An electric seat state recognition method, device and medium

Abstract

The application discloses an electric seat state recognition method and device and a medium. The application obtains the real-time current and the Hall signal of the electric seat motor, calculates the slope of the current and the speed, the speed change rate and the frequency of the Hall signal, and the system can monitor the dynamic state of the motor. If the real-time current, the speed, the speed change rate or the slope exceeds the preset threshold, it is confirmed that the electric seat is in the locked-rotor state. Meanwhile, by analyzing the Hall signal and the frequency thereof, the system can also identify the obstacle state, thereby improving the safety and reliability of the seat, and solving the problem that the state of the electric seat cannot be accurately identified in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric seat state recognition, and in particular to an electric seat state recognition method, device and medium. BACKGROUND

[0002] With the development of automobile intelligence, electric seats have become a common configuration of modern cars. In the control of electric seats, the seat state recognition capability directly affects the function and performance of the entire system. The current mainstream state recognition method is mainly based on current monitoring during motor movement, and its main problems are as follows:

[0003] The stall recognition of the electric seat is achieved by comparing the current motor working current with the calibrated stall threshold current, and when the working current is greater than the preset threshold current, it is recognized that the motor is currently in a stall state. Since the seat power supply system is 9-16V range power supply, the current threshold needs to match the current working voltage, otherwise it will appear that the stall recognition cannot be triggered under low voltage state or the error will be caused by user action (stretching, turning) under high voltage state. The current mainstream method is to calibrate the motor working current under different voltage states, and compare the current stall threshold state in real time according to the current working current of the motor. Although this control method can solve the problem, the current is fluctuating during the entire movement of the seat, and the threshold can only select the maximum point as the trigger point, which greatly affects the state recognition accuracy;

[0004] The obstacle of the electric seat is mainly recognized by the pressure or current detected by the sensor. When encountering obvious obstacles such as people, boxes or hard objects such as screws on the track, the sensor will have a significant state change, and the controller can accurately recognize it. This is also the current mainstream obstacle recognition method. However, if a silk scarf, clothing or other soft objects are rolled into the track, the controller cannot recognize it at the beginning because the sensor state change is not obvious, and it is often not until the object is rolled into the motor shaft that it can be recognized, greatly increasing the difficulty and cost of fault maintenance.

[0005] These problems result in that the prior art cannot accurately recognize the state of the electric seat. SUMMARY

[0006] The present application provides an electric seat state recognition method, device and medium to solve the problem that the prior art cannot accurately recognize the state of the electric seat.

[0007] In a first aspect, the present application provides an electric seat state recognition method, comprising:

[0008] Obtaining each real-time current and each Hall signal of the electric seat motor;

[0009] According to the real-time current and the time of the real-time current, the slope of the real-time current is calculated;

[0010] According to the Hall signal, the speed, the speed change rate of the electric seat motor and the frequency of the Hall signal are calculated;

[0011] If the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold value, it is determined that the electric seat is in the stall state;

[0012] According to the Hall signal and the frequency, it is determined whether the electric seat is in the obstacle state.

[0013] The application can accurately monitor the dynamic state of the motor by obtaining the real-time current and the Hall signal of the electric seat motor, and calculating the slope of the current and the speed, the speed change rate and the frequency of the Hall signal. If the real-time current, the speed, the speed change rate or the slope exceeds the preset threshold value, the system determines that the electric seat is in the stall state, thereby giving an early warning and taking measures. In addition, by analyzing the Hall signal and its frequency, the system can also identify the obstacle state, further enhancing the safety and reliability of the electric seat. This multi-level state monitoring and analysis not only improves the accuracy of stall and obstacle identification, but also reduces the potential risks caused by false state judgment, to solve the problem that the state of the electric seat cannot be accurately identified in the prior art.

[0014] As a preferred embodiment of the first aspect, if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold value, it is determined that the electric seat is in the stall state, specifically:

[0015] Obtaining the real-time voltage of the electric seat motor;

[0016] According to the real-time voltage and the linear interpolation technology, the preset first threshold value is calculated;

[0017] The real-time current is compared with the preset first threshold value, and if the real-time current is greater than the first threshold value, it is determined that the electric seat is in the stall state.

[0018] In this preferred embodiment, the application dynamically adjusts the standard for stall recognition by obtaining the real-time voltage of the electric seat motor and calculating a preset first threshold value using linear interpolation techniques, to adapt to different voltage conditions. The real-time current is compared with this dynamic threshold value, and if the real-time current exceeds the threshold value, it is confirmed that the electric seat is in a stall state. This method of adjusting the threshold value based on real-time voltage improves the accuracy of stall recognition, reduces false positives caused by voltage fluctuations, and enhances the safety and reliability of the electric seat.

