A test control method and system for a DC power supply

Abstract

This invention discloses a test control method and system for DC power supplies, relating to the field of simulation testing. The method includes: constructing a three-dimensional adaptive model to complete the matching and calibration of the system's hardware and software; achieving load calibration through dynamic load simulation and comparison with measured data; employing a three-level startup mechanism for step-by-step power-on with fault protection; performing parameter scanning and real-time recording according to preset modes and test points; introducing a dual closed-loop feedback mechanism, with the inner loop adjusting output parameters and the outer loop predicting and compensating for grid disturbances; dynamically generating safety thresholds based on real-time environmental data to achieve linked protection of approach warning and over-limit protection; and forming a reliable and effective dataset through the fusion processing and dual verification of multi-source test data. The advantages of this invention are: achieving precision and intelligence throughout the entire testing process through dynamic load simulation; constructing a multi-level safety protection system through the three-level startup mechanism; and comprehensively improving the accuracy, safety, and data value of the test.

Description

Technical Field

[0001] This invention relates to the field of analog testing technology, and more specifically, to a test control method and system for a DC power supply. Background Technology

[0002] With the continuous growth in demand for high-performance DC power supplies from industrial equipment, electric vehicles, and new energy fields, higher requirements are being placed on their output accuracy, dynamic response, and reliability. In recent years, automated testing and digital control technologies have gradually become widespread. By integrating programmable electronic loads, data acquisition modules, and host computer software, multi-parameter, sequential testing has been achieved, significantly improving efficiency and accuracy.

[0003] Most current DC power supply testing methods lack multi-dimensional adaptation models, relying solely on manual matching of hardware and software parameters. The calibration process fails to consider power supply rated parameters, test requirements, and environmental baseline data, making them susceptible to temperature, humidity, and grid fluctuations, leading to fundamental test deviations. Dynamic load simulations are often limited to single load types, lacking real-time calibration mechanisms, resulting in load simulation deviations exceeding ±1%, failing to accurately reproduce complex load characteristics such as pulses and nonlinearities in real-world applications. Startup procedures often simplify self-testing and loading steps, lacking tiered startup and abnormal node recording, easily causing equipment failures during startup. Feedback regulation often employs single-loop control, passively correcting output deviations and lacking neural network-based disturbance prediction compensation, resulting in delayed responses to grid abrupt changes and load fluctuations. Safety thresholds are mostly fixed values, failing to dynamically adjust based on the test stage and environment, with simplistic early warning and protection mechanisms, posing safety hazards. Data processing involves only simple screening, lacking layered fusion and dual verification, resulting in weak effective data identification capabilities and output data lacking standardization and correlation, failing to provide high-quality support for power supply performance analysis. Summary of the Invention

[0004] To improve existing methods and systems, a test control method and system for DC power supplies is provided. This method achieves precision and intelligence throughout the entire test process through three-dimensional adaptive calibration, dynamic load simulation, and dual closed-loop feedback regulation. A multi-level safety protection system is constructed through a three-level startup mechanism and dynamic safety thresholds, which comprehensively improves the accuracy, safety, and data value of the test.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for testing and controlling a DC power supply, comprising:

[0007] A three-dimensional adaptation model is constructed based on the rated parameters of the DC power supply under test, test requirements, and test environment benchmark parameters. The hardware configuration and software parameters of the test system are matched and adapted and calibrated.

[0008] Based on the actual application scenario of the DC power supply under test, the dynamic load characteristics are simulated. The actual absorbed voltage and current data of the load are collected in real time and compared with the preset load parameters. The load is calibrated based on the comparison results.

[0009] The test is started through a three-level startup mechanism. The first level is the control module self-test, the second level is the DC power supply under test starting under no-load, and the third level is the gradual connection of the load. If an abnormal situation occurs during the startup process, the shutdown protection is triggered and the abnormal node is recorded.

[0010] A multi-mode test sequence is generated based on pre-configured test requirements. In each mode, parameter scanning test is performed according to the preset test point distribution. During the test, power parameters are recorded in real time.

[0011] A dual closed-loop feedback regulation mechanism is constructed. The inner loop compares the collected power supply parameters with the preset target value to generate a regulation signal. The outer loop monitors the grid parameters, establishes a disturbance impact model based on historical test data, predicts the impact trend of disturbances on power supply output parameters, and outputs compensation signals in advance.

[0012] Based on the rated parameters of the power supply under test, the test stage, and real-time environmental data, a safety boundary threshold is generated. The power supply parameters are collected in real time and compared with the dynamic safety threshold. When the monitored value approaches or exceeds the dynamic safety threshold, the early warning mechanism and interlock protection are triggered respectively.

[0013] Data from multiple sources was collected during the testing process. A hierarchical fusion algorithm was used for data processing, and a dual verification mechanism was used to determine the validity of the data to obtain a valid dataset.

[0014] Preferably, the step of constructing a three-dimensional adaptation model based on the rated parameters of the DC power supply under test, test requirements, and test environment benchmark parameters, and matching the hardware configuration and software parameters of the test system and performing adaptation calibration specifically includes:

[0015] The rated parameters of the DC power supply under test include output voltage, current range, power, and ripple rejection ratio design value.

[0016] The testing requirements include steady-state accuracy, dynamic response, overload capacity, and environmental adaptability testing.

[0017] Environmental baseline parameters, including temperature, humidity, and power grid input voltage fluctuation range, are collected in real time through an integrated temperature and humidity sensor.

[0018] A three-dimensional adaptation model is built based on the collected power supply rated parameters, test requirements, and environmental benchmark parameters. The mapping relationship between parameters is established, and the hardware and software parameters of the test system are matched through the three-dimensional adaptation model. The matched sensors and load modules are then pre-calibrated.

[0019] Preferably, the step of simulating dynamic load characteristics based on the actual application scenario of the DC power supply under test, and comparing the actual absorbed voltage and current data of the load in real time with preset load parameters, and performing load calibration based on the comparison results specifically includes:

[0020] Based on the specific application scenario of the DC power supply under test, dynamic load characteristics are simulated to generate various dynamic loads, including pulse load, nonlinear load, stepped load, and random fluctuating load.

[0021] The system continuously collects the actual voltage and current data absorbed by the load and compares them with the preset load parameters. If the deviation exceeds ±1%, it adjusts the amplitude and phase of the load drive signal and compensates for the nonlinear error and temperature drift of the load module to correct the load simulation deviation.

