Membrane switch performance detection method and system

Abstract

The invention discloses a membrane switch performance detection method and system, relates to the technical field of performance detection, and solves the technical problems that characteristics of different types of membrane switches are difficult to consider, a finer detection evaluation scheme is set for a specific type of membrane switch, and comprehensive performance of various membrane switches is difficult to accurately analyze. By setting different temperature change rates, the working state of the membrane switch in an extreme or complex environment can be simulated. The temperature distribution uniformity of the surface of the switch can be visually displayed by detecting the infrared image, a local overheating or poor heat dissipation area is revealed, and the performance of the membrane switch can be conveniently detected from multiple dimensions by combining operation data. The PET-based resistor temperature drift stability and the FPC-based microcircuit impedance stability are analyzed, the degradation coefficient and the temperature performance evaluation coefficient are combined, comprehensive performance evaluation is carried out, and the reliability of different types of membrane switches is quantified.

Description

Technical Field

[0001] The present invention belongs to the field of performance detection, and specifically relates to a method and system for detecting the performance of a membrane switch. Background Art

[0002] A membrane switch is an operating system that integrates button functions, indicating elements, and instrument panels. It consists of four parts: a panel, an upper circuit, an isolation layer, and a lower circuit. When the membrane switch is pressed, the contacts of the upper circuit deform downward and come into contact with the electrodes of the lower circuit to conduct. After the finger is released, the contacts of the upper circuit bounce back and the circuit is disconnected, triggering a signal in the loop. It is widely used in fields such as electronic communication, electronic measurement instruments, and industrial control. With the increasing demands for intelligence, miniaturization, and high reliability, a performance detection scheme for membrane switches that can meet more requirements is needed.

[0003] Existing performance detection schemes for membrane switches only test resistance or pressing life, lacking comprehensive performance evaluation, making it difficult to consider the characteristics of different types of membrane switches and set more refined detection and evaluation schemes for specific types of membrane switches, and difficult to accurately analyze the comprehensive performance of various membrane switches. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes a method and system for detecting the performance of a membrane switch, which is used to solve the technical problems that it is difficult to consider the characteristics of different types of membrane switches, set more refined detection and evaluation schemes for specific types of membrane switches, and difficult to accurately analyze the comprehensive performance of various membrane switches.

[0005] To solve the above problems, the first aspect of the present invention provides a method and system for detecting the performance of a membrane switch, including: Classify the membrane switches into flexible membrane switches, rigid membrane switches, and composite membrane switches, build a test bench including a current sensor and a voltage sensor, and collect operation data of each classified membrane switch; Press the membrane switch through a preset pressing mode, and collect operation data under different pressing modes in real time to analyze the actual control duration of the switch; Use the pressing parameter as the abscissa and the switch response duration detected by the test bench as the ordinate, and fit the action curve by the polynomial fitting method to obtain a fitting function, and analyze the degradation coefficient of each classified membrane switch; By setting different temperature change rates, collect the operation data and infrared images of each classified membrane switch at the test temperature, and analyze the stable operation time of the membrane switch and the temperature performance evaluation coefficient through the operation data and infrared images; Based on the operation data of the flexible thin-film switch during the whole process of the experiment, analyze the resistance temperature drift stability of the PET-based type in the flexible thin-film switch and the microcircuit impedance stability of the FPC-based type, and combine the deterioration coefficient and the temperature performance evaluation coefficient to analyze the comprehensive performance evaluation value of various thin-film switches.

[0006] Optionally, in an example of the above aspect, press the thin-film switch through a preset pressing mode, and collect the operation data under different pressing modes in real time, and analyze the actual control duration of the switch, including the following steps: The preset pressing mode includes the pressing angle: evenly set several pressing angles between 0° and 90°, the pressing position: evenly set several pressing detection points in the pressing area, and the pressing speed: set low-speed, medium-speed and high-speed pressing speeds, and arrange and combine the preset parameters of the pressing angle, pressing area and pressing speed to obtain several pressing modes; Press the thin-film switch according to the preset pressing mode, and collect the operation data under different pressing modes in real time, including: current and voltage data; Perform wavelet transform on the collected current and voltage signals, extract the wavelet coefficients at different scales, if the change rate of the wavelet coefficients exceeds the preset threshold, judge that the contact resistance of the thin-film switch jitters, and calculate the mean value of the test duration of all pressing modes of the thin-film switch as the actual control duration of the switch.

