Automatic test system for power supply integrity based on multi-parameter intelligent collaboration

Abstract

The invention discloses a power supply integrity automatic test system based on multi-parameter intelligent cooperation, and belongs to the technical field of power supply test. Automatically identifying a power supply type through a power supply type identification and configuration module, and calling preset test parameters from a database to generate a test scheme; the method comprises the following steps: synchronously acquiring multi-modal data output by a power supply through a multi-parameter acquisition module, wherein the multi-modal data comprises test parameters of various power supplies; performing standardized scoring on each test parameter of the power supply through an intelligent scoring module; constructing a three-dimensional incidence matrix through an automatic evaluation module, dynamically adjusting the weight of each graded test parameter, and calculating an evaluation index of the power supply integrity; feedback information is generated through a feedback module, and a visual report of the power supply integrity test is generated according to the visual template; through mutual cooperation of the modules, automation of power supply integrity testing is realized, manpower is saved, and evaluation objectivity is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply testing, and particularly to an automated power integrity testing system based on multi-parameter intelligent collaboration. Background Art

[0002] The integrity testing of a power supply is to ensure that the power supply system can operate normally under various working conditions and provide stable power for the load. This testing covers multiple aspects to ensure the reliability, stability, and adaptability of the power supply.

[0003] With the rapid development of frontier fields such as high-speed digital circuits, 5G communication, artificial intelligence, electric vehicles, and aerospace, electronic devices have put forward higher requirements for power quality. However, traditional power supply testing technologies have been difficult to meet current needs. On the one hand, traditional testing methods are mostly single-parameter isolated tests, with limited coverage and neglecting the collaborative effect among multi-parameters. On the other hand, the degree of manual participation in the existing testing process is too high. From the formulation of the testing plan, the setting of testing instruments, to the adjustment of the compensation network during the testing process, etc., all rely on the manual operation and experience judgment of professional technicians. This not only greatly extends the testing cycle, reduces the testing efficiency, but also easily introduces errors due to human factors, affecting the accuracy and reliability of the testing results. At the same time, the dynamic adaptability of the existing testing system is poor, and it is difficult to flexibly adjust the testing strategy according to the real-time state and performance changes of the object under test, unable to meet the complex and variable actual testing requirements. In addition, the existing automated testing lacks intelligent decision-making ability, and it is difficult to effectively analyze the correlation of parameters in complex scenarios, resulting in low testing efficiency.

[0004] Therefore, there is an urgent need for an automated power integrity testing system based on multi-parameter intelligent collaboration to overcome the above defects. Summary of the Invention

[0005] The purpose of the present invention is to provide an automated power integrity testing system based on multi-parameter intelligent collaboration to solve the problems raised in the above background art.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] An automated power integrity testing system based on multi-parameter intelligent collaboration, the system includes a power supply type identification and configuration module, a multi-parameter acquisition module, an intelligent scoring module, an automated evaluation module, and a feedback module;

[0008] The power supply type identification and configuration module is used to automatically identify the power supply type according to a preset interface and generate a testing plan by calling preset testing parameters from a database;

[0009] A multi-parameter acquisition module, which is used to synchronously acquire multi-modal data output by a power supply according to a test scheme, and the multi-modal data includes test parameters of multiple power supplies;

[0010] An intelligent scoring module, which is used to perform standardized scoring on each test parameter of the power supply;

[0011] An automatic evaluation module, which is used to construct a three-dimensional correlation matrix, dynamically adjust the weights of the scored test parameters, and calculate the evaluation index of power supply integrity;

[0012] A feedback module, which is used to generate feedback information according to the evaluation index and generate a visualization report of the power supply integrity test according to a visualization template.