[0019] As a preferred embodiment of the first aspect, if the real-time current, the speed, the rate of change of speed, or the slope is greater than a preset threshold value, it is confirmed that the electric seat is in a stall state, specifically:

[0020] The real-time current and the rate of change of speed are input into a preset speed control module, so that the speed control module compares the real-time current and the rate of change of speed with a preset second threshold value, and if the real-time current and the rate of change of speed are greater than the second threshold value, it is confirmed that the electric seat is in a stall state.

[0021] In this preferred embodiment, the application can accurately monitor the dynamic performance of the motor by inputting the real-time current and the rate of change of speed of the electric seat motor into a preset speed control module and comparing them with a preset second threshold value. If both the real-time current and the rate of change of speed exceed the second threshold value, the system confirms that the electric seat is in a stall state. This dual monitoring mechanism not only improves the accuracy of stall state recognition, but also reduces the likelihood of false positives through real-time data analysis, thereby enhancing the safety and reliability of the electric seat.

[0022] As a preferred embodiment of the first aspect, if the real-time current, the speed, the rate of change of speed, or the slope is greater than a preset threshold value, it is confirmed that the electric seat is in a stall state, specifically:

[0023] The slope and the speed are compared with a preset third threshold value, and if the slope is greater than the third threshold value, it is confirmed that the electric seat is in a stall state.

[0024] In this preferred embodiment, the application can effectively identify the stall state of the motor by comparing the real-time current slope and speed of the electric seat motor with a preset third threshold value. If the slope exceeds the third threshold value, the system confirms that the electric seat is in a stall state. This slope-based monitoring mechanism uses the sensitivity of current changes to detect potential stall conditions in advance, allowing the system to respond in time and take measures to avoid motor damage or failure, significantly improving the safety and reliability of the electric seat.

[0025] As a preferred embodiment of the first aspect, the confirming whether the electric seat is in the obstacle state according to the respective Hall signals and the respective frequencies specifically comprises:

[0026] collecting respective real-time Hall frequencies of the electric seat motor at respective coordinate positions;

[0027] calculating respective frequencies of the respective Hall signals according to the respective Hall signals and respective time intervals of the respective Hall signals;

[0028] calculating a frequency difference value according to the respective real-time Hall frequencies and the respective frequencies of the respective Hall signals;

[0029] comparing the difference value with preset fourth, fifth and sixth threshold values to confirm whether the electric seat is in the obstacle state.

[0030] The comparing the difference value with preset fourth, fifth and sixth threshold values to confirm whether the electric seat is in the obstacle state specifically comprises:

[0031] if the difference value is greater than or equal to the preset fourth threshold value, confirming that the electric seat is in a normal state;

[0032] if the difference value is less than the fourth threshold value and greater than or equal to the fifth threshold value, confirming that the electric seat is in a winding state;

[0033] if the difference value is less than the fifth threshold value and greater than the sixth threshold value, confirming that the electric seat is in a blocking state;

[0034] if the difference value is less than the sixth threshold value, confirming that the electric seat is in an abnormal state.

[0035] In this preferred embodiment, the application can accurately monitor the obstacles that may be encountered during the movement of the seat by collecting the real-time Hall frequencies of the electric seat motor at respective coordinate positions and calculating the frequencies of the Hall signals and the difference values thereof and the real-time frequencies. By comparing these frequency difference values with the preset fourth, fifth and sixth threshold values, the system can effectively identify the obstacle state, thereby providing early warning and taking appropriate measures. This obstacle detection mechanism based on Hall signal frequency analysis improves the identification ability of the electric seat for obstacles, reduces the potential risks caused by obstacles, and significantly improves the safety of the seat and the reliability of the system.

[0036] In a second aspect, the application provides an electric seat state recognition device. The electric seat state recognition device comprises an acquisition module, a calculation module and a judgment module.

[0037] The acquisition module is configured to acquire respective real-time currents of an electric seat motor and respective Hall signals.

[0038] The computing module is configured to calculate the slope of the real-time current according to the real-time current and the time of the real-time current;

[0039] According to the Hall signals, the speed, the speed change rate of the electric seat motor, and the frequency of the Hall signals are calculated;

[0040] The judging module is configured to determine that the electric seat is in a locked-rotor state if the real-time current, the speed, the speed change rate, or the slope is greater than a preset threshold value;

[0041] According to the Hall signals and the frequencies, it is determined whether the electric seat is in an obstacle state.

