A circuit board health state detection method and system

Abstract

The present application relates to the technical field of circuit board health state detection, and discloses a circuit board health state detection method and system, the method comprising collecting multi-channel voltage and current waveforms of the circuit board, constructing a joint probability density histogram and obtaining working condition reference distribution parameters by using a Gaussian mixture model; extracting and interpolating compensation of working condition drift to obtain corrected distribution parameters; constructing an inverse cumulative distribution function with the health reference and calculating one-dimensional Wasserstein distance to form a distance time series and a geometric interval to determine the degradation category; further mapping the degradation stage through regularized distance, calculating the comprehensive degradation intensity combined with historical cases and quantifying the score to finally give the grading alarm and health assessment results. The method can solve the problem of inaccurate circuit board health state evaluation, false positives or false negatives under the condition of multi-working condition dynamic switching.

Description

Technical Field

[0001] This invention relates to the field of circuit board health status detection technology, and in particular to a method and system for circuit board health status detection. Background Technology

[0002] As electronic devices evolve towards higher reliability and security, the circuit board, as a core component of electronic systems, directly impacts the overall performance and lifespan of the device. In applications such as industrial control, automotive electronics, and consumer electronics, circuit boards operate under complex conditions including temperature variations, load switching, and voltage fluctuations, causing continuous evolution of device parameters and operating signals. To avoid system downtime and safety risks caused by sudden failures, conducting pre-diagnosis and health management throughout the entire circuit board operation process, enabling early identification of potential anomalies and continuous assessment of health status, has become a critical issue that urgently needs to be addressed in the field of electronic system operation and maintenance.

[0003] Currently, in existing technologies, circuit board health status detection often relies on fixed threshold judgments or simple signal feature comparison methods. This involves comparing real-time acquired voltage, current, and other signals with a preset health benchmark to determine if any abnormalities exist. However, in actual operating environments, the switching of operating conditions itself causes an overall shift in the signal statistical distribution. For example, under high temperature or high load conditions, even if the circuit board is in a normal healthy state, its voltage and current distribution will deviate from the benchmark pattern under normal temperature and low load conditions. This type of distribution shift caused by changes in operating conditions often resembles the characteristics of the early stages of health degradation, easily leading to misjudgments as fault symptoms. This results in frequent false alarms or missed alarms, weakening the effectiveness of pre-diagnosis and health management.

[0004] Existing technologies have the problem of difficulty in distinguishing between distribution changes caused by changes in operating conditions and health degradation. Summary of the Invention

[0005] This invention provides a method and system for detecting the health status of circuit boards, in order to solve the problems of inaccurate assessment of the health status of circuit boards under dynamic switching of multiple operating conditions, and the easy generation of false alarms or missed alarms.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a method for detecting the health status of a circuit board, comprising: The operating waveform data of multi-channel voltage and current on the circuit board are collected, and histogram construction processing is performed on the operating waveform data to obtain a joint probability density histogram. Based on the joint probability density histogram, the parameters of the preset initial Gaussian mixture model are set, and the parameters that meet the preset convergence conditions are selected to obtain the baseline distribution parameter set. Based on the reference distributed parameter set, determine the operating condition drift parameters and drift start and end times, and perform interpolation correction to obtain the corrected distributed parameter set; Based on the corrected distribution parameter set and the preset health baseline distribution parameters, construct the corrected inverse cumulative distribution function and the health baseline inverse cumulative distribution function, and calculate the Wasserstein distance value; The distance time series is generated using the Wasserstein distance value and an input feature vector is constructed. Based on the input feature vector, a preset support vector machine classification model is used to determine the health degradation category. The health degradation category is normalized to generate a test feature sequence and a regular distance numerical sequence is calculated by combining it with a preset standard template set. Based on the regular distance numerical sequence, a mapping index is executed to determine the interval number of the degradation process stage. An aging type feature distribution matrix is ​​constructed based on the interval number of the degradation process stage, and the degradation intensity is calculated based on the aging type feature distribution matrix to obtain a comprehensive degradation intensity value. Based on the comprehensive degradation intensity value, a normalization calculation is performed to obtain a quantitative score characterizing the current degree of health degradation of the circuit board. Based on the quantified score, an alarm threshold range is constructed to obtain a specific numerical range. Based on the specific numerical range, an alarm signal of the corresponding level is generated to obtain the final evaluation result of the current health status of the circuit board.

[0007] In a second aspect, the present invention provides a circuit board health status detection system, comprising: The data processing module is used to collect the operating waveform data of multi-channel voltage and current of the circuit board, and perform histogram construction processing on the operating waveform data to obtain a joint probability density histogram. The baseline distribution module is used to set the parameters of the preset initial Gaussian mixture model according to the joint probability density histogram, and to select the parameters that meet the preset convergence conditions to obtain the baseline distribution parameter set. The distributed parameter module is used to determine the operating condition drift parameters and drift start and end times based on the reference distributed parameter set, and to perform interpolation correction to obtain the corrected distributed parameter set. The distance calculation module is used to construct the corrected inverse cumulative distribution function and the health benchmark inverse cumulative distribution function based on the corrected distribution parameter set and the preset health benchmark distribution parameters, and to calculate the Wasserstein distance value; The degradation category module is used to generate a distance time series using the Wasserstein distance value and construct an input feature vector. Based on the input feature vector, a preset support vector machine classification model is used to determine the health degradation category. The numbering confirmation module is used to normalize the amplitude of the health degradation category, generate a test feature sequence and calculate a regular distance numerical sequence in combination with a preset standard template set, and perform a mapping index based on the regular distance numerical sequence to determine the interval number of the degradation process stage. The health quantification module is used to construct an aging type feature distribution matrix based on the interval number of the degradation process stage, calculate the degradation intensity based on the aging type feature distribution matrix to obtain a comprehensive degradation intensity value, and perform normalization calculation based on the comprehensive degradation intensity value to obtain a quantitative score characterizing the current degree of health degradation of the circuit board. The health assessment module is used to construct an alarm threshold range based on the quantified score, obtain a specific numerical range, generate an alarm signal of the corresponding level based on the specific numerical range, and obtain the final assessment result of the current health status of the circuit board.

[0008] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention constructs a joint probability density expression for multi-channel voltage and current waveforms on a circuit board and uses a Gaussian mixture model to parameterize the statistical distribution under operating conditions. Based on the extraction and distribution modeling of stable operating condition segments, the original time-domain waveform can be transformed from "discrete numerical judgment" to "distribution morphology characterization". Thus, when changes in operating conditions such as temperature, load, and voltage fluctuations cause the overall signal to shift, it can still maintain a stable description of the distribution structure characteristics, reduce the sensitivity of the fixed threshold scheme to natural drift, reduce false alarms, and improve the robustness of health benchmark tracking.

[0009] (2) This invention introduces a working condition drift identification and interpolation correction mechanism. By quantifying the differences in distribution parameters between adjacent working condition segments and recording the start and end times of the drift, and combining dynamic weights to perform linear / weighted interpolation on the mean and covariance, a corrected distribution parameter set that eliminates the influence of drift is obtained. This can explicitly separate the overall distribution drift caused by working condition switching from the data, allowing subsequent measurements to focus more on the local deformations caused by health degradation (such as tailing, peak flattening, variance expansion, etc.), thereby alleviating the discrimination confusion caused by the "intertwining of working condition drift and degradation changes" from the source and improving cross-working condition consistency and detection stability.

[0010] (3) Based on the corrected distribution and the health baseline distribution, the present invention constructs an inverse cumulative distribution function and calculates a one-dimensional Wasserstein distance, further forming a distance time series and combining it with geometric intervals to determine the degradation category. At the same time, the degradation stage is aligned by normalized distance, and the comprehensive degradation intensity and quantification score are calculated by integrating the historical case library, and finally the graded alarm results are output. The invention realizes the closed-loop derivation from distribution difference to degradation category, stage positioning, intensity quantification, and finally alarm output, which improves the health assessment from qualitative early warning to interpretable, quantifiable and graded result expression. It can identify the health degradation trend earlier and more accurately in multi-condition operation, reduce maintenance costs and shorten downtime. Attached Figure Description

[0011] Figure 1 This is a schematic flowchart of the circuit board health status detection method provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the circuit board health status detection system provided in the second embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] Reference Figure 1 The first embodiment of the present invention provides a method for detecting the health status of a circuit board, comprising the following steps: S11, Collect the operating waveform data of multi-channel voltage and current of the circuit board, and perform histogram construction processing on the operating waveform data to obtain a joint probability density histogram; S12, set the parameters of the preset initial Gaussian mixture model according to the joint probability density histogram, and select the parameters that meet the preset convergence conditions to obtain the baseline distribution parameter set. S13, determine the operating condition drift parameters and drift start and end times based on the reference distributed parameter set, and perform interpolation correction to obtain the corrected distributed parameter set; S14, construct the corrected inverse cumulative distribution function and the health benchmark inverse cumulative distribution function based on the corrected distribution parameter group and the preset health benchmark distribution parameters, and calculate the Wasserstein distance value; S15, use the Wasserstein distance value to generate a distance time series and construct an input feature vector, and use a preset support vector machine classification model to determine the health degradation category based on the input feature vector; S16, normalize the amplitude of the health degradation category, generate the test feature sequence and calculate the regular distance numerical sequence in combination with the preset standard template set, and perform mapping index based on the regular distance numerical sequence to determine the interval number of the degradation process stage. S17. Construct an aging type feature distribution matrix based on the degradation process stage interval number, and calculate the degradation intensity based on the aging type feature distribution matrix to obtain a comprehensive degradation intensity value. Perform normalization calculation based on the comprehensive degradation intensity value to obtain a quantitative score characterizing the current degree of health degradation of the circuit board. S18, construct an alarm threshold range based on the quantized score to obtain a specific numerical range, generate an alarm signal of the corresponding level based on the specific numerical range, and obtain the final evaluation result of the current health status of the circuit board.

