Response performance test method and system of microswitch

Abstract

The invention relates to a response performance test method and system of a microswitch. The method comprises the following steps: inputting a multi-pulse trigger sequence to a microswitch contact and collecting contact response original data; performing digital filtering processing on the contact response original data to obtain contact response time sequence data; comprehensive response time parameters including contact mechanical action starting time, electrical initial contact time and electrical stable contact time are calculated according to the contact response time sequence data; performing temperature compensation and contact rebound characteristic extraction on the comprehensive response time parameter to obtain contact rebound characteristic evaluation data; according to the contact rebound characteristic evaluation data and the comprehensive response time parameter, the response performance stability of the microswitch under the high-frequency operation condition is calculated, and a reliability evaluation result is generated. According to the invention, the problem of time sequence measurement error caused by the influence of contact jitter in the traditional test is effectively solved, and the accuracy and consistency of contact response time measurement are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microswitches, and particularly relates to a method and system for testing the response performance of a microswitch. Background Art

[0002] Traditional methods for testing the response performance of microswitches mainly rely on single-parameter measurement, such as only measuring the on-off state of the circuit or the displacement of the contact, resulting in large errors in the test results and being unable to comprehensively reflect the actual response characteristics of the switch. Especially under complex conditions such as contact jitter, environmental temperature changes, and high-frequency operation, the accuracy of existing test methods significantly decreases, and accurate performance evaluation results cannot be provided.

[0003] With the development trend of miniaturization, high-frequency operation, and complexity of electronic devices, the working environment of microswitches is becoming increasingly harsh, and the problem of decreasing measurement accuracy of existing test technologies under high-frequency operation conditions is becoming increasingly prominent. The limitations commonly existing in the prior art are also reflected in the inability to distinguish mechanical response delay and electrical contact delay, resulting in the inability to accurately locate the performance bottleneck of microswitches and being unable to provide a reliable basis for fault prediction. In addition, traditional test methods rely on fixed threshold judgment and lack in-depth analysis of contact bounce characteristics, making it difficult to accurately identify the true response performance of microswitches under complex working conditions. Summary of the Invention

[0004] The main object of the present invention is to provide a method and system for testing the response performance of a microswitch. The present invention effectively solves the problem of timing measurement error caused by contact jitter in traditional testing, and significantly improves the accuracy and consistency of measuring the contact response time.

[0005] To achieve the above object, the present invention provides a method for testing the response performance of a microswitch, including the following steps: Input a multi-pulse trigger sequence to the contacts of the microswitch and collect the original contact response data; Perform digital filtering processing on the original contact response data to obtain contact response timing data; Calculate comprehensive response time parameters including the starting time of contact mechanical action, the initial electrical contact time, and the stable electrical contact time according to the contact response timing data; Perform temperature compensation and extract contact bounce characteristics on the comprehensive response time parameters to obtain contact bounce characteristic evaluation data; Calculate the response performance stability of the microswitch under high-frequency operation conditions according to the contact bounce characteristic evaluation data and the comprehensive response time parameters, and generate a reliability evaluation result.

[0006] The present invention also provides a system for testing the response performance of a microswitch, including: The acquisition unit is used to input a multi-pulse trigger sequence to the microswitch contact and acquire the original contact response data; The filtering processing unit is used to perform digital filtering processing on the original contact response data to obtain the contact response timing data; The calculation unit is used to calculate the comprehensive response time parameters including the starting time of the contact mechanical action, the initial electrical contact time, and the stable electrical contact time according to the contact response timing data; The feature extraction unit is used to perform temperature compensation and contact bounce feature extraction on the comprehensive response time parameters to obtain the contact bounce characteristic evaluation data; The generation unit is used to calculate the response performance stability of the microswitch under high-frequency operation conditions according to the contact bounce characteristic evaluation data and the comprehensive response time parameters, and generate a reliability evaluation result.

[0007] In summary, the technical solution provided by the present invention realizes the synchronous high-precision acquisition of contact electrical signals and mechanical displacement signals through multi-parameter integrated contact dynamic characteristic sampling and adaptive sampling frequency technology, effectively improves the integrity and accuracy of test data, and solves the test error problem caused by traditional single-parameter measurement. By using the contact electrical-mechanical characteristic decoupling analysis model, the accurate separation measurement of mechanical response delay and electrical contact delay is realized, breaking through the limitation of traditional technology that cannot distinguish these two delays. Combining the pre-signal conditioning circuit and the post-digital filtering algorithm, the problem of timing measurement error caused by contact jitter in traditional testing is effectively solved, and the accuracy and consistency of contact response time measurement are significantly improved. By introducing temperature, humidity, and vibration compensation algorithms, the interference of environmental factors on the measurement results is eliminated, ensuring stable test performance within a wide temperature range and enhancing the reliability of test results. By using the contact bounce feature extraction algorithm and the dynamic threshold adjustment mechanism, the real-time monitoring of contact bounce, contact wear, and contact resistance change is realized, and the true response performance of the microswitch can be accurately identified under complex working conditions. Description of the Drawings

[0008] Figure 1 It is a schematic diagram of the steps of the microswitch response performance test method in an embodiment of the present invention; Figure 2 It is a structural block diagram of the microswitch response performance test system in an embodiment of the present invention.

[0009] The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed Embodiments

[0010] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present 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 only used to explain the present invention and are not used to limit the present invention.

[0011] Referring to Figure 1 , this embodiment provides a method for testing the response performance of a microswitch, including the following steps: S1, input a multi-pulse trigger sequence to the microswitch contact and collect the original contact response data; Among them, a pulse trigger sequence generation module is constructed. This module uses a precision timing control circuit to set a pulse parameter template internally and generate various standardized electrical excitation sequences including single pulses, double pulses, triple pulses, and variable-frequency pulses. Single pulses are used to obtain the most basic response characteristics of the switch, double pulses and triple pulses are used to evaluate the switch's ability to continuously respond within a short period, and variable-frequency pulses are used to detect the reliability and dynamic stability of the switch under high-frequency operating conditions by gradually increasing the frequency. These trigger sequences are directly applied to the excitation terminal of the microswitch after passing through a pulse amplification module driven by a high-precision timer, causing the microswitch to perform on and off actions under a series of known timing, frequency, and amplitude conditions. The preset signal conditioning circuit unit is triggered by the edge of the excitation signal. This unit is composed of low-noise, high-bandwidth operational amplifiers and combines ultra-low temperature drift precision metal film resistors to form a differential amplification network, thereby amplifying the extremely weak on / off transient voltage signal to an analyzable level without destroying the original signal structure. To avoid contamination of the detection data by power frequency interference and high-frequency parasitic signals, a band-pass filtering link is added in the signal conditioning stage. The center frequency is set to 2 kHz, and the bandwidth range is controlled between 500 Hz and 5 kHz, so that the processed electrical signal retains the key signal change section and suppresses external noise interference. At the same time, in order to synchronously capture the physical behavior changes of the microswitch contact from the mechanical motion dimension, a high-resolution micro-displacement sensor is installed near the switch contact structure. This sensor converts each tiny dynamic change in the contact position into an analog voltage signal in real time through the principle of optical encoding or Hall magnetosensitivity, and then transmits it to the central processor together with the amplified electrical signal after digital processing by a dedicated ADC module. Through this design, the system simultaneously obtains two key data streams on the same time axis: one is the denoised contact electrical signal, which is used to judge the start of electrical conduction, the jitter interval, and the stable conduction state in the subsequent process; the other is the contact mechanical displacement signal, which is used to deduce the mechanical starting point, the maximum speed node, and the stable end point. The central data fusion module pairs the above two types of signals and records them with a unified timestamp, forming a high-precision, multi-dimensional original contact response data sequence including multi-pulse response curves, electrical connection waveforms, and contact physical displacement trajectories.

