Online monitoring system for testing FPC micro-pitch PIN short circuit

Abstract

The invention relates to the technical field of flexible circuit board monitoring, in particular to an online monitoring system for testing FPC (flexible printed circuit) micro-pitch PIN short circuit, which comprises a polarization charge sampling module used for injecting nanosecond pulse excitation to adjacent PINs according to a micro-pitch FPC circuit architecture to trigger parasitic capacitance polarization and acquiring a transient polarization voltage sampling sequence. According to the method, the impedance characteristic Lorentz distribution track is constructed and the potential attenuation Gini coefficient is calculated, so that abnormal individuals causing rapid distribution imbalance can be identified from a group perspective, and the mode is particularly effective for detecting micro-short-circuit faults dominated by serious degradation of a single PIN or a few PINs; according to the method, common-mode interference caused by environment temperature excursion or power supply fluctuation can be filtered out, the signal-to-noise ratio and accuracy of fault recognition are improved, and finally high-precision positioning of a micro-short-circuit fault point is achieved by combining group risk identification with a high Gini coefficient and a leakage resistance characteristic index lower than a mean value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flexible circuit board monitoring, and particularly relates to an online monitoring system for testing FPC micro-pitch PIN short circuit. BACKGROUND

[0002] The field of flexible circuit board monitoring is used for detecting, evaluating and diagnosing the electrical performance, structural integrity and working reliability of a flexible printed circuit (FPC).

[0003] The existing flexible circuit board monitoring technology relies on static insulation resistance measurement, that is, a direct current voltage is applied between adjacent lines and the leakage current is detected, which is limited by its static detection mode. For the incipient micro short circuit in the FPC due to moisture, electrochemical migration or material aging, the fault path often presents characteristics of high impedance, nonlinearity or dynamic change. A static high-impedance test source may not be able to provide sufficient energy to effectively stimulate these implicit fault paths, resulting in a measurement result that is not much different from the actual open circuit state, thereby causing missed detection. For example, a line with a slowly deteriorating insulation resistance from a gigohm level to tens of megohms will be continuously determined to be qualified as long as it does not drop below the hard short circuit threshold of megohms, missing the best opportunity for early warning and maintenance in the early stage of failure. Therefore, improvement is needed. SUMMARY

[0004] The purpose of the present application is to solve the problems existing in the prior art and to provide an online monitoring system for testing FPC micro-pitch PIN short circuit.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: an online monitoring system for testing FPC micro-pitch PIN short circuit comprises: a polarization charge sampling module, configured to inject a nanosecond pulse excitation into adjacent PIN pins according to a micro-pitch FPC line architecture to induce parasitic capacitance polarization, obtain a transient polarization voltage sampling sequence, and operate the transient polarization voltage sampling sequence to generate a charge decay time constant; an attenuation characteristic fitting module, configured to call the charge decay time constant to construct a standard insulation discharge numerical model, calculate a degree of deviation of a measured curve from the standard model, obtain a deviation degree, and perform interval mapping on the deviation degree and a preset insulation impedance reference range to generate a leakage resistance characteristic index; a distribution imbalance degree calculation module, configured to arrange the leakage resistance characteristic indexes corresponding to adjacent PIN pins belonging to a group in ascending order, obtain an impedance characteristic Lorenz distribution trajectory, and calculate a potential decay Gini coefficient according to the impedance characteristic Lorenz distribution trajectory and an absolute average distribution line; A micro-short circuit fault determination module is configured to compare the potential decay Gini coefficient with a set distribution imbalance threshold, obtain a group short circuit risk identifier, associate the group short circuit risk identifier with the leakage resistance characteristic index, locate a micro-short circuit fault point, and generate an FPC micro-gap short circuit monitoring result.

[0006] Preferably, the step of obtaining the charge decay time constant is: According to the micro-gap FPC line architecture, the rising edge width parameter, pulse amplitude parameter, and pulse repetition interval parameter of the nanosecond pulse are configured, adjacent PIN pins are selected as injection objects, a sampling gate is started at the starting time after the excitation source is blocked, the values and time stamps of the potentials at both ends of the PIN pins are recorded at fixed sampling intervals, the overshoot segment at the blocking instant is removed, and a transient polarization voltage sampling sequence is generated. According to the transient polarization voltage sampling sequence, the recorded potential values are sorted by time stamp and non-decay segments are filtered out, an exponential function regression operation is performed to obtain a decay trend, regression residuals are calculated and threshold values are applied to remove discrete points, and the potential values after regression are logarithmically transformed to linearize the decay trend, thereby generating a parameter reflecting the charge release rate. According to the parameter reflecting the charge release rate, the decay slope is converted to a time scale according to a parameter mapping relationship, and a zero offset correction is performed in combination with an intercept term, and the corrected time scale is output as the charge decay time constant.

[0007] Preferably, the step of obtaining the deviation degree is: According to the charge decay time constant, the time stamp range of the transient polarization voltage sampling sequence is read, each time stamp is paired with the corresponding voltage measurement value, a charge decay template that changes with time is established using the charge decay time constant, and the charge decay template is corrected one by one on the time axis with the sampling sequence, thereby generating a standard insulation discharge numerical model. According to the standard insulation discharge numerical model, the deviation degree is calculated.