[0022] Preferably, the test is initiated through a three-level startup mechanism. The first level is a self-test of the control module, the second level is an unloaded startup of the DC power supply under test, and the third level is a gradual connection of the load. Based on any abnormal situation occurring during the startup process, a shutdown protection is triggered and the abnormal node is recorded. Specifically, this includes:

[0023] Perform a self-test of the first-level control module to verify whether the connectivity between modules is normal, and check the working status of each module to ensure that there are no hardware faults or communication interruptions.

[0024] The second-level no-load start-up and monitoring is performed. The DC power supply under test is controlled to enter the no-load start-up mode. The power supply output voltage is controlled to rise from 0V to the test start voltage at a preset slope. The ripple voltage and noise voltage of the power supply output under no-load conditions are monitored in real time to determine whether there is no-load current leakage.

[0025] After performing the third level of graded loading and starting without any abnormalities, gradually connect the load to the power supply according to the load ratios of 10%, 30%, 50%, 80%, and 100%, and continuously monitor the fluctuation range of the power supply output parameters.

[0026] If the ripple voltage exceeds 50% of the design threshold, current leakage is found, or parameter fluctuations exceed ±3% during graded loading, the shutdown protection mechanism will be triggered immediately, and the node data where the abnormality occurred will be recorded.

[0027] Preferably, the generation of multi-mode test sequences based on pre-configured test requirements, wherein parameter scanning tests are performed according to a preset test point distribution in each mode, and the real-time recording of power parameters during the test specifically includes:

[0028] Generate test sequences including constant voltage mode, constant current mode, constant power mode and mixed mode based on pre-configured test requirements;

[0029] Test points are set for each test mode, including rated value, limit value, critical value and random sample value. The limit value test adopts a step-by-step approximation method, with each increment or decrement being 5% of the rated value.

[0030] Based on the set test sequence and test points, parameter scanning tests are performed. During the test, the power supply's output voltage accuracy, output current accuracy, load regulation, source effect regulation, dynamic response time, and output ripple peak-to-peak parameters are monitored in real time.

[0031] Preferably, the construction of the dual closed-loop feedback regulation mechanism, wherein the inner loop compares the collected power supply parameters with preset target values ​​to generate a regulation signal, and the outer loop monitors grid parameters, establishes a disturbance impact model based on historical test data, predicts the impact trend of disturbances on power supply output parameters, and outputs a compensation signal in advance, specifically includes:

[0032] A dual-closed-loop feedback regulation mechanism is constructed, with the inner loop for output parameter feedback and the outer loop for disturbance prediction and compensation.

[0033] The inner loop compares the collected actual data with the preset target value to generate an adjustment signal, which then controls the PWM drive module of the power supply to correct the output deviation.

[0034] The outer ring collects data on grid input voltage fluctuations, ambient temperature changes, and load mutations. Based on historical test data, a disturbance impact model based on an LSTM neural network is established to predict the impact trend of disturbances on power supply output parameters. Based on the prediction results, compensation signals are output in advance.

[0035] When a sudden disturbance such as a sharp drop in grid voltage or a load change exceeding a threshold is detected, an emergency adjustment mode is activated, shortening the feedback adjustment cycle and calling a pre-stored disturbance compensation strategy library to offset the impact of the sudden disturbance.

[0036] Preferably, the step of generating a safety boundary threshold based on the rated parameters of the power supply under test, the test stage, and real-time environmental data, and comparing the power supply parameters with the dynamic safety threshold in real time, triggering an early warning mechanism and activating interlocking protection when the monitored value approaches or exceeds the dynamic safety threshold, specifically includes:

[0037] Based on the rated parameters of the power supply under test, the current test stage, and real-time environmental data, safety boundary thresholds are generated, including the maximum allowable output voltage, the maximum allowable output current, the maximum allowable temperature rise, and the minimum allowable insulation resistance.

[0038] The system continuously collects output parameters, casing temperature, internal component temperature, and insulation resistance value of the power supply. The collected real-time data is compared with the dynamic safety boundary threshold in real time. If the monitored value reaches 90% of the threshold, an early warning mechanism is triggered, and the test load is reduced and the test parameters are adjusted.

[0039] If the monitored value exceeds the safety boundary threshold, the shutdown operation will be carried out in the order of reducing current, reducing voltage, and cutting off power, while disconnecting the electrical connection between the load and the power supply.

[0040] Preferably, the collection and testing process involves multi-source data, employing a hierarchical fusion algorithm for data processing, and using a dual verification mechanism to determine the validity of the data to obtain a valid dataset. Specifically, this includes:

[0041] All types of data from the entire testing process are aggregated, including power output parameter data, load feedback data, environmental monitoring data, disturbance record data, and safety protection data, and a layered fusion algorithm is used for data processing.

[0042] Through the first step of data cleaning in the layered fusion process, outliers and duplicate records in the data are identified and removed, and invalid data interference is eliminated.

[0043] By analyzing the correlation between different data types through the second step of hierarchical fusion, a corresponding mapping between power supply output parameters and environmental changes, load status, and disturbance factors is constructed.

[0044] The effectiveness verification of the third step of the hierarchical integration verifies the consistency of the values ​​of voltage, current, and power, as well as the timing matching of the dynamic response curve with load changes. The key test points of rated values ​​and extreme values ​​are repeatedly tested, and the verified data are integrated to form a standardized and effective dataset.

[0045] Furthermore, a test and control system for a DC power supply is proposed, comprising:

[0046] 3D Adaptation and Calibration Module: Based on power specifications, test requirements, and environmental parameters, a model is built to complete the automatic matching and initial calibration of the test system's hardware and software;

[0047] Dynamic load simulation module: Simulates various dynamic load characteristics under real application scenarios and performs online calibration of load simulation accuracy through real-time data feedback;

[0048] The three-level safe startup module controls the test startup process in the order of system self-test - no-load startup - graded loading, and performs protection and recording in case of abnormality;

[0049] Multi-mode test sequence execution module: Automatically generates and executes parameter scanning test sequences containing multiple working modes and test points based on preset test requirements;

[0050] Dual closed-loop control module: The inner loop quickly corrects the output deviation, while the outer loop predictively compensates for external disturbances, thus maintaining the stability and accuracy of the testing process.