[0012] S2. Perform digital filtering on the original contact response data to obtain contact response timing data; Specifically, classify the original contact response data, which includes data sources from two main channels: one is the denoised electrical signal after pre-amplification and filtering, and the other is the contact mechanical displacement signal collected by the micro-displacement sensor. To suppress pulse-like pseudo-signals caused by electromagnetic interference and operation spikes, a pulse noise identification and suppression algorithm is introduced. This algorithm is based on a threshold discrimination mechanism combined with a median filtering strategy. Mutant points in the signal with a duration less than the set window and an amplitude mutation rate higher than the threshold are regarded as pulse interferences, and are reconstructed using the average value of the front and rear windows or an adaptive interpolation method to generate electrical signal data after removing pulse noise. At the same time, to improve the smoothness and predictability of the contact displacement signal during the high-speed response process, the standard Kalman filtering algorithm is used to process the mechanical displacement signal channel. The Kalman filter uses the principle of linear system state space modeling to fuse the contact displacement measurement value and the system state prediction value, and recursively updates the prediction error covariance matrix at each moment. Estimate the current true displacement state based on the minimum mean square error criterion, thereby significantly weakening the influence of high-frequency noise and low-amplitude jitter, and outputting smoothed contact displacement data with high credibility. Due to the clock drift and response delay differences in the electrical signal and mechanical displacement signal sampling channels, a time alignment method based on the maximum cross-correlation function is used to synchronize the two types of signals. Time index sequences are constructed for the electrical signal and the mechanical signal respectively through a sliding window, and then the cross-correlation coefficient curve is calculated within the overlapping window to find the time offset corresponding to the peak, and the mechanical signal is fine-tuned and synchronized accordingly, so that the two types of signals reach the maximum alignment accuracy on the time axis, obtaining time-synchronized electrical-mechanical signal data. Perform wavelet multi-scale decomposition processing on the time-synchronized electrical-mechanical signal data. Select mother wavelet basis functions with compact support and orthogonality characteristics such as db4 or sym6, perform multi-layer wavelet decomposition on the signal, and separate the detail coefficients and approximation coefficients under each frequency bandwidth. The former represents high-speed disturbance components such as current jitter or instantaneous displacement impact, and the latter represents low-frequency structures such as action trends and stable responses. After removing irrelevant high-frequency noise components, perform signal reconstruction operations, and combine the characteristic coefficients retained at each scale to reconstruct the contact response timing data with clear structure and complete frequency domain information, including synchronized contact timing electrical signals and contact timing mechanical displacement signals.

[0013] S3. Calculate comprehensive response time parameters including the starting time of contact mechanical action, the initial electrical contact time, and the stable electrical contact time according to the contact response timing data; It should be noted that time feature recognition is performed on the mechanical displacement signal and the electrical signal in the contact response timing data respectively. For the contact timing mechanical displacement signal, a set of dynamic displacement thresholds corresponding to the switch stroke structure is set. This threshold is defined as the position where the displacement change amount first reaches or exceeds the preset starting displacement threshold. At this time, it indicates that the actuator of the microswitch has entered the physical movement stage of the pre-trigger segment from the stationary state. Therefore, the system marks the time point when the displacement signal first crosses the threshold as the starting time of the contact mechanical action, and uses this as the reference benchmark for subsequent timing analysis. For the contact timing electrical signal, by performing a first-order derivative process on the current signal, the slope change trend is extracted, and an electrical mutation determination threshold is set to reflect the current climb characteristic generated by the initial conduction of the contact. When the first-order derivative of the current signal first exceeds the set first nominal current change rate threshold value, it means that the contact current enters the initial conduction state from the zero state. The system extracts this time point as the electrical initial contact time accordingly. This step can accurately identify the first effective change point of the current connection and avoid misjudging current noise or bounce as the effective contact moment. Search for the stable section of the current fluctuation in the timing current signal, calculate the instantaneous fluctuation amplitude of the current through a local sliding window. When the current fluctuation range is stably less than the second nominal current within a continuous time period, identify the starting time of this section as the electrical stable contact time. This time marks the transition process of the contact from the first contact to the stable conduction current. Calculate the time difference between the electrical initial contact time and the starting time of the contact mechanical action to obtain the mechanical-electrical response delay time parameter, which is used to reflect the response rate from mechanical trigger to the generation of preliminary conductance; calculate the time difference between the electrical stable contact time and the electrical initial contact time as the contact jitter duration parameter, and this value reflects the electrical jitter stability of the contact during the connection process. According to the actual working conditions or empirical weight coefficients, the mechanical-electrical response delay time parameter and the contact jitter duration parameter are subjected to weighted summation processing to calculate the comprehensive response time parameter representing the overall response performance of the microswitch.

[0014] S4. Perform temperature compensation and contact bounce characteristic extraction on the comprehensive response time parameter to obtain contact bounce characteristic evaluation data; Specifically, a temperature perception mechanism is introduced to build the dynamic thermal feature recognition ability of the test environment. The ambient temperature during the operation of the microswitch is collected in real time through a high-precision digital temperature sensor to obtain the current test environment temperature data, and based on this, a correlation mapping model between temperature and response performance is established. To facilitate the implementation of the compensation strategy, the acquired temperature data is divided according to a preset interval that covers the operating temperature range of the microswitch. For example, the temperature range from -40°C to 85°C is divided into five sections: a low-temperature section (-40°C to -20°C), a sub-low-temperature section (-20°C to 0°C), a normal-temperature section (0°C to 30°C), a sub-high-temperature section (30°C to 60°C), and a high-temperature section (60°C to 85°C). Each section corresponds to different response change characteristics. The pre-calibrated temperature compensation coefficient lookup table is consulted according to the section to which the current temperature belongs. Each interval coefficient in the lookup table is obtained by regression modeling of the measured response time of the standard sample at different temperatures, which can effectively reflect the sensitivity and non-linear change characteristics of the response delay to the ambient temperature. The comprehensive response time parameter is multiplied by the current temperature compensation coefficient to preliminarily correct the response time deviation caused by temperature, and the first temperature compensation response time value is obtained. To improve the compensation accuracy and solve the problem of insufficient accuracy of the single linear correction method in the boundary interval, a secondary correction mechanism is introduced for the first temperature compensation response time value, and a polynomial fitting model is used to perform non-linear regression correction on the compensation result. The general form of the fitting model is a third-order or fourth-order polynomial, with the variable being the first compensation value, and the coefficients are determined by the least mean square error fitting based on historical multi-temperature zone measurement samples. Finally, the second temperature compensation response time value is obtained, which is used as the response time correction result with high confidence for subsequent analysis. On the premise that the temperature effect has been fully corrected, the process of extracting the contact bounce characteristics is executed. Based on the phenomenon that the bounce usually manifests as a violent fluctuation of the current signal within a short period of time, the wavelet transform and local peak detection method are used to perform multi-scale processing on the time-series current signal. The system selects the Daubechies wavelet with good time-frequency localization ability (such as db4) to perform 5-layer wavelet decomposition on the corrected current signal, extracts the detail coefficients therein, and performs threshold analysis and energy concentration detection to locate the possible bounce events at the initial stage of contact closure. For each signal peak determined to be a bounce, its time position, amplitude value, duration, and adjacent bounce interval are extracted, and based on this, parameters such as the bounce count, maximum bounce amplitude, total bounce duration, and bounce attenuation trend are statistically calculated. All these bounce characteristics are integrated into the contact bounce characteristic evaluation data.

