Closing resistance sheet thermal capacity margin test method and system

Abstract

The application provides a closing resistor disc thermal capacity margin test method and system, the method comprises the following steps: constructing a test platform with multi-modal signal synchronous acquisition conditions; matching historical data to generate initial parameters and perform energy impact, synchronously collecting multi-modal signals; standardizing the signals and extracting features, calculating the maximum thermal stress pre-judgment value according to the thermal sensitive features and determining whether it is greater than the first threshold value; if it is greater, stop the energy impact and perform margin level evaluation to obtain the margin level and confidence; when the margin level is excellent, qualified or critical and the confidence is greater than the first preset confidence threshold, output the current limit thermal capacity margin; when the confidence is between the first and second preset confidence thresholds, adjust the parameters in reverse and iterate until convergence, and then output. The application realizes real-time thermal stress pre-judgment protection and accurate limit margin acquisition, and improves the test safety and determination accuracy.

Description

Technical Field

[0001] This invention relates to the field of high-voltage device testing technology in power systems, and in particular to a method and system for testing the thermal capacity margin of a closing resistor. Background Technology

[0002] The closing resistor is a core component in ultra-high voltage (UHV) power systems, responsible for limiting transient overcurrents and ensuring the safe operation of switching equipment. Its short-time thermal capacity margin directly determines the reliability of the power system. Actual operational data shows that over 80% of closing resistor failures originate from internal thermal stress concentration and thermal collapse caused by short-term high-energy injection, rather than traditional electrical performance or mechanical structural defects. Therefore, accurately quantifying the thermal capacity safety margin of the closing resistor under complex operating conditions is crucial for ensuring the safe operation of the power grid. However, existing methods for testing closing resistors generally suffer from limited functionality and insufficient monitoring dimensions. Most can only determine performance based on single indicators such as resistance change rate after the test, failing to detect the temperature distribution and thermal stress evolution within the resistor in real time during energy impact, and making it even more difficult to provide early warning of impending thermal collapse. Furthermore, most existing testing devices can only conduct single tests with fixed parameters, unable to simulate actual operating conditions involving multiple coupled fields such as extreme cold, high temperature, and reclosing impact, and unable to dynamically adjust test parameters based on real-time status feedback during the assessment process. This makes it difficult to accurately obtain the ultimate thermal capacity margin of the closing resistor, resulting in significant deviations between the assessment results and actual performance in engineering settings.

[0003] To address the aforementioned technical issues, some existing solutions attempt to monitor the resistive element by adding temperature sensors or thermal imaging equipment. However, these solutions mostly use externally mounted sensors, which damage the insulating coating on the surface of the resistive element or affect its heat transfer path, leading to distorted monitoring data. Furthermore, existing solutions lack a time-synchronization calibration mechanism for multi-modal signal acquisition, resulting in transmission delay differences between signals acquired by different sensors, causing cumulative errors in subsequent thermal stress calculations and condition assessments. In addition, existing solutions mostly rely on simple threshold comparisons based on a single feature (such as the rate of resistance change or temperature rise) for margin determination, lacking a comprehensive evaluation system based on multi-feature fusion. This results in judgments being highly susceptible to fluctuations in a single feature and a high misjudgment rate. More importantly, existing solutions lack an active safety protection mechanism based on real-time prediction of thermal stress, failing to interrupt the test in time when the resistive element is nearing thermal collapse, easily leading to serious safety accidents such as resistive element explosion and monitoring chamber ablation. Moreover, they cannot accurately obtain the resistive element's ultimate tolerance capacity through iterative approximation. Therefore, there is an urgent need for a method to test the thermal capacity margin of the closing resistor element, which can achieve synchronous acquisition and time calibration of multi-modal signals, in-situ non-destructive sensing, real-time prediction and protection of thermal stress, multi-feature fusion margin assessment and limit margin iterative approximation. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for testing the thermal capacity margin of a closing resistor element. This invention aims to solve the problem that existing methods for testing closing resistor elements cannot simultaneously achieve in-situ non-destructive sensing, multi-modal signal time calibration, real-time thermal stress prediction and protection, multi-feature fusion margin assessment, and limit margin iterative approximation during energy impact processes under complex operating conditions. This results in poor test safety, low margin determination accuracy, and the inability to accurately obtain the limit thermal capacity margin.

[0005] In a first aspect, the present invention provides a method for testing the thermal capacity margin of a closing resistor element, the method comprising:

[0006] The closing resistor is installed in a sealed monitoring chamber, and a multi-dimensional sensor array is attached to the key measuring points on the surface of the resistor in situ. The monitoring chamber is then sealed, filled with insulating atmosphere, and self-calibrated for the signal transmission delay of the entire link, so as to build a test platform with the conditions for synchronous acquisition of multi-modal signals.

[0007] Input test object parameters and scene parameters, match historical valid data according to the test object parameters and scene parameters to generate initial test parameters and perform energy shock. In the energy shock, multi-modal signals are collected through the multi-dimensional sensor array every first preset time. The multi-modal signals include current, voltage, temperature, thermal stress, vibration, insulation state and atmospheric pressure signals.

[0008] The multimodal signal is standardized, and features are extracted from the standardized multimodal signal to obtain thermally sensitive features and corrected features. The maximum thermal stress prediction value is calculated based on the thermally sensitive features, and it is determined whether the maximum thermal stress prediction value is greater than a first threshold.

[0009] If the predicted maximum thermal stress value is greater than the first threshold, the energy impact is stopped, and the margin level is evaluated based on the thermal sensitivity characteristics and correction characteristics at the current moment to obtain the current margin level and confidence level.

[0010] When the margin level is excellent, qualified, or critical, if the confidence level is greater than the first preset confidence threshold, the current limiting heat capacity margin is calculated and output based on the thermal sensitivity characteristics. If the confidence level is less than or equal to the first preset confidence threshold and greater than the second preset confidence threshold, the energy injection parameters are adjusted in reverse based on the current thermal sensitivity characteristics and correction characteristics, and the energy impact and evaluation are re-executed until the deviation of the limiting heat capacity margin values ​​obtained from two consecutive iterations does not exceed the second threshold. Then, the current limiting heat capacity margin is output.

