Alarm control method and system of intelligent door lock

Abstract

The application relates to the technical field of door lock alarm control, and particularly discloses an alarm control method and system of an intelligent door lock, which acquires a current sequence of a clutch electromagnet driving circuit and a system battery voltage in real time, carries out voltage compensation and differential analysis on the current, extracts dynamic characteristics such as a mutation point time, a wave trough depth and a starting slope, compares the characteristics with a self-learned reference characteristic template, generates a characteristic offset set, intelligently identifies a maximum offset item according to the system, and automatically triggers a targeted compensation driving sequence. When the offset is still higher than a threshold value after multiple compensations, it is determined that physical failure occurs and a warning is given. The method decouples mechanical state information from a current waveform, realizes a paradigm shift from fixed threshold value fault alarm to predictive maintenance based on dynamic characteristics, and can identify performance degradation and give a warning before the clutch is completely stuck.

Description

Technical Field

[0001] This invention belongs to the field of door lock alarm control technology, and relates to alarm control methods and systems for smart door locks. Background Technology

[0002] As the first line of defense for home security, the reliability of the clutch mechanism in a smart door lock directly determines the lock's security level and the safety of the user's person and property. The electromagnet, as the core power source of the clutch, drives the mechanical displacement of the iron core through electromagnetic conversion to achieve the unlocking action; its response speed and the stability of the engaging torque are crucial. Meanwhile, the driving current, as the energy carrier for the electromagnet's action, not only reflects the electrical state of the coil but also has an inherent physical coupling relationship with the dynamic response of the mechanical components.

[0003] Current mainstream smart lock control systems have significant blind spots in clutch status monitoring. Existing technologies mostly rely on discrete on / off signals from Hall sensors or limit switches. This binary judgment logic based on "position arrival" can only identify whether the iron core is finally in place, but cannot quantitatively assess the dynamic quality during its movement. This crude monitoring system ignores the multi-dimensional characteristic information contained in the engagement current waveform, making it difficult to detect in time when the clutch is in a "sub-healthy" state such as increased friction or residual magnetization. The lack of in-depth mining of current characteristic fingerprints and dynamic benchmark comparison means that the lock control system can only passively trigger alarms after the mechanical structure is completely jammed or the function fails, and cannot implement adaptive compensation or provide preventive maintenance before the failure occurs, thus creating hidden dangers for the long-term safety of users. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention provides an alarm control method and system for smart door locks to solve the above-mentioned technical problems.

[0005] To achieve the above and other objectives, the technical solution adopted by the present invention is as follows:

[0006] The first aspect of this invention provides an alarm control method for a smart door lock, applied to the main control chip of the smart door lock, the method comprising:

[0007] Upon receiving the unlocking command, an initial drive signal is output to the clutch electromagnet.

[0008] Synchronously acquire the real-time operating current sequence of the clutch electromagnet drive circuit and the current battery voltage data of the door lock system;

[0009] Voltage compensation calculations are performed on the real-time operating current sequence using the current battery voltage data to generate a normalized current sequence;

[0010] Differentiation and extremum extraction are performed on the falling and rising edges of the normalized current sequence to obtain abrupt change time data, trough depth data, and initial slope data.

[0011] The time data of the mutation point, the depth data of the trough, and the initial slope data are respectively compared with the corresponding feature values ​​in the benchmark feature template to generate a set of feature offsets.

[0012] Extract the maximum offset data from the feature offset set. When the maximum offset data is greater than the preset offset threshold, generate the corresponding compensation drive sequence according to the data type of the maximum offset data and output it to the clutch electromagnet.

[0013] After outputting the compensation drive sequence, the system's built-in loop counter is incremented stepwise to obtain the current loop count data, and the process returns to the step of synchronously acquiring the real-time operating current sequence of the clutch electromagnet drive circuit.

[0014] When the current number of loops exceeds the preset limit and the maximum offset in the feature offset set is still greater than the preset offset threshold, it is determined that the electromagnet's engagement action is not in place, a physical failure code for the clutch mechanism is generated, and a safety warning command is triggered based on the physical failure code for the clutch mechanism.

[0015] A second aspect of the present invention provides an alarm control system for a smart door lock, the system comprising:

[0016] Door lock parameter acquisition module: Receives unlocking command, outputs initial drive signal to clutch electromagnet; synchronously acquires real-time operating current sequence of clutch electromagnet drive circuit and current battery voltage data of door lock system;

[0017] Battery parameter calculation module: Calculates voltage compensation for the real-time operating current sequence using the current battery voltage data to generate a normalized current sequence; performs derivative calculations and extracts extreme values ​​for the falling and rising edges of the normalized current sequence to obtain abrupt change time data, trough depth data, and initial slope data.

[0018] Clutch compensation module: The difference between the time data of the sudden change point, the depth data of the trough, and the initial slope data and the corresponding feature values ​​in the reference feature template is calculated to generate a feature offset set; the maximum offset data in the feature offset set is extracted; when the maximum offset data is greater than the preset offset threshold, the corresponding compensation drive sequence is generated according to the data type of the maximum offset data and output to the clutch electromagnet.

[0019] Compensation cycle judgment module: After outputting the compensation drive sequence, it performs step-by-step accumulation calculation on the built-in cycle counter to obtain the current cycle count data, and returns to the step of synchronously acquiring the real-time working current sequence of the clutch electromagnet drive circuit;

[0020] Door lock judgment and warning module: When the current loop count data is greater than the preset count limit and the maximum offset item data in the feature offset set is still greater than the preset offset threshold, it is determined that the electromagnet engagement action is not in place, a physical failure code of the clutch mechanism is generated, and a safety warning command is triggered based on the physical failure code of the clutch mechanism.

[0021] As described above, the alarm control method and system for smart door locks provided by the present invention have at least the following beneficial effects:

[0022] 1. This invention acquires the current sequence of the clutch electromagnet drive circuit and the system battery voltage in real time through the door lock parameter acquisition module, and performs voltage compensation and dynamic feature extraction through the battery parameter calculation module to obtain multi-dimensional feature data such as the time of abrupt change of the normalized current, the depth of the trough, and the initial slope. Then, the system compares the above features with the adaptively updated benchmark feature template to generate a feature offset set and dynamically adjusts the internal judgment threshold accordingly. This allows the assessment of the clutch engagement state to be adaptively adjusted in real time according to the battery condition and the degree of mechanical wear, completely avoiding the rigidity problem of the traditional fixed threshold mechanism that misjudgment failure under voltage fluctuations or component aging, and greatly improving the accuracy and environmental robustness of condition monitoring.

