Connector performance detection method

Abstract

The invention relates to the technical field of electronic connector performance monitoring, in particular to a connector performance detection method, and aims to solve the problem that a method for realizing performance early warning through dynamic correlation analysis of vibration signals and contact resistance in the prior art is not suitable for an L-shaped pin connector without a sheath. According to the method, micro-strain signals and contact resistance signals are synchronously collected, analysis time periods are divided in combination with plugging events, the plastic deformation accumulation degree and signal response delay characteristics are quantitatively evaluated, performance degradation coefficients are generated through fusion, and trend analysis is carried out; the problem that the relevance between the mechanical deformation and the electrical performance cannot be quantitatively evaluated in the prior art is effectively solved, and the method has the advantages of realizing early warning, improving the monitoring accuracy and reducing the maintenance cost.

Description

Technical Field

[0001] This invention relates to the field of electronic connector performance monitoring technology, and specifically to a connector performance testing method. Background Technology

[0002] Existing L-type pin connectors are prone to mechanical and electrical performance degradation during long-term use, leading to connector failure. Specifically, due to the special structure of the L-type pins, repeated insertion and removal cause stress concentration at the pin root, the L-shaped structure's stress point, resulting in accumulated deformation. This leads to contact surface misalignment and ultimately affects the connector's electrical performance.

[0003] In existing technologies, the method of performance early warning through dynamic correlation analysis of vibration signals and contact resistance is not applicable to L-type pin connectors without sheaths. This is because connectors without sheaths lack obvious vibration characteristics, and vibration analysis methods cannot directly reflect the coupling relationship between the mechanical deformation of the pins and contact stability. Therefore, a new method is needed to effectively monitor and provide early warning of performance degradation in L-type pin connectors. Summary of the Invention

[0004] (a) Technical problems to be solved To address the aforementioned issues, this invention proposes a connector performance testing method. This method aims to solve the problem that existing technologies that rely on dynamic correlation analysis of vibration signals and contact resistance to provide performance early warnings are not suitable for L-shaped pin connectors without sheaths.

[0005] (II) Technical Solution The present invention provides a connector performance testing method, the technical solution of which is as follows: Strain gauges are used in the stress concentration area at the root of the pin to simultaneously collect micro-strain signals of the pin and contact resistance signals of the connector. The insertion and removal actions of the connector are detected and the time points of the insertion and removal events are recorded. Independent analysis periods are divided with the insertion and removal events as the dividing points. Perform the first and second operations within each independent analysis period: The first operation involves calculating deformation characteristic values ​​that reflect the degree of cumulative plastic deformation of the insert based on micro-strain signals; The second operation involves calculating the cross-correlation function between the micro-strain signal and the contact resistance signal, and extracting the time shift corresponding to the maximum peak value as the hysteresis characteristic value. The deformation eigenvalues ​​and hysteresis eigenvalues ​​are fused to generate the performance degradation coefficient; A trend separation process is performed on a continuous sequence of performance degradation coefficients, and a performance warning is triggered based on the degree of deviation between the separated trend component and the current coefficient, as well as the rate of deterioration of the trend component.

[0006] Furthermore, this application also proposes that the calculation process for the deformation eigenvalues ​​includes: After extracting the insertion and removal event, the micro-strain data sequence within a set time window is calculated. The squared difference between the strain value at each sampling point and the material yield strain threshold is calculated, and the squared difference is time-weighted and accumulated within the time window to obtain the plastic deformation energy value.

[0007] Furthermore, this application also proposes that the process for obtaining the hysteresis characteristic value includes: The similarity matching calculation is performed between the micro-strain signal and the contact resistance signal after time shift to determine the time shift that maximizes the similarity between the two signals, and the time shift is used as the hysteresis characteristic value reflecting the degree of response delay.

[0008] Furthermore, this application also proposes that the generation of the performance degradation coefficient satisfies the following relationship: The performance degradation coefficient increases monotonically with the increase of the deformation eigenvalue; The performance degradation coefficient increases monotonically with the increase of the hysteresis characteristic value.

[0009] Furthermore, this application proposes that the specific method for generating the performance degradation coefficient is as follows: Logarithmic scaling is applied to the deformation eigenvalues. Normalize by dividing the lag characteristic value by the preset observation duration; Multiply the scaled deformation eigenvalues ​​by the normalized hysteresis eigenvalues.

