Intelligent bed debugging system and method with multi-mode adaptive control function

Abstract

The invention discloses an intelligent bed debugging system and method with a multi-mode self-adaptive control function, and the intelligent bed debugging system with the multi-mode self-adaptive control function comprises a hardware architecture and a software architecture. The hardware architecture is composed of a main control unit, a Bluetooth communication module, a current detection module, a motor driving module, a voice processing module, a remote controller adaptation module, a display and input module and a power management module, and the software architecture is composed of an operating system, a driving system, a middleware system and an application system. By integrating Bluetooth wireless connection, current detection control, PWM speed regulation, voice interaction, manufacturer self-defined configuration and multi-mode self-adaptive control technologies, the adaptive problem of motors of different manufacturers can be solved, and self-defined configuration of personalized functions such as voice control command words, massage modes and dynamic regulation logic is supported; and the novel functions of intelligent snore stopping and sleep inducing dynamic modes are provided, and various remote control devices on the market can be compatible.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent bed debugging, and in particular relates to an intelligent bed debugging system and method with multi-mode adaptive control function. Background Art

[0002] With the gradual popularization of smart homes, smart beds, as a high-end home product that integrates health monitoring, comfort adjustment, and intelligent control, are becoming more and more popular among consumers. In order to make smart beds multi-mode and multi-functional, smart beds usually need to be equipped with: various sensors for monitoring sleep quality, voice components for improving AI interactive functions, and push rod motors or massage motors for achieving comfort adjustment, massage, and assist-to-rise functions.

[0003] The complex structure enables smart beds to have multiple modes and functions, but it also leads to the following problems in the production and development process of smart beds: ① Motor control and debugging are complex. Since motors produced by different manufacturers have significant differences in electrical characteristics, mechanical parameters and control protocols, the debugging process is complicated and inefficient. In addition, some low-cost motors are not equipped with Hall sensors and cannot directly obtain motor position information. Traditional debugging methods are difficult to achieve precise stroke control; ② The demand for personalized function customization is complex. 2. Users have an increasing demand for personalized smart beds, such as customized anti-snoring actions and coaxing sleep modes, but the smart bed debugging system in the existing technology lacks a flexible customization mechanism; ③ The compatibility and adaptability of smart bed control are poor. The control equipment of the smart bed is easily affected by differences in signal protocols and button layouts, making it difficult to be compatible, and cannot be matched and updated with the upgrade of the smart bed functions.

[0004] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a smart bed debugging system and method with multi-mode adaptive control function, which can improve the debugging efficiency, precise controllability and control compatibility of the smart bed and meet the user's personalized customization needs.

[0006] In order to achieve the above object, a technical solution provided by a specific embodiment of the present invention is as follows:

[0007] A smart bed debugging system with multi-mode adaptive control function includes a hardware architecture and a software architecture. The hardware architecture consists of a main control unit, a Bluetooth communication module, a current detection module, a motor drive module, a voice processing module, a remote control adapter module, a display and input module, and a power management module.

[0008] The software architecture consists of an operating system, a drive system, a middleware system and an application system. The middleware system includes a Bluetooth communication management module, a current detection and control algorithm module, a PWM speed control algorithm module, a voice control module, a manufacturer-defined configuration module, a remote control adaptation module, an intelligent anti-snoring control module and a sleep-inducing dynamic control module.

[0009] In one or more embodiments of the present invention, the main control unit is used to coordinate the operation of various modules of the system, process Bluetooth communication data, current detection signals, voice commands and remote control signals, and the main control unit integrates DSP and FPU units to support real-time processing of complex algorithms.

[0010] In one or more embodiments of the present invention, the Bluetooth communication module is used to establish a wireless connection with the smart bed and supports the Bluetooth low energy protocol. The current detection module is used to collect the motor running current in real time. The motor drive module adopts an H-bridge circuit topology, includes four high-power MOSFET switch tubes and a PWM modulator, and can output a PWM waveform with a frequency range of 1-20kHz and an adjustable duty cycle of 0-100%, thereby realizing bidirectional speed control of the motor.

[0011] In one or more embodiments of the present invention, the voice processing module includes a WM8960 microphone array and an LD3320 voice recognition chip, supports far-field voice recognition, integrates a voice synthesis chip WT588D, and supports Chinese and English voice broadcasting. The remote control adapter module is equipped with an infrared receiving head and a radio frequency receiving module, supports multiple signal transmission modes, and has a built-in signal decoding chip that can parse the signal protocols of mainstream remote controls on the market.

[0012] In one or more embodiments of the present invention, the display and input module is used to display the operating parameter information of the smart bed, and the power management module adopts the TPS65910 power management chip to realize power distribution and management of each module and support battery backup function.

[0013] In one or more embodiments of the present invention, the operating system adopts FreeRTOS real-time operating system, which supports task priority scheduling, time slice rotation and interrupt management, and the driving system includes Bluetooth driver, I 2 C driver, SPI driver, PWM driver, ADC driver, UART driver.

[0014] In one or more embodiments of the present invention, the Bluetooth communication management module is used to implement Bluetooth device discovery, pairing, connection and data transmission management, and supports concurrent communication of multiple devices. The current detection and control algorithm module establishes a motor load model based on current detection data, and fuses current and time information through the Kalman filter algorithm to achieve accurate calculation of the bed frame angle and rising speed.