[0008] Preferably, the step of obtaining the leakage resistance characteristic index is: According to the deviation degree, the upper and lower bounds of the insulation impedance reference range are read, the deviation degree is mapped to the insulation impedance reference interval on a logarithmic scale, the decay deviation amplitude is calculated by the interval difference, and the interval mapping result is output to form the leakage resistance characteristic index.

[0009] Preferably, the step of obtaining the impedance characteristic Lorentz distribution trajectory is: Based on the leakage resistance characteristic index, the leakage resistance characteristic indices corresponding to adjacent pins belonging to the same group are sorted in ascending order. The proportion of each leakage resistance characteristic index in the total leakage resistance characteristic index is calculated. The cumulative value of each proportion and the corresponding sample ratio are recorded as coordinate points to form the cumulative distribution data of leakage resistance characteristic index. Based on the cumulative distribution data of the leakage resistance characteristic index, the sample proportion is used as the horizontal axis and the cumulative proportion of the leakage resistance characteristic index is used as the vertical axis. Adjacent coordinate points are connected sequentially to form a closed curve. The connection line between the starting point and the ending point is added to construct a distribution comparison map, thus obtaining the Lorentz distribution trajectory of the impedance characteristics.

[0010] Preferably, the step of obtaining the potential decay Gini coefficient is as follows: The Gini coefficient of potential decay is calculated based on the Lorentz distribution trajectory of the impedance characteristics.

[0011] Preferably, the step of obtaining the group short-circuit risk identifier is as follows: Based on the voltage decay Gini coefficient, the distribution imbalance threshold is read, and the voltage decay Gini coefficient is compared with the distribution imbalance threshold. If the voltage decay Gini coefficient is greater than or equal to the distribution imbalance threshold, an individual with voltage anomaly is marked. If the voltage decay Gini coefficient is less than the distribution imbalance threshold, an individual with voltage anomaly is marked. A group short-circuit risk identifier is generated.

[0012] Preferably, the steps for obtaining the FPC micro-pitch short-circuit monitoring results are as follows: According to the group short circuit risk identifier, if the group short circuit risk identifier indicates that there is an individual with abnormal voltage, the processing continues. The average value of the leakage resistance characteristic index is calculated, and the leakage resistance characteristic index of each pin is compared with the average value of the leakage resistance characteristic index. Pins with a value less than the average value of the leakage resistance characteristic index are selected and marked as micro short circuit fault points, forming a list of micro short circuit fault points. Based on the micro short-circuit fault point list, the group short-circuit risk identifiers are summarized, and the pin locations and leakage resistance characteristic indices of the micro short-circuit fault point list are integrated and organized into a result message according to a fixed field order to generate FPC micro-pitch short-circuit monitoring results.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by injecting pulse excitation into adjacent pins and capturing the transient polarization voltage decay process, the assessment of the insulation state between pins is elevated from traditional static resistance measurement to dynamic process analysis. This generates a charge decay time constant characterizing the charge release rate, enhancing the ability to detect weak leakage currents caused by early insulation degradation. This time constant is then used to construct a standard insulation discharge model, and the measured decay curve is compared with the model to calculate the leakage resistance characteristic index. This not only normalizes the detection results, facilitating cross-batch and cross-sample comparisons, but also transforms the abstract voltage decay deviation into a mapping relationship with the physical insulation impedance. More importantly, the directly related characteristic indicators are analyzed to determine the overall distribution imbalance of a group of adjacent pins. By constructing the Lorentz distribution trajectory of impedance characteristics and calculating the Gini coefficient of potential decay, abnormal individuals causing sharp imbalances in distribution can be identified from a group perspective. This method is particularly effective for detecting micro short-circuit faults dominated by severe degradation of a single or a few pins. It can filter out common-mode interference caused by environmental temperature drift or power fluctuations, improving the signal-to-noise ratio and accuracy of fault identification. Finally, by combining the group risk identifier with a high Gini coefficient and the leakage resistance characteristic index below the mean, high-precision location of micro short-circuit fault points can be achieved. Attached Figure Description

[0014] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] Please see Figure 1 This invention provides a technical solution: an online monitoring system for testing short circuits in FPC micro-pitch PINs, comprising: The polarization charge sampling module is used to inject nanosecond pulses into adjacent pins to excite parasitic capacitance polarization according to the micro-pitch FPC circuit architecture, obtain transient polarization voltage sampling sequence, and perform calculations on the transient polarization voltage sampling sequence to generate charge decay time constant; The attenuation characteristic fitting module is used to construct a standard insulation discharge numerical model by calling the charge attenuation time constant, calculate the degree of deviation of the measured curve from the standard model, obtain the deviation degree, and perform interval mapping between the deviation degree and the preset insulation impedance reference range to generate the leakage resistance characteristic index. The unevenness calculation module is used to sort the leakage resistance characteristic index of adjacent pins in the same group in ascending order, obtain the Lorentz distribution trajectory of impedance characteristics, and calculate and generate the potential decay Gini coefficient based on the Lorentz distribution trajectory of impedance characteristics and the absolute average distribution line. The micro short-circuit fault determination module is used to compare the potential decay Gini coefficient with the set distribution imbalance threshold, obtain the group short-circuit risk identifier, associate the group short-circuit risk identifier with the leakage resistance characteristic index, locate the micro short-circuit fault point, and generate FPC micro-pitch short-circuit monitoring results.