[0013] As a preferred scheme of the present invention, the multi-parameter acquisition module includes an acquisition unit and a synchronization unit:

[0014] The acquisition unit is used to acquire multi-modal data output by the power supply through an anti-interference collaborative working mechanism. The multi-modal data includes voltage, current, efficiency, ripple noise, temperature distribution, and electromagnetic compatibility parameters. Among them: the voltage of the power supply is extracted through a broadband differential probe and a high-precision analog-to-digital converter; the current of the power supply is acquired through a current transformer; the temperature distribution of the power supply is monitored through an infrared thermal imaging sensor; the efficiency of the power supply is monitored through a power analyzer; the ripple noise of the power supply is captured through a high-frequency magnetoelectric coupling probe; the electromagnetic compatibility parameters of the power supply are monitored through a near-field electromagnetic probe and a spectrum analyzer;

[0015] It should be noted that the anti-interference mechanism includes a differential amplification stage to eliminate the common-mode voltage between the power supply ground and the sensor ground connection point, such as the ground potential difference in a motor drive scenario; automatically switching the gain according to the signal amplitude, such as using a high-gain mode to capture microvolt-level ripple when the power supply is lightly loaded and switching to a low-gain mode to avoid saturation when it is heavily loaded; an overload protection circuit instantaneously limits the output amplitude during load mutation, such as during a short-circuit test, to protect the input of the analog-to-digital converter; the adaptive LMS algorithm continuously tracks environmental noise, such as the 10kHz - 1MHz harmonics generated by a frequency converter, to generate an anti-phase cancellation signal; a frequency-domain noise reduction engine automatically identifies and filters out periodic interference, such as the switching frequency of a DC-DC converter and its harmonics.

[0016] The synchronization unit is used to integrate a broadband differential probe, a high-precision analog-to-digital converter, a current transformer, an infrared thermal imaging sensor, a power analyzer, a high-frequency magnetoelectric coupling probe, a near-field electromagnetic probe, and a spectrum analyzer through a broadband composite sensor array, align the time of each sensor through a unified clock signal, and correct the multi-modal data collected by the broadband composite sensor array in real time according to the temperature.

[0017] As a preferred solution of the present invention, the intelligent scoring module is used to perform standardized scoring on each test parameter of the power supply:

[0018] The voltage of the power supply is scored standardly according to the power supply voltage tolerance requirement, and the calculation formula is:

[0019]

[0020] Among them, S V represents the voltage score, V′ represents the rated voltage, V rate represents the real-time voltage, and ΔV max represents the maximum allowable deviation;

[0021] The ripple noise is scored in segments according to the ripple test specification, and the calculation formula is:

[0022]

[0023] Among them, S Ripple represents the score of the ripple noise, and Ripple pp represents the peak-to-peak value;

[0024] It should be noted that the peak-to-peak value is an important parameter for describing the signal fluctuation amplitude. For ripple noise, the peak-to-peak value refers to the difference between the maximum value and the minimum value of the ripple noise signal. It reflects the magnitude of the ripple noise and is one of the most intuitive indicators for measuring the size of ripple noise. By measuring the peak-to-peak value of the ripple noise signal, the fluctuation range of the ripple noise in the power supply output can be clearly understood. For example, if the peak-to-peak value of the ripple noise of a power supply output is 50 mV, it means that a fluctuation signal with a maximum amplitude of 50 mV is superimposed on the DC voltage output by the power supply. In power supply design and applications, the size of the peak-to-peak value is directly related to the quality and stability of the power supply. If the peak-to-peak value is too large, it may cause abnormal operation of the subsequent circuit, such as affecting the accuracy of analog circuits and causing false triggering of digital circuits. The peak-to-peak value of ripple noise may vary in different frequency ranges. Generally speaking, in a switching power supply, the peak-to-peak value of the ripple is relatively large at the switching frequency and its harmonic frequencies because these frequencies are directly related to the operation of the switching transistor. The peak-to-peak value of the noise is randomly distributed in a relatively wide frequency range, but at certain specific frequencies, it may be affected by the resonance of circuit components or external interference and show a large peak-to-peak value. For example, in a switching power supply with a switching frequency of 100 kHz, the peak-to-peak value of the ripple may be more obvious at integer multiples of the switching frequency such as 100 kHz and 200 kHz; and in some frequency bands with strong external electromagnetic interference, the peak-to-peak value of the noise may increase. Component parameters such as capacitors and inductors in the circuit will affect the peak-to-peak value of the ripple noise. Capacitors have a filtering effect and can smooth the output voltage, reducing the peak-to-peak value of the ripple noise. For example, connecting a large capacitor in parallel at the power supply output can increase the low-frequency filtering ability of the circuit, thereby reducing the peak-to-peak value of the ripple noise. Inductors can suppress high-frequency ripple noise through their energy storage and release characteristics. Reasonably selecting the parameters of capacitors and inductors can optimize the filtering effect of the power supply and make the peak-to-peak value of the ripple noise meet the design requirements.