[0014] In step S11, histogram construction processing needs to be performed on the running waveform data to obtain a joint probability density histogram, including: Based on the operating waveform data, perform power accumulation calculation to obtain a cumulative energy value sequence; Calculate the local energy change rate of the cumulative energy numerical sequence, and determine the time window position corresponding to the local energy change rate exceeding the preset mutation threshold as the signal mutation position; The stable segment variance is calculated based on the signal abrupt change location, and the signal abrupt change location corresponding to the stable segment variance being lower than the preset stable threshold is determined as the stable operating condition segment. The frequency distribution of voltage and current amplitudes in the stable operating condition segment is statistically analyzed and a histogram is constructed to generate a joint probability density histogram of voltage and current.

[0015] In the specific implementation, the waveform of the circuit board is continuously acquired by the data acquisition module at a fixed sampling frequency of 20kHz, including at least one channel of voltage signal and one channel of current signal. The acquired raw waveform data is stored in the buffer of the processing unit in chronological order.

[0016] The system uses a fixed sliding time window of 5 milliseconds to process the voltage and current sampling data within each window. Within each time window, the product of the voltage and current values ​​is calculated point-by-point as the instantaneous power value. All instantaneous power values ​​within the time window are then summed to obtain the cumulative energy value corresponding to that time window. Following the order of the time windows, all cumulative energy values ​​are arranged into a cumulative energy value sequence to reflect the energy change trend of the circuit board during continuous operation.

[0017] After obtaining the cumulative energy value sequence, the system calculates the change amplitude between the cumulative energy values ​​of two adjacent time windows to characterize the severity of energy changes. The system selects an initial two-second continuous period of operating data as a baseline interval, statistically analyzes the energy change amplitude within this interval, and obtains the average change level and fluctuation range. This average value is used as the baseline level for energy changes under normal operating conditions, and a preset abrupt change threshold is set as the average energy change amplitude within the baseline interval plus three times the fluctuation range. When the energy change amplitude corresponding to a certain time window exceeds this threshold, that time window is determined to be a signal abrupt change location. This determination method is used to identify significant energy changes caused by load switching, power supply status changes, or operating condition adjustments, without mistaking random noise or minor fluctuations for operating condition changes, effectively eliminating random measurement noise.

[0018] For each identified signal abrupt change, the system uses that location as the center, tracing back 10 milliseconds and extending forward 10 milliseconds to form a candidate operating condition segment of 20 milliseconds. The dispersion of the amplitude of the voltage and current signals within this segment is calculated to determine whether the signal is in a stable state.

[0019] During the system initialization phase, based on 10 minutes of stable operation data under the factory-set healthy state of the circuit board, the amplitude dispersion of the voltage and current signals under stable operating conditions is statistically analyzed, and the statistical results are used as the stability benchmark. If the voltage amplitude dispersion and current amplitude dispersion within a candidate operating condition segment do not exceed 1.2 times the stability benchmark, the segment is determined to be a stable operating condition segment; otherwise, the segment is considered to be in a transitional or perturbed state and will not participate in subsequent statistical analysis.

[0020] Statistical processing is performed on all selected stable operating condition segments. The system discretizes the voltage amplitude range into intervals of 0.5 volts and the current amplitude range into intervals of 0.1 amperes. For each sampling point within a stable operating condition segment, the frequency of occurrence of the corresponding voltage and current interval combinations is counted.

[0021] After completing all statistics, the frequency of occurrence of each voltage-current interval combination was normalized so that the sum of the statistical values ​​of all intervals was 1, thus obtaining the joint probability density histogram of voltage and current. This joint probability density histogram fully reflects the statistical distribution relationship between voltage and current under steady-state conditions and serves as the basic input data for subsequent operating condition distribution modeling and health status analysis.

[0022] In this embodiment, a preset stability threshold is used to determine whether a signal segment is in a stable operating condition, ensuring the consistency and representativeness of the data used for statistical modeling. This threshold is established based on the circuit board's factory health status data. Specifically, during the circuit board's factory testing phase or initial installation and commissioning phase, the circuit board is continuously and stably operated under rated voltage and rated load conditions for no less than 10 minutes, and corresponding voltage and current waveform data are collected. The system segments the collected data in 20-millisecond increments, statistically analyzes the dispersion of voltage and current amplitudes within each segment, and averages all segment results to obtain a baseline dispersion level under stable operating conditions. The system sets 1.2 times the baseline dispersion level as the preset stability threshold to accommodate sensor accuracy errors and slight environmental disturbances. During subsequent operation, when the dispersion of voltage and current amplitudes within a candidate segment does not exceed this stability threshold, the segment is determined to be a stable operating segment; if any signal exceeds the threshold, the segment is determined to be in a transitional or disturbed state and is not included in the construction of the joint probability density histogram.

[0023] In step S12, the parameters of the preset initial Gaussian mixture model need to be set according to the joint probability density histogram, and the parameters that meet the preset convergence conditions are selected to obtain the baseline distribution parameter set, including: The parameters of the initial Gaussian mixture model are set based on the joint probability density histogram; The joint probability density histogram is processed using the expectation-maximization algorithm to calculate the posterior probability of each sampling point for each Gaussian component. The parameters of the initial Gaussian mixture model are iteratively updated according to the posterior probability to obtain the mean vector and covariance matrix, and the increment of the log-likelihood function value is calculated. The initial Gaussian mixture model parameters corresponding to the log-likelihood function value increment satisfying the preset convergence condition are determined as the final Gaussian mixture model parameters; Extract the mean vector and covariance matrix corresponding to the parameters of the final Gaussian mixture model to obtain the baseline distribution parameter set characterizing the current working condition.

[0024] The system treats each non-zero bin center point in the joint probability density histogram as a two-dimensional sample point, with the weight of each sample point taken as the normalized frequency corresponding to that bin. To ensure controllable training scale, this embodiment sets the maximum number of sample points to 5000: when the number of non-zero bins exceeds 5000, they are sorted by frequency from high to low, and the 5000 bin center points with the highest frequency are retained first; when the number of non-zero bins is less than 5000, all non-zero bin center points are used directly.

[0025] The number of Gaussian components is selected using a fixed and reproducible selection rule in engineering. This embodiment sets the number of components to 4, suitable for four common operating condition distribution patterns: "low-load stability," "high-load stability," "minor disturbance," and "significant disturbance." In this embodiment, during the factory calibration phase, at least 60 operating segments of each type (low-load stability, high-load stability, minor disturbance, and significant disturbance) are collected, with each segment lasting 1 second. Joint probability density histograms are then constructed for each segment. Subsequently, the number of Gaussian components is set to 2, 3, 4, 5, and 6, respectively. The same batch of histogram samples is fitted, and the average Bayesian Information Criterion (BIC) value is calculated for each component number. A smaller BIC value indicates a better overall performance in terms of fitting accuracy and parameter complexity. The calibration results show that when the number of components increases from 2 to 4, the average BIC decreases from 18320 and 16875 to 15460, indicating a significant reduction in fitting error. When the number of components continues to increase to 5 and 6, the average BIC is 15435 and 15428, respectively, which is only 0.16% and 0.21% lower than that of 4 components. However, the computational cost of the posterior probability in a single round increases by about 25% and 50%, respectively. Therefore, the number of Gaussian components is set to 4.

[0026] The initial parameters employ "weighted clustering initialization." The system performs weighted K-means clustering on the aforementioned two-dimensional sample points, with 10 random initialization iterations to select the optimal result, and a maximum of 100 iterations. The cluster center serves as the initial mean of a Gaussian component; the weighted dispersion of each sample point across the two dimensions serves as the initial covariance; and the sum of the weights for each sample point serves as the initial mixture weight. To prevent degradation of the initial covariance, this embodiment forcibly adds a fixed minimum noise level to the diagonal of the covariance: an equivalent dispersion of 0.2 volts for the voltage dimension and 0.05 amperes for the current dimension, ensuring the model is stable and trainable in the initial stage.

[0027] After initial parameter settings are completed, the system executes the expectation-maximization algorithm to iteratively fit the joint probability density histogram. For each two-dimensional sample point, based on the current four Gaussian component parameters, the membership degree of the sample point to each Gaussian component is calculated, and the membership degrees of the four components are normalized so that the sum of the membership degrees of the same sample point to the four components is 1. Since each sample point has a frequency weight, the system multiplies the membership degree by the weight of the sample point, which is used as the effective contribution of the sample point to the update of each component parameter. This processing method ensures that "high-frequency regions" in the histogram have a greater impact on model fitting, avoiding interference from sparse noise binning with the fitting results of the baseline distribution.

[0028] In each iteration, the system updates the parameters of the four Gaussian components based on the membership results obtained in the previous iteration. Specifically, the "effective contribution" of all sample points is accumulated for each component to obtain the sum of effective sample weights for that component; then, using this sum of weights as the denominator, a weighted average of the voltage and current values ​​of all sample points is calculated to obtain a new mean for that component; and the dispersion of the sample points relative to this mean is weighted statistically to obtain a new covariance for that component; finally, the mixed weights are updated using the ratio of the sum of effective sample weights for that component to the sum of all sample weights. To avoid numerical instability in the covariance matrix, this embodiment requires that the two diagonal elements of the covariance matrix not be less than a fixed lower limit, the voltage dimension not less than the square of the equivalent discrete quantity of 0.2 volts, and the current dimension not less than the square of the equivalent discrete quantity of 0.05 amperes; if the calculated result is less than this lower limit, it is directly replaced with the lower limit value. This constraint ensures that the model will not experience "overfitting spikes" due to some components being concentrated in a small number of bins.

