Electric seat state identification method and device and medium

Abstract

The invention discloses an electric seat state recognition method and device and a medium. The system can monitor the dynamic state of the motor by acquiring the real-time current and the Hall signal of the motor of the electric seat 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 a preset threshold value, it is confirmed that the electric seat is in a locked-rotor state; meanwhile, by analyzing the Hall signal and the frequency of the Hall signal, the system can identify the state of the obstacle, so that the safety and the reliability of the seat are improved, and the problem that the state of the electric seat cannot be accurately identified in the prior art is solved.

Description

Technical Field

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

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

[0003] The electric seat's stall recognition is achieved by comparing the current working current of the motor with the calibrated stall threshold current. 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 powered in the range of 9-16V, the current threshold needs to match the current working voltage. Otherwise, the stall recognition may not be triggered under low voltage conditions or may be triggered in error due to user actions (stretching, turning around) under high voltage conditions. The current mainstream method is to calibrate the working current of the motor under different voltage conditions, and compare the current state's stall threshold state in real time with the current working current of the motor. Although this control method can solve the problem, the current of the seat fluctuates during the entire movement process, and the threshold can only select the maximum point as the trigger point, which greatly affects the state recognition accuracy;

[0004] The obstacles of the electric seat are mainly identified 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 obvious state changes, and the controller can accurately identify them. This is also the current mainstream obstacle identification method. However, if soft objects such as scarves and clothes are rolled into the track, the controller cannot identify them because the sensor state changes are not obvious at the beginning. Often, the object can only be identified after it has been rolled into the motor shaft, which greatly increases the difficulty and cost of repairing the fault.

[0005] These problems result in the inability of the prior art to accurately identify the status of the electric seat. Summary of the invention

[0006] The present invention provides a method, a device and a medium for identifying the state of an electric seat, so as to solve the problem that the state of the electric seat cannot be accurately identified in the prior art.

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

[0008] Get the real-time current and Hall signals of the electric seat motor;

[0009] Calculate the slope of each real-time current according to each real-time current and each time of each real-time current;

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

[0011] If the real-time current, the speed, the speed change rate or the slope is greater than the preset thresholds, it is confirmed that the electric seat is in a stalled state;

[0012] Whether the electric seat is in an obstacle state is confirmed according to the Hall signals and the frequencies.

[0013] This application can accurately monitor the dynamic state of the motor by obtaining the real-time current and Hall signals of the electric seat motor and calculating the slope of these currents as well as 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 will confirm that the electric seat is in a stalled 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 state of obstacles, 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 misjudgment of the state, so as 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 values, it is confirmed that the electric seat is in a stalled state, specifically:

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

[0016] Calculating and obtaining the preset first threshold value according to the respective real-time voltages and the linear interpolation technique;

[0017] The real-time currents are compared with a preset first threshold value. If the real-time currents are greater than the first threshold value, it is confirmed that the electric seat is in a stalled state.

[0018] In this preferred embodiment, the present application can dynamically adjust the standard for stall identification to adapt to different voltage conditions by obtaining the real-time voltages of the electric seat motors and calculating the preset first threshold using linear interpolation technology. The real-time current is compared with this dynamic threshold. If the real-time current exceeds the threshold, it is confirmed that the electric seat is in a stalled state. This method of adjusting the threshold based on real-time voltage improves the accuracy of stall identification and reduces misjudgment caused by voltage fluctuations, thereby enhancing 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 speed change rate or the slope is greater than the preset threshold values, it is confirmed that the electric seat is in a stalled state, specifically:

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

[0021] In this preferred embodiment, the present application can accurately monitor the dynamic performance of the motor by inputting the real-time current and speed change rate of the electric seat motor into a preset speed control module and comparing it with a preset second threshold. If both the real-time current and speed change rate exceed the second threshold, the system confirms that the electric seat is in a stalled state. This dual monitoring mechanism not only improves the accuracy of stalled state identification, but also reduces the possibility of misjudgment 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 speed change rate or the slope is greater than the preset threshold values, it is confirmed that the electric seat is in a stalled 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 stalled state.