[0017] The steps for obtaining the charge decay time constant are as follows: Based on the micro-pitch FPC circuit architecture, configure the rising edge width parameter, pulse amplitude parameter, and pulse repetition interval parameter of the nanosecond pulse. Select adjacent pins as injection targets. Start sampling gating at the beginning moment after blocking the excitation source. Record the voltage values ​​and timestamps at both ends of the pins at fixed sampling intervals. Eliminate the overshoot segment at the moment of blocking to generate a transient polarization voltage sampling sequence. Based on the transient polarization voltage sampling sequence, the recorded potential values ​​are sorted by timestamp and non-decaying segments are filtered out. Exponential function regression is performed to obtain the decay trend. The regression residual is calculated and a threshold is applied to remove discrete points. The regression potential values ​​are logarithmically transformed to linearize the decay trend and generate parameters that reflect the charge release rate. Based on the parameters reflecting the charge release rate, the decay slope is converted into a time scale according to the parameter mapping relationship. Zero bias correction is performed by combining the intercept term, and the corrected time scale is output as the charge decay time constant.

[0018] Specifically, based on the micro-pitch FPC circuit architecture, the line width and spacing values ​​of the FPC design are retrieved. The theoretical parasitic capacitance value is calculated based on the circuit characteristic impedance model. The rise edge width parameter of the nanosecond pulse is set to one-tenth of the theoretical parasitic capacitance charging time; for example, if the theoretical charging time is 20 nanoseconds, the rise edge width is set to 2 nanoseconds. The pulse amplitude parameter is set to 50% of the FPC insulation layer breakdown voltage; for example, if the breakdown voltage is 10 volts, the pulse amplitude is set to 5 volts. The pulse repetition interval parameter is set to five times the theoretical discharge time constant to ensure complete charge release; for example, if the theoretical discharge time constant is 100 microseconds, the interval is set to 500 microseconds. The output of the generator is connected to one of the two selected adjacent pins, and the other pin is grounded. Sampling gating is started 1 nanosecond after the excitation source is blocked by the trigger command. The analog voltage across the pin is read at a fixed sampling interval of 0.5 nanoseconds using a high-speed analog-to-digital converter, and the corresponding nanosecond-level timestamp is recorded. For the oscillation overshoot generated at the moment of blocking, the voltage change rate of the first ten sampling points is read. The overshoot judgment threshold is set to ten times the average change rate of the subsequent stable decay segment. The initial sampling segment data with a change rate exceeding the overshoot judgment threshold is removed from the record, and only the smooth data segment with monotonically decreasing voltage is retained to generate a transient polarization voltage sampling sequence.

[0019] Based on the transient polarization voltage sampling sequence, the data points in the sequence are sorted in ascending order according to their timestamp values. The sorted voltage values ​​are iterated through. An effective signal noise floor threshold is set to one-thousandth of the full scale of the sampling system; for example, if the full scale is 5 volts, the threshold is 0.005 volts. Data segments with voltage values ​​below this noise floor threshold are removed to filter out non-attenuated segments. A preliminary exponential function fit is performed on the remaining data segments using the least squares method to obtain the fitted curve. The difference between the voltage value at each original sampling point and the theoretical value at the corresponding time point on the fitted curve is calculated. The absolute value of this difference is defined as the regression residual. The regression residuals of all sampling points are statistically analyzed, and the standard deviation of the residuals is calculated. Set a threshold for removing discrete points. ,in To determine the standard deviation of the regression residuals, point-by-point comparisons are made between the regression residuals and the discrete point removal threshold. Residuals with an absolute value greater than a certain threshold are removed. Abnormal sampling points, and the remaining voltage values ​​after cleaning. Perform the natural logarithmic transformation one by one, and the calculation formula is: ,in This is the transformed logarithmic voltage value. The original voltage values ​​after filtering are used to transform the exponential decay curve into a linear form. This transformation is then applied to the data. Perform linear regression analysis, extract the slope of the regression line, and generate parameters that reflect the charge release rate.

[0020] Based on the parameters reflecting the charge release rate, the slope obtained from the linear regression analysis is read. With intercept Based on the logarithmic form of the physical formula for capacitor discharge, the parameter mapping relationship is defined, and the preliminary time constant is calculated. ,in To reflect the slope parameter of the charge release rate, and taking into account the initial potential deviation introduced by the test fixture and circuitry, zero bias correction is performed using the intercept term, and the reference intercept of the standard calibration piece under fault-free conditions is read. Calculate the intercept deviation Set the correction coefficient The coefficient is 0.05. This coefficient is obtained by comparing the test results of a standard resistor with a known resistance value, and the final result is calculated using a correction formula. The calculation formula is as follows: ,in The corrected timescale. For the preliminary calculation of the time constant, For correction factors, To account for the intercept deviation, this calculation process eliminates the minute time drift caused by initial contact resistance or line inductance, and outputs the corrected time scale as the charge decay time constant.

