Electromagnetic induction-based switch mechanical characteristic dynamic test method and system

Abstract

The invention discloses a switch mechanical characteristic dynamic test method and system based on electromagnetic induction. The method comprises the following steps: constructing a non-contact electromagnetic sensing network; collecting an electromagnetic response signal in the action process of the to-be-tested switch through a non-contact electromagnetic sensing network; performing multi-dimensional preprocessing on the electromagnetic response signal to obtain a preprocessed signal; performing feature extraction on the preprocessed signal to obtain a contact stroke, an opening and closing speed and a vibration spectrum feature of the to-be-detected switch; constructing a mechanical property mapping model, and performing parameter adjustment on the mechanical property mapping model according to a preset calibration triggering condition to obtain an abnormal mechanical state classification model; and inputting the contact stroke, the opening and closing speed and the vibration spectrum characteristics into the abnormal mechanical state classification model, and outputting a mechanical performance evaluation report of the to-be-tested switch. The anti-interference capability of an original signal can be enhanced, high-precision analysis of mechanical characteristics is realized, and the method can be widely applied to the technical field of switch testing.

Description

Technical Field

[0001] This application relates to the field of switch testing technology, and in particular to a method and system for dynamic testing of the mechanical characteristics of switches based on electromagnetic induction. Background Technology

[0002] As the core control unit of a power system, the mechanical characteristics of switchgear directly affect the accuracy of opening and closing, arc suppression capability, and system reliability. Currently, traditional mechanical characteristic testing mainly employs contact sensors (such as displacement sensors and accelerometers) or optical measurement techniques. However, these methods have significant drawbacks: displacement sensors need to be directly mounted on moving parts, which increases the additional mass load, alters the original dynamic characteristics of the mechanism, and leads to distorted test results; furthermore, under high-voltage environments, these sensors are susceptible to electromagnetic interference. More importantly, existing testing schemes often analyze mechanical parameters in isolation, failing to comprehensively assess degradation effects such as insulation aging and contact erosion.

[0003] In recent years, although non-contact measurement solutions based on ultrasound and radio frequency identification have emerged, these technologies still suffer from problems such as narrow frequency response range and low spatial resolution, making it difficult to meet the urgent needs of smart grids for online monitoring and predictive maintenance of switching equipment. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a high-precision, highly interference-resistant dynamic testing method and system for the mechanical characteristics of switches based on electromagnetic induction.

[0005] To achieve the above objectives, one aspect of this application proposes a dynamic testing method for the mechanical characteristics of a switch based on electromagnetic induction, comprising the following steps:

[0006] Constructing a non-contact electromagnetic sensing network;

[0007] The electromagnetic response signal during the operation of the switch under test is collected through the non-contact electromagnetic sensing network.

[0008] The electromagnetic response signal is preprocessed in multiple dimensions to obtain a preprocessed signal;

[0009] Feature extraction is performed on the preprocessed signal to obtain the contact stroke, opening and closing speed, and vibration spectrum characteristics of the switch under test;

[0010] A mechanical performance mapping model is constructed, and the parameters of the mechanical performance mapping model are adjusted according to the preset calibration trigger conditions to obtain an abnormal mechanical state classification model.

[0011] The contact stroke, the opening and closing speed, and the vibration spectrum characteristics are input into the abnormal mechanical state classification model, and the mechanical performance evaluation report of the switch under test is output.

[0012] In some embodiments, the non-contact electromagnetic sensing network includes a sensing unit, a transmitting unit, and a receiving unit. The construction of the non-contact electromagnetic sensing network specifically includes:

[0013] The frequency band of the sensing unit is configured;

[0014] The transmitting unit is fixed in the axial detection area of ​​the movement trajectory of the switch under test, and the receiving unit is fixed in the radial detection area of ​​the movement trajectory of the switch under test.

[0015] By setting the frequency sweep mode of high-frequency electromagnetic waves, the constructed non-contact electromagnetic sensing network is obtained.

[0016] The frequency sweep mode includes frequency sweep range constraints and power threshold constraints.

[0017] In some embodiments, the non-contact electromagnetic sensing network includes a receiving unit, and the acquisition of electromagnetic response signals during the operation of the switch under test through the non-contact electromagnetic sensing network specifically includes:

[0018] During the operation of the switch under test, the receiving unit collects the phase signal and amplitude signal of the electromagnetic wave according to a preset sampling rate;

[0019] The phase signal and the amplitude signal are quadrature demodulated to obtain the electromagnetic response signal during the operation of the switch under test;

[0020] The electromagnetic response signal is bound to the identification code of the switch under test;

[0021] The voltage transition edges of the switch-controlled relay are analyzed to mark the current operation type of the switch under test.

[0022] In some embodiments, the multi-dimensional preprocessing of the electromagnetic response signal to obtain a preprocessed signal specifically includes:

[0023] The electromagnetic response signal is phase unwrapped to obtain the first electromagnetic response signal;

[0024] The mechanical motion signal and random noise in the first electromagnetic response signal are suppressed to obtain the second electromagnetic response signal;

[0025] The second electromagnetic response signal is time-aligned through multiple channels to obtain the preprocessed signal.