[0042] The device uses three modules to work in a coordinated manner to better identify the state of the electric seat. The application can accurately monitor the dynamic state of the motor by obtaining the real-time current and the Hall signals of the electric seat motor, calculating the slope of the current, and calculating the speed, the speed change rate, and the frequency of the Hall signals. If the real-time current, the speed, the speed change rate, or the slope exceeds the preset threshold value, the system determines that the electric seat is in a locked-rotor state, thereby providing early warning and taking measures. In addition, by analyzing the Hall signals and their frequencies, the system can also identify the obstacle state, further enhancing the safety and reliability of the electric seat. This multi-level state monitoring and analysis not only improves the accuracy of locked-rotor and obstacle identification, but also reduces the potential risks caused by state misjudgment, thereby solving the problem of inaccurate identification of the state of the electric seat in the prior art.

[0043] As a preferred embodiment of the second aspect, according to the Hall signals and the frequencies, it is determined whether the electric seat is in an obstacle state, specifically:

[0044] The real-time Hall frequencies of each coordinate position of the electric seat motor are collected;

[0045] According to the Hall signals and the time intervals of the Hall signals, the frequencies of the Hall signals are calculated;

[0046] According to the real-time Hall frequencies and the frequencies of the Hall signals, a frequency difference value is calculated;

[0047] The difference value is compared with a preset fourth threshold value, a fifth threshold value, and a sixth threshold value to determine whether the electric seat is in an obstacle state.

[0048] The difference is compared with a preset fourth threshold, a fifth threshold and a sixth threshold, and it is determined whether the electric seat is in an obstacle state, specifically:

[0049] If the difference is greater than or equal to the preset fourth threshold, it is determined that the electric seat is in a normal state.

[0050] If the difference is less than the fourth threshold and greater than or equal to the fifth threshold, it is determined that the electric seat is in a winding state.

[0051] If the difference is less than the fifth threshold and greater than the sixth threshold, it is determined that the electric seat is in a blocked state.

[0052] If the difference is less than the sixth threshold, it is determined that the electric seat is in an abnormal state.

[0053] In this preferred embodiment, the application can accurately monitor the obstacles that may be encountered during the movement of the seat by collecting the real-time Hall frequency of each coordinate position of the motor of the electric seat and calculating the frequency of the Hall signal and the difference between the real-time frequency. By comparing these frequency differences with the preset fourth, fifth and sixth thresholds, the system can effectively identify the obstacle state, thereby providing early warning and taking appropriate measures. This obstacle detection mechanism based on Hall signal frequency analysis improves the identification ability of the electric seat for obstacles, reduces the potential risks caused by obstacles, and significantly improves the safety of the seat and the reliability of the system.

[0054] In a third aspect, the application provides a computer readable storage medium comprising a stored computer program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the electric seat state identification method as described when the computer program is running. The beneficial effects are the same as those of the electric seat state identification method provided in the first aspect of the application. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 : a flowchart of an embodiment of the electric seat state identification method provided by the application;

[0056] Figure 2 : a structural schematic diagram of an embodiment of the motor movement state change rate provided by the application;

[0057] Figure 3 : a structural schematic diagram of an embodiment of the stall algorithm identification algorithm model provided by the application;

[0058] Figure 4 : a structural schematic diagram of an embodiment of the Hall signal in the obstacle blocked state provided by the application;

[0059] Figure 5 : A schematic diagram of one embodiment of the electric seat status recognition device provided in this application. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Example 1

[0062] Please refer to Figure 1 This invention provides a method for recognizing the status of an electric seat.

[0063] In this embodiment, the process of the electric seat state recognition method in this application is described in detail through steps S01-S05.

[0064] S01: Acquire the real-time current and Hall signals of the electric seat motor.

[0065] S02: Calculate the slope of each real-time current based on each real-time current and each time of each real-time current.

[0066] As a preferred embodiment of Embodiment 1, the step of calculating the slope of each real-time current based on each real-time current and each time of each real-time current specifically involves:

[0067] like Figure 2 As shown, the rate of change of the current motor's motion state, k,kn=f(In,tn), is calculated from the change in motor current ΔI within a unit time Δt. The current change ΔI, time change Δt, and current change slope k,ΔI=In-I(n-1), Δt=tn-t(n-1), k=ΔI / Δt. Where In is the current sampled value, I(n-1) is the previous sampled value, tn is the current current sampling system time, and t(n-1) is the previous current sampling system time.