[0021] The steps to obtain the deviation are as follows: Based on the charge decay time constant, the timestamp range of the transient polarization voltage sampling sequence is read, each timestamp is paired with the corresponding measured voltage value, a potential decay template that varies with time is established using the charge decay time constant, and the potential decay template is corrected one by one with the sampling sequence on the time axis to generate a standard insulation discharge numerical model. The deviation is calculated based on the standard insulation discharge numerical model, using the following formula: ; Where D is the deviation, m is the number of sampling points, and i is the sampling point index, from 1 to m. Let be the potential value of the transient polarization voltage sampling sequence at the i-th time point. Let be the theoretical potential value of the standard insulation discharge numerical model at the i-th time point. The timestamp corresponding to the i-th sampling point. The charge decay time constant is The normalization function is calculated as follows: ,in This represents the maximum potential value in the transient polarization voltage sampling sequence.

[0022] Specifically, based on the charge decay time constant, the transient polarization voltage sampling sequence in the FPC test record is read, and the metadata of the sequence is parsed to obtain the start and end timestamps. The effective coverage range of the time axis is determined, for example, the sampling interval from 0 nanoseconds to 500 nanoseconds. Each discrete time point within this interval is traversed, and the corresponding measured voltage value is read to form a time-voltage data pair. The previously calculated charge decay time constant is then used to determine the voltage range. Construct the expression for the exponentially decaying function under ideal conditions, i.e. ,in The peak voltage in the sampling sequence is set as the potential decay template. A set of theoretical voltage values ​​that continuously change with time is generated. A cross-correlation algorithm is used to calculate the optimal matching position between the potential decay template and the measured sampling sequence on the time axis. The potential decay template is then translated on the time axis so that they are aligned at the first peak point after the rising edge ends, with the alignment error controlled within 0.5 nanoseconds. For each sampling time point, the corresponding theoretical voltage value is extracted from the aligned potential decay template. If the sampling time does not fall on a discrete point defined by the template, linear interpolation is used to calculate the theoretical value at that moment. The calculation formula is as follows: ,in This is the current timestamp. The theoretical voltage to be determined is... and For each measured voltage point, a standard theoretical reference value is associated with two adjacent known points in the template. This removes high-frequency jitter components caused by environmental noise, retains the smooth attenuation trend, integrates the theoretical voltage values ​​at all time points, and generates a standard insulation discharge numerical model.

[0023] In the deviation calculation formula, the weighted average deviation calculation with time weight is introduced to focus on the voltage matching degree in the early stage of discharge, because in micro-spacing short circuit faults, the early charge leakage behavior has the most significant impact on voltage decay, thereby improving the detection sensitivity of micro short circuit defects. parameter The acquisition steps are as follows: count the total number of valid data points in the transient polarization voltage sampling sequence. For example, in a complete discharge test cycle, if the sampling frequency is 2GS / s and the duration is 500ns, then the number of sampling points acquired is... 1000; parameter The steps to obtain it are as follows: establish a system starting from 1 and incrementing to... An integer index sequence is used to identify the position of each sampling point in the sequence, for example... ; parameter The acquisition steps are as follows: directly read the first value from the transient polarization voltage sampling sequence. The measured voltage value corresponding to each timestamp is in volts (V). For example, the voltage value measured at the 50th point is 4.5V. parameter The steps to obtain the value are as follows: extract the first value from the standard insulation discharge numerical model. The theoretical voltage value corresponding to each timestamp is calculated based on an ideal exponential decay curve and is expressed in volts (V). For example, the theoretical value corresponding to the 50th point is 4.8V. parameter The steps to obtain it are as follows: read the first... The relative time of each sampling point with respect to the trigger start time is in nanoseconds (ns) and is recorded by a high-precision timer. For example, the timestamp of the 50th point is 25 ns. parameter The steps to obtain it are as follows: Call the charge decay time constant, which is output in the previous step after logarithmic regression analysis and zero bias correction. The unit is nanoseconds (ns). For example, the calculated... The value is 150 ns; parameter The steps to obtain the voltage value are as follows: traverse the entire transient polarization voltage sampling sequence, compare the voltage values ​​of all sampling points, and filter out the maximum value, for example, the peak voltage in the sequence is 5.0V. Calculations based on parameters: Three key sampling points are selected for a computational example demonstration, and the following settings are provided. , .

[0024] Point 1: , , .

[0025] Point 2: , , (ideal attenuation value) ).

[0026] Point 3: , , (ideal attenuation value) ).

[0027] Calculate the normalization function : , .

[0028] , .

[0029] , .

[0030] Calculate the weight term : .

[0031] .

[0032] .

[0033] Calculate the squared term of the deviation : .

[0034] .

[0035] .

[0036] Calculate the cumulative sum of the numerators: .

[0037] .

[0038] Calculate the sum of the denominators: . .

[0039] Calculate deviation : .

[0040] The results indicate that the measured voltage curve agrees well with the standard insulation model, and the calculated deviation is within acceptable limits. The value is 0.00173. The closer the value is to 0, the better the insulation performance matches the theoretical model. A significant increase in the value, such as exceeding 0.05, indicates the presence of a clear leakage path leading to abnormally accelerated voltage decay. The deviation is then calculated based on the standard insulation discharge numerical model.

[0041] The steps for obtaining the characteristic index of leakage resistance are as follows: Based on the deviation, the upper and lower boundaries of the insulation impedance reference range are read, and the deviation is mapped to the insulation impedance reference range on a logarithmic scale. The attenuation deviation magnitude is calculated through the interval difference, and the interval mapping result is output to form the leakage resistance characteristic index.