[0029] After each round of parameter updates, the system calculates the goodness of fit of the model to all two-dimensional sample points, representing the fit with a scalar value, and calculates the increment by subtracting the result from the previous round. In this embodiment, the goodness of fit calculation is based on the comprehensive probability value of all sample points under the current model. The frequency weight of the sample points participates in the accumulation of this comprehensive probability, ensuring that high-frequency regions dominate the fit evaluation. The system uses the difference in goodness of fit between two adjacent rounds as the "log-likelihood increment" to determine whether to continue iteration.

[0030] To ensure reproducibility and controllable runtime, this embodiment fixes the maximum number of iterations of the expectation-maximization algorithm at 80 and sets a two-condition convergence criterion: First, if the log-likelihood increment of five consecutive iterations is less than 0.0001, convergence is determined and iteration stops; second, if the first condition is not met after 80 iterations, the algorithm is forcibly stopped, and the parameters of the 80th iteration are taken as the final parameters. This setting can control the training time within the range of 0.3 seconds to 1 second in most operating conditions (on common industrial edge computing units), avoiding delays in output from online systems due to excessively long training times.

[0031] The maximum number of iterations and the convergence threshold are both determined by recording the weighted log-likelihood value after each round of expected maximization iterations in four types of calibration segments: low-load stability, high-load stability, slight disturbance, and significant disturbance. The relative increment between adjacent rounds is calculated by dividing the absolute value of the difference between the current round's weighted log-likelihood value and the previous round's weighted log-likelihood value by the absolute value of the previous round's weighted log-likelihood value. When this relative increment is less than 0.0001, it indicates that the fitting improvement brought by the parameter update in this round is less than 0.01%, and the correction to the mean vector and covariance matrix by continued iteration is less than the change that the voltage and current binning accuracy can distinguish. Therefore, 0.0001 is used as the convergence increment threshold. To avoid premature termination due to accidental drops in a single round, the system requires that the relative increment be less than 0.0001 for five consecutive rounds before convergence is determined. This number of consecutive rounds is determined by the short-term recovery situation in the calibration samples, covering the likelihood increment recovery of 1 to 3 rounds while retaining a margin. The maximum number of iterations is determined based on the number of iterations required for all calibrated samples to reach the above continuous convergence condition. First, take the value in the first 95% of these iterations. For example, if the first 95% corresponds to 78 iterations, round it up to get 80 iterations. Therefore, the maximum number of iterations for the expectation maximization algorithm is set to 80.

[0032] After iteration, the system outputs and stores the parameters of the final Gaussian mixture model as the "baseline distribution parameter set" for the current operating condition. This includes three types of information: the mixing weights of the four components, the mean parameters of the four components, and the covariance parameters of the four components. To facilitate subsequent drift detection and interpolation correction, this embodiment encapsulates the mean and covariance of each component into structured data in a fixed order and binds them with timestamps. Simultaneously, it records the time period number for generating the joint probability density histogram of the input in this round, ensuring that subsequent steps can accurately trace the operating condition segment corresponding to this baseline distribution parameter set.

[0033] It should be noted that, in this embodiment, the preset convergence condition is used to determine whether the parameters of the Gaussian mixture model have achieved a stable fit, so as to control the online computation time while ensuring modeling accuracy. The convergence condition is fixedly configured during the system initialization phase and does not change dynamically during operation.

[0034] In practice, after each round of parameter updates, the system calculates the overall fit of the current model to the joint probability density histogram and compares it with the fitting result of the previous round to obtain the fitting change in this round. The system continuously records the fitting change in the last 5 iterations to determine whether the model has entered a stable state. When the fitting change in each of the last 5 iterations is less than 0.0001, the system determines that the model parameters have sufficiently converged, immediately terminates subsequent iterations, and determines the model parameters of the current round as the final Gaussian mixture model parameters. This setting is used to avoid continuing invalid iterations after the model has stabilized numerically, ensuring the consistency and repeatability of the modeling results. At the same time, to prevent situations where the convergence condition cannot be met for a long time under abnormal operating conditions or noise interference, the system sets the maximum number of iterations to 80. When the number of iterations reaches 80 but the above-mentioned convergence condition of 5 consecutive rounds has not been met, the system forcibly terminates the iteration and outputs the model parameters obtained in the 80th iteration as the final Gaussian mixture model parameters. This dual constraint mechanism ensures that the model training time is controllable in engineering and is suitable for online health status detection and real-time operating condition modeling scenarios. By setting the above convergence conditions, the Gaussian mixture model can stably complete the training process under different working conditions and data scales, which avoids parameter oscillations caused by overfitting and prevents underfitting caused by insufficient iteration.

[0035] In step S13, it is necessary to determine the operating condition drift parameters and drift start and end times based on the reference distributed parameter set, and perform interpolation correction to obtain the corrected distributed parameter set, including: Calculate the Euclidean distance between the mean vectors of the baseline distributions of adjacent working condition segments based on the aforementioned baseline distribution parameter set. The reference distribution parameter group corresponding to the Euclidean distance value exceeding the preset distance threshold is determined as the working condition drift parameter and the start and end times of the drift are recorded. A dynamic weighting factor sequence is generated based on the drift start and end times. Linear interpolation and weighted interpolation are then performed on the mean vector and covariance matrix corresponding to the drift parameters based on the dynamic weighting factor sequence, and the results are combined to obtain a corrected distribution parameter set that eliminates the drift effect.

[0036] In practice, the system converts the set of reference distributed parameters corresponding to each operating condition segment into a fixed-length numerical sequence. This sequence is obtained by sequentially concatenating the mean parameters of four Gaussian components, where each component contains the mean value in the voltage direction and the mean value in the current direction, for a total of eight values. The order of these values ​​remains fixed during the system initialization phase. For ease of description, this invention refers to the set of reference distributed parameters corresponding to the operating condition segments where operating condition drift occurs as operating condition drift parameters, which are used for subsequent correction.

[0037] The system uses 1 second as the smallest time unit for a work condition segment, generating a set of baseline distribution parameters every second. For the two sets of mean parameter sequences corresponding to the nth and (n+1)th seconds, the system calculates the difference between each parameter, sums the squares, and then takes the square root of the result to obtain a single value representing the change in the distribution center between two adjacent work condition segments. This value directly reflects the degree of continuous change of the work condition over time.

[0038] The system extracts sample segments for threshold calibration from controlled calibration data or on-site quasi-stable operation data. The quasi-stable operation data refers to operation segments where voltage fluctuations, load current fluctuations, and temperature change rates are all within preset allowable ranges during continuous data acquisition. Specifically, the system generates baseline distribution parameters in 1-second increments and uses a continuous 5-minute period as a candidate calibration interval. Within this candidate interval, if the deviation of the supply voltage from the average voltage of the interval does not exceed ±2%, the deviation of the load current from the average current of the interval does not exceed ±5%, and the ambient temperature change rate does not exceed 1°C per minute, then the candidate interval is determined as a quasi-stable calibration interval. If load switching, start-stop transients, or rapid temperature changes occur within the candidate interval, the system removes the corresponding time period and continues searching for quasi-stable segments that meet the above conditions from subsequent operation data until at least 5 minutes of valid calibration data are accumulated.

[0039] The system generates a set of baseline distribution parameters every second from the valid calibration data and calculates the change in distribution center between adjacent seconds. When the valid calibration data consists of multiple discontinuous quasi-stable segments, the system only calculates the change in distribution center between adjacent seconds within the same quasi-stable segment, without calculating across segments, to avoid the actual operating condition differences between different load segments from entering the stability fluctuation statistics. The system sorts all valid change values ​​and takes the value corresponding to the 95th percentile as the benchmark for the maximum change in stable operating conditions. In this embodiment, this value falls within the range of 0.6 to 0.9; this range is only an example range obtained statistically from a typical circuit board under the above-mentioned quasi-stable screening conditions.

[0040] The preset allowable range is jointly determined by the power supply deviation, load regulation range, and sensor measurement accuracy permitted in the circuit board product specifications. In practice, the system first reads the rated power supply voltage, rated load current, and ambient temperature operating range of this type of circuit board, and then determines the screening boundaries by combining the sampling circuit accuracy and short-term fluctuation statistics under factory health conditions. For example, when the product specifications allow a power supply deviation of ±5% and a normal load current fluctuation range of ±10%, the calibration screening stage adopts a more stringent ±2% voltage deviation and ±5% load current deviation to ensure that the data entering the calibration are indeed in a quasi-stable state.

[0041] The system sets a preset distance threshold of 1.5 times the baseline value corresponding to the 95th percentile, used to distinguish between normal operating condition fluctuations and significant drift caused by operating condition switching. The amplification factor for the preset distance threshold is calculated by collecting two types of data: the first type is quasi-stable operating data without operating condition switching, used to calculate the mean vector distance between adjacent operating condition segments and obtain a set of normal fluctuation distances; the second type is operating condition switching data with manually recorded switching times, used to calculate the mean vector distance between adjacent operating condition segments before and after the switching and obtain a set of actual switching distances. Subsequently, the system uses the 95th percentile value of the normal fluctuation distance set as the upper limit of normal fluctuations and the 5th percentile value of the actual switching distance set as the lower limit of effective switching. The system then uses 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2.0 as candidate amplification factors, multiplied by the upper limit of normal fluctuations, to obtain multiple sets of candidate distance thresholds. For each candidate distance threshold, the false trigger ratio in the normal fluctuation data and the missed detection ratio in the actual switching data are calculated. If a candidate distance threshold results in a false trigger rate of no more than 5% and a false miss rate of no more than 5%, then the candidate value is considered to meet the requirement of distinguishing between normal fluctuations and operating condition switching. Among the candidate values ​​that meet the requirements, the smallest amplification factor is selected first to ensure high sensitivity to operating condition switching. After the above calibration search, the distance threshold corresponding to 1.5 times can simultaneously meet the requirements for both false trigger rate and false miss rate. Therefore, the preset distance threshold is set to 1.5 times the 95th percentile baseline value.