[0068] S03: Based on the Hall signals, calculate the speed of the electric seat motor, the rate of change of speed, and the frequencies of the Hall signals.

[0069] As a preferred embodiment of Example 1, the calculation of the speed and rate of change of the electric seat motor based on the various Hall signals specifically involves:

[0070] By converting the Hall signal into speed, the rate of change of the current speed of the motor is obtained.

[0071] S04: If the real-time current, the speed, the rate of change of the speed, or the slope is greater than a preset threshold value, it is determined that the electric seat is in a locked-rotor state.

[0072] As a preferred embodiment of embodiment one, if the real-time current, the speed, the rate of change of the speed, or the slope is greater than a preset threshold value, it is determined that the electric seat is in a locked-rotor state, specifically:

[0073] The real-time voltage of the motor of the electric seat is obtained.

[0074] According to the real-time voltage and the linear interpolation technique, the preset first threshold value is calculated.

[0075] The real-time current is compared with the preset first threshold value, and if the real-time current is greater than the first threshold value, it is determined that the electric seat is in a locked-rotor state.

[0076] More specifically, as shown in the figure, the above process belongs to a threshold control algorithm module: after the locked-rotor threshold value of the highest and lowest working voltage is calibrated, the current threshold value Thrcur is calculated according to the real-time system working voltage through linear interpolation, and when the real-time current I is greater than the locked-rotor threshold value Thrcur, the threshold control module will be activated (activation means that the motor is in a locked-rotor state at this time). Figure 3

[0077] In this preferred embodiment, the application can dynamically adjust the standard of locked-rotor recognition by obtaining the real-time voltage of the motor of the electric seat and calculating the preset first threshold value using the linear interpolation technique, so as to adapt to different voltage conditions. The real-time current is compared with the dynamic threshold value, and if the real-time current exceeds the threshold value, it is determined that the electric seat is in a locked-rotor state. This method of adjusting the threshold value based on the real-time voltage improves the accuracy of locked-rotor recognition, reduces the misjudgment caused by voltage fluctuations, and thus enhances the safety and reliability of the electric seat.

[0078] As a preferred embodiment of embodiment one, if the real-time current, the speed, the rate of change of the speed, or the slope is greater than a preset threshold value, it is determined that the electric seat is in a locked-rotor state, specifically:

[0079] The real-time current and the rate of change of the speed are input into a preset speed control module, so that the speed control module compares the real-time current and the rate of change of the speed with a preset second threshold value, and if the real-time current and the rate of change of the speed are greater than the second threshold value, it is determined that the electric seat is in a locked-rotor state. ​

[0080] More specifically, as shown in Figure 3 The above process belongs to the speed control algorithm module: by converting the Hall signal into speed, the rate of change of the current motor speed is obtained. When the motor appears speed drop and current rise state, the system activates the speed control algorithm, and the algorithm judges according to the rising ratio of real-time current I and the falling ratio of motor speed V. When the trigger threshold is continuously met, the speed control module will be activated (activation means that the motor is in the state of locked rotor at this time).

[0081] In this preferred embodiment, the application can accurately monitor the dynamic performance of the motor by inputting the real-time current and the rate of change of the speed of the motor into the preset speed control module and comparing it with the preset second threshold. If the real-time current and the rate of change of the speed exceed the second threshold, the system confirms that the electric seat is in the state of locked rotor. This double monitoring mechanism not only improves the accuracy of the locked rotor state recognition, but also reduces the possibility of misjudgment through real-time data analysis, thereby enhancing the safety and reliability of the electric seat.

[0082] As a preferred embodiment of embodiment one, if the real-time current, the speed, the rate of change of the speed, or the slope is greater than the preset threshold, it is confirmed that the electric seat is in the state of locked rotor, specifically:

[0083] The slope and the speed are compared with the preset third threshold, and if the slope is greater than the third threshold, it is confirmed that the electric seat is in the state of locked rotor.

[0084] More specifically, as shown in Figure 3 The above process belongs to the slope control algorithm module: by monitoring the current slope change state when the motor is moving, when the current slope k and the motor speed V continuously meet the threshold Thrslop, the slope control module will be activated (activation means that the motor is in the state of locked rotor at this time).

[0085] In this preferred embodiment, the application can effectively identify the locked rotor state of the motor by comparing the real-time current slope and the speed of the motor with the preset third threshold. If the slope exceeds the third threshold, the system confirms that the electric seat is in the state of locked rotor. This slope-based monitoring mechanism uses the sensitivity of current change to detect potential locked rotor conditions in advance, thereby allowing the system to respond in time and take measures to avoid motor damage or failure, significantly improving the safety and reliability of the electric seat.