[0023] 2. When abnormal offset is detected after the compensation cycle exceeds a preset number, this invention automatically determines it as a physical failure of the clutch mechanism, triggering local and remote safety warnings. By tracking the temporal trend of characteristic offsets over a long period, the lifespan degradation of components can be predicted in advance, achieving an intelligent transition from immediate fault repair to proactive maintenance. This endows smart door locks with unprecedented self-diagnosis and early warning capabilities, greatly improving product reliability and user security. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram showing the connections between the steps of the method of the present invention.

[0026] Figure 2 This is a schematic diagram illustrating the construction logic of the inductor characteristic response model in this invention.

[0027] Figure 3 This is a schematic diagram showing the connections of the various modules in the system of the present invention. Detailed Implementation

[0028] The following description, in conjunction with the implementation of this invention, is merely an example and illustration of the concept of this invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the inventive concept or exceed the scope defined in these claims, all of which should fall within the protection scope of this invention.

[0029] In traditional smart door lock clutch status monitoring, static judgment mechanisms based on fixed current thresholds or simple timing logic are commonly used. This mechanism cannot respond to the drift of electromagnet dynamic parameters caused by battery voltage decay, mechanical component wear, or changes in ambient temperature. When the waveform characteristics of the drive current change slowly or non-linearly due to these factors, the system cannot establish a dynamic correlation model between the current characteristics and the mechanical health state of the clutch, leading to a mismatch between the state criterion and the actual physical state. This static, binary judgment logic drowns out weak characteristic signals indicating a "sub-healthy" state, causing the warning mechanism to receive only extreme information of "complete conduction" or "complete disconnection," ultimately affecting the timeliness and accuracy of maintenance warnings.

[0030] For example, after prolonged use, the frictional resistance between the clutch electromagnet core and the sleeve gradually increases due to the drying of grease. The current waveform during engagement shows a gradually delayed abrupt change in the timing of the abrupt change, but the depth of the trough may not change significantly. In this case, if a traditional system only sets a fixed action completion time threshold or a simple current amplitude threshold, it will still consider the engagement "normal" for a considerable period because the current eventually reaches its saturation value without exceeding the timeout. The crucial action delay characteristic is ignored as noise, causing the system to miss the optimal opportunity to issue a warning during the "slow action" phase. This continues until an instance of excessive resistance or slightly low voltage directly leads to engagement failure, exposing the user to the risk of a "false lockout."

[0031] If the aforementioned problems are not addressed, the lag in this monitoring mechanism will render the early warning function ineffective, failing to provide any effective alerts before a true safety malfunction (i.e., the clutch fails to physically lock) occurs. The rigidity of the feature sensing mechanism will hinder the system from capturing the critical state from "performance degradation" to "functional failure," delaying the window for maintenance intervention (such as adding lubrication). Furthermore, the coupling effect between battery voltage fluctuations and changes in mechanical condition, if not decoupled and analyzed, will cause false alarms or missed alarms. For example, increased resistance due to increased lubricant viscosity at low temperatures may be incorrectly attributed to insufficient battery power, thus reducing the reliability of condition diagnosis and ultimately failing to form an effective predictive maintenance loop.

[0032] After introducing the basic concept of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0033] Example 1:

[0034] Please see Figure 1-2 As shown, the alarm control method for smart door locks includes the following steps:

[0035] Upon receiving the unlocking command, the system outputs an initial drive signal to the clutch electromagnet; simultaneously, it acquires the real-time operating current sequence of the clutch electromagnet drive circuit and the current battery voltage data of the door lock system.

[0036] In a specific embodiment, the encrypted unlocking level logic is first obtained through the wireless communication module or touch panel, which triggers the main control chip to switch from low-power sleep mode to working mode. The main control chip calls the built-in analog-to-digital converter channel to sample the two ends of the battery pack and obtain the current battery voltage data.

[0037] To eliminate the fluctuations in electromagnet attraction force caused by battery power loss, a voltage-pulse width modulation compensation model is established based on the principle of energy conservation. This model maintains a constant coil magnetomotive force by adjusting the duty cycle of the initial drive signal in real time. The calculation formula is as follows: ;

[0038] This is the target duty cycle value after voltage compensation; This is the preset value of the reference duty cycle under standard voltage conditions. This value is the optimal starting value determined based on the clutch spring preload and armature mass balance experiment. The standard operating voltage reference value for the system design; This is the current battery voltage data that has been collected.

[0039] Subsequently, the main control chip followed The gate of the control power MOSFET injects a high-frequency PWM drive current into the clutch electromagnet coil and simultaneously starts a high-frequency current sampling process. This sampling process converts the current signal into a millivolt-level voltage signal through a precision current sensing resistor connected in series at the bottom of the drive circuit, and then performs signal tuning through an operational amplifier.

[0040] The acquired raw current data is converted into a real-time operating current sequence using the following conversion formula:

[0041] ;

[0042] I(n) is the real-time operating current value at the nth sampling point, in amperes; This represents the raw digital value output by the sampling channel; N is the reference voltage of the microcontroller's ADC, and N is the resolution bit of the ADC, which is 12, representing that the reference voltage is divided into 4096 equal parts. The resistance value of the current sensing resistor is selected as 0.05 ohms. This resistance value is chosen to balance the signal-to-noise ratio and the power loss of the circuit. The closed-loop gain of the operational amplifier is set to 20 times, which is used to amplify the weak sampling voltage to the optimal range of the ADC.

[0043] Voltage compensation calculations are performed on the real-time operating current sequence using current battery voltage data to generate a normalized current sequence that eliminates the effects of voltage fluctuations, including:

[0044] Calculate the voltage deviation of the current battery voltage data relative to the system's built-in standard reference voltage;

[0045] The voltage deviation factor is substituted into the inductor characteristic response model for mapping calculation, and the current amplitude gain factor and time axis scaling ratio are extracted.