[0015] In this embodiment, the second temperature compensation response time value is used as the main time reference benchmark for analysis. The electrical signal segments in the corresponding contact response timing data are selected and processed. The dynamic characteristics at the initial stage of contact conduction are focused on and analyzed within this compensation time window, so as to improve the time focus of bounce detection and the accuracy of feature extraction. The selected electrical signal segments are processed using the wavelet multi-scale decomposition method. Orthogonal wavelet basis functions such as db4 or coif5 with good transient capture ability and frequency resolution are selected, and a multi-level decomposition operation is performed to obtain a set of detail coefficients at multiple frequency scales. These detail coefficients can clearly depict the sudden jump and high-frequency fluctuation components in the signal, and are an important source of dynamic characteristics reflecting the contact bounce behavior. A multi-threshold peak detection operation is performed on all scale detail coefficients. Combining the multi-scale energy distribution and the threshold adaptive mechanism, the peak points with significant mutation characteristics in all current signals are identified, and the occurrence time of the bounce event is recorded in the form of time index, forming the contact bounce event time series. Based on the bounce time series, the time interval is calculated, the time difference between any two consecutive bounce events is analyzed to obtain the bounce interval time parameter, and before the entire contact conduction stage is completed, the total number of bounce events that occur is counted to obtain the bounce number parameter. The above two parameters reflect the contact instability degree at the initial stage of the microswitch being turned on, and are important measurement indicators for measuring the dynamic frequency and response rhythm in the bounce characteristics. For each bounce event, its local current peak is extracted, and a ratio calculation is performed with the final stable current value to obtain the single bounce amplitude parameter; then the amplitude values of multiple bounce events are formed into a sequence, and amplitude normalization is performed. The normalization reference is the maximum amplitude of the first bounce event. Subsequently, a non-linear exponential decay model is used to fit the normalized sequence, and a fitting function with the amplitude decreasing with the bounce sequence number is established. The exponential decay factor of this function is used as the bounce decay rate parameter to quantify whether the bounce phenomenon shows good convergence characteristics. Through this method, the existence of bounce is judged, and the amplitude change trend and energy decay path of the bounce are depicted. The bounce number parameter, bounce interval time parameter, bounce amplitude parameter, and bounce decay rate parameter are encapsulated in a unified format to generate the contact bounce characteristic evaluation data. This data set is used for horizontal comparison during multiple tests, vertical performance evaluation between different models of microswitches, and as a key feature vector input in the response performance trend analysis and life prediction algorithm.

[0016] S5. According to the contact bounce characteristic evaluation data and the comprehensive response time parameter, calculate the response performance stability of the microswitch under high-frequency operation conditions, and generate a reliability evaluation result.

[0017] Among them, an experimental process based on a variable-frequency pulse excitation mode is constructed. An excitation signal that gradually increases from low frequency to high frequency is applied to the microswitch through a pulse sequence controller. The frequency range covers 0.1 Hz to 100 Hz, and multiple groups of test samplings are carried out at set time intervals. The system synchronously records the corresponding comprehensive response time parameters and contact bounce characteristic evaluation data at different frequencies to form a response performance data set. To objectively quantify the response consistency and volatility of the microswitch under different frequency loads, based on the comprehensive response time parameters, the response time statistical index of each frequency point is calculated, and its full-band standard deviation is calculated to obtain the dispersion index of the microswitch response time varying with the operating frequency. This standard deviation is the response stability data of the microswitch at each frequency point. The smaller the standard deviation, the higher its stability to frequency perturbation, and vice versa, indicating serious performance fluctuations. At the same time, a visual display platform is constructed for the contact bounce characteristic evaluation data. The four dimensions of the bounce count, bounce duration, bounce amplitude, and bounce decay rate are mapped onto an equilateral coordinate axis in the form of a radar chart to show the different performance of different microswitch samples in the bounce dynamic characteristics. The area and shape of the graph can intuitively reflect the concentration degree of bounce energy, decay characteristics, and short-term contact stability, thus assisting in judging the dynamic reliability level of the microswitch during the conduction stage. On the basis of forming the stability data and the visual representation of the bounce characteristics, the system integrates the above two types of core indicators to construct a comprehensive performance scoring system for the microswitch. In the scoring model, the comprehensive response time standard deviation is the main evaluation factor, the bounce count and bounce amplitude are the dynamic interference factors, and the decay rate and bounce duration are the supplementary items. A normalized comprehensive score result is generated through a weighted scoring formula. After obtaining the static score result, to reflect its dynamic evolution characteristics during use, a life cycle test is performed on the microswitch, that is, on a set mechanical and electrical life platform, the switch is continuously driven by high-frequency pulses until its structure or contact performance reaches the critical deterioration point. During this process, the change curves of its comprehensive response time and bounce parameters with the number of operations are recorded in stages, and time series modeling algorithms, such as exponential smoothing models, ARIMA, or polynomial regression, are used to predict and model the performance change trend, and then the change rates of each key performance parameter are extracted. Combining the comprehensive score and the performance change trend curve, according to the preset performance deterioration threshold, the remaining number of operations required for the performance to reach an unacceptable state is calculated, and the expected remaining life of the microswitch is deduced by normalizing the current cumulative number of operations. Combining the current score, change trend, and remaining life prediction results, a reliability assessment report including performance level evaluation, reliability score, life prediction period, and warning suggestions is generated.

[0018] In one example, a multi-pulse trigger sequence is input to the microswitch contact and the original contact response data is collected, including: A multi-pulse trigger sequence including single pulses, double pulses, triple pulses and variable-frequency pulses is generated by a pulse trigger sequence generator, and the microswitch contacts are electrically excited according to the multi-pulse trigger sequence to obtain a microswitch contact excitation signal; Based on the microswitch contact excitation signal, a pre-signal conditioning circuit is triggered to amplify the electrical signals generated during the on and off processes of the microswitch contacts, obtaining an amplified contact electrical signal; The amplified contact electrical signal is filtered to obtain a denoised contact electrical signal, and a micro-displacement sensor is used to track the mechanical movement of the microswitch contacts in real time to obtain a contact mechanical displacement signal; Contact response raw data is generated based on the denoised contact electrical signal and the contact mechanical displacement signal.