[0011] Secondly, the present invention provides a system for testing the thermal capacity margin of a closing resistor element, the system comprising:

[0012] The test platform construction module is used to install the closing resistor in a sealed monitoring chamber, attach a multi-dimensional sensor array to the key measuring points on the surface of the resistor in situ, and perform monitoring chamber sealing, insulation atmosphere filling and end-to-end signal transmission delay self-calibration to build a test platform with multi-modal signal synchronous acquisition conditions.

[0013] The signal acquisition module is used to input test object parameters and scene parameters, match historical valid data according to the test object parameters and scene parameters to generate initial test parameters and perform energy shock. During the energy shock, multi-modal signals are acquired through the multi-dimensional sensor array every first preset time. The multi-modal signals include current, voltage, temperature, thermal stress, vibration, insulation state and atmospheric pressure signals.

[0014] The feature extraction module is used to standardize the multimodal signal, extract features from the standardized multimodal signal to obtain thermally sensitive features and corrected features, calculate the maximum thermal stress prediction value based on the thermally sensitive features, and determine whether the maximum thermal stress prediction value is greater than a first threshold.

[0015] The margin level assessment module is used to stop the energy impact if the predicted maximum thermal stress value is greater than the first threshold, and to assess the margin level based on the thermal sensitivity characteristics and correction characteristics at the current moment to obtain the current margin level and confidence level.

[0016] The margin output module is used to calculate and output the current limiting heat capacity margin based on the thermal sensitivity characteristics when the margin level is excellent, qualified, or critical. If the confidence level is greater than the first preset confidence threshold, the module will output the margin when the confidence level is less than or equal to the first preset confidence threshold and greater than the second preset confidence threshold. If the confidence level is less than or equal to the first preset confidence threshold and greater than the second preset confidence threshold, the module will adjust the energy injection parameters in reverse according to the thermal sensitivity characteristics and correction characteristics at the current moment and return to re-execute the energy impact and evaluation until the deviation of the limiting heat capacity margin values ​​obtained from two consecutive iterations does not exceed the second threshold. Then, the module will output the current limiting heat capacity margin.

[0017] Thirdly, the present invention provides a storage medium that stores one or more programs, which, when executed by a processor, implement the above-described method for testing the thermal capacity margin of the closing resistor.

[0018] Fourthly, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein:

[0019] The memory is used to store computer programs;

[0020] When the processor executes the computer program stored in the memory, it implements the above-mentioned method for testing the thermal capacity margin of the closing resistor.

[0021] Compared with the prior art, the present invention has the following advantages:

[0022] This invention achieves high-fidelity synchronous acquisition of multi-modal signals, including current, voltage, temperature, thermal stress, vibration, insulation status, and atmospheric pressure, during energy impact by installing a closing resistor in a sealed monitoring chamber and in-situ attaching a multi-dimensional sensor array to key measurement points on the surface. This is combined with the sealing of the monitoring chamber, the filling of the insulating atmosphere, and self-calibration of the end-to-end signal transmission delay. Based on this, the invention standardizes and extracts features from the acquired multi-modal signals to obtain thermally sensitive and corrected features. The maximum predicted thermal stress value is calculated based on the thermally sensitive features, and the energy impact is immediately stopped when it exceeds a first threshold. Furthermore, a margin level assessment is performed based on the current thermally sensitive and corrected features to obtain a margin level and confidence level, achieving active safety protection and real-time status assessment during the impact process. Further, when the margin level is excellent... When the resistance is rated as excellent, qualified, or critical and the confidence level is greater than the first preset confidence threshold, the current limit thermal capacity margin is calculated based on the thermal sensitivity characteristics and directly output. When the confidence level is less than or equal to the first preset confidence threshold and greater than the second preset confidence threshold, the energy injection parameters are adjusted in reverse based on the current thermal sensitivity characteristics and correction characteristics, and the energy impact and evaluation are re-executed until the deviation of the limit thermal capacity margin values ​​obtained from two consecutive iterations does not exceed the second threshold. Thus, the true limit thermal capacity margin of the resistor is accurately converged to through iterative approximation. This effectively solves the technical problems in the existing technology, such as poor test safety due to the lack of multi-mode signal synchronous acquisition and real-time thermal stress prediction mechanism, and low margin determination accuracy and inability to accurately obtain the limit thermal capacity margin due to the lack of confidence level evaluation and iterative approximation mechanism. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for testing the thermal capacity margin of a closing resistor element according to an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of the structure of a closing resistor thermal capacity margin testing system proposed in an embodiment of the present invention.

[0025] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but does not exclude other elements or objects.

[0027] like Figure 1 and Figure 2 As shown, an embodiment of the present invention proposes a method for testing the thermal capacity margin of a closing resistor element. The method includes steps S101 to S105, wherein:

[0028] Step S101: Install the closing resistor in the sealed monitoring chamber, and attach a multi-dimensional sensor array to the key measuring points on the surface of the resistor in situ. Then, seal the monitoring chamber, fill the insulating atmosphere, and perform self-calibration of the full-link signal transmission delay to build a test platform with the conditions for synchronous acquisition of multi-modal signals.