[0010] Furthermore, this application also proposes that the trend separation processing employs a filtering algorithm that decomposes the time series into long-term trend components and short-term fluctuation components; The warning trigger conditions are met: The absolute deviation between the current performance degradation coefficient and the trend component exceeds the first threshold; The rate of change of the trend component per unit time exceeds the second threshold; The warning trigger condition is that the weighted sum exceeds a threshold.

[0011] Furthermore, this application also proposes integrating a temperature sensor next to the strain gauge and introducing a temperature compensation mechanism: The material yield strain threshold is dynamically adjusted based on real-time temperature. The deformation characteristic values ​​are calibrated using a temperature-dependent correction factor.

[0012] Furthermore, this application also proposes that the micro-strain signal and the contact resistance signal be acquired using a sampling frequency of ≥1kHz; The two signals are synchronized in time through a hardware synchronization clock.

[0013] Furthermore, this application also proposes a computing device comprising: At least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the above-described method.

[0014] Furthermore, this application also proposes a non-transitory machine-readable storage medium storing executable instructions that, when executed, cause the machine to perform the above-described method.

[0015] (III) Beneficial Effects Compared with the prior art, the beneficial effects of the present invention are as follows: (1) In this invention, by synchronously acquiring micro-strain signals and contact resistance signals, and combining the insertion and removal events to divide the analysis period, the degree of plastic deformation accumulation and signal response delay characteristics are quantitatively evaluated, and the performance degradation coefficient is generated and trend analysis is performed. This effectively solves the problem that the existing technology cannot quantitatively evaluate the correlation between mechanical deformation and electrical performance, and has the advantages of achieving early warning, improving monitoring accuracy and reducing maintenance costs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic diagram of the overall logic structure of the connector performance testing method; Figure 2 This is a schematic diagram of the waveforms of the micro-strain signal and the contact resistance signal; Figure 3 A waveform diagram of the characteristic values ​​of plastic deformation; Figure 4 A schematic diagram of the frame structure of the device.

[0018] 1. Processor, 2. Memory, 3. Communication interface, 4. Communication bus. Detailed Implementation

[0019] L-type pin connectors, lacking a sheath structure, present challenges in monitoring their mechanical properties. Traditional vibration analysis methods cannot capture the deformation characteristics of the stress concentration area at the pin root. The cumulative plastic deformation caused by repeated insertion and removal has a coupling effect with changes in contact resistance, but current technologies lack monitoring methods specifically for sheathless structures, making it difficult to establish a correlation model between mechanical deformation and electrical performance degradation. For example, in the field of smart home appliances, power connectors that are frequently inserted and removed are prone to poor contact due to fatigue fracture at the pin root, and traditional contact resistance detection cannot provide early warning during the deformation accumulation stage.

[0020] The inventors discovered that plastic deformation in the stress concentration zone at the base of the insertion pin is the root cause of performance degradation, but existing strain monitoring methods do not correlate it with insertion and removal actions. Experiments revealed a time lag between changes in contact resistance and mechanical deformation, providing a breakthrough for establishing a coupled model. Further research showed that dividing the time into independent periods based on insertion and removal events can eliminate interference from historical data, while cross-correlation analysis can effectively quantify the temporal relationship between deformation and resistance. Ultimately, a complete monitoring scheme was developed, incorporating synchronous acquisition of strain-resistance signals, time period segmentation, feature fusion, and trend analysis.

[0021] Example 1

[0022] like Figures 1-3 As shown, this application proposes a connector performance testing method, which specifically includes the following steps: S100. Use strain gauges in the stress concentration area at the root of the pin to simultaneously collect the micro-strain signal of the pin and the contact resistance signal of the connector. S200: Detect the insertion and removal actions of the connector and record the time point when the insertion and removal events occur, and divide the independent analysis period into the dividing point of the insertion and removal events; S300, Perform the first and second operations within each independent analysis period: The first operation involves calculating deformation characteristic values ​​that reflect the degree of cumulative plastic deformation of the insert based on the micro-strain signal; The second operation involves calculating the cross-correlation function between the micro-strain signal and the contact resistance signal, and extracting the time shift corresponding to the maximum peak value as the hysteresis characteristic value. S400, The deformation characteristic value and the hysteresis characteristic value are fused to generate a performance degradation coefficient; S500 performs trend separation processing on the continuous performance degradation coefficient sequence, and triggers performance warnings based on the degree of deviation between the separated trend component and the current coefficient and the deterioration rate of the trend component.

