Method and system for testing functional parameters of DC / DC power supply and computer equipment

Abstract

The invention relates to the technical field of DC / DC power supplies, and discloses a DC / DC power supply function parameter testing method and system and computer equipment, and the method comprises the steps: injecting a preset disturbance signal into an input end of a tested DC / DC power supply through a signal conditioning circuit, and obtaining output voltage response data and output current response data; adopting a dynamic multi-parameter test system and collecting target test parameter data; calculating a characteristic parameter set, and executing voltage-temperature control model predictive control analysis to obtain a control parameter sequence; and controlling the power output value of the electric heating wire, the rotating speed value of the heat dissipation fan and the position value of the electric push rod in the test box body to obtain an intelligent adjustment test environment and generate an aging test result and a test strategy adjustment instruction, thereby overcoming the problem of parameter detection blind areas in a traditional test method, and improving the test accuracy even under the condition that the output voltage is accurately matched with the set value. And the power supply performance abnormity can be effectively identified, and the reliability and accuracy of the test are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of DC / DC power supplies, and in particular to a method and system for testing functional parameters of a DC / DC power supply, and computer equipment. Background Art

[0002] Traditional DC / DC power supply testing methods often suffer from parameter detection blind spots. This is particularly true when the power supply's output voltage precisely matches the set value, making it difficult to detect potential performance anomalies. This results in many power supplies passing tests in the early stages of degradation only to quickly fail in actual use. Furthermore, traditional burn-in testing utilizes fixed test sequences and a constant high-temperature environment. This not only creates inconsistencies between the test environment and the actual application, but also leads to significant deviations in test results, failing to truly reflect the power supply's performance under complex operating conditions.

[0003] Existing DC / DC power supply test equipment generally uses high-power continuous heating for aging testing, resulting in significant energy consumption and low test efficiency. On the one hand, the high temperature environment must be maintained for extended periods during testing, resulting in energy waste. On the other hand, test parameter configuration lacks flexibility, making it impossible to dynamically adjust the test strategy based on the real-time performance status of the power supply under test. Consequently, even power supplies in certain abnormal states must undergo a complete test cycle, wasting time and preventing timely detection and resolution of abnormalities. Furthermore, when a power supply is damaged during testing, traditional test equipment reacts slowly, making it difficult to remove the heat source in a timely manner. This accelerates damage to the power supply and can even damage the test equipment itself. Summary of the Invention

[0004] The present invention provides a DC / DC power supply functional parameter testing method, system, and computer equipment. The present invention overcomes the parameter detection blind spot problem in traditional testing methods. Even when the output voltage precisely matches the set value, it can effectively identify power supply performance anomalies, significantly improving the reliability and accuracy of the test.

[0005] In a first aspect, the present invention provides a method for testing functional parameters of a DC / DC power supply, the method comprising: Injecting a preset disturbance signal into the input terminal of the DC / DC power supply under test through a signal conditioning circuit to obtain output voltage response data and output current response data; Inputting the output voltage response data and the output current response data into a dynamic multi-parameter test system in a test box, and collecting target test parameter data; Calculating a characteristic parameter set according to the target test parameter data, and performing voltage-temperature control model predictive control analysis to obtain a control parameter sequence for the test box; According to the control parameter sequence, the power output value of the heating wire, the speed value of the cooling fan and the position value of the electric push rod in the test box are controlled to obtain an intelligently adjustable test environment; An aging test result and a test strategy adjustment instruction of the DC / DC power supply under test are generated in the intelligent adjustment test environment.

[0006] In a second aspect, the present invention provides a DC / DC power supply functional parameter testing system, the DC / DC power supply functional parameter testing system comprising: An injection module is used to inject a preset disturbance signal into the input terminal of the DC / DC power supply under test through a signal conditioning circuit to obtain output voltage response data and output current response data; an acquisition module, configured to input the output voltage response data and the output current response data into a dynamic multi-parameter test system in a test box, and acquire target test parameter data; a calculation module, configured to calculate a characteristic parameter set based on the target test parameter data, and perform predictive control analysis of a voltage-temperature control model to obtain a control parameter sequence for the test box; A control module, configured to control the power output value of the heating wire, the speed value of the cooling fan, and the position value of the electric push rod in the test box according to the control parameter sequence, so as to obtain an intelligently adjustable test environment; A generating module is used to generate an aging test result and a test strategy adjustment instruction of the DC / DC power supply under the intelligent adjustment test environment.

[0007] A third aspect of the present invention provides a computer device comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned DC / DC power supply functional parameter testing method.

[0008] The technical solution provided by the present invention overcomes the parameter detection blind spot problem in traditional test methods by injecting a precisely controllable disturbance signal into the input terminal of the DC / DC power supply under test. Even when the output voltage precisely matches the set value, it can effectively identify power supply performance anomalies, significantly improving the reliability and accuracy of the test. A dynamic multi-parameter test system is adopted to achieve high consistency between the test environment and the actual application environment, support flexible test parameter combinations and load mode configurations, and solve the test blind spot problem existing in traditional test equipment. By bandpass filtering the test data and calculating parameters based on the first signal equivalent circuit model, combined with a multidimensional anomaly detection algorithm, accurate identification and classification of DC / DC power supply performance anomalies are achieved. Based on the voltage-temperature control integrated model predictive control algorithm, temperature control is directly integrated into the test cost function, achieving dual-objective optimization of test efficiency and energy consumption. Test parameters are dynamically adjusted according to the real-time performance of the power supply, reducing test complexity and energy consumption. Through nonlinear mapping functions and closed-loop control strategies, precise control of the test box heating wire power, cooling fan speed, and electric push rod position is achieved, ensuring that the test environment is consistent with the algorithm expectations and providing stable and reliable conditions for parameter testing. Through parameter trend prediction and anomaly diagnosis and feedback mechanisms, adaptive adjustment of test strategies is achieved, minimizing test time while ensuring test effectiveness. A comprehensive anomaly classification and hierarchical feedback strategy has been established, improving test efficiency and reliability. When a serious power supply anomaly is detected, the system immediately terminates the test and initiates protective measures, including disconnecting the input power, retracting the heating plate, and activating the cooling fan, effectively preventing further damage to the power supply and protecting the test equipment.

[0009] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0010] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of an embodiment of a method for testing functional parameters of a DC / DC power supply according to an embodiment of the present invention; Figure 2 Schematic diagram of a DC / DC power supply functional parameter testing system according to an embodiment of the present invention; Figure 3 FIG. 1 is a schematic diagram of an embodiment of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0013] The terms "including," "having," and any variations thereof, as used in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or device.

[0014] To facilitate understanding of this embodiment, a method for testing the functional parameters of a DC / DC power supply disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, this method includes the following steps: 101. Injecting a preset disturbance signal into the input terminal of the DC / DC power supply under test through a signal conditioning circuit to obtain output voltage response data and output current response data; It is understandable that the execution subject of the present invention may be a DC / DC power supply function parameter test system, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0015] Specifically, a systematic analysis of the rated electrical parameters of the DC / DC power supply under test (DUT), including its rated input voltage, rated output voltage and current, steady-state load capability, and transient response characteristics, is performed to construct a disturbance signal design space. Within this parameter space, sensitivity analysis and system identification techniques are then combined, drawing on historical test data, frequency-domain response characteristics, and equivalent circuit models, to generate an optimal disturbance signal parameter configuration table. This table specifies the disturbance signal type (e.g., step, sinusoidal, pulse, or pseudo-random sequence) and details the frequency ranges and corresponding amplitude ranges for each type of signal. This ensures that the DUT's dynamic characteristics are maximized while remaining within its safe operating range and providing sufficient input stimulus for characteristic extraction. Based on this configuration table, the signal generator is parameterized to output the target disturbance signal in real time according to the optimal disturbance configuration. In hardware implementation, the target disturbance signal is injected via capacitive coupling, using series coupling capacitors to form an AC path at high frequencies, effectively superimposing the high-frequency disturbance component with the DC signal while avoiding any impact on the DC bias. To prevent the disturbance signal from causing a persistent unstable response in the system, a feedback suppression loop is introduced as a dynamic control mechanism. This loop receives feedback signals from the output end in real time, monitors the disturbance injection state, and immediately adjusts the duration of the disturbance injection when the response amplitude exceeds the set threshold or the system shows signs of a critical stable state, thereby avoiding irreversible output offset or damage caused by the disturbance. During this process, the test system performs synchronous high-frequency sampling of the voltage and current data at the output end of the DC / DC power supply. The sampling rate should be no less than 100kHz to ensure that the subtle response changes caused by high-frequency disturbances are fully captured. These response data are recorded in the test data processing unit after front-end amplification, filtering, and analog-to-digital conversion, obtaining output voltage response data and output current response data.

