtained from a temperature prediction model.

[0061] As can be seen from the above technical solution, this embodiment provides a method for monitoring the temperature of a superconducting tape. This method, applied to electronic equipment, is used to monitor whether the operating temperature of a superconducting tape in a superconducting magnet is abnormal. Specifically, the method comprises obtaining multiple operating parameters collected from the superconducting magnet and the real-time temperature of the superconducting tape; inputting these multiple operating parameters into a pre-trained temperature prediction model to calculate a predicted temperature value for the superconducting tape; and determining whether the operating temperature is abnormal based on the degree of match between the predicted temperature value and the real-time temperature value. This solution can determine whether the operating temperature of the superconducting tape is normal, enabling the system or maintenance personnel to take timely remedial measures when an abnormality occurs.

[0062] In addition, the temperature monitoring method provided in this embodiment also includes the following steps, as shown in FIG2 .

[0063] S102: Preprocess multiple working parameters.

[0064] Specifically, the multiple working parameters acquired are processed for deduplication, outlier removal, and interpolation. Deduplication refers to the sensor collecting multiple or multiple data points at a certain rate, and averaging multiple data points per second. Outlier removal involves interpolating the five numbers before and after a particular data point (if there are fewer than five, as many as possible are used). If the error between the interpolated data and the collected data exceeds a reasonable range, the collected data is considered an outlier and replaced with the interpolated data. Interpolation is used to supplement missing data. If a sensor does not collect data in a certain second, the data is supplemented by interpolating the five data points before and after that second (if there are fewer than five, as many as possible are used).

[0065] In addition, this embodiment further includes the following steps, as shown in FIG3 , which are used to train the temperature prediction model required for the above-mentioned temperature prediction.

[0066] S301. Obtain historical data of superconducting magnets and construct a training sample set.

[0067] Specifically, a training sample set is constructed by acquiring historical data from the superconducting magnet. This data includes multiple historical operating parameters and historical real-time temperature values ​​of the superconducting tape. These parameters include the vacuum level within the superconducting magnet cavity, the temperature between the cold head and the connection block, and the voltage and magnetic field strength of the superconducting tape. After obtaining this historical data, it can optionally be preprocessed. This preprocessing is similar to the preprocessing of operating parameters described above and will not be detailed here.

[0068] S302: Perform model training based on the training sample set.

[0069] This application is to input the training sample set into the long short-term memory neural network for model training, thereby obtaining the temperature prediction model. This application uses Keras to build the long short-term memory neural network. For the long short-term memory neural network, a layer of LSTM is set as the hidden layer to accept input, a layer of DENSE is used for output, the loss function uses mean square error, and the optimization algorithm uses Adam. At the same time, in order to prevent overfitting, the Dropout layer is used with a parameter set to 0.2 to enhance the generalization of the model. For the hyperparameters involved, the number of hidden layer neurons is 24, the learning rate is 0.01, the batch size batch_size is 128, and the number of iterations is 100.

[0070] The temperature prediction model can be obtained by the above scheme. In addition, the following steps can also be included, as shown in Figure 4:

[0071] S303: Calculate historical predicted temperature values ​​based on the temperature prediction model.

[0072] That is, the trained temperature prediction model is used to process multiple historical working parameters to obtain historical predicted temperature values.

[0073] S304: Calculate a preset residual threshold range based on the historical predicted temperature value and the historical real-time temperature value.

[0074] That is, calculate the residual between the historical predicted temperature value and the corresponding historical real-time temperature value, and determine the residual threshold range based on the 3σ criterion. The calculation formula of σ is as follows:

[0075] where r i is the residual, and n is the number of samples in the training set. The probability that the value is distributed in [-σ, σ] is 0.6826, the probability that the value is distributed in [-2σ, 2σ] is 0.9544, and the probability that the value is distributed in [-3σ, 3σ] is 0.9974. We can see that the probability that the residual is not in [-3σ, 3σ] is less than 0.3%.

[0076] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0077] Although the operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0078] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0079] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0080] FIG5 is a block diagram of a temperature monitoring process for a superconducting tape according to an embodiment of the present application.