[0025] The temperature gradient is scored according to the requirement of thermal distribution uniformity, and the calculation formula is:

[0026]

[0027] where S T represents the score of the temperature gradient, represents the temperature gradient, represents the standard value of the temperature gradient;

[0028] The electromagnetic compatibility parameters are scored according to the electromagnetic compatibility standard, and the calculation formula is:

[0029]

[0030] where S EMIIt represents the score of electromagnetic compatibility parameters, w(f) represents the preset frequency band weight, Limit(f) represents the CISPR 32 Class B limit, and EMI(f) represents the electromagnetic compatibility parameters;

[0031] The dynamic response time is scored according to the power supply design guide, and the calculation formula is:

[0032]

[0033] Among them, S response represents the score of the response time, t rise represents the actual rise time, t min represents the theoretical minimum value, t max represents the allowable maximum value;

[0034] It should be explained that the actual rise time generally refers to the time required for the signal to rise from 10% of the steady-state value to 90%. For example, when the load suddenly decreases, the output voltage recovers from the falling state to the target value; when the load suddenly increases, the output current climbs from a low value to the required value. The trigger condition is the load step change.

[0035] As a preferred solution of the present invention, the automatic evaluation module includes a preprocessing unit, a matrix construction unit, a dynamic adjustment unit, and an evaluation unit:

[0036] The preprocessing unit is used to extract the multi-modal data collected by the multi-parameter acquisition module to perform data alignment and preprocess the multi-modal data; including: converting the voltage and current into per-unit values, where the reference value is taken as 110% of the rated parameters; normalizing the temperature based on the thermal resistance model:

[0037] Among them, T represents the normalized temperature value, T al represents the actually measured temperature value, T an represents the reference value of the temperature, R th represents the thermal resistance coefficient;

[0038] Remove the high-frequency interference in the ripple noise through wavelet transform; select the db4 wavelet basis and the decomposition level; perform multi-level wavelet decomposition on the noisy signal to obtain the approximation coefficients and detail coefficients of each layer; apply a threshold rule, such as a hard threshold or a soft threshold, to the high-frequency detail coefficients to remove the noise-dominated coefficients; reconstruct the denoised signal with the processed coefficients.

[0039] It should be noted that the purpose of performing data alignment is to avoid the timing deviation caused by the difference in the response time of sensors. The purpose of the preprocessing unit is to convert the heterogeneous sensor data into comparable data in a unified mathematical space; eliminate the systematic error caused by the asynchronous hardware sampling; meet the standardization requirements of the input data distribution for machine learning algorithms and prepare for the model input.

[0040] The matrix construction unit is used to construct a three-dimensional correlation matrix based on the processed multimodal data, which is expressed as follows:

[0041]

[0042] Among them, V(t) represents the real-time voltage value, represents the partial derivative of current with respect to voltage, reflecting the dynamic characteristics of the load; Φ T (V) represents the voltage-temperature transfer function, quantifying the impact of voltage fluctuations on the temperature field; represents the temperature gradient, indicating the spatial change rate of temperature per unit time, η(V, I) represents the efficiency of the power supply, and ψ EMI (f) represents the EMI coupling coefficient, describing the correlation strength between the voltage spectrum and electromagnetic interference; Ripple pp represents the peak-to-peak ripple, characterizing the output voltage fluctuation range, represents the power change rate, reflecting the transient load response ability; Γ THD represents the total harmonic distortion correlation factor, quantifying the impact of harmonic distortion on the system stability;

[0043] The dynamic adjustment unit is used for the weight coefficients of each scored test parameter;

[0044] The evaluation unit is used to calculate the evaluation index of the power supply.