[0016] 102. Input the output voltage response data and the output current response data into the dynamic multi-parameter test system in the test box, and collect target test parameter data; Specifically, the output voltage and current response data are input via a high-speed communication bus to the dynamic multi-parameter test system's main control unit. This unit, equipped with an embedded control core and signal preprocessing modules, performs preliminary processing on the input signals, including amplitude compression, timing calibration, and boundary identification. Based on these response characteristics, the main control unit, combined with a pre-set test process model and a historical power supply performance database, automatically generates test configuration instructions, including sampling frequency, signal channel configuration, load scheduling strategy, and temperature control requirements, to construct an optimal test strategy framework for the power supply under test. This test configuration instruction is then sent to the multi-channel data acquisition unit as a basis for initializing its parameters. Based on this, the data acquisition system configures the sampling rate, trigger threshold, and filter parameters according to the required monitoring indicators for each channel, such as voltage, current, ripple, and response time, to form a sampling configuration. This configuration result is also linked to the system's load simulation unit, dynamically setting its operating mode, thereby enabling the construction of simulation scenarios based on different operating conditions. The load simulation scheme covers various modes, including constant current, constant resistance, constant power, and dynamic loads. Millisecond-level switching is achieved through an electronic switch array and power feedback module to test the DC / DC power supply's output stability and response speed under various load disturbances. Based on the rated operating temperature range of the DC / DC power supply under test, control commands are sent to the temperature control unit to develop a corresponding temperature control scheme. This scheme sets the baseline ambient temperature within the test chamber and uses PID control logic to control the heating wire output power and fan speed in real time, ensuring a stable and controllable thermal environment. The load simulation and temperature control schemes are then input into the test environment of the DC / DC power supply under test for parameter testing. The test system's high-precision synchronous sampling mechanism is activated, and each channel collects key performance parameters such as voltage, current, power, efficiency, conversion time, and output noise with unified timestamps, generating a continuous data stream. The back-end data processing module then performs data decoding, anomaly detection, and parameter extraction to generate structured target test parameter data.

[0017] 103. Calculate a characteristic parameter set based on the target test parameter data, and perform predictive control analysis of a voltage-temperature control model to obtain a control parameter sequence for the test box; Specifically, a bandpass filtering operation is performed on the target test parameter data. The filter design takes into account the characteristic frequency band and noise frequency distribution of the test signal. A digital bandpass filter based on Butterworth or Chebyshev characteristics is selected to effectively retain the key change patterns in the response signal while eliminating the interference of high-frequency noise and low-frequency drift on the analysis results, thereby obtaining the filtered processed data. On this basis, two types of equivalent circuit models are constructed based on the power supply response behavior, namely the first-signal equivalent circuit model and the second-signal equivalent circuit model. The former is centered on the output voltage response and uses a small-signal modeling method to characterize the voltage response characteristics of the DC / DC power supply under steady-state and small-disturbance conditions. The latter is centered on the output current response and establishes a current modeling network containing inductors, capacitors, equivalent series resistors, and output current feedback branches through the transmission relationship between load disturbances and responses. Using the two equivalent models described above, core characteristic parameters are extracted from the filtered data to form a set of characteristic parameters describing the DC / DC power supply's performance. This set includes output voltage stability (represented by the ratio of the output voltage standard deviation to the mean), output current stability (measured by the coefficient of variation of the output current), temperature coefficient (indicating the sensitivity of the power supply output to temperature changes), output impedance (determined by the ratio of the voltage change rate to the current disturbance), transient response time (the time it takes for the system to recover to the steady-state range after a disturbance), and voltage regulation (the ability to adjust the output voltage to a change in input voltage). To determine the stability trend of the power supply under test, statistical analysis is performed on the output voltage stability and output current stability in the characteristic parameter set. The mean, variance, and range are extracted using a sliding window statistical method, and the parameter time series are plotted to obtain stability assessment results. Multidimensional anomaly detection algorithms, such as those based on Gaussian mixture models, isolation forests, or local outlier factors, are introduced to comprehensively analyze the entire characteristic parameter set, identifying the degree of deviation and anomaly clustering tendencies in high-dimensional space, and outputting a continuous parameter anomaly metric. Based on parameter anomaly metrics, a performance anomaly detection result is generated, clearly indicating the specific anomaly type (e.g., excessive voltage jitter, frequent current fluctuations, thermal drift imbalance, etc.) and the corresponding anomaly severity score. This score is normalized on a scale of 0 to 1, with higher values ​​indicating a more severe anomaly. The control system uses this performance anomaly detection result as input to drive a model predictive control algorithm for combined voltage and temperature control, performing predictive analysis and scheduling optimization of the test chamber's environmental control parameters. Taking into account multiple factors such as future temperature trends, voltage response behavior, and equipment thermal inertia, the controller generates a control parameter sequence, including the heating wire PWM signal, fan speed adjustment value, and heat exchanger operating state setpoints, thereby enabling proactive adjustment and state compensation of the test environment.

[0018] In this embodiment, a system state-space model is constructed based on the performance anomaly detection results. In this model, the system's state variables include the input voltage, output voltage, output current, and temperatures at key internal points of the DC / DC power supply under test. These state variables collectively reflect the power supply's operating state during dynamic testing. Control inputs include key control parameters such as heating power, fan speed, load current regulation, and input disturbance amplitude. External disturbances, such as ambient temperature fluctuations and power supply fluctuations, are also considered. Therefore, the model not only includes a mapping between state and control inputs but also incorporates disturbance factors to characterize the coupling between the test environment and power supply behavior. A first objective function is constructed based on the system state-space model. This function guides the control strategy for the future, maintaining the key parameters of the power supply under test within target ranges while avoiding system instability caused by controller overshoot. To improve responsiveness to different anomaly conditions, the weights of the parameters in the control objective function are dynamically adjusted based on the anomaly detection results. For example, when the system identifies a dominant temperature anomaly, the control function prioritizes constraining the deviation of temperature-related variables. However, when significant output voltage fluctuations are identified, the system prioritizes voltage response as the core control metric. To ensure the feasibility and safety of the control inputs, multiple physical and logical constraints are added to the objective function. These constraints include the upper limit of the heater power, the allowable range of fan speed, the maximum slope of the load current change, and the safety limit of the voltage disturbance amplitude. This constrained control problem is modeled as an optimization problem in the controller and solved using a standard optimization algorithm to obtain the optimal control input sequence for a future prediction period. A rolling horizon control strategy is implemented for the optimal control sequence, executing only the first control input in the current cycle and re-evaluating the system status and abnormal conditions in the next control cycle. This continuously updates the optimization results and achieves continuous rolling updates of the control strategy. The control parameter sequence output by the system is used to drive the actuators of the test chamber, including adjusting the heater power, setting the fan speed to control the heat dissipation capacity, adjusting the load current to simulate different operating conditions, and superimposing specific voltage disturbances to enhance the dynamic coverage of the test environment.