[0019] In this example, a programmable high-precision pulse trigger sequence generation module is built in the system. This module is implemented based on a digital pulse width modulation counter and a stable clock control circuit. By presetting parameters, it can generate various forms of pulse sequences, including single pulses, double pulses, triple pulses, and variable-frequency pulse signals whose frequency changes with time. Among them, the single-pulse mode is used to measure the basic response characteristics of the switch under a single excitation. The double-pulse mode is used to verify the accuracy and consistency of the switch's repeated response within a short time. The triple-pulse and above composite pulses are used to simulate the stability evaluation under high-density signal excitation. The variable-frequency pulse mode gradually increases the pulse frequency to detect the performance fluctuation trend of the switch in a high-frequency working environment. The pulse width, period, duty cycle, and frequency change range of each pulse sequence are finely configured and completed through the drive of an FPGA or a high-speed microprocessor, ensuring that the time accuracy of the pulse trigger is controlled within ±0.01 ms. When the multi-pulse trigger sequence is output, it is directly applied as an external electrical excitation signal to the excitation input terminal of the micro switch, enabling it to perform periodic on-off actions under different types of excitation conditions and forming a physical response behavior synchronized with the excitation. At this time, the system uses the rising edge or falling edge of the excitation signal as the trigger condition to start the signal conditioning module. The signal conditioning module consists of a high-precision low-noise operational amplifier, a precision resistor array, and a band-pass filter network. Its primary function is to amplify the weak voltage fluctuations generated by the micro switch at the moment of contact closure and disconnection without distortion, so that the signal amplitude is increased to the input level range acceptable to the ADC. The operational amplifier device needs to have a low bias current and an extremely low input offset voltage to ensure a stable gain in the micro-voltage signal domain. The precision resistors are composed of metal film resistors with a temperature drift coefficient not exceeding ±5 ppm / °C to form a gain control network, thus ensuring the consistency of the amplification factor in different temperature environments. After amplification, the system connects to a band-pass filter to perform frequency-domain processing on the amplified signal. The center frequency of the band-pass filter is set to 2 kHz, and the bandwidth is between 500 Hz and 5 kHz to effectively filter out 50 Hz power frequency noise, interference signals from surrounding wireless devices, and high-frequency spike components, and retain the transient change curve that truly has physical significance during the contact on-off process. To simultaneously capture the mechanical movement behavior of the contact, a micro displacement sensor is synchronously configured for position tracking. The sensor uses principles such as eddy current, laser triangulation, or magneto-sensitive Hall effect, and continuously detects the displacement change of the micro switch contact through a high sampling rate (greater than 10 kHz), thereby achieving a full-time domain record of the switch's movement path, start time point, movement speed, and stable position. After the displacement signal is analog output by the sensor, it undergoes amplification and anti-aliasing filtering processing, and then is digitized by a high-speed ADC module to ensure that it has the same level of time resolution and amplitude accuracy when aligned with the electrical signal.After all signal conditioning and preprocessing are completed, time alignment processing is performed on the denoised electrical signals and synchronously acquired mechanical displacement signals. The time calibration method based on the maximum cross-correlation coefficient is adopted. Through the sliding window mechanism, the optimal synchronization point of the two signal streams on the time axis is found, and the time axis is uniformly reconstructed to obtain a set of accurately aligned electrical-mechanical response data pairs. These data pairs are encapsulated into a multi-channel sampling data structure and stored in a buffer or high-speed memory. They are stored in segments according to the data length corresponding to each excitation pulse. Each segment of data contains pulse numbers, timestamps, denoised voltage values, current values, displacement values, and their first and second derivative-derived features. These structured data form the original dataset of contact responses.

[0020] In one example, digital filtering processing is performed on the original contact response data to obtain contact response time-series data, including: Perform impulse noise suppression processing on the denoised contact electrical signals in the original contact response data to obtain electrical signal data after removing impulse noise; Perform Kalman filtering processing on the contact mechanical displacement signals in the original contact response data to obtain smoothed contact displacement data; Perform time alignment and signal cross-correlation analysis on the electrical signal data after removing impulse noise and the smoothed contact displacement data to obtain time-synchronized electrical-mechanical signal data; Perform multi-scale decomposition on the time-synchronized electrical-mechanical signal data to obtain signal details and approximation coefficients at different frequency scales; Perform signal reconstruction based on the signal details and approximation coefficients at different frequency scales to obtain contact response time-series data, which includes contact time-series electrical signals and contact time-series mechanical displacement signals.

[0021] In this example, pulse noise suppression processing is performed on the denoised contact electrical signal in the original contact response data. This algorithm combines a transient mutation discrimination mechanism based on median filtering and a sliding window statistical method. The electrical signal is traversed with a fixed-length window, and the local amplitude variance and amplitude change rate of the sample points within each window are calculated. When a certain sampling point shows positive or negative mutations greater than the set threshold relative to the window mean and only lasts for a very short time, the system determines it as pulse noise and replaces the abnormal point with the mean of adjacent sample points or cubic spline interpolation to obtain an electrical signal with better continuity and excluding high-frequency burst disturbances, that is, the electrical signal data after removing pulse noise. The Kalman filter algorithm is introduced to process the contact mechanical displacement signal. As an optimal estimation method based on Bayesian inference, the Kalman filter is applicable to the state estimation scenario of linear Gaussian systems and continuously corrects the displacement data measured by the sensor through a prediction-update mechanism. A state space model is established, considering the displacement and velocity of the contact as state variables, and defining the state transition matrix, observation matrix, and covariance matrices of process noise and observation noise. At each moment, the current displacement value is predicted based on the previous moment's state and compared with the actual output of the sensor, and then the state estimation is adjusted to output the smoothed contact displacement data in the sense of minimum mean square error. The electrical signal data after removing pulse noise is time-aligned with the smoothed contact displacement data to ensure data consistency in subsequent analysis. The signal cross-correlation analysis method is used for time synchronization processing. Calculate the cross-correlation coefficient curves of the two signals at each time offset, find the time delay corresponding to the maximum correlation value, and perform a time-axis translation operation on the signal sequence accordingly, so that the time points of the electrical signal and the mechanical signal coincide at key events (such as the instant of connection), obtaining time-synchronized electrical-mechanical signal data. Multiscale signal decomposition processing is performed on the synchronized electrical-mechanical combined signal data. The wavelet transform is selected as the core tool, and orthogonal wavelet bases suitable for capturing transient changes, such as db4 or coif5, are used to decompose the signal at multiple levels, decomposing the original signal into low-frequency approximation coefficients and high-frequency detail coefficients respectively. The low-frequency part corresponds to the trend evolution component of the system response, while the high-frequency detail coefficients contain local events such as the dynamic characteristics at the instant of switch connection, mechanical rebound, and micro-vibration. The high-frequency details of the electrical signal are used to discriminate the rebound phenomenon and contact jitter behavior, while the high-frequency components in the mechanical displacement signal reveal acceleration mutations and feedback disturbances during contact movement. Select appropriate scale coefficients from the multiscale signals according to the analysis task requirements and perform a reconstruction operation, recombining the low-frequency approximation component and the retained high-correlation high-frequency detail part to reconstruct the optimized contact timing electrical signal and contact timing mechanical displacement signal respectively, which together constitute the final contact response timing data.