[0026] In some embodiments, the vibration spectrum characteristics include frequency domain energy distribution characteristics and wear characteristics. The feature extraction of the preprocessed signal to obtain the contact stroke, opening and closing speed, and vibration spectrum characteristics of the switch under test specifically includes:

[0027] The short-time Fourier transform of the preprocessed signal is performed to calculate the contact stroke and the opening and closing speed.

[0028] The power spectral density of the impulse phase signal of the switch under test is estimated to obtain the frequency domain energy distribution characteristics.

[0029] The wear characteristics are obtained based on the frequency domain energy distribution characteristics.

[0030] In some embodiments, the construction of the mechanical performance mapping model, which involves adjusting the parameters of the mechanical performance mapping model according to preset calibration triggering conditions to obtain an abnormal mechanical state classification model, specifically includes:

[0031] Collect a dataset, which includes electromagnetic signals and corresponding spectral characteristics under different mechanical parameters;

[0032] The mechanical performance mapping model is obtained by training a preset support vector machine model based on the dataset.

[0033] The calibration trigger conditions are set, including a temperature drift threshold and a threshold for the number of consecutive abnormal data.

[0034] Collect the current ambient temperature change rate and count the number of consecutive abnormal data.

[0035] When the current ambient temperature change rate exceeds the temperature drift threshold, or when the number of consecutive abnormal data exceeds the consecutive abnormal data number threshold, the parameters of the mechanical performance mapping model are adjusted.

[0036] A test signal is acquired, and the mechanical performance mapping model after parameter adjustment is verified by the test signal to obtain the abnormal mechanical state classification model.

[0037] In some embodiments, the step of inputting the contact stroke, the opening and closing speed, and the vibration spectrum characteristics into the abnormal mechanical state classification model and outputting a mechanical performance evaluation report of the switch under test specifically includes:

[0038] The contact stroke, the opening and closing speed, and the vibration spectrum characteristics are input into the abnormal mechanical state classification model.

[0039] The characteristic deviation is obtained by comparing the contact stroke, the opening and closing speed, and the vibration spectrum characteristics.

[0040] Based on the wear characteristics and the frequency domain energy distribution characteristics, calculate the percentage of wear life consumed by the switch under test;

[0041] Based on the characteristic deviation and the wear life consumption percentage, the mechanical performance evaluation report of the switch under test is output.

[0042] To achieve the above objectives, another aspect of this application proposes a dynamic testing system for the mechanical characteristics of a switch based on electromagnetic induction, comprising:

[0043] The first module is used to construct a non-contact electromagnetic sensing network;

[0044] The second module is used to collect electromagnetic response signals during the operation of the switch under test through the non-contact electromagnetic sensing network.

[0045] The third module is used to perform multi-dimensional preprocessing on the electromagnetic response signal to obtain a preprocessed signal;

[0046] The fourth module is used to extract features from the preprocessed signal to obtain the contact stroke, opening and closing speed, and vibration spectrum characteristics of the switch under test.

[0047] The fifth module is used to construct a mechanical performance mapping model. The parameters of the mechanical performance mapping model are adjusted according to the preset calibration trigger conditions to obtain an abnormal mechanical state classification model.

[0048] The sixth module is used to input the contact stroke, the opening and closing speed, and the vibration spectrum characteristics into the abnormal mechanical state classification model, and output a mechanical performance evaluation report of the switch under test.

[0049] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, it implements the dynamic testing method for the mechanical characteristics of a switch based on electromagnetic induction as described above.

[0050] To achieve the above objectives, another aspect of the embodiments of this application proposes a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, which can be executed by one or more processors to implement the dynamic testing method for the mechanical characteristics of switches based on electromagnetic induction as described above.

[0051] The beneficial effects of this invention are as follows: The dynamic testing method and system for the mechanical characteristics of switches based on electromagnetic induction first constructs a non-contact electromagnetic sensor network. Through this network, electromagnetic response signals during the operation of the switch under test are acquired. Next, the electromagnetic response signals undergo multi-dimensional preprocessing to obtain preprocessed signals. Feature extraction is then performed on these preprocessed signals to obtain the contact travel, opening and closing speeds, and vibration spectrum characteristics of the switch under test. Furthermore, a mechanical performance mapping model is constructed. Based on preset calibration trigger conditions, the parameters of the mechanical performance mapping model are adjusted to obtain an abnormal mechanical state classification model. Finally, the contact travel, opening and closing speeds, and vibration spectrum characteristics are input into the abnormal mechanical state classification model, and a mechanical performance evaluation report of the switch under test is output. This invention, by constructing a non-contact electromagnetic sensor network, captures the electromagnetic response signals during the operation of the switch under test in real time and performs multi-dimensional preprocessing on these signals, enhancing the anti-interference capability of the original signals. Furthermore, by automatically adjusting the parameters of the constructed mechanical performance mapping model according to calibration trigger conditions, it achieves high-precision analysis of mechanical characteristics, possesses real-time online calibration capabilities, and ensures long-term stable and high-precision operation of the system. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments of the present invention are described below. It should be understood that the drawings described below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart illustrating the steps of a dynamic testing method for the mechanical characteristics of a switch based on electromagnetic induction, provided in one embodiment of the present invention.