[0086] The locked rotor identification algorithm model monitors in different dimensions through three modules. When any module detects that the motor is in the state of locked rotor, the system considers that the current motor is in the state of locked rotor, which can greatly improve the accuracy of motor state recognition.

[0087] S05: According to the respective Hall signals and the respective frequencies, it is determined whether the electric seat is in an obstacle state.

[0088] As a preferred embodiment of Embodiment I, the determination of whether the electric seat is in an obstacle state according to the respective Hall signals and the respective frequencies is specifically:

[0089] As shown in FIG. 1, the interval time T of adjacent Hall signals is collected, and the Hall signal generation frequency f = 1 / T is calculated. The model records the Hall frequency of each coordinate position in the movement process in real time and stores it in the data buffer f(m, n). As shown in formula 1, the buffer data uses a first-in, first-out storage method to store the latest n times of coordinate axis data. Figure 4

[0090] The system calls the buffer data and the calibration data weight factor Fac(n, 1) at each operation to calculate the recorded frequency value Rec(m, 1) of each Hall position, as shown in formula 2.

[0091]

[0092] During the movement process, the difference value Δf is calculated by comparing the frequency of the current Hall position with the recorded data. The model will recognize the obstacle state according to the deviation of the difference value:

[0093] a) Normal state: Δf ≥ Thr1 (calibration threshold 1), in the normal state range, no processing is performed;

[0094] b) Winding state: Thr1 > Δf ≥ Thr2 (calibration threshold 2), activate the foreign matter winding monitoring, at this time the motor state changes but there is no large change. If the state lasts more than the threshold time, the model will judge that the track is wound by foreign matter;

[0095] c) Blocking state: Thr2 > Δf > Thr3 (calibration threshold 3), the blocking state duration threshold Tthr is calculated based on the current frequency using a linear difference method based on the calibration time threshold Tthrmax (the greater the frequency, the greater the duration), when the condition is met, the algorithm will set it to the blocking state;

[0096] d) Abnormal state: Δf < Thr3, when the frequency is lower than the lowest threshold, it is judged as a loss of Hall signal.

[0097]

[0098] ​In this preferred embodiment, the application can accurately monitor the obstacles that may be encountered during the movement of the electric seat by collecting the real-time Hall frequency of each coordinate position of the electric seat motor and calculating the frequency of the Hall signal and the difference between the real-time frequency. By comparing these frequency differences with the preset fourth, fifth, and sixth threshold values, the system can effectively identify the obstacle state, thereby providing early warning and taking appropriate measures. This obstacle detection mechanism based on Hall signal frequency analysis improves the identification ability of the electric seat for obstacles, reduces the potential risks caused by obstacles, and significantly improves the safety of the seat and the reliability of the system.

[0099] The application can accurately monitor the dynamic state of the motor by obtaining each real-time current and Hall signal of the electric seat motor and calculating the slope of these currents and the speed, speed change rate, and frequency of the Hall signal. If the real-time current, speed, speed change rate, or slope exceeds the preset threshold value, the system confirms that the electric seat is in a locked-rotor state, thereby providing early warning and taking measures. In addition, by analyzing the Hall signal and its frequency, the system can also identify the obstacle state, further enhancing the safety and reliability of the electric seat. This multi-level state monitoring and analysis not only improves the accuracy of locked-rotor and obstacle identification, but also reduces the potential risks caused by state misjudgment, to solve the problem of inaccurate identification of the state of the electric seat in the prior art.

[0100] Embodiment Two

[0101] Please refer to Figure 5 The electric seat state identification device provided in the embodiments of the application.

[0102] In this embodiment, the electric seat state identification device includes an acquisition module 10, a calculation module 20, and a judgment module 30.

[0103] The acquisition module 10 is used to obtain each real-time current and each Hall signal of the electric seat motor.

[0104] The calculation module 20 is used to calculate the slope of each real-time current according to each real-time current and the time of each real-time current.

[0105] As a preferred embodiment of Embodiment Two, the calculation of the slope of each real-time current according to each real-time current and the time of each real-time current is specifically:

[0106] As Figure 2As shown, the rate of change of the current motor's motion state, k,kn=f(In,tn), is calculated from the change in motor current ΔI within a unit time Δt. The current change ΔI, time change Δt, and current change slope k,ΔI=In-I(n-1), Δt=tn-t(n-1), k=ΔI / Δt. Where In is the current sampled value, I(n-1) is the previous sampled value, tn is the current current sampling system time, and t(n-1) is the previous current sampling system time.