[0022] In one example, calculating a comprehensive response time parameter including the starting time of the contact mechanical action, the initial electrical contact time, and the stable electrical contact time based on the contact response timing data, includes: Performing threshold detection on the contact timing mechanical displacement signal in the contact response timing data, and determining the moment when the contact timing mechanical displacement signal exceeds the distance target value as the starting time of the contact mechanical action; Calculating the first derivative of the contact timing electrical signal in the contact response timing data to obtain the first derivative value, and finding the time point when the first derivative value first exceeds the first nominal current, which is determined as the initial electrical contact time; Performing stability analysis on the contact timing electrical signal in the contact response timing data, and finding the starting moment when the current fluctuation amplitude is less than the second nominal current, which is determined as the stable electrical contact time; Calculating the time difference between the initial electrical contact time and the starting time of the contact mechanical action to obtain the mechanical-electrical response delay time parameter; Calculating the time difference between the stable electrical contact time and the initial electrical contact time to obtain the contact jitter duration parameter; Performing weighted summation on the mechanical-electrical response delay time parameter and the contact jitter duration parameter to obtain the comprehensive response time parameter.

[0023] In this example, the contact timing mechanical displacement signal is extracted from the contact response timing data, and a threshold detection operation is performed on this signal to determine the initial time point when the microswitch structure starts. A minimum effective displacement threshold value that is significantly different from the static state of the contact is set, which is about 0.01 mm according to the mechanical tolerance of the switch structure. The system scans the displacement signal sequence with a continuous time window and compares the sampled value with the set threshold in real time. When it is detected that the contact displacement first exceeds this distance threshold, it is determined that the mechanical drive action of the switch has changed from the static state to the dynamic process, and this moment is defined as the starting time of the contact mechanical action. This time point marks from a mechanical perspective that the external excitation has successfully driven the physical displacement of the switch actuator. Perform a first-order derivative operation on the contact timing electrical signal, calculate the instantaneous growth rate or change speed of each adjacent sampled point in the current curve, and form a sequence of first-order derivative values. Since the current will change suddenly when the microswitch changes from the non-conducting state to the conducting state, and this mutation appears as a steep rise segment in the derivative sequence, the occurrence time of the closing event is judged by analyzing the derivative peak. A change rate threshold value reflecting the conduction mutation is set, that is, the first nominal current threshold. When the first-order derivative value of the current first exceeds this threshold and maintains a positive growth trend, the system determines that the microswitch has had its first effective electrical contact, and this time point is defined as the electrical initial contact time. This node marks the first formation of a low-resistance channel in the circuit loop and is the starting point of time when the electrical behavior enters the dominant stage. Perform a stability analysis on the contact electrical signal to identify the transition moment when the current changes from the initial conduction state to the steady-state conduction. In this stage, a sliding window is constructed to statistically analyze the fluctuation range of the current in a continuous time section in real time and compare it with the second nominal current threshold. The second threshold is set to the stable fluctuation range allowed by the electrical system, such as ±1% of the nominal current. When the system detects that the fluctuation amplitude of the current is always less than this set value in several consecutive sampling periods, it is confirmed that the contact has completed the convergence of the conduction jitter stage, and its contact state has tended to be stable, and the starting moment of this section is marked as the electrical stable contact time. This time point reflects the contact jitter time and contact reliability, and the corresponding physical process is highly related to phenomena such as rebound, microarc, and material adhesion. Perform response delay and stability quantization calculations. Extract the time difference between the electrical initial contact time and the starting time of the contact mechanical action to represent the response delay from the occurrence of the microswitch structure action to the first conduction of the current. This parameter is the mechanical-electrical response delay time, representing the dielectric breakdown time, surface contamination layer penetration time, and dynamic contact lag during the closing process of the electrical contact; calculate the time difference between the electrical stable contact time and the electrical initial contact time. This difference reflects the time taken for the contact to transition from the initial connection to the current becoming stable, which is defined as the contact jitter duration. This parameter quantifies the short-term stability of the contact state. The longer the jitter duration, the more complex the contact surface state, the higher the rebound frequency, or the lower the surface contact quality.The mechanical-electrical response delay time parameter and the contact bounce duration parameter are weighted and summed to obtain a comprehensive response time parameter.

[0024] In one example, temperature compensation and contact bounce feature extraction are performed on the comprehensive response time parameter to obtain contact bounce characteristic evaluation data, including: The temperature value of the working environment of the microswitch is collected in real time through a temperature sensor to obtain the current test environment temperature data; The current test environment temperature data is divided into intervals to obtain temperature compensation interval parameters; According to the temperature compensation interval parameters, a temperature compensation coefficient look-up table is queried to obtain the current temperature compensation coefficient; The comprehensive response time parameter and the current temperature compensation coefficient are multiplied to obtain a first temperature compensation response time value; The first temperature compensation response time value is quadratically corrected through a polynomial fitting model to obtain a second temperature compensation response time value; Based on the second temperature compensation response time value, contact bounce feature extraction is performed to obtain contact bounce characteristic evaluation data.