[0029] It should be noted that, in order to build a standardized test environment that can accurately reproduce the actual operating conditions of the closing resistor and achieve high-fidelity synchronous acquisition of multiple types of signals, the authenticity and comparability of all subsequent acquired data are ensured from four dimensions: physical installation, sensor coupling, insulation atmosphere, and signal timing. First, the closing resistor is fixed on a precision positioning platform inside the monitoring chamber. Through automatic coaxiality calibration and dynamic flatness adjustment, the coaxiality of the resistor installation is ensured to not exceed the third threshold (e.g., 0.005 mm), and the flatness fluctuation is ensured to not exceed the fourth threshold (e.g., within ±0.01 mm). This high-precision positioning requirement stems from the significant influence of the electric field distribution and mechanical constraints on the thermo-mechanical response of the closing resistor during energy impact; installation deviations will cause the measured data to deviate from the true value. Then, a distributed fiber optic temperature measurement array (8 channels, -40℃-800℃, accuracy ±0.3℃), a miniature thermal stress sensor (sampling rate 1MHz, 0-500MPa), a piezoelectric vibration sensor (measurement range 0.1-10μm, accuracy ±0.05μm), and an insulation coating non-destructive testing sensor (damage identification accuracy 0.1mm) are used. 2By non-destructively attaching a flexible, thermally conductive, and insulating substrate to key measuring points on the surface of the resistive element, this method neither damages the resistive element itself nor affects its heat transfer and insulation performance. Simultaneously, it ensures tight thermal contact and mechanical coupling between the sensor and the resistive element surface, allowing the measured signals such as temperature and thermal stress to accurately reflect the physical state of the resistive element itself rather than external environmental interference. After closing the monitoring chamber, it is filled with... The gas pressure was increased to 0.5 MPa and allowed to stand for 10 minutes, then passed through... The gas circulation and recovery module maintains a gas purity of no less than 99.5% to reproduce the real insulation atmosphere environment inside ultra-high voltage switchgear. At the same time, the high and low temperature environment chamber is started to simulate the target operating temperature such as extreme cold (e.g. -40℃) and high temperature and is kept at a constant temperature for 30 minutes. After the surface temperature of the resistor sheet stabilizes, the device is started to complete the dynamic self-calibration of signal transmission delay to ensure that the delay does not exceed the delay threshold, the self-calibration of sensor acquisition frequency, and the self-verification of environmental parameters. Among them, the delay self-calibration is particularly critical. Due to the different transmission paths and physical principles of each sensor, there are timing deviations. If calibration is not performed, the temperature, thermal stress, vibration and other signals in the subsequent thermal stress calculation will be misaligned on the time axis, making it impossible to accurately depict the causal relationship in the energy impact process.

[0030] Step S102: Input test object parameters and scene parameters, generate initial test parameters by matching historical valid data according to the test object parameters and scene parameters, and perform energy shock. In the energy shock, multi-modal signals are collected through the multi-dimensional sensor array every first preset time. The multi-modal signals include current, voltage, temperature, thermal stress, vibration, insulation state and atmospheric pressure signals.

[0031] It should be noted that operators input the core parameters of the closing resistor (initial resistance, nominal thermal capacity margin, material) and key parameters of the application scenario (ambient temperature, number of reclosing cycles) through the cloud-based collaborative unit. The system automatically matches the closest historical valid test data from the historical assessment database based on the material, ambient temperature, and number of reclosing cycles. This database contains industry standards and historical assessment records. When a perfectly matching operating condition group exists, it is directly called; otherwise, the average value of each parameter under the closest operating condition group is used as the initial value. The parameter range is adapted to the hardware capabilities as follows: The system operates within a range of 0-800kJ, 0-35kV, and 100-1000ms. Specifically, when the deviation between the nominal thermal capacity margin of the current resistor element and the historical average exceeds 15%, the system fine-tunes the initial parameters within ±10% of the deviation. This adaptive matching mechanism ensures that the initial test parameters do not deviate too far from the actual tolerance range of the current test object, avoiding both multiple invalid tests due to excessively low parameters and direct explosion accidents caused by excessively high parameters. After the parameters are sent to the energy storage regulation unit, the device is activated. The voltage is boosted via a 35kVA electric voltage regulator and a 35kV / 1A SF6 dry-type test transformer. A vacuum switch group with a response time ≤5ms applies a single energy impact of up to 800kJ to the resistor element. Simultaneously, the data fusion processing module synchronously acquires current, voltage, temperature, thermal stress, vibration, insulation status, and other data at a frequency of 1kHz. Seven types of multimodal signals of air pressure were collected. This high-frequency synchronous acquisition ensured the complete recording of the transient response of the resistor during the entire energy injection process (typical injection time 10ms). The collected data was stored in real time to the local database and backed up to the cloud, providing raw data support for subsequent feature extraction and thermal stress prediction.

[0032] Step S103: Standardize the multimodal signal, extract features from the standardized multimodal signal to obtain thermally sensitive features and corrected features, calculate the maximum thermal stress prediction value based on the thermally sensitive features, and determine whether the maximum thermal stress prediction value is greater than the first threshold.

[0033] It should be noted that in this step, when the raw signals (current, voltage, temperature, etc.) are directly output from the sensor, there are problems such as inconsistent dimensions (temperature unit: °C, stress unit: MPa, current unit: kA), significant differences in amplitude range (temperature signals change smoothly but stress signals fluctuate violently), and varying degrees of acquisition noise. If these raw signals are directly fed into the subsequent feature extraction and thermal stress prediction model, signals with large dimensions will dominate the calculation results while signals with small dimensions will be submerged, and noise signals may be misjudged as physical features, causing the prediction and evaluation to fail completely. Therefore, the standardization process in this invention is specifically as follows: mutual information filtering. By calculating the mutual information between each signal channel and the final margin evaluation target, noise channels and redundant signals with extremely weak correlation to the thermal capacity margin of the resistor are eliminated, and only the retained signals are entered into the subsequent feature extraction, as detailed below;

[0034] Energy deposition rate The ratio of the actual energy absorbed by the resistor to the total energy injected reflects the energy utilization efficiency. The formula is: ,in, Where is instantaneous power, C is total capacitance, and U is charging voltage, all in units of %.

[0035] Average temperature rise rate The difference between the highest temperature and the initial temperature, divided by the energy injection time, reflects the rate of temperature rise. The calculation formula is as follows: ,in, The highest temperature, The initial temperature, This refers to the injection duration.

[0036] Peak thermal-temperature coupling stress The core characteristics of integrating the absolute value of thermal stress and the temperature-force synergistic effect are shown in the calculation formula. Where E is the elastic modulus. The coefficient of thermal expansion is For the maximum temperature difference, To measure the peak thermal stress, The rated reference thermal stress for the material is 500 MPa (for carbon ceramic materials), in MPa.

[0037] Energy time-domain cumulative distortion rate The degree of energy accumulation deviation within the effective pulse width is calculated using the following formula: , For actual accumulated energy, Energy accumulated for theoretical ideals, in units of %.

[0038] Current bandwidth The frequency range with power spectral density ≥ 50% of the main peak reflects the concentration of frequency distribution. It is the frequency difference between the two sides of the main peak, in Hz.

[0039] Mean of low-frequency coefficients of wavelet The approximate mean of coefficients after 3-level decomposition of the db8 wavelet reflects the low-frequency trend of thermal stability correlation. The arithmetic mean is taken and has no unit.