[0023] The stress concentration area at the root refers to the inner surface of the L-shaped bend of the pin, where the maximum bending stress is experienced. The specific location of the patch can be determined using finite element stress analysis to capture the initiation point of plastic deformation. The insertion / removal event boundary is determined by detecting sudden changes in plug displacement signals, for example, by using a Hall effect sensor to monitor the plug's positioning, thus achieving precise segmentation of action events.

[0024] Deformation eigenvalues ​​are obtained by summing the squared deviations of the strain signal from the material's yield threshold, for example, using a time-weighted accumulation algorithm to quantify the irreversible accumulation of plastic deformation. Hysteresis eigenvalues ​​are calculated by the peak offset of the cross-correlation function, for example, using a sliding window cross-correlation algorithm to capture the time delay from strain signal change to resistance response.

[0025] The performance degradation coefficient fusion employs nonlinear operations, such as multiplying the logarithmically scaled deformation eigenvalues ​​by the normalized hysteresis eigenvalues, to enhance sensitivity to abnormal operating conditions. Trend separation processing uses time series decomposition algorithms, such as Hodrick-Prescott filtering, to separate long-term trends from short-term fluctuation components.

[0026] Strain gauges are attached to the inner curved surface of the insertion pin root to collect micro-strain signals in real time, reflecting the material's deformation state. When the insertion / removal action is triggered, a Hall sensor records the event timestamp, dividing the data between two adjacent insertion / removal events into independent analysis periods. Within each period, micro-strain data of the stable phase after insertion / removal are first extracted, and the squared difference between the strain value at each sampling point and the material's yield threshold is calculated. This difference is then weighted and accumulated using time-exponential decay to obtain the deformation characteristic value, which increases with the accumulation of plastic deformation.

[0027] Simultaneously, cross-correlation calculations are performed on the micro-strain signal and the contact resistance signal. A sliding time window is used to find the time offset corresponding to the maximum similarity as the hysteresis feature value. The two feature values ​​are multiplied after logarithmic scaling and normalization to generate a coefficient that comprehensively reflects the degradation of mechanical and electrical performance. Trend decomposition is performed on the coefficient sequence over multiple consecutive time periods to extract the long-term trend component. When the absolute deviation between the current coefficient and the trend value exceeds a threshold and the rate of trend change continues to rise, an early warning signal is triggered.

[0028] Compared to existing technologies, traditional methods rely on vibration signal analysis to determine the loosening state of sheathed connectors, which is not applicable to L-shaped pins without sheaths. This solution directly monitors the micro-strain signal in the stress concentration zone at the root, and combined with the hysteresis response analysis of contact resistance, establishes a direct correlation model between deformation accumulation and contact performance degradation.

[0029] Existing contact resistance threshold methods can only detect the final stage of contact failure, while the performance degradation coefficient fusion mechanism of this solution can identify anomalies in the early stages of plastic deformation, enabling early warning. Furthermore, the time-segmented processing based on insertion / removal events avoids interference from historical data and more accurately reflects the impact of a single insertion / removal action compared to continuous time series analysis.

[0030] This application enables real-time monitoring of the cumulative mechanical deformation process of sheathless L-type pin connectors, accurately quantifying the coupling relationship between deformation and contact resistance changes. In smart home appliance power connector scenarios, it can provide early warning when pins exhibit invisible bending deformation, preventing equipment failures caused by poor contact. This method effectively reduces the false alarm rate by separating long-term degradation trends from short-term interference signals, while simultaneously enhancing the detection sensitivity for early performance degradation through nonlinear feature fusion.

[0031] This application further proposes a technical solution to use strain gauges in the stress concentration area at the root of the pin, simultaneously acquire the micro-strain signal of the pin and the contact resistance signal of the connector, detect the insertion and removal actions of the connector and record the time point of the insertion and removal event, divide the independent analysis period into independent analysis periods with the insertion and removal event as the dividing point, and perform the first operation and the second operation in each independent analysis period.

[0032] The setting of the time window refers to the data segment of a fixed duration that is extracted after the insertion / removal event ends. Specifically, a time window of 5 seconds can be used. This window is used to isolate the deformation process caused by a single insertion / removal action and eliminate interference from historical cumulative effects.

[0033] The difference square operation involves subtracting the strain value at each sampling point from the material's yield strain threshold and then squaring the result. Specifically, a yield strain threshold of 0.35% for hard beryllium copper can be used as a baseline. This operation zeroes out the elastic deformation data and amplifies the contribution of plastic deformation. Time-weighted accumulation involves integrating and accumulating the squared difference results of all sampling points within the window in chronological order. This can be achieved using the sampling time interval Δt as the integration step size. This accumulation process converts instantaneous plastic deformation into a cumulative amount of energy.

