Superconducting magnet low-temperature performance testing method, system and equipment based on controllable nuclear fusion and medium

Abstract

The invention relates to a superconducting magnet low-temperature performance testing method, system and device based on controllable nuclear fusion and a medium. The method comprises the following steps: acquiring multi-source original data and active excitation signal data of a to-be-detected magnet; based on a preset unified sampling frequency parameter, performing time synchronization processing on the multi-source original data and the active excitation signal data to obtain synchronous multi-source data; extracting change characteristics of the synchronous multi-source data to obtain multi-source change rate data; based on the synchronous multi-source data, the unified sampling frequency parameter and a preset virtual disturbance sequence, calculating a critical state parameter of the magnet to be measured; the critical state parameters comprise equivalent critical temperature and equivalent critical current; generating a test adjustment instruction based on the multi-source change rate data, the critical state parameters and the synchronous multi-source data; the test adjustment instruction is used for indicating to adjust the test state of the to-be-tested magnet. By adopting the method, the test efficiency and accuracy can be improved.

Description

Technical Field

[0001] This invention belongs to the field of superconducting magnet testing, and in particular relates to a method, system, equipment and medium for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion. Background Technology

[0002] Currently, cryogenic superconducting magnets are widely used in controlled nuclear fusion devices to generate strong magnetic fields to confine high-temperature plasma. Existing cryogenic performance tests of superconducting magnets are typically conducted in liquid helium or other cryogenic cooling environments. By applying an excitation current to the sample and monitoring its voltage changes, the critical current for the transition from superconductivity to normal conduction is determined. This type of testing method generally employs a multi-channel data acquisition system to record parameters such as current, voltage, temperature, magnetic field, and strain, and then performs data alignment and analysis after the test.

[0003] However, existing technologies have significant shortcomings in multi-channel data processing. Each acquisition channel typically has different sampling frequencies and independent time bases, resulting in inconsistent timestamps for the raw data. After testing, manual or software alignment and resampling are required. This post-processing is complex and prone to errors, hindering real-time computation and online control. Furthermore, the inability to guarantee time synchronization between different channels affects the accuracy of subsequent model building, risk calculation, and adaptive control based on real-time data. This makes it difficult to meet the demands for real-time response and precise analysis in complex low-temperature performance testing, leading to reduced testing efficiency and accuracy. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, system, equipment, and medium for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion, which can improve testing efficiency and accuracy, in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion, including:

[0006] Acquire multi-source raw data and active excitation signal data of the magnet under test; the multi-source raw data includes at least excitation current data, voltage data, temperature data, and magnetic field data; the excitation current data, voltage data, temperature data, magnetic field data, and active excitation signal data all have sampling timestamps;

[0007] Based on preset unified sampling frequency parameters, time synchronization processing is performed on multi-source raw data and active excitation signal data to obtain synchronized multi-source data; and the variation characteristics of synchronized multi-source data are extracted to obtain multi-source change rate data; the multi-source change rate data includes at least voltage change rate data, temperature change rate data, stability change rate parameter and synchronized ohmic power data; the synchronized multi-source data includes at least synchronized temperature data, synchronized magnetic field data, synchronized electric field data, synchronized excitation current data and synchronized active excitation signal data;

[0008] Based on synchronous multi-source data, unified sampling frequency parameters, and a preset virtual perturbation sequence, the critical state parameters of the magnet under test are calculated; the critical state parameters include the equivalent critical temperature and the equivalent critical current.

[0009] Test adjustment instructions are generated based on multi-source rate of change data, critical state parameters, and synchronous multi-source data; these instructions are used to instruct on adjusting the test state of the magnet under test.

[0010] Furthermore, based on a preset unified sampling frequency parameter, time synchronization processing is performed on the multi-source raw data and the active excitation signal data to obtain synchronized multi-source data; and the variation characteristics of the synchronized multi-source data are extracted to obtain multi-source change rate data, including:

[0011] Based on multi-source raw data, a sampling timestamp that meets the preset synchronization reference timestamp requirements is selected from the multi-source raw data and active excitation signal data and used as a unified reference sampling timestamp.

[0012] Based on a unified sampling frequency parameter and a unified reference sampling timestamp, time-unified processing is performed on multi-source raw data and active excitation signal data to obtain synchronized multi-source data; the synchronized multi-source data includes synchronized excitation current data, synchronized voltage data, synchronized temperature data, synchronized magnetic field data, and synchronized active excitation signal data;

[0013] Based on the synchronization voltage data and the preset voltage sampling length, the synchronization electric field data is calculated using the following formula:

[0014]

[0015] in, It is synchronous electric field data. It is synchronous voltage data. It is the voltage sampling length;

[0016] Based on synchronous electric field data, synchronous excitation current data, synchronous voltage data, and synchronous temperature data, the synchronous rate of change solution data is obtained.

[0017] Based on the synchronization voltage data and synchronization excitation current data, the synchronization ohmic power data is calculated using the following formula:

[0018]

[0019] in, It is synchronous ohmic power data. It is synchronous excitation current data. It is synchronous voltage data;

[0020] Based on a unified sampling frequency parameter and a preset derivative weighting coefficient, the rate of change of the synchronous rate of change solution data is calculated to obtain synchronous rate of change data; the synchronous rate of change data includes current rate of change data, voltage rate of change data, temperature rate of change data, and electric field rate of change data;

[0021] Based on the preset stability calculation time window, synchronous excitation current data, and synchronous electric field data, the stability of the magnet under test is quantified to obtain the stability change rate parameter; and based on the synchronous change rate data, the stability change rate parameter, and the synchronous ohmic power data, multi-source change rate data is obtained.

[0022] Furthermore, the virtual disturbance sequence includes the extreme condition pulse sequence and the extreme condition magnetic disturbance sequence;

[0023] Based on synchronized multi-source data, unified sampling frequency parameters, and a preset virtual perturbation sequence, the critical state parameters of the magnet under test are calculated, including:

[0024] Based on the synchronous active excitation signal data, the synchronous temperature data is segmented to obtain sampled temperature data; the synchronous ohmic power data is segmented to obtain sampled ohmic power data.

[0025] Based on sampled temperature data, sampled ohmic power data, unified sampling frequency parameters, and synchronous active excitation signal data, the temperature response of the magnet under test is quantified to obtain the thermal pulse response function.

[0026] The equivalent temperature rise data is calculated based on the thermal pulse response function, unified sampling frequency parameters, and extreme condition pulse sequence; the extreme condition pulse sequence includes the preset excitation signal strength and the corresponding sampling timestamp.

[0027] Based on synchronous multi-source data, synchronous temperature data and equivalent temperature rise data are fused to obtain the equivalent critical temperature;

[0028] Based on synchronous magnetic field data, equivalent critical temperature, and a preset baseline critical current function, the baseline critical current is calculated.

[0029] Based on the extreme operating condition magnetic disturbance sequence, the baseline critical current is adjusted to obtain the equivalent critical current; and based on the equivalent critical temperature and equivalent critical current, the critical state parameters are obtained.

[0030] Furthermore, based on sampled temperature data, sampled ohmic power data, unified sampling frequency parameters, and synchronous active excitation signal data, the temperature response of the magnet under test is quantified to obtain the thermal pulse response function, including:

[0031] Based on a unified sampling frequency parameter, the sampled temperature data, sampled ohmic power data, and synchronous active excitation signal data with the same sampling timestamp are combined to obtain a function-constructed data vector for each sampling timestamp.

[0032] Based on the preset initialization model parameters and functions, a data vector is constructed, and the thermal pulse response equations for each sampling time stamp are obtained.

[0033] Based on the preset forgetting factor and thermal impulse response equation, the gain coefficient of the thermal impulse response equation for each sampling time stamp is iteratively solved to obtain the gain matrix;

[0034] The dynamic model parameters are obtained by updating the initial model parameters based on the gain matrix; the dynamic model parameters include at least the first model parameters and the second model parameters.

[0035] Based on the dynamic model parameters, the unified sampling frequency parameters, and the preset delay time length parameters, the thermal pulse response function is constructed.

[0036] The thermal pulse response function has the following form:

[0037]

[0038] in, It is the thermal impulse response function. It is a unified sampling frequency parameter. It is the delay time length parameter. It is any delay time length. These are the first model parameters. These are the parameters of the second model.

[0039] Furthermore, based on the thermal pulse response function, unified sampling frequency parameters, and extreme condition pulse sequence, equivalent temperature rise data are calculated, including:

[0040] Based on the thermal pulse response function, a unified sampling frequency parameter, and the extreme condition pulse sequence, the equivalent temperature rise data is calculated using the following formula:

[0041]

[0042] in, It is any sampling timestamp in the extreme condition pulse sequence. The sampling timestamp is Equivalent temperature rise data at that time It is the delay time length parameter. It is any delay time length. It is the thermal impulse response function. It is a unified sampling frequency parameter. It is the pulse sequence under extreme conditions at the sampling timestamp The intensity of the excitation signal.

[0043] Furthermore, test adjustment instructions are generated based on multi-source rate of change data, critical state parameters, and synchronous multi-source data, including:

[0044] Based on multi-source rate of change data and critical state parameters, voltage rate of change data, temperature rate of change data, stability rate of change parameters and equivalent critical temperature are integrated to obtain comprehensive risk parameters.

[0045] Based on the preset comprehensive risk threshold parameter, the difference between the comprehensive risk parameter and the comprehensive risk threshold parameter is calculated to obtain the risk deviation value;

[0046] Based on the risk deviation value, stability change rate parameter and preset excitation current adjustment range threshold, the adjustment amount of synchronous excitation current data is analyzed to obtain the excitation current adjustment value.