[0054] Figure 2 A schematic diagram of the structure of a dynamic testing system for the mechanical characteristics of a switch based on electromagnetic induction, provided in one embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0057] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0058] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0059] As the core control unit of a power system, the mechanical characteristics of switchgear directly affect the accuracy of opening and closing, arc suppression capability, and system reliability. Currently, traditional mechanical characteristic testing mainly employs contact sensors (such as displacement sensors and accelerometers) or optical measurement techniques. However, these methods have significant drawbacks: displacement sensors need to be directly mounted on moving parts, which increases the additional mass load, alters the original dynamic characteristics of the mechanism, and leads to distorted test results; furthermore, under high-voltage environments, these sensors are susceptible to electromagnetic interference. More importantly, existing testing schemes often analyze mechanical parameters in isolation, failing to comprehensively assess degradation effects such as insulation aging and contact erosion.

[0060] In recent years, although non-contact measurement solutions based on ultrasound and radio frequency identification have emerged, these technologies still suffer from problems such as narrow frequency response range and low spatial resolution, making it difficult to meet the urgent needs of smart grids for online monitoring and predictive maintenance of switching equipment.

[0061] To address this, this invention proposes a dynamic testing method for the mechanical characteristics of switches based on electromagnetic induction. First, a non-contact electromagnetic sensor network is constructed to collect electromagnetic response signals during the operation of the switch under test. Next, the electromagnetic response signals undergo multi-dimensional preprocessing to obtain preprocessed signals. Feature extraction is then performed on these preprocessed signals to obtain the contact travel, opening and closing speeds, and vibration spectrum characteristics of the switch under test. Subsequently, a mechanical performance mapping model is constructed. Based on preset calibration trigger conditions, the parameters of the mechanical performance mapping model are adjusted to obtain an abnormal mechanical state classification model. Finally, the contact travel, opening and closing speeds, and vibration spectrum characteristics are input into the abnormal mechanical state classification model to output a mechanical performance evaluation report for the switch under test. This invention, by constructing a non-contact electromagnetic sensor network to capture electromagnetic response signals during the operation of the switch under test in real time and performing multi-dimensional preprocessing on these signals to enhance the anti-interference capability of the original signals, and then automatically adjusting the parameters of the constructed mechanical performance mapping model according to calibration trigger conditions, enables high-precision analysis of mechanical characteristics and provides real-time online calibration functionality, ensuring long-term stable and high-precision operation of the system.

[0062] Reference Figure 1 , Figure 1 This is a flowchart illustrating the steps of a dynamic testing method for the mechanical characteristics of a switch based on electromagnetic induction, according to an embodiment of the present invention. The embodiment of the present invention proposes a dynamic testing method for the mechanical characteristics of a switch based on electromagnetic induction, which includes steps S101 to S106:

[0063] S101. Construct a non-contact electromagnetic sensing network;

[0064] It should be noted that, by constructing a non-contact electromagnetic sensing network, this embodiment of the invention can collect electromagnetic response signals during the operation of the switch under test in real time, thus avoiding the additional weight interference caused by direct contact of traditional contact sensors.

[0065] As an optional implementation, the non-contact electromagnetic sensing network includes a sensing unit, a transmitting unit, and a receiving unit. Step S101 can be further divided into the following steps S1011 to S1013:

[0066] S1011. Configure the frequency band of the sensing unit;

[0067] S1012. Fix the transmitting unit in the axial detection area of ​​the motion trajectory of the switch under test, and fix the receiving unit in the radial detection area of ​​the motion trajectory of the switch under test.

[0068] S1013. Set the frequency sweep mode of the high-frequency electromagnetic wave to obtain the constructed non-contact electromagnetic sensing network.

[0069] The frequency sweep mode includes frequency sweep range constraints and power threshold constraints.

[0070] Specifically, the frequency band of the sensing unit is configured through the electromagnetic field control module, the transmitting unit and the receiving unit are respectively bound to the axial detection area and radial detection area of ​​the motion trajectory of the switch under test, and the frequency sweep mode of the high-frequency electromagnetic wave is set. The frequency sweep mode includes the frequency sweep range and power threshold constraint.

[0071] In some optional embodiments, the frequency sweep range constraint formula for the frequency sweep mode is:

[0072]

[0073] Where f0 represents the initial frequency, Δf represents the frequency step size, and T s This indicates the dwell time for each frequency. This represents the floor function, used for discretizing time segments. The transmission frequency range and power level are set, and different frequencies are automatically switched. In this embodiment of the invention, it starts at 12.5MHz and jumps every 0.5MHz.

[0074] The power threshold constraint formula is:

[0075]

[0076] Where k represents the safety factor, ε r μ represents the relative permittivity of the outer casing material. r σ represents the relative permeability of air, σ represents the conductivity of the contact material, and d represents the relative permeability of air. safe Indicates the safe detection distance, A coil This indicates the effective area of ​​the transmitting coil.

[0077] S102. Collect the electromagnetic response signal during the operation of the switch under test through a non-contact electromagnetic sensing network.

[0078] In some optional embodiments, the dynamic electromagnetic response signal during the operation of the switch under test is collected, and the phase and amplitude information of the electromagnetic wave modulated by mechanical motion is captured in real time by the receiving unit. The switch number, operation type and timestamp are synchronously associated, and the spectral characteristic data of the environmental interference source are recorded.

[0079] As an optional implementation, step S102 can be further divided into the following steps S1021 to S1024:

[0080] S1021. During the operation of the switch under test, the phase signal and amplitude signal of the electromagnetic wave are collected by the receiving unit according to the preset sampling rate.