[0034] Once the insertion / removal action is triggered, the system automatically captures a sequence of micro-strain data over the next 5 seconds. For each sampling point, the difference between the measured strain value and the preset material yield threshold is calculated. When the strain value exceeds the yield threshold, the square of the difference produces a positive output; otherwise, the output is zero.

[0035] The plastic deformation energy value is obtained by multiplying the calculation results of all sampling points by the sampling time interval and summing them. This energy value directly reflects the damage accumulation state of the material at the root of the insert by quantifying the degree of irreversible deformation caused by a single insertion and extraction. For example, when the insert undergoes slight plastic bending, strain data exceeding the yield threshold continuously appear within the time window, and the sum of its squares increases linearly with the duration of deformation, thus establishing a correspondence between deformation and energy value. The specific expression of the plastic deformation energy formula is as follows: in Indicates the first The real-time strain values ​​at each sampling point, measured in microstrain (με), are acquired using strain gauges mounted at the base of the L-shaped pin. It represents the absolute value of strain and treats tensile and compressive deformation uniformly. This represents the yield strain threshold of a material, determined by a tensile test. For example, it is 3500 με for hard beryllium copper, 1000 με for semi-hard phosphor bronze, and 600 με for pure copper. This indicates the sampling interval, which is 1ms in this embodiment for 1kHz sampling. This represents the total number of sampling points within the time window; in this embodiment, it's a 5-second window. , This represents the amplitude component of the plastic strain. The output is zero when the absolute value of the strain does not exceed the yield threshold.

[0036] This application further proposes a process for obtaining hysteresis characteristics, including performing similarity matching calculations between the micro-strain signal and the time-shifted contact resistance signal, determining the time shift that maximizes the similarity between the two signals, and using the time shift as a hysteresis characteristic that reflects the degree of response delay.

[0037] Micro-strain signals refer to the deformation data of the stress concentration area at the root of the insert acquired by strain gauges. Specifically, they can be implemented by using a resistive strain gauge combined with a Wheatstone bridge circuit to characterize the degree of mechanical deformation of the insert.

[0038] The time-shifted contact resistance signal refers to the signal sequence after shifting the contact resistance signal along the time axis. Specifically, it can be achieved by using a sliding window algorithm to perform cyclic displacement operations on the contact resistance signal, which is used to simulate the signal alignment state under different time shift conditions.

[0039] Similarity matching calculation refers to quantifying the degree of correlation between two signals in the time domain. Specifically, it can be implemented using a normalized cross-correlation function or a dynamic time warping algorithm to find the optimal time relationship between mechanical deformation and electrical performance changes.

[0040] Specifically, when the insert deforms, the change in contact resistance exhibits a physical transmission delay. By dynamically adjusting the time shift of the contact resistance signal, its cross-correlation function with the micro-strain signal is calculated. When the cross-correlation function reaches its maximum peak, the corresponding shift is the hysteresis characteristic value. This process simulates the complete transmission path from the occurrence of mechanical deformation to the resulting change in contact resistance. For example, when plastic bending occurs at the root of the insert, the contact surface shift requires a material stress relaxation process before the contact resistance increases. In this case, the hysteresis characteristic value characterizes the time span of this relaxation process. Hysteresis characteristic The cross-correlation function is obtained through peak search and its specific expression is as follows: in This represents the time shift, with a search range set to ±50ms and a step size of 1ms. In this embodiment, the duration of the analysis window is indicated. , Indicates time micro-strain values, This represents the contact resistance value after translation, in mΩ. This indicates the maximum delay time, which is recommended in this embodiment. The stress relaxation process of the covering material This represents the signal energy normalization factor, used to eliminate the effects of amplitude drift. The formula above specifies... The search range excludes non-physical delay noise. When When the deformation and resistance change are synchronized, it indicates good contact. The time indicates the delay in the transmission of mechanical deformation to the electrical response, indicating a risk of loose pins.

[0041] Traditional methods assess correlation by directly calculating the correlation coefficient between micro-strain and contact resistance, but they cannot distinguish dynamic delay effects between signals. This solution introduces a time shift search mechanism to accurately capture the duration of the physical process from mechanical deformation to electrical performance degradation, thus solving the misjudgment problem caused by neglecting timing delays in existing technologies. For example, when a pin undergoes elastic deformation, the contact resistance may fluctuate due to instantaneous pressure changes, but there is no actual delay effect. In this case, the hysteresis characteristic value output by this solution approaches zero, effectively distinguishing transient interference from actual performance degradation.