[0045] As a preferred solution of the present invention, the dynamic adjustment unit includes:

[0046] Set the basic weights of each scored test parameter, and predefined n working condition templates, where each working condition template includes a corresponding weight adjustment coefficient table;

[0047] Extract key indicators from the three-dimensional correlation matrix, and use the k-nearest neighbor algorithm to match the current matrix features with the feature vectors in the working condition templates;

[0048] Update the weight coefficients according to the matched working condition template, and perform normalization processing on the updated weight coefficients so that the weighted sum of all weight coefficients is equal to 1, completing the comprehensive normalization of weights;

[0049] Perform weighted fusion on the standardized score values of each test parameter of the power supply calculated by the intelligent scoring module, and calculate the comprehensive score of the power supply. The formula is as follows:

[0050]

[0051] Among them, KA represents the comprehensive score of the power supply, and S i represents the standardized score of the i-th real-time test parameter of the power supply, and wi represents the weight coefficient of the real-time test parameters of the i-th power supply, and n represents the total number of types of real-time test parameters of the power supply for comprehensive scoring.

[0052] As a preferred embodiment of the present invention, the feedback module includes a response unit and a visualization unit;

[0053] The response unit is used to output a prompt message indicating good power integrity when the comprehensive score of the power supply is greater than or equal to the scoring threshold; when the comprehensive score of the power supply is less than the scoring threshold, it outputs a warning message indicating that there is a fault in the power supply;

[0054] The visualization unit is used to generate a power integrity test report according to the visualization template for all the data generated by the power type identification and configuration module, the multi-parameter acquisition module, the intelligent scoring module, the automated evaluation module, and the feedback module, and display it in the graphical interface.

[0055] As a preferred embodiment of the present invention, the power type identification and configuration module is used to automatically identify the power type according to the preset interface and generate a unique identification code, and call the corresponding preset test parameters from the database according to the unique identification code to generate a test plan.

[0056] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In the power integrity automated test system based on multi-parameter intelligent collaboration provided by the present invention, the power type identification and configuration module automatically identifies the power type and calls the preset test parameters from the database to generate a test plan; the multi-parameter acquisition module synchronously acquires multi-modal data output by the power supply, and the multi-modal data includes test parameters of multiple power supplies; the intelligent scoring module performs standardized scoring on each test parameter of the power supply; the automated evaluation module constructs a three-dimensional correlation matrix and dynamically adjusts the weights of the scored test parameters to calculate the evaluation index of power integrity; the feedback module generates feedback information and generates a visualization report of the power integrity test according to the visualization template; the present invention realizes the automation of the power integrity test through the mutual cooperation of the above-mentioned various modules, which not only saves manpower but also improves the objectivity of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention.

[0058] Figure 1 It is a schematic structural diagram of the power integrity automated test system based on multi-parameter intelligent collaboration of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0060] Please refer to Figure 1 , in the first embodiment: a power integrity automated test system based on multi-parameter intelligent collaboration is provided. The system includes a power type identification and configuration module, a multi-parameter acquisition module, an intelligent scoring module, an automated evaluation module, and a feedback module;

[0061] The power type identification and configuration module is used to automatically identify the power type according to a preset interface and generate a test plan by calling preset test parameters from a database;

[0062] The multi-parameter acquisition module is used to synchronously acquire multi-modal data output by the power supply according to the test plan. The multi-modal data includes test parameters of multiple power supplies;

[0063] The intelligent scoring module is used to perform standardized scoring on each test parameter of the power supply;

[0064] The automated evaluation module is used to construct a three-dimensional correlation matrix, dynamically adjust the weights of the scored test parameters, and calculate the evaluation index of power integrity;

[0065] The feedback module is used to generate feedback information according to the evaluation index and generate a visualization report of the power integrity test according to a visualization template.

[0066] Preferably, the multi-parameter acquisition module includes an acquisition unit and a synchronization unit:

[0067] The acquisition unit is used to acquire multi-modal data output by the power supply through an anti-interference collaborative working mechanism. The multi-modal data includes test parameters of multiple power supplies. The parameters include voltage, current, efficiency, ripple noise, temperature distribution, and electromagnetic compatibility parameters. Among them: the voltage of the power supply is extracted through a broadband differential probe and a high-precision analog-to-digital converter; the current of the power supply is acquired through a current transformer; the temperature distribution of the power supply is monitored through an infrared thermal imaging sensor; the efficiency of the power supply is monitored through a power analyzer; the ripple noise of the power supply is captured through a high-frequency magnetoelectric coupling probe; the electromagnetic compatibility parameters of the power supply are monitored through a near-field electromagnetic probe and a spectrum analyzer;

[0068] The synchronization unit is used to integrate a broadband differential probe, a high-precision analog-to-digital converter, a current transformer, an infrared thermal imaging sensor, a power analyzer, a high-frequency magnetoelectric coupling probe, a near-field electromagnetic probe, and a spectrum analyzer through a broadband composite sensor array, align the time of each sensor through a unified clock signal, and correct the multimodal data collected by the broadband composite sensor array in real time according to the temperature.