[0042] During online operation, when the change in the distribution center corresponding to a certain adjacent second exceeds the preset distance threshold, the system immediately determines that a condition drift has occurred at that time point, and records the timestamp of the previous second as the drift start time and the timestamp of the next second as the drift end time. If multiple consecutive seconds exceed the threshold, they are merged into the same drift interval, and only the earliest start time and the latest end time are recorded.

[0043] For each recorded operating condition drift interval, the system first calculates the total duration of that interval. In this embodiment, the shortest duration of the drift interval is 1 second, and the longest is no more than 30 seconds. The system discretizes the drift interval in time with a fixed step size of 100 milliseconds.

[0044] At the first time point of the drift interval, the system sets the dynamic weight to 0; at the last time point of the drift interval, the system sets the dynamic weight to 1. For each discrete time point in between, the system generates a corresponding weight value according to its relative position within the drift interval, increasing at equal intervals. For example, if the drift interval is 10 seconds long, there are 100 discrete points, and the weight increment step for each discrete point is fixed at 0.01. This weight sequence is completely cached after generation and used to drive the interpolation correction process of the distribution parameters.

[0045] During the correction phase, the system processes the mean parameter and covariance parameter separately. For the mean parameter, the system takes the mean parameter of the baseline distribution corresponding to the start time of the drift interval and the mean parameter of the baseline distribution corresponding to the end time of the drift interval as two endpoint parameters. For each discrete time point within the drift interval, the system performs linear interpolation between the two endpoint parameters according to the corresponding dynamic weights to generate the corrected mean parameter for that time point.

[0046] For the covariance parameter, the system uses the same dynamic weight for weighted interpolation, that is, a smooth transition is performed proportionally between the covariance parameter at the start of drift and the covariance parameter at the end of drift. To avoid abnormal contraction or expansion of the covariance during the interpolation process, the system limits the diagonal elements of the interpolation result to not be less than the lower limit set in the initialization phase. The lower limit is fixed at 0.2 for the voltage direction and 0.05 for the current direction.

[0047] At each discrete time point, the system combines the interpolated mean parameter and the interpolated covariance parameter into a complete set of corrected distribution parameters and outputs them in chronological order.

[0048] After interpolation correction, the system replaces the original uncorrected parameters with all corrected distributed parameter sets within the drift interval, while retaining the original baseline distributed parameters within the non-drift interval. This results in a distributed parameter sequence that is time-continuous, without abrupt changes, and has eliminated the overall drift effect of the operating conditions.

[0049] It should be noted that during the circuit board factory testing or initial deployment phase, the circuit board must operate continuously and stably for at least 5 minutes under constant power supply voltage and constant load conditions. During this phase, the system generates a set of baseline distribution parameters in 1-second time units and calculates the difference in the mean parameter of the baseline distribution for adjacent seconds, obtaining a continuous set of difference value samples. The system statistically sorts these difference value samples and selects the value at the top 95 percentile as the maximum normal variation reference value under stable operating conditions. In actual engineering environments, this reference value is typically within the range of 0.6 to 0.9. The system multiplies this reference value by a fixed amplification factor of 1.5 to obtain the final preset distance threshold, used to cover sensor accuracy errors and minor environmental disturbances. During online operation, when the difference in the mean parameter of the baseline distribution for any adjacent operating condition exceeds the preset distance threshold, the system immediately determines that an operating condition drift has occurred at that location and triggers subsequent drift start and end time recording and distribution parameter correction processes; when the difference value does not exceed the threshold, it is determined to be a normal statistical fluctuation under the same operating condition and does not enter the drift processing process.

[0050] In step S14, it is necessary to construct the corrected inverse cumulative distribution function and the health baseline inverse cumulative distribution function based on the corrected distribution parameter set and the preset health baseline distribution parameters, and calculate the Wasserstein distance value, including: Based on the corrected distribution parameter set and the preset health baseline distribution parameters, a corrected inverse cumulative distribution function and a health baseline inverse cumulative distribution function are constructed. Equal-probability interval sampling is performed on the corrected inverse cumulative distribution function and the health baseline inverse cumulative distribution function to generate the corrected quantile sequence and the health baseline quantile sequence; The mean absolute difference between the corrected quantile sequence and the healthy baseline quantile sequence is calculated to obtain the Wasserstein distance value, which characterizes the intrinsic difference between the current state and the healthy state.

[0051] In specific implementation, the system processes the corrected distribution parameter set and the health baseline distribution parameters separately. The health baseline distribution parameters are obtained by continuously collecting data for no less than 10 minutes under stable operating conditions in the circuit board's factory healthy state and modeling the data. The parameter structure is consistent with the corrected distribution parameter set, both containing the mixture weights, mean parameters, and covariance parameters of the four Gaussian components. Specifically, after the circuit board passes the factory inspection, it should be continuously and stably operated for at least 30 minutes under rated operating voltage, rated load, and standard ambient temperature specified in the product specifications. During this period, voltage and current waveform data of all channels to be monitored are collected. This data is then used to train the Gaussian mixture model, including power accumulation, abrupt change detection, stable segment extraction, and construction of a joint probability density histogram. By setting GMM parameters, iterative convergence of the EM algorithm is performed. The optimal model parameters obtained from the final training, including the mean vector, covariance matrix, and mixture weights of each component, are completely saved as the preset health baseline distribution parameters of the circuit board and stored in non-volatile memory.

[0052] The system first maps the probability distributions corresponding to the corrected distribution parameter set to a one-dimensional scalar space. In this embodiment, the voltage direction is selected as the unified metric dimension. The system synthesizes the statistical characteristics of each Gaussian component in the voltage direction according to mixed weights to generate the corrected one-dimensional probability distribution. Similarly, the system performs the same processing on the health benchmark distribution parameters to generate the one-dimensional probability distribution corresponding to the health benchmark.

[0053] After obtaining the two one-dimensional probability distributions mentioned above, the system constructs their inverse cumulative distribution functions to describe the voltage values ​​at different cumulative probability levels. To ensure numerical stability, the construction range of the inverse cumulative distribution function is limited to the cumulative probability interval from 1% to 99%, automatically ignoring extreme tail noise. After the inverse cumulative distribution function is constructed, the system performs equal-probability interval sampling on the corrected inverse cumulative distribution function and the healthy baseline inverse cumulative distribution function. In this embodiment, the cumulative probability interval from 1% to 99% is uniformly divided into 50 sampling points, and the probability interval between each sampling point is fixed at approximately 2%.

[0054] Specifically, based on the one-dimensional probability distribution determined by the corrected distribution parameter set, within the range of cumulative probability from 1% to 99%, a numerical solution method is used to calculate each preset probability value, such as the voltage quantile corresponding to 50 equally spaced probability points. Each pair of probability values ​​and corresponding voltage values ​​are recorded as discrete points, and these discrete points constitute the numerical approximation of the inverse cumulative distribution function. The inverse cumulative distribution function of the health benchmark is constructed using the exact same steps to ensure the comparability of the two in subsequent sampling and distance calculation.

[0055] For each probability sampling point, the system reads the corresponding voltage value from the corrected inverse cumulative distribution function to form a corrected quantile sequence; simultaneously, it reads the corresponding voltage value from the health baseline inverse cumulative distribution function to form a health baseline quantile sequence. This results in two quantile sequences of identical length and with one-to-one probability positions, ensuring comparability in subsequent difference calculations.

[0056] After obtaining the corrected quantile sequence and the healthy baseline quantile sequence, the system calculates the difference between the voltage values ​​at each corresponding position in the two sequences and takes the absolute value to characterize the degree of deviation between the current state and the healthy state at that probability position.

[0057] The system accumulates the absolute differences corresponding to all probability positions and divides them by the number of quantiles, 50, to obtain a single scalar value, which serves as the Wasserstein distance between the current circuit board state and the healthy state. The larger this value, the more significant the overall difference in statistical distribution between the current state and the healthy baseline; when the value is close to zero, it indicates that the current state is highly consistent with the healthy state.

[0058] It should be noted that in calculating the Wassstein distance, this application chooses to project the two-dimensional joint distribution onto the voltage direction for one-dimensional calculation. This process has been verified by a large number of experiments. The distribution changes of circuit board health degradation in the voltage dimension, such as mean drift, variance expansion, and tailing, are more significant and stable than in the current dimension. Moreover, the main changes in the current dimension have been indirectly reflected by the joint probability density histogram modeling and operating condition drift correction process in steps S11-S13. Therefore, using the one-dimensional Wassstein distance in the voltage direction as a degradation metric can effectively capture the main degradation characteristics while ensuring computational efficiency, without causing the loss of key information. This simplified process has maintained a high degree of consistency with the two-dimensional calculation results in the historical case library verification (correlation coefficient ≥ 0.95).

[0059] In step S15, a distance time series needs to be generated using the Wassstein distance value and an input feature vector needs to be constructed. Based on the input feature vector, a preset support vector machine classification model is used to determine the health degradation category, including: The Wasserstein distance values ​​are used to generate a distance-time series reflecting the state evolution; Calculate the overall growth slope and local variance of the distance time series, and construct an input feature vector describing the evolution law; The input feature vector is input into a preset support vector machine classification model to calculate the geometric margin distance of the optimal classification hyperplane; The distance time series corresponding to the geometric interval distance satisfying the preset health degradation conditions are determined as the health degradation category.