[0042] Specifically, based on the degree of deviation, consult the technical specifications of the FPC product to obtain the acceptable standard for insulation resistance, and set the lower limit of the insulation resistance reference range. for Ohm (i.e., 1M) ), set upper bound for Ohm (i.e. 10G) These two boundary values ​​are determined based on the IPC-TM-650 test standard and the characteristics of the product's insulation material. A mapping model from deviation to insulation impedance is constructed. Considering that the variation in insulation impedance usually spans multiple orders of magnitude, a logarithmic linear mapping relationship is adopted, and a maximum allowable deviation is set. The minimum deviation is set to 0.1 (an empirical value derived from the statistical distribution of historical fault data; a deviation greater than 0.1 usually corresponds to a short circuit). The value is 0.0001 (corresponding to the ideal insulation state). The current equivalent impedance value is calculated using the logarithmic interpolation formula, which is as follows: ,in The deviation calculated so far, if Less than Then take directly ,like Greater than Then take directly The calculated equivalent impedance value Compare the impedance value with the reference range of insulation resistance and calculate the percentage of its relative position within the reference range. For example, if for Ohms, in the middle position, is used as the percentage of the relative position as the quantitative indicator of the attenuation deviation. Finally, the quantitative indicator is converted into a value between 0 and 100. The lower the value, the higher the insulation resistance, and the higher the value, the greater the leakage risk. The output range mapping result is used to form the leakage resistance characteristic index.

[0043] The steps for obtaining the Lorentz distribution trajectory of impedance characteristics are as follows: Based on the leakage resistance characteristic index, the leakage resistance characteristic indices of adjacent pins belonging to the same group are sorted in ascending order. The proportion of each leakage resistance characteristic index in the total leakage resistance characteristic index is calculated. The cumulative value of each proportion and the corresponding sample proportion are recorded as coordinate points to form the cumulative distribution data of leakage resistance characteristic index. Based on the cumulative distribution data of the leakage resistance characteristic index, the sample proportion is used as the horizontal axis and the cumulative proportion of the leakage resistance characteristic index is used as the vertical axis. Adjacent coordinate points are connected sequentially to form a closed curve. The connection line between the starting point and the ending point is added to construct a distribution comparison map, thus obtaining the Lorentz distribution trajectory of the impedance characteristics.

[0044] Specifically, based on the leakage resistance characteristic index, the FPC line grouping configuration information defined in this test task is read, and the physical numbers of adjacent pins belonging to the same group are identified. For example, pins 1 to 50 at the flexible circuit board interface are divided into an independent monitoring group. The leakage resistance characteristic index of each pin in this group, calculated in the previous step, is retrieved in batches from the real-time database. A data cleaning program is executed to remove null values ​​or invalid data marked as fault codes caused by poor probe contact. A fast sorting algorithm is called to sort the valid leakage resistance characteristic indices in the group in ascending order, so that normal insulation samples with smaller values ​​are placed at the beginning of the sequence, and potential leakage samples with larger values ​​are placed at the end of the sequence. The sorted index list is traversed, and the leakage resistance characteristic indices of all pins are accumulated to obtain the value. Take the sum of the overall features. For example, if there are 50 valid samples in the group, add the values ​​of these 50 specific feature indices to get a total value. Then, iterate through the sorted list again, divide the feature index of each pin by the sum of the overall features, and calculate the relative contribution of each pin to the overall leakage risk. Create a double-precision floating-point array to store the cumulative value. Starting from the first data after sorting, add the current percentage value to the cumulative result of the previous item. At the same time, generate the corresponding sample proportion sequence, which is the current position index value divided by the total number of pins in the group. Pair the calculated cumulative proportion of leakage resistance feature index with the corresponding cumulative proportion of samples to ensure that each pair of data accurately corresponds to the sorted logical position, forming the cumulative distribution data of leakage resistance feature index.

[0045] Based on the cumulative distribution data of the leakage resistance characteristic index, a standardized two-dimensional rectangular coordinate system is constructed to describe the unevenness of the impedance distribution. The horizontal axis is defined as the cumulative proportion axis of the samples, with a range of 0 to 1. The vertical axis is defined as the cumulative proportion axis of the leakage resistance characteristic index, with a range of 0 to 1 as well. The origin (0,0) is marked in the coordinate system as the starting reference point of the distribution curve. This point represents zero samples contributing zero characteristic values. Each previously generated two-dimensional data pair is read, and the corresponding data points are plotted one by one in the coordinate system according to the order of the sample proportion from smallest to largest. For example, when the horizontal axis is 0.2, the vertical axis may only be 0.05, which reflects the order of the samples. The contribution rate of the low impedance characteristic samples in the overall risk is significantly lower than their quantity proportion. The cubic spline interpolation algorithm is used to connect adjacent coordinate points in sequence to smooth the transition and eliminate the sense of broken lines caused by discrete sampling, forming a closed curve that extends to the upper right and has a concave shape. Finally, the theoretical absolute average distribution line (i.e., the diagonal of y=x) between the last data point (1,1) and the starting point (0,0) is used as a reference. The actual curve trajectory is compared with the reference line in spatial geometry to define the enclosing area between the two. The shape and area of ​​this area directly reflect the degree of dispersion of impedance distribution within the group, thus obtaining the Lorentz distribution trajectory of impedance characteristics.