[0069] Preferably, the intelligent scoring module is used to perform standardized scoring on each test parameter of the power supply:

[0070] Perform standardized scoring on the voltage of the power supply according to the power supply voltage tolerance requirement, and the calculation formula is:

[0071]

[0072] Among them, S V represents the voltage score, V′ represents the rated voltage, V rate represents the real-time voltage, and ΔV max represents the maximum allowable voltage deviation;

[0073] Perform segmented scoring on the ripple noise according to the ripple test specification, and the calculation formula is:

[0074]

[0075] Among them, S Ripple represents the score of the ripple noise, and Ripple pp represents the peak-to-peak value;

[0076] Perform scoring on the temperature gradient according to the requirement of thermal distribution uniformity, and the calculation formula is:

[0077]

[0078] Among them, S T represents the score of the temperature gradient, represents the temperature gradient, represents the standard value of the temperature gradient;

[0079] Perform scoring on the electromagnetic compatibility parameters according to the electromagnetic compatibility standard, and the calculation formula is:

[0080]

[0081] Among them, S EMI represents the score of the electromagnetic compatibility parameters, w(f) represents the preset frequency band weight, Limit(f) represents the CISPR 32 Class B limit, and EMI(f) represents the electromagnetic compatibility parameters;

[0082] Score the dynamic response time according to the power supply design guide, and the calculation formula is:

[0083]

[0084] Among them, S response represents the score of the response time, t rise represents the actual rise time, t min represents the theoretical minimum value, t max represents the allowable maximum value.

[0085] Preferably, the automatic evaluation module includes a preprocessing unit, a matrix construction unit, a dynamic adjustment unit and an evaluation unit:

[0086] The preprocessing unit is used to extract the multimodal data collected by the multi-parameter acquisition module to perform data alignment and preprocess the multimodal data; including: converting the voltage and current into per-unit values; normalizing the temperature based on the thermal resistance model; removing the high-frequency interference in the ripple noise through wavelet transform;

[0087] The matrix construction unit is used to construct a three-dimensional correlation matrix according to the processed multimodal data, which is expressed as follows:

[0088]

[0089] Among them, V(t) represents the real-time voltage value, represents the partial derivative of the current with respect to the voltage reflecting the dynamic characteristics of the load; Φ T (V) represents the voltage-temperature transfer function used to quantify the influence of voltage fluctuations on the temperature field; represents the temperature gradient used to reflect the spatial change rate of temperature per unit time, η(V, I) represents the efficiency of the power supply, ψ EMI (f) represents the electromagnetic compatibility coupling coefficient used to describe the correlation strength between the voltage spectrum and electromagnetic interference; Ripple pp represents the peak-to-peak value used to characterize the output voltage fluctuation range, represents the power change rate used to reflect the transient load response ability; Γ THD represents the total harmonic distortion correlation factor used to quantify the influence of harmonic distortion on system stability;

[0090] The dynamic adjustment unit is used to dynamically adjust the weight coefficients of the tested parameters after scoring;

[0091] The evaluation unit is used to calculate the evaluation index of the power supply.

[0092] Preferably, the dynamic adjustment unit includes:

[0093] Set the basic weights of the test parameters after each scoring, and pre-define n working condition templates, each of which includes a corresponding weight adjustment coefficient table;

[0094] Extract key indicators from the three-dimensional correlation matrix, and use the k-nearest neighbor algorithm to match the current matrix features with the feature vectors in the working condition templates;

[0095] For example: in the three-dimensional correlation matrix of the transient working condition (Rate of change of power) increases significantly, triggering an increase in the voltage / current weight in the dynamic weight model; in the three-dimensional correlation matrix of the thermal stress working condition (Temperature gradient) rises abnormally, automatically enhancing the analysis weight of the temperature gradient.