[0051] Safety boundary protection module: Calculates and updates safety thresholds in real time, and ensures test safety through multi-level early warning and gradient interlocking protection mechanisms;

[0052] Multi-source data fusion processing module: collects data from the entire testing process, cleans it, performs correlation analysis and double verification, and finally outputs a standardized and valid dataset;

[0053] Processor: The processor is used to handle the calculation process of each formula and the construction calculation process of each model.

[0054] Compared with the prior art, the advantages of the present invention are:

[0055] By constructing a 3D adaptation model and combining power supply rated parameters, test requirements, and environmental data, precise matching and calibration of hardware and software are achieved, laying a reliable foundation for testing. Dynamic load simulation can reproduce various load characteristics of real application scenarios, and deviations are controlled within ±1% through real-time calibration, improving scenario adaptability. A three-level startup mechanism with layer-by-layer verification avoids abnormal risks during the startup phase, and multi-mode test sequences cover all-dimensional test points, making parameter scanning more comprehensive. The innovative dual-loop feedback regulation, which integrates real-time inner-loop correction and LSTM-based outer-loop disturbance prediction compensation, can also cope with sudden disturbances and significantly improve test stability. Dynamic safety thresholds combined with multi-level early warning and gradient protection ensure test safety from the source. In addition, the layered fusion algorithm and dual verification mechanism perform refined processing of multi-source data, eliminating invalid data and verifying correlations. The final standardized dataset provides high-quality support for power supply performance analysis, achieving a synergistic improvement in test efficiency, accuracy, and safety. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the method proposed in this invention;

[0057] Figure 2 This is a schematic diagram of the three-dimensional adaptation model proposed in this invention;

[0058] Figure 3 This is a schematic diagram of the load calibration proposed in this invention;

[0059] Figure 4 This is a schematic diagram of the three-level startup mechanism proposed in this invention;

[0060] Figure 5 This is a schematic diagram illustrating the generation of multi-mode test sequences proposed in this invention;

[0061] Figure 6 This is a schematic diagram of the dual closed-loop feedback regulation mechanism proposed in this invention;

[0062] Figure 7 This is a schematic diagram illustrating the generation of the safety boundary threshold proposed in this invention;

[0063] Figure 8 This is a schematic diagram illustrating the method for obtaining a valid dataset as proposed in this invention. Detailed Implementation

[0064] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0065] A test and control system for a DC power supply, comprising:

[0066] 3D Adaptation and Calibration Module: Based on power specifications, test requirements, and environmental parameters, a model is built to complete the automatic matching and initial calibration of the test system's hardware and software;

[0067] Dynamic load simulation module: Simulates various dynamic load characteristics under real application scenarios and performs online calibration of load simulation accuracy through real-time data feedback;

[0068] The three-level safe startup module controls the test startup process in the order of system self-test - no-load startup - graded loading, and performs protection and recording in case of abnormality;

[0069] Multi-mode test sequence execution module: Automatically generates and executes parameter scanning test sequences containing multiple working modes and test points based on preset test requirements;

[0070] Dual closed-loop control module: The inner loop quickly corrects the output deviation, while the outer loop predictively compensates for external disturbances, thus maintaining the stability and accuracy of the testing process.

[0071] Safety boundary protection module: Calculates and updates safety thresholds in real time, and ensures test safety through multi-level early warning and gradient interlocking protection mechanisms;

[0072] Multi-source data fusion processing module: collects data from the entire testing process, cleans it, performs correlation analysis and double verification, and finally outputs a standardized and valid dataset;

[0073] Processor: The processor is used to handle the calculation process of each formula and the calculation process of constructing each model.

[0074] See Figure 1 As shown, a test control method for a DC power supply includes:

[0075] Step 1: Construct a three-dimensional adaptation model based on the rated parameters of the DC power supply under test, test requirements, and test environment benchmark parameters; match the hardware configuration and software parameters of the test system and perform adaptation calibration.

[0076] Step 2: Simulate dynamic load characteristics based on the actual application scenario of the DC power supply under test. Collect the actual absorbed voltage and current data of the load in real time, compare them with the preset load parameters, and perform load calibration based on the comparison results.

[0077] Step 3: Start the test through a three-level startup mechanism. The first level is the control module self-test, the second level is the no-load startup of the DC power supply under test, and the third level is the gradual connection of the load. If an abnormal situation occurs during the startup process, the shutdown protection is triggered and the abnormal node is recorded.

[0078] Step 4: Generate a multi-mode test sequence based on the pre-configured test requirements. In each mode, perform parameter scanning tests according to the preset test point distribution. During the test, record the power parameters in real time.

[0079] Step 5: Construct a dual closed-loop feedback regulation mechanism. The inner loop compares the collected power supply parameters with the preset target value to generate a regulation signal. The outer loop monitors the grid parameters, establishes a disturbance impact model based on historical test data, predicts the impact trend of disturbances on power supply output parameters, and outputs compensation signals in advance.

[0080] Step 6: Generate a safety boundary threshold based on the rated parameters of the power supply under test, the test stage, and real-time environmental data. Compare the power supply parameters with the dynamic safety threshold in real time. When the monitored value approaches or exceeds the dynamic safety threshold, trigger the early warning mechanism and start the interlocking protection respectively.

[0081] Step 7: Collect multi-source data during the testing process, use a hierarchical fusion algorithm for data processing, and use a dual verification mechanism to determine the validity of the data to obtain a valid dataset.

[0082] See Figure 2 As shown, a three-dimensional adaptation model is constructed based on the rated parameters of the DC power supply under test, test requirements, and test environment benchmark parameters. This model is then matched with the hardware configuration and software parameters of the test system, and adaptation calibration is performed. Specifically, this includes:

[0083] The rated parameters of the DC power supply under test include output voltage, current range, power, and ripple rejection ratio design value.

[0084] Testing requirements include steady-state accuracy, dynamic response, overload capacity, and environmental adaptability testing.

[0085] Environmental baseline parameters, including temperature, humidity, and power grid input voltage fluctuation range, are collected in real time through an integrated temperature and humidity sensor.

[0086] A three-dimensional adaptation model is built based on the collected power supply rated parameters, test requirements, and environmental benchmark parameters. The mapping relationship between parameters is established, and the hardware and software parameters of the test system are matched through the three-dimensional adaptation model. The matched sensors and load modules are then pre-calibrated.