[0046] The current amplitude gain factor is used to perform a linear scaling mapping on the real-time operating current sequence to obtain an amplitude-aligned current sequence, thereby eliminating the influence of voltage level on the current waveform amplitude.

[0047] By using the time axis scaling ratio to resample and phase-calibrate the amplitude-aligned current sequence, the current response characteristics under different voltage backgrounds are transformed into a unified time coordinate system, generating a normalized current sequence that can reflect the real mechanical motion state of the clutch electromagnet.

[0048] It should be added that the construction logic of the inductor characteristic response model is as follows:

[0049] Establish a reference training set containing historical operating current reference sequences of multiple clutch electromagnets under different voltage gradients and ambient temperature gradients;

[0050] For each set of historical operating current reference sequences in the reference training set, the steady-state current peak value and the induction inflection point trigger time are extracted as model input samples.

[0051] The ratio of the voltage value corresponding to each group of historical working current reference sequences to the standard voltage reference value is marked as the voltage independent variable label, and the ratio of the model input sample to the corresponding physical parameter under the standard state is marked as the response coefficient target value.

[0052] A multidimensional nonlinear mapping function containing resistance heating effect correction terms, magnetic circuit saturation compensation terms, and mechanical damping correlation terms is constructed as the main structure of the model.

[0053] A set including the coil resistivity variation coefficient, the iron core permeability decay constant, and the armature motion inertia weight is set as the model iteration parameters;

[0054] Using a nonlinear least squares algorithm, with the voltage independent variable label as input, the model iteration parameters are adjusted through multiple iterations to minimize the sum of squared deviations between the output value of the multidimensional nonlinear mapping function and the target value of the response coefficient.

[0055] When the sum of squared deviations is less than the preset convergence threshold, the iteration stops and the coefficients of each order in the main structure of the model are locked, thus completing the construction of the inductor characteristic response model.

[0056] Preferably, the steps for extracting the current amplitude gain factor and the time axis scaling ratio are as follows:

[0057] Obtain the current ambient temperature data of the smart door lock, use the current ambient temperature data to perform thermistor correction calculation on the voltage deviation ratio, and output the real-time comprehensive impedance deviation coefficient;

[0058] The inductance characteristic excitation value of the electromagnet under the current driving environment is obtained by multiplying the real-time integrated impedance offset coefficient with the pre-stored geometric constant of the number of coil turns.

[0059] The inductor characteristic excitation value is input into the inductor characteristic response model. The phase shift of the current sequence during the magnetic field establishment stage is identified through the electromagnetic transient simulation logic built into the model. The phase shift is defined as the time axis scaling ratio.

[0060] The motional electromotive force loss generated by the electromagnet core at different pull-in stroke nodes is calculated based on the time axis scaling ratio. The motional electromotive force loss is used to perform nonlinear gain compensation on the real-time integrated impedance offset coefficient, and a current amplitude gain factor is generated to correct the normalized current sequence amplitude intensity.

[0061] In a specific embodiment, the voltage deviation ratio of the current battery voltage data relative to the system's built-in standard reference voltage (representing the standard DC level under full charge) is first calculated using a division operation. The voltage deviation ratio reflects the linear shift in the energy intensity of the driving source. Subsequently, the inductor characteristic response model is activated. The model was constructed through a deep mapping process, pre-constructed by conducting excitation experiments on 100 sets of electromagnets of the same specifications under different voltage and ambient temperature gradients in a constant-temperature laboratory environment. During model construction, a nonlinear least squares algorithm was used to iteratively solve the main structure of the multidimensional nonlinear mapping function.

[0062] ;in, The target value for the response coefficient includes the current amplitude response and the time offset response; The coil resistivity variation coefficient is used to correct the current reduction caused by the increase in resistance with increasing temperature; It is the core permeability decay constant, reflecting the magnetic circuit saturation characteristics; This is the coefficient of the rate of change of ambient temperature; This represents the difference between the current ambient temperature and the standard temperature of 25°C. The armature's moment of inertia weight represents the strength of the mechanical load's feedback to changes in the inductor current. A larger value means that the current waveform of the electromagnet is very sensitive to the mechanical motion state; conversely, a smaller value indicates lower sensitivity. During model iteration, using... To preset the convergence threshold, the coefficients of each order are locked by minimizing the sum of squared deviations. Represents magnetic flux. denoted as , where is the rate of change of magnetic flux over time.

[0063] During the real-time extraction phase, the current ambient temperature data collected by the NTC thermistor sensor is acquired, and the real-time comprehensive impedance offset coefficient is output using the following formula. ; This is the current ambient temperature data. The reference temperature is 25℃. The preset thermistor resistance correction coefficient is set to 0.004. The current is multiplied by a pre-stored geometric constant for the number of coil turns to generate an inductor characteristic excitation value. This excitation value is then fed into a locked inductor characteristic response model to identify the phase shift of the current sequence during the magnetic field establishment phase, defined as the time axis scaling factor. Simultaneously, considering the phenomenon of core displacement cutting magnetic lines of force, the motional electromotive force loss is calculated. And use this loss amount to Nonlinear gain compensation is performed to ultimately generate the current amplitude gain factor. The calculation formula is as follows:

[0064] ;

[0065] use Perform a linear scaling mapping on the real-time operating current sequence to obtain an amplitude-aligned current sequence, and then utilize... The amplitude-aligned current sequence is resampled, mapping the sampling points from the observation time axis to a standard time coordinate system. For example, when a current is detected... At 5.0V and an ambient temperature of 45℃, It is 0.833. The calculated value is approximately 0.771. The system amplifies the amplitude by approximately 1.29 times and stretches the time axis accordingly to generate a normalized current sequence.

[0066] In a specific embodiment, when the iron core moves in the magnetic field, its displacement causes a change in the magnetic reluctance of the magnetic circuit, which in turn causes an instantaneous change in the coil inductance. The resulting back electromotive force is the motional electromotive force loss. The formula is: ; t represents the loss of motional electromotive force at time t, in volts; I(t) represents the instantaneous current value at the current sampling time in the normalized current sequence; v(t) represents the instantaneous velocity of the iron core at the current time, in meters per second. This data can be estimated by the reciprocal transformation of the time data at the abrupt change point and the displacement stroke. The gradient coefficient of the electromagnet coil inductance with respect to the iron core displacement is expressed in Henry per meter. This parameter is an inherent physical property of the electromagnet and characterizes the degree of change in inductance caused by a unit displacement change. Its value is pre-stored in the storage module through previous finite element simulation or laser ranging calibration experiments.