[0047] Based on the synchronous excitation current data and critical state parameters, the difference between the synchronous excitation current data and the equivalent critical current is calculated to obtain the excitation current difference; and the excitation current difference is compared with the preset excitation current difference threshold to obtain the excitation current difference comparison result.

[0048] Based on the excitation current adjustment value, the comparison result of the excitation current difference, the risk deviation value, and the synchronous electric field data, a test adjustment command is generated.

[0049] Secondly, this application also provides a low-temperature performance testing system for superconducting magnets based on controlled nuclear fusion, comprising:

[0050] The data acquisition module is used to acquire multi-source raw data and active excitation signal data of the magnet under test; the multi-source raw data includes at least excitation current data, voltage data, temperature data and magnetic field data; the excitation current data, voltage data, temperature data, magnetic field data and active excitation signal data all have sampling timestamps;

[0051] The data synchronization module is used to perform time synchronization processing on multi-source raw data and active excitation signal data based on preset unified sampling frequency parameters to obtain synchronized multi-source data; and to extract the variation characteristics of the synchronized multi-source data to obtain multi-source change rate data; the multi-source change rate data includes at least voltage change rate data, temperature change rate data, stability change rate parameters, and synchronized ohmic power data; the synchronized multi-source data includes at least synchronized temperature data, synchronized magnetic field data, synchronized electric field data, synchronized excitation current data, and synchronized active excitation signal data;

[0052] The critical parameter calculation module is used to calculate the critical state parameters of the magnet under test based on synchronous multi-source data, unified sampling frequency parameters, and a preset virtual disturbance sequence; the critical state parameters include the equivalent critical temperature and the equivalent critical current.

[0053] The test instruction module is used to generate test adjustment instructions based on multi-source rate of change data, critical state parameters, and synchronous multi-source data; the test adjustment instructions are used to instruct the adjustment of the test state of the magnet under test.

[0054] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the methods for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion as described in the first aspect of this application.

[0055] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any of the methods for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion as described in the first aspect of this application.

[0056] The aforementioned method, system, equipment, and medium for testing the cryogenic performance of superconducting magnets based on controlled nuclear fusion acquire multi-source raw data and active excitation signal data of the magnet under test; based on a preset unified sampling frequency parameter, the multi-source raw data and active excitation signal data are time-synchronized to obtain synchronized multi-source data; the variation characteristics of the synchronized multi-source data are extracted to obtain multi-source rate of change data; based on the synchronized multi-source data, the unified sampling frequency parameter, and a preset virtual perturbation sequence, the critical state parameters of the magnet under test are calculated; the critical state parameters include the equivalent critical temperature and the equivalent critical current; test adjustment instructions are generated based on the multi-source rate of change data, critical state parameters, and synchronized multi-source data; the test adjustment instructions are used to instruct the adjustment of the test state of the magnet under test. This transforms the traditional offline static analysis mode of "test first, then align" into a closed-loop dynamic testing mode of "real-time synchronization, online modeling, virtual simulation, and intelligent control," solving the problem of real-time calculation and control failure caused by asynchronous multi-source data, and improving the safety, accuracy, and intelligence level of cryogenic performance testing of superconducting magnets. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A schematic flowchart illustrating a method for testing the low-temperature performance of a superconducting magnet based on controlled nuclear fusion, provided as an embodiment of this application;

[0059] Figure 2 A schematic flowchart illustrating a method for testing the low-temperature performance of a superconducting magnet based on controlled nuclear fusion, provided as an embodiment of this application;

[0060] Figure 3 This is a schematic diagram of a low-temperature performance testing system for a superconducting magnet based on controlled nuclear fusion, provided as an embodiment of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] First, a brief introduction to the terms used in the embodiments of this application will be given.

[0063] Excitation current is a DC or pulsed current injected into an electromagnetic coil (such as a superconducting magnet) to generate a magnetic field of the required strength. It is the energy source for exciting the magnetic field, and its magnitude directly determines the strength of the generated magnetic field. It is a core parameter for the operation and control of the superconducting magnet.

[0064] A superconducting magnet is an electromagnetic coil wound using superconducting materials. When cooled below its critical temperature, the coil resistance drops to zero, allowing it to carry extremely large direct currents without Joule heat loss and generate a steady-state strong magnetic field far exceeding that of conventional electromagnets. It is a core component of high-end equipment such as controlled nuclear fusion devices, particle accelerators, and magnetic resonance imaging.

[0065] In one embodiment, such as Figure 1As shown, a method for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps S101-S104, wherein:

[0066] S101, acquire multi-source raw data and active excitation signal data of the magnet under test; the multi-source raw data includes at least excitation current data, voltage data, temperature data and magnetic field data; the excitation current data, voltage data, temperature data, magnetic field data and active excitation signal data all have sampling timestamps.

[0067] Specifically, the terminal acquires multi-source raw data from the superconducting magnet under test by connecting to various sensors and signal sources connected to the testing platform. This multi-source raw data includes at least excitation current data, voltage data, temperature data, and magnetic field data, all of which are timestamped. Specifically, the excitation current data is the excitation current value flowing through the magnet under test, acquired through a high-precision current sensor or power monitoring interface; this data is presented as a series of numerical pairs with high-precision clock timestamps. , For sampling timestamps, The current instantaneous value at the sampling timestamp is expressed in amperes (A). Voltage data consists of voltage values ​​measured using a nanovoltmeter or a high-precision differential voltage acquisition card, obtained from the specific voltage lead terminals of the magnet under test. The data format is as follows: , For sampling timestamps, The voltage instantaneous value at the sampling timestamp is in volts (V). Temperature data are collected by low-temperature compatible temperature sensors placed at key locations on the magnet (such as winding hotspots), and the data format is... , For sampling timestamps, The temperature is the instantaneous value at the sampling timestamp, in Kelvin (K). The magnetic field data is the magnetic field strength of the background environment surrounding the magnet under test, measured by a Hall probe or fluxmeter, and is in the form of... , For sampling timestamps, This represents the instantaneous value of the magnetic flux density at the sampling timestamp, in Tesla (T). The active excitation signal data is a known, small-amplitude thermal disturbance power signal applied to the magnet under test by a separate, low-power heater driven by the control terminal at the initial stage of testing. This signal is used to excite the magnet's thermal response for model identification, and its data format is a sequence with start and end timestamps. ,in for The excitation signal strength at any given time, expressed in watts per cubic meter (W / m³).

[0068] S102, based on preset unified sampling frequency parameters, performs time synchronization processing on multi-source raw data and active excitation signal data to obtain synchronized multi-source data; and extracts the variation characteristics of synchronized multi-source data to obtain multi-source change rate data; the multi-source change rate data includes at least voltage change rate data, temperature change rate data, stability change rate parameter and synchronized ohmic power data; the synchronized multi-source data includes at least synchronized temperature data, synchronized magnetic field data, synchronized electric field data, synchronized excitation current data and synchronized active excitation signal data.

[0069] Specifically, the terminal performs time synchronization preprocessing on the acquired asynchronous multi-source data according to a preset terminal configuration to establish a unified analysis benchmark. The synchronized multi-source data includes at least synchronized temperature data, synchronized magnetic field data, synchronized electric field data, synchronized excitation current data, and synchronized active excitation signal data. The terminal then analyzes the variation characteristics of the synchronized multi-source data to obtain multi-source rate-of-change data. This multi-source rate-of-change data includes at least voltage rate-of-change data, temperature rate-of-change data, stability rate-of-change parameters, and synchronized ohmic power data. The preset unified sampling frequency parameter controls the sampling timestamp interval of the synchronized data and can be set based on the minimum sampling interval of each data point in the original multi-source data. This embodiment does not further limit the setting of this preset unified sampling frequency parameter. The synchronized temperature data, synchronized magnetic field data, synchronized electric field data, synchronized excitation current data, and synchronized active excitation signal data are data with unified sampling timestamps. The synchronized electric field data is obtained by dividing the synchronized voltage data by a preset voltage sampling length and is used as a standardized criterion for superconducting testing. The synchronized ohmic power data characterizes the Joule heat source intensity inside the magnet. The stability rate of change parameter is a key risk perception indicator, characterizing the instantaneous sensitivity of the electric field to changes in current. Voltage rate of change data is used to characterize the voltage changes of the magnet under test during the test. Temperature rate of change data is used to characterize the temperature changes of the magnet under test during the test.

[0070] S103 calculates the critical state parameters of the magnet under test based on synchronous multi-source data, unified sampling frequency parameters, and a preset virtual disturbance sequence; the critical state parameters include the equivalent critical temperature and the equivalent critical current.

[0071] Specifically, the terminal calculates the performance boundary of the magnet under assumed harsh operating conditions based on synchronous multi-source data and a preset virtual disturbance sequence, obtaining critical state parameters including the equivalent critical temperature and equivalent critical current. The preset virtual disturbance sequence includes an excitation signal characterizing the extreme operating conditions, which can be set according to actual operation; the equivalent critical temperature characterizes the heating effect on the magnet under test under extreme conditions. The terminal also calculates the equivalent critical current based on the equivalent critical temperature. The equivalent critical current characterizes the critical excitation current value that the magnet under test may currently possess under the worst-case assumption of superimposed equivalent critical temperature and magnetic disturbance.

[0072] S104 generates test adjustment instructions based on multi-source rate of change data, critical state parameters, and synchronous multi-source data; the test adjustment instructions are used to indicate the adjustment of the test state of the magnet under test.

[0073] Specifically, the terminal comprehensively analyzes the test risks of the magnet under test by integrating multi-source change rate data, critical state parameters, and synchronous multi-source data, and generates test adjustment instructions. The test adjustment instructions are used to indicate the adjustment of the test state of the magnet under test, including instructions to stop the test and instructions to adjust the excitation current data.