[0087] Specifically, an adaptation model is built based on the collected rated parameters of the power supply under test as the basic dimension, the test requirements as the functional dimension, and the environmental benchmark parameters as the adaptation dimension. This model automatically matches the hardware configuration of the test system, selects the corresponding resistive or electronic type according to the power level of the power supply, determines the sampling rate specification of the data acquisition module according to the test type, and matches the sensor range with the rated voltage and current range to ensure that the hardware is accurately matched with the test object and test requirements. At the same time, the software parameters are matched synchronously, the data acquisition cycle and single-item test duration are set according to the test accuracy requirements, and the parameter judgment thresholds are defined according to the power supply design standards to form an initial configuration scheme for hardware and software collaboration.

[0088] By calling the built-in calibration parameter library, the system matches the corresponding standard calibration curve and temperature compensation coefficient according to the sensor model and load module type; it applies stepped reference signals (such as zero point, full scale point, and midpoint) to the sensor through a standard signal source, collects sensor feedback data in real time and compares it with the reference value, and dynamically corrects its linearity and zero drift; it applies preset static and dynamic load modes (such as constant resistance and constant current switching) to the load module, and iteratively adjusts the amplitude / phase compensation parameters of its drive circuit based on the deviation between the actual absorbed power and the command value; it simultaneously integrates real-time temperature and humidity data to perform online compensation for sensor temperature drift and temperature characteristics of load power devices.

[0089] See Figure 3 As shown, based on the actual application scenario of the DC power supply under test, the dynamic load characteristics are simulated. Real-time data on the actual absorbed voltage and current of the load are collected and compared with preset load parameters. Load calibration is then performed based on the comparison results, specifically including:

[0090] Based on the specific application scenario of the DC power supply under test, dynamic load characteristics are simulated to generate various dynamic loads, including pulse load, nonlinear load, stepped load, and random fluctuating load.

[0091] The system continuously collects the actual voltage and current data absorbed by the load and compares them with the preset load parameters. If the deviation exceeds ±1%, it adjusts the amplitude and phase of the load drive signal and compensates for the nonlinear error and temperature drift of the load module to correct the load simulation deviation.

[0092] Specifically, dynamic loads are generated to simulate the complex load characteristics faced by the power supply in actual working conditions, such as pulse loads generated by the start and stop of industrial equipment, nonlinear loads caused by frequency converters, stepped loads during battery charging, and random loads caused by power grid fluctuations, to ensure that the test environment is close to reality.

[0093] Simulate the load change process in actual working conditions, such as the pulse load when industrial equipment starts and stops, and the nonlinear load fluctuation when electronic circuits are working. Simultaneously, use high-frequency acquisition to capture the actual voltage and current data absorbed by the load in real time, record the actual load value at each time point, and form a continuous load operation data curve.

[0094] The actual load data collected is compared with the preset load parameters point by point, and the deviation value between the two is calculated. The deviation is focused on key stages such as load type switching and peak fluctuation. A deviation judgment standard is set. When the deviation value of three consecutive collection cycles exceeds ±1% or the single deviation exceeds ±3%, it is judged that the load simulation accuracy is not up to standard, and the load calibration mechanism is immediately triggered.

[0095] Preset load parameters include:

[0096] Waveform characteristic parameters:

[0097] Pulsed load: peak current, rise / fall time, duty cycle;

[0098] Nonlinear loads: harmonic spectrum, power factor;

[0099] Stepped load: step amplitude, duration, step interval;

[0100] Random load: fluctuation frequency range, maximum offset amplitude;

[0101] Electrical performance benchmark values:

[0102] Target voltage / current curve, theoretical power consumption curve, impedance change timing;

[0103] To address nonlinear errors, a compensation algorithm is used to correct the load output characteristics, making them conform to the preset nonlinear curve. To address deviations caused by temperature drift, the calibration coefficient is adjusted to offset the temperature effect by combining the real-time temperature data of the load module. After calibration, the deviation between the actual load data and the preset value is continuously monitored until the deviation stabilizes within ±1%, forming a closed-loop control of simulation-acquisition-comparison-calibration-verification.

[0104] See Figure 4 As shown, the test is initiated through a three-level startup mechanism. The first level is a self-test of the control module, the second level is a no-load startup of the DC power supply under test, and the third level is a gradual connection of the load. Based on any abnormal situations that occur during startup, a shutdown protection mechanism is triggered and the abnormal nodes are recorded. Specific abnormal nodes include:

[0105] Perform a self-test of the first-level control module to verify whether the connectivity between modules is normal, and check the working status of each module to ensure that there are no hardware faults or communication interruptions.

[0106] The second-level no-load start-up and monitoring is performed. The DC power supply under test is controlled to enter the no-load start-up mode. The power supply output voltage is controlled to rise from 0V to the test start voltage at a preset slope. The ripple voltage and noise voltage of the power supply output under no-load conditions are monitored in real time to determine whether there is no-load current leakage.

[0107] After performing the third level of graded loading and starting without any abnormalities, gradually connect the load to the power supply according to the load ratios of 10%, 30%, 50%, 80%, and 100%, and continuously monitor the fluctuation range of the power supply output parameters.

[0108] If the ripple voltage exceeds 50% of the design threshold, current leakage is found, or parameter fluctuations exceed ±3% during graded loading, the shutdown protection mechanism will be triggered immediately, and the node data where the abnormality occurred will be recorded.

[0109] Specifically, the slope setting is dynamically determined based on the power supply rating, test start voltage, and ambient temperature. For high-power power supplies (>1kW): a gentler slope (e.g., 0.5-1V / s) is used to avoid surge current caused by sudden voltage application; for low-power power supplies (<200W): a steeper slope (e.g., 5-10V / s) is used to accelerate the startup process; for high-temperature environments (>40℃): the slope is reduced by 20%-30% to prevent excessive temperature rise.

[0110] The start-up control module performs a comprehensive self-test, focusing on verifying the working status of the core components of the test system; it checks the signal transmission continuity of each data acquisition channel, confirming the voltage and current acquisition links are uninterrupted by using simulated signal input; it checks the command response capability of the load drive module, sending an unloaded drive signal to verify whether the module is responding normally; it tests the connection stability of the communication module to ensure that there is no delay or loss in the transmission of control commands and feedback data; if any module is found to have abnormal connectivity or a malfunction during the self-test, the startup process is immediately paused.