[0067] It needs to be explained that, , as well as The specific acquisition logic is as follows:

[0068] In the initial stage, 100 sets of electromagnets of the same specifications were subjected to full-condition scanning in a constant-temperature laboratory: for each combination of voltage and temperature, the actual current response waveform was recorded, and the true response coefficient was extracted from it. Substitute the experimental data into At this time, , , and All are known experimental measurements. A nonlinear least squares algorithm is used iteratively to find a set of (…). , If the sum of errors of all equations is minimized, then this set of equations ( , The value corresponding to ) is ( , The final value of ).

[0069] Differentiation and extremum extraction are performed on the falling and rising edges of the normalized current sequence to obtain abrupt change time data, trough depth data, and initial slope data. The specific steps include:

[0070] Extract local time window sampling points in the early stage of current growth from the normalized current sequence, and use the least squares method to perform linear regression fitting on the local time window sampling points to obtain the slope value of the regression line and define it as the initial slope data.

[0071] Perform first-order difference calculation on the normalized current sequence to generate an instantaneous derivative sequence that reflects the slope of the current value change; search for zero-crossing points in the instantaneous derivative sequence where the value changes from negative to positive, and mark the sampling time index corresponding to the zero-crossing points as the abrupt change point time data;

[0072] Extract the minimum value of the current trough corresponding to the time data of the abrupt change in the normalized current sequence. At the same time, calculate the average current amplitude of the normalized current sequence after entering the steady state stage. Subtract the average current amplitude from the minimum value of the current trough to obtain the trough depth data.

[0073] It should be added that the logic for judging the steady-state phase is as follows:

[0074] Starting with the time data of the mutation point, the absolute value of the derivative at each sampling point is traversed backward in the instantaneous derivative sequence. The starting point where the absolute value of the derivative first drops below a preset convergence threshold and remains below the preset convergence threshold in subsequent sampling periods of a predetermined number of bits is defined as the steady-state entry node. Starting from the steady-state entry node, a sampling data set with a fixed time length is extracted from the normalized current sequence to construct a steady-state analysis vector. The dispersion characteristic value of the current values ​​in the steady-state analysis vector is calculated. When the dispersion characteristic value is less than a preset stability threshold, the normalized current sequence is determined to have entered the steady-state stage.

[0075] In a specific embodiment, sampling points within a local time window that are in the early stages of current growth are extracted from the normalized current sequence, and the least squares method is used to perform linear regression fitting on the sampling points within this window to calculate the initial slope data. ; The slope of the regression line is expressed in amperes per second; N is the total number of sampling points within the local time window. This represents the time offset of the i-th sampling point relative to the starting point. This corresponds to the normalized current value. Subsequently, a first-order difference calculation is performed on the normalized current sequence to generate the instantaneous derivative sequence. , The current value at the current sampling point. The sampling frequency is preset. Then, the zero-crossing point where the value changes from negative to positive is retrieved from the instantaneous derivative sequence. This is a physical inflection point caused by the core moving to its end of its stroke, resulting in the disappearance of the back electromotive force and the start of current recovery. The sampling time index corresponding to this point is marked as the abrupt change point time data. .

[0076] by Traverse backwards from the starting point The absolute value of the derivative is used to determine the steady-state entry point. The logic is as follows: when the absolute value of the derivative first falls below a preset convergence threshold and remains below that threshold for a predetermined number of digits, this point is recorded as the steady-state entry node. A sample set of length L is extracted from this node to construct the steady-state analysis vector, and its discrete eigenvalues ​​are calculated. Where the discrete eigenvalue represents the standard deviation of the sampling points within the vector, when When the current falls below a preset stability threshold, it is determined that the current has officially entered the steady-state phase. The average current amplitude is obtained by calculating the arithmetic mean of the current values ​​during the steady-state phase. .

[0077] Finally, extract the normalized current sequence from the data. Corresponding current trough minimum value And calculate the valley depth data. , The unit is ampere.

[0078] The time data of the mutation point, the depth data of the trough, and the initial slope data are respectively compared with the corresponding feature values ​​in the reference feature template to generate a set of feature offsets. The reference feature template is constructed by weighted average of the normalized current sequence of historical normal pull-in states stored in the system.

[0079] In a specific embodiment, a baseline feature template is constructed by retrieving pre-stored historical normal engagement state data. This template is not a fixed static value, but rather calculated using an exponentially weighted moving average algorithm based on a normalized current sequence extracted by the system during the first 50 to 100 successful unlocking cycles after the door lock is installed. Each baseline feature value in the baseline feature template... The calculation logic is as follows:

[0080] ;

[0081] Represents the benchmark mutation point time in the benchmark feature template Baseline trough depth Or the baseline initial slope M represents the total number of historical normal unlocking cycles involved in the calculation. Let be the corresponding feature value of the j-th historical period; The preset time decay weighting coefficient is calculated using the following formula: ,in A preset forgetting factor is used, the logic of which is to give higher weight to historical data closer to the current moment, thereby dynamically adapting to minor baseline drifts caused by grease aging or seasonal changes. The extracted physical features are then interpolated with the aforementioned baseline feature template to generate a feature offset set consisting of three independent dimensions. .

[0082] Extract the maximum offset data from the feature offset set. When the maximum offset data is greater than the preset offset threshold, generate the corresponding compensation drive sequence according to the data type of the maximum offset data and output it to the clutch electromagnet. The compensation drive sequence includes a reverse demagnetizing pulse signal to cancel the residual magnetization of the iron core and a high-frequency oscillation drive signal to overcome mechanical resistance.

[0083] Preferably, a corresponding compensation driving sequence is generated based on the data type of the maximum offset term, including:

[0084] Perform a modulus comparison operation on the starting slope data offset, abrupt change point time data offset, and trough depth data offset in the feature offset set, and lock the dimension with the highest degree of deviation as the dominant failure feature;

[0085] The dominant failure feature is matched with the pre-stored physical fault attribute library. When the dominant failure feature is the initial slope data offset, the current failure type is determined to be magnetic circuit polarization anomaly. When the dominant failure feature is the trough depth data offset, the current failure type is determined to be mechanical damping overload.