[0042] This application can accurately quantify the response delay time between mechanical deformation and changes in contact resistance, overcoming the limitations of traditional signal correlation analysis methods in dynamic delay scenarios. This hysteresis characteristic value provides a time-series quantitative indicator for assessing the potential impact of pin deformation accumulation on electrical performance. For example, after multiple insertions and removals, a continuous increase in the hysteresis characteristic value indicates a decrease in deformation conduction efficiency, which can provide early warning of contact failure risk.

[0043] This application further proposes that the generation of the performance degradation coefficient satisfies the following relationships: the performance degradation coefficient increases monotonically with the increase of the deformation eigenvalue; the performance degradation coefficient increases monotonically with the increase of the hysteresis eigenvalue.

[0044] Deformation eigenvalues ​​are quantitative indicators reflecting the cumulative degree of plastic deformation of the insert. Specifically, they can be achieved by time-weighted summation of the squared differences between micro-strain data and the material's yield strain threshold within a set time window after the insertion / removal event. Their function is to transform discrete strain signals into continuous energy values ​​characterizing material fatigue damage. Hysteresis eigenvalues ​​are quantitative indicators of the response delay between the micro-strain signal and the contact resistance signal. Specifically, they can be achieved by the time shift corresponding to the maximum peak value of the cross-correlation function. Their function is to capture the time lag effect of mechanical deformation being transmitted to changes in electrical performance.

[0045] When plastic deformation occurs at the root of the insert, the deformation characteristic value quantifies the irreversible damage to the material's microstructure by calculating the deviation of the strain data from the yield threshold. When oxidation or wear occurs at the contact interface, the hysteresis characteristic value characterizes the deterioration of the mechanical-electrical response correlation by measuring the time shift between the strain signal and the resistance signal. The performance degradation coefficient forms a two-dimensional degradation assessment mechanism by establishing a monotonically increasing relationship with the two types of characteristic values: the coefficient inevitably increases when plastic deformation intensifies, and the coefficient increases synchronously when the response delay worsens; both failure modes are forcibly mapped to a unidirectional trend of coefficient change. Specifically, the expression for the performance degradation coefficient is: in To take the natural logarithm of the plastic deformation energy, compress the numerical order of magnitude, enhance the sensitivity to early damage, and represent the cumulative severity of deformation, To normalize the time shift and eliminate the influence of observation duration, this represents the degree of response delay. When When indicates the normal state; when The current level indicates a medium risk, requiring continuous monitoring; when This indicates a high risk and requires immediate repair.

[0046] This application further proposes a specific method for generating the performance degradation coefficient: logarithmically scaling the deformation eigenvalues; normalizing the hysteresis eigenvalues ​​by dividing them by a preset observation duration; and multiplying the scaled deformation eigenvalues ​​by the normalized hysteresis eigenvalues.

[0047] Logarithmic scaling refers to a nonlinear transformation of plastic deformation energy. Specifically, it can use the natural logarithm or common logarithm to numerically compress deformation eigenvalues, balancing differences in damage accumulation rates at different stages. Normalization eliminates the influence of observation time on hysteresis eigenvalues. This can be achieved by dividing the time shift by the total observation time to convert it into a scaling factor, ensuring data comparability across different usage periods. Multiplication couples two types of features, using algebraic multiplication to fuse mechanical deformation and electrical response parameters, enhancing sensitivity to synchronous degradation scenarios.

[0048] After logarithmic transformation, the growth trend of early, small plastic deformation is amplified, while the magnitude of later, large deformation is suppressed, thus linearizing the damage accumulation process. The hysteresis characteristics are time-normalized, converting the time shift into a hysteresis coefficient independent of the observation duration, avoiding data bias caused by different detection periods. Multiplying the two types of characteristics after processing allows the performance degradation coefficient to simultaneously reflect the irreversibility of mechanical damage and the time mismatch of electromechanical response. When both deteriorate simultaneously, the product operation significantly amplifies the degradation signal.

[0049] This application further proposes a filtering algorithm to decompose a time series into a long-term trend component and a short-term fluctuation component for trend separation processing, and sets an early warning trigger condition that simultaneously satisfies the following conditions: the absolute deviation between the current performance degradation coefficient and the trend component exceeds a first threshold, and the rate of change of the trend component per unit time exceeds a second threshold.