[0107] The calculation module 20 is also used to calculate the speed of the electric seat motor, the rate of change of speed, and the frequency of each of the Hall signals based on the Hall signals.

[0108] As a preferred embodiment of Embodiment 2, the calculation of the speed and rate of change of the electric seat motor based on the various Hall signals specifically involves:

[0109] The current rate of change of motor speed is obtained by converting the Hall signal into speed.

[0110] The judgment module 30 is used to determine whether the electric seat is in a stall state if the real-time current, the speed, the speed change rate, or the slope is greater than the preset threshold.

[0111] As a preferred embodiment of Embodiment 2, the step of confirming that the electric seat is in a stall state if the real-time current, the speed, the rate of change of speed, or the slope is greater than the preset threshold values ​​specifically involves:

[0112] Obtain the real-time voltages of the electric seat motor;

[0113] The preset first threshold is calculated based on the various real-time voltages and linear interpolation techniques.

[0114] The real-time currents are compared with a preset first threshold. If the real-time currents are greater than the first threshold, the electric seat is confirmed to be in a stalled state.

[0115] More specifically, such as Figure 3 As shown, the above process belongs to the threshold control algorithm module: after calibrating the stall thresholds of the highest and lowest operating voltages, the current threshold Thrcur is calculated by linear interpolation based on the real-time system operating voltage. When the real-time current I is greater than the stall threshold Thrcur, the threshold control module will be activated (activation means that the motor is considered to be in a stall state at this time).

[0116] In this preferred embodiment, the present application dynamically adjusts the standard for identifying the stall condition by obtaining the real-time voltage of the motor of the electric seat and calculating the preset first threshold value using linear interpolation technology, so as to adapt to different voltage conditions. The real-time current is compared with the dynamic threshold value, and if the real-time current exceeds the threshold value, it is confirmed that the electric seat is in the stall condition. This method of adjusting the threshold value based on the real-time voltage improves the accuracy of stall condition identification, reduces the misjudgment caused by voltage fluctuations, and thus enhances the safety and reliability of the electric seat.

[0117] As a preferred embodiment of embodiment two, if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold value, it is confirmed that the electric seat is in the stall condition, specifically:

[0118] The real-time current and the speed change rate are input into the preset speed control module, so that the speed control module compares the real-time current and the speed change rate with the preset second threshold value, and if the real-time current and the speed change rate are greater than the second threshold value, it is confirmed that the electric seat is in the stall condition.

[0119] More specifically, as shown in Figure 3 The above process belongs to the speed control algorithm module: the speed change rate of the current motor is obtained by converting the Hall signal into speed. When the motor is in the state of speed drop and current rise, the system activates the speed control algorithm, which judges according to the rising ratio of real-time current I and the falling ratio of motor speed V. When the trigger threshold value is continuously met, the speed control module will be activated (activation means that the motor is in the stall condition at this time).

[0120] In this preferred embodiment, the present application can accurately monitor the dynamic performance of the motor by inputting the real-time current and the speed change rate of the motor of the electric seat into the preset speed control module and comparing them with the preset second threshold value. If the real-time current and the speed change rate both exceed the second threshold value, the system confirms that the electric seat is in the stall condition. This double monitoring mechanism not only improves the accuracy of stall condition identification, but also reduces the possibility of misjudgment through real-time data analysis, thereby enhancing the safety and reliability of the electric seat.

[0121] As a preferred embodiment of embodiment two, if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold value, it is confirmed that the electric seat is in the stall condition, specifically:

[0122] The slope and the speed are compared with the preset third threshold value, and if the slope is greater than the third threshold value, it is confirmed that the electric seat is in the stall condition.

[0123] More specifically, as shown inFigure 3 As shown in the above process, the above process belongs to the slope control algorithm module: by monitoring the current slope change state when the motor is moving, when the slope k and the motor speed V continuously satisfy the threshold Thrslop, the slope control module will be activated (activation is considered that the motor is in the state of locked-rotor at this time).

[0124] In this preferred embodiment, the application can effectively identify the locked-rotor state of the motor by comparing the real-time current slope and speed of the motor with the preset third threshold value. If the slope exceeds the third threshold value, the system confirms that the electric seat is in the locked-rotor state. This slope-based monitoring mechanism uses the sensitivity of current change to detect potential locked-rotor conditions in advance, thereby allowing the system to respond in time and take measures to avoid motor damage or failure, significantly improving the safety and reliability of the electric seat.