[0007] Optionally, in an example of the above aspect, use the pressing parameter as the abscissa and the switch response duration detected by the test bench as the ordinate, and fit the action curve by the polynomial fitting method to obtain the fitting function, including the following steps: When the test bench detects and presses the thin-film switch according to the preset pressing mode, obtain the switch response duration after pressing, and obtain the pressing parameter and switch response duration data of the preset pressing mode; According to the pressing angle and pressing speed of each pressing detection point in the pressing area, use the pressing angle and pressing speed parameters in the pressing parameter as the abscissa respectively, obtain the corresponding switch response duration according to the abscissa parameter and take the mean value, and use the mean value of the corresponding switch response duration as the ordinate, and use a data visualization tool to draw a scatter plot; Use the numpy.polyfit function in the numerical calculation tool Python to perform polynomial fitting on the data; Calculate the mean square error of the fitting. If it does not exceed the preset threshold, the fitting of the action curve of the switch response duration and the pressing parameter is completed. If it exceeds the preset threshold, increase the order of the polynomial and re-perform polynomial fitting until the mean square error of the fitting does not exceed the preset threshold.

[0008] Optionally, in an example of the above aspect, analyzing the deterioration coefficients of membrane switches of each classification includes the following steps: Obtain the action curves of the switch response duration and actuation parameters obtained by fitting each pressing detection point in the pressing area of the membrane switch, and calculate the variance S1 and mean tr1 of the action curves at different pressing angles, as well as the variance S2 and mean tr2 of the action curves at different pressing speeds; According to the actual control duration of the membrane switch and the preset standard control duration, analyze the control duration decay coefficient of the membrane switch f1 = (actual control duration of the switch - preset standard control duration) / preset standard control duration; According to the variance and mean of the action curve, and the control duration decay coefficient f1 of the membrane switch, analyze the deterioration coefficient through the following formula: ; where D is the deterioration coefficient of the membrane switch, Δθi is the absolute value of the deviation between the actual pressing angle and the standard angle for the i-th pressing angle, θ0 is the preset standard angle, tai is the switch response duration for the i-th pressing angle, n is the total number of pressing angle settings, i ∈ (1, 2,..., n), ΔVj is the absolute value of the deviation between the actual pressing speed and the standard speed for the j-th pressing speed, V0 is the preset standard speed, taj is the switch response duration for the j-th pressing speed, j ∈ (1, 2, 3), and w1, w2, w3, w4, and w5 are the corresponding weights.

[0009] Optionally, in an example of the above aspect, by setting different temperature change rates, collect the operation data and infrared images of membrane switches of each classification at the test temperature, and analyze the stable operation time of the membrane switch through the operation data and infrared images, including the following steps: Set a temperature change rate of 1 °C / min and 5 °C / min for flexible membrane switches, a temperature change rate of 5 °C / min for rigid membrane switches, and a temperature change rate of 1 °C / min and 5 °C / min for composite membrane switches; Detect and collect the operation data of membrane switches of each classification at the test temperature through a test bench, and simultaneously collect the infrared image data of the membrane switch during the experiment; Obtain the historical infrared image data of the membrane switch tested at different temperatures, and mark the areas with bad points generated during the membrane switch test in the historical infrared image data; Train a deep learning model through the marked historical infrared image data to mark the bad points in the real-time collected infrared image data of the membrane switch, and extract the test time length when there are no bad points in the infrared image data of the membrane switch according to the recognition results; Analyze the volatility and deviation of different types of flexible membrane switches, set the volatility threshold and deviation threshold of the operating data, extract the time length during which the volatility of the operating data does not exceed the volatility threshold and the deviation of the operating data does not exceed the deviation threshold, and compare it with the test time length, and take the minimum time as the stable operating time of the membrane switch.

[0010] Optionally, in an example of the above aspect, analyzing the volatility and deviation of different types of flexible membrane switches, setting the volatility threshold and deviation threshold of the operating data, includes the following steps: Perform weighted averaging on the operating data for different types of flexible membrane switches at different preset temperature change rates; According to the weighted average operating data, calculate volatility = operating data variance / (square of operating data mean + 1), deviation = ∣operating data peak - preset maximum operating data threshold∣ / preset maximum operating data threshold; Screen the operating data within a fixed time threshold before the occurrence of a failure in the historical data, and calculate the volatility and deviation through the screened data as the corresponding volatility threshold and deviation threshold.

[0011] Optionally, in an example of the above aspect, analyzing the temperature performance evaluation coefficient includes the following steps: According to the stable operating time of the membrane switch, as well as the volatility and deviation of the flexible membrane switch, analyze the temperature performance evaluation coefficient through the following formula: ; where TPEC is the temperature performance evaluation coefficient, Fvo is the volatility of the flexible membrane switch, Fde is the deviation of the flexible membrane switch, and Tf is the stable operating time of the membrane switch.