[0086] The real-time energy gain coefficient is calculated based on the numerical magnitude of the dominant failure characteristics. The duty cycle of the initial drive signal is then progressively increased using the real-time energy gain coefficient to generate a high-frequency oscillating drive signal that matches the current mechanical resistance.

[0087] The polarity direction data of the dominant failure characteristics are obtained, and the current vector direction of the demagnetizing pulse is determined by combining the polarity direction data. The current vector direction of the demagnetizing pulse is logically combined with the high-frequency oscillation drive signal in the time domain to synthesize a compensation drive sequence customized for the current physical failure mode.

[0088] Preferably, generating a high-frequency oscillating drive signal that matches the current mechanical resistance includes:

[0089] The target compensation duty cycle value is generated by multiplying the real-time energy gain coefficient with the original duty cycle value of the initial drive signal.

[0090] Obtain the static frictional resistance of the internal core of the clutch electromagnet under the current failure type, retrieve the optimal disturbance frequency from the system's built-in dynamic response table based on the static frictional resistance, and define the optimal disturbance frequency as the oscillation modulation frequency.

[0091] By using the oscillation modulation frequency to perform timing logic mapping processing on the target compensation duty cycle value, the continuous level signal is converted into a pulse wave group with alternating switching characteristics, so that the integral energy of the pulse wave group in the time domain and the energy loss generated by the current mechanical resistance reach a dynamic balance.

[0092] The pulse wave group is defined as a high-frequency oscillation drive signal output to the clutch electromagnet.

[0093] It should be added that the static frictional resistance of the internal core of the clutch electromagnet under the current failure type should be obtained, including:

[0094] Extract the valley depth offset values ​​corresponding to the valley depth data from the feature offset set, and use them as a mechanical resistance quantification index characterizing the current degree of jamming of the clutch mechanical transmission component.

[0095] The system obtains the current ambient temperature data of the smart door lock and determines the viscosity coefficient of the lubricating grease corresponding to the current ambient temperature data by querying the temperature change characteristic curve of the lubricating grease stored in the system.

[0096] The mechanical resistance quantification index and the lubricating grease viscosity coefficient are weighted and aggregated to generate the comprehensive dynamic resistance index that the iron core needs to overcome at the moment of startup.

[0097] By using the comprehensive dynamic impedance index to perform inverse derivation in the electromagnetic force-friction force balance model, the critical force value for maintaining the iron core in a static locked state is calculated, and the critical force value is marked as the static friction resistance.

[0098] It should be added that achieving a dynamic balance between the integrated energy of the pulse wave group in the time domain and the energy loss caused by the current mechanical resistance includes:

[0099] The oscillation modulation frequency is converted by reciprocal to determine the oscillation switching period per unit time and the total duration data of a single period is extracted and marked as the pulse period width;

[0100] By multiplying the target compensation duty cycle value with the pulse period width, the effective conduction time of the drive signal being in a high-level state within each pulse period is obtained.

[0101] Based on the proportion of the effective conduction time within the pulse period width, the high and low levels are logically switched on the time axis to generate alternating switching signals.

[0102] The pulse group time-domain integral energy is obtained by performing an integral summation of the instantaneous current and voltage product over time on the alternating switching signal.

[0103] The estimated energy loss generated by static friction resistance during the core displacement stroke is extracted. By comparing the relationship between the time-domain integral energy of the pulse group and the estimated energy loss, the gain of the pulse width of the alternating switching signal is finely adjusted to synthesize a pulse group that meets the requirements of dynamic energy balance.

[0104] Preferably, determining the current vector direction of the demagnetizing pulse includes:

[0105] By comparing the numerical relationship between the dominant failure feature and its corresponding feature value in the baseline feature template, the sign attribute of the deviation between the two is extracted and defined as polarity direction data.

[0106] Synchronously analyze the current flow direction of the clutch electromagnet during the initial drive signal execution phase, and determine the current flow direction of the initial drive signal as the initial magnetization vector direction;

[0107] Logical mapping analysis of the initial magnetization vector direction is performed using polarity direction data. When the shift in the polarity direction data, which represents the current dominant failure feature, is caused by the residual magnetism interference of the iron core, the initial magnetization vector direction is subjected to an inverse phase conversion process, and the compensation magnetic field polarity, which is completely opposite to the initial driving signal magnetic field polarity, is calculated.

[0108] The polarity of the compensating magnetic field is converted into the corresponding pulse current direction and set as the current vector direction of the demagnetizing pulse.

[0109] It should be added that the dominant failure feature is matched with the pre-stored electromagnetic characteristic correlation matrix. When the dominant failure feature is identified as the initial slope data offset and the polarity direction data shows a positive increase, the offset is determined to be caused by the residual magnetic interference of the iron core. When the dominant failure feature is identified as the trough depth data offset or the abrupt change point time data offset, the offset is determined to be caused by the residual magnetic interference of the non-iron core, and the cause of failure is defined as mechanical transmission obstruction.

[0110] In a specific embodiment, the offset of the initial slope data in the set Time offset of mutation point and trough depth data offset Perform a modulus comparison operation, identify the dimension with the highest deviation as the dominant failure feature, and match it with a pre-stored physical fault attribute library; the decision logic is set as follows: when the dominant failure feature is... Furthermore, when the polarity direction data is increasing positively (indicating an abnormally rapid magnetic circuit establishment rate due to residual magnetism superposition), the current failure type is determined to be abnormal magnetic circuit polarization, and a priority for residual magnetism elimination is assigned; when the dominant failure characteristic is or When the current dip depth is reduced or the motion time is delayed due to armature obstruction, the current failure type is determined to be mechanical damping overload, and energy enhancement priority is allocated.