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

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

[0026] Collecting each real-time Hall frequency of each coordinate position of the electric seat motor;

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

[0028] Calculate the frequency difference according to the real-time Hall frequencies and the frequencies of the Hall signals;

[0029] The difference is compared with a preset fourth threshold, a fifth threshold and a sixth threshold to confirm whether the electric seat is in an obstacle state.

[0030] The step of comparing the difference with a preset fourth threshold, a fifth threshold, and a sixth threshold to determine whether the electric seat is in an obstacle state is specifically as follows:

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

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

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

[0034] If the difference is less than the sixth threshold, it is confirmed that the electric seat is in an abnormal state.

[0035] In this preferred embodiment, the present application can accurately monitor obstacles that may be encountered during the movement of the 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 Hall signal and the real-time frequency. By comparing these frequency differences with the preset fourth, fifth and sixth thresholds, the system can effectively identify the state of obstacles, thereby giving early warnings and taking corresponding measures. This obstacle detection mechanism based on Hall signal frequency analysis improves the electric seat's ability to identify obstacles, reduces 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 present application provides a device for identifying the state of an electric seat. The electric seat state identification comprises an acquisition module, a calculation module and a judgment module;

[0037] The acquisition module is used to obtain each real-time current and each Hall signal of the electric seat motor;

[0038] The calculation module is used to calculate the slope of each real-time current according to each real-time current and each time of each real-time current;

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

[0040] The judging module is used to judge if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold values, confirming that the electric seat is in a stalled state;

[0041] Whether the electric seat is in an obstacle state is confirmed according to the Hall signals and the frequencies.

[0042] This device uses three modules to divide the work and coordinate work to better identify the status of the electric seat. This application can accurately monitor the dynamic state of the motor by obtaining the real-time current and Hall signals 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 a stalled 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 state of obstacles, 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 misjudgment of the state, so as to solve the problem that the state of the electric seat cannot be accurately identified in the prior art.

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

[0044] Collecting each real-time Hall frequency of each coordinate position of the electric seat motor;

[0045] Calculating the frequencies of the Hall signals according to the Hall signals and the time intervals of the Hall signals;

[0046] Calculate the frequency difference according to the real-time Hall frequencies and the frequencies of the Hall signals;

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

[0048] The step of comparing the difference with a preset fourth threshold, a fifth threshold, and a sixth threshold to determine whether the electric seat is in an obstacle state is specifically as follows:

[0049] If the difference is greater than or equal to a preset fourth threshold, confirming 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, confirming that the electric seat is in a entangled state;

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

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

[0053] In this preferred embodiment, the present application can accurately monitor obstacles that may be encountered during the movement of the 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 Hall signal and the real-time frequency. By comparing these frequency differences with the preset fourth, fifth and sixth thresholds, the system can effectively identify the state of obstacles, thereby giving early warnings and taking corresponding measures. This obstacle detection mechanism based on Hall signal frequency analysis improves the electric seat's ability to identify obstacles, reduces 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 present application provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a method for identifying the state of an electric seat as described. The beneficial effect is the same as the method for identifying the state of an electric seat provided in the first aspect of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 : A flow chart of an embodiment of a method for identifying the state of an electric seat provided in the present application;

[0056] Figure 2 : A structural schematic diagram of an embodiment of the motor motion state change rate provided in the present application;

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

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

[0059] Figure 5 : A structural schematic diagram of an embodiment of an electric seat state identification device provided in the present application. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Embodiment 1

[0062] Please refer to Figure 1 , which is a method for identifying the state of an electric seat provided in an embodiment of the present invention.

[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: Obtain the real-time current and Hall signals of the electric seat motor.

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

[0066] As a preferred embodiment of the first embodiment, the slopes of the real-time currents are calculated based on the real-time currents and the times of the real-time currents, specifically:

[0067] like Figure 2 As shown, the change rate k of the current motor motion state is calculated by the change ΔI of the motor current within the unit time Δt, kn=f(In,tn), the current change ΔI, the time change Δt and the current change slope k, ΔI=In-I(n-1), Δt=tn-t(n-1), k=ΔI / Δt. Among them, In is the current current sampling value, I(n-1) is the last current sampling value, tn is the current current sampling system time, and t(n-1) is the last current sampling system time.