[0046] The steps for obtaining the potential decay Gini coefficient are as follows: Based on the Lorentz distribution trajectory of the impedance characteristic, the Gini coefficient of potential decay is calculated using the following formula: ; in, Here, h is the Gini coefficient for potential decay, h is the number of pins in the same group, and k is the index of the k-th pin. The cumulative proportion of the k-th sample. This represents the cumulative proportion of the characteristic index of the k-th leakage resistance. The leakage resistance characteristic index is the value corresponding to the j-th pin. This is the fault amplification factor, used to nonlinearly amplify the influence of high-order samples.

[0047] Specifically, in the formula for calculating the potential decay Gini coefficient, the above formula makes a nonlinear correction to the traditional Gini coefficient calculation by introducing a power function term with position weight. It focuses on amplifying the difference effect at the tail of the distribution curve or in a specific interval, so that when there is a small local short circuit in the FPC line that causes a slight skew in the impedance distribution, this imbalance feature can be significantly amplified by the coefficient combination, thereby improving the sensitivity of early micro short circuit fault identification. parameter The steps to obtain this number are as follows: directly read the number of pins in the same group defined in the test configuration file. This value is determined by the hardware connection relationship and the total number of pins in the FPC design, and is a dimensionless integer. For example, in the test of a precision flexible circuit board, the number of pins in the monitoring group of a power strip is set to 5, i.e. ; parameter The steps to obtain it are as follows: Establish a loop index variable whose value ranges from 1 to... This is used to identify the specific position of each sorted sample point in the sequence. It is a dimensionless integer, for example, in the group of 5 pins mentioned above. The values ​​are 1, 2, 3, 4, and 5 in sequence. parameter The steps for obtaining these values ​​are as follows: First, retrieve the sorted values ​​from the list of leakage resistance characteristic indices generated in the previous steps. These values ​​are dimensionless indices between 0 and 100, obtained based on deviation mapping. Higher values ​​indicate a higher leakage risk. For example, the sorted sequence is... , where 80 represents the existence of an outlier; parameter The steps to obtain it are as follows: calculate the first... The cumulative proportion of each sample is calculated using the formula. This directly indicates that the first [part of] the sorted sequence [is] the [first part of] the [second part of] the [third part]. The proportion of a sample to the total sample size, ranging from 0 to 1, is dimensionless. For example, when... hour, ; parameter The steps to obtain it are as follows: calculate the first... The cumulative proportion of each leakage resistance characteristic index is first determined by the sorted characteristic index sequence. The former Summing the terms, we get Then calculate the sum of the entire sequence. Finally, divide the former by the latter to get This value represents the previous The proportion of the total feature value contributed by each sample to the whole, ranging from 0 to 1, is dimensionless. parameter The steps for obtaining the value are as follows: Based on the balance curve between fault detection rate and false alarm rate, a fault amplification factor is set; micro-short-circuit defects with different resistance values ​​are artificially introduced onto a standard FPC sample; the distribution range of the conventional Gini coefficient is recorded; and adjustments are made. The value is set to maximize the distinguishability between faulty and normal samples. ; Calculations based on parameters: Example setting: , .

[0048] Feature index sequence (Simulating 4 normal circuits and 1 micro-short circuit).

[0049] Calculate the sum: .

[0050] Calculate each point and : : , .

[0051] : , .

[0052] : , .

[0053] : , .

[0054] : , .

[0055] Calculate the weight term : .

[0056] .

[0057] .

[0058] .

[0059] .

[0060] : : .

[0061] : .

[0062] : .

[0063] : .

[0064] : .

[0065] Total of numerators .

[0066] Calculate the terms in the denominator : : .

[0067] : .

[0068] : .

[0069] : .

[0070] : .

[0071] Sum of denominators .

[0072] calculate : .

[0073] The results show that the calculated potential decay Gini coefficient is 0.369, which is significantly higher than the normal uniform distribution (close to 0). This clearly reflects the existence of a few PINs with abnormally high values ​​(i.e. micro-short circuit points) within the group, verifying the high sensitivity of the coefficient in sparse fault scenarios. The potential decay Gini coefficient is calculated based on the Lorentz distribution trajectory of the impedance characteristics.

[0074] The steps to obtain the group short circuit risk indicator are as follows: Based on the potential decay Gini coefficient, the distribution imbalance threshold is read, and the potential decay Gini coefficient is compared with the distribution imbalance threshold. If the potential decay Gini coefficient is greater than or equal to the distribution imbalance threshold, an individual with abnormal voltage is marked. If the potential decay Gini coefficient is less than the distribution imbalance threshold, an individual with abnormal voltage is marked. A group short circuit risk identifier is generated.