[0111] For mechanical damping overload, the trough depth offset value is first extracted as a quantitative index of mechanical sluggishness. Simultaneously acquire the current ambient temperature data of the smart door lock. By querying the pre-stored temperature change characteristic curve of the lubricating grease, which is fitted by experimental data on the dynamic viscosity of the lubricating grease from -20℃ to 60℃, the viscosity coefficient of the lubricating grease at the current temperature is determined. The comprehensive dynamic impedance index is generated using a weighted aggregation formula. ;

[0112] The mechanical resistance conversion coefficient, ranging from 1.5 to 2.5, is used to map the current amplitude shift into mechanical losses. This is the temperature compensation gain coefficient, set to 0.08, used to quantify the weight of the contribution of the low-temperature environment to the resistance. The preset reference current offset value is taken as the average value of the historical normal fluctuation range, and the unit is amperes. It is used to normalize the current offset. This is the standard viscosity coefficient at the reference ambient temperature, measured in Pascals per second, used to normalize viscosity parameters.

[0113] Then The electromagnetic force-friction equilibrium model is then used for reverse derivation. This model is based on the armature dynamics equations. Constructed, where m represents the mass of the armature (moving iron core), The electromagnetic attraction is generated by an electric current. To reset the spring preload, The static frictional resistance is the unknown; the model is adjusted iteratively. The preset parameters ensure that the integral trajectory of the armature acceleration 'a' under the input current sequence is consistent with the actual observed abrupt change time data. Matching is achieved when the residual variance between the simulated trajectory and the actual trajectory is less than a preset threshold, locking is completed, the critical force value is calculated and marked as static friction resistance f.

[0114] Then, based on f, the optimal perturbation frequency is retrieved from the dynamic response table and defined as the oscillation modulation frequency. Simultaneously, the real-time energy gain coefficient is calculated using the numerical magnitude of the dominant failure characteristics. Compare it with the original duty cycle of the initial drive signal. Perform a product operation to generate the target compensation duty cycle value. .

[0115] use right To perform sequential logic mapping, first calculate the pulse period width. Next, the effective conduction time within each pulse cycle is calculated. And generate alternating switching signals with discrete characteristics on the time axis. To ensure the effectiveness of energy compensation, the following energy dynamic balance verification formula is executed:

[0116] ;

[0117] in, For pulse group compensation duration Time-domain integral energy; This represents the current battery voltage; f represents the static frictional resistance. This is the core engagement stroke; The preset mechanical loss margin coefficient is denoted by I(t), which represents the instantaneous current value at the current sampling moment in the normalized current sequence.

[0118] To address magnetic circuit polarization anomalies, the system extracts polarity direction data by comparing the sign attributes of the dominant failure characteristics and the baseline characteristic template values, and simultaneously analyzes the initial magnetization vector direction of the initial drive signal. When the polarity direction data is positive, the system determines that the offset is caused by remanence in the same direction. The system then performs an inverse phase conversion on the initial magnetization vector direction to generate the current vector direction of the demagnetizing pulse. Finally, the system logically combines the demagnetizing pulse and the high-frequency oscillation drive signal in the time domain (the preset logic is to first perform 0.5ms of reverse demagnetization, followed by 2ms of high-frequency oscillation to clear magnetic circuit interference and then overcome mechanical static friction), synthesizing a compensation drive sequence customized for the current physical failure mode and outputting it to the clutch electromagnet, achieving precise self-repair of clutch jamming or remanence faults.

[0119] It should be added that the dominant failure characteristics are calculated first. Relative to the corresponding reference value in the reference feature template The relative deviation rate is then substituted into the following nonlinear saturation mapping function for calculation to generate the real-time energy gain coefficient. The specific calculation formula is as follows:

[0120] ;

[0121] in It is a real-time energy gain coefficient used to limit the upper limit of the drive signal energy and prevent the electromagnetic coil from overheating; The numerical magnitude of the dominant failure feature is expressed in seconds when the dominant failure feature is the time data offset of the abrupt change point, in amperes when it is the data offset of the trough depth, and in amperes per second when it is the data offset of the initial slope. The reference feature value in the reference feature template corresponds to the dominant failure feature, with units of and . To maintain consistency, in order to eliminate the dimensional effects caused by individual differences in different electromagnets; The preset response sensitivity coefficient is used to control the rise rate of the gain curve. The design logic of this parameter is to ensure that when the deviation rate is less than the threshold, the system maintains a stable output, while when the deviation rate increases, the gain coefficient can quickly respond to the target range. The preset maximum gain amplitude coefficient represents the maximum allowable additional energy output ratio of the system. This preset logic is set based on the heat dissipation capacity of the clutch electromagnet coil and the strength limit of the mechanical transmission components to ensure that the hardware structure is not damaged while eliminating jamming; e is a natural constant.

[0122] The real-time energy gain coefficient formula is derived from a variation of the exponential saturation function. Traditional linear proportional formulas lack robustness when deviations are extremely small or large, easily leading to output oscillations due to noise interference or output saturation overflow due to excessive deviations. Using the negative exponential form in this embodiment achieves a control effect of "low gain with small deviations, stable saturation with large deviations," meaning that when the relative deviation rate approaches zero... When the relative deviation rate approaches 1, the original driving capability is maintained; when the relative deviation rate increases significantly, Smoothness approaches It automatically locks within the safe energy threshold.

[0123] It should be added that the preset logic of the dynamic response table is based on experimental calibration and the principle of minimum power consumption. Specifically, different frictional resistance loads are simulated in a laboratory environment, the driving frequency is scanned one by one, the frequency value that enables the iron core to successfully overcome the resistance and start with the lowest power consumption is recorded, and a mapping relationship is established between this value and the corresponding frictional resistance.

[0124] After outputting the compensation drive sequence, the system's built-in loop counter is incremented stepwise to obtain the current loop count data, and the process returns to the step of synchronously acquiring the real-time operating current sequence of the clutch electromagnet drive circuit. When the current loop count data exceeds the preset limit and the maximum offset item data in the feature offset set is still greater than the preset offset threshold, it is determined that the electromagnet engagement action is not in place, a physical failure code for the clutch mechanism is generated, and a safety warning command is triggered based on the physical failure code for the clutch mechanism.