[0074] This embodiment provides a method for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion. By synchronizing multi-source raw data and active excitation signal data along a unified time axis, synchronous multi-source data and multi-source rate-of-change data are obtained. The method then uses active excitation to identify a thermal model specific to the magnet under test online. Based on this model and a preset virtual perturbation sequence, the equivalent critical temperature and equivalent critical current of the magnet under extreme hypothetical operating conditions are calculated in real time, yielding critical state parameters. Furthermore, the method dynamically generates test adjustment commands by integrating multi-source rate-of-change data, critical state parameters, and synchronous multi-source data. This represents a shift from traditional offline static analysis to real-time synchronous closed-loop dynamic testing, solving the problem of real-time calculation and control failure.

[0075] In one embodiment, based on a preset unified sampling frequency parameter, time synchronization processing is performed on multi-source raw data and active excitation signal data to obtain synchronized multi-source data; and the variation characteristics of the synchronized multi-source data are extracted to obtain multi-source change rate data, including:

[0076] S201, based on multi-source raw data, selects a sampling timestamp that meets the preset synchronization reference timestamp requirements from the multi-source raw data and active excitation signal data, and uses it as a unified reference sampling timestamp.

[0077] Specifically, the preset synchronization reference timestamp requirement is that the sampling timestamp be the earliest sampling timestamp from multiple sources. The terminal uses the earliest sampling timestamp from both the raw data and the active excitation signal data as the unified reference sampling timestamp, based on the preset synchronization reference timestamp requirement.

[0078] S202, based on unified sampling frequency parameters and unified reference sampling timestamps, performs time-unified processing on multi-source raw data and active excitation signal data to obtain synchronized multi-source data; the synchronized multi-source data includes synchronized excitation current data, synchronized voltage data, synchronized temperature data, synchronized magnetic field data and synchronized active excitation signal data.

[0079] Specifically, the terminal uses the following formula: The uniform sampling interval is calculated based on the uniform sampling frequency parameter. It is a uniform sampling interval. It uses a unified sampling frequency parameter. The terminal uses a unified reference sampling timestamp. Starting from a uniform sampling interval Generate a uniform time series with a step size of 1. For each generated target time... The terminal uses an interpolation algorithm to process multi-source raw data and active excitation signal data. Estimate the value at time [time] to obtain its [time]. Approximate value of time This process is repeated for each data point in the multi-source raw data and active excitation signal data, ultimately yielding the final result. Strictly aligned synchronous excitation current data Synchronous voltage data Synchronized temperature data Synchronous magnetic field data and synchronous active excitation signal data .

[0080] S203, based on the synchronization voltage data and the preset voltage sampling length, calculates the synchronization electric field data using the following formula:

[0081]

[0082] in, It is synchronous electric field data. It is synchronous voltage data. It is the voltage sampling length.

[0083] Specifically, the terminal uses a formula to calculate the synchronization electric field data based on the synchronization voltage data and a preset voltage sampling length. The synchronization voltage data... This is the synchronization voltage data obtained through S202 synchronization processing. The preset voltage sampling length... It is a fixed geometric parameter representing the effective span of the voltage measurement lead on the winding of the magnet under test, in meters (m), and can be set according to the actual situation of the magnet under test.

[0084] S204, based on synchronous electric field data, synchronous excitation current data, synchronous voltage data, and synchronous temperature data, obtains synchronous rate of change solution data.

[0085] Specifically, the terminal splices together the synchronous electric field data, synchronous excitation current data, synchronous voltage data, and synchronous temperature data to obtain the synchronous rate of change solution data.

[0086] Based on the synchronous voltage data and synchronous excitation current data, the synchronous ohmic power data is calculated using the following formula:

[0087]

[0088] in, It is synchronous ohmic power data. It is synchronous excitation current data. It is synchronous voltage data.

[0089] Specifically, the terminal uses a formula to calculate the synchronous ohmic power data based on the synchronous voltage data and the synchronous excitation current data. Among these, the synchronous excitation current data... This is the synchronous excitation current data obtained from the S202 synchronous processing. Synchronous voltage data. This is the synchronization voltage data obtained through S202 synchronization processing. Synchronization ohmic power data. Includes a sampling timestamp to indicate that it is based on synchronous voltage data. The instantaneous voltage and current values ​​are calculated from the corresponding sampling timestamps in the synchronous excitation current data.

[0090] S206, based on the unified sampling frequency parameter and the preset derivative weighting coefficient, calculates the rate of change of the synchronous rate of change solution data to obtain synchronous rate of change data; the synchronous rate of change data includes current rate of change data, voltage rate of change data, temperature rate of change data and electric field rate of change data.

[0091] Specifically, the terminal uses a differential method based on local polynomial fitting to numerically differentiate four data sequences—synchronous excitation current, synchronous voltage, synchronous temperature, and synchronous electric field—from the synchronous rate of change solution data to estimate their rates of change over time, obtaining current, voltage, temperature, and electric field rate of change data respectively. These are then concatenated to obtain the synchronous electric field data. The preset derivative weighting coefficients are the coefficients used by the local polynomial fitting differential method to calculate the first derivative under a specific window size and polynomial order, and can be set according to actual work requirements. The terminal calculates a uniform sampling interval based on a uniform sampling frequency parameter and uses this uniform sampling interval as the time scale to convolve each data sequence with the preset derivative weighting coefficients, obtaining the current rate of change data. (Unit: A / s) Voltage change rate data (Unit: V / s) Temperature change rate data (Unit: K / s) and electric field change rate data (Unit: V / m / s).

[0092] S207 quantifies the stability of the magnet under test based on a preset stability calculation time window, synchronous excitation current data, and synchronous electric field data, and obtains the stability change rate parameter; and obtains multi-source change rate data based on synchronous change rate data, stability change rate parameter, and synchronous ohmic power data.

[0093] Specifically, the terminal selects a synchronization point within a preset stability calculation time window. Data pairs. A linear fit is performed within this window, and the slope of the fitted line is used as the stability rate of change parameter. The physical meaning of the stability rate of change parameter is: how much the electric field is expected to change for every unit increase in current; this value usually increases significantly before quench loss. The terminal concatenates the synchronous rate of change data, the stability rate of change parameter, and the synchronous ohmic power data to obtain multi-source rate of change data. Exemplarily, the preset stability calculation time window can be set according to the actual data stability time requirements in operation; this embodiment does not further limit the setting of this preset stability calculation time window.

[0094] This embodiment provides a method for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion. By mapping heterogeneous data from multiple sources, such as current, voltage, temperature, magnetic field, and active excitation signals from different clock references and sampling rates, onto a unified discrete time axis, it generates synchronous multi-source data with strict time alignment. Based on this, the derived quantities and rate of change characteristics are systematically calculated. This eliminates the analytical obstacles caused by asynchronous multi-channel data and provides reliable and consistent input data for subsequent real-time modeling and simulation calculations.

[0095] In one embodiment, the virtual disturbance sequence includes an extreme condition pulse sequence and an extreme condition magnetic disturbance sequence;

[0096] Based on synchronized multi-source data, unified sampling frequency parameters, and a preset virtual perturbation sequence, the critical state parameters of the magnet under test are calculated, including:

[0097] S301, based on the synchronous active excitation signal data, segments the synchronous temperature data to obtain sampled temperature data; segments the synchronous ohmic power data to obtain sampled ohmic power data.

[0098] Specifically, the terminal extracts the start sampling timestamp and end sampling timestamp of the synchronous active excitation signal data from the synchronous active excitation signal data, and segments the synchronous temperature data and synchronous electric field data according to the start sampling timestamp and end sampling timestamp, and extracts the synchronous temperature data and synchronous electric field data whose sampling timestamps are in the start sampling timestamp and end sampling timestamp, so as to obtain the corresponding sampling temperature data and sampling ohmic power data.

[0099] S302 quantifies the temperature response of the magnet under test based on sampled temperature data, sampled ohmic power data, unified sampling frequency parameters, and synchronous active excitation signal data, and obtains the thermal pulse response function.

[0100] Specifically, the terminal constructs a thermal pulse response function based on sampled temperature data, sampled ohmic power data, unified sampling frequency parameters, and synchronous active excitation signal data. The thermal pulse response function characterizes the relationship between the temperature of the magnet under test and the input excitation signal data.

[0101] S303 calculates the equivalent temperature rise data based on the thermal pulse response function, unified sampling frequency parameters, and extreme condition pulse sequence; the extreme condition pulse sequence includes the preset excitation signal strength and the corresponding sampling timestamp.

[0102] Specifically, the extreme condition pulse sequence is a hypothetical thermal disturbance power waveform, including a preset excitation signal strength and a corresponding sampling timestamp. The extreme condition pulse sequence is used to simulate extreme transient thermal events that may be encountered during the operation of a fusion device (e.g., plasma thermal radiation pulses lasting hundreds of milliseconds and reaching megawatt-level per cubic meter), and can be configured according to actual operating conditions. The terminal calculates the temperature impact generated by the extreme condition pulse sequence using convolution operations based on the thermal pulse response function and a uniform sampling frequency parameter, obtaining equivalent temperature rise data characterizing the temperature rise sequence that may result from the extreme condition pulse sequence.

[0103] S304, based on synchronous multi-source data, fuses synchronous temperature data and equivalent temperature rise data to obtain the equivalent critical temperature.

[0104] Specifically, the terminal combines equivalent temperature rise data and synchronous temperature data to assess the thermal state under worst-case conditions and obtain the equivalent critical temperature. The terminal uses the formula: Linear superposition: The equivalent critical temperature was calculated. It is the equivalent critical temperature, which characterizes the temperature level that the magnet under test may reach under the current measured operating conditions after being superimposed with a thermal load representing the extreme operating conditions. It is the synchronized temperature data from the synchronized multi-source data obtained by S102. These are the equivalent temperature rise data obtained from S303.