[0019] 104. According to the control parameter sequence, the power output value of the heating wire, the speed value of the cooling fan and the position value of the electric push rod in the test box are controlled to obtain an intelligently adjusted test environment; Specifically, a control mapping function set is established based on the control parameter sequence. This mapping function set converts the abstract control variables in the control parameter sequence into specific, executable physical quantities. This mapping function set includes a heating wire power mapping function, a cooling fan speed mapping function, an electric actuator position mapping function, and a load current mapping function to assist in load regulation. Upon receiving the control parameter sequence generated in the previous stage, the system inputs the heating power parameter into the heating wire power mapping function. This function uses a nonlinear transformation to perform amplitude correction and dynamic compensation on the original input to adapt it to the nonlinear thermal inertia characteristics and operating power curve of the heating wire, thereby obtaining the heating wire power output value. The cooling fan speed parameter is then normalized and mapped. This process uses linear interpolation and soft limiting to ensure that the fan speed varies within a safe operating range while balancing heat dissipation efficiency and noise levels. A specific speed command is generated to drive the fan motor. Furthermore, the difference between the temperature parameter in the control parameter sequence and the current ambient temperature inside the cabinet determines whether the heat convection structure needs to be re-arranged. This difference serves as the trigger logic to activate the electric actuator position control mapping function. This function converts the relationship between temperature error and temperature threshold into a set displacement for the electric actuator, which is used to adjust the position of the cooling duct, airflow path, or shielding structure, thereby intelligently regulating the direction and distribution of ambient heat flow. All control command values ​​generated by the mapping function, including heating wire power output, fan speed, and actuator position, are transmitted via a standardized control interface to the corresponding actuators within the test chamber. These actuators, including power driver modules, PWM modulation circuits, and stepper motor drivers, immediately execute the commands and change the physical state of the test environment, producing the initial control effect. To ensure high consistency between control actions and the desired environmental state, feedback signals from each actuator are continuously collected, including temperature curves, wind speed distribution, duct pressure changes, and actuator position sensor data. These feedback signals are then compared with the desired control target. If an error is detected outside the allowable range, the system immediately triggers a control error correction mechanism, automatically adjusting the output ratio of the current control parameter or reactivating the mapping function calculation path until the error converges within the set range, forming a complete closed-loop control chain and achieving an intelligently regulated test environment.

[0020] 105. Generate aging test results and test strategy adjustment instructions for the DC / DC power supply under test in an intelligent adjustment test environment.

[0021] Specifically, time series analysis and processing are performed on various voltage, current, temperature, and environmental control data continuously collected within the intelligent control environment. Multidimensional data timestamp alignment technology, combined with a window smoothing algorithm and outlier rejection mechanisms, ensures that each data set is accurately modeled on a logically consistent timeline. A parameter trend model is constructed by extracting the temporal trends of key parameters. This model uses linear regression, polynomial fitting, or sliding window-based moving average analysis on the collected data to reflect the dynamic evolution of the DC / DC power supply's output stability, load response characteristics, and thermal performance during aging. Based on the established parameter trend model, key parameter trends at multiple future time points are predicted, forming a continuous series of parameter predictions. These predictions are then compared with actual parameter changes obtained through monitoring. The difference between the predicted and measured values ​​is calculated to obtain a parameter deviation metric, which reflects model accuracy and indicates sudden changes in power supply performance and potential anomaly risks. A larger parameter deviation metric indicates that the current system behavior deviates further from the predicted trajectory, and that potential instability or aging characteristics are more pronounced. To extract structural information from deviations, the deviation metrics are graded and, combined with a multi-threshold judgment mechanism or clustering algorithm, classified into different levels of anomaly, such as normal, mild anomaly, moderate anomaly, and severe anomaly. Based on the characteristic manifestations of each anomaly type, a graded feedback strategy is implemented. Specifically, corresponding test strategy adjustment instructions are automatically formulated for anomaly types of varying severity. For example, these adjustments may include reducing the disturbance amplitude to avoid overload risk, extending the test time to observe parameter convergence, or increasing the sampling density to improve detection resolution. This allows the test process to maximize the regularity of power supply aging behavior while controlling risks. Throughout the test, anomaly diagnosis results are generated, and test environment parameters such as internal temperature control records, cooling wind speed curves, and electric actuator movement frequency are structured and organized. These results are then integrated with the test duration, number of sampling points, number of anomaly records, parameter trend fitting results, and energy consumption statistics to form the aging test results.

[0022] In an embodiment of the present invention, by injecting a precisely controllable disturbance signal into the input of the DC / DC power supply under test, the parameter detection blind spot problem in traditional test methods is overcome. Even when the output voltage precisely matches the set value, power supply performance anomalies can be effectively identified, significantly improving the reliability and accuracy of the test. A dynamic multi-parameter test system is used to achieve high consistency between the test environment and the actual application environment, supporting flexible test parameter combinations and load mode configurations, and solving the test blind spot problem existing in traditional test equipment. By bandpass filtering the test data and calculating parameters based on the first signal equivalent circuit model, combined with a multidimensional anomaly detection algorithm, accurate identification and classification of DC / DC power supply performance anomalies are achieved. Based on the voltage-temperature control integrated model predictive control algorithm, temperature control is directly integrated into the test cost function, achieving dual-objective optimization of test efficiency and energy consumption. Test parameters are dynamically adjusted according to the real-time performance of the power supply, reducing test complexity and energy consumption. Through a nonlinear mapping function and closed-loop control strategy, precise control of the test box heating wire power, cooling fan speed, and electric push rod position is achieved, ensuring that the test environment is consistent with the algorithm expectations and providing stable and reliable conditions for parameter testing. Through parameter trend prediction and anomaly diagnosis and feedback mechanisms, adaptive adjustment of test strategies is achieved, minimizing test time while ensuring test effectiveness. A comprehensive anomaly classification and hierarchical feedback strategy has been established, improving test efficiency and reliability. When a serious power supply anomaly is detected, the system immediately terminates the test and initiates protective measures, including disconnecting the input power, retracting the heating plate, and activating the cooling fan, effectively preventing further damage to the power supply and protecting the test equipment.

[0023] In a specific embodiment, the process of executing step 101 may specifically include the following steps: Analyze the rated parameters of the DC / DC power supply under test and generate an optimal disturbance signal parameter configuration table, which includes the disturbance signal type, frequency value and amplitude range; Set the parameters of the signal generator according to the optimal disturbance signal parameter configuration table to generate the target disturbance signal; The target disturbance signal is superimposed on the DC input voltage of the DC / DC power supply under test through capacitive coupling, and the feedback suppression loop is started at the same time; Through the feedback suppression loop, the injection duration of the target disturbance signal is dynamically adjusted, and the output end of the DC / DC power supply under test is monitored in real time to obtain output voltage response data and output current response data.