[0012] Optionally, in an example of the above aspect, according to the operating data of the flexible membrane switch during the whole experiment process, analyze the resistance temperature drift stability of the PET-based type and the microcircuit impedance stability of the FPC-based type in the flexible membrane switch, including the following steps: Obtain the temperature and operating data of the flexible membrane switch during the experiment process, calculate the real-time resistance of the flexible membrane switch according to the operating data, and align the resistance data with the temperature data of the flexible membrane switch; Label the corresponding data of the PET-based type and the FPC-based type in the flexible membrane switch; For the PET-based flexible membrane switch, according to the aligned data, randomly select several groups of data with different temperatures and resistance values within the stable operation time of the membrane switch and the data during the actual control time of the switch. Calculate the pairwise permutations and combinations of the selected data, and calculate the temperature coefficient of resistance (TCR) of the combined data. Take the ratio of the variance of all the obtained TCRs to the mean value of the TCRs as the resistance temperature drift stability of the PET-based type in the flexible membrane switch; For the FPC-based flexible membrane switch, measure the impedance value of the membrane switch after the experiment using a TDR (Time Domain Reflectometer). According to the recorded initial impedance value and the impedance value after the experiment of the FPC-based flexible membrane switch, calculate the impedance change rate as the microcircuit impedance stability of the FPC-based type.

[0013] Optionally, in an example of the above aspect, analyzing the comprehensive performance evaluation value of various membrane switches includes the following steps: For the PET-based flexible membrane switch, based on the resistance temperature drift stability, combined with the deterioration coefficient and the temperature performance evaluation coefficient, perform normalized weighted averaging to analyze the comprehensive performance evaluation value of the flexible membrane switch; For the FPC-based flexible membrane switch, based on the microcircuit impedance stability, combined with the deterioration coefficient and the temperature performance evaluation coefficient, perform normalized weighted averaging to analyze the comprehensive performance evaluation value of the flexible membrane switch; For the rigid membrane switch and the composite membrane switch, obtain the corresponding deterioration coefficient and temperature performance evaluation coefficient, perform normalized weighted averaging on the deterioration coefficient and the temperature performance evaluation coefficient of the membrane switch to analyze the comprehensive performance of the membrane switch.

[0014] According to another aspect of the present disclosure, there is provided a performance detection system for a membrane switch, characterized in that the system uses a performance detection system for a membrane switch as described above to implement the performance detection of the membrane switch.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By setting different temperature change rates, the present invention can simulate the working state of the membrane switch in extreme or complex environments and accurately evaluate its performance limits under different temperature gradients. By detecting the infrared image, it is convenient to visually display the temperature distribution uniformity on the surface of the switch, reveal local overheating or poor heat dissipation areas, and combined with the operation data, it is convenient to detect the performance of the membrane switch from multiple dimensions. By defining the temperature performance evaluation coefficient, the influence of temperature on the switch performance can be converted into a quantifiable value, which is convenient for more accurate analysis of the switch performance.

[0016] The present invention can reduce the resistance drift caused by temperature changes through its stable temperature drift characteristics, thereby reducing the risk of switch failure. Through the whole process of experimental data, the resistance value changes of the PET-based membrane switch at different temperatures are accurately measured, and its temperature drift coefficient is quantified; this helps to evaluate the stability of the PET-based resistor in a temperature fluctuation environment. The impedance stability of the FPC-based microcircuit directly affects the signal transmission quality. By analyzing the impedance fluctuation through experimental data, the integrity of high-speed signal transmission can be ensured, reflections and crosstalk can be reduced, and through the deterioration coefficient and temperature performance evaluation coefficient, the comprehensive performance evaluation can be carried out to quantify the reliability of different types of membrane switches. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flow chart of the method of the present invention; Figure 2 It is a schematic flow chart for analyzing the actual control duration of the switch of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0020] Please refer to Figure 1 - Figure 2 , an embodiment of the first aspect of the present invention provides a method and system for detecting the performance of a membrane switch, including: Classify the membrane switches into flexible membrane switches, rigid membrane switches, and composite membrane switches, build a test bench including a current sensor and a voltage sensor, and collect the operation data of each classified membrane switch; Press the membrane switch through a preset pressing mode, and collect the operation data under different pressing modes in real time to analyze the actual control duration of the switch; Take the pressing parameter as the abscissa and the switch response duration detected by the test bench as the ordinate, and fit the action curve by the polynomial fitting method to obtain the fitting function, and analyze the deterioration coefficient of each classified membrane switch; By setting different temperature change rates, collect the operation data and infrared images of the membrane switches of each category at the test temperature. Through the operation data and infrared images, analyze the stable operation time of the membrane switches and analyze the temperature performance evaluation coefficient; According to the operation data of the flexible membrane switch during the whole experiment process, analyze the resistance temperature drift stability of the PET-based type and the microcircuit impedance stability of the FPC-based type in the flexible membrane switch, and combine the deterioration coefficient and the temperature performance evaluation coefficient to analyze the comprehensive performance evaluation value of various membrane switches.

[0021] Specifically, in this embodiment, According to the substrate flexibility, application scenarios and structural characteristics, the membrane switches are divided into the following three categories: ; The test bench realizes the acquisition of current and voltage data and supports the compatibility test of three types of switches, and collects the operation data of the membrane switches of each category.