[0068] S03: According to the Hall signals, the speed of the electric seat motor, the speed change rate and the frequencies of the Hall signals are calculated.

[0069] As a preferred embodiment of the first embodiment, the speed and speed change rate of the electric seat motor are calculated according to the Hall signals, specifically:

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

[0071] S04: If the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold values, it is confirmed that the electric seat is in a stalled state.

[0072] As a preferred embodiment of the first embodiment, if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold values, it is confirmed that the electric seat is in a stalled state, specifically:

[0073] Get the real-time voltage of each electric seat motor;

[0074] Calculating and obtaining the preset first threshold value according to the respective real-time voltages and the linear interpolation technique;

[0075] The real-time currents are compared with a preset first threshold value. If the real-time currents are greater than the first threshold value, it is confirmed that the electric seat is in a stalled state.

[0076] More specifically, Figure 3 As shown, the above process belongs to the threshold control algorithm module: after calibrating the stall thresholds of the highest and lowest working voltages, the current threshold Thrcur is calculated by linear interpolation according to the real-time system working voltage. When the real-time current I is greater than the stall threshold Thrcur, the threshold control module will be activated (if activated, it is considered that the motor is in a stall state at this time).

[0077] In this preferred embodiment, the present application can dynamically adjust the standard for stall identification to adapt to different voltage conditions by obtaining the real-time voltages of the electric seat motors and calculating the preset first threshold using linear interpolation technology. The real-time current is compared with this dynamic threshold. If the real-time current exceeds the threshold, it is confirmed that the electric seat is in a stalled state. This method of adjusting the threshold based on real-time voltage improves the accuracy of stall identification and reduces misjudgment caused by voltage fluctuations, thereby enhancing the safety and reliability of the electric seat.

[0078] As a preferred embodiment of the first embodiment, if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold values, it is confirmed that the electric seat is in a stalled state, specifically:

[0079] The real-time currents and the speed change rates are input into a preset speed control module, so that the speed control module compares the real-time currents and the speed change rates with a preset second threshold value. If the real-time currents and the speed change rates are greater than the second threshold value, it is confirmed that the electric seat is in a stalled state.

[0080] More specifically, Figure 3 As shown, the above process belongs to the speed control algorithm module: by converting the Hall signal into speed, the current motor speed change rate is obtained. When the motor speed decreases and the current increases, the system activates the speed control algorithm. The algorithm makes a judgment based on the increase ratio of the real-time current I and the decrease ratio of the motor speed V. When the trigger threshold is continuously met, the speed control module will be activated (if activated, it is considered that the motor is in a stalled state at this time).

[0081] In this preferred embodiment, the present application can accurately monitor the dynamic performance of the motor by inputting the real-time current and speed change rate of the electric seat motor into a preset speed control module and comparing it with a preset second threshold. If both the real-time current and speed change rate exceed the second threshold, the system confirms that the electric seat is in a stalled state. This dual monitoring mechanism not only improves the accuracy of stalled state identification, 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 the first embodiment, if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold values, it is confirmed that the electric seat is in a stalled state, specifically:

[0083] 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 stalled state.

[0084] More specifically, Figure 3 As shown, the above process belongs to the slope control algorithm module: by monitoring the current slope change state when the motor moves, when the slope k and the motor speed V continuously meet the threshold Thrslop, the slope control module will be activated (if activated, it is considered that the motor is in a stalled state at this time).

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

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

[0087] S05: Confirm whether the electric seat is in an obstacle state according to the respective Hall signals and the respective frequencies.

[0088] As a preferred embodiment of Embodiment 1, the step of confirming whether the electric seat is in an obstacle state according to the respective Hall signals and the respective frequencies is specifically as follows:

[0089] As Figure 4 shown, collect the interval time T between adjacent Hall signals, and calculate the Hall signal generation frequency f = 1 / T. The model records the Hall frequencies at each coordinate position during the movement process in real time and stores them in the data cache f(m,n). As shown in Equation 1, the cache data uses a first-in-first-out storage method to store the most recent n-axis data.