[0024] Specifically, a parameter parsing module was established within the test system to perform structured analysis of the technical specifications of the selected DC / DC power supply. By automatically identifying performance indicators such as the power supply's rated input voltage, rated output voltage, maximum output current, steady-state ripple range, load regulation, temperature drift coefficient, and transient response time, a parameter feature vector was established. The power supply's structural characteristics, such as topology (e.g., buck, boost, flyback), control mode (e.g., voltage mode, peak current mode, average current mode), and compensation loop configuration, were then used to determine its sensitivity to disturbances of varying frequencies and amplitudes. This process utilized a historical experience database to perform pattern matching on the response curves of power supplies of different structural types under various excitation conditions. Sensitivity analysis was then used to identify the signal types most likely to induce dynamic responses. Based on this analysis, a multi-factor decision-making mechanism was used to generate an optimal disturbance signal parameter configuration table. This table clearly identifies the preferred disturbance signal types (e.g., square wave, sine wave, triangle wave, step signal, random sequence), and details the optimal frequency range and amplitude modulation range for each signal. The voltage perturbation upper and lower limits are determined based on the power supply's minimum load startup voltage and maximum shutdown voltage, ensuring that the perturbation stimulus effectively triggers dynamic behavior without triggering protection mechanisms or causing damage. After the perturbation parameter configuration table is established, the test control unit automatically calls the configuration results and sets the signal generator parameters to output the target perturbation signal that meets the specified type, frequency, and amplitude requirements. This signal is injected into the DC input of the DC / DC power supply under test via a capacitive coupling circuit. Capacitive coupling, the core signal injection method of this system, electrically blocks DC and passes AC, thus maintaining the power supply's average input voltage while efficiently superimposing medium- and high-frequency perturbation signals. During the coupling process, the capacitor value forms an impedance matching network with the signal source's internal resistance and the input bus impedance. The system dynamically selects the coupling capacitor value based on the test object's input resistance to avoid signal reflections, waveform distortion, and reduced injection efficiency. To prevent the perturbation signal from adversely affecting power supply stability over a long period of time, a feedback suppression loop is activated during signal injection. This loop uses the sampled signal at the output of the power supply under test as its core input variable, monitors the response characteristics of the output voltage and current to disturbance injection in real time, and establishes a functional relationship between the response amplitude and system stability within the disturbance time window. During operation, the feedback suppression loop continuously determines whether the output response exceeds the safety threshold, such as when the output voltage drops beyond the specified range or the current ripple amplitude exceeds the set limit. When it detects that the response behavior is approaching the abnormal boundary, the system actively controls the signal generator to pause or reduce the amplitude of the disturbance signal, and recalibrates the injection rhythm and duration through the closed-loop channel. At the same time, the output response data acquisition module activates the high-speed sampling logic to perform multi-channel synchronous acquisition of the output voltage and current.The sampling system utilizes a high-resolution analog-to-digital converter to ensure the complete reproduction of the power supply's dynamic response even in the presence of kHz-level disturbance signals. The voltage signal undergoes buffering, anti-aliasing filtering, and multi-stage conditioning, while the current signal is collected via a high-precision shunt or Hall effect sensor and fed into a digital channel. All collected data is uniformly timestamped and written into a cache for transmission to a data processing center, serving as the basis for subsequent response analysis, frequency domain identification, and dynamic modeling.

[0025] In a specific embodiment, the process of executing step 102 may specifically include the following steps: Inputting the output voltage response data and the output current response data into the main control unit of the dynamic multi-parameter test system for processing to obtain a test configuration instruction; Setting parameters of a multi-channel data acquisition unit of a dynamic multi-parameter test system according to a test configuration instruction to obtain a sampling configuration scheme; Performing load mode configuration on a load simulation unit of a dynamic multi-parameter test system based on a sampling configuration scheme to obtain a load simulation scheme, wherein the load simulation scheme includes at least one of a constant current mode, a constant resistance mode, a constant power mode, and a dynamic load mode; The temperature of the temperature control unit of the dynamic multi-parameter test system is set according to the rated operating temperature of the DC / DC power supply under test to obtain a temperature control scheme; Input the load simulation scheme and temperature control scheme into the test environment of the DC / DC power supply under test, perform parameter testing, and obtain real-time test data stream; Perform synchronous sampling processing on the real-time test data stream to collect the target test parameter data of the DC / DC power supply under test.

[0026] Specifically, a master control algorithm module with high scalability and logical reasoning capabilities was established. This module is capable of preprocessing, feature analysis, and test strategy generation for multi-channel high-frequency response data. When the voltage and current response data from the DC / DC power supply under test are fed into the master control unit in real time, the system initiates a signal synchronization mechanism to align the time axes of the two signal channels and identify waveform features. A unified timestamp calibration is performed using a high-precision clock to ensure millisecond-level response matching of waveform changes in subsequent operations. The master control unit then uses an integrated fast analysis model to perform a preliminary analysis of the signal waveform, extracting characteristic metrics such as voltage rise time, current slope, maximum overshoot, steady-state ripple amplitude, response time, and steady-state recovery time. This is then quickly compared against pre-set standard performance templates to determine whether the DC / DC power supply is within typical operating conditions. Based on the power supply's structural type and historical testing experience, the master control unit automatically generates test configuration instructions, including recommended settings for parameters such as data acquisition rate, sampling accuracy, channel combination strategy, and load simulation strategy. This configuration command considers the characteristics of the current response data, taking into account various factors such as the power supply's operating frequency, topology, control method, and expected aging performance. It also optimizes the test sequence based on a priority scheduling mechanism. The resulting configuration is highly operational, highly focused, and responsive. This configuration command is then sent to the multi-channel data acquisition unit, which automatically sets the sampling channels, filtering parameters, amplification factors, and analog-to-digital conversion accuracy to form a sampling configuration. This configuration specifies the physical signal type corresponding to each sampling channel, such as one channel dedicated to output voltage, another to output current, and another to input voltage or temperature. This ensures consistency across all signal channels in both physical wiring and software logic. Based on the parameter constraints of the sampling configuration, the load simulation unit is controlled to select the appropriate load mode based on the current power supply's response capabilities and configuration objectives. In practice, constant current mode is selected to evaluate the power supply's current regulation capability, constant resistance mode is selected to verify voltage regulation, constant power mode is used to simulate typical energy load behavior, and dynamic load mode is enabled to set a cyclically changing current trajectory or simulate sudden load changes in real-world scenarios, enhancing the evaluation of the power supply's response under non-steady-state conditions. The load simulation scheme isn't static; instead, it maintains continuous communication with the main control unit and adjusts based on real-time response characteristics during the test, ensuring the load simulation remains in the most representative test state. Simultaneously, the test system configures the temperature control unit based on the rated operating temperature parameters of the DC / DC power supply under test and generates a corresponding temperature control scheme. The system reads the optimal operating temperature range indicated in the power supply's technical specifications and sets a constant or variable temperature program within the test environment based on this range. The temperature control strategy employs either a single-point constant temperature maintenance strategy or a step-by-step temperature increase to simulate the changing ambient temperature trends during the aging process.The temperature control unit regulates the environment by controlling the heating wire power and fan speed. Throughout the test cycle, the system monitors the deviation between the ambient temperature and the power supply's key thermal points and adjusts the heating or cooling strategy appropriately to ensure the temperature trajectory remains consistent with the expected path. Once the load simulation and temperature control schemes are fully configured, the system enters the parameter testing phase, applying both schemes to the power supply under test, implementing a full-cycle, continuous load-bearing operation. During the test, the test platform activates the high-frequency data acquisition module to sample all parameters, including voltage, current, power, efficiency, ripple, and load change rate. All sampled data forms a real-time test data stream. This data stream is organized with a unified timestamp and encapsulated in a hierarchical channel structure. It is transmitted to the main control data processing module via a high-speed data bus to ensure high consistency and integrity during subsequent data analysis. Upon acquiring the real-time data stream, the system simultaneously activates the data stream processing engine, invoking a synchronized sampling algorithm to collaboratively process data from all signal channels. This process includes signal amplitude calibration, drift elimination, noise suppression, and phase compensation, as well as simultaneous extraction of time- and frequency-domain features of the data waveform. Through a high-speed cache mechanism and multi-core parallel processing architecture, the system completes the verification, matching and reorganization of sampled data from all signal channels within a millisecond response time, and ultimately extracts target test parameter data representing the true performance status of the DC / DC power supply under the current test conditions, covering dimensions such as voltage stability, current regulation capability, load response speed, energy conversion efficiency, heat dissipation capability and thermal stability.