[0081] As shown in FIG5 , the temperature monitoring device provided in this embodiment is used to monitor the temperature of a superconducting tape of a superconducting magnet. The temperature monitoring device includes a parameter acquisition module 10 , a first calculation module 30 , and a state judgment module 50 .

[0082] The parameter acquisition module is used to obtain multiple operating parameters of the superconducting magnet and the real-time temperature value of the superconducting tape.

[0083] The multiple operating parameters include the vacuum level within the superconducting magnet cavity and the temperature between the cold head and the connecting block, which are key components of the superconducting magnet. They also include the voltage and magnetic field strength of the superconducting tape. These multiple operating parameters and real-time temperature values ​​are detected by sensors installed in corresponding locations of the superconducting magnet.

[0084] The first calculation module is used to input multiple operating parameters into the temperature prediction model for calculation.

[0085] This temperature prediction model extracts a trained neural network model. When implementing the present invention, the temperature prediction model calculates these operating parameters based on the aforementioned operating parameters to obtain a predicted temperature value for the superconducting tape. This predicted temperature value refers to the ideal operating temperature of the superconducting tape under the operating conditions determined by the aforementioned operating parameters. This serves as a benchmark for comparing the operating temperature of the superconducting tape. Theoretically, the closer the actual temperature of the superconducting tape is to the predicted temperature value, the more normal its operating conditions are.

[0086] The status judgment module is used to judge whether the operating temperature is normal based on the predicted temperature value and the real-time temperature value.

[0087] After obtaining the ideal operating temperature of the superconducting tape, i.e., the predicted temperature value, the predicted temperature value is compared with the real-time temperature value to determine whether the operating temperature of the superconducting tape is normal. This is specifically implemented by the following means:

[0088] First, the difference between the predicted temperature value and the real-time temperature value is calculated to obtain the temperature residual between the two.

[0089] Then, a determination is made as to whether the temperature residual is within a preset residual threshold range [-3σ, 3σ]. If so, the operating temperature of the superconducting tape is determined to be normal; if not, the operating temperature is determined to be abnormal. The preset residual threshold range can be obtained from a temperature prediction model.

[0090] As can be seen from the above technical solution, this embodiment provides a superconducting tape temperature monitoring device, applicable to electronic equipment, for monitoring whether the operating temperature of a superconducting tape in a superconducting magnet is abnormal. Specifically, the device obtains multiple operating parameters collected from the superconducting magnet and the real-time temperature of the superconducting tape; inputs these operating parameters into a pre-trained temperature prediction model to calculate a predicted temperature value for the superconducting tape; and determines whether the operating temperature is abnormal based on the degree of match between the predicted temperature value and the real-time temperature value. This solution can determine whether the operating temperature of the superconducting tape is normal, allowing the system or maintenance personnel to take timely remedial measures when an abnormality occurs.

[0091] In addition, the temperature monitoring device provided in this embodiment further includes a pre-processing module 20 , as shown in FIG6 .

[0092] The preprocessing module is used to preprocess multiple working parameters.

[0093] Specifically, the multiple working parameters acquired are processed for deduplication, outlier removal, and interpolation. Deduplication refers to the sensor collecting multiple or multiple data points at a certain rate, and averaging multiple data points per second. Outlier removal involves interpolating the five numbers before and after a particular data point (if there are fewer than five, as many as possible are used). If the error between the interpolated data and the collected data exceeds a reasonable range, the collected data is considered an outlier and replaced with the interpolated data. Interpolation is used to supplement missing data. If a sensor does not collect data in a certain second, the data is supplemented by interpolating the five data points before and after that second (if there are fewer than five, as many as possible are used).

[0094] In addition, this embodiment further includes a sample set construction module 31 and a model training module 32, as shown in FIG7 , for training the temperature prediction model required for the above-mentioned temperature prediction.

[0095] The sample set construction module is used to obtain historical data of superconducting magnets and construct a training sample set.