[0022] By presetting the pressing mode, press the membrane switch and collect the operation data under different pressing modes in real time, and analyze the actual control duration of the switch; taking the pressing parameter as the abscissa and the switch response duration detected by the test bench as the ordinate, and fitting the action curve by the polynomial fitting method to obtain the fitting function, and analyze the deterioration coefficient of the membrane switches of each category; The preset pressing mode can simulate the actual use scenario, comprehensively evaluate the mechanical and electrical performance of the switch, and the real-time data acquisition is convenient for capturing transient effects and long-term stability. Through the curve fitting of the pressing parameter and the response duration, the fitting function replaces a large number of repeated experiments, and the deterioration speed of the switch under different use conditions can be quantified. It is convenient to predict the life of the switch under specific use conditions. Combined with the accelerated aging test, the test cycle can be shortened and the effect of design improvement can be quickly verified.

[0023] By setting different temperature change rates, collect the operation data and infrared images of the membrane switches of each category at the test temperature. Through the operation data and infrared images, analyze the stable operation time of the membrane switches and analyze the temperature performance evaluation coefficient; By setting different temperature change rates, the working state of the membrane switch in extreme or complex environments can be simulated, and its performance limit under different temperature gradients can be accurately evaluated. By detecting the infrared image, it is convenient to visually display the temperature distribution uniformity on the surface of the switch, reveal local overheating or poor heat dissipation areas, and combined with the operation data, it is convenient to detect the performance of the membrane switch from multiple dimensions. By defining the temperature performance evaluation coefficient, the influence of temperature on the switch performance can be converted into a quantifiable value, which is convenient for more accurate analysis of the switch performance.

[0024] Based on the operation data of the flexible membrane switch during the whole process of the experiment, analyze the resistance temperature drift stability of the PET-based type in the flexible membrane switch and the microcircuit impedance stability of the FPC-based type, and combine the deterioration coefficient and the temperature performance evaluation coefficient to analyze the comprehensive performance evaluation value of various membrane switches.

[0025] A stable temperature drift characteristic can reduce the resistance drift caused by temperature changes and lower the risk of switch failure. Through the whole-process experimental data, accurately measure the resistance value change of the PET-based membrane switch at different temperatures and quantify its temperature drift coefficient. This helps to evaluate the stability of the PET-based resistance in a temperature fluctuation environment. The impedance stability of the FPC-based microcircuit directly affects the signal transmission quality. By analyzing the impedance fluctuation through experimental data, the integrity of high-speed signal transmission can be ensured, reducing reflection and crosstalk. At the same time, through the deterioration coefficient and the temperature performance evaluation coefficient, a comprehensive performance evaluation is carried out to quantify the reliability of different types of membrane switches.

[0026] Based on the deterioration coefficient and the temperature performance evaluation coefficient, the life and failure risk of the switch under specific usage conditions can be predicted, providing data support for product maintenance and replacement cycles. Combining the temperature drift stability, impedance stability, deterioration coefficient and temperature performance evaluation coefficient can comprehensively evaluate the electrical performance, mechanical performance and environmental adaptability of the membrane switch, avoiding the one-sidedness of single-index evaluation.

[0027] In one embodiment of the present invention, by presetting the pressing mode, press the membrane switch and collect the operation data under different pressing modes in real time, and analyze the actual control duration of the switch, including the following steps: Preset the pressing mode, including the pressing angle: evenly set several pressing angles between 0° and 90°. In this embodiment, the pressing angles are set to 15°, 30°, 45°, 60°, 75° and 90°. The pressing position: evenly set several pressing detection points in the pressing area, and the pressing speed: set low-speed, medium-speed and high-speed pressing speeds. In this embodiment, it is specifically set to 5mm / s, 20mm / s and 50mm / s. Arrange and combine the preset parameters of the pressing angle, pressing area and pressing speed to obtain several pressing modes; Press the membrane switch according to the preset pressing mode and collect the operation data under different pressing modes in real time, including: current and voltage data; Perform wavelet transform on the collected current and voltage signals, extract the wavelet coefficients at different scales. If the change rate of the wavelet coefficients exceeds the preset threshold, judge that the contact resistance of the membrane switch jitters, and calculate the mean value of the test duration of all pressing modes of the membrane switch as the actual control duration of the switch.

[0028] Calculate the actual control duration, which can be obtained by accumulating the number of normal operation presses and the average time per press. For example, if 100 presses are made and the average time per press is 0.1 second, the actual control duration is 10 seconds.