[0081] Specifically, the original phase and amplitude signals of electromagnetic waves are acquired by the receiving unit in the non-contact electromagnetic sensor network at a preset sampling rate. The set sampling rate is synchronously matched with the frequency sweep step rate of the high-frequency electromagnetic waves (e.g., when switching frequencies every 10μs, the sampling rate is ≥100MS / s) to ensure complete coverage of each frequency band.

[0082] S1022. Perform quadrature demodulation on the phase signal and amplitude signal to obtain the electromagnetic response signal during the operation of the switch under test;

[0083] Specifically, quadrature demodulation technology is used to separate the in-phase and quadrature components of the signal, generating a raw data packet containing time-varying phase and amplitude to eliminate the influence of carrier frequency offset.

[0084] S1023. Bind the electromagnetic response signal to the identification code of the switch under test;

[0085] Specifically, based on the trigger signal of the switch control system, the electromagnetic response signal is bound to the unique identifier of the switch under test. A hash algorithm can be used to encode the switch ID into a 12-bit digital tag (such as 0xA3D) and embed it in the data frame header.

[0086] S1024. Analyze the voltage transition edge of the switch control relay and mark the current action type of the switch under test.

[0087] Specifically, by analyzing the voltage transition edge of the switch control relay (rising edge for closing, falling edge for opening), the current action type is marked as either opening or closing operation.

[0088] Furthermore, after marking the current action type of the switch under test, a microsecond-level timestamp is generated based on the IEEE 1588 protocol and time-aligned with the electromagnetic signal data packets to ensure precise synchronization of sampling data from multiple devices on the time axis. Simultaneously, a backup receiving channel is activated to scan the background noise across the entire frequency band, generating an environmental interference spectrum snapshot, marking the interfering frequency bands, and calculating the power spectral density of each band. Finally, the interference characteristics are stored in an interference characteristic database. When sudden high-frequency interference is detected, an adaptive filtering parameter update mechanism is immediately triggered (e.g., when a 20dB power surge in a certain frequency band is detected, it is automatically marked and switched to anti-interference mode).

[0089] S103. Perform multi-dimensional preprocessing on the electromagnetic response signal to obtain the preprocessed signal;

[0090] In some optional embodiments, the original electromagnetic response signal is preprocessed in multiple dimensions, including phase unwrapping, motion noise suppression, and time alignment. Phase unwrapping is used to eliminate the ambiguity in displacement calculation caused by periodic jumps in the electromagnetic response signal.

[0091] As an optional implementation, step S103 can be further divided into the following steps S1031 to S1033:

[0092] S1031. Perform phase unwrapping processing on the electromagnetic response signal to obtain the first electromagnetic response signal;

[0093] Specifically, the phase unwrapping process detects periodic transition points in the phase signal of the original electromagnetic response signal. When the phase difference between adjacent sampling points exceeds π radians, it is marked as a transition event. Then, a global phase accumulation strategy is used to correct the phase continuity of the marked transition points, eliminating the 2π periodic ambiguity and obtaining the first electromagnetic response signal. Among them, the transition detection of phase unwrapping uses the differential comparison method to identify phase change points (threshold: ±2.8 rad); the global correction generates a continuous displacement curve through phase accumulation to avoid the cumulative error of traditional local unwrapping.

[0094] S1032. Suppress the mechanical motion signal and random noise in the first electromagnetic response signal to obtain the second electromagnetic response signal;

[0095] Specifically, the sym4 wavelet basis function is selected, and a wavelet thresholding denoising algorithm is used to separate the mechanical motion signal and random noise in the first electromagnetic response signal. The first electromagnetic response signal is decomposed into 8 layers of wavelets to separate the detail coefficients and approximation coefficients of different frequency bands. Then, the threshold is calculated based on the rigrsure adaptive rule, and soft thresholding is performed on the high-frequency detail coefficients (corresponding to random noise). A motion noise feature library is constructed, which contains typical noise spectrum templates in the switching process (such as electromagnetic coil excitation interference). Then, based on the noise frequency bands in the motion noise feature library, band-stop filtering is performed on the natural vibration frequency of the switching mechanism.

[0096] Among them, mechanical motion signals refer to the high-frequency vibration signals generated by the physical motion (collision, friction, vibration, etc.) of mechanical components such as metal contacts, springs, and connecting rods during the operation of power switches (such as opening / closing operations). These signals are collected by acceleration sensors or vibration sensors and may be mixed with electromagnetic signals to form noise.

[0097] S1033. Perform multi-channel time alignment on the second electromagnetic response signal to obtain a preprocessed signal.

[0098] Specifically, data from each received channel is aligned based on a reference signal, and interpolation compensation is performed on channels with delay deviations. During time alignment, the reference signal is synchronized, using the electrical pulses of the switching operation command as the absolute time base, and interpolation compensation is considered, employing a cubic spline interpolation algorithm to correct inter-channel transmission delays. The reference signal is the time base signal for multi-channel data synchronization, including a hardware synchronization signal and a main channel signal.

[0099] S104. Perform feature extraction on the preprocessed signal to obtain the contact stroke, opening and closing speed and vibration spectrum characteristics of the switch under test;

[0100] In some optional embodiments, the contact stroke, opening and closing speed and vibration spectrum characteristics are extracted from the preprocessed signal based on the time-frequency analysis algorithm, and the mapping relationship between electromagnetic response parameters and mechanical performance indicators is established.

