A multi-gear posture memory control method and system for an electric sofa

Abstract

The application discloses a multi-gear posture memory control method and system for an electric sofa, which comprises five core steps of initial posture calibration and multi-gear division, multi-user independent posture parameter memory, user identity recognition and automatic switching, closed-loop high-precision posture restoration and intelligent learning optimization of use habits; the system comprises a main control module, a posture acquisition module, a multi-channel driving module, a multi-user storage module, a man-machine interaction module, a user recognition module and an intelligent learning module. Through multi-gear scene division, multi-user independent memory, closed-loop PID control and intelligent learning algorithm, the application solves the core problems of single posture memory, low restoration precision, inconvenience for multiple users and lack of adaptive ability of the existing electric sofa, can support multiple custom posture gears and multiple user independent memory spaces, has high posture restoration precision, greatly improves use experience and intelligent level, and is widely applicable to the intelligent control requirements of various electric functional sofas.

Description

Technical Field

[0001] This invention belongs to the field of smart home and smart furniture control technology, specifically relating to a multi-position posture memory control method and system for an electric sofa. Background Technology

[0002] With the rapid development of smart home technology and the improvement of people's living standards, electric reclining sofas have become mainstream furniture products in homes, offices, and leisure spaces due to their comfortable experience and convenient operation. The core function of an electric sofa is to adjust the posture of its movable joints, such as the backrest, footrest, headrest, and lumbar support, through motor-driven adjustment, meeting the user's needs in different scenarios such as leisure, reading, watching movies, and sleeping. The posture memory function, as a core intelligent feature of electric sofas, helps users quickly restore frequently used postures, avoiding the hassle of manual adjustment every time they use the sofa.

[0003] However, existing posture memory control technology for electric sofas has the following unavoidable core technical defects in practical applications, which are also the technical pain points that this invention specifically addresses: The limited range of posture memory settings fails to meet the needs of various usage scenarios. Most existing electric sofas only support 1-2 posture memory settings, with a few high-end products supporting up to 4. This cannot cover the posture needs of users in various scenarios such as leisure, reading, watching movies, sleeping, working, and massage. Users still need to frequently adjust their posture manually, resulting in a poor user experience.

[0004] Low posture restoration accuracy and poor consistency. Existing electric sofas generally use an open-loop control method, which controls the posture position only by the motor running time. Due to the influence of factors such as changes in motor load, mechanical wear, and power supply voltage fluctuations, the posture restoration error is usually between 3° and 5°. After multiple restorations, the error will accumulate and increase, resulting in inconsistent postures each time, which seriously affects the comfort of use.

[0005] The lack of independent memory function for multiple users makes it inconvenient for multiple users. The memory parameters of existing electric sofas are globally shared and cannot distinguish between different users. When multiple family members use the sofa, the adjustment of the later user will overwrite the memory parameters of the previous user, which means that each user needs to readjust the settings every time they use the sofa. This is especially difficult for the elderly and children.

[0006] Lacking intelligent learning and adaptive capabilities, current electric sofas use fixed memory parameters that cannot automatically optimize based on user habits or adjust posture parameters according to different users' heights and weights. This results in a low level of intelligence and an inability to meet personalized usage needs.

[0007] The control methods are limited and the operation is not convenient enough. Existing electric sofas mainly rely on physical buttons for control, and some products support remote control, but all of these require manual operation by the user and cannot achieve voice control, remote control via mobile APP, or automatic user posture recognition and switching, making operation inconvenient.

[0008] The safety protection mechanism is inadequate. Existing electric sofas lack effective overload protection, obstacle detection, and anti-pinch protection during posture adjustment. When encountering obstacles or motor overload, they can easily lead to motor burnout, mechanical damage, or even injury to the user, posing a safety hazard.