[0040] Overall insulation degradation The comprehensive index integrating the effects of thermally induced insulation aging and leakage current is calculated using the following formula: ,in, For the integrity rate of the insulating coating, For leakage current, The area is the electrode area.

[0041] Rate of change of resistance The relative change in resistance after the test compared to the initial resistance reflects thermal stability. The calculation formula is... ,unit%.

[0042] air pressure stability During the assessment process The maximum air pressure fluctuation value reflects the stability of the insulation environment. The calculation formula is as follows: ,in, At maximum air pressure, This is the minimum air pressure, expressed in MPa.

[0043] average vibration amplitude The effective value of the vibration signal of the resistor during the assessment reflects the structural integrity. It is collected by a piezoelectric sensor, with a unit of μm and a weight of 0.05.

[0044] Furthermore, in some embodiments, the predicted maximum thermal stress value is calculated according to the following formula:

[0045] ;

[0046] in, This is the thermal stress coupling correction factor. The temperature exponential decay coefficient is... The average temperature. This is the temperature coupling index term, which reflects the nonlinear effect of temperature on thermal stress. For time-varying correction factors, The impact duration, measured in milliseconds (ms), reflects the time-domain decay characteristics of thermal stress; dimensionless normalization coefficient. , Weighting coefficient , , .

[0047] By substituting the three thermally sensitive features—energy deposition rate, average temperature rise rate, and peak thermal-temperature coupled stress—into the prediction formula according to the time-series recursive relationship where energy deposition first drives the average temperature rise rate, and the average temperature rise rate then drives the peak thermal-temperature coupled stress, the prediction formula can output the maximum predicted thermal stress value within 3ms after the start of the energy impact with an error of no more than 3%. This provides an approximately 3ms lead time for subsequent active safety protection, ensuring that the protection action occurs before rather than after thermal collapse.

[0048] Step S104: If the predicted maximum thermal stress value is greater than the first threshold, the energy impact is stopped, and the margin level is evaluated based on the thermal sensitivity characteristics and correction characteristics at the current moment to obtain the current margin level and confidence level.

[0049] It should be noted that in this step, the current operating condition is first obtained based on the material of the resistor sheet, the ambient temperature, and the number of reclosing cycles. Then, the corresponding standard operating condition name, the margin level under the standard operating condition, and the margin threshold range corresponding to each margin level are obtained. The margin level includes four categories: excellent, qualified, critical, and unqualified. The four threshold ranges for each feature do not overlap or intersect, ensuring the scientific nature and uniqueness of the threshold calibration.

[0050] Then, the heat-sensitive features and corrected features under the current operating condition are concatenated to obtain the target feature vector, and the similarity between the current operating condition and the standard operating condition is calculated:

[0051] ;

[0052] in, The similarity between the current operating condition and the standard operating condition. This represents the target feature vector under the current operating conditions. This refers to the standard feature vector under standard operating conditions.

[0053] If the similarity is less than the fifth threshold, then the various margin threshold ranges are adjusted according to the following formula:

[0054] ;

[0055] in, These are the corrected margin threshold ranges. These are the various margin threshold ranges before correction;

[0056] Determine the margin threshold range of each feature in the target feature vector, determine the target margin level corresponding to each feature based on the margin threshold range of each feature, obtain the weight value corresponding to each feature from the preset weight association table based on the target margin level, and add the weight values ​​of all target margin levels of the same type to obtain the cumulative weight value of each type of target margin level.

[0057] The maximum cumulative weight value is selected from all the cumulative weight values, and the margin level corresponding to the maximum cumulative weight value is used as the current margin level.

[0058] The preset weight association table includes multiple features and the weight value for each feature at each margin level.

[0059] In some embodiments, the confidence level of the margin level The calculation formula is as follows:

[0060] ;

[0061] in, The total weight of the 12-dimensional features is set to 1. The features corresponding to the final margin level are cumulatively weighted.

[0062] For example, consider a domestically produced carbon ceramic closing resistor for ultra-high voltage applications. Made of carbon ceramic, its application scenario is in high-altitude, cold regions with an ambient temperature of -40°C and two reclosing operations. Based on the input test object parameters (material = carbon ceramic) and scenario parameters (ambient temperature = -40°C, number of reclosing operations = 2), the system automatically identifies the current operating condition as "carbon ceramic, -40°C, 2 reclosing operations". Then, it searches the historical assessment database using this operating condition name to obtain the standard operating condition name that perfectly matches the current condition: "carbon ceramic_-40°C_2 reclosing_standard". This standard operating condition has accumulated 30 sets of historical assessment data for qualified resistors, each set containing a complete record of 10-dimensional features and the corresponding margin level judgment result.

[0063] Next, the pre-calibrated four-category margin threshold ranges for each feature under the standard operating condition are retrieved from the database. For example, the four-category ranges for the peak thermal-temperature coupling stress under the standard operating condition are: Excellent ≤280MPa, Acceptable (280,320], Critical (320,360], Unacceptable >360MPa; the four-category ranges for energy deposition rate are: Excellent ≥90%, Acceptable [80%,90%), Critical [70%,80%), Unacceptable <70%; and the four-category ranges for average temperature rise rate are: Excellent ≤25K / ms, Acceptable (25,35], Critical (35,45], Unacceptable >45K / ms. The other seven features each have corresponding four-category ranges, and the excellent, acceptable, critical, and unacceptable ranges for each feature do not overlap or intersect.

[0064] In summary, a three-tiered progressive mechanism—"calibration of the measured threshold library, dynamic correction of feature similarity, and weighted cumulative judgment of ten-dimensional features"—achieves a precise and comprehensive assessment of the margin level. First, operating conditions are grouped according to material, ambient temperature, and number of reclosing cycles, and four non-overlapping threshold ranges are pre-calibrated for each group, ensuring the judgment benchmark precisely corresponds to the actual service conditions of the resistor element and avoiding systematic deviations caused by using a uniform fixed threshold under different operating conditions. Simultaneously, cosine similarity is used to adaptively correct the threshold range; when the similarity falls below the fifth threshold, the threshold is shifted overall according to the design formula, ensuring the judgment standard... The system dynamically adjusts the threshold range based on the deviation between the current operating conditions and the standard operating conditions to ensure the applicability of the threshold range under different ambient temperatures or reclosing times. Then, it further assigns weights to all ten features according to their contribution to the causal chain and accumulates the scores of each level. The level with the highest accumulated weight is used as the final margin level and the confidence level is calculated. Compared with the traditional judgment method that only relies on the comparison of a single indicator threshold, this method avoids misjudgment caused by the random fluctuation of a single feature, improves the accuracy of margin level judgment, and provides a reliable margin judgment basis for subsequent confidence level grading and limit margin iteration.