[0111] After the self-test passes, the second-level no-load start-up procedure of the DC power supply under test is executed. The voltage is gradually increased from 0V to the test start voltage according to the preset adjustable slope. The ripple voltage and noise voltage data of the power supply output under no-load conditions are collected in real time, and the power supply output terminal is continuously monitored for any no-load current leakage. During this period, the peak value and fluctuation frequency of the ripple voltage, the amplitude of the noise voltage, and the presence or absence of leakage current are closely monitored. The collected parameters are compared with the preset safety thresholds in real time to ensure the basic performance of the power supply under no-load conditions is stable.

[0112] After the no-load test passes, the third stage of gradual load connection begins. Loads are applied in stages according to proportions of 10%, 30%, 50%, 80%, and 100%. After each stage, a 3-10 second stabilization period is maintained to allow time for the power supply output parameters to adjust. During each stabilization period, the fluctuation range of the power supply's output voltage and current is continuously monitored, and the parameter change curves are recorded. If the parameter fluctuations are stable during the loading process and the fluctuation range is controlled within the allowable range during the stabilization period, the next stage of load loading is executed. If abnormal fluctuations such as sudden increases or decreases in parameters occur, the loading process is immediately paused.

[0113] Design thresholds refer to the permissible limits in the power supply's manufacturer's technical specifications, including ripple voltage threshold (typically 1%-3% of the rated output voltage), noise voltage threshold (generally 1.5-2 times the ripple threshold), and leakage current threshold (set according to safety standards). During graded loading, the instantaneous fluctuations of output voltage and output current are monitored, and the fluctuation amplitude is obtained by dividing the difference between the maximum and minimum values ​​of output voltage and output current by the rated output voltage and output current values.

[0114] See Figure 5 As shown, a multi-mode test sequence is generated based on pre-configured test requirements. In each mode, parameter scanning tests are performed according to the preset test point distribution. During the test, power parameters are recorded in real time, including:

[0115] Generate test sequences including constant voltage mode, constant current mode, constant power mode and mixed mode based on pre-configured test requirements;

[0116] Test points are set for each test mode, including rated value, limit value, critical value and random sample value. The limit value test adopts a step-by-step approximation method, with each increment or decrement being 5% of the rated value.

[0117] Based on the set test sequence and test points, parameter scanning tests are performed. During the test, the power supply's output voltage accuracy, output current accuracy, load regulation, source effect regulation, dynamic response time, and output ripple peak-to-peak parameters are monitored in real time.

[0118] Specifically, the step-by-step execution process first performs basic mode tests such as constant voltage and constant current. In constant voltage mode, the target value of the power supply output voltage is fixed, and the load current is gradually adjusted through the load module, with real-time monitoring of voltage accuracy and load regulation. In constant current mode, the target value of the output current is fixed, the load resistance is adjusted, and the current accuracy and source effect regulation are recorded. After the basic mode tests are completed, constant power mode and hybrid mode are executed: constant power mode maintains stable output power by coordinating voltage and current adjustment, and power stability and parameter coordination adjustment response speed are monitored. In hybrid mode, the working mode is switched strictly according to preset rules, and the parameter transition curve and stabilization time are recorded at each switch.

[0119] See Figure 6 As shown, a dual-loop feedback regulation mechanism is constructed. The inner loop compares the collected power supply parameters with preset target values ​​to generate a regulation signal. The outer loop monitors grid parameters, establishes a disturbance impact model based on historical test data, predicts the impact trend of disturbances on power supply output parameters, and outputs compensation signals in advance. Specifically, this includes:

[0120] A dual-closed-loop feedback regulation mechanism is constructed, with the inner loop for output parameter feedback and the outer loop for disturbance prediction and compensation.

[0121] The inner loop compares the collected actual data with the preset target value to generate an adjustment signal, which then controls the PWM drive module of the power supply to correct the output deviation.

[0122] The outer ring collects data on grid input voltage fluctuations, ambient temperature changes, and load mutations. Based on historical test data, a disturbance impact model based on an LSTM neural network is established to predict the impact trend of disturbances on power supply output parameters. Based on the prediction results, compensation signals are output in advance.

[0123] When a sudden disturbance such as a sharp drop in grid voltage or a load change exceeding a threshold is detected, an emergency adjustment mode is activated, shortening the feedback adjustment cycle and calling a pre-stored disturbance compensation strategy library to offset the impact of the sudden disturbance.

[0124] Specifically, the inner loop output parameter feedback adjustment process is initiated. A fixed data acquisition cycle of 1ms-5ms is set, and a high-speed acquisition module continuously captures real-time voltage and current data at the power supply output. The acquired real-time data is compared cycle-by-cycle with the target value pre-configured in step one, and the parameter deviation is calculated, with a focus on the trend of deviation changes. Based on the deviation characteristics, a targeted adjustment signal is generated. If the voltage is too high, a voltage reduction command is sent to the adjustment module; if the current is too low, a current boost control signal is output. By precisely adjusting the internal output link parameters of the power supply, the deviation is quickly offset, maintaining stable output parameters.

[0125] The outer loop disturbance prediction and compensation operation continuously monitors three core disturbance factors: instantaneous fluctuations in grid input voltage, sudden changes in test environment temperature, and sudden increases and decreases in load power. Disturbance signals are captured by dedicated sensors and uploaded to the control module in real time.

[0126] By accumulating historical test data, we collect disturbance characteristic data and corresponding output parameter change data to establish a correlation database. The disturbance characteristic data includes: disturbance type (grid input voltage fluctuation, ambient temperature change, load abrupt change, etc.), disturbance intensity (e.g., grid voltage drop ≥10%, load current change rate ≥15% / ms), duration (e.g., drop lasting more than 20ms), and environmental parameters (e.g., current temperature, humidity). The output parameter change data includes: the deviation of power supply output parameters (voltage, current, power) under the corresponding disturbance (e.g., voltage deviation ±2%), change trend (e.g., continuous rise / fall), and recovery time (e.g., recovery to stability within 10ms).