[0060] In practice, after each work segment ends, the system generates a corresponding Wasserstein distance value and stores it in chronological order. The system uses a fixed time step of 1 second, meaning that a Wasserstein distance value is generated and recorded every 1 second.

[0061] By continuously recording data for at least 60 seconds, the system generates a distance time series of length 60, which characterizes the statistical distribution difference between the circuit board and the health benchmark over a recent period. This time series is updated using a sliding window during online operation; that is, for each new distance value, the oldest value is discarded to ensure the series length remains constant at 60.

[0062] After obtaining a distance time series of length 60, the system performs feature extraction on the series to quantify its evolution patterns. Specifically, this includes two types of features: the first type is the overall trend, where the system compares the values ​​at the beginning and end of the time series and combines this with the changes in the intermediate values ​​to calculate the overall growth rate of distance over time, reflecting whether the differences are showing a continuously expanding trend; the second type is local fluctuation characteristics, where the system divides the time series into six consecutive segments with 10-second sub-windows, calculates the degree of fluctuation in distance values ​​within each segment, and averages the fluctuation results of the six segments to obtain a feature value reflecting local stability.

[0063] The system combines the overall trend features and local fluctuation features in a fixed order to form an input feature vector of length 2, which is used for subsequent classification. The feature order is fixed during system initialization to avoid inconsistencies in dimensionality during operation. In this embodiment, the support vector machine classification model is used to perform binary classification on the evolution features of the distance time series, and its output distinguishes between "healthy state" and "healthy deterioration state". This model is trained offline before system deployment and its parameters remain fixed during online operation.

[0064] In practice, the support vector machine classification model adopts a linear classification model without using nonlinear kernel functions to ensure the interpretability of the classification boundary and reduce online computational complexity. The model input dimension is fixed at 2, corresponding to the overall trend features and local fluctuation features of the distance time series, respectively. The feature extraction method is completely consistent with that in the online operation phase.

[0065] The training samples are derived from historical operational data and accelerated aging experiment data, with a total sample size of no less than 240 groups, including no less than 120 groups of healthy state samples and no less than 120 groups of healthy deterioration state samples. Each sample group corresponds to a continuous 60-second distance time series, and the overall trend features and local fluctuation features are extracted according to the same rules in step S15 to ensure the statistical consistency between the training samples and the online data.

[0066] During model training, the penalty coefficient is fixed at 1.0, and no grid search or dynamic parameter tuning is performed. The training process uses five-fold cross-validation to verify classification stability, requiring an overall classification accuracy of no less than 90% on the validation set, and a misclassification rate of no more than 10% for both healthy and degenerate states. After training, the final model parameters are stored in the system storage unit and used only for geometric interval distance calculation during online operation, without further updates or adaptive adjustments.

[0067] During the online operation phase, the system inputs the currently constructed input feature vector into the support vector machine classification model and calculates the geometric margin distance from this feature vector to the optimal classification boundary. This geometric margin distance is used to measure the relative positional relationship between the current state and the healthy or degenerate category.

[0068] During the system initialization phase, statistical analysis is performed on the distribution of geometric interval distances based on offline training samples. The system uses the mean geometric interval distance corresponding to healthy state samples as a reference for the healthy center, and shifts this mean by 0.3 in the direction of degradation as the threshold for determining health degradation.

[0069] During online operation, when the geometric interval distance corresponding to a certain distance time series is less than the health degradation judgment threshold, the system classifies the time series as a health degradation category; when the geometric interval distance is greater than or equal to the threshold, it is classified as a non-degradation category. To avoid misjudgment caused by occasional fluctuations, the system requires that the degradation judgment conditions be met for three consecutive time windows before finally confirming entry into the health degradation category.

[0070] It should be noted that, in this embodiment, the preset health degradation condition is used to transform the geometric margin distance output by the support vector machine classification model into a clear health degradation judgment result. This condition is generated statistically based on training samples during the system initialization phase and remains fixed during the online operation phase.

[0071] In practice, after model training, the system statistically analyzes the geometric interval distance distribution corresponding to all healthy state samples and calculates its average value and discrete range. The system uses the average value of the healthy state geometric interval distances as a health center reference and offsets this reference value by a fixed distance of 0.3 in the degradation direction as a health degradation judgment threshold. This offset is used to cover the generalization error of the model on different batches of circuit boards, avoiding misjudgments caused by boundary samples. During online operation, when the geometric interval distance corresponding to the current distance time series is less than the health degradation judgment threshold, the system determines that the time series meets the health degradation condition; when the geometric interval distance is greater than or equal to the threshold, it is determined that no health degradation has occurred.

[0072] To further improve the stability of the judgment, the system introduces a time consistency constraint. The system will only confirm that the circuit board has entered a health degradation state if the health degradation conditions are met for three consecutive adjacent time windows (each window is 60 seconds long). If the conditions are not met in any of the windows, the original health status judgment will remain unchanged.

[0073] It should be noted that the geometric margin distance output by the support vector machine classification model in this embodiment refers to the absolute value of the vertical distance from the sample point to the optimal classification hyperplane. Since the model maps healthy state samples to the positive class and degenerate state samples to the negative class during training, the geometric margin from healthy state sample points to the hyperplane is larger, while the geometric margin of degenerate state sample points is smaller because they are closer to the hyperplane or even located on the negative class side. Therefore, when the geometric margin distance is less than a preset threshold, it indicates that the sample point is close to or has entered the degenerate region and is judged as a healthy or degenerate category. Conversely, when the geometric margin distance is greater than or equal to the threshold, it is judged as a healthy state. This judgment logic is based on the statistical distribution of the training samples and has been guaranteed to be accurate through five-fold cross-validation during the initialization phase.

[0074] In step S16, the health degradation category needs to be normalized to generate a test feature sequence and a regularized distance numerical sequence is calculated by combining it with a preset standard template set. Based on the regularized distance numerical sequence, a mapping index is executed to determine the degradation process stage interval number, including: The distance time series corresponding to the health degradation category is normalized with the preset historical degradation case library to generate the feature sequence to be tested; The minimum curvature path between the feature sequence to be tested and the preset standard template set is calculated to obtain a normalized distance numerical sequence; The target degradation mode template is determined based on the minimum value in the normalized distance numerical sequence; The relative position coordinates of the feature sequence to be tested on the target degradation pattern template are determined by using the mapping relationship of the regular distance numerical sequence; The degradation process stage interval number is determined based on the landing point interval of the relative position coordinates in the preset index.

[0075] In practice, the system uses the distance time series identified as belonging to the health degradation category in step S15 as the input feature sequence to be tested. The length of this time series is fixed at 60 sampling points, corresponding to 60 consecutive seconds of state evolution information.

[0076] The system simultaneously accesses a pre-defined historical degradation case library. This library consists of no fewer than 30 degradation cases, each originating from at least the following sources: accelerated aging test data of circuit boards (under constant power supply conditions, by increasing ambient temperature and load levels to allow the circuit board to undergo a complete degradation process within a controllable time); or long-term field operation records, selecting the operational history of circuit boards that have confirmed performance degradation but have not experienced sudden failure. For each degradation case, the system continuously extracts the time series from the healthy phase before degradation begins until it approaches the stage before functional failure. All degradation cases are uniformly trimmed into time series of 100 sampling points and stored in chronological order. If the original sequence length exceeds 100, it is downsampled at equal intervals; if the length is less than 100, it is smoothed at the end according to the most recent trend. Before adding cases to the library, the system performs a consistency check on each degradation sequence, removing data containing sudden power outages, significant external shocks, or abnormal data acquisition. Degradation sequences that pass the check are marked as standard degradation mode templates and stored in the historical degradation case library in numerical order. The case library is only used for reading during system operation and is not updated online to ensure the repeatability and consistency of degradation judgment results.

[0077] To eliminate the impact of amplitude differences between different circuit boards and test batches, the system performs uniform amplitude normalization on the feature sequence under test and all standard template sequences; the minimum value of each sequence is mapped to 0, the maximum value is mapped to 1, and the remaining sampling points are scaled linearly. After normalization, only the degradation trend morphology information is retained, unaffected by the absolute value magnitude.

[0078] After normalization, the system performs time-normalization comparisons between the test feature sequence and a set of preset standard templates. Specifically, the system uses each standard template as a reference path to calculate the minimum bending alignment path between the test feature sequence and that template. During normalization, the system imposes path continuity constraints: each sample point of the test sequence is allowed to establish a correspondence with a maximum of three adjacent sample points in the template to avoid unreasonable time jumps. For each standard template, the system outputs a corresponding normalization distance value to characterize the similarity between the test sequence and the overall degradation morphology of that template.

[0079] After performing the above operations on all standard templates, the system obtains a set of normalized distance values, arranged in order of template number to form a normalized distance value sequence. All normalized distance values ​​are compared, and the standard template with the smallest value is selected as the target degradation mode template. This template represents the historical degradation path that most closely resembles the current circuit board state in terms of overall degradation morphology.

[0080] To avoid accidental template selection due to random noise, the system introduces a stability judgment rule: if the difference between the minimum regularization distance and the second smallest regularization distance is less than 5%, the system retains both templates and takes their intersection in subsequent interval determination stages; otherwise, only the template corresponding to the minimum regularization distance is used as the unique target degradation pattern template. After the target degradation pattern template is determined, the system uses the path mapping relationship generated during the regularization process to determine the corresponding position interval of the test feature sequence in the target template. In specific implementation, the system records the template index position corresponding to the last sampling point of the test feature sequence in the target template.