[0096] Specifically, a training sample set is constructed by acquiring historical data from the superconducting magnet. This data includes multiple historical operating parameters and historical real-time temperature values ​​of the superconducting tape. These parameters include the vacuum level within the superconducting magnet cavity, the temperature between the cold head and the connection block, and the voltage and magnetic field strength of the superconducting tape. After obtaining this historical data, it can optionally be preprocessed. This preprocessing is similar to the preprocessing of operating parameters described above and will not be detailed here.

[0097] The model training module is used to train the model based on the training sample set.

[0098] This application is to input the training sample set into the long short-term memory neural network for model training, thereby obtaining the temperature prediction model. This application uses Keras to build the long short-term memory neural network. For the long short-term memory neural network, a layer of LSTM is set as the hidden layer to accept input, a layer of DENSE is used for output, the loss function uses mean square error, and the optimization algorithm uses Adam. At the same time, in order to prevent overfitting, the Dropout layer is used with a parameter set to 0.2 to enhance the generalization of the model. For the hyperparameters involved, the number of hidden layer neurons is 24, the learning rate is 0.01, the batch size batch_size is 128, and the number of iterations is 100.

[0099] The temperature prediction model can be obtained by the above scheme. In addition, it can also include a prediction processing module 33 and a second calculation module 34, as shown in Figure 8:

[0100] The prediction processing module is used to calculate historical predicted temperature values ​​based on the temperature prediction model.

[0101] That is, the trained temperature prediction model is used to process multiple historical working parameters to obtain historical predicted temperature values.

[0102] The second calculation module is used to calculate a preset residual threshold range based on the historical predicted temperature value and the historical real-time temperature value.

[0103] That is, calculate the residual between the historical predicted temperature value and the corresponding historical real-time temperature value, and determine the residual threshold range based on the 3σ criterion. The calculation formula of σ is as follows:

[0104] where r i is the residual, and n is the number of samples in the training set. The probability that the value is distributed in [-σ, σ] is 0.6826, the probability that the value is distributed in [-2σ, 2σ] is 0.9544, and the probability that the value is distributed in [-3σ, 3σ] is 0.9974. We can see that the probability that the residual is not in [-3σ, 3σ] is less than 0.3%.

[0105] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses."

[0106] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0107] FIG9 is a block diagram of an electronic device according to an embodiment of the present application.

[0108] Reference is now made to FIG9 , which illustrates a schematic diagram of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. This electronic device is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0109] The electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 902 or a program loaded from an input device 906 into a random access memory RAM 903. Various programs and data required for the operation of the electronic device are also stored in the RAM. The processing device, ROM, and RAM are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0110] Typically, the following devices may be connected to the I / O interface: input devices including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 907 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 908 including, for example, a magnetic tape, hard disk, etc.; and communication devices 909. Communication devices 909 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figures illustrate electronic devices with various devices, it should be understood that not all of the devices shown are required to be implemented or present. More or fewer devices may be implemented or present instead.

[0111] This application also provides an embodiment of a computer-readable medium, which is applied to an electronic device and carries one or more computer programs. When the one or more computer programs are executed by the electronic device, the electronic device monitors whether the operating temperature of the superconducting tape of the superconducting magnet is abnormal. Specifically, the electronic device obtains multiple operating parameters collected from the superconducting magnet and the real-time temperature value of the superconducting tape; inputs the multiple operating parameters into a pre-trained temperature prediction model for calculation to obtain a predicted temperature value of the superconducting tape; and determines whether the operating temperature is abnormal based on the degree of match between the predicted temperature value and the real-time temperature value. This solution can determine whether the operating temperature of the superconducting tape is normal, and enables the system or operation and maintenance personnel to take timely remedial measures when an abnormality occurs.

[0112] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0113] In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0114] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0115] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0116] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0117] The technical solution provided by the present invention is introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for monitoring the temperature of a superconducting tape, applied to electronic equipment, for monitoring whether the operating temperature of the superconducting tape of the superconducting magnet is abnormal, characterized in that: The temperature monitoring method comprises the steps of: Acquiring a plurality of operating parameters collected from the superconducting magnet and a real-time temperature value of the superconducting tape; Inputting the plurality of operating parameters into a pre-trained temperature prediction model for calculation to obtain a predicted temperature value of the superconducting tape; Whether the operating temperature is abnormal is determined based on a matching degree between the predicted temperature value and the real-time temperature value.