[0009] In view of the above-mentioned shortcomings of existing technologies, there is currently no effective integrated solution in the industry. Therefore, the development of an electric sofa posture memory control method and system that supports multi-level and multi-user memory, high-precision restoration, intelligent learning, and multi-mode control has become an urgent need in the smart furniture industry, and has extremely high market value and user experience improvement value. Summary of the Invention

[0010] To address the problems raised in the background art, the purpose of this invention is to provide a multi-position posture memory control method and system for electric sofas. This invention solves the core problems of existing electric sofas, such as single posture memory, low restoration accuracy, inconvenience for multiple users, and lack of adaptive capability, through multi-position scenario division, multi-user independent memory, closed-loop PID control, and intelligent learning algorithms. It can support multiple custom posture positions and multiple user independent memory spaces, with high posture restoration accuracy, significantly improved user experience and intelligence level, and is widely adaptable to the intelligent control needs of various electric functional sofas.

[0011] To achieve the above-mentioned technical objectives, the technical solution adopted by the present invention is as follows: A multi-position posture memory control system for an electric sofa includes the following steps: S1 Initial Posture Calibration and Multi-Level Division: Perform extreme position calibration on all movable joints of the electric sofa, establish a posture coordinate system, pre-divide multiple scenario-based basic levels, and reserve multiple user-defined levels; S2 Multi-position Attitude Parameter Memory Storage: Receives the user's attitude saving command, collects the real-time attitude parameters of all movable joints, binds the parameters with the corresponding position number, and stores them in the user's independent memory partition. S3 User Gear Invocation Command Reception: Receives the user's gear invocation command through at least one interactive method, and parses the user identifier and target gear number in the command; S4 Closed-Loop High-Precision Attitude Restoration: Based on the attitude parameters corresponding to the target gear, a closed-loop control algorithm is used to drive the motors of each joint to run synchronously, collect joint attitude feedback in real time and correct the output until all joints reach the target position. S5 Attitude Operation Status Real-time Monitoring: During attitude adjustment, the motor operation status and environmental signals are monitored in real time. When an abnormality is detected, operation is stopped immediately and a protection mechanism is triggered.

[0012] Further specifying, the initial attitude calibration of S1 specifically includes the following steps: S11 drives each joint to move sequentially to the forward and reverse limit positions, and records the corresponding sensor reference values. S12 calculates the effective travel range of each joint based on the extreme position reference value and establishes the mapping relationship between joint posture and sensor output value; The S13 pre-divides multiple basic gears to cover different usage scenarios, writes system default parameters, and reserves multiple blank custom gears for users to set.

[0013] Further specifying, the multi-level attitude parameter memory storage of S2 specifically includes the following steps: S21 receives the user's gear selection command and save command, and confirms the target storage gear; S22 collects the real-time attitude parameters of each joint multiple times and takes the average value as the standard attitude parameter for that gear. S23 obtains the unique identifier of the current user, binds the standard attitude parameters with the gear number, and writes them to the independent storage partition corresponding to the user. S24 provides the user with a notification that the memory was successfully saved and updates the gear status on the human-computer interaction interface.

[0014] Furthermore, S3 also includes an automatic user identification step: S31 scans nearby paired user mobile devices through the wireless communication module to obtain the device identifier as the user's unique identifier; When S32 detects that a registered user has entered the communication coverage area, it automatically loads all the user's memory settings parameters. When S33 detects that multiple registered users are online at the same time, it loads the corresponding priority user's level parameters by default, based on usage records or user priority.

[0015] Further specifying, the closed-loop high-precision attitude restoration of S4 specifically includes the following steps: S41 reads the target attitude values ​​of each joint corresponding to the target gear and calculates the difference between the current attitude and the target attitude of each joint. S42 uses a closed-loop control algorithm to calculate the output control quantity of each motor based on the attitude difference, and drives the motor to move toward the target position. The S43 collects the posture feedback of each joint in real time, updates the input value of the controller, and dynamically adjusts the motor output. When the attitude error of all joints is within the preset range, S44 stops the motor output and completes the attitude restoration.

[0016] Further clarification includes intelligent learning optimization steps: S61 records each posture adjustment operation performed by the user, including the adjustment level, modified parameters, usage duration, and usage time. S62 regularly performs statistical analysis on user data to calculate the usage frequency and commonly modified parameters for each gear level; When the usage frequency of a certain custom gear exceeds a preset threshold, S63 automatically upgrades it to a frequently used gear. S64 automatically optimizes the default parameters for the corresponding gear based on the user's frequently modified parameters and pushes optimization suggestions to the user.