[0025] In this example, a high-precision digital temperature sensor is deployed in the test system and arranged in the environmental area adjacent to the microswitch body to collect the temperature changes of the environment where the microswitch is located in real time during the test. This temperature sensor has high sampling frequency, low drift characteristics and wide temperature range adaptation ability, such as a digital output-based thermal resistor or a MEMS integrated thermometer, and its measurement error does not exceed ±0.2°C. The system reads the temperature value from the sensor at fixed time intervals and records it in time series, and the obtained continuous temperature samples constitute the original source of the current test environment temperature data. According to the law of the response time difference of the microswitch in different temperature environments, the temperature values are partitioned, and an adaptive compensation strategy is introduced. The temperature range is divided with reference to the operating environment temperature grade of the switch or the IEC test standard, and the entire temperature range (for example, from -40°C to +85°C) is divided into multiple non-overlapping closed intervals, such as -40°C to -20°C, -20°C to 0°C, 0°C to 30°C, 30°C to 60°C, and 60°C to 85°C. Each interval represents a type of thermal environment condition, and within these intervals, phenomena such as the thermal expansion and contraction of the switch material, the change of the contact resistance of the contacts, and the bounce time delay all show regular differences. The system maps the interval parameters according to the interval to which the current temperature value belongs to obtain the corresponding temperature compensation interval parameters. Based on these interval parameters, a temperature compensation coefficient lookup table is consulted. This lookup table is a temperature-response compensation coefficient mapping matrix constructed in advance through a large number of measured samples, and each interval corresponds to a temperature correction coefficient determined through statistical analysis. The method for determining the coefficient is to collect the average comprehensive response time of multiple microswitches in different temperature intervals under constant temperature conditions, and calculate the response ratio of other temperature regions to the reference region with the normal temperature region as the reference. According to the current interval parameters, the table entry is quickly retrieved to obtain the current temperature compensation coefficient in actual application, and this coefficient is multiplied by the previously calculated comprehensive response time parameter to obtain the first temperature compensation response time value. This value represents the estimated response time after linear correction under the existing temperature conditions, and the deviation component caused by the environmental thermal state has been eliminated. Due to the non-linear characteristics of the microswitch in temperature-sensitive response, especially in the extremely low or extremely high temperature regions, its compensation error shows an asymmetric distribution. A non-linear fitting correction strategy is adopted, and a polynomial fitting model is applied for secondary correction on the basis of the first compensation value. The fitting model selects a third-order or fourth-order polynomial form, and the first compensation value is input-transformed through this fitting function to obtain a second temperature compensation response time value that is more in line with the measured trend. This value is used as the reference time window for subsequent bounce identification to ensure that the time period when a valid bounce phenomenon actually occurs is always selected for analysis under different temperature backgrounds. Based on the time index corrected by this temperature, the contact bounce feature extraction stage is entered.During this process, an analysis window of the contact electrical response curve is located according to the second temperature compensation response time value, and the complete conduction process data before and after this moment is intercepted as a high-resolution local subsequence. The wavelet transform algorithm is applied to this subsequence for multi-scale analysis. Using orthogonal compactly supported wavelet functions such as db4, the current curve is decomposed into detail coefficients of multiple frequency band scales, and the occurrence time of the bounce event is marked by identifying the position of the mutation peak in the high-frequency coefficients. Feature extraction operations are performed on all identified bounce peaks to obtain parameters such as the amplitude, interval, duration, and amplitude decay rate of each bounce event. Especially during the amplitude extraction process, each bounce peak is normalized with the final current stable value to judge the relative intensity of its bounce energy; while in the decay rate calculation, an exponential regression model is used to fit the sequence of multiple bounce peaks, and its exponential coefficient is used as a characterization index of the bounce decay speed. All the above bounce-related parameters are encapsulated into structured contact bounce characteristic evaluation data, including the number of bounces, bounce duration, average bounce amplitude, maximum bounce amplitude, bounce interval sequence, bounce decay coefficient, etc., and are accompanied by the current temperature label and the corrected response time value.

[0026] In one example, based on the second temperature compensation response time value, contact bounce feature extraction is performed to obtain contact bounce characteristic evaluation data, including: Using the second temperature compensation response time value, the contact timing electrical signal in the contact response timing data is decomposed to obtain multi-scale detail coefficients reflecting the contact bounce characteristics; Based on the multi-scale detail coefficients, peak detection is performed to obtain the time series of the occurrence of contact bounce events; According to the time series of the occurrence of contact bounce events, the time interval between adjacent bounce events is calculated, and the number of bounces before the contact is completely turned on is counted to obtain the bounce number parameter and the bounce interval time parameter; The current amplitude of each bounce event is extracted, and the ratio of the bounce amplitude to the stable value is calculated to obtain the bounce amplitude parameter, and the amplitudes of consecutive multiple bounce events are normalized and exponentially fitted to obtain the bounce decay rate parameter; According to the bounce number parameter, bounce interval time parameter, bounce amplitude parameter, and bounce decay rate parameter, contact bounce characteristic evaluation data is generated.

[0027] In this example, the second temperature compensation response time value is adopted to construct a time-domain analysis window that includes the pre- and post-current-conduction stages. This window covers the short buffer period before the initial electrical contact to the fully stable period after stable conduction to ensure capturing all possible bounce behaviors. The contact timing electrical signals within this time period are processed at multiple scales using wavelet decomposition. By decomposing the original signal into different levels of detail at multiple frequency scales, the high-frequency local fluctuation characteristics present in the current can be effectively extracted. Peak detection operations are performed on the detail coefficients corresponding to each scale. The detection process depends on factors such as peak amplitude, time interval between adjacent peaks, and waveform symmetry to jointly judge and exclude non-physical signal perturbations caused by power supply noise and electromagnetic interference. For each peak event determined to be a real bounce, its occurrence time is recorded in a unified time series, forming a list of bounce event occurrence times. Based on this time series, the time intervals between adjacent bounce events are calculated one by one, and the total number of bounces during the entire conduction process is counted. To ensure that the statistical results of bounce behaviors are not affected by the natural fluctuations of the current in the later stage, only the bounce peaks occurring before the electrical stable contact time are counted, obtaining two most basic bounce characteristic parameters: one is the bounce count, which is used to describe the frequency of unstable current jumps during the conduction process; the other is the bounce interval time, which is used to reflect the rhythm pattern of the jumps and their periodic stability. The current peak amplitude corresponding to each bounce event is extracted, and with the value in the final stable state of the current as the reference benchmark, these bounce amplitudes are subjected to proportional normalization to eliminate misjudgments caused by global amplitude drift due to factors such as power supply voltage and load resistance, ensuring that the evaluation of bounce amplitudes is only related to the physical jumps during the contact process. After normalization, all bounce amplitudes are arranged in chronological order, and their change trends are analyzed. If the amplitude shows a gradually decreasing trend over time, it indicates that the contact bounce tends to converge and the contact state is evolving towards stability; if the amplitude does not converge or even increases for a long time, it indicates that there are problems with the contact performance or significant structural rebound. To quantify this change trend, the decay ability is estimated by analyzing the change pattern of the normalized amplitude sequence. A model is constructed to simulate the convergence speed of the amplitude. The faster the sequence converges, the faster the contact stabilizes in a short time and the stronger the bounce decay ability. Conversely, if the amplitude remains at a high level for a long time or only weakens slowly, it indicates strong contact instability and a long-term jitter risk. This decay trend ultimately boils down to a parameter reflecting the bounce convergence ability, namely the bounce decay rate. This parameter, combined with the previously extracted bounce count, bounce interval time, and bounce amplitude parameters, constitutes a set of bounce characteristic evaluation indicators. The above four types of key bounce indicators are packaged into standardized contact bounce characteristic evaluation data to describe the dynamic behavior stability of the microswitch during the electrical connection process.