[0096] Update the weight coefficients according to the matched working condition template, and perform normalization processing on the updated weight coefficients so that the weighted sum of all weight coefficients is equal to 1, completing the comprehensive normalization of weights;

[0097] Perform weighted fusion on the standardized scoring values of each test parameter of the power supply calculated by the intelligent scoring module to calculate the comprehensive score of the power supply. The formula is as follows:

[0098]

[0099] Among them, KA represents the comprehensive score of the power supply, S i represents the standardized score of the i-th real-time test parameter of the power supply, w i represents the weight coefficient of the i-th real-time test parameter of the power supply, and n represents the total number of types of real-time test parameters of the power supply for the comprehensive score.

[0100] Preferably, the feedback module includes a response unit and a visualization unit;

[0101] The response unit is used to output a prompt message indicating good power supply integrity when the comprehensive score of the power supply is greater than or equal to the scoring threshold; when the comprehensive score of the power supply is less than the scoring threshold, output a warning message indicating that there is a fault in the power supply;

[0102] The visualization unit is used to generate a power supply integrity test report based on the visualization template for all data generated by the power supply type identification and configuration module, the multi-parameter acquisition module, the intelligent scoring module, the automated evaluation module, and the feedback module, and display it in the graphical interface.

[0103] Preferably, the power supply type identification and configuration module is used to automatically identify the power supply type according to the preset interface and generate a unique identification code, and call the corresponding preset test parameters from the database according to the unique identification code to generate a test plan.

[0104] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variation thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0105] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An automated power integrity test system based on multi-parameter intelligent collaboration, characterized in that, The system includes a power supply type identification and configuration module, a multi-parameter acquisition module, an intelligent scoring module, an automated evaluation module, and a feedback module; The power supply type identification and configuration module is used to automatically identify the power supply type according to a preset interface and generate a test plan by calling preset test parameters from a database; The multi-parameter acquisition module is used to synchronously acquire multi-modal data output by the power supply according to the test plan, and the multi-modal data includes test parameters of multiple power supplies; The intelligent scoring module is used to perform standardized scoring on each test parameter of the power supply; The automated evaluation module is used to construct a three-dimensional correlation matrix, dynamically adjust the weights of the scored test parameters, and calculate the evaluation index of power supply integrity; The feedback module is used to generate feedback information according to the evaluation index and generate a visualization report of the power supply integrity test according to a visualization template.

2. The power integrity automated test system based on multi-parameter intelligent collaboration according to claim 1, wherein The multi-parameter acquisition module includes an acquisition unit and a synchronization unit: The acquisition unit is used to acquire multi-modal data output by the power supply through an anti-interference collaborative working mechanism. The multi-modal data includes test parameters of multiple power supplies, and the parameters include voltage, current, efficiency, ripple noise, temperature distribution, and electromagnetic compatibility parameters. Among them: the voltage of the power supply is extracted through a broadband differential probe and a high-precision analog-to-digital converter; the current of the power supply is acquired through a current transformer; the temperature distribution of the power supply is monitored through an infrared thermal imaging sensor; the efficiency of the power supply is monitored through a power analyzer; the ripple noise of the power supply is captured through a high-frequency magnetoelectric coupling probe; the electromagnetic compatibility parameters of the power supply are monitored through a near-field electromagnetic probe and a spectrum analyzer; The synchronization unit is used to integrate a broadband differential probe, a high-precision analog-to-digital converter, a current transformer, an infrared thermal imaging sensor, a power analyzer, a high-frequency magnetoelectric coupling probe, a near-field electromagnetic probe, and a spectrum analyzer by setting up a broadband composite sensor array, align the time of each sensor through a unified clock signal, and correct the multi-modal data acquired by the broadband composite sensor array in real time according to the temperature.