[0050] The filtering algorithm specifically refers to the Hodrick-Prescott filtering algorithm, which solves the trend component by optimizing the objective function. Specifically, it can be implemented by setting the smoothing parameter λ>200. This parameter setting can effectively preserve the step-type inflection point characteristics in the performance degradation process.

[0051] The first threshold refers to the critical value of the deviation between the performance degradation coefficient and the trend component. It can be obtained by training with historical failure data and is used to identify instantaneous contact failure events.

[0052] The second threshold refers to the critical value of the rate of change of the trend component, which is implemented by an absolute value judgment mechanism. It only responds to the positive rate of change to eliminate interference caused by performance recovery.

[0053] Specifically, this technical solution decomposes the performance degradation coefficient sequence into trend and fluctuation components using the Hodrick-Prescott filtering algorithm. During the filtering process, the rigidity of the trend component is controlled by the smoothing parameter λ. When λ is set greater than 200, the algorithm can accurately capture trend jumps caused by sudden cracks in the insert. Specifically, the expression for the Hodrick-Prescott filtering algorithm is: in Represents the sequence of performance degradation coefficients. This component represents the long-term trend of performance degradation. Indicates the degree of rigidity in controlling the trend and In this embodiment Preserve the trend abrupt change caused by sudden cracks. This represents the total number of sampling points for the performance degradation coefficient during the analysis period. Indicates the sampling point. Warning trigger conditions: in In this embodiment, the threshold used for training with historical failure data is represented. , In this embodiment, the weighting coefficient is represented. To avoid delaying alarms for transient faults due to slow degradation, Indicates the first The performance degradation coefficient for each sampling point, dimensionless. Represents the discrete-time index in a time series. This is the residual term, also known as the first threshold, used to ensure the system responds quickly to transient anomalies, such as poor contact caused by pin vibration. The trend derivative term, also known as the second threshold, is used to monitor the slope change of the trend component. When the residual term > 0.3, it indicates instantaneous detection failure, such as pin vibration; when the trend derivative term > 0.6, it indicates accelerated degradation of the identification, such as root crack propagation.

[0054] In the early warning triggering mechanism, the residual condition detects instantaneous contact failure caused by pin vibration by calculating the absolute deviation between the current coefficient and the trend component; the trend deterioration condition identifies accelerated degradation in the later stages of fatigue accumulation by monitoring the rate of change of the trend component per unit time. The logical AND relationship design of the two conditions ensures that the system triggers an early warning only when the residual exceeds the limit and the trend continues to deteriorate, avoiding misjudgments caused by a single condition.

[0055] This application further proposes a technical solution of integrating a temperature sensor next to the strain gauge and introducing a temperature compensation mechanism. This mechanism dynamically adjusts the material yield strain threshold according to the real-time temperature, and at the same time uses a temperature-related correction coefficient to calibrate the deformation characteristic value.

[0056] Among them, the temperature sensor refers to the temperature sensing element set in the stress concentration area at the root of the pin. Specifically, it can be implemented by a miniature thermocouple or a thin-film resistance temperature detector, and is used to synchronously collect temperature change data of the working area of ​​the pin.

[0057] Temperature compensation mechanism refers to a systematic method for dynamically correcting the deformation detection process through temperature data. Specifically, it includes two related links: yield strain threshold adjustment and deformation characteristic value calibration, which are used to eliminate the coupling effect of temperature on material mechanical properties and sensor output.

[0058] The yield strain threshold of a material refers to the critical strain value at which a metallic material undergoes plastic deformation. It is dynamically calculated using an exponential function model based on the material's thermodynamic constitutive relationship to accurately reflect the softening phenomenon caused by temperature increases. The temperature-related correction coefficient is a proportionality factor related to the thermal expansion effect, calculated using an inverse proportional function constructed from the thermal expansion coefficient. It is used to suppress baseline drift of the strain signal caused by temperature gradients.

[0059] The temperature sensor and strain gauge are installed in a coplanar integrated manner to ensure spatial consistency of their sensing areas. When the ambient temperature changes, the temperature sensor outputs the temperature data at the base of the pin in real time, and the control system dynamically calculates the material yield strain threshold based on the current temperature value. This threshold is updated using an exponential decay function model, which lowers the yield strain judgment benchmark in high-temperature environments and raises the benchmark value in low-temperature environments. The calibration of the deformation characteristic value is achieved by multiplying the original strain data with a temperature-related correction coefficient to eliminate the strain signal offset caused by the thermal expansion and contraction of the material. The calibrated deformation characteristic value only reflects the plastic deformation caused by mechanical stress, avoiding misjudging temperature-induced elastic deformation as permanent damage. This application further proposes a technical solution that uses a sampling frequency of not less than 1 kHz to acquire the micro-strain signal and the contact resistance signal, and the two signals are synchronized in time through a hardware synchronization clock.