[0028] In one example, based on the contact bounce characteristic evaluation data and the comprehensive response time parameter, the response performance stability of the microswitch under high-frequency operation conditions is calculated to generate a reliability evaluation result, including: The microswitch is tested in a variable-frequency pulse mode to obtain a response performance data set at different frequencies; The standard deviation of the comprehensive response time parameter is calculated based on the response performance data set at different frequencies to obtain the response stability data of the microswitch at each frequency point; A radar chart is constructed based on the contact bounce characteristic evaluation data, and the bounce times, bounce duration, bounce amplitude, and bounce decay rate are mapped onto the radar chart to obtain a visual representation of the contact bounce characteristics; The comprehensive performance score of the microswitch is obtained according to the contact bounce characteristic evaluation data and the comprehensive response time parameter. At the same time, a life cycle test is performed on the microswitch to obtain a performance change trend curve of the microswitch; Based on the comprehensive performance score of the microswitch and the performance change trend curve of the microswitch, the expected remaining life of the microswitch is calculated to obtain a reliability evaluation result.

[0029] In this example, a switching excitation platform supporting variable-frequency pulse control is constructed. This platform dynamically adjusts the output frequency through a pulse controller. On the premise of ensuring that the pulse amplitude, voltage waveform, and rising edge rate remain constant, multiple rounds of periodic on-off tests are conducted on the same switch sample in ascending order of frequency (such as gradually rising from 0.1 Hz to 100 Hz). At each frequency point, a sufficient number of test cycles are run, for example, 100 times, to ensure that the collected response data is representative and statistically stable. After each test, the contact response behavior of the microswitch is recorded in real time, mainly including nodes such as the starting time of mechanical action, the initial electrical contact time, and the stable electrical contact time. Based on this, the comprehensive response time parameter is calculated. At the same time, characteristics such as the number of bounces and bounce amplitude in the electrical waveform are recorded to form a response performance data set, where each frequency point corresponds to a set of response data with multi-dimensional indicators. Based on this data set, the standard deviation of the comprehensive response time parameter collected at each frequency point is calculated to measure the stability of the switch response performance under that frequency condition. The smaller the standard deviation, the smaller the fluctuation of the switch response time and the more consistent the performance at that frequency; the larger the standard deviation, the more it reflects the decline in response reliability or the existence of structural fatigue problems under high-frequency excitation. Through this step, a response stability distribution map is formed in the frequency domain, revealing the weaknesses of the switch in specific frequency regions. At the same time, combining the evaluation data of the contact bounce characteristics, the four core bounce characteristic parameters of the number of bounces, bounce duration, bounce amplitude, and bounce decay rate are mapped into a visual graphic structure, and the radar chart is an intuitive and effective multi-dimensional representation form. In this chart, each dimension corresponds to a bounce index, which is normalized and plotted on the symmetric coordinate axes, and the numerical points of each index are connected in a polygon envelope manner to form a recognizable geometric figure. A large area, an asymmetric figure, and a bias in a certain direction indicate complex bounce behavior and high contact instability; a compact and balanced figure represents that the bounce behavior is easy to converge and the performance is good. Based on the stability data of the response time and the visual information of the bounce characteristics in the radar chart, a comprehensive performance scoring model of the microswitch is constructed. This scoring model is composed according to a weighted evaluation mechanism, where the mean and standard deviation of the response time represent the response speed and response consistency, which are the basic indicators. The number of bounces and duration reflect the short-term contact instability, and the bounce amplitude and decay rate reflect the trend of contact energy convergence. All indicators together constitute the evaluation factor set. Weights are assigned to each indicator and normalized, and the comprehensive score from 0 to 100 is summarized. The higher the score, the better the performance, the wider the applicable frequency range, the faster the response, and the more controllable the bounce. To verify the stability and durability of the scoring model in actual use, a life cycle test is carried out synchronously. The switch is subjected to long-term high-frequency excitation, and indicators such as the response time, bounce parameters, and resistance change at fixed operation intervals are recorded. These indicators are plotted as a performance change trend curve with the operation times as the abscissa.These trend curves include the response time growth curve, the cumulative number of bounces curve, and the current stabilization time drift curve, etc. The rate of change reflects the speed of performance degradation. Based on the comprehensive performance score of the microswitch and the microswitch performance change trend curve, the time point or number of operations when the future performance reaches the limit threshold is judged by fitting historical change data. For example, when the response time increases by 30% compared to the initial value, or the number of bounces exceeds the set tolerance value, the system considers that the performance reaches the critical state, and based on this, the ratio between the current moment and the critical point is deduced, which is the basic logic of the expected remaining life estimation process. The remaining life is given in the form of the number of operations or on-off cycles, reflecting how long the switch can operate stably under the current usage conditions. Combining the comprehensive score results with the performance change trend, a reliability assessment report including structured scoring, trend charts, remaining life estimation, and risk grading suggestions is formed.

[0030] Referring to Figure 2 , this embodiment provides a response performance test system for a microswitch, including: An acquisition unit 1, configured to input a multi-pulse trigger sequence to the contacts of the microswitch and acquire the original contact response data; A filtering processing unit 2, configured to perform digital filtering processing on the original contact response data to obtain the contact response timing data; A calculation unit 3, configured to calculate a comprehensive response time parameter including the starting time of the contact mechanical action, the electrical initial contact time, and the electrical stable contact time according to the contact response timing data; A feature extraction unit 4, configured to perform temperature compensation and contact bounce feature extraction on the comprehensive response time parameter to obtain contact bounce characteristic evaluation data; A generation unit 5, configured to calculate the response performance stability of the microswitch under high-frequency operation conditions according to the contact bounce characteristic evaluation data and the comprehensive response time parameter, and generate a reliability assessment result.

[0031] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to that described in the above method embodiment, and details are not described herein again.

[0032] It should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, system, article or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, system, article or method. Without further limitations, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, system, article or method including that element.

[0033] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for testing the response performance of a microswitch, characterized in that, Including: Input a multi-pulse trigger sequence to the microswitch contact and collect the original data of the contact response; Perform digital filtering on the original data of the contact response to obtain the contact response time-series data; Perform threshold detection, first derivative analysis, and stability analysis on the contact response time-series data respectively to determine the starting time of the contact mechanical action, the initial electrical contact time, and the electrical stable contact time. Calculate the mechanical-electrical response delay time parameter based on the initial electrical contact time and the starting time of the contact mechanical action. Determine the contact jitter duration parameter based on the difference between the electrical stable contact time and the initial electrical contact time, and perform weighted summation on the mechanical-electrical response delay time parameter and the contact jitter duration parameter to obtain the comprehensive response time parameter; Perform temperature compensation and contact bounce feature extraction on the comprehensive response time parameter to obtain the contact bounce characteristic evaluation data; Calculate the response performance stability of the microswitch under high-frequency operation conditions based on the contact bounce characteristic evaluation data and the comprehensive response time parameter, and generate a reliability evaluation result.

2. The response performance testing method of the microswitch according to claim 1, wherein The inputting a multi-pulse trigger sequence to the microswitch contact and collecting the original data of the contact response includes: Generate a multi-pulse trigger sequence including single pulse, double pulse, triple pulse, and variable frequency pulse through a pulse trigger sequence generator, and perform electrical excitation on the microswitch contact according to the multi-pulse trigger sequence to obtain a microswitch contact excitation signal; Trigger a pre-signal conditioning circuit based on the microswitch contact excitation signal, and amplify the electrical signals generated during the on and off processes of the microswitch contact to obtain an amplified contact electrical signal; Perform filtering on the amplified contact electrical signal to obtain a denoised contact electrical signal, and perform real-time tracking on the mechanical movement of the microswitch contact through a micro-displacement sensor to obtain a contact mechanical displacement signal; Generate the original data of the contact response based on the denoised contact electrical signal and the contact mechanical displacement signal.