[0090]

[0091] The system calls the cache data and the calibration data weight factor Fac(n,1) during each operation to calculate the recorded frequency value Rec(m,1) at each Hall position, as shown in Equation 2.

[0092]

[0093] During the movement process, compare the frequency at the current Hall position with the recorded data to calculate the difference value Δf. The model will identify the obstacle state according to the deviation of the difference value:

[0094] a) Normal state: Δf ≥ Thr1 (calibration threshold 1), within the normal state range, no processing is required;

[0095] b) Winding state: Thr1 > Δf ≥ Thr2 (calibration threshold 2), activate the foreign object winding monitoring in the active state. At this time, the motor state changes but there is no significant change. If the state persists for more than the threshold time, the model will judge that the track is wound by a foreign object;

[0096] c) Blocking state: Thr2 > Δf > Thr3 (calibration threshold 3), the blocking state duration threshold Tthr is calculated using a linear interpolation method based on the current frequency and 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;

[0097] d) Abnormal state: Δf < Thr3. When the frequency is lower than the lowest threshold, it is judged that the Hall signal is lost.

[0098] In this preferred embodiment, the present application can accurately monitor obstacles that may be encountered during the movement of the 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 Hall signal and the real-time frequency. By comparing these frequency differences with the preset fourth, fifth and sixth thresholds, the system can effectively identify the state of obstacles, thereby giving early warnings and taking corresponding measures. This obstacle detection mechanism based on Hall signal frequency analysis improves the electric seat's ability to identify obstacles, reduces potential risks caused by obstacles, and significantly improves the safety of the seat and the reliability of the system.

[0099] This application can accurately monitor the dynamic state of the motor by obtaining the real-time current and Hall signals of the electric seat motor and calculating the slope of these currents as well as 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 will confirm that the electric seat is in a stalled 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 state of obstacles, 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 misjudgment of the state, so as to solve the problem that the state of the electric seat cannot be accurately identified in the prior art.

[0100] Embodiment 2

[0101] Please refer to Figure 5 , is an electric seat status identification device provided in an embodiment of the present application.

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

[0103] The acquisition module 10 is used to acquire various real-time currents and various Hall signals 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 each time of each real-time current.

[0105] As a preferred embodiment of the second embodiment, the slope of each real-time current is calculated based on each real-time current and each time of each real-time current, specifically:

[0106] like Figure 2As shown, the change rate k of the current motor motion state is calculated by the change ΔI of the motor current within the unit time Δt, kn=f(In,tn), the current change ΔI, the time change Δt and the current change slope k, ΔI=In-I(n-1), Δt=tn-t(n-1), k=ΔI / Δt. Among them, In is the current current sampling value, I(n-1) is the last current sampling value, tn is the current current sampling system time, and t(n-1) is the last current sampling system time.

[0107] The calculation module 20 is further used to calculate the speed of the electric seat motor, the speed change rate and the frequencies of the Hall signals according to the Hall signals.

[0108] As a preferred embodiment of the second embodiment, the speed and speed change rate of the electric seat motor are calculated according to the Hall signals, specifically:

[0109] By converting the Hall signal into speed, the current speed change rate of the motor can be obtained.

[0110] The judgment module 30 is used to judge if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold values, and confirm that the electric seat is in a stalled state.

[0111] As a preferred embodiment of the second embodiment, if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold values, it is confirmed that the electric seat is in a stalled state, specifically:

[0112] Get the real-time voltages of the electric seat motors;

[0113] Calculating the preset first threshold value according to the respective real-time voltages and the linear interpolation technique;

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

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

[0116] In this preferred embodiment, the present application can dynamically adjust the standard for stall identification to adapt to different voltage conditions by obtaining the real-time voltages of the electric seat motors and calculating the preset first threshold using linear interpolation technology. The real-time current is compared with this dynamic threshold. If the real-time current exceeds the threshold, it is confirmed that the electric seat is in a stalled state. This method of adjusting the threshold based on real-time voltage improves the accuracy of stall identification and reduces misjudgment caused by voltage fluctuations, thereby enhancing the safety and reliability of the electric seat.