[0105] S305 calculates the baseline critical current based on synchronous magnetic field data, equivalent critical temperature, and a preset baseline critical current function.

[0106] Specifically, the preset baseline critical current function is used to describe the relationship between the magnetic flux density and temperature of the magnet under test under steady-state conditions and the current. This function is established through extensive prior standard tests and can take the form of… This embodiment does not further limit the setting of the preset baseline critical current function. The terminal takes the current synchronous magnetic field data and the calculated equivalent critical temperature as input and substitutes them into the baseline critical current function. Calculations are performed to obtain the baseline critical current value corresponding to the current synchronous magnetic field data and the equivalent critical temperature. The baseline critical current value characterizes the maximum current that the magnet under test is expected to carry in the current thermomagnetic environment under steady-state conditions and without other dynamic disturbances.

[0107] S306, based on the extreme operating condition magnetic disturbance sequence, adjusts the baseline critical current to obtain the equivalent critical current; and based on the equivalent critical temperature and equivalent critical current, obtains the critical state parameters.

[0108] Specifically, the extreme condition magnetic disturbance sequence is a normalized sequence used to simulate background magnetic field fluctuations. Its value range is typically between [0,1], where 1 represents the preset maximum design disturbance intensity. This value can be set according to the actual working conditions, and its form can be... The terminal also reduces the baseline critical current based on a preset magnetic disturbance influence function and a maximum correction coefficient, using the formula: The equivalent critical current was calculated. It is the equivalent critical current, which characterizes the critical current that the magnet under test may have under a composite virtual working condition that comprehensively considers the measured magnetic field, the equivalent critical temperature (including virtual thermal disturbance), and the virtual magnetic disturbance. It is the baseline critical current calculated by S305. It is the maximum correction factor, used to control the impact of disturbances caused by the magnetic disturbance sequence under extreme operating conditions on the baseline critical current. It can be set according to the actual operation. It is a preset magnetic disturbance influence function, which describes the nonlinear relationship between the disturbance intensity brought about by the magnetic disturbance sequence under extreme working conditions and the critical current reduction. It can be calculated based on actual work. It is a magnetic disturbance sequence under extreme operating conditions. The terminal splices the equivalent critical temperature and equivalent critical current to obtain the critical state parameters.

[0109] This embodiment provides a method for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion. By utilizing synchronous active excitation signal data and the resulting temperature and power responses within an absolutely safe low-current range, the unique thermal pulse response function of the magnet under test is identified in real time. This thermal pulse response function is then used to perform real-time simulations of preset extreme operating condition pulse sequences representing virtual heat under extreme operating conditions and extreme operating condition magnetic disturbance sequences representing magnetic disturbances under extreme operating conditions. The equivalent critical temperature and equivalent critical current of the magnet under these hypothetical harsh conditions are calculated, forming critical state parameters. This allows for a forward-looking and dynamic assessment of the magnet's true performance margin and safety boundary in a non-steady-state, multi-disturbance coupled fusion operating environment.

[0110] In one embodiment, based on sampled temperature data, sampled ohmic power data, unified sampling frequency parameters, and synchronous active excitation signal data, the temperature response of the magnet under test is quantified to obtain a thermal pulse response function, including:

[0111] S401, based on a unified sampling frequency parameter, combines the sampled temperature data, sampled ohmic power data, and synchronous active excitation signal data with the same sampling timestamp to obtain a function-constructed data vector for each sampling timestamp.

[0112] Specifically, the terminal concatenates the time-aligned sampled temperature data, sampled ohmic power data, and synchronous active excitation signal data according to their common sampling timestamp. For each sampling timestamp, a function is constructed to build a data vector. .in, It is any sampling timestamp. The sampling timestamp in the sampled temperature data is Temperature value. The sampling timestamp in the sampled ohmic power data is The ohmic power value. The sampling timestamp in the synchronous active excitation signal data is The intensity of the excitation signal.

[0113] S402, based on the preset initialization model parameters and functions, constructs a data vector and obtains the thermal pulse response equation for each sampling time stamp.

[0114] Specifically, the preset initialization model parameters include the initial weighted parameters of each dimension of the data vector constructed by the function and the initial covariance matrix, where the initial covariance matrix is ​​a preset identity matrix. The preset initialization model parameters are in the form of... ,in It is the initial weighted parameter set for each dimension. This involves initializing the covariance matrix. For each sampling timestamp, the terminal constructs the thermal impulse response equation for that timestamp based on the initial weighted parameter set for each dimension. The thermal impulse response equation is in the form of a difference equation. And obtained through discretization ,in It is the true parameter vector to be determined. It is a modeling error.

[0115] S403, based on the preset forgetting factor and thermal impulse response equation, iteratively solves the gain coefficient of the thermal impulse response equation for each sampling time stamp to obtain the gain matrix.

[0116] Specifically, the terminal uses a recursive least squares method for online iterative solution. At each new sampling time... The algorithm performs the following core steps: First, it calculates the gain matrix. .in, It is the covariance matrix of the previous time step, reflecting the uncertainty of parameter estimation; It is the data vector at the current moment; It is a preset forgetting factor ( The settings can be adjusted according to actual work requirements; this embodiment does not further limit the setting of the forgetting factor. Gain Matrix This determines the weight of the contribution of the new observation data at the current moment to the parameter update. In the denominator... It serves as a regularization mechanism to prevent numerical computation problems. At the same time, a value less than 1 means that the algorithm will gradually "forget" old data and pay more attention to new data, thus enabling the model to track the slow changes in system characteristics.

[0117] S404, update the initial model parameters based on the gain matrix to obtain dynamic model parameters; the dynamic model parameters include at least the first model parameters and the second model parameters.

[0118] Specifically, the terminal updates the parameter estimates based on the gain matrix, first calculating the prior error. And use this difference and gain matrix to correct the parameter estimate: And update the covariance matrix: This reflects the reduction in estimation uncertainty. As the iteration progresses, the parameter vector estimates... The model will gradually converge, yielding dynamic model parameters. These dynamic model parameters include at least the first model parameters and the second model parameters, i.e. In and .

[0119] S404, based on dynamic model parameters, unified sampling frequency parameters and preset delay time length parameters, constructs the thermal pulse response function;

[0120] The thermal pulse response function has the following form:

[0121]

[0122] in, It is the thermal impulse response function. It is a unified sampling frequency parameter. It is the delay time length parameter. It is any delay time length. These are the first model parameters. These are the parameters of the second model.

[0123] Specifically, the terminal derives the thermal pulse response function based on dynamic model parameters, a unified sampling frequency parameter, and a preset delay time parameter. The terminal uses the following formula: The uniform sampling interval is calculated based on the uniform sampling frequency parameter. For a given difference equation... The first-order system described, which is in The response to a unit impulse excitation at any given time can be obtained through recursive calculation. At any moment, the response is After this, the incentive is zero and the system evolves freely: Therefore, in the delay sampling interval When, the impulse response value is After factoring After being absorbed into the processing of the input signal, it becomes... The thermal impulse response function of the form, and defined .parameter It is the preset delay time length parameter, indicating when hour, The value has decayed to a negligible level. The preset delay time parameter can be set according to the actual work requirements.

[0124] This embodiment provides a method for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion. By constructing a standard format, initializing the model, and pre-setting a forgetting factor, the method iteratively executes the steps of "calculating the gain matrix, updating parameter estimates, and updating the covariance matrix" in each sampling period. Using the converged core parameters, a thermal pulse response function is constructed through mathematical derivation. This method achieves the transformation from measured "excitation-response" data to an abstract dynamic system model, closely reflecting the individual differences and real-time state of the tested magnet, and laying an accurate and reliable foundation for subsequent high-fidelity virtual operating condition simulations.

[0125] In one embodiment, equivalent temperature rise data is calculated based on the thermal pulse response function, a uniform sampling frequency parameter, and an extreme condition pulse sequence, including:

[0126] S501, based on the thermal pulse response function, unified sampling frequency parameters, and extreme condition pulse sequence, uses the following formula to calculate the equivalent temperature rise data:

[0127]

[0128] in, It is any sampling timestamp in the extreme condition pulse sequence. The sampling timestamp is Equivalent temperature rise data at that time It is the delay time length parameter. It is any delay time length. It is the thermal impulse response function. It is a unified sampling frequency parameter. It is the pulse sequence under extreme conditions at the sampling timestamp The intensity of the excitation signal.

[0129] Specifically, the terminal calculates the equivalent temperature rise data using a formula based on the thermal pulse response function, a unified sampling frequency parameter, and the extreme operating condition pulse sequence. Among these, It is any sampling timestamp in the extreme condition pulse sequence, representing the time from the past to the present under the input extreme condition pulse sequence. The total temperature rise caused by the accumulation of all virtual thermal disturbances at any given moment. It is any number of delay steps. It is the thermal impulse response function The effective length, i.e., the delay time length parameter. It is the system after being excited by a unit pulse, after Each sampling interval (time is) The remaining response strength when ). It is in the past moment The power intensity of the applied virtual thermal perturbation. Multiplying the two gives the result. The impact of momentary disturbance on the present The contribution of constant temperature rise.

[0130] This embodiment provides a method for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion. By performing discrete convolution operations on the identified thermal pulse response function and a preset extreme condition pulse sequence, the equivalent temperature rise sequence caused by virtual thermal disturbances is calculated in real time. This achieves simulation of linear thermal systems, transforming the high-risk thermal load experiments in traditional testing, which require actual application to observe effects, into safe, fast, and repeatable "digital experiments" in a computer. This allows for the early detection of potential performance shortcomings and safety risks, realizing a shift from a testing mode of "passively accepting results" to "actively predicting boundaries."