[0127] The dynamic disturbance impact model (LSTM neural network) is trained by taking disturbance feature data as input and output parameter change data as output. The model learns the nonlinear mapping relationship between disturbances and the impact of output parameters. Model inputs include disturbance type (encoded numerically), disturbance intensity (e.g., a 10% drop in power grid voltage is denoted as 0.1), duration, and ambient temperature. Model outputs include the prediction deviation of output parameters (voltage / current) (e.g., ±2.5%) and the predicted trend (e.g., "increasing" / "decreasing"). The model parameters (number of hidden layer nodes, learning rate, number of iterations) are adjusted using gradient descent to minimize the mean squared error (MSE) between the predicted and actual values, ensuring the model's prediction accuracy for different disturbance scenarios (e.g., cosine similarity ≥ 90%).

[0128] The system collects disturbance data in real time during the test using sensor modules (voltage sensor, current sensor, temperature and humidity sensor) and extracts key features: Disturbance type identification includes judging the disturbance type (such as power grid fluctuation, load change) by signal characteristics (such as voltage waveform drop, current change); Disturbance intensity calculation includes quantifying the disturbance degree (such as instantaneous drop in power grid input voltage / rated voltage × 100%, load current change rate di / dt); Environmental parameter fusion corrects the disturbance intensity by synchronously collecting current temperature and humidity.

[0129] The perturbation feature parameters extracted in real time are input into the trained LSTM model, and the output prediction results are: Impact magnitude: the deviation value of the power supply output parameters caused by the perturbation; Impact trend: the direction of change of the output parameters; Confidence assessment: the confidence of the prediction results given by the model. If the confidence is lower than 80%, a fallback strategy is triggered (such as using historical average compensation parameters).

[0130] Based on the predicted impact magnitude and trend, and combined with the pre-stored "disturbance feature-compensation parameter" mapping library, a targeted compensation signal is generated; historical disturbance scenarios are analyzed using an LSTM model to generate corresponding optimal compensation parameters, which are stored according to "disturbance type + intensity"; real-time disturbance features are matched with entries in the mapping library to retrieve the corresponding compensation parameters and generate a digital compensation signal; the compensation signal is superimposed on the inner loop of the dual closed-loop regulation mechanism to drive the power supply hardware module to correct the output deviation in advance and offset the potential impact of the disturbance.

[0131] A voltage sag is triggered when the instantaneous drop in grid input voltage is ≥10% of the rated voltage or the sag lasts for more than 20ms. The load sag amplitude is determined using the current change rate (di / dt) as the core indicator. The base threshold is 15% of the power supply's rated current / ms, and dynamic corrections include a 20% reduction in the threshold under high-temperature environments (>40℃) and a 10% increase in the threshold during extreme testing.

[0132] The feedback adjustment cycle is shortened through hardware acceleration. When a sudden disturbance triggers the emergency mode, the inner loop adjustment cycle is compressed from the usual 1-5ms to 200-500μs. The FPGA is used to process the PWM signal in real time and bypass the software protocol layer. Sensor data is directly read through memory mapping technology to eliminate communication delay.

[0133] The pre-storage process of the compensation strategy library is based on historical disturbance data. It collects historical data of typical disturbance scenarios during DC power supply testing, including: disturbance characteristics: grid sag, load change, ambient temperature change, recording the type, intensity, and duration of the disturbance; power supply response data: output voltage / current deviation and dynamic response time of the power supply when the disturbance occurs; compensation parameters used at that time, and output deviation after compensation;

[0134] Key time-series features are extracted from historical data and used as input to the LSTM model, including: disturbance type encoding: classifying and encoding grid sags, load mutations, etc.; disturbance intensity quantification: grid sags are represented by "percentage drop in rated voltage", and load mutations are represented by "current change rate (di / dt)"; environmental co-features: incorporating ambient temperature and humidity to correct for the impact of disturbances on the power supply; and data cleaning and normalization.

[0135] LSTM neural network training is used to explore the nonlinear temporal relationship between disturbance features and optimal compensation parameters: Input layer: temporal data such as disturbance type, intensity, and environmental co-features (e.g., the disturbance intensity sequence of the past 5 sampling points); Output layer: corresponding optimal compensation parameters (e.g., PWM duty cycle adjustment, current loop gain coefficient); Minimize the mean square error (MSE) between "model predicted compensation parameters" and "historical effective compensation parameters" to ensure that the model can predict the optimal compensation scheme under different disturbance scenarios;

[0136] The trained LSTM model generates the optimal compensation parameters for each type of historical disturbance scenario, and stores them as policy entries according to the type and intensity of the disturbance, forming a "disturbance feature-compensation parameter" mapping library.

[0137] Strategy invocation and disturbance cancellation are executed through dynamic matching: the disturbance characteristics monitored in real time are matched with the entries in the strategy library (e.g., if the cosine similarity is >90%, it is directly invoked); the compensation signal is superimposed on the inner loop regulation output to drive the PWM module to correct the deviation in advance; the compensation effect is verified within 10ms, and if the deviation is still >2%, gradient compensation is started (increasing the compensation amount by 20%) until the output returns to stability (deviation ≤1%).

[0138] See Figure 7 As shown, a safety boundary threshold is generated based on the rated parameters of the power supply under test, the test stage, and real-time environmental data. The power supply parameters are compared in real-time with the dynamic safety threshold. When the monitored value approaches or exceeds the dynamic safety threshold, an early warning mechanism and interlocking protection are triggered, respectively. Specifically, this includes:

[0139] Based on the rated parameters of the power supply under test, the current test stage, and real-time environmental data, safety boundary thresholds are generated, including the maximum allowable output voltage, the maximum allowable output current, the maximum allowable temperature rise, and the minimum allowable insulation resistance.

[0140] The system continuously collects output parameters, casing temperature, internal component temperature, and insulation resistance value of the power supply. The collected real-time data is compared with the dynamic safety boundary threshold in real time. If the monitored value reaches 90% of the threshold, an early warning mechanism is triggered, and the test load is reduced and the test parameters are adjusted.

[0141] If the monitored value exceeds the safety boundary threshold, the shutdown operation will be carried out in the order of reducing current, reducing voltage, and cutting off power, while disconnecting the electrical connection between the load and the power supply.

[0142] Specifically, the collected monitoring data are compared with the dynamic safety threshold at the millisecond level. When the monitored value reaches 90% of the safety threshold, the early warning mechanism is immediately triggered: the current test load is automatically reduced or the test parameters are adjusted, and an audible and visual warning signal is issued to remind the operator to pay attention to the equipment status. If the monitored value continues to rise or directly exceeds the safety threshold after the warning, the interlock protection program is immediately activated, and the shutdown operation is strictly performed according to the gradient of current reduction, voltage reduction, and power cut-off. First, the output current is quickly reduced to a safe range, then the output voltage is gradually reduced to 0V, and finally the main power supply circuit is cut off, and the electrical connection between the load and the power supply is disconnected simultaneously.