[0065] Step S105: When the margin level is excellent, qualified or critical, if the confidence level is greater than the first preset confidence threshold, the current limiting heat capacity margin is calculated and output based on the thermal sensitivity characteristics. If the confidence level is less than or equal to the first preset confidence threshold and greater than the second preset confidence threshold, the energy injection parameters are adjusted in reverse based on the current thermal sensitivity characteristics and correction characteristics, and the energy impact and evaluation are re-executed until the deviation of the limiting heat capacity margin values ​​obtained from two consecutive iterations does not exceed the second threshold. Then the current limiting heat capacity margin is output.

[0066] It should also be noted that if the margin level is unqualified and / or the confidence level is less than or equal to the second preset confidence threshold, the test is deemed invalid; for example, when To ensure the reliability of the judgment, Validation needs to be repeated. If the current assessment is deemed invalid, the parameters must be readjusted before the assessment can proceed to ensure the rigor and accuracy of the margin assessment.

[0067] The current limiting heat capacity margin value is calculated using the following formula:

[0068] ;

[0069] in, This represents the current limiting heat capacity margin value.

[0070] In summary, by setting a first pre-set reliability threshold as the reliability threshold and a second pre-set reliability threshold as the goodness threshold, the confidence level of the margin assessment results is verified in stages: when the confidence level is not lower than 80%, the assessment result is directly determined to be reliable, avoiding unnecessary repeated verification of high-confidence results and thus wasting experimental resources; when the confidence level is between 50% and 80%, the assessment verification needs to be repeated once, and the judgment bias caused by random factors such as occasional sensor noise and energy injection fluctuations in a single test is eliminated through secondary experimental verification, ensuring that the results are verifiable; when the confidence level is lower than 50%, the assessment is determined to be invalid, avoiding the risk of subsequent decision-making caused by outputting low-reliability results as valid conclusions.

[0071] For example, using a laboratory precision positioning platform as the installation reference, the insulation-thermal stress co-monitoring chamber is fixed to the platform surface, housing a built-in resistive element and a multi-dimensional sensor array (including an 8-channel distributed fiber optic temperature measurement array, a miniature thermal stress sensor, a piezoelectric vibration sensor, and a non-destructive testing sensor for the insulation coating). The energy storage and regulation unit controls a 35kVA electric voltage regulator to adjust the voltage, providing 35kV / 1A. A dry-type test transformer and a vacuum switch group with a response time ≤5ms inject energy into the resistor. The data fusion processing module receives the signal from the sensor array and uploads the collected multi-modal signals to the edge computing unit with end-to-end delay in real time to complete the unified summary and transmission of the raw data. After processing the data, the edge computing unit pushes it to the thermal stress prediction unit to perform thermal stress calculation and safety judgment. The monitoring results of the thermal stress prediction unit are sent back to the cloud collaboration unit and control the energy storage regulation unit to perform energy regulation. The gas circulation and recovery module is connected to the monitoring chamber to realize gas circulation. The gas recovery rate is ≥95%. When the purity is lower than 99.5%, the purification process is automatically started.

[0072] The test object was a domestically produced carbon ceramic closing resistor for ultra-high voltage applications. The specific parameters of this resistor are as follows: outer diameter 150mm, inner diameter 40mm, thickness 25mm, initial resistance 5Ω±0.2Ω, nominal heat capacity margin 400J, material is carbon ceramic, elastic modulus 220GPa, and coefficient of thermal expansion is... thermal conductivity The application scenario is in high-altitude and cold regions (ambient temperature -40℃) and involves two reclosing operations. The specific implementation steps are as follows:

[0073] Step S1: Setting up the assessment environment and debugging the device;

[0074] Based on the closing resistor element's properties under extreme cold / high temperature / high altitude conditions, and the impact of reclosing. The actual operating conditions under insulating atmosphere and multi-field coupling of heat, force, and electricity were simulated using a modular and precise replication method to build the testing environment. The testing device was fixed on a precision positioning platform in the laboratory. Automatic coaxiality calibration and dynamic flatness adjustment were used to achieve coordinated positioning of the device, ensuring that the coaxiality of the resistor element installation was ≤0.005mm and the flatness fluctuation was ≤±0.01mm. A distributed fiber optic temperature measurement array, a miniature thermal stress sensor, a piezoelectric vibration sensor, and a non-destructive testing sensor for the insulating coating were non-destructively attached to key measuring points on the surface of the resistor element in situ. The sensors were fixed using a flexible thermally conductive insulating substrate, without damaging the resistor element itself or affecting heat transfer and insulation performance. The insulation-thermal stress co-monitoring chamber was closed and filled with... The gas pressure was increased to 0.5 MPa and allowed to stand for 10 minutes, then passed through... The gas circulation and recovery module achieves dynamic closed-loop calibration of gas purity (purity ≥ 99.5%); the high and low temperature environment chamber is started to simulate the target operating temperature, and the surface temperature of the resistor is confirmed to be stable after 30 minutes of constant temperature; the device is started to complete the dynamic self-calibration of signal transmission delay, self-calibration of sensor acquisition frequency, and self-verification of environmental parameters.

[0075] Step S2: Parameter configuration and multimodal signal acquisition;

[0076] By inputting core parameters and scenario parameters through the cloud-based collaborative unit and matching historical data, the initial parameters were determined to be: energy injection 40kJ, voltage 24kV, reclosing interval 500ms, and energy injection duration 10ms. After starting the device, multi-modal signals from two reclosing processes were collected: peak reclosing impulse current of 4.8kA, voltage 24kV±0.2kV, maximum resistor temperature of 480℃, thermal stress of 0-272MPa, vibration amplitude of 0.32μm, and insulation coating integrity rate of 99.0%. The air pressure is 0.5MPa±0.003MPa and the leakage current is 0.000018A. The actual energy absorbed by the resistor is calculated to be 36.5kJ through voltage-current data integration. The collected data is stored in the local database in real time and backed up to the cloud.