[0131] In one embodiment, test adjustment instructions are generated based on multi-source rate of change data, critical state parameters, and synchronous multi-source data, including:

[0132] S601, based on multi-source rate of change data and critical state parameters, integrates voltage rate of change data, temperature rate of change data, stability rate of change parameters and equivalent critical temperature to obtain comprehensive risk parameters.

[0133] Specifically, the terminal uses the following formula: Calculate By fusing voltage change rate data, temperature change rate data, stability change rate parameters, and equivalent critical temperature, a comprehensive risk parameter is obtained. Among these, It is a comprehensive risk parameter. It is the stability rate of change parameter. It is voltage change rate data. It is data on the rate of temperature change. It is the equivalent critical temperature. These are stability weights; It is voltage weighting; It is a temperature weight; It is the critical temperature weight; , , and The sum is 1, which can be set according to the actual work.

[0134] S602, based on the preset comprehensive risk threshold parameter, calculate the difference between the comprehensive risk parameter and the comprehensive risk threshold parameter to obtain the risk deviation value.

[0135] Specifically, the preset comprehensive risk threshold parameter represents the expected value of the comprehensive risk parameter under "normal and safe" operating conditions, and can be set according to actual work requirements. The terminal calculates the risk deviation value at each sampling timestamp. This deviation value directly quantifies the degree to which the current risk level deviates from the safety baseline. If A positive and large value indicates that the system risk is significantly higher than expected, requiring control measures such as deceleration; if A negative value or a small positive value indicates that the system is in a safe or low-risk state, and the testing progress can be maintained or accelerated appropriately.

[0136] S603 analyzes the adjustment amount of synchronous excitation current data based on risk deviation value, stability change rate parameter and preset excitation current adjustment amplitude threshold to obtain excitation current adjustment value.

[0137] Specifically, the excitation current adjustment value It is calculated using a composite control law: The formula consists of two parts: the first part... It is a proportional feedback term based on risk deviation, risk deviation The larger the value, the greater the need for a rate reduction; Part Two It is a feedforward term based on electric field sensitivity. This is a stability rate of change parameter, serving as a precursor signal for quench failure; its increase can trigger a faster and more direct control response. Proportional coefficient. and It is a preset setting used to adjust the intensity of feedback and feedforward, and can be set according to actual work requirements. The calculated... This is the currently recommended rate adjustment (unit: A / s²). The terminal will also... The adjustment is compared with a preset excitation current adjustment threshold to ensure that a single adjustment is not too drastic. The preset excitation current adjustment threshold can be set according to the actual operation requirements of the excitation current adjustment.

[0138] S604: Based on the synchronous excitation current data and critical state parameters, calculate the difference between the synchronous excitation current data and the equivalent critical current to obtain the excitation current difference; and compare the excitation current difference with the preset excitation current difference threshold to obtain the excitation current difference comparison result.

[0139] Specifically, the terminal calculates the difference between the synchronous excitation current data and the equivalent critical current to obtain the excitation current difference. This excitation current difference represents the safe distance between the current and the performance boundary under the worst-case operating condition considering virtual disturbances. The terminal compares this difference with a preset excitation current difference threshold to obtain the excitation current difference comparison result. The preset excitation current difference threshold can be set according to the actual excitation current safety requirements in operation; this embodiment does not further limit the setting of this preset excitation current difference threshold. The comparison result is a logical state; if... This indicates that there is still sufficient margin; if This indicates that the safety margin is insufficient or exhausted. Among them, It is the difference in excitation current. It is a preset excitation current difference threshold.

[0140] S605 generates test adjustment commands based on the excitation current adjustment value, the comparison result of the excitation current difference, the risk deviation value, and the synchronous electric field data.

[0141] Specifically, the terminal compares the synchronous electric field data with a preset electric field threshold to obtain an electric field comparison result; the terminal also compares the risk deviation value with a preset risk difference threshold to obtain a risk difference comparison result; the terminal simultaneously monitors the electric field comparison result, the risk difference comparison result, and the excitation current difference comparison result. If the electric field comparison result indicates that the synchronous electric field data is greater than the preset electric field threshold, or the risk deviation value is greater than the preset risk difference threshold, or the excitation current difference comparison result indicates that the excitation current difference is greater than the preset excitation current difference threshold, the terminal generates a test adjustment command based on the excitation current adjustment value to instruct the adjustment of the excitation current magnitude.

[0142] This embodiment provides a method for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion. By fusing multi-dimensional signals such as voltage change rate, temperature change rate, electric field-current sensitivity, and equivalent temperature rise into a comprehensive risk parameter, and using a composite control law to generate in real-time a triple independent safety criterion based on the measured electric field, comprehensive risk index, and equivalent critical current margin, along with an adaptive current ramp-up rate adjustment command, a closed-loop linkage between the testing process and the real-time state of the magnet is achieved, improving testing efficiency and safety. This method changes the traditional passive protection mode that relies on a single voltage threshold, achieving a revolutionary leap from "open-loop program control, reactive protection" to "closed-loop intelligent adjustment, proactive early warning and protection," ensuring the safety of valuable superconducting magnets in extreme performance testing.

[0143] To further illustrate the solution of the application embodiment in this embodiment, a specific example is provided below:

[0144] This application provides a method for solving target motion elements using beam domain line spectra, referring to... Figure 2 This includes the following steps:

[0145] S01, unified processing of data acquisition and time.

[0146] Specifically, during the test, the acquisition equipment obtains current, voltage, temperature, and magnetic field signals, as well as optional strain signals, of the superconducting magnet under test in a low-temperature environment. Each acquisition channel can be connected to the test control system via a standard data acquisition interface, and each channel records the sampling time and the corresponding physical quantity value. The acquired raw data contains different sampling frequencies and different time bases, which need to be uniformly processed before entering the model calculation.

[0147] First, map the time series data acquired from each channel to a unified time axis. Let the unified time axis be... in

[0148]

[0149] Indicates a unified start time. Indicates the sampling step size. This is a discrete-time index.

[0150] For any original signal channel Its original sampling points are Through interpolation operators Mapping to a unified time axis yields the signal values ​​at a unified time point:

[0151]

[0152] in: Indicates a unified point in time The signal value on; Represents the original sampled value; Indicates the original sampling time; The interpolation algorithm can be represented by piecewise linear interpolation or piecewise cubic interpolation that preserves elasticity.

[0153] Through the above processing, the current Voltage temperature magnetic field and strain Once all channel signals are unified to the same time base.

[0154] After interpolation, the time derivative of a portion of the signal needs to be calculated to describe the rate of change. For any discrete signal... , at time The derivative estimate is:

[0155]

[0156] in: To standardize the sampling step size: For local weighting coefficients, through length The sample is obtained by fitting a polynomial within the sampling window; The window radius is used to ensure the weighted sequence is centrally symmetric in order to reduce phase deviation.

[0157] This derivative calculation is used to obtain the rate of change of the electric field with respect to the current. Voltage change rate Rate of temperature change These key quantities provide the basic inputs for risk indicator calculation and control laws. Regarding electric field data processing, the electric field... Calculated from voltage and sampling length:

[0158]

[0159] in: Indicates the voltage sampling length: Indicates time The collected voltage value.

[0160] After the above unified processing, the following synchronized data sequence is obtained: excitation current sequence electric field sequence Temperature sequence Magnetic field sequence Optional strain sequence

[0161] These data are collected using a uniform sampling step size. Record, along with a sequence of derivatives. and This provides direct input for subsequent thermal model identification, virtual operating condition calculation, adaptive control, and quench prediction.

[0162] S02, The thermal and electrothermal coupling model is established.

[0163] Specifically, it includes three main steps: perturbation excitation within a safe range, thermal dynamic behavior modeling, and discretization and parameter identification.

[0164] In the initial stage of the test, the excitation current was gradually increased from zero at a small rate, kept well below the expected critical current, and the current was recorded simultaneously. Voltage temperature With magnetic field The time series. By applying small-amplitude step or ramp changes to the current or temperature settings. A system response suitable for identification is generated. During this phase, entry into the critical current region is avoided to ensure the magnet remains in a safe state. For the description of thermal behavior, a first-order lumped-parameter model is used to represent the magnet's heat capacity and thermal resistance characteristics. The model's continuous-time expression is as follows:

[0165]

[0166] in: Indicates the temperature at the magnet or a key testing point. Reference temperature indicating a low-temperature environment It represents the equivalent heat capacity, used to characterize the amount of heat required for a magnet to undergo a unit temperature rise; Represents the equivalent thermal resistance, used to describe the thermal conductivity between a magnet and a cold source: Indicates ohmic heating power, according to calculate; This represents the virtual thermal disturbance input power density used for modeling.

[0167] Discretizing the above continuous model on a unified time axis yields the difference equation form:

[0168]

[0169] in: Indicates at discrete time Temperature value: Indicates at time Ohmic power: Indicates at time Virtual thermal disturbance input: These are the parameters to be identified after discretization.

[0170] Parameter identification is achieved using the least squares or recursive least squares method. Let the acquired temperature sequence be... Ohmic power sequence is The virtual thermal perturbation sequence is {q} v If k ∈ {,k}, then the discrete equation can be rewritten in linear regression form:

[0171]

[0172] in This represents the fitting error term. The parameters are obtained by minimizing the squared error between the predicted and measured temperatures. The estimated value. The recursive least squares method can be updated online during real-time data acquisition, allowing the model to be dynamically corrected based on the latest data during testing.