[0075] Specifically, based on the potential decay Gini coefficient, the currently calculated Gini coefficient value is read from memory. This value reflects the degree of impedance imbalance within the current pin group. A preset parameter configuration file is used to obtain the imbalance threshold. The threshold was set based on statistical analysis of a large number of qualified FPC products. Specifically, the process involved selecting 1,000 rigorously verified "gold sample" FPC boards without short-circuit faults for testing, calculating the potential decay Gini coefficient of each gold sample to form a benchmark dataset, and then calculating the average value of this dataset. (e.g., 0.05) and standard deviation (e.g., 0.01), based on the normal distribution 3 Based on principles and combined with the stringent requirements for false alarm rates in industrial settings, the following settings were established. Calculation This setting ensures that impedance differences under normal process fluctuations will not trigger alarms, and will be calculated in real time. (For example, 0.369 obtained from the previous steps) and the set Perform numerical comparison, and the judgment logic is as follows: This indicates the presence of a significant outlier within the group, namely an abnormal pin with extremely rapid or slow charge decay. In this case, a state Boolean variable is instantiated in memory and assigned the value True, representing the presence of an individual with an abnormal voltage within the group. If the value is false, it indicates that the attenuation characteristics of each pin in the group are highly consistent. The state variable is assigned the value False, which means that there are no individual with abnormal voltage. This judgment process is completed in a nanosecond-level processing cycle and does not involve disk I / O operations. Finally, the state variable is output as a group short circuit risk indicator.

[0076] The steps for obtaining FPC micro-pitch short-circuit monitoring results are as follows: Based on the group short circuit risk indicator, if the group short circuit risk indicator indicates the presence of an individual with abnormal voltage, the process continues. The average value of the leakage resistance characteristic index is calculated, and the leakage resistance characteristic index of each pin is compared with the average value of the leakage resistance characteristic index. Pins with leakage resistance characteristic index values ​​less than the average value of the leakage resistance characteristic index are selected and marked as micro short circuit fault points, thus forming a list of micro short circuit fault points. Based on the list of micro short-circuit fault points, the group short-circuit risk identifiers are summarized, and the pin locations and leakage resistance characteristic indices of the micro short-circuit fault point list are integrated and organized into a result message according to a fixed field order to generate FPC micro-pitch short-circuit monitoring results.

[0077] Specifically, based on the group short-circuit risk indicator, the state variables in memory are checked. If the indicator is false, subsequent fault location steps are skipped to save computing power. If the indicator is true, the anomaly screening procedure is immediately started, retrieving the leakage resistance characteristic index sequence of all pins in the same group. For example, the sequence is... (Here, for example, the exponent is positively correlated with impedance, meaning the lower the value, the lower the impedance and the greater the risk of short circuit, in accordance with the logic of "a value less than the mean indicates a fault"), iterate through the sequence and accumulate all values, then divide the sum by the total number of pins. (For example, 5), the mean value of the characteristic index of leakage resistance is calculated. The calculation process is as follows Then, for each feature index in the sequence... Perform iterative access, for each With the calculated mean The comparison is based on the following conditions: In this example, the first four values ​​are all greater than 78, which is considered normal. The fifth value, 10, is less than 78, which is considered abnormal. The program captures the physical location index of the abnormal point (e.g., PIN 5) and its corresponding characteristic index value, and constructs a dynamic linked list or array structure in memory. The information of the selected abnormal PIN is added to the structure as a node. The recorded content includes the PIN number, the measured index value, and the drop relative to the mean. After traversing all PINs in the group, the dynamic structure only contains those nodes that are significantly lower than the average insulation level. These nodes are then fixed and output to form a list of micro short-circuit fault points.

[0078] Based on the list of micro-short-circuit fault points, a standardized data structure object is initialized to encapsulate the final monitoring results. First, the task ID and timestamp of the current test are written to the header field of the object. Then, the group short-circuit risk identifier is read, converted into a single-byte status code (e.g., 0x01 represents risk, 0x00 represents no risk), and written to the status field. Next, it checks if the list of micro-short-circuit fault points is empty. If not empty, the number of fault points in the list is counted and written to the counter field. Each fault node in the list is traversed, and the physical location coordinates of the pin (e.g., connector number and pin number) and the corresponding leakage resistance characteristic index are extracted. The data is serialized into a binary stream or JSON format key-value pairs according to a predefined communication protocol format (e.g., [PIN_ID: 2Bytes, Index_Value: 4Bytes]), for example, generating {"PinID": 5, "Value":}. 10} These detailed fault data blocks are appended sequentially to the data payload area of ​​the result object. Finally, the cyclic redundancy check code (CRC32) of the entire data packet is calculated and appended to the end of the message to ensure transmission integrity. This process is entirely performed in the processor's cache, without generating temporary files. Finally, the assembled data object is pushed to the host computer communication interface or human-machine interface buffer to generate the FPC micro-pitch short circuit monitoring result.

Claims

1. An online monitoring system for testing short circuits in FPC micro-pitch PINs, characterized in that, The system includes: The polarization charge sampling module is used to inject nanosecond pulses into adjacent pins to excite parasitic capacitance polarization according to the micro-pitch FPC circuit architecture, obtain transient polarization voltage sampling sequence, and perform calculations on the transient polarization voltage sampling sequence to generate charge decay time constant; The attenuation characteristic fitting module is used to call the charge attenuation time constant to construct a standard insulation discharge numerical model, calculate the degree of deviation of the measured curve from the standard model, obtain the deviation degree, and perform interval mapping between the deviation degree and the preset insulation impedance reference range to generate the leakage resistance characteristic index. The distribution imbalance calculation module is used to sort the leakage resistance characteristic indexes of adjacent pins belonging to the same group in ascending order, obtain the impedance characteristic Lorentz distribution trajectory, and calculate and generate the potential decay Gini coefficient based on the impedance characteristic Lorentz distribution trajectory and the absolute average distribution line. The micro short-circuit fault determination module is used to compare the potential decay Gini coefficient with a set distribution imbalance threshold, obtain a group short-circuit risk identifier, associate the group short-circuit risk identifier with the leakage resistance characteristic index, locate the micro short-circuit fault point, and generate FPC micro-pitch short-circuit monitoring results.