[0125] The locked-rotor identification algorithm model monitors in different dimensions through three modules. When any module detects that the motor is locked, the system considers that the current motor is in the locked-rotor state, which can greatly improve the accuracy of motor state identification.

[0126] The determination module 30 is also used to confirm whether the electric seat is in an obstacle state according to the respective Hall signals and the respective frequencies.

[0127] As a preferred embodiment of embodiment two, the confirmation of whether the electric seat is in an obstacle state according to the respective Hall signals and the respective frequencies is specifically:

[0128] As shown in the above process, the above process belongs to the slope control algorithm module: by monitoring the current slope change state when the motor is moving, when the slope k and the motor speed V continuously satisfy the threshold Thrslop, the slope control module will be activated (activation is considered that the motor is in the state of locked-rotor at this time). Figure 4 As shown in the above process, the above process belongs to the slope control algorithm module: by monitoring the current slope change state when the motor is moving, when the slope k and the motor speed V continuously satisfy the threshold Thrslop, the slope control module will be activated (activation is considered that the motor is in the state of locked-rotor at this time).

[0129]

[0130] The system calls the cache data and the calibration data weight factor Fac(n, 1) at each operation to calculate the recorded frequency value Rec(m, 1) of each Hall position, as shown in formula 2.

[0131]

[0132] During the movement, the difference value Δf is calculated by comparing the frequency of the current Hall position with the recorded data. The model will identify the obstacle state according to the deviation of the difference value:

[0133] a) Normal state: Δf≥Thr1 (calibration threshold 1), within the normal state range, no processing is performed;

[0134] b) Wrapping state: Thr1 > Δf ≥ Thr2 (calibrated threshold 2), activate foreign object wrapping monitoring, at this time the motor state changes but does not change significantly, if the state continues to exceed the threshold time, the model will judge that the track is wrapped by a foreign object;

[0135] c) Blocking state: Thr2 > Δf > Thr3 (calibrated threshold 3), the blocking state duration threshold Tthr is calculated based on the current frequency using a linear difference method based on the calibrated time threshold Tthrmax (the greater the frequency, the greater the duration), when the condition is met, the algorithm will set it to the blocking state;

[0136] d) Abnormal state: Δf < Thr3, when the frequency is lower than the minimum threshold, it is judged as a loss of Hall signal.

[0137] In this preferred embodiment, the application can accurately monitor the obstacles that may be encountered during the movement of the electric seat by collecting the real-time Hall frequency of each coordinate position of the electric seat motor and calculating the frequency of the Hall signal and the difference between the real-time frequency. By comparing these frequency differences with the fourth, fifth and sixth threshold values, the system can effectively identify the obstacle state, thereby providing early warning and taking appropriate measures. This obstacle detection mechanism based on Hall signal frequency analysis improves the identification ability of the electric seat for obstacles, reduces the potential risks caused by obstacles, and significantly improves the safety of the seat and the reliability of the system.

[0138] The device uses three modules to work better in identifying the state of the electric seat. The application can accurately monitor the dynamic state of the motor by obtaining the real-time current and Hall signal of the electric seat motor and calculating the slope of these currents and the speed, speed change rate and frequency of the Hall signal. If the real-time current, speed, speed change rate or slope exceeds the preset threshold, the system confirms that the electric seat is in the stall state, thereby providing early warning and taking measures. In addition, by analyzing the Hall signal and its frequency, the system can also identify the obstacle state, further enhancing the safety and reliability of the electric seat. This multi-level state monitoring and analysis not only improves the accuracy of stall and obstacle identification, but also reduces the potential risks caused by state misjudgment, to solve the problem that the state of the electric seat cannot be accurately identified in the prior art.

[0139] Embodiment three:

[0140] The embodiment of the application provides a computer readable storage medium, the computer readable storage medium comprises a stored computer program, wherein the computer program controls the device where the computer readable storage medium is located to execute the electric seat state identification method when the computer program runs.

[0141] The one electric seat state recognition method can be stored in a computer readable storage medium if it is realized in the form of a software function unit and used as an independent product. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0142] The above specific embodiments further specifically describe the purposes, technical solutions, and beneficial effects of the present application. It should be understood that the above description is only for specific embodiments of the present application and is not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for recognizing the state of an electric seat, characterized in that, include: Acquire the real-time current and Hall effect signals of the electric seat motor; The slope of each real-time current is calculated based on each real-time current and each time of each real-time current. Based on the Hall signals, the speed, rate of change of speed of the electric seat motor and the frequencies of the Hall signals are calculated. If the real-time current, the speed, the rate of change of speed, or the slope is greater than the preset threshold, the electric seat is confirmed to be in a stall state. Based on the various Hall signals and frequencies, it is determined whether the electric seat is in an obstacle state.