[0173] To further describe the dynamic effect of external disturbances on temperature, a unit impulse response kernel is defined. Used to represent the time interval of a unit thermal disturbance. The resulting temperature change. Solving the discretized model yields:

[0174]

[0175] in: This represents the equivalent temperature rise caused by virtual thermal disturbance; Indicates the delay time The unit impulse response; Indicates a uniform sampling step size. Unit impulse response kernel. The expression can be based on the identified parameters. The method is derived and used to calculate the equivalent temperature rise when subsequent virtual operating conditions are superimposed.

[0176] Through the above modeling process, a discrete model was established to describe the thermal and electrothermal coupling relationship of a magnet in a low-temperature environment. This model takes the collected current, voltage, temperature, and virtual thermal disturbance as inputs, and outputs a dynamic temperature response, providing a direct calculation basis for virtual thermal disturbance superposition, equivalent critical current calculation, and risk index construction.

[0177] S03, Virtual working condition generation and equivalent parameter calculation.

[0178] Specifically, it includes four main steps: virtual thermal disturbance generation, virtual magnetic disturbance processing, equivalent temperature calculation, and equivalent critical current calculation.

[0179] First, virtual thermal perturbation is used to describe the effect of external heat injection on the magnet temperature. The unit impulse response kernel identified by the aforementioned model is then used. Used to calculate the temperature change caused by a virtual thermal perturbation. For time... Defined virtual thermal perturbation input sequence The equivalent temperature rise it produces on a unified time axis Represented in convolutional form as follows:

[0180]

[0181] in: Indicates time Temperature increment caused by virtual thermal disturbance: Indicates the unit impulse response kernel in the time delay The value; Indicates time Virtual thermal disturbance input: This indicates a uniform sampling step size.

[0182] The equivalent temperature is defined as: ,in: Indicates time The equivalent temperature; Indicates time The actual measured temperature.

[0183] Secondly, virtual magnetic disturbances are used to reflect the correction of the critical current by external magnetic field fluctuations. During the test, the baseline critical current is first determined using electric field criteria. In a magnetic field With temperature Under certain conditions, when the electric field reaches a preset electric field threshold The excitation current corresponding to the critical current point is defined as the critical current point. A baseline functional relationship between the critical current and the magnetic field and temperature is established through a series of steady-state or quasi-steady-state data.

[0184]

[0185] in: Indicates the baseline critical current: This indicates the magnetic field at the operating point of the magnet; This indicates the temperature at the operating point.

[0186] After introducing virtual magnetic disturbance, a correction coefficient is defined. and mapping function Adjust the baseline critical current to the equivalent critical current:

[0187]

[0188] in: Indicates time Equivalent critical current: For magnetic disturbance correction factors:

[0189] The virtual magnetic disturbance effect function has the following input: It is a normalized quantity representing the fluctuation or rate of change of the magnetic field.

[0190] Through the above processing, the equivalent temperature With equivalent critical current It can calculate in real time during testing to dynamically reflect the state of the magnet under hypothetical thermal and magnetic loads. This equivalent state can serve as an important input parameter for subsequent risk index construction and adaptive excitation control.

[0191] To ensure real-time performance, the calculation of virtual thermal disturbance and virtual magnetic disturbance is performed after the data acquisition and processing module completes time alignment. The updated model parameters and the latest measured signals are used to continuously calculate and output the data, so that the latest equivalent state variables can always be obtained during the entire test.

[0192] S04, Risk Indicator Construction and Adaptive Control.

[0193] Specifically, this includes: the construction of risk indicators based on multi-source measurement data and virtual calculation results. The system acquires the following quantities on a unified time axis:

[0194] Real-time electric field From voltage measurement value With sampling length Calculated excitation current and its rate of change Voltage change rate ;temperature and rate of temperature change The equivalent temperature rise obtained from the aforementioned virtual thermal disturbance calculation The equivalent critical current calculated above

[0195] The basic inputs for risk indicators include:

[0196] 1. The rate of change of electric field with respect to electric current is denoted as: It is used to reflect the sensitivity of the electric field to changes in current.

[0197] 2. Rate of change of voltage, denoted as: It is used to describe the dynamic change of voltage over time.

[0198] 3. The rate of temperature change, denoted as: It is used to reflect the increasing trend of temperature over time.

[0199] 4. Equivalent temperature rise caused by virtual thermal disturbance Used to represent potential heat accumulation under the current modeling conditions.

[0200] The system combines the above input quantities according to preset weights to form risk indicators.

[0201]

[0202] in, Indicates time Risk indicators These are weighting coefficients set before testing to adjust the contribution of each input to the overall risk indicator.

[0203] Risk indicators The data is updated in real time during the test, serving as the basis for dynamically adjusting the rate of rise of the excitation current. The rate of rise of the excitation current is denoted as... Its adjustment follows the adaptive update law:

[0204]

[0205] in: This indicates the rate of increase of the excitation current at the next moment; This indicates the rate of increase of the excitation current at the current moment. Adjustment coefficient based on risk indicator deviation: Here is the adjustment coefficient based on the rate of change of current by the electric field: This is a risk reference value; and These are the preset minimum and maximum current rise rates, respectively; This represents the limiting function, used to ensure that the rate adjustment does not exceed the preset range.

[0206] In special circumstances, when risk indicators When the temperature approaches a threshold or shows a rapid growth trend, the system can simultaneously adjust the rate of change of the magnetic field and the rate of change of the temperature. The rate of change of the magnetic field is denoted as... The rate of temperature change is denoted as Its update strategy can refer to the limiting and weighted correction method similar to that used for current rate. Through the above processing, the system can achieve the following process during testing: 1. Collect synchronous data and calculate each input quantity at each time step; 2. Calculate real-time risk indicators based on the input quantities. 3. Compare risk indicators with reference values Based on the rate of change of the electric field with respect to the current, update the rate of rise of the excitation current. 4. When the risk indicators approach the threshold or change abnormally, limit or reduce the current rise rate, and simultaneously reduce the rate of change of magnetic field or temperature; 5. Continue to cycle the above steps until the stop condition is triggered or the test is completed.

[0207] S05, Go-out prediction and protection.

[0208] Specifically, to prevent uncontrollable quenching from occurring when the superconducting magnet under test approaches a critical state during testing, this invention predicts quenching based on real-time data and virtual operating condition calculations, and sets multiple stopping conditions. When any stopping condition is met, the system immediately stops the current from rising and performs a safe current reduction, ensuring that the test ends within a controllable range.

[0209] The quench prediction is based on the following real-time calculations: measured electric field , by voltage With sampling length The excitation current was calculated to be: Equivalent critical current Risk indicators were calculated based on virtual operating conditions. The reference threshold is formed by a combination of the rate of change of current, the rate of change of voltage, the rate of change of temperature, and the equivalent temperature rise, including the electric field threshold. Risk threshold and safety margin

[0210] First, the measured electric field criterion is used to detect critical states. When at a certain time interval... satisfy: When the magnet has reached the critical electric field condition, the current should be stopped immediately.

[0211] Secondly, risk indicators are used to predict the trend of loss of control in advance. At a certain time... satisfy: If the magnet is considered to be in a high-risk state, protective measures should be triggered immediately.

[0212] Secondly, the equivalent critical current difference criterion is used to determine the safety margin based on virtual operating conditions. At a certain moment... satisfy: If the difference between the current excitation current and the equivalent critical current is deemed insufficient to guarantee safety, the current boosting process must be terminated.

[0213] in: For at any time Measured electric field: A preset electric field threshold is used to determine the critical state: For at any time Risk indicators; To preset risk thresholds; For at any time Excitation current: For at any time The equivalent critical current: This is used as a safety margin to limit the minimum difference between the current and the equivalent critical current.

[0214] When any of the above conditions are met, the system immediately issues a stop command and executes the safety current reduction process. The safety current reduction process includes: 1. Reducing the rate of increase of the excitation current. 1. Adjust to a non-positive value and gradually reduce the current within the preset maximum allowable rate of decrease: 2. Continuously monitor the electric field, voltage, temperature, and magnetic field until the electric field is below the electric field threshold and the temperature returns to a stable range; 3. Maintain data acquisition for all channels during the current reduction process and record the complete sequence of current, voltage, temperature, magnetic field, and model parameters; 4. End the test cycle when it is confirmed that the magnet has returned to a safe state.

[0215] To ensure reliability, the stop conditions are handled using a logical "OR" relationship, meaning that the protection action is triggered if any one of the conditions is met. The aforementioned multiple criteria combine real-time measurement signals and virtual calculated states, enabling timely response when the critical electric field is reached, and early intervention when risk indicators or the difference in equivalent critical current suggest potential quenching, thereby preventing the magnet from entering an uncontrollable superconducting-normal-conducting transition state.

[0216] S06, Test data recording and model iteration updates.

[0217] Specifically, this includes: First, after each increase in excitation current or change in temperature or magnetic field conditions, the system continuously collects synchronized current data. Voltage temperature magnetic field electric field and optional strain and maintain consistency with the timeline. Alignment. The newly acquired data is used to perform online parameter correction on the previously established thermal-electrothermal coupling model. The discrete form of the thermal model is:

[0218]

[0219] in: Indicates at time Temperature; Indicates at time ohmic power; Indicates at time Virtual thermal disturbance input: These are the model parameters.

[0220] During the model update process, the system utilizes newly acquired data. Re-execute the least squares or recursive least squares algorithm on the data to calculate new parameter estimates. It also replaces old parameters in real time. The recursive least squares algorithm introduces a forgetting factor. Implement weight updates to ensure that the latest data has a higher impact on the model, thereby improving the model's adaptability to changes in test conditions.

[0221] After the model parameters are updated, the unit impulse response kernel It is also recalculated to correct for the temperature rise caused by virtual thermal disturbances. Recalculate the equivalent temperature based on the new model output: And the new equivalent critical current: The meanings of the symbols are consistent with the aforementioned definitions. Model updates enable the virtual operating condition calculation results to reflect changes in current heat conduction conditions and magnet status in a timely manner. After each model update, the system will recalculate the risk indicators:

[0222]

[0223] And adjust the excitation current rise rate accordingly. And, if necessary, the rate of change of magnetic field and the rate of change of temperature, to ensure that the control strategy matches the latest state.