[0125] In a specific embodiment, a register read instruction is invoked to access the loop counter value stored at a specific address in the random access memory, and an increment operation is performed on the value to obtain the current loop count. , This is the updated current loop count data; This is the count value before the previous iteration, which is reset to 0 by default during system initialization or when the unlocking task starts. The system then triggers a program pointer jump, returning to the step of synchronously acquiring the real-time operating current sequence of the clutch electromagnet drive circuit. After each loop, the system executes dual failure determination logic to calculate the failure lockout flag. The specific determination formula is as follows:

[0126] ;

[0127] in, This represents the data for the largest offset item in the feature offset set, with units of amperes per second, seconds, or amperes depending on the feature type. The preset offset threshold is in units of... Consistent; To limit the number of attempts, it is recommended to set it to an integer range of 3 to 5, preferably 3. This preset logic aims to balance the success rate of fault repair with system energy consumption, and prevent the battery from being depleted or the electromagnetic coil from overheating and burning out due to an infinite number of ineffective compensation attempts. H(x) is a unit step function. When the input parameter x is greater than 0, it outputs 1, otherwise it outputs 0.

[0128] If and only if When the value equals 1, it means that the current loop count data is satisfied simultaneously. More than the preset number of times and the maximum offset data Still greater than the preset offset threshold Under these two conditions, it is determined that the electromagnet's engagement action is not complete, confirming that the current hardware fault has exceeded the boundary of the software's self-repair capability.

[0129] Example 2:

[0130] like Figure 3 As shown, the alarm control system of the smart door lock includes a door lock parameter acquisition module, a battery parameter calculation module, a clutch compensation module, a compensation cycle judgment module, and a door lock judgment and early warning module.

[0131] The various modules are connected via wired and / or wireless connections to enable data transmission between them;

[0132] Door lock parameter acquisition module: Receives unlocking command, outputs initial drive signal to clutch electromagnet; synchronously acquires real-time operating current sequence of clutch electromagnet drive circuit and current battery voltage data of door lock system;

[0133] Battery parameter calculation module: Calculates voltage compensation for the real-time operating current sequence using the current battery voltage data to generate a normalized current sequence; performs derivative calculations and extracts extreme values ​​for the falling and rising edges of the normalized current sequence to obtain abrupt change time data, trough depth data, and initial slope data.

[0134] Clutch compensation module: The difference between the time data of the sudden change point, the depth data of the trough, and the initial slope data and the corresponding feature values ​​in the reference feature template is calculated to generate a feature offset set; the maximum offset data in the feature offset set is extracted; when the maximum offset data is greater than the preset offset threshold, the corresponding compensation drive sequence is generated according to the data type of the maximum offset data and output to the clutch electromagnet.

[0135] Compensation cycle judgment module: After outputting the compensation drive sequence, it performs step-by-step accumulation calculation on the built-in cycle counter to obtain the current cycle count data, and returns to the step of synchronously acquiring the real-time working current sequence of the clutch electromagnet drive circuit;

[0136] Door lock judgment and warning module: When the current loop count data is greater than the preset count limit and the maximum offset item data in the feature offset set is still greater than the preset offset threshold, it is determined that the electromagnet engagement action is not in place, a physical failure code of the clutch mechanism is generated, and a safety warning command is triggered based on the physical failure code of the clutch mechanism.

[0137] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0138] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0139] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0140] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling an alarm of an intelligent door lock, characterized by, include: Upon receiving the unlocking command, an initial drive signal is output to the clutch electromagnet. Synchronously acquire the real-time operating current sequence of the clutch electromagnet drive circuit and the current battery voltage data of the door lock system; Voltage compensation calculations are performed on the real-time operating current sequence using the current battery voltage data to generate a normalized current sequence; Differentiation and extremum extraction are performed on the falling and rising edges of the normalized current sequence to obtain abrupt change time data, trough depth data, and initial slope data. The time data of the mutation point, the depth data of the trough, and the initial slope data are respectively compared with the corresponding feature values ​​in the benchmark feature template to generate a set of feature offsets. Extract the maximum offset data from the feature offset set. When the maximum offset data is greater than the preset offset threshold, generate the corresponding compensation drive sequence according to the data type of the maximum offset data and output it to the clutch electromagnet. After outputting the compensation drive sequence, the system's built-in loop counter is incremented stepwise to obtain the current loop count data, and the process returns to the step of synchronously acquiring the real-time operating current sequence of the clutch electromagnet drive circuit. When the current number of loops exceeds the preset limit and the maximum offset in the feature offset set is still greater than the preset offset threshold, it is determined that the electromagnet's engagement action is not in place, a physical failure code for the clutch mechanism is generated, and a safety warning command is triggered based on the physical failure code for the clutch mechanism.

2. The alarm control method for a smart door lock according to claim 1, characterized in that, Generate a normalized current sequence, including: Calculate the voltage deviation of the current battery voltage data relative to the system's built-in standard reference voltage; The voltage deviation factor is substituted into the inductor characteristic response model for mapping calculation, and the current amplitude gain factor and time axis scaling ratio are extracted. The current amplitude gain factor is used to perform a linear scaling mapping on the real-time operating current sequence to obtain an amplitude-aligned current sequence. By using the time axis scaling ratio to resample and phase-calibrate the amplitude-aligned current sequence, the current response characteristics under different voltage backgrounds are transformed into a unified time coordinate system, generating a normalized current sequence.

3. The alarm control method for a smart door lock according to claim 2, characterized in that, The current amplitude gain factor and time axis scaling factor are extracted, including: Obtain the current ambient temperature data of the smart door lock, use the current ambient temperature data to perform thermistor correction calculation on the voltage deviation ratio, and output the real-time comprehensive impedance deviation coefficient; The inductance characteristic excitation value of the electromagnet under the current driving environment is obtained by multiplying the real-time integrated impedance offset coefficient with the pre-stored geometric constant of the number of coil turns. The inductor characteristic excitation value is input into the inductor characteristic response model. The phase shift of the current sequence during the magnetic field establishment stage is identified through the electromagnetic transient simulation logic built into the model. The phase shift is defined as the time axis scaling ratio. The motional electromotive force loss generated by the electromagnet core at different pull-in stroke nodes is calculated based on the time axis scaling ratio. The motional electromotive force loss is then used to perform nonlinear gain compensation on the real-time integrated impedance offset coefficient to generate the current amplitude gain factor.