[0017] Further specifying, the anomaly protection of S5 specifically includes the following steps: The S51 monitors the operating current of each motor in real time. When the current exceeds the preset threshold and the duration exceeds the preset time, it is determined to be an overload and the motor is stopped immediately. The S52 monitors the running speed of each joint in real time, and reduces the motor output power when the speed exceeds the preset maximum speed. The S53 uses environmental sensors to detect obstacles in the sofa's movement path. When an obstacle is detected, it immediately stops running and automatically resumes running after the obstacle is removed.

[0018] Further specifying, the interaction method of S3 includes at least one of physical buttons, touch screen, voice control, mobile terminal application, and remote control; the posture parameters include at least one of backrest angle, footrest angle, headrest angle, and lumbar support extension.

[0019] A multi-position posture memory control system for an electric sofa includes: The main control module, as the core control unit of the system, is used to execute control algorithms and logic processing; The attitude acquisition module is electrically connected to the main control module and is used to acquire the attitude parameters of each movable joint of the electric sofa in real time. The multi-channel drive module is electrically connected to the main control module and is used to drive the motors of each joint according to the instructions of the main control module. A multi-user storage module, electrically connected to the main control module, is used to store the independent attitude memory parameters of multiple users; The human-computer interaction module is electrically connected to the main control module and is used to receive user commands and provide feedback on the system status. The user identification module is electrically connected to the main control module and is used to identify the user and automatically load the corresponding memory parameters. The intelligent learning module, electrically connected to the main control module, is used to analyze user habits and optimize posture parameters.

[0020] Furthermore, the attitude acquisition module includes multiple angle sensors, which are respectively installed on each movable joint; The multi-channel drive module includes multiple independent motor drive units, each drive unit corresponding to a joint motor; The multi-user storage module is divided into multiple independent user memory partitions; The user identification module includes a wireless communication unit and a human body detection unit; The human-computer interaction module includes a button unit, a display unit, a voice recognition unit, and a wireless communication unit.

[0021] The beneficial effects of this invention are: This invention supports multiple posture settings, covering almost all usage scenarios such as leisure, reading, watching movies, sleeping, working, and massage. Users do not need to frequently adjust manually, greatly improving the user experience. This invention supports independent user memory partitions, with each user having their own dedicated memory space for multiple posture settings, ensuring that parameters from different users do not interfere with each other. It also supports automatic user recognition; once a user is seated, their dedicated memory parameters are automatically loaded, eliminating the need for manual switching and providing an excellent experience for multiple users.

[0022] This invention features intelligent learning capabilities, automatically recording and analyzing user habits to optimize gear parameters, automatically upgrade frequently used gears, and even automatically adjust posture based on the user's height and weight, achieving personalized control for each individual. The invention supports control methods including physical buttons, touchscreen, voice control, mobile app, and remote control, allowing users to choose the most convenient method based on their preferences; especially offline voice control, which enables voice command control without an internet connection, making it even more convenient to use. Attached Figure Description

[0023] The present invention can be further illustrated by the non-limiting embodiments given in the accompanying drawings; Figure 1 This is a flowchart illustrating the steps of an embodiment of a multi-position posture memory control method and system for an electric sofa according to the present invention. Figure 2 This is a system block diagram of an embodiment of a multi-position posture memory control method and system for an electric sofa according to the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments. The technical solutions in 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 a part of the embodiments of the present invention, and not all of the 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. The technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention. It should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0025] like Figure 1 As shown, the present invention discloses a multi-position posture memory control method for an electric sofa, which includes the following steps: S1 Initial Posture Calibration and Multi-Level Division: Perform extreme position calibration on all movable joints of the electric sofa, including the backrest, footrest, headrest, and lumbar support, establish a posture coordinate system, pre-divide at least 4 scenario-based basic levels, and reserve at least 4 user-defined levels. S2 Multi-position Attitude Parameter Memory Storage: Receives the user's attitude saving command, collects the real-time attitude parameters of all movable joints, binds the parameters with the corresponding position number, and stores them in the user's independent memory partition. S3 User Gear Invocation Command Reception: Receives the user's gear invocation command through at least one interactive method, and parses the user identifier and target gear number in the command; S4 Closed-Loop High-Precision Attitude Restoration: Based on the attitude parameters corresponding to the target gear, a PID closed-loop control algorithm is used to drive the motors of each joint to run synchronously, collect joint attitude feedback in real time and correct the output until all joints accurately reach the target position. S5 Attitude Operation Status Real-time Monitoring: During attitude adjustment, the motor current, running speed and obstacle signals are monitored in real time. When an abnormality is detected, operation is stopped immediately and the protection mechanism is triggered.