[0224] The entire test can trigger model iteration under the following conditions: 1. When the excitation current is increased to a new stage and remains stable; 2. After the temperature or magnetic field setpoint changes; 3. When the risk index deviates significantly or the deviation between the model prediction value and the measured value exceeds the preset threshold.

[0225] Each iteration includes the following steps: applying a small perturbation within a safe current range, acquiring new response data, and updating the model parameters. and response core Recalculate the equivalent temperature rise, equivalent temperature, and equivalent critical current caused by the virtual thermal disturbance; update the risk indicators and adaptive control law, and continue testing.

[0226] Through the aforementioned dynamic model updates and iterative testing, this invention achieves real-time adaptation between the model and control during the testing process. The entire method can maintain an accurate description of the magnet's thermal properties and critical states under different operating conditions, providing a continuously effective computational foundation for risk assessment and protection triggering in subsequent steps.

[0227] S07, Test data recording and output.

[0228] Specifically, this includes recording and storing the collected data and established model parameters in real time throughout the entire testing process for subsequent analysis.

[0229] The aforementioned method for testing the cryogenic performance of superconducting magnets based on controlled nuclear fusion involves acquiring multi-source raw data and active excitation signal data of the magnet under test; performing time synchronization processing on the multi-source raw data and active excitation signal data based on a preset unified sampling frequency parameter to obtain synchronized multi-source data; extracting the variation characteristics of the synchronized multi-source data to obtain multi-source rate of change data; calculating the critical state parameters of the magnet under test based on the synchronized multi-source data, the unified sampling frequency parameter, and a preset virtual perturbation sequence; the critical state parameters include the equivalent critical temperature and the equivalent critical current; and generating test adjustment instructions based on the multi-source rate of change data, critical state parameters, and synchronized multi-source data; these instructions are used to instruct adjustments to the test state of the magnet under test. This method revolutionizes the traditional offline static analysis mode of "test first, then align" into a closed-loop dynamic testing mode of "real-time synchronization, online modeling, virtual simulation, and intelligent control," solving the problem of real-time calculation and control failure caused by asynchronous multi-source data, and improving the safety, accuracy, and intelligence level of cryogenic performance testing of superconducting magnets.

[0230] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0231] Based on the same inventive concept, this application also provides a controlled nuclear fusion-based superconducting magnet cryogenic performance testing system for implementing the aforementioned method for testing the cryogenic performance of a superconducting magnet based on controlled nuclear fusion. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the controlled nuclear fusion-based superconducting magnet cryogenic performance testing system provided below can be found in the limitations of the controlled nuclear fusion-based superconducting magnet cryogenic performance testing method described above, and will not be repeated here.

[0232] In one exemplary embodiment, such as Figure 3 As shown, a low-temperature performance testing system 200 for superconducting magnets based on controlled nuclear fusion is provided, comprising:

[0233] The data acquisition module 201 is used to acquire multi-source raw data and active excitation signal data of the magnet under test; the multi-source raw data includes at least excitation current data, voltage data, temperature data and magnetic field data; the excitation current data, voltage data, temperature data, magnetic field data and active excitation signal data all have sampling timestamps;

[0234] The data synchronization module 202 is used to perform time synchronization processing on multi-source raw data and active excitation signal data based on preset unified sampling frequency parameters to obtain synchronized multi-source data; and to extract the variation characteristics of the synchronized multi-source data to obtain multi-source change rate data; the multi-source change rate data includes at least voltage change rate data, temperature change rate data, stability change rate parameters and synchronized ohmic power data; the synchronized multi-source data includes at least synchronized temperature data, synchronized magnetic field data, synchronized electric field data, synchronized excitation current data and synchronized active excitation signal data;

[0235] The critical parameter calculation module 203 is used to calculate the critical state parameters of the magnet under test based on synchronous multi-source data, unified sampling frequency parameters and preset virtual disturbance sequence; the critical state parameters include equivalent critical temperature and equivalent critical current;

[0236] The test instruction module 204 is used to generate test adjustment instructions based on multi-source rate of change data, critical state parameters and synchronous multi-source data; the test adjustment instructions are used to indicate the adjustment of the test state of the magnet under test.

[0237] Furthermore, the data synchronization module is also used for:

[0238] Based on multi-source raw data, a sampling timestamp that meets the preset synchronization reference timestamp requirements is selected from the multi-source raw data and active excitation signal data and used as a unified reference sampling timestamp.

[0239] Based on a unified sampling frequency parameter and a unified reference sampling timestamp, time-unified processing is performed on multi-source raw data and active excitation signal data to obtain synchronized multi-source data; the synchronized multi-source data includes synchronized excitation current data, synchronized voltage data, synchronized temperature data, synchronized magnetic field data, and synchronized active excitation signal data;

[0240] Based on the synchronization voltage data and the preset voltage sampling length, the synchronization electric field data is calculated using the following formula:

[0241]

[0242] in, It is synchronous electric field data. It is synchronous voltage data. It is the voltage sampling length;

[0243] Based on synchronous electric field data, synchronous excitation current data, synchronous voltage data, and synchronous temperature data, the synchronous rate of change solution data is obtained.

[0244] Based on the synchronization voltage data and synchronization excitation current data, the synchronization ohmic power data is calculated using the following formula:

[0245]

[0246] in, It is synchronous ohmic power data. It is synchronous excitation current data. It is synchronous voltage data;

[0247] Based on a unified sampling frequency parameter and a preset derivative weighting coefficient, the rate of change of the synchronous rate of change solution data is calculated to obtain synchronous rate of change data; the synchronous rate of change data includes current rate of change data, voltage rate of change data, temperature rate of change data, and electric field rate of change data;

[0248] Based on the preset stability calculation time window, synchronous excitation current data, and synchronous electric field data, the stability of the magnet under test is quantified to obtain the stability change rate parameter; and based on the synchronous change rate data, the stability change rate parameter, and the synchronous ohmic power data, multi-source change rate data is obtained.

[0249] Furthermore, the virtual disturbance sequence includes the extreme condition pulse sequence and the extreme condition magnetic disturbance sequence;

[0250] The critical parameter calculation module includes:

[0251] The data segmentation unit is used to segment the synchronous temperature data to obtain sampled temperature data based on the synchronous active excitation signal data; and to segment the synchronous ohmic power data to obtain sampled ohmic power data.

[0252] The function construction unit is used to quantify the temperature response of the magnet under test based on sampled temperature data, sampled ohmic power data, unified sampling frequency parameters and synchronous active excitation signal data, and obtain the thermal pulse response function.

[0253] The equivalent temperature rise calculation unit is used to calculate the equivalent temperature rise data based on the thermal pulse response function, the unified sampling frequency parameter, and the extreme condition pulse sequence; the extreme condition pulse sequence includes the preset excitation signal strength and the corresponding sampling timestamp.

[0254] The critical temperature calculation unit is used to fuse synchronous temperature data and equivalent temperature rise data based on synchronous multi-source data to obtain the equivalent critical temperature.

[0255] The baseline current calculation unit is used to calculate the baseline critical current based on synchronous magnetic field data, equivalent critical temperature and preset baseline critical current function;

[0256] The critical state parameter construction unit is used to adjust the baseline critical current based on the extreme operating condition magnetic disturbance sequence to obtain the equivalent critical current; and to obtain the critical state parameters based on the equivalent critical temperature and the equivalent critical current.

[0257] Furthermore, function building units are also used for:

[0258] Based on a unified sampling frequency parameter, the sampled temperature data, sampled ohmic power data, and synchronous active excitation signal data with the same sampling timestamp are combined to obtain a function-constructed data vector for each sampling timestamp.

[0259] Based on the preset initialization model parameters and functions, a data vector is constructed, and the thermal pulse response equations for each sampling time stamp are obtained.

[0260] Based on the preset forgetting factor and thermal impulse response equation, the gain coefficient of the thermal impulse response equation for each sampling time stamp is iteratively solved to obtain the gain matrix;

[0261] The dynamic model parameters are obtained by updating the initial model parameters based on the gain matrix; the dynamic model parameters include at least the first model parameters and the second model parameters.

[0262] Based on the dynamic model parameters, the unified sampling frequency parameters, and the preset delay time length parameters, the thermal pulse response function is constructed.

[0263] The thermal pulse response function has the following form:

[0264]

[0265] in, It is the thermal impulse response function. It is a unified sampling frequency parameter. It is the delay time length parameter. It is any delay time length. These are the first model parameters. These are the parameters of the second model.

[0266] Furthermore, the equivalent temperature rise calculation unit is also used for:

[0267] Based on the thermal pulse response function, a unified sampling frequency parameter, and the extreme condition pulse sequence, the equivalent temperature rise data is calculated using the following formula:

[0268]

[0269] in, It is any sampling timestamp in the extreme condition pulse sequence. The sampling timestamp is Equivalent temperature rise data at that time It is the delay time length parameter. It is any delay time length. It is the thermal impulse response function. It is a unified sampling frequency parameter. It is the pulse sequence under extreme conditions at the sampling timestamp The intensity of the excitation signal.

[0270] Furthermore, the test instruction module is also used for:

[0271] Based on multi-source rate of change data and critical state parameters, voltage rate of change data, temperature rate of change data, stability rate of change parameters and equivalent critical temperature are integrated to obtain comprehensive risk parameters.