[0060] A sampling frequency of not less than 1kHz refers to the ability of the signal acquisition device to acquire at least 1000 data points per second. Specifically, this can be achieved by using a high-speed analog-to-digital converter in conjunction with an anti-aliasing filter. This frequency setting can fully capture the transient mechanical response characteristics of the stress concentration area at the root of the pin.

[0061] Hardware synchronization clock refers to a physical clock source that provides a unified time base for a multi-channel signal acquisition system. Specifically, it can be implemented using a global clock tree of a field-programmable gate array or a GPS synchronization pulse generator. This synchronization mechanism ensures that the data acquired by different sensors are strictly aligned on the time axis.

[0062] Specifically, during the insertion and removal of the pins, the micro-strain signal generated in the stress concentration area at the root contains millisecond-level dynamic changes. Using a sampling frequency of no less than 1kHz can effectively avoid aliasing distortion of high-frequency signal components. The contact resistance signal exhibits rapid fluctuation characteristics during insertion and removal. The synchronously acquired hardware clock directly controls the sampling trigger time of each channel through physical circuitry, eliminating time jitter caused by software scheduling. The time alignment accuracy of the two signals is controlled within a single sampling interval, providing an accurate time-domain reference for subsequent calculation of the cross-correlation function, enabling accurate quantification of the dynamic coupling relationship between the micro-strain signal and the contact resistance signal.

[0063] Traditional methods, employing sampling frequencies below 500Hz, result in the loss of high-frequency mechanical deformation characteristics, and rely on software timestamp synchronization, leading to millisecond-level time deviations. This solution overcomes the technical obstacle of establishing a correlation model between mechanical deformation and electrical performance in unsheathed L-type pin connectors due to the lack of obvious vibration characteristics, through the synergistic effect of high-frequency sampling and hardware clocking.

[0064] Through the above technical solution, this application achieves high-precision synchronous acquisition of micro-strain signals and contact resistance signals, effectively avoiding the distortion problem of dynamic correlation analysis caused by insufficient sampling rate or time reference deviation, and providing a reliable data foundation for performance degradation monitoring of sheathless L-type pin connectors.

[0065] Example 2

[0066] This invention provides a computer-readable storage medium.

[0067] The computer-readable storage medium provided in this embodiment of the invention stores a computer program, which, when executed by a processor, can implement the steps of any of the connector performance testing methods described above.

[0068] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] For a description of the computer-readable storage medium provided in the embodiments of the present invention, please refer to the above method embodiments; the present invention will not be described in detail here.

[0070] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0071] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0072] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0073] Example 3

[0074] This invention provides an execution device.

[0075] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the structure of an execution device provided by the present invention. The execution device may include: Memory, used to store computer programs; The processor, when executing a computer program, can implement the steps of any of the connector performance testing methods described above.

[0076] like Figure 4 The diagram shows the structural composition of an execution device, which may include: a processor 1, a memory 2, a communication interface 3, and a communication bus 4. The processor 1, memory 2, and communication interface 3 all communicate with each other via the communication bus 4.

[0077] In this embodiment of the invention, processor 1 may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field-programmable gate array, or other programmable logic devices.

[0078] Processor 1 can call the program stored in memory 2. Specifically, processor 1 can execute the operations in the embodiment of the button switch fault detection method.

[0079] The memory 2 is used to store one or more programs. The programs may include program code, which includes computer operation instructions. In this embodiment of the invention, the memory 2 stores at least a program for implementing the following functions: Strain gauges are used in the stress concentration area at the root of the pin to simultaneously collect micro-strain signals of the pin and contact resistance signals of the connector. The insertion and removal actions of the connector are detected and the time points of the insertion and removal events are recorded. Independent analysis periods are divided with the insertion and removal events as the dividing points. Perform the first and second operations within each independent analysis period: The first operation involves calculating deformation characteristic values ​​that reflect the degree of cumulative plastic deformation of the insert based on the micro-strain signal; The second operation involves calculating the cross-correlation function between the micro-strain signal and the contact resistance signal, and extracting the time shift corresponding to the maximum peak value as the hysteresis characteristic value. The deformation characteristic value and the hysteresis characteristic value are fused to generate a performance degradation coefficient; A trend separation process is performed on a continuous sequence of performance degradation coefficients, and a performance warning is triggered based on the degree of deviation between the separated trend component and the current coefficient, as well as the rate of deterioration of the trend component.