[0143] See Figure 8 As shown, during the testing process, multi-source data was collected, and a hierarchical fusion algorithm was used for data processing. A dual verification mechanism was used to determine the validity of the data, and the specific details of obtaining a valid dataset include:

[0144] All types of data from the entire testing process are aggregated, including power output parameter data, load feedback data, environmental monitoring data, disturbance record data, and safety protection data, and a layered fusion algorithm is used for data processing.

[0145] Through the first step of data cleaning in the layered fusion process, outliers and duplicate records in the data are identified and removed, and invalid data interference is eliminated.

[0146] By analyzing the correlation between different data types through the second step of hierarchical fusion, a corresponding mapping between power supply output parameters and environmental changes, load status, and disturbance factors is constructed.

[0147] The effectiveness verification of the third step of the hierarchical integration verifies the consistency of the values ​​of voltage, current, and power, as well as the timing matching of the dynamic response curve with load changes. The key test points of rated values ​​and extreme values ​​are repeatedly tested, and the verified data are integrated to form a standardized and effective dataset.

[0148] Specifically, multi-dimensional data association and fusion are implemented to construct a hierarchical fusion system. The first layer performs basic data association, mapping power output data, load data, and environmental data at the same time stamp to form a basic parameter-load-environment association unit. The second layer conducts deep logical mapping, analyzing the causal relationship between output parameter changes and factors such as increased ambient temperature, sudden load changes, and grid fluctuations, establishing a logical association model for multi-dimensional data. For example, it clarifies the correspondence between output voltage fluctuations over a certain period and sudden drops in grid voltage during the same period. Through hierarchical fusion, scattered single-dimensional data are integrated into a comprehensive dataset with logical relationships, clearly presenting the impact patterns of various factors on power supply performance.

[0149] The dual validity verification mechanism has two layers. The first layer is logical verification, which checks the logical consistency of the values ​​of voltage, current, and power to ensure that the actual collected data conforms to the basic working principle of the electrical equipment and has no obvious logical contradictions. It also verifies the timing matching of dynamic response data with load changes, confirming that the changing trend of power supply output parameters is synchronized with the load adjustment timing, with no obvious lag or lead deviation. The second layer is repeated verification, which retrieves the results of 3-5 repeated tests for key test points such as rated values ​​and extreme values, analyzes the degree of data variation, and determines that the test point data is valid if the dispersion of the test results is within an acceptable range.

[0150] The formula for calculating data discreteness is:

[0151] ;

[0152] in, The relative standard deviation, The standard deviation is... The average of n repeated test data. This represents the actual value collected during the i-th test.

[0153] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0154] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0155] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for testing and controlling a DC power supply, characterized in that, include: A three-dimensional adaptation model is constructed based on the rated parameters of the DC power supply under test, test requirements, and test environment benchmark parameters. The hardware configuration and software parameters of the test system are matched and adapted and calibrated. Based on the actual application scenario of the DC power supply under test, the dynamic load characteristics are simulated. The actual absorbed voltage and current data of the load are collected in real time and compared with the preset load parameters. The load is calibrated based on the comparison results. The test is started through a three-level startup mechanism. The first level is the control module self-test, the second level is the DC power supply under test starting under no-load, and the third level is the gradual connection of the load. If an abnormal situation occurs during the startup process, the shutdown protection is triggered and the abnormal node is recorded. A multi-mode test sequence is generated based on pre-configured test requirements. In each mode, parameter scanning test is performed according to the preset test point distribution. During the test, power parameters are recorded in real time. A dual closed-loop feedback regulation mechanism is constructed. The inner loop compares the collected power supply parameters with the preset target value to generate a regulation signal. The outer loop monitors the grid parameters, establishes a disturbance impact model based on historical test data, predicts the impact trend of disturbances on power supply output parameters, and outputs compensation signals in advance. Based on the rated parameters of the power supply under test, the test stage, and real-time environmental data, a safety boundary threshold is generated. The power supply parameters are collected in real time and compared with the dynamic safety threshold. When the monitored value approaches or exceeds the dynamic safety threshold, the early warning mechanism and interlock protection are triggered respectively. Collect data from multiple sources during the testing process, use a hierarchical fusion algorithm for data processing, and use a dual verification mechanism to determine the validity of the data to obtain a valid dataset; The aforementioned dual-closed-loop feedback regulation mechanism, where the inner loop compares collected power supply parameters with preset target values ​​to generate a regulation signal, and the outer loop monitors grid parameters, establishes a disturbance impact model based on historical test data, predicts the impact trend of disturbances on power supply output parameters, and outputs compensation signals in advance, specifically includes: A dual-closed-loop feedback regulation mechanism is constructed, with the inner loop for output parameter feedback and the outer loop for disturbance prediction and compensation. The inner loop compares the collected actual data with the preset target value to generate an adjustment signal, which then controls the PWM drive module of the power supply to correct the output deviation. The outer ring collects data on grid input voltage fluctuations, ambient temperature changes, and load mutations. Based on historical test data, a disturbance impact model based on an LSTM neural network is established to predict the impact trend of disturbances on power supply output parameters. Based on the prediction results, compensation signals are output in advance. When a sudden disturbance such as a sharp drop in grid voltage or a load change exceeding a threshold is detected, an emergency adjustment mode is activated, shortening the feedback adjustment cycle and calling the pre-stored disturbance compensation strategy library to offset the impact of the sudden disturbance.

2. The test and control method for a DC power supply according to claim 1, characterized in that, The process of constructing a three-dimensional adaptation model based on the rated parameters of the DC power supply under test, test requirements, and test environment benchmark parameters, and matching the hardware configuration and software parameters of the test system with the adaptation calibration specifically includes: The rated parameters of the DC power supply under test include output voltage, current range, power, and ripple rejection ratio design value. The testing requirements include steady-state accuracy, dynamic response, overload capacity, and environmental adaptability testing. Environmental baseline parameters, including temperature, humidity, and power grid input voltage fluctuation range, are collected in real time through an integrated temperature and humidity sensor. A three-dimensional adaptation model is built based on the collected power supply rated parameters, test requirements, and environmental benchmark parameters. The mapping relationship between parameters is established, and the hardware and software parameters of the test system are matched through the three-dimensional adaptation model. The matched sensors and load modules are then pre-calibrated.