[0272] Based on the preset comprehensive risk threshold parameter, the difference between the comprehensive risk parameter and the comprehensive risk threshold parameter is calculated to obtain the risk deviation value;

[0273] Based on the risk deviation value, stability change rate parameter and preset excitation current adjustment range threshold, the adjustment amount of synchronous excitation current data is analyzed to obtain the excitation current adjustment value.

[0274] Based on the synchronous excitation current data and critical state parameters, the difference between the synchronous excitation current data and the equivalent critical current is calculated to obtain the excitation current difference; and the excitation current difference is compared with the preset excitation current difference threshold to obtain the excitation current difference comparison result.

[0275] Based on the excitation current adjustment value, the comparison result of the excitation current difference, the risk deviation value, and the synchronous electric field data, a test adjustment command is generated.

[0276] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method for testing the low-temperature performance of a superconducting magnet based on controlled nuclear fusion as described above.

[0277] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0278] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0279] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for testing the low-temperature performance of superconducting magnets based on controlled nuclear fusion, characterized in that, The method includes: Acquire multi-source raw data and active excitation signal data of the magnet under test; the multi-source raw data includes at least excitation current data, voltage data, temperature data, and magnetic field data; the excitation current data, voltage data, temperature data, magnetic field data, and active excitation signal data all have sampling timestamps; Based on preset unified sampling frequency parameters, the multi-source raw data and the active excitation signal data are time-synchronized to obtain synchronized multi-source data; and the variation characteristics of the synchronized multi-source data are extracted to obtain multi-source change rate data; the multi-source change rate data includes at least voltage change rate data, temperature change rate data, stability change rate parameter and synchronized ohmic power data; the synchronized multi-source data includes at least synchronized temperature data, synchronized magnetic field data, synchronized electric field data, synchronized excitation current data and synchronized active excitation signal data; Based on the synchronous multi-source data, the unified sampling frequency parameters, and the preset virtual perturbation sequence, the critical state parameters of the magnet under test are calculated; the critical state parameters include the equivalent critical temperature and the equivalent critical current. Test adjustment instructions are generated based on the multi-source rate of change data, the critical state parameters, and the synchronous multi-source data; the test adjustment instructions are used to instruct the adjustment of the test state of the magnet under test.

2. The method according to claim 1, characterized in that, Based on a preset unified sampling frequency parameter, the multi-source raw data and the active excitation signal data are time-synchronized to obtain synchronized multi-source data. The change characteristics of the synchronous multi-source data are extracted to obtain multi-source change rate data, including: Based on the multi-source raw data, a sampling timestamp that meets the preset synchronization reference timestamp requirements is selected from the multi-source raw data and the active excitation signal data and used as a unified reference sampling timestamp; Based on the unified sampling frequency parameter and the unified reference sampling timestamp, the multi-source raw data and the active excitation signal data are subjected to time-unified processing to obtain the synchronous multi-source data; the synchronous multi-source data includes the synchronous excitation current data, synchronous voltage data, synchronous temperature data, synchronous magnetic field data, and synchronous active excitation signal data; Based on the synchronous voltage data and the preset voltage sampling length, the synchronous electric field data is calculated using the following formula: in, It is synchronous electric field data. It is synchronous voltage data. It is the voltage sampling length; Based on the synchronous electric field data, the synchronous excitation current data, the synchronous voltage data, and the synchronous temperature data, the synchronous rate of change solution data is obtained; Based on the synchronous voltage data and the synchronous excitation current data, the synchronous ohmic power data is calculated using the following formula: in, It is synchronous ohmic power data. It is synchronous excitation current data. It is synchronous voltage data; Based on the unified sampling frequency parameters and the preset derivative weighting coefficients, the rate of change of the synchronous rate of change solution data is calculated to obtain synchronous rate of change data; the synchronous rate of change data includes the current rate of change data, the voltage rate of change data, the temperature rate of change data, and the electric field rate of change data; Based on a preset stability calculation time window, the synchronous excitation current data, and the synchronous electric field data, the stability of the magnet under test is quantified to obtain the stability change rate parameter; and based on the synchronous change rate data, the stability change rate parameter, and the synchronous ohmic power data, the multi-source change rate data is obtained.

3. The method according to claim 1, characterized in that, The virtual disturbance sequence includes an extreme condition pulse sequence and an extreme condition magnetic disturbance sequence. The calculation of the critical state parameters of the magnet under test based on the synchronous multi-source data, the unified sampling frequency parameters, and the preset virtual perturbation sequence includes: Based on the synchronous active excitation signal data, the synchronous temperature data is segmented to obtain sampled temperature data; the synchronous ohmic power data is segmented to obtain sampled ohmic power data. Based on the sampled temperature data, the sampled ohmic power data, the unified sampling frequency parameters, and the synchronous active excitation signal data, the temperature response of the magnet under test is quantified to obtain the thermal pulse response function. Based on the thermal pulse response function, the unified sampling frequency parameter, and the extreme condition pulse sequence, the equivalent temperature rise data is calculated; the extreme condition pulse sequence includes a preset excitation signal strength and the corresponding sampling timestamp. Based on the synchronous multi-source data, the synchronous temperature data and the equivalent temperature rise data are fused to obtain the equivalent critical temperature; Based on the synchronous magnetic field data, the equivalent critical temperature, and the preset baseline critical current function, the baseline critical current is calculated. Based on the extreme operating condition magnetic disturbance sequence, the baseline critical current is adjusted to obtain the equivalent critical current; and based on the equivalent critical temperature and the equivalent critical current, the critical state parameters are obtained.

4. The method according to claim 3, characterized in that, The process of quantifying the temperature response of the magnet under test based on the sampled temperature data, the sampled ohmic power data, the unified sampling frequency parameters, and the synchronous active excitation signal data to obtain the thermal pulse response function includes: Based on the unified sampling frequency parameter, the sampled temperature data, the sampled ohmic power data, and the synchronous active excitation signal data with the same sampling timestamp are combined to obtain a function-constructed data vector for each sampling timestamp; Based on the preset initialization model parameters and the function, a data vector is constructed, and the thermal pulse response equation for each sampling timestamp is obtained. Based on the preset forgetting factor and the thermal pulse response equation, the gain coefficients of the thermal pulse response equations for each sampling timestamp are iteratively solved to obtain the gain matrix; The initial model parameters are updated based on the gain matrix to obtain dynamic model parameters; the dynamic model parameters include at least a first model parameter and a second model parameter. Based on the dynamic model parameters, the unified sampling frequency parameters, and the preset delay time length parameters, the thermal pulse response function is constructed. The thermal pulse response function is in the following form: in, It is the thermal impulse response function. It is a unified sampling frequency parameter. It is the delay time length parameter. It is any delay time length. These are the first model parameters. These are the parameters of the second model.

5. The method according to claim 4, characterized in that, The equivalent temperature rise data calculated based on the thermal pulse response function, the unified sampling frequency parameter, and the extreme condition pulse sequence includes: Based on the thermal pulse response function, the unified sampling frequency parameter, and the extreme condition pulse sequence, the equivalent temperature rise data is calculated using the following formula: in, It is any sampling timestamp in the extreme condition pulse sequence. The sampling timestamp is Equivalent temperature rise data at that time It is the delay time length parameter. It is any delay time length. It is the thermal impulse response function. It is a unified sampling frequency parameter. It is the pulse sequence under extreme conditions at the sampling timestamp The intensity of the excitation signal.

6. The method according to claim 1, characterized in that, The generation of test adjustment instructions based on the multi-source rate of change data, the critical state parameters, and the synchronous multi-source data includes: Based on the multi-source rate of change data and critical state parameters, the voltage rate of change data, the temperature rate of change data, the stability rate of change parameters, and the equivalent critical temperature are integrated to obtain a comprehensive risk parameter; Based on a preset comprehensive risk threshold parameter, the difference between the comprehensive risk parameter and the comprehensive risk threshold parameter is calculated to obtain the risk deviation value; Based on the risk deviation value, the stability change rate parameter, and the preset excitation current adjustment range threshold, the adjustment amount of the synchronous excitation current data is analyzed to obtain the excitation current adjustment value. Based on the synchronous excitation current data and the critical state parameters, the difference between the synchronous excitation current data and the equivalent critical current is calculated to obtain the excitation current difference; and the excitation current difference is compared with a preset excitation current difference threshold to obtain the excitation current difference comparison result. The test adjustment command is generated based on the excitation current adjustment value, the excitation current difference comparison result, the risk deviation value, and the synchronization electric field data.

7. A low-temperature performance testing system for superconducting magnets based on controlled nuclear fusion, characterized in that, The system includes: The data acquisition module is used to acquire multi-source raw data and active excitation signal data of the magnet under test; the multi-source raw data includes at least excitation current data, voltage data, temperature data and magnetic field data; the excitation current data, the voltage data, the temperature data, the magnetic field data and the active excitation signal data all have sampling timestamps; The data synchronization module is used to perform time synchronization processing on the multi-source raw data and the active excitation signal data based on a preset unified sampling frequency parameter to obtain synchronized multi-source data; and to extract the variation characteristics of the synchronized multi-source data to obtain multi-source change rate data; the multi-source change rate data includes at least voltage change rate data, temperature change rate data, stability change rate parameter, and synchronized ohmic power data; the synchronized multi-source data includes at least synchronized temperature data, synchronized magnetic field data, synchronized electric field data, synchronized excitation current data, and synchronized active excitation signal data; The critical parameter calculation module is used to calculate the critical state parameters of the magnet under test based on the synchronous multi-source data, the unified sampling frequency parameters, and the preset virtual disturbance sequence; the critical state parameters include the equivalent critical temperature and the equivalent critical current. The test instruction module is used to generate test adjustment instructions based on the multi-source rate of change data, the critical state parameters, and the synchronous multi-source data; the test adjustment instructions are used to instruct the adjustment of the test state of the magnet under test.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images