4. The alarm control method for a smart door lock according to claim 1, characterized in that, Obtain the time data of the abrupt change point, the depth data of the trough, and the initial slope data, including: Extract local time window sampling points in the early stage of current growth from the normalized current sequence, and use the least squares method to perform linear regression fitting on the local time window sampling points to obtain the slope value of the regression line and define it as the initial slope data. Perform first-order difference calculation on the normalized current sequence to generate an instantaneous derivative sequence that reflects the slope of the current value change; search for zero-crossing points in the instantaneous derivative sequence where the value changes from negative to positive, and mark the sampling time index corresponding to the zero-crossing points as the abrupt change point time data; Extract the minimum value of the current trough corresponding to the time data of the abrupt change in the normalized current sequence. At the same time, calculate the average current amplitude of the normalized current sequence after entering the steady state stage. Subtract the average current amplitude from the minimum value of the current trough to obtain the trough depth data.

5. The alarm control method for a smart door lock according to claim 1, characterized in that, Generate the corresponding compensation driving sequence based on the data type of the maximum offset term, including: Perform a modulus comparison operation on the starting slope data offset, abrupt change point time data offset, and trough depth data offset in the feature offset set, and lock the dimension with the highest degree of deviation as the dominant failure feature; The dominant failure feature is matched with the pre-stored physical fault attribute library. When the dominant failure feature is the initial slope data offset, the current failure type is determined to be magnetic circuit polarization anomaly. When the dominant failure feature is the trough depth data offset, the current failure type is determined to be mechanical damping overload. The real-time energy gain coefficient is calculated based on the numerical magnitude of the dominant failure characteristics. The duty cycle of the initial drive signal is then progressively increased using the real-time energy gain coefficient to generate a high-frequency oscillating drive signal that matches the current mechanical resistance. The polarity direction data of the dominant failure characteristics are obtained, and the current vector direction of the demagnetizing pulse is determined by combining the polarity direction data. The current vector direction of the demagnetizing pulse is logically combined with the high-frequency oscillation drive signal in the time domain to synthesize a compensation drive sequence customized for the current physical failure mode.

6. The alarm control method for a smart door lock according to claim 5, characterized in that, Generate a high-frequency oscillating drive signal that matches the current mechanical resistance, including: The target compensation duty cycle value is generated by multiplying the real-time energy gain coefficient with the original duty cycle value of the initial drive signal. Obtain the static frictional resistance of the internal core of the clutch electromagnet under the current failure type, retrieve the optimal disturbance frequency from the system's built-in dynamic response table based on the static frictional resistance, and define the optimal disturbance frequency as the oscillation modulation frequency. By using the oscillation modulation frequency to perform timing logic mapping processing on the target compensation duty cycle value, the continuous level signal is converted into a pulse wave group, so that the integral energy of the pulse wave group in the time domain and the energy loss generated by the current mechanical resistance reach a dynamic balance; the pulse wave group is defined as a high-frequency oscillation drive signal and output to the clutch electromagnet.

7. The alarm control method for a smart door lock according to claim 6, characterized in that, Obtain the static frictional resistance of the internal core of the clutch electromagnet under the current failure type, including: Extract the valley depth offset values ​​corresponding to the valley depth data from the feature offset set, and use them as a mechanical lag quantification index. The system obtains the current ambient temperature data of the smart door lock and determines the viscosity coefficient of the lubricating grease corresponding to the current ambient temperature data by querying the temperature change characteristic curve of the lubricating grease stored in the system. The mechanical resistance quantification index and the lubricating grease viscosity coefficient are weighted and aggregated to generate the comprehensive dynamic resistance index that the iron core needs to overcome at the moment of startup. By using the comprehensive dynamic impedance index to perform inverse derivation in the electromagnetic force-friction force balance model, the critical force value for maintaining the iron core in a static locked state is calculated, and the critical force value is marked as the static friction resistance.

8. The alarm control method for a smart door lock according to claim 5, characterized in that, Determining the direction of the current vector of the demagnetizing pulse includes: By comparing the numerical relationship between the dominant failure feature and its corresponding feature value in the baseline feature template, the sign attribute of the deviation between the two is extracted and defined as polarity direction data. Synchronously analyze the current flow direction of the clutch electromagnet during the initial drive signal execution phase, and determine the current flow direction of the initial drive signal as the initial magnetization vector direction; Logical mapping analysis of the initial magnetization vector direction is performed using polarity direction data. When the shift in the polarity direction data, which represents the current dominant failure characteristic, is caused by the residual magnetism interference of the iron core, the initial magnetization vector direction is subjected to inverse phase conversion processing, and the polarity of the compensation magnetic field is calculated. The polarity of the compensating magnetic field is converted into the corresponding pulse current direction and set as the current vector direction of the demagnetizing pulse.

9. An alarm control system for a smart door lock, characterized in that, It is implemented based on the alarm control method of the smart door lock according to any one of claims 1-8, including: Door lock parameter acquisition module: Receives unlocking command, outputs initial drive signal to clutch electromagnet; synchronously acquires real-time operating current sequence of clutch electromagnet drive circuit and current battery voltage data of door lock system; Battery parameter calculation module: Calculates voltage compensation for the real-time operating current sequence using the current battery voltage data to generate a normalized current sequence; performs derivative calculations and extracts extreme values ​​for the falling and rising edges of the normalized current sequence to obtain abrupt change time data, trough depth data, and initial slope data. Clutch compensation module: The difference between the time data of the sudden change point, the depth data of the trough, and the initial slope data and the corresponding feature values ​​in the reference feature template is calculated to generate a feature offset set; the maximum offset data in the feature offset set is extracted; when the maximum offset data is greater than the preset offset threshold, the corresponding compensation drive sequence is generated according to the data type of the maximum offset data and output to the clutch electromagnet. Compensation cycle judgment module: After outputting the compensation drive sequence, it performs step-by-step accumulation calculation on the built-in cycle counter to obtain the current cycle count data, and returns to the step of synchronously acquiring the real-time working current sequence of the clutch electromagnet drive circuit; Door lock judgment and warning module: When the current loop count data is greater than the preset count limit and the maximum offset item data in the feature offset set is still greater than the preset offset threshold, it is determined that the electromagnet engagement action is not in place, a physical failure code of the clutch mechanism is generated, and a safety warning command is triggered based on the physical failure code of the clutch mechanism.

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