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

[0118] The real-time currents and the speed change rates are input into a preset speed control module, so that the speed control module compares the real-time currents and the speed change rates with a preset second threshold value. If the real-time currents and the speed change rates are greater than the second threshold value, it is confirmed that the electric seat is in a stalled state.

[0119] More specifically, Figure 3 As shown, the above process belongs to the speed control algorithm module: by converting the Hall signal into speed, the current motor speed change rate is obtained. When the motor speed decreases and the current increases, the system activates the speed control algorithm. The algorithm makes a judgment based on the increase ratio of the real-time current I and the decrease ratio of the motor speed V. When the trigger threshold is continuously met, the speed control module will be activated (if activated, it is considered that the motor is in a stalled state 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 speed change rate of the electric seat motor into a preset speed control module and comparing it with a preset second threshold. If both the real-time current and speed change rate exceed the second threshold, the system confirms that the electric seat is in a stalled state. This dual monitoring mechanism not only improves the accuracy of stalled state 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 the second embodiment, if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold values, it is confirmed that the electric seat is in a stalled state, specifically:

[0122] 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 stalled state.

[0123] More specifically, Figure 3 As shown, the above process belongs to the slope control algorithm module: by monitoring the current slope change state when the motor moves, when the slope k and the motor speed V continuously meet the threshold Thrslop, the slope control module will be activated (if activated, it is considered that the motor is in a stalled state at this time).

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

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

[0126] The determination module 30 is further configured to determine whether the electric seat is in an obstacle state according to the Hall signals and the frequencies.

[0127] As a preferred embodiment of the second embodiment, the step of confirming whether the electric seat is in an obstacle state according to the Hall signals and the frequencies is specifically as follows:

[0128] like Figure 4 As shown in the figure, 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 real time during the movement and stores it in the data cache f(m,n). As shown in formula 1, the cache data uses a first-in-first-out storage method to store the most recent n coordinate axis data.

[0129]

[0130] The system calls the cache data and the calibration data weight factor Fac(n,1) during 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 frequency of the current Hall position is compared with the recorded data to calculate the difference value Δf. The model will identify the obstacle status 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) Winding state: Thr1 > Δf ≥ Thr2 (calibration threshold 2), activating the monitoring of foreign object winding in the active state. At this time, the motor state changes but does not change significantly. If the state lasts for more than the threshold time, the model will judge that the track is wound by a foreign object;

[0135] c) Blocking state: Thr2 > Δf > Thr3 (calibration threshold 3). The duration threshold Tthr of the blocking state is calculated using the linear interpolation method based on the current frequency and 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;

[0136] d) Abnormal state: Δf < Thr3. When the frequency is lower than the lowest threshold, it is judged that the Hall signal is lost.

[0137] In this preferred embodiment, the present application can accurately monitor the obstacles that may be encountered during the movement of the seat by collecting the real-time Hall frequencies of each coordinate position of the electric seat motor and calculating the frequency of the Hall signal and the difference between it and 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, thus giving an early warning and taking corresponding measures. This obstacle detection mechanism based on the analysis of the Hall signal frequency improves the ability of the electric seat to identify obstacles, reduces the potential risks caused by obstacles, and significantly enhances the safety of the seat and the reliability of the system.

[0138] This device uses three modules to work in division and coordination to better identify the state of the electric seat. The present application can accurately monitor the dynamic state of the motor by obtaining the real-time currents and Hall signals of each part of the electric seat motor and calculating the slopes of these currents and the speeds, speed change rates, and frequencies of the Hall signals. If the real-time current, speed, speed change rate, or slope exceeds the preset threshold, the system will confirm that the electric seat is in the locked-rotor state, thus 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 locked-rotor and obstacle identification but also reduces the potential risks caused by misjudgment of the state, so as to solve the problem that the state of the electric seat cannot be accurately identified in the prior art.