[0029] In one embodiment of the present invention, with the pressing parameter as the abscissa and the switch response duration detected by the test bench as the ordinate, and the action curve is fitted by the polynomial fitting method to obtain the fitting function, including the following steps: When the test bench detects the pressing test of the membrane switch according to the preset pressing mode, obtain the switch response duration after pressing, and acquire the pressing parameter and switch response duration data of the preset pressing mode. According to the pressing angle and pressing speed of each pressing detection point in the pressing area, respectively take the pressing angle and pressing speed parameters in the pressing parameter as the abscissa, obtain the corresponding switch response duration according to the abscissa parameter and take the mean value, and use the mean value of the corresponding switch response duration as the ordinate, and use a data visualization tool (such as Matplotlib in Python, Excel, etc.) to draw a scatter plot. Use the numpy.polyfit function in the numerical calculation tool Python to perform polynomial fitting on the data. Calculate the mean squared error of the fitting. If it does not exceed the preset threshold, the action curve fitting of the switch response duration and the pressing parameter is completed. If it exceeds the preset threshold, increase the order of the polynomial and re - perform polynomial fitting until the mean squared error of the fitting does not exceed the preset threshold.

[0030] In this embodiment, the switch response duration is obtained by accumulating the number of normal operation presses and the average time per press. The mean squared error of the fitting exceeds the preset threshold, and the polynomial increases by two orders for fitting; the mean squared error (Mean Squared Error, MSE) is set according to actual requirements. In the membrane switch experiment, if the control duration error needs to be ≤ 5%, assuming the mean control duration is 0.5 and the error limit is 0.025, MSE = error limit², then the MSE threshold can be set to 0.0025.

[0031] In one embodiment of the present invention, analyze the deterioration coefficient of each type of membrane switch, including the following steps: Obtain the action curve of the switch response duration and the pressing parameter obtained by fitting for each pressing detection point in the pressing area of the membrane switch, and calculate the variance S1 and mean tr1 of the action curves at different pressing angles, and the variance S2 and mean tr2 of the action curves at different pressing speeds. According to the actual control duration of the membrane switch and the preset standard control duration, analyze the control duration decay coefficient of the membrane switch f1 = (actual control duration of the switch - preset standard control duration) / preset standard control duration; According to the variance and mean of the action curve, and the control duration decay coefficient f1 of the membrane switch, analyze the deterioration coefficient through the following formula: ; where D is the deterioration coefficient of the membrane switch, Δθi is the absolute value of the deviation between the actual pressing angle and the standard angle for the i-th pressing angle, θ0 is the preset standard angle, tai is the switch response duration for the i-th pressing angle, n is the total number of pressing angle settings, i ∈ (1, 2,..., n), ΔVj is the absolute value of the deviation between the actual pressing speed and the standard speed for the j-th pressing speed, V0 is the preset standard speed, taj is the switch response duration for the j-th pressing speed, j ∈ (1, 2, 3), and w1, w2, w3, w4, and w5 are the corresponding weights. In this embodiment, the weights are set to 0.4, 0.2, 0.2, 0.1, and 0.1 respectively.

[0032] In one embodiment of the present invention, by setting different temperature change rates, collect the operation data and infrared images of each type of membrane switch at the test temperature, and analyze the stable operation time of the membrane switch through the operation data and infrared images, including the following steps: Set a temperature change rate of 1 °C / min and 5 °C / min for the flexible membrane switch, a temperature change rate of 5 °C / min for the rigid membrane switch, and a temperature change rate of 1 °C / min and 5 °C / min for the composite membrane switch; Detect and collect the operation data of each type of membrane switch at the test temperature through the test bench, and simultaneously collect the infrared image data of the membrane switch during the experiment process; Obtain the historical infrared image data of the membrane switch tested at different temperatures, and mark the areas with bad points generated during the membrane switch test in the historical infrared image data; Train a deep learning model through the marked historical infrared image data, mark the bad points in the real-time collected infrared image data of the membrane switch, and extract the test time length when there are no bad points in the infrared image data of the membrane switch according to the recognition result; Analyze the volatility and deviation of different types of flexible membrane switches, set the volatility threshold and deviation threshold of the operation data, extract the time length when the volatility of the operation data does not exceed the volatility threshold and the deviation of the operation data does not exceed the deviation threshold, and compare it with the test time length, and take the minimum time as the stable operation time of the membrane switch.

[0033] In one embodiment of the present invention, to analyze the volatility and deviation of different types of flexible membrane switches, and set the volatility threshold and deviation threshold of the operating data, the following steps are included: Under different preset temperature change rates of different types of flexible membrane switches, perform weighted averaging on the operating data; According to the weighted-averaged operating data, calculate the volatility = operating data variance / (square of the operating data mean + 1), and the deviation = |operating data peak - preset maximum operating data threshold| / preset maximum operating data threshold; Screen the operating data within a fixed time threshold before a failure in the historical data, and calculate the volatility and deviation through the screened data as the corresponding volatility threshold and deviation threshold.

[0034] In this embodiment, during weighted averaging, the weights of the operating data of the flexible membrane switch at temperature change rates of 1 °C / min and 5 °C / min are 0.4 and 0.6 respectively, and for the rigid membrane switch and the composite membrane switch, the weights of the operating data at temperature change rates of 1 °C / min and 5 °C / min are 0.6 and 0.4 respectively.