[0081] After normalization, the template index position is mapped to a relative position coordinate between 0 and 1, representing the current state's progress proportion within the complete degradation path. The closer this relative position coordinate is to 1, the closer the degradation process is to failure; the closer it is to 0, the more it indicates the early degradation stage. During system initialization, the complete degradation path is divided into several fixed stage intervals based on the relative position coordinates. In this embodiment, the degradation path is evenly divided into 5 stage intervals, corresponding to interval numbers 1 to 5, with each interval having a fixed width of 0.2.

[0082] The degradation path is evenly divided into five stages, determined based on the magnitude of state changes and the level of maintenance and handling in historical degradation cases. The complete degradation process is corresponding to five states: early deviation, mild degradation, moderate degradation, accelerated degradation, and critical degradation, which correspond to five handling methods: continuous observation, increased inspection frequency, scheduled maintenance, priority replacement of key components, and shutdown. When there are fewer than five stages, moderate degradation and accelerated degradation are easily merged, making it impossible to distinguish the differences between maintenance and replacement. When there are more than five stages, the differences in handling measures between adjacent stages become smaller, which increases the complexity of judgment.

[0083] The system directly determines the corresponding degradation process stage interval number based on the range of the current relative position coordinates. For example, if the relative position coordinates fall within the interval of 0.4 to 0.6, the current state is determined to be in stage 3. This stage interval number serves as a direct index parameter for subsequent degradation intensity calculations and alarm level determinations.

[0084] It should be noted that, in this embodiment, the preset index is used to map the relative position of the feature sequence to be tested in the target degradation mode template to discrete degradation process stage interval numbers, so as to realize the staged expression of degradation state.

[0085] In practice, during the initialization phase, the system establishes a unified stage indexing rule for each standard degradation mode template. The complete length of the template is evenly divided into 5 consecutive intervals, each interval corresponding to a degradation stage number, which increases sequentially from 1 to 5. Each interval covers 20% of the template length, and the interval boundaries are fixed in the system configuration file and do not change with the template type.

[0086] In step S16, after the system determines the corresponding position of the feature sequence to be tested in the target template based on the regularized path, it converts the position into a relative proportion in the template length and searches for its corresponding interval in the preset index. If the relative position falls into the first 20% interval, the stage interval number is determined to be 1; if it falls into the second 20% interval, the stage interval number is determined to be 2; and so on, until the number is 5.

[0087] When there are two parallel candidate templates for the target degradation mode, the system calculates the stage interval number corresponding to the two templates respectively, and takes the stage number with the larger value as the final degradation process stage interval number to ensure that the degradation assessment result is biased towards the safe side.

[0088] In step S17, an aging type feature distribution matrix needs to be constructed based on the degradation process stage interval number, and the degradation intensity is calculated based on the aging type feature distribution matrix to obtain a comprehensive degradation intensity value. Based on the comprehensive degradation intensity value, a normalization calculation is performed to obtain a quantitative score characterizing the current degree of health degradation of the circuit board, including: Based on the historical degradation case library preset by the degradation process stage interval number index, the set of component aging type labels and the cumulative offset distance numerical sequence within the interval are obtained; The set of component aging type labels and the cumulative offset distance numerical sequence are aggregated and calculated to generate an aging type feature distribution matrix. The comprehensive degradation intensity value is obtained by using the aging type feature distribution matrix and the preset fault severity weighting vector; The comprehensive degradation intensity value is input into a preset normalization mapping function to calculate a quantitative score that characterizes the current degree of health degradation of the circuit board.

[0089] In practice, the system selects degradation case segments belonging to the same stage interval from a preset historical degradation case library based on the degradation process stage interval number corresponding to the current circuit board. In the historical degradation case library, each standard degradation pattern template has been divided into 5 stage intervals according to the same rules during the construction phase, and each stage interval is pre-labeled with the corresponding component aging type label and cumulative offset distance value.

[0090] When the current degradation process stage interval is a certain stage number, the system extracts data fragments within the corresponding stage interval from all historical degradation cases to form a case set for that stage. Each case fragment contains at least two pieces of information: first, the confirmed component aging type label within that stage; and second, the corresponding cumulative offset distance numerical sequence within that stage, used to reflect the cumulative degree of degradation over time.

[0091] After obtaining the case set within the stage interval, the system statistically aggregates the component aging type tags within it. In this embodiment, the aging type tags include at least five categories: solder joint fatigue, dielectric aging, increased on-resistance, capacitance parameter drift, and thermal stress damage. The tag system is fixed during the system initialization phase. The system counts the number of times each aging type appears in the case set and simultaneously sums the cumulative offset distance values ​​of the corresponding case segments to obtain the comprehensive contribution of each aging type in the current stage. Subsequently, the system arranges the comprehensive contributions of each aging type in a fixed order to form an aging type feature distribution matrix, which is used to characterize the relative influence of different aging mechanisms on the overall degradation within the current stage.

[0092] Specifically, an initial data table is constructed using the component aging type label as the row index and the cumulative offset distance value sequence as the value domain. Next, the cumulative offset distance values ​​under the same aging type label are summed to obtain the total cumulative offset for that type of aging. Finally, all aging types are arranged in a fixed order, such as solder joint fatigue, dielectric aging, increased on-resistance, capacitor parameter drift, and thermal stress damage, and the corresponding total cumulative offsets are sequentially filled into the column vectors of the matrix, forming a 5×1 aging type feature distribution matrix. Each element of this matrix represents the cumulative contribution of the corresponding aging type in the current degradation stage.

[0093] During system initialization, a fixed severity weight is pre-set for each aging type to reflect its impact on the overall performance and reliability of the circuit board. In this embodiment, solder joint fatigue is weighted at 1.0, dielectric aging at 0.8, increased on-resistance at 0.9, capacitance parameter drift at 0.7, and thermal stress damage at 1.2. These weights remain unchanged after system deployment.

[0094] The system performs weighted calculations on the contribution of each aging type in the aging type feature distribution matrix and its corresponding fault severity weight, and then sums the results to obtain a single comprehensive degradation intensity value. This value simultaneously reflects the diversity of aging types, the degree of accumulation, and the importance of different aging mechanisms within the current degradation stage.

[0095] To facilitate horizontal comparisons between different circuit boards and different operating cycles, the system performs normalization mapping processing on the comprehensive degradation intensity value. In this embodiment, during the system initialization phase, based on the distribution of comprehensive degradation intensity values ​​of all cases in the historical degradation case library, the minimum and maximum reference values ​​are determined and used as the upper and lower limits of the normalization mapping interval.

[0096] During online operation, the system maps the currently calculated comprehensive degradation intensity value to a numerical range of 0 to 100, obtaining a quantitative score characterizing the current degree of health degradation of the circuit board. The higher the quantitative score, the more severe the health degradation of the circuit board; when the quantitative score is close to 0, it indicates that the circuit board is close to the health baseline.

[0097] It should be noted that, in this embodiment, a preset normalization mapping function is used to uniformly map the comprehensive degradation intensity value to a standardized health degradation quantification score, so as to enable horizontal comparison and alarm classification between different circuit boards and different operating stages.

[0098] In practice, before system deployment, based on all degradation cases in the preset historical degradation case library, the corresponding comprehensive degradation intensity value is calculated for each case according to step S17, forming a complete intensity distribution sample set. The system selects the smallest comprehensive degradation intensity value from the sample set as the lower normalization reference value, and selects the comprehensive degradation intensity value located in the top 98 percentile as the upper normalization reference value, in order to avoid unreasonable stretching of the mapping interval by extreme abnormal cases.

[0099] The system maps the aforementioned lower reference value to a quantization score of 0, and the aforementioned upper reference value to a quantization score of 100. During online operation, when the currently calculated comprehensive degradation intensity value is lower than the lower reference value, a quantization score of 0 is directly output; when it exceeds the upper reference value, a quantization score of 100 is directly output; in other cases, it is linearly mapped to the range of 0 to 100 to obtain the final health degradation quantization score. This mapping function remains fixed during system operation and does not adaptively adjust over time.

[0100] It should be noted that, in this embodiment, the preset fault severity weighted vector is used to characterize the impact of different component aging types on the overall health status and functional reliability of the circuit board. It is set during the system initialization phase and remains unchanged during system operation.

[0101] In practice, the system first performs statistical analysis on the main failure modes of the circuit board. Combining historical repair records and accelerated aging test results, it classifies component aging types into five categories: solder joint fatigue, dielectric aging, increased on-resistance, capacitor parameter drift, and thermal stress damage. For each aging type, the system sets a corresponding severity weight value based on its direct impact on the circuit board's functional failure, its frequency of occurrence, and the repair cost after failure.

[0102] In this embodiment, the severity weight of solder joint fatigue is fixed at 1.0, the severity weight of dielectric aging is fixed at 0.8, the severity weight of increased on-resistance is fixed at 0.9, the severity weight of capacitance parameter drift is fixed at 0.7, and the severity weight of thermal stress damage is fixed at 1.2. These weight values ​​are stored in a fixed array format in the system configuration file and correspond one-to-one with the aging type label.

[0103] During the calculation of comprehensive degradation intensity, the system calls the fault severity weighting vector in a fixed order to weight the feature contribution of different aging types, so that high-risk and high-impact aging types have a higher weight in the comprehensive degradation intensity, thereby ensuring the rationality and safety of the final health degradation assessment results in engineering decision-making.

[0104] In step S18, an alarm threshold range needs to be constructed based on the quantized score to obtain a specific numerical range. Based on this specific numerical range, an alarm signal of the corresponding level is generated to obtain the final assessment result of the circuit board's current health status, including: The quantized score is compared with a preset multi-level alarm threshold sequence to lock the specific numerical range used to index the preset interval state mapping matrix; Extract the corresponding alarm level identifier and circuit board health status code from the specific numerical range; The alarm level identifier is modulated to generate a digital alarm signal, and the digital alarm signal is encapsulated with the circuit board health status code to form the final evaluation result of the current health status of the circuit board.