3. The test and control method for a DC power supply according to claim 1, characterized in that, The process of simulating dynamic load characteristics based on the actual application scenario of the DC power supply under test, and comparing the actual absorbed voltage and current data of the load in real time with preset load parameters, and performing load calibration based on the comparison results, specifically includes: Based on the specific application scenario of the DC power supply under test, dynamic load characteristics are simulated to generate various dynamic loads, including pulse load, nonlinear load, stepped load, and random fluctuating load. The system continuously collects the actual voltage and current data absorbed by the load and compares them with the preset load parameters. If the deviation exceeds ±1%, it adjusts the amplitude and phase of the load drive signal and compensates for the nonlinear error and temperature drift of the load module to correct the load simulation deviation.

4. The test and control method for a DC power supply according to claim 1, characterized in that, The test is initiated through a three-level startup mechanism. The first level is a self-test of the control module, the second level is an unloaded startup of the DC power supply under test, and the third level is a gradual connection of the load. Based on any abnormal situations that occur during startup, a shutdown protection mechanism is triggered and the abnormal nodes are recorded. Specifically, these include: Perform a self-test of the first-level control module to verify whether the connectivity between modules is normal, and check the working status of each module to ensure that there are no hardware faults or communication interruptions. The second-level no-load start-up and monitoring is performed. The DC power supply under test is controlled to enter the no-load start-up mode. The power supply output voltage is controlled to rise from 0V to the test start voltage at a preset slope. The ripple voltage and noise voltage of the power supply output under no-load conditions are monitored in real time to determine whether there is no-load current leakage. After performing the third level of graded loading and starting without any abnormalities, gradually connect the load to the power supply according to the load ratios of 10%, 30%, 50%, 80%, and 100%, and continuously monitor the fluctuation range of the power supply output parameters. If the ripple voltage exceeds 50% of the design threshold, current leakage is found, or parameter fluctuations exceed ±3% during graded loading, the shutdown protection mechanism is immediately triggered, and the node data where the abnormality occurred is recorded.

5. The test and control method for a DC power supply according to claim 1, characterized in that, The multi-mode test sequence is generated based on pre-configured test requirements. In each mode, parameter scanning tests are performed according to a preset test point distribution. During the test, power parameters are recorded in real time, including: Generate test sequences including constant voltage mode, constant current mode, constant power mode and mixed mode based on pre-configured test requirements; Test points are set for each test mode, including rated value, limit value, critical value and random sample value. The limit value test adopts a step-by-step approximation method, with each increment or decrement being 5% of the rated value. Based on the set test sequence and test points, parameter scanning tests are performed. During the test, the power supply's output voltage accuracy, output current accuracy, load regulation, source effect regulation, dynamic response time, and output ripple peak-to-peak parameters are monitored in real time.

6. The test and control method for a DC power supply according to claim 1, characterized in that, The process of generating a safety boundary threshold based on the rated parameters, testing phase, and real-time environmental data of the power supply under test, and comparing the power supply parameters with the dynamic safety threshold in real time, triggering an early warning mechanism and activating interlocking protection when the monitored value approaches or exceeds the dynamic safety threshold, specifically includes: Based on the rated parameters of the power supply under test, the current test stage, and real-time environmental data, safety boundary thresholds are generated, including the maximum allowable output voltage, the maximum allowable output current, the maximum allowable temperature rise, and the minimum allowable insulation resistance. The system continuously collects output parameters, casing temperature, internal component temperature, and insulation resistance value of the power supply. The collected real-time data is compared with the dynamic safety boundary threshold in real time. If the monitored value reaches 90% of the threshold, an early warning mechanism is triggered, and the test load is reduced and the test parameters are adjusted. If the monitored value exceeds the safety boundary threshold, the shutdown operation will be carried out in the order of reducing current, reducing voltage, and cutting off power, while disconnecting the electrical connection between the load and the power supply.

7. The test and control method for a DC power supply according to claim 1, characterized in that, The collection and testing process involves multi-source data, employing a hierarchical fusion algorithm for data processing, and using a dual verification mechanism to determine the validity of the data. Specifically, obtaining a valid dataset includes: All types of data from the entire testing process are aggregated, including power output parameter data, load feedback data, environmental monitoring data, disturbance record data, and safety protection data, and a layered fusion algorithm is used for data processing. Through the first step of data cleaning in the layered fusion process, outliers and duplicate records in the data are identified and removed, and invalid data interference is eliminated. By analyzing the correlation between different data types through the second step of hierarchical fusion, a corresponding mapping between power supply output parameters and environmental changes, load status, and disturbance factors is constructed. The effectiveness verification of the third step of the hierarchical integration verifies the consistency of the values ​​of voltage, current, and power, as well as the timing matching of the dynamic response curve with load changes. The key test points of rated values ​​and extreme values ​​are repeatedly tested, and the verified data are integrated to form a standardized and effective dataset.

8. A test and control system for a DC power supply, used to implement the test and control method for a DC power supply as described in any one of claims 1-7, characterized in that, include: 3D Adaptation and Calibration Module: Based on power specifications, test requirements, and environmental parameters, a model is built to complete the automatic matching and initial calibration of the test system's hardware and software; Dynamic load simulation module: Simulates various dynamic load characteristics under real application scenarios and performs online calibration of load simulation accuracy through real-time data feedback; The three-level safe startup module controls the test startup process in the order of system self-test - no-load startup - graded loading, and performs protection and recording in case of abnormality; Multi-mode test sequence execution module: Automatically generates and executes parameter scanning test sequences containing multiple working modes and test points based on preset test requirements; Dual closed-loop control module: The inner loop quickly corrects the output deviation, while the outer loop predictively compensates for external disturbances, thus maintaining the stability and accuracy of the test process. Safety boundary protection module: Calculates and updates safety thresholds in real time, and ensures test safety through multi-level early warning and gradient interlocking protection mechanisms; Multi-source data fusion processing module: collects data from the entire testing process, cleans it, performs correlation analysis and double verification, and finally outputs a standardized and valid dataset; Processor: The processor is used to handle the calculation process of each formula and the construction calculation process of each model.

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