[0035] In one embodiment of the present invention, to analyze the temperature performance evaluation coefficient, the following steps are included: According to the stable operation time of the membrane switch, as well as the volatility and deviation of the flexible membrane switch, analyze the temperature performance evaluation coefficient through the following formula: ; where TPEC is the temperature performance evaluation coefficient, Fvo is the volatility of the flexible membrane switch, Fde is the deviation of the flexible membrane switch, and Tf is the stable operation time of the membrane switch.

[0036] In one embodiment of the present invention, according to the operating data of the flexible membrane switch during the whole experimental process, analyze the resistance temperature drift stability of the PET-based type and the microcircuit impedance stability of the FPC-based type in the flexible membrane switch, including the following steps: Obtain the temperature and operating data of the flexible membrane switch during the experiment, calculate the real-time resistance of the flexible membrane switch according to the operating data, and align the resistance data with the temperature data of the flexible membrane switch; Label the corresponding data of the PET-based type and the FPC-based type in the flexible membrane switch; For the PET-based flexible membrane switch, according to the aligned data, randomly select several groups of data with different temperatures and resistance values within the stable operation time of the membrane switch and the actual control duration of the switch, calculate the pairwise permutations and combinations of the selected data, calculate the temperature coefficient of resistance (TCR) of the combined data, and take the ratio of the variance of all obtained TCRs to the mean value of TCRs as the resistance temperature drift stability of the PET-based type in the flexible membrane switch. For the FPC-based flexible membrane switch, measure the impedance value of the membrane switch after the experiment using a TDR (Time Domain Reflectometer). According to the recorded initial impedance value and the impedance value after the experiment of the FPC-based flexible membrane switch, calculate the impedance change rate as the microcircuit impedance stability of the FPC-based type.

[0037] The TCR (temperature coefficient of resistance) is used to measure the stability of the resistance value with respect to temperature change, and its calculation formula is: TCR = (R2 - R1) / R1 × (T2 - T1), where: R1 and R2 are the resistance values at temperatures T1 and T2 respectively.

[0038] The smaller the value of TCR, the smaller the change of the resistance with temperature, and the higher the stability.

[0039] The calculation formula for the microcircuit impedance Z0 is: ; where, εr is the dielectric constant of the base material (the typical value of FR4 is 4.2 - 4.5, and that of PI film is 3.2 - 3.5); h is the thickness of the dielectric layer (mm), w is the designed width of the wire (mm), and t is the thickness of the copper foil (mm, 1oz = 0.035mm).

[0040] Calculate the impedance change rate: ΔZ = (Zafter - Zbefore) / Zbefore, where Zafter is the impedance value after the experiment and Zbefore is the initial impedance value.

[0041] In one embodiment of the present invention, analyzing the comprehensive performance evaluation value of various types of membrane switches includes the following steps: For the PET-based flexible membrane switch, based on the resistance temperature drift stability, combined with the deterioration coefficient and the temperature performance evaluation coefficient, perform normalization and weighted average to analyze the comprehensive performance evaluation value of the flexible membrane switch; in this embodiment, the weights for the weighted average are set as follows: the weight of the combination of the resistance temperature drift stability and the deterioration coefficient is 0.2, the weight of the deterioration coefficient is 0.4, and the weight of the temperature performance evaluation coefficient is 0.4; For the FPC-based flexible membrane switch, according to the impedance stability of the microcircuit, combined with the deterioration coefficient and the temperature performance evaluation coefficient, after normalization, weighted averaging is performed to analyze the comprehensive performance evaluation value of the flexible membrane switch; in this embodiment, the weights for weighted averaging are set such that the weight of the impedance stability of the microcircuit combined with the deterioration coefficient is 0.2, the weight of the deterioration coefficient is 0.4, and the weight of the temperature performance evaluation coefficient is 0.4; For the rigid membrane switch and the composite membrane switch, the corresponding deterioration coefficient and temperature performance evaluation coefficient are obtained, and after normalizing the deterioration coefficient and temperature performance evaluation coefficient of the membrane switch, weighted averaging is performed to analyze the comprehensive performance of the membrane switch. In this embodiment, the weight of the deterioration coefficient is 0.4, and the weight of the temperature performance evaluation coefficient is 0.6.

[0042] In this embodiment, the historical detection data of the membrane switch is obtained. According to the maintenance feedback, the historical detection data of the membrane switch with the best maintenance feedback batch and passing the quality inspection is selected, and the mean and minimum values of the comprehensive performance evaluation values of various types of membrane switches are calculated. The obtained mean and minimum values are weighted averaged, and the resulting value is used as the analysis threshold for the comprehensive performance evaluation value. If it is greater than or equal to the corresponding threshold, the performance of the corresponding membrane switch is excellent.