[0077] Step S3: Feature vector construction and standardization;

[0078] Based on the collected signals, 10-dimensional features were extracted, including an energy deposition rate of 91.25%, an average temperature rise rate of 27.12 K / ms, a peak thermal-temperature coupling stress of 304.9 MPa, an energy time-domain cumulative distortion rate of 1.12%, a current bandwidth of 65 Hz, an average wavelet low-frequency coefficient of 0.82, an overall insulation degradation of 13.165, a resistance change rate of 2.48%, an air pressure stability of 0.005 MPa, and an average vibration amplitude of 0.32 μm.

[0079] Step S4: Thermal stress prediction and margin level assessment:

[0080] (1) Real-time prediction of thermal stress:

[0081] First, calculate the average temperature. Extracting the core heat-sensitive feature: energy deposition rate characteristic value. Characteristic value of average temperature rise rate 2. Peak characteristic value of thermal-temperature coupled stress Combining the fixed-dimensional normalization coefficients and the prediction-specific weights, and substituting them into the optimized nonlinear fusion prediction formula, we obtain... The predicted value was less than the 280MPa protection threshold. The vacuum switch group was not triggered to disconnect the discharge circuit and the injected energy was not reduced. The two reclosing tests were performed stably throughout.

[0082] (2) Three-level assessment of margin level:

[0083] Step 1: Actual threshold library matching:

[0084] The operating conditions are divided into groups based on carbon ceramic material, -40℃ ambient temperature, and two reclosing operations. The 10-dimensional features and four-category threshold ranges of 30 sets of qualified resistors under each operating condition group are called. The threshold values ​​of each feature do not overlap or intersect, thus meeting the requirement of uniqueness in judgment.

[0085] Step 2: Dynamic adjustment of feature similarity:

[0086] Similarity calculated using the cosine similarity formula There is no need to adjust the threshold range; the original calibrated threshold can be used directly for judgment.

[0087] Step 3: Weighted accumulation and determination of 10-dimensional features:

[0088] The current 10-dimensional feature values ​​are compared one by one with the corrected threshold to determine the margin level corresponding to each feature. The margin levels are then accumulated according to preset weights: the weight for excellent level is 0.862, the weight for qualified level is 0.138, and the weight for critical and unqualified levels is 0. The excellent level with the highest accumulated weight is taken as the final margin level.

[0089] (3) Confidence calculation and verification:

[0090] The confidence level for this assessment was calculated using the confidence level formula. 86.2% ≥ 80%, the judgment result is reliable and no repeated assessment or verification is required.

[0091] (4) Dynamic energy adjustment and report output:

[0092] The assessment margin level is excellent. No parameter adjustment logic needs to be triggered. A complete comprehensive assessment report is directly output. The report includes the ultimate heat capacity margin value of 450J, a thermal stress visualization cloud map, a 10-dimensional feature sensitivity ranking, and safety usage recommendations for double reclosing in high-altitude and cold regions.

[0093] The hardware involved is described below:

[0094] The insulation-thermal stress co-monitoring chamber incorporates a multi-dimensional sensor array, including a distributed fiber optic temperature measurement array (8 channels, -40℃-800℃, accuracy ±0.3℃), a miniature thermal stress sensor (sampling rate 1MHz, 0-500MPa, surface mount), a piezoelectric vibration sensor (measurement range 0.1-10μm, accuracy ±0.05μm), and a non-destructive testing sensor for the insulation coating (damage identification accuracy 0.1mm²). The sensors are fixed using a surface mount method, eliminating the need to damage the resistor body and enabling synchronous acquisition of multi-modal signals.

[0095] Vacuum switchgear, response time ≤5ms, paired with a 35kVA electric voltage regulator and a 35kV / 1A... The dry-type test transformer has an adjustable output voltage of 0-35kV, a maximum single injection energy of 800kJ, and supports millisecond-level disconnection of the discharge circuit to regulate the injected energy.

[0096] The edge computing unit uses a high-performance embedded core board, integrating data preprocessing and real-time inference functions. All data processing is completed locally, reducing reliance on the cloud.

[0097] The gas circulation and recovery module integrates a leak detection sensor and a cryogenic liquefaction recovery device, achieving a gas recovery rate of ≥95%. When the purity is below 99.5%, the purification process is automatically activated, taking into account both environmental protection and insulation safety.

[0098] In summary, this invention achieves high-fidelity synchronous acquisition of multi-modal signals such as current, voltage, temperature, thermal stress, vibration, insulation status, and atmospheric pressure during energy impact by installing a closing resistor in a sealed monitoring chamber and in-situ attaching a multi-dimensional sensor array to key measurement points on the surface. This is combined with the sealing of the monitoring chamber, the filling of the insulating atmosphere, and self-calibration of the end-to-end signal transmission delay. Based on this, the invention standardizes and extracts features from the acquired multi-modal signals to obtain thermally sensitive and corrected features. The maximum predicted thermal stress value is calculated based on the thermally sensitive features, and the energy impact is immediately stopped when it exceeds a first threshold. Furthermore, a margin level assessment is performed based on the current thermally sensitive and corrected features to obtain the margin level and confidence level, thus achieving active safety protection and real-time status assessment during the impact process. Further, when the margin level... When the value is excellent, qualified, or critical and the confidence level is greater than the first preset confidence threshold, the current limiting thermal capacity margin is calculated based on the thermal sensitivity characteristics and directly output. When the confidence level is less than or equal to the first preset confidence threshold and greater than the second preset confidence threshold, the energy injection parameters are adjusted in reverse based on the current thermal sensitivity characteristics and correction characteristics, and the energy impact and evaluation are re-executed until the deviation of the limiting thermal capacity margin value obtained from two consecutive iterations does not exceed the second threshold. Thus, the true limiting thermal capacity margin of the resistor is accurately converged to through iterative approximation. This effectively solves the technical problems in the existing technology, such as poor test safety due to the lack of multi-mode signal synchronous acquisition and real-time thermal stress prediction mechanism, and low margin determination accuracy and inability to accurately obtain the limiting thermal capacity margin due to the lack of confidence level evaluation and iterative approximation mechanism.