[0026] In the practical application of this embodiment, the initial attitude calibration in step S1 specifically includes: S11 drives each joint to move sequentially to the forward and reverse limit positions, and records the corresponding sensor reference values. S12 calculates the effective travel range of each joint based on the limit position reference value and establishes a linear mapping relationship between the joint angle and the sensor output value. The S13 pre-divides four basic modes: leisure, reading, movie watching, and sleep, and writes the system default parameters, while reserving four blank custom modes for users to set.

[0027] In the practical application of this embodiment, the multi-level attitude parameter memory storage in step S2 specifically includes: S21 receives the user's gear selection command and save command, and confirms the target storage gear; S22 continuously collects the real-time posture parameters of each joint three times, and takes the average value as the standard posture parameter for that gear. S23 obtains the unique identifier of the current user, binds the standard attitude parameters with the gear number, and writes them into the independent Flash memory partition corresponding to the user. S24 provides the user with a notification that the memory was successfully saved and updates the gear status on the human-computer interaction interface.

[0028] In practical applications of this embodiment, step S3 further includes an automatic user identification step: S31 scans nearby paired user mobile devices via Bluetooth module and obtains the device MAC address as the user's unique identifier. When the S32 detects a registered user entering the Bluetooth coverage area, it automatically loads all the user's memorized profile parameters. When S33 detects that multiple registered users are online at the same time, it loads the primary user's profile parameters by default, based on the most recent usage records or user priority.

[0029] In the practical application of this embodiment, the closed-loop high-precision attitude restoration in step S4 specifically includes: S41 reads the target angle values ​​of each joint corresponding to the target gear and calculates the difference between the current angle and the target angle of each joint; S42 uses an incremental PID algorithm to calculate the output PWM duty cycle of each motor based on the angle difference, and drives the motor to move towards the target position. The S43 collects real-time angle feedback from each joint every 10ms, updates the input value of the PID controller, and dynamically adjusts the motor output. When the angle error of all joints is ≤0.5°, S44 stops the motor output and completes the posture restoration.

[0030] Furthermore, it also includes intelligent learning optimization steps: S61 records each user's posture adjustment operation, including the adjustment level, the angle modified, the usage duration, and the usage time. S62 performs weekly statistical analysis of user data, calculating the usage frequency and commonly modified parameters for each gear level. When the usage frequency of a certain custom gear exceeds a preset threshold, S63 automatically upgrades it to a frequently used gear. S64 automatically optimizes the default angle value for the corresponding gear based on the user's frequently modified parameters and pushes optimization suggestions to the user.

[0031] In the practical application of this embodiment, the anomaly protection in step S5 specifically includes: The S51 monitors the operating current of each motor in real time. When the current exceeds 1.5 times the rated current and the duration exceeds 500ms, it is determined to be an overload and the motor is stopped immediately. The S52 monitors the running speed of each joint in real time, and reduces the motor output power when the speed exceeds the preset maximum speed. The S53 uses an infrared sensor to detect obstacles in the sofa's movement path. When an obstacle is detected, it immediately stops running and automatically resumes running after the obstacle is removed.

[0032] In the practical application of this embodiment, the interaction method of step S3 includes at least one of physical buttons, touch screen, voice control, mobile APP, and remote control; the posture parameters include at least one of backrest angle, footrest angle, headrest angle, and lumbar support extension.