2. The electric seat status recognition method according to claim 1, characterized in that, If any of the real-time currents, the speed, the rate of change of speed, or the slope is greater than a preset threshold, the electric seat is confirmed to be in a stall state, specifically: Obtain the real-time voltages of the electric seat motor; Based on the aforementioned real-time voltages and linear interpolation techniques, a preset first threshold is calculated; The real-time currents are compared with a preset first threshold. If the real-time currents are greater than the first threshold, the electric seat is confirmed to be in a stalled state.

3. The electric seat status recognition method according to claim 1, characterized in that, If any of the real-time currents, the speed, the rate of change of speed, or the slope is greater than a preset threshold, the electric seat is confirmed to be in a stall state, specifically: The real-time currents and the rate of change of speed are input to a preset speed control module, so that the speed control module compares the real-time currents and the rate of change of speed with a preset second threshold. If the real-time currents and the rate of change of speed are greater than the second threshold, the electric seat is confirmed to be in a stall state.

4. The electric seat status recognition method according to claim 1, characterized in that, If any of the real-time currents, the speed, the rate of change of speed, or the slope is greater than a preset threshold, the electric seat is confirmed to be in a stall state, specifically: The slope and speed are compared with a preset third threshold. If the slope is greater than the third threshold, the electric seat is confirmed to be in a stalled state.

5. The electric seat status recognition method according to claim 1, characterized in that, The step of determining whether the electric seat is in an obstacle state based on the various Hall signals and the various frequencies specifically involves: Collect the real-time Hall frequencies at each coordinate position of the electric seat motor; Based on each Hall signal and each time interval of each Hall signal, each frequency of each Hall signal is calculated. The frequency difference is calculated based on the real-time Hall frequencies and the frequencies of the Hall signals. The difference is compared with preset fourth, fifth and sixth thresholds to confirm whether the electric seat is in an obstacle state.

6. The electric seat status recognition method according to claim 5, characterized in that, The step of comparing the difference with preset fourth, fifth, and sixth thresholds to determine whether the electric seat is in an obstacle state specifically involves: If the difference is greater than or equal to the preset fourth threshold, the electric seat is confirmed to be in normal condition. If the difference is less than the fourth threshold and greater than or equal to the fifth threshold, it is confirmed that the electric seat is in a tangled state. If the difference is less than the fifth threshold and greater than the sixth threshold, the electric seat is confirmed to be in a blocked state. If the difference is less than the sixth threshold, the electric seat is confirmed to be in an abnormal state.

7. An electric seat status recognition device, characterized in that, It includes an acquisition module, a calculation module, and a judgment module; The acquisition module is used to acquire the real-time currents and Hall signals of the electric seat motor; The calculation module is used to calculate the slope of each real-time current based on each real-time current and each time of each real-time current. Based on the Hall signals, the speed, rate of change of speed of the electric seat motor and the frequencies of the Hall signals are calculated. The judgment module is used to determine whether the electric seat is in a stall state if the real-time current, the speed, the rate of change of speed, or the slope is greater than the preset threshold. Based on the various Hall signals and frequencies, it is determined whether the electric seat is in an obstacle state.

8. The electric seat status recognition device according to claim 7, characterized in that, After acquiring the various real-time currents, real-time voltages, and Hall signals of the electric seat motor, the method further includes: Collect the real-time Hall frequencies at each coordinate position of the electric seat motor; Based on each Hall signal and each time interval of each Hall signal, each frequency of each Hall signal is calculated. The frequency difference is calculated based on the real-time Hall frequencies and the frequencies of the Hall signals. The difference is compared with preset fourth, fifth and sixth thresholds to confirm whether the electric seat is in an obstacle state.

9. The electric seat status recognition device according to claim 8, characterized in that, The step of comparing the difference with preset fourth, fifth, and sixth thresholds to determine whether the electric seat is in an obstacle state specifically involves: If the difference is greater than or equal to the preset fourth threshold, the electric seat is confirmed to be in normal condition. If the difference is less than the fourth threshold and greater than or equal to the fifth threshold, it is confirmed that the electric seat is in a tangled state. If the difference is less than the fifth threshold and greater than the sixth threshold, the electric seat is confirmed to be in a blocked state. If the difference is less than the sixth threshold, the electric seat is confirmed to be in an abnormal state.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the electric seat state recognition method as described in any one of claims 1 to 6.

Images