[0099] like Figure 2 As shown, one embodiment of the present invention proposes a thermal capacity margin testing system for a closing resistor element, the system comprising:

[0100] The test platform construction module 10 is used to install the closing resistor in a sealed monitoring chamber, attach a multi-dimensional sensor array to the key measuring points on the surface of the resistor in situ, and perform monitoring chamber sealing, insulation atmosphere filling and full-link signal transmission delay self-calibration to build a test platform with multi-modal signal synchronous acquisition conditions.

[0101] The signal acquisition module 20 is used to input test object parameters and scene parameters, match historical valid data according to the test object parameters and scene parameters to generate initial test parameters and perform energy shock. In the energy shock, multi-modal signals are acquired through the multi-dimensional sensor array every first preset time. The multi-modal signals include current, voltage, temperature, thermal stress, vibration, insulation state and atmospheric pressure signals.

[0102] The feature extraction module 30 is used to standardize the multimodal signal, extract features from the standardized multimodal signal to obtain thermally sensitive features and corrected features, calculate the maximum thermal stress prediction value based on the thermally sensitive features, and determine whether the maximum thermal stress prediction value is greater than a first threshold.

[0103] The margin level assessment module 40 is used to stop the energy impact if the predicted maximum thermal stress value is greater than the first threshold, and to perform a margin level assessment based on the thermal sensitivity characteristics and correction characteristics at the current moment to obtain the current margin level and confidence level.

[0104] The margin output module 50 is used to calculate and output the current limiting heat capacity margin based on the thermal sensitivity characteristics when the margin level is excellent, qualified, or critical. If the confidence level is greater than the first preset confidence threshold, the module is used to calculate and output the current limiting heat capacity margin based on the thermal sensitivity characteristics and the correction characteristics at the current moment. If the confidence level is less than or equal to the first preset confidence threshold and greater than the second preset confidence threshold, the module is used to adjust the energy injection parameters in reverse according to the thermal sensitivity characteristics and correction characteristics at the current moment and return to re-execute the energy impact and evaluation until the deviation of the limiting heat capacity margin values ​​obtained from two consecutive iterations does not exceed the second threshold. Then, the module outputs the current limiting heat capacity margin.

[0105] In another aspect, the present invention also proposes a storage medium having stored one or more programs thereon, which, when executed by a processor, implement the above-described method for testing the thermal capacity margin of the closing resistor.

[0106] In another aspect, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the above-mentioned method for testing the thermal capacity margin of the closing resistor.

[0107] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain stored, communicated, propagated, or transmitted programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0108] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0109] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0110] While embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations fall within the scope and spirit of the invention as set forth in the claims. Furthermore, the invention described herein may have other embodiments and can be implemented or carried out in various ways.

Claims

1. A method for testing the thermal capacity margin of a closing resistor element, characterized in that, The method includes: The closing resistor is installed in a sealed monitoring chamber, and a multi-dimensional sensor array is attached to the key measuring points on the surface of the resistor in situ. The monitoring chamber is then sealed, filled with insulating atmosphere, and self-calibrated for the signal transmission delay of the entire link, so as to build a test platform with the conditions for synchronous acquisition of multi-modal signals. Input test object parameters and scene parameters, match historical valid data according to the test object parameters and scene parameters to generate initial test parameters and perform energy shock. In the energy shock, multi-modal signals are collected through the multi-dimensional sensor array every first preset time. The multi-modal signals include current, voltage, temperature, thermal stress, vibration, insulation state and atmospheric pressure signals. The multimodal signal is standardized, and features are extracted from the standardized multimodal signal to obtain thermally sensitive features and corrected features. The maximum thermal stress prediction value is calculated based on the thermally sensitive features, and it is determined whether the maximum thermal stress prediction value is greater than a first threshold. If the predicted maximum thermal stress value is greater than the first threshold, the energy impact is stopped, and the margin level is evaluated based on the thermal sensitivity characteristics and correction characteristics at the current moment to obtain the current margin level and confidence level. When the margin level is excellent, qualified, or critical, if the confidence level is greater than the first preset confidence threshold, the current limiting heat capacity margin is calculated and output based on the thermal sensitivity characteristics. If the confidence level is less than or equal to the first preset confidence threshold and greater than the second preset confidence threshold, the energy injection parameters are adjusted in reverse based on the current thermal sensitivity characteristics and correction characteristics, and the energy impact and evaluation are re-executed until the deviation of the limiting heat capacity margin values ​​obtained from two consecutive iterations does not exceed the second threshold. Then, the current limiting heat capacity margin is output.

2. The method for testing the thermal capacity margin of the closing resistor element according to claim 1, characterized in that, The steps of installing the closing resistor in a sealed monitoring chamber, attaching a multi-dimensional sensor array to key measuring points on the resistor surface in situ, and performing monitoring chamber sealing, insulating atmosphere filling, and end-to-end signal transmission delay self-calibration to construct a test platform capable of synchronous acquisition of multi-modal signals include: The closing resistor is fixed on a precision positioning platform inside the monitoring cabin. Through coaxiality calibration and flatness adjustment, the coaxiality of the resistor installation is ensured to not exceed the third threshold, and the flatness fluctuation is ensured to not exceed the fourth threshold. The multi-dimensional sensor array is attached to the surface of the resistor sheet using a flexible thermally conductive insulating substrate; Shut down the monitoring chamber and fill it with... The gas is brought to the target pressure and allowed to stand for a set time, while maintaining the gas purity at or above the preset purity threshold. Start the high and low temperature environment chamber to simulate the target operating temperature and stabilize it. After confirming that the surface temperature of the resistor has reached thermal equilibrium, perform signal transmission delay self-calibration to ensure that the delay does not exceed the delay threshold, sensor acquisition frequency self-calibration, and environmental parameter self-verification.

3. The method for testing the thermal capacity margin of the closing resistor element according to claim 1, characterized in that, The step of inputting test object parameters and scenario parameters, and generating initial test parameters by matching historical valid data with the test object parameters and scenario parameters includes: The test object parameters include at least initial resistance, nominal thermal capacity margin, and material; the scenario parameters include at least ambient temperature and number of reclosing cycles. Based on the material, ambient temperature, and number of reclosing cycles, the closest historical valid data in the historical assessment database is matched, and the average value of energy injection, charging voltage, and reclosing interval in the historical valid data is taken as the initial test parameters.