[0033] A multi-position posture memory control system for an electric sofa includes: The main control module, as the core control unit of the system, is used to execute control algorithms and logic processing. The main control module uses an STM32F103RCT6 microcontroller as the core controller with a main frequency of 72MHz, 256KB Flash and 48KB RAM, ADC and communication interface to meet the control and computing requirements of the system. The attitude acquisition module is electrically connected to the main control module and is used to acquire the attitude parameters of each movable joint of the electric sofa in real time. The multi-channel drive module is electrically connected to the main control module and is used to drive the motors of each joint according to the instructions of the main control module. A multi-user storage module, electrically connected to the main control module, is used to store the independent attitude memory parameters of multiple users; The human-computer interaction module is electrically connected to the main control module and is used to receive user commands and provide feedback on the system status. The user identification module is electrically connected to the main control module and is used to identify the user and automatically load the corresponding memory parameters. The intelligent learning module, electrically connected to the main control module, is used to analyze user habits and optimize posture parameters.

[0034] In the practical application of this embodiment, the attitude acquisition module uses four AS5600 high-precision Hall angle sensors, which are respectively installed on the rotation axis of the backrest, footrest, headrest, and lumbar support, with an angle measurement range of 0°-360°. The multi-channel drive module includes multiple independent motor drive units, using four DRV8833 H-bridge motor drive chips, with each chip driving one joint motor. The multi-user storage module is divided into multiple independent user memory partitions. Specifically, the multi-user storage module includes a 16MB SPI Flash chip, which is divided into 16 independent user memory partitions. Each partition can store 8 levels of attitude parameters. The user identification module includes a wireless communication unit and a human body detection unit; specifically, the user identification module includes a Bluetooth 5.0 module and an HC-SR501 infrared human body sensor. The Bluetooth module is used to identify the user's mobile device, and the infrared human body sensor is used to detect whether the user has taken a seat.

[0035] The human-computer interaction module includes a button unit, a display unit, a voice recognition unit, and a wireless communication unit. Specifically, the human-computer interaction module includes capacitive touch buttons, an OLED display screen, an offline voice recognition module, and a WiFi module. The capacitive touch buttons are used for gear switching and saving, the OLED display screen is used to display the current gear and system status, the offline voice recognition module is used for voice control, and the WiFi module is used for communication with the mobile APP.

[0036] The core working principle of this invention consists of six core components: initial calibration, memory storage, posture restoration, user identification, intelligent learning, and security protection, as detailed below: When the system is first run, it automatically drives each joint to its limit position, records the reference value of the Hall sensor, and establishes a linear mapping relationship between the joint angle and the sensor output value, providing an accurate reference for subsequent attitude acquisition and control.

[0037] After the user adjusts to a satisfactory posture, presses the save button and selects the corresponding gear. The system continuously collects the angle values ​​of each joint three times and takes the average value as the standard parameter for that gear. Then, it is bound to the user's identifier and stored in the corresponding independent memory partition to complete the memory storage.

[0038] When a user selects a specific gear, the system reads the target angle value corresponding to that gear, calculates the difference between the current angle and the target angle, uses an incremental PID algorithm to calculate the PWM value output by the motor, and drives the motor to run; at the same time, it collects real-time angle feedback every 10ms, dynamically adjusts the motor output, and completes accurate restoration.

[0039] The system continuously scans for paired user phones in the vicinity via Bluetooth. When a user phone is detected to be within the coverage area, the system automatically identifies the user and loads their unique memory parameters. Once the user is seated, the infrared human body sensor detects the human body signal, and the system automatically switches to the user's preferred mode.

[0040] The system backend continuously records all user operation data and performs statistical analysis once a week to calculate the usage frequency of each gear and the user's commonly used modification angles. When a gear is used very frequently, it is automatically upgraded to a frequently used gear. At the same time, based on the user's modification habits, the default parameters of that gear are automatically optimized, and optimization suggestions are pushed to the user.