[0101] As an optional implementation, the vibration spectrum characteristics include frequency domain energy distribution characteristics and wear characteristics. Step S104 can be further divided into the following steps S1041 to S1043:

[0102] S1041. Perform a short-time Fourier transform on the preprocessed signal to calculate the contact stroke and opening / closing speed;

[0103] Specifically, a Short-Time Fourier Transform (STFT) is performed on the preprocessed signal with a window length of 10 ms and an overlap rate of 75% to extract energy ridges from the time-frequency matrix, and to calculate the instantaneous values ​​of contact travel and opening / closing speed. A pre-established, experimentally calibrated mapping model is used to correlate the extracted energy ridge features (mainly instantaneous frequencies) with the actual physical travel (position) of the switch contacts. By inputting the feature values ​​of each time point on the energy ridges into this mapping model, the corresponding instantaneous contact travel can be derived. Finally, the instantaneous opening / closing speed is obtained by numerically differentiating the calculated instantaneous travel time series (calculating the rate of change of travel with respect to time).

[0104] S1042. Estimate the power spectral density of the impulse phase signal of the switch under test to obtain the frequency domain energy distribution characteristics;

[0105] S1043. Based on the frequency domain energy distribution characteristics, the wear characteristics are obtained.

[0106] Specifically, power spectral density (PSD) estimation is performed on the signal during the impact phase (5 ms before and after contact closure), and the proportion of vibration energy in the 1-10 kHz frequency band is extracted as wear characteristics. The core purpose of PSD estimation is to quantify the frequency domain energy distribution characteristics of the signal during the brief period (±5 ms) before and after contact closure. This process first requires accurately locating the precise contact closure time point t_closure, which can be achieved by analyzing abrupt changes in the previously calculated contact stroke / velocity curve, identifying characteristic events in the original electromagnetic signal (such as a sharp current increase or specific transient peaks), or utilizing synchronous auxiliary measurements (such as contact on / off state). Once t_closure is determined, a 10 ms segment of signal data ([t_closure-5ms, t_closure+5ms]) is extracted from the preprocessed signal. Considering the short duration of this signal and the presence of non-stationary components caused by the impact, this embodiment of the invention employs Welch's method for PSD estimation because it effectively reduces the variance of the estimation results through piecewise averaging, yielding a smoother and more reliable spectrum. In the specific implementation of the Welch method, the 10ms signal segment is further divided into several shorter sub-segments with a certain overlap rate (e.g., 50%). A window function (such as the Hanning window) is applied to each sub-segment to reduce spectral leakage. Then, the Fast Fourier Transform (FFT) of each sub-segment is calculated to obtain its power spectrum (FFT amplitude squared). Finally, the power spectra of all sub-segments are normalized (e.g., converted to units such as V² / Hz or W / Hz) and averaged to obtain the final PSD estimation curve P(f). The P(f) curve shows the distribution of signal energy at different frequencies f during the contact closure impact, laying the foundation for subsequently extracting the energy proportion of a specific frequency band (e.g., 1-10kHz) as wear characteristics. Throughout the process, the selection of parameters such as sampling frequency, sub-segment length of the Welch method, overlap rate, and window function type can be reasonably configured according to the actual signal characteristics and analysis objectives, and are not limited here.

[0107] S105. Construct a mechanical performance mapping model, and adjust the parameters of the mechanical performance mapping model according to the preset calibration trigger conditions to obtain an abnormal mechanical state classification model.

[0108] As an optional implementation, step S105 can be further divided into the following steps S1051 to S1056:

[0109] S1051. Collect a dataset, which includes electromagnetic signals and corresponding spectral characteristics under different mechanical parameters;

[0110] S1052. Train the preset support vector machine model based on the dataset to obtain the mechanical performance mapping model;

[0111] Specifically, a dataset is first acquired through extensive experiments. These experiments intentionally alter key mechanical parameters of the switch (e.g., replacing springs with different elastic coefficients or using components with varying degrees of wear), and electromagnetic signals and corresponding known mechanical states are recorded synchronously during each opening and closing operation. Next, the aforementioned steps are applied to extract spectral features from the acquired electromagnetic signals. Then, using these feature-state paired datasets, statistical analysis or machine learning methods are employed to construct a mechanical performance mapping model.

[0112] In this embodiment of the invention, a Support Vector Machine (SVM) model is employed to classify and identify abnormal mechanical states of switching equipment. Considering the potentially complex nonlinear relationship between changes in mechanical state and electromagnetic signal characteristics, a Radial Basis Function (RBF) is specifically chosen as the kernel function, which can effectively construct flexible nonlinear decision boundaries in the feature space. The feature vector input to the SVM for classification consists of three key indicators extracted from the signal: time-frequency center of gravity, used to capture the concentrated changes in signal energy in the time-frequency domain; kurtosis, used to measure the sharpness of the signal amplitude distribution, reflecting the amount of impact components; and impulse factor, used to quantify the prominence of the signal peak relative to the average level, also sensitive to impact signals. Through training, the SVM model learns how to distinguish data points representing "normal" mechanical states from data points representing "abnormal" states (such as changes in elasticity coefficients, excessive wear, etc.) based on the combined values ​​of these three features and the complex boundaries defined by the RBF kernel, thereby achieving automatic classification and discrimination of the mechanical states corresponding to unknown signals.