2. The temperature monitoring method according to claim 1, characterized in that: The plurality of operating parameters include the vacuum degree in the cavity of the superconducting magnet and the temperature between the cold head and the connection block, and also include the voltage and magnetic field strength of the superconducting tape.

3. The temperature monitoring method according to claim 1, characterized in that: The step of determining whether the operating temperature is abnormal based on the matching degree between the predicted temperature value and the real-time temperature value comprises the following steps: Calculating the difference between the predicted temperature value and the real-time temperature value to obtain a temperature residual; A judgment is made based on the temperature residual and a preset residual threshold range. If the temperature residual is within the preset residual threshold range, it is determined that the operating temperature is normal; otherwise, it is determined that the operating temperature is abnormal.

4. The temperature monitoring method according to claim 1, characterized in that: Before calculating the predicted temperature value, the method further comprises the following steps: The plurality of working parameters are preprocessed, wherein the preprocessing includes part or all of deduplication processing, outlier removal processing and interpolation processing.

5. The temperature monitoring method according to any one of claims 1 to 4, characterized in that: The temperature monitoring method further comprises the steps of: Constructing a training sample set by acquiring historical data of the superconducting magnet; The temperature prediction model is obtained by training a long short-term memory neural network based on the training sample set.

6. The temperature prediction method according to claim 5, characterized in that: The historical data includes a plurality of historical operating parameters of the superconducting magnet and historical real-time temperature values ​​of the superconducting tape, and the temperature monitoring method further includes the steps of: Processing the plurality of historical operating parameters based on the temperature prediction model to obtain historical predicted temperature values; The difference between the historical predicted temperature value and the historical real-time temperature value is calculated to obtain a historical residual, and the preset residual threshold range is determined based on the historical residual.

7. A temperature monitoring device for a superconducting tape, applied to electronic equipment, for monitoring whether the operating temperature of the superconducting tape of the superconducting magnet is abnormal, characterized in that: The temperature monitoring device comprises: A parameter acquisition module, configured to acquire a plurality of operating parameters collected from the superconducting magnet and a real-time temperature value of the superconducting tape; A first calculation module is configured to input the plurality of operating parameters into a pre-trained temperature prediction model for calculation to obtain a predicted temperature value of the superconducting tape; The state judgment module is configured to determine whether the operating temperature is abnormal based on the matching degree between the predicted temperature value and the real-time temperature value.

8. The temperature monitoring device according to claim 7, characterized in that: Also includes: The preprocessing module is configured to preprocess the multiple operating parameters before the first calculation module calculates the predicted temperature value, and the preprocessing includes part or all of deduplication processing, outlier removal processing and interpolation processing.

9. The temperature monitoring device according to claim 7 or 8, characterized in that: The temperature monitoring device also includes: A sample set building module is configured to build a training sample set by acquiring historical data of the superconducting magnet; The model training module is configured to perform training using a long short-term memory neural network based on the training sample set to obtain the temperature prediction model.

10. The temperature prediction device according to claim 9, characterized in that: The historical data includes a plurality of historical operating parameters of the superconducting magnet and historical real-time temperature values ​​of the superconducting tape, and the temperature monitoring device further includes: A prediction processing module, configured to process the plurality of historical operating parameters based on the temperature prediction model to obtain a historical predicted temperature value; The second calculation module is configured to calculate the difference between the historical predicted temperature value and the historical real-time temperature value to obtain a historical residual, and determine the preset residual threshold range based on the historical residual.

11. An electronic device, characterized in that: The electronic device comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instruction so that the electronic device implements the temperature monitoring method of the superconducting tape according to any one of claims 1 to 6.

12. A medium, applied to electronic equipment, characterized in that: The medium carries one or more computer programs, and the computer programs can be executed by the electronic device, so that the electronic device implements the temperature monitoring method of the superconducting tape according to any one of claims 1 to 6.

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