[0041] The system monitors the operating current and speed of each motor in real time. When the current exceeds the threshold, it is determined to be an overload and the motor is stopped immediately. At the same time, it uses infrared sensors to detect obstacles in the sofa's movement path. When an obstacle is detected, the system stops running immediately to prevent injury to the user or damage to the equipment.

[0042] During software use: After the system is powered on, it first performs hardware initialization and system self-test, then reads the system configuration parameters and the current user's memory settings; after entering the main loop, it scans the input signals of the human-machine interaction module, the angle signals of the posture acquisition module, the user signals of the user recognition module, and the safety monitoring signals in real time, and executes the corresponding control logic according to the input signals.

[0043] When using the system for the first time, press and hold the save button for 5 seconds to enter calibration mode. The system will then drive the backrest, footrest, headrest, and lumbar support to their forward and reverse limits in sequence, record the corresponding sensor values, calculate the linear mapping relationship, write it to the system configuration area, and complete the calibration.

[0044] After the user adjusts to a satisfactory posture, they press the save button, and then press the target gear button. The system continuously collects the angle values ​​of each joint three times, takes the average value, binds the parameters with the current user identifier, and writes them into the corresponding memory partition. The OLED display shows the "Memory Successful" message.

[0045] When a user presses a gear button, the system reads the target angle value corresponding to that gear, calculates the difference between the current angle and the target angle, and starts the PID controller to drive the motor. The real-time angle is collected every 10ms, and the PID output is updated until the error of all joints is ≤0.5°. Then the motor stops, and the OLED display shows the name of the current gear.

[0046] The Bluetooth module continuously scans for nearby devices. When it detects the MAC address of a paired mobile phone, it automatically loads the user's memory parameters. When the infrared sensor detects that the user has sat down, the system automatically switches to the user's most frequently used mode.

[0047] The system backend records user usage data every hour and automatically analyzes the data every Sunday morning to calculate the usage frequency and average modification angle of each gear. When the usage frequency of a certain custom gear exceeds 30%, it is automatically upgraded to a frequently used gear and optimization suggestions are pushed to the user.

[0048] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for controlling the multi-position posture memory of an electric sofa, characterized in that, Includes the following steps: S1 Initial Posture Calibration and Multi-Level Division: Perform extreme position calibration on all movable joints of the electric sofa, establish a posture coordinate system, pre-divide multiple scenario-based basic levels, and reserve multiple user-defined levels; S2 Multi-position Attitude Parameter Memory Storage: Receives the user's attitude saving command, collects the real-time attitude parameters of all movable joints, binds the parameters with the corresponding position number, and stores them in the user's independent memory partition. S3 User Gear Invocation Command Reception: Receives the user's gear invocation command through at least one interactive method, and parses the user identifier and target gear number in the command; S4 Closed-Loop High-Precision Attitude Restoration: Based on the attitude parameters corresponding to the target gear, a closed-loop control algorithm is used to drive the motors of each joint to run synchronously, collect joint attitude feedback in real time and correct the output until all joints reach the target position. S5 Attitude Operation Status Real-time Monitoring: During attitude adjustment, the motor operation status and environmental signals are monitored in real time. When an abnormality is detected, operation is stopped immediately and a protection mechanism is triggered.

2. The method for multi-position posture memory control of an electric sofa according to claim 1, characterized in that, The initial attitude calibration of S1 specifically includes the following steps: S11 drives each joint to move sequentially to the forward and reverse limit positions, and records the corresponding sensor reference values. S12 calculates the effective travel range of each joint based on the extreme position reference value and establishes the mapping relationship between joint posture and sensor output value; The S13 pre-divides multiple basic gears to cover different usage scenarios, writes system default parameters, and reserves multiple blank custom gears for users to set.

3. The method for multi-position posture memory control of an electric sofa according to claim 1, characterized in that, The multi-level attitude parameter memory storage of S2 specifically includes the following steps: S21 receives the user's gear selection command and save command, and confirms the target storage gear; S22 collects the real-time attitude parameters of each joint multiple times and takes the average value as the standard attitude parameter for that gear. S23 obtains the unique identifier of the current user, binds the standard attitude parameters with the gear number, and writes them to the independent storage partition corresponding to the user. S24 provides the user with a notification that the memory was successfully saved and updates the gear status on the human-computer interaction interface.