2. The online monitoring system for testing short circuits in FPC micro-pitch PINs according to claim 1, characterized in that, The steps for obtaining the charge decay time constant are as follows: Based on the micro-pitch FPC circuit architecture, configure the rising edge width parameter, pulse amplitude parameter, and pulse repetition interval parameter of the nanosecond pulse. Select adjacent pins as injection targets. Start sampling gating at the beginning moment after blocking the excitation source. Record the voltage values ​​and timestamps at both ends of the pins at fixed sampling intervals. Eliminate the overshoot segment at the moment of blocking to generate a transient polarization voltage sampling sequence. Based on the transient polarization voltage sampling sequence, the recorded potential values ​​are sorted by timestamp and non-attenuated segments are filtered out. An exponential function regression operation is performed to obtain the attenuation trend. The regression residual is calculated and a threshold is applied to remove discrete points. The logarithmic transformation is performed on the regressed potential values ​​to linearize the attenuation trend and generate parameters that reflect the charge release rate. Based on the parameters reflecting the charge release rate, the decay slope is converted into a time scale according to the parameter mapping relationship. Zero bias correction is performed by combining the intercept term, and the corrected time scale is output as the charge decay time constant.

3. The online monitoring system for testing short circuits in FPC micro-pitch PINs according to claim 1, characterized in that, The steps for obtaining the deviation are as follows: Based on the charge decay time constant, the timestamp range of the transient polarization voltage sampling sequence is read, each timestamp is paired with the corresponding measured voltage value, a potential decay template that varies with time is established using the charge decay time constant, and the potential decay template is corrected one by one with the sampling sequence on the time axis to generate a standard insulation discharge numerical model. The deviation is calculated based on the standard insulation discharge numerical model.

4. The online monitoring system for testing short circuits in FPC micro-pitch PINs according to claim 1, characterized in that, The steps for obtaining the leakage resistance characteristic index are as follows: Based on the deviation, the upper and lower boundaries of the insulation impedance reference range are read, and the deviation is mapped to the insulation impedance reference range on a logarithmic scale. The attenuation deviation amplitude is calculated through the interval difference, and the interval mapping result is output to form the leakage resistance characteristic index.

5. The online monitoring system for testing short circuits in FPC micro-pitch PINs according to claim 1, characterized in that, The steps for obtaining the Lorentz distribution trajectory of the impedance characteristic are as follows: Based on the leakage resistance characteristic index, the leakage resistance characteristic indices corresponding to adjacent pins belonging to the same group are sorted in ascending order. The proportion of each leakage resistance characteristic index in the total leakage resistance characteristic index is calculated. The cumulative value of each proportion and the corresponding sample ratio are recorded as coordinate points to form the cumulative distribution data of leakage resistance characteristic index. Based on the cumulative distribution data of the leakage resistance characteristic index, the sample proportion is used as the horizontal axis and the cumulative proportion of the leakage resistance characteristic index is used as the vertical axis. Adjacent coordinate points are connected sequentially to form a closed curve. The connection line between the starting point and the ending point is added to construct a distribution comparison map, thus obtaining the Lorentz distribution trajectory of the impedance characteristics.

6. The online monitoring system for testing short circuits in FPC micro-pitch PINs according to claim 1, characterized in that, The steps for obtaining the potential decay Gini coefficient are as follows: The Gini coefficient of potential decay is calculated based on the Lorentz distribution trajectory of the impedance characteristics.

7. The online monitoring system for testing short circuits in FPC micro-pitch PINs according to claim 1, characterized in that, The steps for obtaining the group short-circuit risk identifier are as follows: Based on the voltage decay Gini coefficient, the distribution imbalance threshold is read, and the voltage decay Gini coefficient is compared with the distribution imbalance threshold. If the voltage decay Gini coefficient is greater than or equal to the distribution imbalance threshold, an individual with voltage anomaly is marked. If the voltage decay Gini coefficient is less than the distribution imbalance threshold, an individual with voltage anomaly is marked. A group short-circuit risk identifier is generated.

8. The online monitoring system for testing short circuits in FPC micro-pitch PINs according to claim 1, characterized in that, The steps for obtaining the FPC micro-pitch short-circuit monitoring results are as follows: According to the group short circuit risk identifier, if the group short circuit risk identifier indicates that there is an individual with abnormal voltage, the processing continues. The average value of the leakage resistance characteristic index is calculated, and the leakage resistance characteristic index of each pin is compared with the average value of the leakage resistance characteristic index. Pins with a value less than the average value of the leakage resistance characteristic index are selected and marked as micro short circuit fault points, forming a list of micro short circuit fault points. Based on the micro short-circuit fault point list, the group short-circuit risk identifiers are summarized, and the pin locations and leakage resistance characteristic indices of the micro short-circuit fault point list are integrated and organized into a result message according to a fixed field order to generate FPC micro-pitch short-circuit monitoring results.

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