[0043] In an embodiment of another aspect of the present invention, a performance detection system for a membrane switch is provided, characterized in that the system implements the performance detection of the membrane switch by using a performance detection system for a membrane switch as described above.

[0044] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A performance detection method for a membrane switch, characterized in that, Including: Classify membrane switches into flexible membrane switches, rigid membrane switches, and composite membrane switches, set up a test bench with current sensors and voltage sensors, and collect operation data for membrane switches of each classification; Press the membrane switch through a preset pressing mode, and collect operation data in real time under different pressing modes to analyze the actual control duration of the switch; Use the pressing parameter as the abscissa and the switch response duration detected by the test bench as the ordinate, and fit the action curve by polynomial fitting method to obtain a fitting function, and analyze the degradation coefficient of membrane switches of each classification; By setting different temperature change rates, collect the operation data and infrared images of membrane switches of each classification at the test temperature, and analyze the stable operation time of the membrane switches and the temperature performance evaluation coefficient through the operation data and infrared images; According to the operation data of the flexible membrane switch during the whole experimental process, analyze the resistance temperature drift stability of the PET-based type and the microcircuit impedance stability of the FPC-based type in the flexible membrane switch, and combine the degradation coefficient and the temperature performance evaluation coefficient to analyze the comprehensive performance evaluation value of various membrane switches.

2. The performance detection method of a membrane switch according to claim 1, characterized in that, Press the membrane switch through a preset pressing mode, and collect operation data in real time under different pressing modes to analyze the actual control duration of the switch, including the following steps: Preset the pressing mode, including the pressing angle: evenly set several pressing angles between 0° and 90°, the pressing position: evenly set several pressing detection points in the pressing area, and the pressing speed: set low, medium, and high pressing speeds, and arrange and combine the preset parameters of the pressing angle, pressing area, and pressing speed to obtain several pressing modes; Press the membrane switch according to the preset pressing mode, and collect operation data in real time under different pressing modes, including: current and voltage data; Perform wavelet transform on the collected current and voltage signals, extract wavelet coefficients at different scales. If the change rate of the wavelet coefficients exceeds the preset threshold, judge the contact resistance jitter of the membrane switch, and calculate the mean value of the test duration of all pressing modes of the membrane switch as the actual control duration of the switch.

3. The performance detection method of a membrane switch according to claim 1, characterized in that, Use the pressing parameter as the abscissa and the switch response duration detected by the test bench as the ordinate, and fit the action curve by polynomial fitting method to obtain a fitting function, including the following steps: When the test bench detects the pressing test of the membrane switch according to the preset pressing mode, record the switch response duration after pressing, and obtain the pressing parameter and switch response duration data of the preset pressing mode; According to the pressing angle and pressing speed of each pressing detection point in the pressing area, use the pressing angle and pressing speed parameters in the pressing parameter as the abscissa respectively, obtain the corresponding switch response duration according to the abscissa parameter and take the mean value, and use the mean value of the corresponding switch response duration as the ordinate, and use a data visualization tool to draw a scatter plot; Use the numpy.polyfit function in the numerical calculation tool Python to perform polynomial fitting on the data; Calculate the mean square error of the fitting. If it does not exceed the preset threshold, the fitting of the switch response duration and the action curve of the pressing parameter is completed. If it exceeds the preset threshold, increase the order of the polynomial and perform polynomial fitting again until the mean square error of the fitting does not exceed the preset threshold.

4. A performance detection method for a membrane switch according to claim 1, characterized in that, Analyze the deterioration coefficients of the membrane switches of each classification, including the following steps: Obtain the action curves of the switch response duration and the pressing parameter obtained by fitting at each pressing detection point in the pressing area of the membrane switch, and calculate the variance S1 and mean tr1 of the action curves at different pressing angles, as well as the variance S2 and mean tr2 of the action curves at different pressing speeds. According to the actual control duration of the membrane switch and the preset standard control duration, analyze the control duration decay coefficient of the membrane switch f1 = (actual control duration of the switch - preset standard control duration) / preset standard control duration. According to the variance and mean of the action curve and the control duration decay coefficient f1 of the membrane switch, analyze the deterioration coefficient through the following formula: ； Where D is the deterioration coefficient of the membrane switch, Δθi is the absolute value of the deviation between the actual pressing angle and the standard angle of the i-th pressing angle, θ0 is the preset standard angle, tai is the switch response duration of the i-th pressing angle, n is the total number of pressing angle settings, i ∈ (1, 2,..., n), ΔVj is the absolute value of the deviation between the actual pressing speed and the standard speed of the j-th pressing speed, V0 is the preset standard speed, taj is the switch response duration of the j-th pressing speed, j ∈ (1, 2, 3), and w1, w2, w3, w4, and w5 are the corresponding weights.