[0105] In practical implementation, the system presets a fixed multi-level alarm threshold sequence during the initialization phase to divide the health degradation quantification score into several continuous numerical intervals. In this embodiment, the quantification score ranges from 0 to 100, and this range is divided into four alarm level intervals. When the quantification score is less than 25, it is determined to be in the first-level interval, corresponding to "healthy state"; when the quantification score is not less than 25 and less than 50, it is determined to be in the second-level interval, corresponding to "mild degradation state"; when the quantification score is not less than 50 and less than 75, it is determined to be in the third-level interval, corresponding to "moderate degradation state"; and when the quantification score is not less than 75, it is determined to be in the fourth-level interval, corresponding to "severe degradation state". The system compares the current quantification score sequentially with the multi-level alarm threshold sequence and locks the specific numerical interval range accordingly, which serves as the input for subsequent state indexing.

[0106] The multi-level alarm threshold sequence is determined jointly by the quantitative score distribution in the historical degradation case library and the corresponding operation and maintenance results, rather than by arbitrarily dividing it into equal intervals from 0 to 100. During the initialization phase, the system first extracts confirmed healthy, mildly degraded, moderately degraded, and severely degraded sample records from the historical degradation case library that matches the current circuit board type, rated power supply range, load range, and ambient temperature range. Each record includes the quantitative score at the corresponding time, the manual inspection conclusion, the maintenance result, and the subsequent operating status. Subsequently, the quantitative score distribution of the four types of samples is statistically analyzed, and the boundary point is determined by the 95th percentile coverage relationship between adjacent two status samples. Specifically, if more than 95% of the quantitative scores in healthy samples are below 25, and more than 95% of the quantitative scores in mildly degenerated samples are not below 25, then 25 is determined as the boundary threshold between healthy and mildly degenerated states; if more than 95% of the quantitative scores in mildly degenerated samples are below 50, and more than 95% of the quantitative scores in moderately degenerated samples are not below 50, then 50 is determined as the boundary threshold between mild and moderately degenerated states; if more than 95% of the quantitative scores in moderately degenerated samples are below 75, and more than 95% of the quantitative scores in severely degenerated samples are not below 75, then 75 is determined as the boundary threshold between moderate and severely degenerated states.

[0107] During system initialization, a pre-established interval state mapping matrix is ​​used to bind quantized score intervals to alarm level identifiers and circuit board health status codes in a one-to-one correspondence. For example, the alarm level identifier for the first-level interval is set to L0, and the corresponding circuit board health status code is set to H0; the alarm level identifier for the second-level interval is set to L1, and the corresponding circuit board health status code is set to H1; the alarm level identifier for the third-level interval is set to L2, and the corresponding circuit board health status code is set to H2; and the alarm level identifier for the fourth-level interval is set to L3, and the corresponding circuit board health status code is set to H3.

[0108] Once the system locks in a specific numerical range, it directly extracts the corresponding alarm level identifier and circuit board health status code from the range state mapping matrix, avoiding dynamic generation or judgment during operation and ensuring the stability of the mapping relationship.

[0109] After obtaining the alarm level identifier, the system performs digital modulation processing on the identifier to generate a corresponding digital alarm signal. In this embodiment, the system maps the alarm level identifier to a fixed-length binary code. For example, L0 is mapped to 00, L1 to 01, L2 to 10, and L3 to 11, which are used to embed communication frames or log records.

[0110] The system then encapsulates the digital alarm signal with the corresponding circuit board health status code to form a final evaluation result data packet containing a timestamp, device identifier, health status code, and alarm signal. This data packet can be output to a host computer, cloud platform, or operation and maintenance system via serial communication, bus communication, or network interface for real-time display, historical tracing, and maintenance decision-making.

[0111] It should be noted that, in this embodiment, a preset multi-level alarm threshold sequence is used to divide the circuit board health degradation quantification score obtained in step S17 into discrete health status levels, so as to realize graded alarms and differentiated operation and maintenance responses. The alarm threshold sequence is set once during the system initialization phase and remains unchanged during system operation.

[0112] In practice, the quantization score is fixed at a range of 0 to 100. The system divides this range into four consecutive alarm threshold intervals, with the boundary values ​​of each interval determined jointly by statistical results of historical degradation cases and operational experience. In this embodiment, the alarm threshold sequence is explicitly set to four levels: when the quantization score is in the range of 0 to 24, it is determined to be a no-alarm state; when the quantization score is in the range of 25 to 49, it is determined to be a low-alarm state; when the quantization score is in the range of 50 to 74, it is determined to be a medium-alarm state; and when the quantization score is greater than or equal to 75, it is determined to be a high-alarm state.

[0113] The aforementioned threshold classification covers the entire process from minor performance degradation to severe failure risk and directly corresponds to subsequent maintenance strategies. During online operation, the system determines its alarm level range simply by comparing the current quantification score with the threshold sequence, without involving dynamic adjustment or adaptive updates.

[0114] It should be noted that, in this embodiment, the preset interval state mapping matrix is ​​used to map the alarm threshold interval to which the quantized score belongs to a standardized alarm level identifier and circuit board health status code, so as to facilitate inter-system communication, log recording and alarm linkage.

[0115] In practical implementation, the system establishes a fixed interval state mapping matrix during the initialization phase. This matrix uses the alarm threshold interval as the index dimension and corresponds one-to-one with the alarm level identifier and the circuit board health status code. In this embodiment, the mapping relationship is as follows: the no-alarm interval corresponds to the alarm level identifier L0 and the corresponding circuit board health status code H0; the low-level alarm interval corresponds to the alarm level identifier L1 and the corresponding circuit board health status code H1; the medium-level alarm interval corresponds to the alarm level identifier L2 and the corresponding circuit board health status code H2; and the high-level alarm interval corresponds to the alarm level identifier L3 and the corresponding circuit board health status code H3.

[0116] The interval state mapping matrix is ​​stored in the system configuration area in a lookup table manner. During operation, the system directly indexes the corresponding row according to the interval to which the quantified score belongs, extracts the alarm level identifier and health status code, avoids complex logical judgments, and ensures the consistency and real-time nature of the evaluation results output.

[0117] The system employs a hierarchical hardware architecture consisting of a sampling control unit and an edge computing unit. The sampling control unit is responsible for acquiring multi-channel voltage and current waveforms, writing timestamps, counting sliding window bins, and managing cache. It can utilize an ARM Cortex-M7 controller with a hardware floating-point unit, an STM32H7 series controller, or an industrial control chip with equivalent computing power. This type of controller is responsible for real-time data reception and histogram counting updates at a 20kHz sampling frequency, but does not handle computationally intensive tasks such as complete Gaussian mixture parameter iteration and normalized distance matching. The edge computing unit reads the compressed non-zero bin center points and their normalized frequencies after each operating condition segment, and performs Gaussian mixture parameter fitting, Wasserstein distance calculation, support vector machine determination, and historical degradation template normalized distance calculation. It can utilize an ARM Cortex-A53 processor with a clock speed of at least 1GHz and hardware floating-point capabilities, an embedded Linux computing board, an industrial gateway processor, or a DSP processor with equivalent floating-point capabilities.

[0118] When the on-site hardware is only configured with an ARM Cortex-M level controller, the system uses parameter pre-training and online table lookup to reduce the computational load. The number of Gaussian mixture components, initial mean range, lower limit of covariance, support vector machine classification parameters, and standard degradation template are written to non-volatile memory after factory calibration or server-side calculation; during the online operation phase, only binning statistics, mean distance calculation, drift detection, interpolation correction, and low-dimensional feature comparison are performed.

[0119] In summary, the present invention provides a method and system for detecting the health status of circuit boards, in order to solve the problems of inaccurate assessment of the health status of circuit boards under dynamic switching of multiple operating conditions, and the easy generation of false alarms or missed alarms.

[0120] Reference Figure 2 The second embodiment of the present invention provides a circuit board health status detection system, comprising: The data processing module is used to collect the operating waveform data of multi-channel voltage and current of the circuit board, and perform histogram construction processing on the operating waveform data to obtain a joint probability density histogram. The baseline distribution module is used to set the parameters of the preset initial Gaussian mixture model according to the joint probability density histogram, and to select the parameters that meet the preset convergence conditions to obtain the baseline distribution parameter set. The distributed parameter module is used to determine the operating condition drift parameters and drift start and end times based on the reference distributed parameter set, and to perform interpolation correction to obtain the corrected distributed parameter set. The distance calculation module is used to construct the corrected inverse cumulative distribution function and the health benchmark inverse cumulative distribution function based on the corrected distribution parameter set and the preset health benchmark distribution parameters, and to calculate the Wasserstein distance value; The degradation category module is used to generate a distance time series using the Wasserstein distance value and construct an input feature vector. Based on the input feature vector, a preset support vector machine classification model is used to determine the health degradation category. The numbering confirmation module is used to normalize the amplitude of the health degradation category, generate a test feature sequence and calculate a regular distance numerical sequence in combination with a preset standard template set, and perform a mapping index based on the regular distance numerical sequence to determine the interval number of the degradation process stage. The health quantification module is used to construct an aging type feature distribution matrix based on the interval number of the degradation process stage, calculate the degradation intensity based on the aging type feature distribution matrix to obtain a comprehensive degradation intensity value, and perform normalization calculation based on the comprehensive degradation intensity value to obtain a quantitative score characterizing the current degree of health degradation of the circuit board. The health assessment module is used to construct an alarm threshold range based on the quantified score, obtain a specific numerical range, generate an alarm signal of the corresponding level based on the specific numerical range, and obtain the final assessment result of the current health status of the circuit board.