[0027] In a specific embodiment, the process of executing step 103 may specifically include the following steps: Performing bandpass filtering on the target test parameter data to obtain filtered processed data; constructing a first signal equivalent circuit model and a second signal equivalent circuit model of the DC / DC power supply under test based on the filtered processed data; Calculating a characteristic parameter set of the DC / DC power supply under test based on the first signal equivalent circuit model and the second signal equivalent circuit model, the characteristic parameter set including output voltage stability, output current stability, temperature coefficient, output impedance, transient response time, and regulation rate; Perform statistical analysis on the output voltage stability and output current stability in the characteristic parameter set to obtain parameter stability evaluation results; According to the parameter stability evaluation results, a multi-dimensional anomaly detection algorithm is used to conduct a comprehensive analysis of the characteristic parameter set to obtain the parameter anomaly measurement value; Generate performance anomaly detection results based on parameter anomaly measurement values, including anomaly type identification and anomaly degree score; According to the performance anomaly detection results, the voltage-temperature control model predictive control analysis is performed to obtain the control parameter sequence of the test box.

[0028] Specifically, the target test parameter data is bandpass filtered. By appropriately setting the filter's upper and lower cutoff frequencies, the filter retains frequency components reflecting the power supply's dynamic characteristics while effectively suppressing high- and low-frequency interference introduced by background noise, test environment disturbances, and system nonlinearities. The bandpass filter design must consider the DC / DC power supply's operating frequency, the load disturbance frequency, and the dominant frequency of the ripple signal to ensure that the filtering result accurately reproduces the power supply's true response characteristics during testing. Based on the filtered data, a first-signal equivalent circuit model and a second-signal equivalent circuit model are constructed. The first model describes the DC / DC power supply's output response under voltage excitation. Based on the control loop model and output stage compensation network, it constructs an equivalent network consisting of equivalent capacitors, inductors, resistors, and feedback gains, reflecting the dynamic behavior of the power supply's output voltage under disturbance conditions. The second-signal equivalent circuit model focuses on modeling the current response path, simulating the output current response to input disturbances or load fluctuations. This model includes a current sampling network, an error amplifier modeling module, and a representation of the load's equivalent behavior. Together, these two models provide a mathematical abstraction of the power supply's electrical response in the frequency and time domains, enabling the system to accurately calculate key electrical performance indicators based on model calculations. Based on the modeling results, the system then deduces and calculates a set of characteristic parameters for the DC / DC power supply under test. This set encompasses multiple dimensions of the power supply's steady-state, dynamic, and thermal behavior. First, output voltage stability, derived by the ratio of the mean square deviation of filtered voltage data to its mean, reflects the output's ability to maintain a constant voltage under a constant load. Second, output current stability, calculated similarly but focusing on the degree of current variation at the output end, reflects the impact of ambient temperature changes on the power supply's key performance parameters (such as output voltage and conversion efficiency). This is determined by linearly fitting the temperature-output relationship. Output impedance, a key indicator of the power supply's dynamic load regulation capability, is derived from the ratio of voltage change to current change under a small signal disturbance. Transient response time reflects the time delay from the onset of a disturbance until the output returns to steady state. Regulation measures the speed and ability of the power supply to maintain output stability under varying input or load conditions. Each of the above parameters is calculated and extracted through the electrical characteristic nodes or equivalent links in the model to ensure that the results are engineering reproducible and physically interpretable. Statistical analysis is performed on the output voltage stability and output current stability in the characteristic parameter set, and the long-term trends and short-term fluctuations of these two parameters are evaluated. The analysis process adopts a sliding window processing strategy to calculate the mean, variance, and range of the stability in different time segments, and then construct a stability time evolution curve. The stability level classification result is obtained through clustering or normalized mapping, forming the parameter stability assessment result under the current state of the system. Based on the parameter stability assessment results, a multi-dimensional anomaly detection algorithm is used to conduct a comprehensive analysis of the characteristic parameter set.Based on a high-dimensional feature space, algorithms such as isolation forests, local anomaly factors, support vector bounds, or Gaussian mixture distribution models are used to model and identify potential abnormal relationships between parameters. By analyzing the cluster density, distance distribution, and degree of projection anomaly of the overall parameters in multidimensional space, a comprehensive parameter anomaly metric is calculated. This metric represents the degree to which the current power supply operating state deviates from the normal operating range. Larger values ​​indicate more significant deviations and reflect a more severe performance degradation trend. Based on the parameter anomaly metric, performance anomaly detection results are generated. The results consist of two key components: an anomaly type identifier, clarifying whether the current anomaly belongs to a voltage fluctuation, abnormal current response, excessive thermal drift, or transient regulation instability; and an anomaly severity score, expressed as a normalized numerical value, reflecting the system's assessment of the severity of the current anomaly. Based on the performance anomaly detection results, the test system initiates the predictive control analysis process of the voltage-temperature control model. The current anomaly type and severity score are loaded into the control engine. Based on control characteristics such as power supply thermal inertia, voltage stability window, and feedback regulation rate, the system dynamically adjusts the weight parameters of the control objective function. The optimized control objective is then input into the model predictive control algorithm module, which uses rolling horizon optimization to generate the optimal environmental adjustment sequence for several future control cycles. This sequence encompasses key control variables such as heating power output level, cooling fan speed variation curve, load current perturbation scheme, and voltage input perturbation strategy. Ultimately, it is implemented in the test chamber's physical control unit through a closed-loop control interface, enabling intelligent, coordinated adjustment of the environment and test load.

[0029] In a specific embodiment, the step of performing a voltage-temperature control model predictive control analysis based on the performance anomaly detection result to obtain a control parameter sequence for the test box may specifically include the following steps: A system state space model is constructed based on the performance anomaly detection results. The state variables of the system state space model include the DC / DC power supply input voltage, output voltage, output current, and internal key point temperatures. The state equation is a linear combination of the state vector, the control input vector, and the disturbance vector. Constructing a first objective function based on the system state space model, and dynamically adjusting the weight coefficient in the first objective function according to the performance anomaly detection result to obtain a second objective function; Set constraints on the second objective function to obtain a constrained optimization problem, and use the quadratic programming method to solve the constrained optimization problem to obtain the optimal control sequence in the future control time domain; A rolling time domain control strategy is implemented for the optimal control sequence. Only the control input of the first control cycle is executed, and the optimization calculation is re-performed in the next control cycle to obtain the control parameter sequence of the test box. The control parameter sequence includes heating power parameters, cooling fan speed parameters, load current parameters and input voltage disturbance parameters.