3. The power integrity automated test system based on multi-parameter intelligent collaboration according to claim 2, characterized in that, The intelligent scoring module is used to perform standardized scoring on each test parameter of the power supply: Perform standardized scoring on the voltage of the power supply according to the power supply voltage tolerance requirement. The calculation formula is: Among them, S V represents the voltage score, V′ represents the rated voltage, and V rate represents the real-time voltage, and ΔV max represents the maximum allowable voltage deviation; Perform segmented scoring on the ripple noise according to the ripple test specification. The calculation formula is: Among them, S Ripple represents the score of the ripple noise, Ripple pp represents the peak-to-peak value; Score the temperature gradient according to the requirement of thermal distribution uniformity. The calculation formula is: Among them, S T represents the score of the temperature gradient, represents the temperature gradient, represents the standard value of the temperature gradient; Score the electromagnetic compatibility parameters according to the electromagnetic compatibility standard. The calculation formula is: Among them, S ENI represents the score of the electromagnetic compatibility parameter, w(f) represents the preset frequency band weight, Limit(f) represents the CISPR32 Class B limit, and EMI(f) represents the electromagnetic compatibility parameter; Score the dynamic response time according to the power supply design guide. The calculation formula is: Among them, S response represents the score of the response time, t rise represents the actual rise time, t min represents the theoretical minimum value, t max represents the allowable maximum value.

4. The power integrity automated test system based on multi-parameter intelligent collaboration according to claim 3, characterized in that The automated evaluation module includes a preprocessing unit, a matrix construction unit, a dynamic adjustment unit, and an evaluation unit: The preprocessing unit is used to extract the multi-modal data acquired by the multi-parameter acquisition module to perform data alignment and preprocess the multi-modal data; including: converting the voltage and current into per-unit values; normalizing the temperature based on a thermal resistance model; removing high-frequency interference in the ripple noise through wavelet transform; The matrix construction unit is used to construct a three-dimensional correlation matrix according to the processed multi-modal data, which is expressed as follows: Among them, V(t) represents the real-time voltage value, represents the partial derivative of current with respect to voltage reflecting the dynamic characteristics of the load; Φ T (V) represents the voltage-temperature transfer function used to quantify the impact of voltage fluctuations on the temperature field; represents the temperature gradient used to reflect the spatial change rate of temperature per unit time, η(V, I) represents the efficiency of the power supply, ψ EMI (f) represents the electromagnetic compatibility coupling coefficient used to describe the correlation strength between the voltage spectrum and electromagnetic interference; Ripple pp represents the peak-to-peak value used to characterize the output voltage fluctuation range, represents the power change rate used to reflect the transient load response ability; Γ THD represents the total harmonic distortion correlation factor used to quantify the impact of harmonic distortion on system stability; The dynamic adjustment unit is used to dynamically adjust the weight coefficients of the scored test parameters; The evaluation unit is used to calculate the evaluation index of the power supply.

5. The power integrity automated test system based on multi-parameter intelligent collaboration according to claim 4, characterized in that The dynamic adjustment unit includes: Set the basic weights of the tested parameters after scoring, and predefined n working condition templates, each of which includes a corresponding weight adjustment coefficient table; Extract key indicators from the three-dimensional correlation matrix, and use the k-nearest neighbor algorithm to match the current matrix features with the feature vectors in the working condition templates; Update the weight coefficients according to the matched working condition templates, and normalize the updated weight coefficients so that the weighted sum of all weight coefficients is equal to 1, completing the comprehensive normalization of weights; Perform weighted fusion on the standardized score values of the tested parameters of the power supply calculated by the intelligent scoring module, and calculate the comprehensive score of the power supply. The formula is as follows: Among them, KA represents the comprehensive score of the power supply, and S i represents the standardized score of the real-time test parameters of the i-th power supply, and w i represents the weight coefficient of the real-time test parameters of the i-th power supply, and n represents the total number of types of real-time test parameters of the power supply for the comprehensive score.

6. The power integrity automated test system based on multi-parameter intelligent collaboration according to claim 5, wherein The feedback module includes a response unit and a visualization unit; The response unit is used to output a prompt message indicating good power supply integrity when the comprehensive score of the power supply is greater than or equal to the score threshold; When the comprehensive score of the power supply is less than the score threshold, output a warning message indicating that there is a fault in the power supply; The visualization unit is used to generate a power supply integrity test report according to the visualization template for all data generated by the power supply type identification and configuration module, the multi-parameter acquisition module, the intelligent scoring module, the automatic evaluation module and the feedback module, and display it in the graphical interface.

7. The power integrity automated test system based on multi-parameter intelligent collaboration according to claim 6, characterized in that The power supply type identification and configuration module is used to automatically identify the power supply type according to the preset interface and generate a unique identification code, and call the corresponding preset test parameters from the database according to the unique identification code to generate a test plan.

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