3. The response performance testing method of the microswitch according to claim 1, characterized in that, The performing digital filtering on the original data of the contact response to obtain the contact response time-series data includes: Perform pulse noise suppression on the denoised contact electrical signal in the original data of the contact response to obtain electrical signal data after removing pulse noise; Perform Kalman filtering on the contact mechanical displacement signal in the original data of the contact response to obtain smoothed contact displacement data; Perform time alignment and signal cross-correlation analysis on the electrical signal data after removing pulse noise and the smoothed contact displacement data to obtain time-synchronized electrical-mechanical signal data; Perform multi-scale decomposition on the time-synchronized electrical-mechanical signal data to obtain signal details and approximation coefficients at different frequency scales; Perform signal reconstruction based on the signal details and approximation coefficients at different frequency scales to obtain the contact response time-series data, and the contact response time-series data includes contact time-series electrical signals and contact time-series mechanical displacement signals.

4. The response performance test method of the microswitch according to claim 1, characterized in that Threshold detection, first derivative analysis, and stability analysis are respectively performed on the contact response timing data to determine the starting time of the mechanical action of the contact, the initial electrical contact time, and the stable electrical contact time. The mechanical-electrical response delay time parameter is calculated based on the initial electrical contact time and the starting time of the mechanical action of the contact. The contact jitter duration parameter is determined based on the difference between the stable electrical contact time and the initial electrical contact time. The mechanical-electrical response delay time parameter and the contact jitter duration parameter are weighted and summed to obtain the comprehensive response time parameter, including: Threshold detection is performed on the contact timing mechanical displacement signal in the contact response timing data, and the moment when the contact timing mechanical displacement signal exceeds the distance target value is determined as the starting time of the mechanical action of the contact; The first derivative of the contact timing electrical signal in the contact response timing data is calculated to obtain the first derivative value, and the time point when the first derivative value first exceeds the first nominal current is found and determined as the initial electrical contact time; Stability analysis is performed on the contact timing electrical signal in the contact response timing data, and the starting moment when the current fluctuation amplitude is less than the second nominal current is found and determined as the stable electrical contact time; The time difference between the initial electrical contact time and the starting time of the mechanical action of the contact is calculated to obtain the mechanical-electrical response delay time parameter; The time difference between the stable electrical contact time and the initial electrical contact time is calculated to obtain the contact jitter duration parameter; The mechanical-electrical response delay time parameter and the contact jitter duration parameter are weighted and summed to obtain the comprehensive response time parameter.

5. The response performance testing method of the microswitch according to claim 1, characterized in that Temperature compensation and contact bounce feature extraction are performed on the comprehensive response time parameter to obtain contact bounce characteristic evaluation data, including: The temperature value of the working environment of the microswitch is collected in real time through a temperature sensor to obtain the current test environment temperature data; The current test environment temperature data is divided into intervals to obtain the temperature compensation interval parameter; According to the temperature compensation interval parameter, the temperature compensation coefficient lookup table is queried to obtain the current temperature compensation coefficient; The comprehensive response time parameter and the current temperature compensation coefficient are multiplied to obtain the first temperature compensation response time value; The first temperature compensation response time value is corrected quadratically through a polynomial fitting model to obtain the second temperature compensation response time value; Contact bounce feature extraction is performed based on the second temperature compensation response time value to obtain contact bounce characteristic evaluation data.

6. The response performance test method of the microswitch according to claim 5, characterized in that, Contact bounce feature extraction is performed based on the second temperature compensation response time value to obtain contact bounce characteristic evaluation data, including: Using the second temperature compensation response time value, the contact timing electrical signal in the contact response timing data is decomposed to obtain multi-scale detail coefficients reflecting the contact bounce characteristics; Peak detection is performed based on the multi-scale detail coefficients to obtain the time series of the occurrence of contact bounce events; Calculate the time intervals between adjacent bounce events according to the time series of the occurrence of the contact bounce events, and count the number of bounces before the contact is fully closed to obtain the bounce number parameter and the bounce interval time parameter; Extract the current amplitude of each bounce event and calculate the ratio of the bounce amplitude to the stable value to obtain the bounce amplitude parameter, and perform normalization processing and exponential fitting on the amplitudes of multiple consecutive bounce events to obtain the bounce decay rate parameter; Generate contact bounce characteristic evaluation data according to the bounce number parameter, the bounce interval time parameter, the bounce amplitude parameter, and the bounce decay rate parameter.

7. The response performance test method of the microswitch according to claim 1, characterized in that, Calculating the response performance stability of the microswitch under high-frequency operation conditions according to the contact bounce characteristic evaluation data and the comprehensive response time parameter, and generating a reliability evaluation result, including: Perform a variable-frequency pulse mode test on the microswitch to obtain a response performance data set at different frequencies; Calculate the standard deviation of the comprehensive response time parameter according to the response performance data set at different frequencies to obtain the response stability data of the microswitch at each frequency point; Construct a radar chart based on the contact bounce characteristic evaluation data, and map the bounce number, bounce duration, bounce amplitude, and bounce decay rate onto the radar chart to obtain a visual representation of the contact bounce characteristics; Score the comprehensive performance of the microswitch according to the contact bounce characteristic evaluation data and the comprehensive response time parameter, and at the same time perform a life cycle test on the microswitch to obtain a performance change trend curve of the microswitch; Calculate the expected remaining life of the microswitch according to the comprehensive performance score of the microswitch and the performance change trend curve of the microswitch to obtain a reliability evaluation result.

8. A response performance test system for a microswitch, characterized in that, For implementing the steps of the method according to any one of claims 1 to 7, the response performance test system of the microswitch includes: An acquisition unit for inputting a multi-pulse trigger sequence to the microswitch contact and acquiring the original contact response data; A filtering processing unit for performing digital filtering processing on the original contact response data to obtain contact response time series data; A calculation unit for respectively performing threshold detection, first-order derivative analysis, and stability analysis on the contact response time series data to determine the starting time of the contact mechanical action, the initial electrical contact time, and the stable electrical contact time, calculating the mechanical-electrical response delay time parameter according to the initial electrical contact time and the starting time of the contact mechanical action, determining the contact jitter duration parameter according to the difference between the stable electrical contact time and the initial electrical contact time, and performing weighted summation on the mechanical-electrical response delay time parameter and the contact jitter duration parameter to obtain the comprehensive response time parameter; A feature extraction unit for performing temperature compensation and contact bounce feature extraction on the comprehensive response time parameter to obtain contact bounce characteristic evaluation data; A generation unit for calculating the response performance stability of the microswitch under high-frequency operation conditions according to the contact bounce characteristic evaluation data and the comprehensive response time parameter, and generating a reliability evaluation result.

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