[0030] Specifically, a system state-space model is constructed based on the performance anomaly detection results. The core of this model is to define a set of state variables representing the current operating status of the power supply, with control inputs and external disturbances as the two main factors influencing state evolution. These state variables include the DC / DC power supply's input voltage, output voltage, and output current, three key parameters that directly reflect power conversion efficiency and load capacity. They also include temperature information at key internal points, such as the temperature rise at the output inductor or power MOSFETs. These temperature variables are collected in real time by embedded sensors and serve as an important reference for determining thermal control strategies. To establish a mathematical structure that directly interacts with the control system, these state variables are organized into state vectors and linearly combined with control input vectors representing the control behavior of the test system and disturbance vectors reflecting external environmental disturbances. This results in a multi-input, multi-output dynamic system state equation. The evolution of each state set depends not only on its own state at the previous moment but also on control inputs such as heater power output, fan speed, disturbance amplitude at the power supply input, and dynamic load current settings. Furthermore, the state variables are affected by non-structural disturbances such as ambient temperature, ventilation conditions, and grid fluctuations. Based on the system state-space model, a first objective function is constructed. This objective function evaluates the overall performance of a control scheme in the future time domain. It comprises a weighted sum of two components: state error and control energy consumption. The state error measures the degree of deviation between the current power supply operating state and the desired target, while the control energy consumption constrains the energy consumption and execution intensity of the control strategy during actual execution. To account for various performance anomalies that may occur during actual testing, such as unstable output voltage, abnormal temperature rise, or sluggish current response, the weight coefficients in the first objective function are dynamically adjusted based on the performance anomaly detection results. The latest performance anomaly detection results are input into the control weight reconstruction mechanism, which automatically adjusts the weight coefficients in the original objective function based on the anomaly type, severity score, and anomaly impact dimension, generating a second objective function that adapts to the current power supply state. Constraints are imposed on the second objective function to form a constrained control optimization problem. These constraints include the maximum output capacity of heating power, the safe operating range of fan speed, the maximum load current disturbance amplitude that the power supply can withstand, and the amplitude limit of input disturbances. Based on the constrained problem framework, the control system calls the solver module and uses the quadratic programming method to numerically solve the optimization problem. This method quickly obtains the optimal control sequence for multiple future control cycles while ensuring global convergence. This control sequence defines how to adjust the heater's operating power, fan speed, simulated load current intensity, and input side disturbance injection amplitude at each time step, thereby achieving continuous adjustment and dynamic correction of the test environment.The control system uses a rolling horizon control strategy to dynamically update the control sequence in real time. At the beginning of each control cycle, only the first control input within the current cycle is executed, while the remaining control actions are temporarily suspended. In the next cycle, the state-space model and objective function are rebuilt and the control optimization problem is solved based on the latest system state, updated performance anomaly detection results, and possible new disturbances. This results in a new control sequence, which is then repeated repeatedly. This optimization process is performed once per control cycle, improving the system's ability to respond quickly to anomalies and enhance the control system's adaptability to dynamic changes. This is particularly useful for situations where the power supply under test experiences gradual performance degradation or nonlinear drift during long-term aging tests. The first control input generated during each rolling optimization cycle constitutes the control parameter sequence for the test chamber. This sequence includes heating power parameters for regulating ambient temperature, cooling fan speed parameters for controlling heat convection, load current parameters for simulating load fluctuations, and input voltage disturbance parameters for interfering with the power supply input. These parameters are then distributed to the corresponding execution units, providing real-time feedback on the control effect via a high-speed bus.

[0031] In a specific embodiment, the process of executing step 104 may specifically include the following steps: Establishing a control mapping function set according to the control parameter sequence, the control mapping function set includes a heating wire power mapping function, a cooling fan speed mapping function, an electric push rod position mapping function and a load current mapping function; Perform nonlinear mapping on the heating power parameter in the control parameter sequence to obtain the power output value of the heating wire; Mapping conversion is performed based on the cooling fan speed parameter in the control parameter sequence to obtain the heat dissipation fan speed value; The electric push rod is controlled according to the comparison result of the temperature parameter in the control parameter sequence and the temperature threshold value to obtain the position value of the electric push rod; The heating wire power output value, cooling fan speed value and electric push rod position value are sent to the corresponding execution unit through the control interface to obtain the initial control effect; The initial control effect is closed-loop controlled, and the control error is corrected through real-time feedback signals to obtain an intelligent adjustment test environment.

[0032] Specifically, a control mapping function set is established based on the control parameter sequence. A set of control mapping functions corresponding to different execution units is constructed, consisting of a heating wire power mapping function, a cooling fan speed mapping function, an electric push rod position mapping function, and a load current mapping function. The core of these functions is to establish a continuously differentiable, monotonically stable mapping relationship between the parameter space and the execution action, so as to achieve precise regulation of multiple types of control objects such as heat, electricity, wind, and machinery. During the actual execution process, the system receives the heating power parameter in the control parameter sequence. This parameter is a relative control quantity of the desired heat output and cannot be directly used as the driving signal of the heating wire. Therefore, a nonlinear mapping conversion is performed through the heating wire power mapping function. This nonlinear mapping function takes into account actual physical factors such as the thermal inertia, heat capacity, response delay, and nonlinear heating characteristics of the heating wire. It adopts a segmented response model or an exponential decay model to model the control parameter and convert it into the heating wire PWM duty cycle or constant power output value. After calculation by this function, the power output of the heating wire during the current control cycle is obtained. This signal is then sent to the power control module via the hardware control interface, driving the heating wire and achieving a targeted increase in ambient temperature. The fan speed parameter from the control parameter sequence is input into the cooling fan speed mapping function. While this function structure is relatively linear, it still needs to account for factors such as the fan characteristic curve, the influence of power supply voltage, and nonlinear air flow. Based on this, a fan control mapping model is constructed. This model maps control parameters to desired speeds and adapts to the drive logic of different fan models, converting control commands into PWM signals, voltage control, or frequency regulation signals, and outputting the target fan speed. Simultaneously, the temperature control strategy is used to control the movement of the electric actuator in the airflow guidance mechanism within the test environment. The temperature parameter from the control parameter sequence is extracted and compared in real time with the system-set temperature threshold. The comparison result is then fed as an input signal into the electric actuator position mapping function. This function determines the actuator displacement based on the relative position of the current temperature, thereby automatically adjusting the air duct opening, wind deflector position, or the angle of the guide structure. For example, when the temperature continues to be higher than the set threshold and the system determines that ventilation needs to be increased, the mapping function will output a target position value that pushes the electric push rod in the opening direction; if the temperature is lower than the preset value, the system will retract the push rod or remain stationary to reduce heat loss. This displacement control mechanism based on temperature state, combined with the two-way adjustment of the fan and heating element, can achieve spatial redistribution of thermal dynamics in complex environments and improve the temperature control accuracy of the system. After completing the calculation of the above three types of control values, the integrated control interface module will convert the heating wire power output value, the cooling fan speed value and the electric push rod position value into corresponding drive signals, and send them to the corresponding execution unit through standard communication protocols such as CAN, Modbus or PWM analog signals.After receiving the instruction, the execution unit starts the corresponding execution action, thereby changing the temperature distribution, ventilation intensity and air flow structure of the test environment at the physical level, forming the initial control effect of the system. In order to ensure that the actual results of environmental adjustment are highly consistent with the control target, a feedback mechanism is constructed and a closed-loop control strategy is implemented. In each control cycle, multiple feedback parameters such as the real-time temperature around the heating wire, the wind speed at the fan outlet, the average ambient temperature, the current position of the electric push rod, etc. are collected and input into the control system through the sensor array. They are compared with the target value to calculate the actual control error. If the error exceeds the dynamic stability range allowed by the system, the system automatically starts the error correction module to fine-tune the control parameters of the previous round. The adjustment amplitude is determined according to the error direction and the error integral value, so that corrections are made in the control input of the next cycle to obtain an intelligently adjusted test environment.

[0033] In a specific embodiment, the process of executing step 105 may specifically include the following steps: Conduct time series analysis on test data collected in the intelligent adjustment test environment and establish parameter trend models; Based on the parameter trend model, the future change trend of the parameters of the DC / DC power supply under test is predicted to obtain a parameter prediction value sequence; Calculate the deviation between the parameter prediction value sequence and the actual parameter change to obtain the parameter deviation measurement value; Based on the parameter deviation measurement value, the abnormality level is divided into different levels to obtain the abnormality diagnosis results. Based on the abnormality diagnosis results, a hierarchical feedback strategy is implemented to obtain the test strategy adjustment instructions. The abnormality diagnosis results and test environment parameters are integrated into the aging test results of the DC / DC power supply under test. The aging test results include test environment parameters, test duration, number of test points, abnormality diagnosis results, parameter trend analysis and energy consumption statistics.