5. A performance detection method for a membrane switch according to claim 1, characterized in that By setting different temperature change rates, collect the operation data and infrared images of the membrane switches of each classification at the test temperature. Analyze the stable operation time of the membrane switches through the operation data and infrared images, including the following steps: Set a temperature change rate of 1 °C / min and 5 °C / min for the flexible membrane switch, a temperature change rate of 5 °C / min for the rigid membrane switch, and a temperature change rate of 1 °C / min and 5 °C / min for the composite membrane switch. Detect and collect the operation data of the membrane switches of each classification at the test temperature through the test bench, and simultaneously collect the infrared image data of the membrane switches during the experiment. Obtain the historical infrared image data of the membrane switch tested at different temperatures, and mark the bad point areas generated during the test of the membrane switch in the historical infrared image data. Train a deep learning model through the marked historical infrared image data to label the bad points in the real-time collected infrared image data of the membrane switch, and extract the test time length when there are no bad points in the infrared image data of the membrane switch according to the recognition result. Analyze the volatility and deviation of different types of flexible membrane switches, set the volatility threshold and deviation threshold of the operation data, extract the time length when the volatility of the operation data does not exceed the volatility threshold and the deviation of the operation data does not exceed the deviation threshold, and compare it with the test time length, and take the minimum time as the stable operation time of the membrane switch.

6. The performance detection method of a membrane switch according to claim 5, characterized in that Analyze the volatility and deviation of different types of flexible membrane switches, and set the volatility threshold and deviation threshold of the operating data, including the following steps: For different types of flexible membrane switches under different preset temperature change rates, perform weighted averaging on the operating data; According to the weighted average operating data, calculate the volatility = variance of operating data / (square of the mean of operating data + 1), and the deviation = ∣peak value of operating data - maximum threshold of preset operating data∣ / maximum threshold of preset operating data; Screen the operating data within a fixed time threshold before the occurrence of a failure in the historical data, and calculate the volatility and deviation through the screened data as the corresponding volatility threshold and deviation threshold.

7. A performance detection method for a membrane switch according to claim 1, characterized in that Analyze the temperature performance evaluation coefficient, including the following steps: According to the stable operating time of the membrane switch, as well as the volatility and deviation of the flexible membrane switch, analyze the temperature performance evaluation coefficient through the following formula: ； Among them, TPEC is the temperature performance evaluation coefficient, Fvo is the volatility of the flexible membrane switch, Fde is the deviation of the flexible membrane switch, and Tf is the stable operating time of the membrane switch.

8. A performance detection method for a membrane switch according to claim 1, characterized in that According to the operating data of the flexible membrane switch during the whole experimental process, analyze the resistance temperature drift stability of the PET-based type and the microcircuit impedance stability of the FPC-based type in the flexible membrane switch, including the following steps: Obtain the temperature and operating data of the flexible membrane switch during the experimental process, calculate the real-time resistance of the flexible membrane switch according to the operating data, and align the resistance data with the temperature data of the flexible membrane switch; Label the corresponding data of the PET-based type and FPC-based type in the flexible membrane switch; For the PET-based flexible membrane switch, according to the aligned data, randomly select several groups of different temperature and resistance value data within the stable operating time of the membrane switch and the data within the actual control duration of the switch, calculate the pairwise permutation and combination of the screened data, and calculate the resistance temperature drift TCR of the combined data. Take the ratio of the variance of all obtained resistance temperature drift TCR to the mean of the resistance temperature drift TCR as the resistance temperature drift stability of the PET-based type in the flexible membrane switch; For the FPC-based flexible membrane switch, measure the impedance value of the membrane switch after the experiment through a TDR time domain reflectometer, and calculate the impedance change rate according to the recorded initial impedance value and the impedance value after the experiment of the FPC-based flexible membrane switch as the microcircuit impedance stability of the FPC-based type.

9. A performance detection method for a membrane switch according to claim 1, characterized in that Analyze the comprehensive performance evaluation value of various types of membrane switches, including the following steps: For the PET-based flexible membrane switch, according to the resistance temperature drift stability, combined with the degradation coefficient and the temperature performance evaluation coefficient, perform normalized weighted averaging to analyze the comprehensive performance evaluation value of the flexible membrane switch; For the FPC-based flexible membrane switch, according to the microcircuit impedance stability, combined with the degradation coefficient and the temperature performance evaluation coefficient, perform normalized weighted averaging to analyze the comprehensive performance evaluation value of the flexible membrane switch; For the rigid membrane switch and the composite membrane switch, obtain the corresponding degradation coefficient and temperature performance evaluation coefficient, normalize the degradation coefficient and temperature performance evaluation coefficient of the membrane switch and then perform weighted averaging to analyze the comprehensive performance of the membrane switch.

10. A performance detection system for a membrane switch, characterized in that, The system uses a performance detection system for a membrane switch as described in any one of claims 1-9 to implement the performance detection of the membrane switch.

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