[0121] It should be noted that the circuit board health status detection system provided in this embodiment of the invention is used to execute all the process steps of the circuit board health status detection method in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0122] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0123] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for detecting the health status of a circuit board, characterized in that, include: The operating waveform data of multi-channel voltage and current on the circuit board are collected, and histogram construction processing is performed on the operating waveform data to obtain a joint probability density histogram. Based on the joint probability density histogram, the parameters of the preset initial Gaussian mixture model are set, and the parameters that meet the preset convergence conditions are selected to obtain the baseline distribution parameter set. Based on the reference distributed parameter set, determine the operating condition drift parameters and drift start and end times, and perform interpolation correction to obtain the corrected distributed parameter set; Based on the corrected distribution parameter set and the preset health baseline distribution parameters, construct the corrected inverse cumulative distribution function and the health baseline inverse cumulative distribution function, and calculate the Wasserstein distance value; The distance time series is generated using the Wasserstein distance value and an input feature vector is constructed. Based on the input feature vector, a preset support vector machine classification model is used to determine the health degradation category. The health degradation category is normalized to generate a test feature sequence and a regular distance numerical sequence is calculated by combining it with a preset standard template set. Based on the regular distance numerical sequence, a mapping index is executed to determine the interval number of the degradation process stage. An aging type feature distribution matrix is ​​constructed based on the interval number of the degradation process stage, and the degradation intensity is calculated based on the aging type feature distribution matrix to obtain a comprehensive degradation intensity value. The comprehensive degradation intensity value is then normalized to obtain a quantitative score characterizing the current degree of health degradation of the circuit board. Based on the quantified score, an alarm threshold range is constructed to obtain a specific numerical range. Based on the specific numerical range, an alarm signal of the corresponding level is generated to obtain the final evaluation result of the current health status of the circuit board.

2. The circuit board health status detection method according to claim 1, characterized in that, The step of performing histogram construction processing on the running waveform data to obtain a joint probability density histogram includes: Based on the operating waveform data, perform power accumulation calculation to obtain a cumulative energy value sequence; Calculate the local energy change rate of the cumulative energy numerical sequence, and determine the time window position corresponding to the local energy change rate exceeding the preset mutation threshold as the signal mutation position; The stable segment variance is calculated based on the signal abrupt change location, and the signal abrupt change location corresponding to the stable segment variance being lower than the preset stable threshold is determined as the stable operating condition segment. The frequency distribution of voltage and current amplitudes in the stable operating condition segment is statistically analyzed and a histogram is constructed to generate a joint probability density histogram of voltage and current.

3. The circuit board health status detection method according to claim 1, characterized in that, The parameters of the preset initial Gaussian mixture model are set according to the joint probability density histogram, and the parameters that meet the preset convergence conditions are selected to obtain the baseline distribution parameter set, including: The parameters of the initial Gaussian mixture model are set based on the joint probability density histogram; The joint probability density histogram is processed using the expectation-maximization algorithm to calculate the posterior probability of each sampling point for each Gaussian component. The parameters of the initial Gaussian mixture model are iteratively updated according to the posterior probability to obtain the mean vector and covariance matrix, and the increment of the log-likelihood function value is calculated. The initial Gaussian mixture model parameters corresponding to the log-likelihood function value increment satisfying the preset convergence condition are determined as the final Gaussian mixture model parameters; Extract the mean vector and covariance matrix corresponding to the parameters of the final Gaussian mixture model to obtain the baseline distribution parameter set characterizing the current working condition.

4. The circuit board health status detection method according to claim 1, characterized in that, The step involves determining the operating condition drift parameters and drift start and end times based on the reference distributed parameter set, and performing interpolation correction to obtain the corrected distributed parameter set, including: Calculate the Euclidean distance between the mean vectors of the baseline distributions of adjacent working condition segments based on the aforementioned baseline distribution parameter set. The reference distribution parameter group corresponding to the Euclidean distance value exceeding the preset distance threshold is determined as the working condition drift parameter and the start and end times of the drift are recorded. A dynamic weighting factor sequence is generated based on the drift start and end times. Linear interpolation and weighted interpolation are then performed on the mean vector and covariance matrix corresponding to the drift parameters based on the dynamic weighting factor sequence, and the results are combined to obtain a corrected distribution parameter set that eliminates the drift effect.

5. The circuit board health status detection method according to claim 1, characterized in that, The process of constructing the corrected inverse cumulative distribution function and the health baseline inverse cumulative distribution function based on the corrected distribution parameter set and the preset health baseline distribution parameters, and calculating the Wasserstein distance, includes: Based on the corrected distribution parameter set and the preset health baseline distribution parameters, a corrected inverse cumulative distribution function and a health baseline inverse cumulative distribution function are constructed. Equal-probability interval sampling is performed on the corrected inverse cumulative distribution function and the health baseline inverse cumulative distribution function to generate the corrected quantile sequence and the health baseline quantile sequence; The mean absolute difference between the corrected quantile sequence and the healthy baseline quantile sequence is calculated to obtain the Wasserstein distance value, which characterizes the intrinsic difference between the current state and the healthy state.

6. The circuit board health status detection method according to claim 1, characterized in that, The process of generating a distance time series using the Wassstein distance value and constructing an input feature vector, and then determining the health degradation category using a pre-defined support vector machine classification model based on the input feature vector, includes: The Wasserstein distance values ​​are used to generate a distance-time series reflecting the state evolution; Calculate the overall growth slope and local variance of the distance time series, and construct an input feature vector describing the evolution law; The input feature vector is input into a preset support vector machine classification model to calculate the geometric margin distance of the optimal classification hyperplane; The distance time series corresponding to the geometric interval distance satisfying the preset health degradation conditions are determined as the health degradation category.

7. The circuit board health status detection method according to claim 1, characterized in that, The process of normalizing the health degradation category, generating a test feature sequence, calculating a regular distance numerical sequence based on a preset standard template set, and performing a mapping index based on the regular distance numerical sequence to determine the interval number of the degradation process stage includes: The distance time series corresponding to the health degradation category is normalized with the preset historical degradation case library to generate the feature sequence to be tested; The minimum curvature path between the feature sequence to be tested and the preset standard template set is calculated to obtain a normalized distance numerical sequence; The target degradation mode template is determined based on the minimum value in the normalized distance numerical sequence; The relative position coordinates of the feature sequence to be tested on the target degradation pattern template are determined by using the mapping relationship of the regular distance numerical sequence; The degradation process stage interval number is determined based on the landing point interval of the relative position coordinates in the preset index.

8. The circuit board health status detection method according to claim 1, characterized in that, The process involves constructing an aging type feature distribution matrix based on the interval numbering of the degradation process stage, calculating the degradation intensity based on the aging type feature distribution matrix to obtain a comprehensive degradation intensity value, and performing normalization calculation based on the comprehensive degradation intensity value to obtain a quantitative score characterizing the current degree of health degradation of the circuit board, including: Based on the preset historical degradation case library according to the degradation process stage interval number index, the set of component aging type labels and the cumulative offset distance numerical sequence within the interval are obtained; The aging type label set of the components and the cumulative offset distance numerical sequence are aggregated and calculated to generate an aging type feature distribution matrix; The comprehensive degradation intensity value is obtained by using the aging type feature distribution matrix and the preset fault severity weighting vector; The comprehensive degradation intensity value is input into a preset normalization mapping function to calculate a quantitative score that characterizes the current degree of health degradation of the circuit board.

9. The circuit board health status detection method according to claim 1, characterized in that, The process involves constructing an alarm threshold range based on the quantized score to obtain a specific numerical interval range, generating an alarm signal of the corresponding level based on the specific numerical interval range, and obtaining the final assessment result of the current health status of the circuit board, including: The quantized score is compared with a preset multi-level alarm threshold sequence to lock the specific numerical range used to index the preset interval state mapping matrix; Extract the corresponding alarm level identifier and circuit board health status code from the specific numerical range; The alarm level identifier is modulated to generate a digital alarm signal, and the digital alarm signal is encapsulated with the circuit board health status code to form the final evaluation result of the current health status of the circuit board.

10. A circuit board health status detection system, characterized in that, include: The data processing module is used to collect the operating waveform data of multi-channel voltage and current of the circuit board, and perform histogram construction processing on the operating waveform data to obtain a joint probability density histogram. The baseline distribution module is used to set the parameters of the preset initial Gaussian mixture model according to the joint probability density histogram, and to select the parameters that meet the preset convergence conditions to obtain the baseline distribution parameter set. The distributed parameter module is used to determine the operating condition drift parameters and drift start and end times based on the reference distributed parameter set, and to perform interpolation correction to obtain the corrected distributed parameter set. The distance calculation module is used to construct the corrected inverse cumulative distribution function and the health benchmark inverse cumulative distribution function based on the corrected distribution parameter set and the preset health benchmark distribution parameters, and to calculate the Wasserstein distance value; The degradation category module is used to generate a distance time series using the Wasserstein distance value and construct an input feature vector. Based on the input feature vector, a preset support vector machine classification model is used to determine the health degradation category. The numbering confirmation module is used to normalize the amplitude of the health degradation category, generate a test feature sequence and calculate a regular distance numerical sequence in combination with a preset standard template set, and perform a mapping index based on the regular distance numerical sequence to determine the interval number of the degradation process stage. The health quantification module is used to construct an aging type feature distribution matrix based on the interval number of the degradation process stage, calculate the degradation intensity based on the aging type feature distribution matrix to obtain a comprehensive degradation intensity value, and perform normalization calculation based on the comprehensive degradation intensity value to obtain a quantitative score characterizing the current degree of health degradation of the circuit board. The health assessment module is used to construct an alarm threshold range based on the quantified score, obtain a specific numerical range, generate an alarm signal of the corresponding level based on the specific numerical range, and obtain the final assessment result of the current health status of the circuit board.

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