[0113] S1053. Set calibration trigger conditions, including temperature drift threshold and consecutive abnormal data count threshold;

[0114] S1054. Collect the current ambient temperature change rate and count the number of consecutive abnormal data.

[0115] S1055. When the current ambient temperature change rate exceeds the temperature drift threshold, or the number of consecutive abnormal data exceeds the consecutive abnormal data number threshold, adjust the parameters of the mechanical performance mapping model.

[0116] S1056. Obtain test signals and verify the mechanical performance mapping model after parameter adjustment using test signals to obtain an abnormal mechanical state classification model.

[0117] Specifically, the mechanical performance mapping model parameters are dynamically adjusted based on preset mechanical motion benchmark values. Calibration trigger conditions include temperature drift thresholds and consecutive abnormal data count thresholds. The current ambient temperature change rate is monitored in real time; calibration is triggered when the temperature drift exceeds the set value, and the number of consecutive abnormal data points is counted. Recalibration is forced after more than five consecutive abnormal data points. During calibration, the displacement-phase mapping coefficients are updated using a recursive least squares method with a forgetting factor, and the inertial delay error in vibration feature extraction is corrected using a Kalman filter. The forgetting factor λ = 0.95, and the initial covariance matrix P = 1e3·I. State variables include displacement, velocity, and acceleration, and the observation matrix H = [1,0,0]. Finally, a test signal with a known displacement is injected. This test signal serves as the benchmark input signal to verify the model's accuracy. It simulates a standard displacement of 0.1-2.0 mm using a piezoelectric ceramic actuator, and the corresponding real and accurate contact displacement time history is known in advance. Auxiliary measurements are used to verify the accuracy of the calibrated model. If the error still exceeds the limit after calibration, the system switches to a backup sensor fusion mode and enables a laser displacement sensor for auxiliary measurement.

[0118] S106. Input the contact stroke, opening and closing speed and vibration spectrum characteristics into the abnormal mechanical state classification model, and output the mechanical performance evaluation report of the switch under test.

[0119] As an optional implementation, step S106 can be further divided into the following steps S1061 to S1064:

[0120] S1061. Input the contact stroke, opening and closing speed and vibration spectrum characteristics into the abnormal mechanical state classification model;

[0121] S1062. Based on the contact stroke, opening and closing speed and vibration spectrum characteristics, a feature comparison is performed to obtain the feature deviation.

[0122] In some optional embodiments, dynamic response feature comparison is performed by retrieving standard feature curves of the same type of switch from the historical database through an abnormal mechanical state classification model, and calculating the deviation between the current feature parameters and the standard values.

[0123] Specifically, first, the model retrieves standard characteristic parameters or curves of the same model of switch under healthy or ideal conditions from the historical database. Then, it compares the extracted characteristic parameters (closing and opening speeds, frequency domain energy distribution characteristics, contact travel, etc.) with these standard values. The method for calculating the deviation depends on the characteristic type: for single numerical characteristics, the absolute deviation, relative percentage deviation, or standardized Z-score is calculated; for curve-type characteristics (such as contact travel, speed curves), indicators such as root mean square error (RMSE), mean absolute error (MAE), maximum deviation, and dynamic time warping (DTW) distance are used to measure the overall shape or point-to-point differences, or to compare the deviations of key characteristic points on the curve (such as peak values, specific time points). The final output of one or more deviation values ​​provides a quantitative basis for subsequently determining whether the current operating state of the switch deviates from the normal range.

[0124] S1063. Calculate the percentage of wear life consumed by the switch under test based on the wear characteristics and frequency domain energy distribution characteristics.

[0125] In some alternative embodiments, the degradation rate is quantitatively assessed by calculating the degradation rate of the elastic coefficient over time based on the Arrhenius model; the percentage of wear life consumed is estimated by the accumulation of high-frequency vibration energy.

[0126] It should be noted that the Arrhenius model is an empirical model widely used to describe the relationship between chemical reaction rates or material aging rates and temperature. It assumes that the mechanical wear process is accompanied by the generation or enhancement of specific high-frequency vibration signals, and that the accumulated high-frequency vibration energy is correlated with the accumulated wear amount or lifespan loss. The calculation process mainly includes: High-frequency energy extraction: Processing the vibration signals (or signals reflecting vibration) acquired during each switching operation (especially the impact phase) to identify and select specific high-frequency bands strongly correlated with wear. Then, calculating the energy value of the signal within this high-frequency band during each operation (energy can be calculated through PSD integration or time-domain filtering); Energy accumulation: Recording the total number of operations N of the switch under test, and summing the high-frequency energy values ​​calculated for each operation to obtain the total accumulated high-frequency energy; Lifespan loss mapping: Using a pre-established mapping relationship calibrated based on a large number of lifespan experiments or field data, converting the currently calculated total accumulated high-frequency energy into a percentage of wear lifespan loss. This mapping defines the expected total accumulated energy value at the end of the lifespan. For example, a simple linear mapping can be used, i.e., lifespan consumption % = (current total accumulated energy / energy at the end of lifespan) * 100%.

[0127] S1064. Based on the characteristic deviation and wear life consumption percentage, output the mechanical performance evaluation report of the switch under test.