[0080] In one possible implementation, the memory 2 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; and the data storage area may store data created during use.

[0081] In addition, memory 2 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0082] Communication interface 3 can be the interface of the communication module, used to connect with other devices or systems.

[0083] Of course, it should be noted that, Figure 4 The structure shown does not constitute a limitation on the execution device in the embodiments of the present invention. In practical applications, the execution device may include more than Figure 4 More or fewer components as shown, or combinations of certain components.

[0084] The preferred embodiments of the present invention have been described above, but this is not intended to limit the concept and scope of the invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the inventive concept should fall within the protection scope of the present invention. All the technical contents for which protection is sought in this invention are fully described in the claims.

[0085] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the concept and scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the inventive concept should fall within the protection scope of the present invention. All technical contents for which protection is sought in this invention are fully described in the claims.

Claims

1. A method for testing connector performance, characterized in that, include: Strain gauges are used in the stress concentration area at the root of the pin to simultaneously collect micro-strain signals of the pin and contact resistance signals of the connector. The insertion and removal actions of the connector are detected and the time points of the insertion and removal events are recorded. Independent analysis periods are divided with the insertion and removal events as the dividing points. Perform the first and second operations within each independent analysis period: The first operation involves calculating deformation characteristic values ​​that reflect the degree of cumulative plastic deformation of the insert based on the micro-strain signal; The second operation involves calculating the cross-correlation function between the micro-strain signal and the contact resistance signal, and extracting the time shift corresponding to the maximum peak value as the hysteresis characteristic value. The deformation characteristic value and the hysteresis characteristic value are fused to generate a performance degradation coefficient; A trend separation process is performed on a continuous sequence of performance degradation coefficients, and a performance warning is triggered based on the degree of deviation between the separated trend component and the current coefficient, as well as the rate of deterioration of the trend component.

2. The method according to claim 1, characterized in that, The calculation process for the deformation characteristic value includes: After extracting the insertion and removal event, the micro-strain data sequence within a set time window is calculated. The squared difference between the strain value at each sampling point and the material yield strain threshold is calculated, and the squared difference is time-weighted and accumulated within the time window to obtain the plastic deformation energy value.

3. The method according to claim 1, characterized in that, The process of obtaining the hysteresis characteristic value includes: The similarity matching calculation is performed between the micro-strain signal and the contact resistance signal after time shift to determine the time shift amount that maximizes the similarity between the two signals, and the time shift amount is used as the hysteresis characteristic value reflecting the degree of response delay.

4. The method according to claim 2 or 3, characterized in that, The generation of the performance degradation coefficient satisfies the following relationship: The performance degradation coefficient increases monotonically with the increase of the deformation eigenvalue; The performance degradation coefficient increases monotonically with the increase of the hysteresis characteristic value.

5. The method according to claim 4, characterized in that, The specific method for generating the performance degradation coefficient is as follows: Logarithmic scaling is applied to the deformation eigenvalues. Normalize by dividing the lag characteristic value by the preset observation duration; Multiply the scaled deformation eigenvalues ​​by the normalized hysteresis eigenvalues.

6. The method according to claim 1, characterized in that, The trend separation process employs a filtering algorithm that decomposes the time series into long-term trend components and short-term fluctuation components. The warning trigger conditions are met: The absolute deviation between the current performance degradation coefficient and the trend component exceeds the first threshold; The rate of change of the trend component per unit time exceeds the second threshold; The warning trigger condition is that the weighted sum exceeds a threshold.

7. The method according to claim 1, characterized in that, Also includes: A temperature sensor is integrated next to the strain gauge, and a temperature compensation mechanism is introduced: The material yield strain threshold is dynamically adjusted based on real-time temperature. The deformation characteristic values ​​are calibrated using a temperature-dependent correction factor.

8. The method according to claim 7, characterized in that, The micro-strain signal and contact resistance signal are acquired using a sampling frequency of ≥1kHz; The two signals are synchronized in time through a hardware synchronization clock.

9. A computing device, characterized in that, include: At least one processor; And a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the method as described in any one of claims 1-8.

10. A non-transitory machine-readable storage medium, characterized in that, It stores executable instructions that, when executed, cause the machine to perform the method as described in any one of claims 1-8.

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