[0139] Embodiment 3:

[0140] The embodiment of the present application provides a computer-readable storage medium, and the computer-readable storage medium includes a stored computer program. Among them, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the above-mentioned method for identifying the state of an electric seat;

[0141] Wherein, if the electric seat state recognition method is implemented in the form of a software functional unit and used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0142] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying the state of an electric seat, characterized in that: include: Get the real-time current and Hall signals of the electric seat motor; Calculate the slope of each real-time current according to each real-time current and each time of each real-time current; According to the Hall signals, the speed of the electric seat motor, the speed change rate and the frequencies of the Hall signals are calculated; If the real-time current, the speed, the speed change rate or the slope is greater than the preset thresholds, it is confirmed that the electric seat is in a stalled state; Whether the electric seat is in an obstacle state is confirmed according to the Hall signals and the frequencies.

2. The electric seat state recognition method according to claim 1, characterized in that: If the real-time current, the speed, the speed change rate or the slope is greater than the preset thresholds, it is confirmed that the electric seat is in a stalled state, specifically: Get the real-time voltage of each electric seat motor; Calculating and obtaining the preset first threshold value according to the respective real-time voltages and the linear interpolation technique; The real-time currents are compared with a preset first threshold value. If the real-time currents are greater than the first threshold value, it is confirmed that the electric seat is in a stalled state.

3. The electric seat state recognition method according to claim 1, characterized in that: If the real-time current, the speed, the speed change rate or the slope is greater than the preset thresholds, it is confirmed that the electric seat is in a stalled state, specifically: The real-time currents and the speed change rates are input into a preset speed control module, so that the speed control module compares the real-time currents and the speed change rates with a preset second threshold value. If the real-time currents and the speed change rates are greater than the second threshold value, it is confirmed that the electric seat is in a stalled state.

4. The electric seat state recognition method according to claim 1, characterized in that: If the real-time current, the speed, the speed change rate or the slope is greater than the preset thresholds, it is confirmed that the electric seat is in a stalled state, specifically: 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 stalled state.

5. The electric seat state recognition method according to claim 1, characterized in that: The determining whether the electric seat is in an obstacle state according to the Hall signals and the frequencies is specifically as follows: Collecting each real-time Hall frequency of each coordinate position of the electric seat motor; Calculating the frequencies of the Hall signals according to the Hall signals and the time intervals of the Hall signals; Calculate the frequency difference according to the real-time Hall frequencies and the frequencies of the Hall signals; The difference is compared with a preset fourth threshold, a fifth threshold and a sixth threshold to confirm whether the electric seat is in an obstacle state.

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

7. An electric seat state recognition device, characterized in that: It includes an acquisition module, a calculation module and a judgment module; The acquisition module is used to obtain each real-time current and each Hall signal of the electric seat motor; The calculation module is used to calculate the slope of each real-time current according to each real-time current and each time of each real-time current; According to the Hall signals, the speed of the electric seat motor, the speed change rate and the frequencies of the Hall signals are calculated; The judging module is used to judge if the real-time current, the speed, the speed change rate or the slope is greater than the preset threshold values, confirming that the electric seat is in a stalled state; Whether the electric seat is in an obstacle state is confirmed according to the Hall signals and the frequencies.

8. The electric seat state recognition device according to claim 7, characterized in that: After obtaining each real-time current, each real-time voltage and each Hall signal of the electric seat motor, the method further includes: Collecting each real-time Hall frequency of each coordinate position of the electric seat motor; Calculating the frequencies of the Hall signals according to the Hall signals and the time intervals of the Hall signals; Calculate the frequency difference according to the real-time Hall frequencies and the frequencies of the Hall signals; The difference is compared with a preset fourth threshold, a fifth threshold and a sixth threshold to confirm whether the electric seat is in an obstacle state.

9. The electric seat state recognition device according to claim 8, characterized in that: The step of comparing the difference with a preset fourth threshold, a fifth threshold, and a sixth threshold to determine whether the electric seat is in an obstacle state is specifically as follows: If the difference is greater than or equal to a preset fourth threshold, confirming that the electric seat is in a normal state; If the difference is less than the fourth threshold and greater than or equal to the fifth threshold, confirming that the electric seat is in a entangled state; If the difference is less than the fifth threshold and greater than the sixth threshold, confirming that the electric seat is in a blocked state; If the difference is less than the sixth threshold, it is confirmed that the electric seat is 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, the device where the computer-readable storage medium is located is controlled to execute the electric seat state recognition method according to any one of claims 1 to 6.

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