[0128] Specifically, a grading threshold is set based on the characteristic deviation degree and the percentage of wear life consumption. A health status level is generated based on the grading threshold, which includes four levels: normal, attention, warning, and danger. A 3D visualization report containing trend charts of key indicators is output. The visualization report renders a 3D mechanism model based on the WebGL engine and overlays a heat map to display the wear distribution.

[0129] It should be noted that the health level is divided into two categories based on the percentage of characteristic deviation and the percentage of wear life consumption. The specific classification method can be set according to actual needs. For example, the categories are: Normal (characteristic deviation <5% and wear life consumption <20%), Caution (characteristic deviation 5%-15% and wear life consumption 20%-50%), Warning (characteristic deviation 15%-30% and wear life consumption 50%-80%), and Danger (characteristic deviation >30% and wear life consumption >80%).

[0130] The above describes the dynamic testing method for the mechanical characteristics of switches based on electromagnetic induction according to embodiments of the present invention. It can be understood that, compared with traditional mechanical characteristic testing, embodiments of the present invention, on the one hand, significantly improve testing accuracy by constructing a non-contact electromagnetic sensing network, coupled with a specific frequency sweep mode and power threshold constraints, to capture electromagnetic response signals during switching operations in real time, and by performing multi-dimensional preprocessing on the electromagnetic response signals to enhance the anti-interference capability of the original signals; on the other hand, by combining dynamic calibration algorithms and multi-physics compensation mechanisms, it possesses real-time online calibration capabilities, ensuring long-term stable and high-precision operation of the system.

[0131] Reference Figure 2 This invention also provides a dynamic testing system for the mechanical characteristics of switches based on electromagnetic induction, comprising:

[0132] The first module is used to construct a non-contact electromagnetic sensing network;

[0133] The second module is used to collect electromagnetic response signals during the operation of the switch under test through a non-contact electromagnetic sensing network.

[0134] The third module is used to perform multi-dimensional preprocessing on the electromagnetic response signal to obtain the preprocessed signal;

[0135] The fourth module is used to extract features from the preprocessed signal to obtain the contact stroke, opening and closing speed, and vibration spectrum characteristics of the switch under test.

[0136] The fifth module is used to construct a mechanical performance mapping model. The parameters of the mechanical performance mapping model are adjusted according to the preset calibration trigger conditions to obtain an abnormal mechanical state classification model.

[0137] The sixth module is used to input contact stroke, opening and closing speed, and vibration spectrum characteristics into the abnormal mechanical state classification model, and output a mechanical performance evaluation report of the switch under test.

[0138] The content of the above embodiments of the dynamic testing method for the mechanical characteristics of switches based on electromagnetic induction is applicable to this embodiment of the dynamic testing system for the mechanical characteristics of switches based on electromagnetic induction. The specific functions implemented by this embodiment of the dynamic testing system for the mechanical characteristics of switches based on electromagnetic induction are the same as those of the above embodiments of the dynamic testing method for the mechanical characteristics of switches based on electromagnetic induction, and the beneficial effects achieved are also the same as those achieved by the above embodiments of the dynamic testing method for the mechanical characteristics of switches based on electromagnetic induction.

[0139] This invention also provides an electronic device, comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for communication between the processor and the memory. When the program is executed by the processor, it implements the aforementioned dynamic testing method for the mechanical characteristics of a switch based on electromagnetic induction. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0140] like Figure 3 The diagram shown is a hardware structure schematic of an electronic device provided in an embodiment of the present invention. (Refer to...) Figure 3 This invention provides an electronic device, comprising:

[0141] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0142] The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 to execute the dynamic testing method for the mechanical characteristics of switches based on electromagnetic induction according to the embodiments of this invention.

[0143] Input / output interface 1003 is used to implement information input and output;

[0144] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0145] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0146] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0147] This invention also provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, which can be executed by one or more processors to implement the above-described method for dynamic testing of the mechanical characteristics of switches based on electromagnetic induction.

[0148] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0149] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform... Figure 1 The method shown.

[0150] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the aforementioned blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0151] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0152] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0154] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or, if necessary, processing in other suitable ways, and then stored in computer memory.

[0155] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0156] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0157] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0158] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A dynamic testing method for the mechanical characteristics of a switch based on electromagnetic induction, characterized in that, Includes the following steps: Constructing a non-contact electromagnetic sensing network; The electromagnetic response signal during the operation of the switch under test is collected through the non-contact electromagnetic sensing network. The electromagnetic response signal is preprocessed in multiple dimensions to obtain a preprocessed signal; Feature extraction is performed on the preprocessed signal to obtain the contact stroke, opening and closing speed, and vibration spectrum characteristics of the switch under test; A mechanical performance mapping model is constructed, and the parameters of the mechanical performance mapping model are adjusted according to the preset calibration trigger conditions to obtain an abnormal mechanical state classification model. The contact stroke, the opening and closing speed, and the vibration spectrum characteristics are input into the abnormal mechanical state classification model, and the mechanical performance evaluation report of the switch under test is output.

2. The method for dynamic testing of the mechanical characteristics of a switch based on electromagnetic induction according to claim 1, characterized in that, The non-contact electromagnetic sensing network includes a sensing unit, a transmitting unit, and a receiving unit. The construction of the non-contact electromagnetic sensing network specifically includes: The frequency band of the sensing unit is configured; The transmitting unit is fixed in the axial detection area of ​​the movement trajectory of the switch under test, and the receiving unit is fixed in the radial detection area of ​​the movement trajectory of the switch under test. By setting the frequency sweep mode of high-frequency electromagnetic waves, the constructed non-contact electromagnetic sensing network is obtained. The frequency sweep mode includes frequency sweep range constraints and power threshold constraints.