4. The method for multi-position posture memory control of an electric sofa according to claim 1, characterized in that, The S3 also includes an automatic user identification step: S31 scans nearby paired user mobile devices through the wireless communication module to obtain the device identifier as the user's unique identifier; When S32 detects that a registered user has entered the communication coverage area, it automatically loads all the user's memory settings parameters. When S33 detects that multiple registered users are online at the same time, it loads the corresponding priority user's level parameters by default, based on usage records or user priority.

5. The method for multi-position posture memory control of an electric sofa according to claim 1, characterized in that, The closed-loop high-precision attitude restoration of S4 specifically includes the following steps: S41 reads the target attitude values ​​of each joint corresponding to the target gear and calculates the difference between the current attitude and the target attitude of each joint. S42 uses a closed-loop control algorithm to calculate the output control quantity of each motor based on the attitude difference, and drives the motor to move toward the target position. The S43 collects the posture feedback of each joint in real time, updates the input value of the controller, and dynamically adjusts the motor output. When the attitude error of all joints is within the preset range, S44 stops the motor output and completes the attitude restoration.

6. The method for multi-position posture memory control of an electric sofa according to claim 1, characterized in that, It also includes intelligent learning optimization steps: S61 records each posture adjustment operation performed by the user, including the adjustment level, modified parameters, usage duration, and usage time. S62 regularly performs statistical analysis on user data to calculate the usage frequency and commonly modified parameters for each gear level; When the usage frequency of a certain custom gear exceeds a preset threshold, S63 automatically upgrades it to a frequently used gear. S64 automatically optimizes the default parameters for the corresponding gear based on the user's frequently modified parameters and pushes optimization suggestions to the user.

7. The method for multi-position posture memory control of an electric sofa according to claim 1, characterized in that, The anomaly protection of S5 specifically includes the following steps: The S51 monitors the operating current of each motor in real time. When the current exceeds the preset threshold and the duration exceeds the preset time, it is determined to be an overload and the motor is stopped immediately. The S52 monitors the running speed of each joint in real time, and reduces the motor output power when the speed exceeds the preset maximum speed. The S53 uses environmental sensors to detect obstacles in the sofa's movement path. When an obstacle is detected, it immediately stops running and automatically resumes running after the obstacle is removed.

8. The method for multi-position posture memory control of an electric sofa according to claim 1, characterized in that: The interaction method of S3 includes at least one of physical buttons, touch screen, voice control, mobile terminal application, and remote control; the posture parameters include at least one of backrest angle, footrest angle, headrest angle, and lumbar support extension.

9. A multi-position posture memory control system for an electric sofa that implements the method of any one of claims 1-8, characterized in that, include: The main control module, as the core control unit of the system, is used to execute control algorithms and logic processing; The attitude acquisition module is electrically connected to the main control module and is used to acquire the attitude parameters of each movable joint of the electric sofa in real time. The multi-channel drive module is electrically connected to the main control module and is used to drive the motors of each joint according to the instructions of the main control module. A multi-user storage module, electrically connected to the main control module, is used to store the independent attitude memory parameters of multiple users; The human-computer interaction module is electrically connected to the main control module and is used to receive user commands and provide feedback on the system status. The user identification module is electrically connected to the main control module and is used to identify the user and automatically load the corresponding memory parameters. The intelligent learning module, electrically connected to the main control module, is used to analyze user habits and optimize posture parameters.

10. The multi-position posture memory control system for an electric sofa according to claim 9, characterized in that: The attitude acquisition module includes multiple angle sensors, which are respectively installed on each movable joint; The multi-channel drive module includes multiple independent motor drive units, each drive unit corresponding to a joint motor; The multi-user storage module is divided into multiple independent user memory partitions; The user identification module includes a wireless communication unit and a human body detection unit; The human-computer interaction module includes a button unit, a display unit, a voice recognition unit, and a wireless communication unit.

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