[0034] Specifically, time series analysis is performed on the multi-dimensional, multi-timepoint test data collected during the test. This data includes information such as output voltage, output current, input voltage, internal key point temperatures, conversion efficiency, load current response, control signal execution results, and ambient temperature and humidity. All data is indexed by timestamp and organized into a time series dataset with a unified sampling period. A synchronized sampling mechanism ensures time-axis consistency between signals. After receiving the raw time series data, the system uses a preprocessing module to remove noise, correct outliers, and perform data interpolation to ensure the integrity and analyzability of the time series data. Entering the parameter trend modeling stage, appropriate modeling methods are selected based on the physical characteristics of different parameters. For example, polynomial fitting is used for temperature trends, and sliding average and exponentially weighted moving average models are used for output voltage stability. For parameters with strong nonlinear variations, such as efficiency or ripple response, ARIMA models, wavelet decomposition models, or deep learning prediction models based on long-short-term memory networks are used. Multi-model fusion improves the robustness and generalization of trend modeling. Each trend model fits a corresponding trend function or state transition pattern based on historical data within the current time window, constructing a set of parameter trend models that describe the time evolution characteristics of various performance indicators of the DC / DC power supply under test under current environmental and load conditions. Future parameter change trends of the DC / DC power supply under test are predicted based on the parameter trend models, resulting in a set of parameter prediction values ​​in the form of a time series. The time span, sampling interval, and prediction accuracy of the parameter prediction value sequence are dynamically adjusted based on the test strategy. For example, the prediction granularity can be slightly larger in the early stages of testing, while the prediction step size can be reduced and the prediction density increased when signs of system instability are detected, thereby enhancing early warning capabilities for potential anomalies. This predicted sequence is compared with the subsequent actual parameter sequence collected, and the difference between the two is calculated to form a parameter deviation metric. This metric reflects the degree of consistency between the actual system operating status and the model prediction. When the deviation metric value continuously deviates from the model prediction and shows an increasing trend, it indicates that the operating status of the power supply under test has begun to deviate into an abnormal range during the test, indicating potential issues such as thermal runaway, control lag, or internal device performance degradation. Anomaly identification and classification are performed based on the parameter deviation metric. Abnormality classification relies on a multi-level error threshold system. Each parameter or parameter combination corresponds to a set of sensitivity-adjusted threshold intervals, resulting in four levels: normal, slight deviation, moderate abnormality, and severe abnormality. The system assigns a corresponding priority and response intensity to each abnormality type. The system then normalizes the overall abnormality level by combining the weight distribution of different parameters to produce a normalized score for the abnormality diagnosis result. A graded feedback strategy is implemented based on the abnormality diagnosis results.The hierarchical feedback strategy assigns different response strategy paths based on the abnormality level. For example, for minor abnormalities, the sampling density and short-term monitoring frequency are increased. For moderate abnormalities, short-term encrypted control adjustments, local test condition degradation, or automatic extension of the test cycle are performed. When a serious abnormality is detected, the protection mechanism is activated, the current test cycle is terminated, the abnormal snapshot is recorded, and manual intervention is prompted. The feedback strategy not only acts on the control system level, but also links the data acquisition system, the abnormality recording module, and the test strategy engine to form new test strategy adjustment instructions by updating the test process schedule, adjusting the disturbance injection intensity, switching the load mode or temperature regulation method, etc. The system integrates all abnormality diagnosis results detected during this test cycle with the current environmental parameters, control strategy execution records, parameter trend modeling, and predictive analysis results to generate an aging test result report for the DC / DC power supply under test. The report contains structured data in multiple dimensions, including test environment parameters such as temperature, humidity, wind speed, and thermal convection status; test duration and cumulative operating cycles; number of test points and number of samples taken at each stage; complete details of abnormal diagnosis results; parameter trend charts, comparison charts of predicted trajectories and actual fluctuation curves; parameter deviation development trends and aggregated evaluation indicators; and complete energy consumption statistics, including heating energy consumption, cooling energy consumption, total load power consumption, and other information for each stage.

[0035] The above describes the DC / DC power supply function parameter test method according to the embodiment of the present invention. The following describes the DC / DC power supply function parameter test system according to the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a DC / DC power supply function parameter testing system includes: The injection module 201 is used to inject a preset disturbance signal into the input terminal of the DC / DC power supply under test through a signal conditioning circuit to obtain output voltage response data and output current response data; An acquisition module 202 is configured to input the output voltage response data and the output current response data into a dynamic multi-parameter test system in a test box and acquire target test parameter data; The calculation module 203 is used to calculate the characteristic parameter set according to the target test parameter data, and perform voltage-temperature control model predictive control analysis to obtain the control parameter sequence of the test box; The control module 204 is used to control the power output value of the heating wire, the speed value of the cooling fan and the position value of the electric push rod in the test box according to the control parameter sequence to obtain an intelligently adjustable test environment; The generating module 205 is used to generate an aging test result and a test strategy adjustment instruction of the DC / DC power supply under test in an intelligent adjustment test environment.

[0036] Through the collaborative efforts of these components, the system injects a precisely controlled disturbance signal into the input of the DC / DC power supply under test, overcoming the parameter detection blind spots inherent in traditional testing methods. This system effectively identifies power supply performance anomalies even when the output voltage precisely matches the set value, significantly improving test reliability and accuracy. The dynamic multi-parameter test system achieves high consistency between the test environment and the actual application environment, supporting flexible test parameter combinations and load mode configurations, addressing the testing blind spots inherent in traditional test equipment. By bandpass filtering the test data and calculating parameters based on a first-signal equivalent circuit model, combined with a multidimensional anomaly detection algorithm, the system accurately identifies and classifies DC / DC power supply performance anomalies. Based on a voltage-temperature integrated model predictive control algorithm, temperature control is directly integrated into the test cost function, achieving dual-objective optimization of test efficiency and energy consumption. Dynamically adjusting test parameters based on the power supply's real-time performance reduces test complexity and energy consumption. A nonlinear mapping function and closed-loop control strategy enable precise control of the test chamber's heating wire power, cooling fan speed, and electric actuator position, ensuring the test environment is consistent with the algorithm's expectations and providing stable and reliable conditions for parameter testing. Through parameter trend prediction and anomaly diagnosis and feedback mechanisms, adaptive adjustment of test strategies is achieved, minimizing test time while ensuring test effectiveness. A comprehensive anomaly classification and hierarchical feedback strategy has been established, improving test efficiency and reliability. When a serious power supply anomaly is detected, the system immediately terminates the test and initiates protective measures, including disconnecting the input power, retracting the heating plate, and activating the cooling fan, effectively preventing further damage to the power supply and protecting the test equipment.

[0037] above Figure 2 The DC / DC power supply functional parameter testing system in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The computer device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0038] Figure 3This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. The computer device 300 may vary significantly due to different configurations or performance. It may include one or more processors (central processing units, CPUs) 310 (e.g., one or more processors), memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage medium 330 may be either transient or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown), each of which may include a series of instruction operations within the computer device 300. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, and the series of instruction operations stored in the storage medium 330 may be executed on the computer device 300 to implement the steps of the above-described DC / DC power supply functional parameter testing method.

[0039] The computer device 300 may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The illustrated computer device structure does not constitute a limitation on the computer device provided by the present invention, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0040] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0041] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0042] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for testing functional parameters of a DC / DC power supply, characterized in that: include: Injecting a preset disturbance signal into the input terminal of the DC / DC power supply under test through a signal conditioning circuit to obtain output voltage response data and output current response data; Inputting the output voltage response data and the output current response data into a dynamic multi-parameter test system in a test box, and collecting target test parameter data; Calculating a characteristic parameter set according to the target test parameter data, and performing voltage-temperature control model predictive control analysis to obtain a control parameter sequence for the test box; According to the control parameter sequence, the power output value of the heating wire, the speed value of the cooling fan and the position value of the electric push rod in the test box are controlled to obtain an intelligently adjustable test environment; An aging test result and a test strategy adjustment instruction of the DC / DC power supply under test are generated in the intelligent adjustment test environment.