4. The method for testing the thermal capacity margin of the closing resistor element according to claim 1, characterized in that, The steps of standardizing the multimodal signal and extracting features from the standardized multimodal signal to obtain thermally sensitive features and corrected features include: The thermally sensitive characteristics include energy deposition rate. Average temperature rise rate and thermal-temperature coupled stress peak ; The energy deposition rate is calculated using the following formula: ； in, This is the real-time voltage across the resistor. Let t be the real-time current flowing through the resistor, C be the total capacitance, U be the charging voltage, and t be the time t. The average temperature rise rate is calculated using the following formula: ； in, The highest temperature, The initial temperature, Injection duration; The peak value of the thermal-temperature coupling stress is calculated using the following formula: ； Where E is the elastic modulus. The coefficient of thermal expansion is For the maximum temperature difference, To measure the peak thermal stress, The rated reference thermal stress for the material; The correction feature includes energy temporal cumulative distortion rate. Current bandwidth Mean of low-frequency coefficients of wavelet Overall insulation degradation Rate of change of resistance air pressure stability Average vibration amplitude ; The energy time-domain cumulative distortion rate is calculated using the following formula: ； in, For actual accumulated energy, Accumulate energy for theoretical ideals; The overall insulation degradation degree is calculated using the following formula: ； in, For the integrity rate of the insulating coating, For leakage current, The electrode area; The rate of change of resistance is calculated using the following formula: ； in, The measured resistance value of the closing resistor after the thermal capacity margin test is completed. To test the initial resistance value of the closing resistor; The air pressure stability is calculated using the following formula: ； in, At maximum air pressure, This is the minimum air pressure.

5. The method for testing the thermal capacity margin of the closing resistor element according to claim 4, characterized in that, The step of calculating the predicted maximum thermal stress value based on the thermal sensitivity characteristics includes: The predicted maximum thermal stress value is calculated using the following formula: ； in, This is the predicted value for maximum thermal stress. This is the thermal stress coupling correction factor. The temperature exponential decay coefficient is... The average temperature. All are weighting coefficients. , , All are normalized coefficients. For time-varying correction factors, To impact duration.

6. The method for testing the thermal capacity margin of the closing resistor element according to claim 4, characterized in that, The step of evaluating the margin level based on the current thermal sensitivity characteristics and correction characteristics to obtain the current margin level and confidence level includes: The current operating condition is obtained based on the resistor material, ambient temperature, and number of reclosing cycles. The corresponding standard operating condition name, margin level under the standard operating condition, and margin threshold range corresponding to each margin level are also obtained. The margin level includes four categories: excellent, qualified, critical, and unqualified. The heat-sensitive features and corrected features under the current operating condition are concatenated to obtain the target feature vector, and the similarity between the current operating condition and the standard operating condition is calculated: ； in, The similarity between the current operating condition and the standard operating condition. This represents the target feature vector under the current operating conditions. This refers to the standard feature vector under standard operating conditions. If the similarity is less than the fifth threshold, then the various margin threshold ranges are adjusted according to the following formula: ； in, These are the corrected margin threshold ranges. These are the various margin threshold ranges before correction; Determine the margin threshold range of each feature in the target feature vector, determine the target margin level corresponding to each feature based on the margin threshold range of each feature, obtain the weight value corresponding to each feature from the preset weight association table based on the target margin level, and add the weight values ​​of all target margin levels of the same type to obtain the cumulative weight value of each type of target margin level. The maximum cumulative weight value is selected from all the cumulative weight values, and the margin level corresponding to the maximum cumulative weight value is used as the current margin level. The preset weight association table includes multiple features and the weight value for each feature at each margin level.

7. The method for testing the thermal capacity margin of the closing resistor element according to claim 6, characterized in that, The method further includes: If the margin level is unqualified and / or the confidence level is less than or equal to the second preset confidence threshold, then the test is deemed invalid. The current limiting heat capacity margin value is calculated using the following formula: ； in, This represents the current limiting heat capacity margin value.

8. A system for testing the thermal capacity margin of a closing resistor element, characterized in that, The system includes: The test platform construction module is used to install the closing resistor in a sealed monitoring chamber, attach a multi-dimensional sensor array to the key measuring points on the surface of the resistor in situ, and perform monitoring chamber sealing, insulation atmosphere filling and end-to-end signal transmission delay self-calibration to build a test platform with multi-modal signal synchronous acquisition conditions. The signal acquisition module is used to input test object parameters and scene parameters, match historical valid data according to the test object parameters and scene parameters to generate initial test parameters and perform energy shock. During the energy shock, multi-modal signals are acquired through the multi-dimensional sensor array every first preset time. The multi-modal signals include current, voltage, temperature, thermal stress, vibration, insulation state and atmospheric pressure signals. The feature extraction module is used to standardize the multimodal signal, extract features from the standardized multimodal signal to obtain thermally sensitive features and corrected features, calculate the maximum thermal stress prediction value based on the thermally sensitive features, and determine whether the maximum thermal stress prediction value is greater than a first threshold. The margin level assessment module is used to stop the energy impact if the predicted maximum thermal stress value is greater than the first threshold, and to assess the margin level based on the thermal sensitivity characteristics and correction characteristics at the current moment to obtain the current margin level and confidence level. The margin output module is used to calculate and output the current limiting heat capacity margin based on the thermal sensitivity characteristics when the margin level is excellent, qualified, or critical. If the confidence level is greater than the first preset confidence threshold, the module will output the margin when the confidence level is less than or equal to the first preset confidence threshold and greater than the second preset confidence threshold. If the confidence level is less than or equal to the first preset confidence threshold and greater than the second preset confidence threshold, the module will adjust the energy injection parameters in reverse according to the thermal sensitivity characteristics and correction characteristics at the current moment and return to re-execute the energy impact and evaluation until the deviation of the limiting heat capacity margin values ​​obtained from two consecutive iterations does not exceed the second threshold. Then, the module will output the current limiting heat capacity margin.

9. A storage medium, characterized in that, The storage medium stores one or more programs that, when executed by a processor, implement the thermal capacity margin test method for the closing resistor element as described in any one of claims 1-7.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the method for testing the thermal capacity margin of the closing resistor as described in any one of claims 1-7.

Images