3. The method for dynamic testing of the mechanical characteristics of a switch based on electromagnetic induction according to claim 1, characterized in that, The non-contact electromagnetic sensing network includes a receiving unit. The process of acquiring electromagnetic response signals during the operation of the switch under test through the non-contact electromagnetic sensing network specifically includes: During the operation of the switch under test, the receiving unit collects the phase signal and amplitude signal of the electromagnetic wave according to a preset sampling rate; The phase signal and the amplitude signal are quadrature demodulated to obtain the electromagnetic response signal during the operation of the switch under test; The electromagnetic response signal is bound to the identification code of the switch under test; The voltage transition edges of the switch-controlled relay are analyzed to mark the current operation type of the switch under test.

4. The method for dynamic testing of the mechanical characteristics of a switch based on electromagnetic induction according to claim 1, characterized in that, The multi-dimensional preprocessing of the electromagnetic response signal to obtain a preprocessed signal specifically includes: The electromagnetic response signal is phase unwrapped to obtain the first electromagnetic response signal; The mechanical motion signal and random noise in the first electromagnetic response signal are suppressed to obtain the second electromagnetic response signal; The second electromagnetic response signal is time-aligned through multiple channels to obtain the preprocessed signal.

5. The method for dynamic testing of the mechanical characteristics of a switch based on electromagnetic induction according to claim 1, characterized in that, The vibration spectrum characteristics include frequency domain energy distribution characteristics and wear characteristics. The feature extraction of the preprocessed signal yields the contact travel, opening and closing speeds, and vibration spectrum characteristics of the switch under test, specifically including: The short-time Fourier transform of the preprocessed signal is performed to calculate the contact stroke and the opening and closing speed. The power spectral density of the impulse phase signal of the switch under test is estimated to obtain the frequency domain energy distribution characteristics. The wear characteristics are obtained based on the frequency domain energy distribution characteristics.

6. The method for dynamic testing of the mechanical characteristics of a switch based on electromagnetic induction according to claim 1, characterized in that, The construction of the mechanical performance mapping model, which involves adjusting the parameters of the model according to preset calibration triggering conditions to obtain an abnormal mechanical state classification model, specifically includes: Collect a dataset, which includes electromagnetic signals and corresponding spectral characteristics under different mechanical parameters; The mechanical performance mapping model is obtained by training a preset support vector machine model based on the dataset. The calibration trigger conditions are set, including a temperature drift threshold and a threshold for the number of consecutive abnormal data. Collect the current ambient temperature change rate and count the number of consecutive abnormal data. When the current ambient temperature change rate exceeds the temperature drift threshold, or when the number of consecutive abnormal data exceeds the consecutive abnormal data number threshold, the parameters of the mechanical performance mapping model are adjusted. A test signal is acquired, and the mechanical performance mapping model after parameter adjustment is verified by the test signal to obtain the abnormal mechanical state classification model.

7. The method for dynamic testing of the mechanical characteristics of a switch based on electromagnetic induction according to claim 5, characterized in that, The process of inputting the contact stroke, the opening and closing speed, and the vibration spectrum characteristics into the abnormal mechanical state classification model and outputting a mechanical performance evaluation report of the switch under test specifically includes: The contact stroke, the opening and closing speed, and the vibration spectrum characteristics are input into the abnormal mechanical state classification model. The characteristic deviation is obtained by comparing the contact stroke, the opening and closing speed, and the vibration spectrum characteristics. Based on the wear characteristics and the frequency domain energy distribution characteristics, calculate the percentage of wear life consumed by the switch under test; Based on the characteristic deviation and the wear life consumption percentage, the mechanical performance evaluation report of the switch under test is output.

8. A dynamic testing system for the mechanical characteristics of a switch based on electromagnetic induction, characterized in that, include: The first module is used to construct a non-contact electromagnetic sensing network; The second module is used to collect electromagnetic response signals during the operation of the switch under test through the non-contact electromagnetic sensing network. The third module is used to perform multi-dimensional preprocessing on the electromagnetic response signal to obtain a preprocessed signal; The fourth module is used to extract features from the preprocessed signal to obtain the contact stroke, opening and closing speed, and vibration spectrum characteristics of the switch under test. The fifth module is used to construct a mechanical performance mapping model. The parameters of the mechanical performance mapping model are adjusted according to the preset calibration trigger conditions to obtain an abnormal mechanical state classification model. The sixth module is used to input the contact stroke, the opening and closing speed, and the vibration spectrum characteristics into the abnormal mechanical state classification model, and output a mechanical performance evaluation report of the switch under test.

9. An electronic device, characterized in that, The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for establishing communication between the processor and the memory. When the program is executed by the processor, it implements the steps of the dynamic testing method for the mechanical characteristics of a switch based on electromagnetic induction as described in any one of claims 1 to 7.

10. A storage medium, said storage medium being a computer-readable storage medium for computer-readable storage, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the dynamic testing method for the mechanical characteristics of a switch based on electromagnetic induction as described in any one of claims 1 to 7.

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