2. The DC / DC power supply function parameter testing method according to claim 1, characterized in that: The method includes injecting a preset disturbance signal into the input terminal of the DC / DC power supply under test through a signal conditioning circuit to obtain output voltage response data and output current response data, including: Analyze the rated parameters of the DC / DC power supply under test and generate an optimal disturbance signal parameter configuration table, wherein the optimal disturbance signal parameter configuration table includes a disturbance signal type, a frequency value, and an amplitude range; Setting parameters of a signal generator according to the optimal disturbance signal parameter configuration table to generate a target disturbance signal; Superimposing the target disturbance signal on the DC input voltage of the DC / DC power supply under test by capacitive coupling, and simultaneously starting a feedback suppression loop; The injection duration of the target disturbance signal is dynamically adjusted through the feedback suppression loop, and the output end of the DC / DC power supply under test is monitored in real time to obtain output voltage response data and output current response data.

3. The DC / DC power supply function parameter testing method according to claim 1, characterized in that: Inputting the output voltage response data and the output current response data into a dynamic multi-parameter test system in a test box and collecting target test parameter data includes: Inputting the output voltage response data and the output current response data into a main control unit of a dynamic multi-parameter test system for processing to obtain a test configuration instruction; Setting parameters of a multi-channel data acquisition unit of the dynamic multi-parameter test system according to the test configuration instruction to obtain a sampling configuration scheme; Performing load mode configuration on the load simulation unit of the dynamic multi-parameter test system based on the sampling configuration scheme to obtain a load simulation scheme, wherein the load simulation scheme includes at least one of a constant current mode, a constant resistance mode, a constant power mode, and a dynamic load mode; Setting the temperature of the temperature control unit of the dynamic multi-parameter test system according to the rated operating temperature of the DC / DC power supply under test to obtain a temperature control scheme; Inputting the load simulation scheme and the temperature control scheme into the test environment of the DC / DC power supply under test, performing parameter testing, and obtaining a real-time test data stream; The real-time test data stream is synchronously sampled to collect target test parameter data of the DC / DC power supply under test.

4. The DC / DC power supply function parameter testing method according to claim 1, characterized in that: The step of calculating a characteristic parameter set based on the target test parameter data and performing voltage-temperature control model predictive control analysis to obtain a control parameter sequence for the test box includes: Performing bandpass filtering on the target test parameter data to obtain filtered processed data; Constructing a first signal equivalent circuit model and a second signal equivalent circuit model of the DC / DC power supply under test based on the filtered processed data; Calculating a characteristic parameter set of the DC / DC power supply under test according to the first signal equivalent circuit model and the second signal equivalent circuit model, the characteristic parameter set including output voltage stability, output current stability, temperature coefficient, output impedance, transient response time, and regulation rate; Performing statistical analysis on the output voltage stability and the output current stability in the characteristic parameter set to obtain a parameter stability evaluation result; Based on the parameter stability evaluation results, a multi-dimensional anomaly detection algorithm is used to comprehensively analyze the characteristic parameter set to obtain a parameter anomaly measurement value; Generate a performance anomaly detection result based on the parameter anomaly measurement value, wherein the performance anomaly detection result includes an anomaly type identifier and an anomaly degree score; A voltage-temperature control model predictive control analysis is performed based on the performance anomaly detection result to obtain a control parameter sequence of the test box.

5. The DC / DC power supply function parameter testing method according to claim 4, characterized in that: The performing of voltage-temperature control model predictive control analysis according to the performance abnormality detection result to obtain a control parameter sequence of the test box includes: Constructing a system state space model based on the performance anomaly detection result, wherein the state variables of the system state space model include the DC / DC power supply input voltage, output voltage, output current and internal key point temperature, and the state equation is in the form of a linear combination of a state vector, a control input vector and a disturbance vector; Constructing a first objective function based on the system state space model, and dynamically adjusting a weight coefficient in the first objective function according to the performance anomaly detection result to obtain a second objective function; Setting constraints on the second objective function to obtain a constrained optimization problem, and solving the constrained optimization problem using a quadratic programming method to obtain an optimal control sequence in a future control time domain; A rolling time domain control strategy is implemented on the optimal control sequence, only the control input of the first control cycle is executed, and the optimization calculation is re-performed in the next control cycle to obtain the control parameter sequence of the test box, which includes heating power parameters, cooling fan speed parameters, load current parameters and input voltage disturbance parameters.

6. The DC / DC power supply function parameter testing method according to claim 1, characterized in that: The method of controlling the power output value of the heating wire, the speed value of the cooling fan, and the position value of the electric push rod in the test box according to the control parameter sequence to obtain an intelligently adjusted test environment includes: Establishing a control mapping function set according to the control parameter sequence, wherein the control mapping function set includes a heating wire power mapping function, a cooling fan speed mapping function, an electric push rod position mapping function, and a load current mapping function; Performing nonlinear mapping on the heating power parameter in the control parameter sequence to obtain a heating wire power output value; Performing mapping conversion based on the cooling fan speed parameter in the control parameter sequence to obtain a heat dissipation fan speed value; Controlling the electric push rod according to a comparison result between the temperature parameter in the control parameter sequence and the temperature threshold value to obtain a position value of the electric push rod; The heating wire power output value, the cooling fan speed value and the electric push rod position value are sent to the corresponding execution unit through the control interface to obtain an initial control effect; The initial control effect is subjected to closed-loop control, and the control error is corrected through a real-time feedback signal to obtain an intelligent adjustment test environment.

7. The DC / DC power supply function parameter testing method according to claim 1, characterized in that: The step of generating an aging test result and a test strategy adjustment instruction for the DC / DC power supply under the intelligent adjustment test environment includes: Performing time series analysis on the test data collected under the intelligent adjustment test environment and establishing a parameter trend model; Predicting future change trends of parameters of the DC / DC power supply under test based on the parameter trend model to obtain a parameter prediction value sequence; Calculating the deviation between the parameter prediction value sequence and the actual parameter change to obtain a parameter deviation measurement value; Performing abnormality level classification according to the parameter deviation measurement value to obtain abnormality diagnosis results, and implementing a hierarchical feedback strategy based on the abnormality diagnosis results to obtain a test strategy adjustment instruction; The abnormality diagnosis results and test environment parameters are integrated into the aging test results of the tested DC / DC power supply, and the aging test results include the test environment parameters, test duration, number of test points, abnormality diagnosis results, parameter trend analysis and energy consumption statistics.

8. A DC / DC power supply function parameter test system, characterized in that: Used to perform the DC / DC power supply function parameter testing method according to any one of claims 1 to 7, the DC / DC power supply function parameter testing system comprising: An injection module is used to inject a preset disturbance signal into the input terminal of the DC / DC power supply under test through a signal conditioning circuit to obtain output voltage response data and output current response data; an acquisition module, configured to input the output voltage response data and the output current response data into a dynamic multi-parameter test system in a test box, and acquire target test parameter data; a calculation module, configured to calculate a characteristic parameter set based on the target test parameter data, and perform predictive control analysis of a voltage-temperature control model to obtain a control parameter sequence for the test box; A control module, configured to control the power output value of the heating wire, the speed value of the cooling fan, and the position value of the electric push rod in the test box according to the control parameter sequence, so as to obtain an intelligently adjustable test environment; A generating module is used to generate an aging test result and a test strategy adjustment instruction of the DC / DC power supply under the intelligent adjustment test environment.

9. A computer device, characterized in that: The computer device includes: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the computer device to execute the DC / DC power supply functional parameter testing method according to any one of claims 1 to 7.

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