[0015] In one or more embodiments of the present invention, the PWM speed control algorithm module is based on fuzzy PID control theory and automatically adjusts the PWM frequency and duty cycle in combination with the motor load characteristics. The voice control module supports the command word customization function, adopts the deep neural network (DNN) algorithm to realize voice recognition, and combines natural language processing (NLP) technology to understand user intentions.

[0016] In one or more embodiments of the present invention, the intelligent anti-snoring control module integrates a snoring recognition algorithm, collects ambient sounds through a microphone array, and uses Mel-frequency cepstral coefficient (MFCC) feature extraction and convolutional neural network (CNN) classifier to identify snoring signals. The dynamic sleep-coaxing control module has built-in multiple sleep-coaxing mode curves and adopts an adaptive control algorithm to dynamically adjust the sleep-coaxing parameters according to user body movement feedback.

[0017] A debugging method for an intelligent bed debugging system with a multi-mode adaptive control function comprises the following steps:

[0018] S1. Establish a wireless connection with the smart bed through the Bluetooth communication module;

[0019] S2. The motor operating current is collected through the current detection module, the bed frame angle and rising speed are calculated based on the current data, the PWM speed control algorithm module is used to control the motor operation, and the PWM parameters are automatically adjusted according to the motor load characteristics;

[0020] S3. Configure the anti-snoring mode, collect and identify snoring signals through the intelligent anti-snoring control module, and trigger the bedside angle adjustment action after identification;

[0021] S4. Configure the dynamic mode of coaxing to sleep. Use the dynamic control module to set the swing amplitude, frequency and vibration intensity of the smart bed, and dynamically optimize it according to the user's status.

[0022] S5, remote control matching, uses the remote control adapter module to parse the remote control signal protocol through automatic recognition or manual learning to achieve button function mapping.

[0023] Compared with the existing technology, the smart bed debugging system and method disclosed in the present invention, which has a multi-mode adaptive control function, can solve the adaptation problem of motors from different manufacturers by integrating Bluetooth wireless connection, current detection control, PWM speed regulation, voice interaction, manufacturer-defined configuration and multi-mode adaptive control technology, and achieve precise stroke control and speed adjustment, thereby improving the stability and reliability of the smart bed.

[0024] It supports custom configuration of personalized functions such as voice control commands, massage modes, and dynamic adjustment logic to meet the differentiated needs of different brands and users. It also provides innovative functions such as intelligent snoring prevention and dynamic sleep-inducing modes, enhancing the health-care attributes and user comfort of the smart bed.

[0025] It is compatible with a variety of remote control devices on the market, achieving a unified operating experience, improving user convenience, simplifying the debugging process, reducing debugging costs, and shortening the R&D and production cycle of smart beds. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 Schematic diagram of the hardware architecture of an intelligent bed debugging system with multi-mode adaptive control function according to one embodiment of the present invention;

[0028] Figure 2 This is a flowchart of the independent mode control of the software architecture of the intelligent bed debugging system with multi-mode adaptive control function in one embodiment of the present invention;

[0029] Figure 3 This is a flow chart of the motor power-off protection of the software architecture of the intelligent bed debugging system with multi-mode adaptive control function in one embodiment of the present invention;

[0030] Figure 4 This is a flow chart of the dynamic mode control of the software architecture of the intelligent bed debugging system with multi-mode adaptive control function in one embodiment of the present invention;

[0031] Figure 5 This is a schematic diagram of a motor drive circuit with an H-bridge circuit topology structure according to an embodiment of the present invention;

[0032] Figure 6 This is a flow chart of PWM speed regulation control in one embodiment of the present invention;

[0033] Figure 7 A flowchart of customizing voice control command words in one embodiment of the present invention;

[0034] Figure 8 This is a logic diagram of intelligent snoring control in one embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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 making creative efforts should fall within the scope of protection of the present invention.

[0036] like Figures 1 to 8 As shown, an intelligent bed debugging system with multi-mode adaptive control function in one embodiment of the present invention includes a hardware architecture and a software architecture. The hardware architecture is composed of a main control unit, a Bluetooth communication module, a current detection module, a motor drive module, a voice processing module, a remote control adaptation module, a display and input module and a power management module.

[0037] like Figure 1 As shown, the main control unit coordinates the operation of various system modules and processes Bluetooth communication data, current detection signals, voice commands, and remote control signals. The main control unit integrates DSP and FPU units to support real-time processing of complex algorithms. Running an embedded real-time operating system (RTOS) ensures efficient task scheduling.

[0038] It is worth noting that the main control unit uses the STM32H7 series high-performance microprocessor with a main frequency of 480MHz, 2MB of flash memory and 1MB of RAM.

[0039] like Figure 1 As shown, the Bluetooth communication module is used to establish a wireless connection with the smart bed and supports the Bluetooth low energy protocol.

[0040] The Bluetooth communication module integrates the Nordic nRF52840 Bluetooth 5.0 chip, supporting a 2Mbps data rate and long-range mode, with a transmission distance of 30 meters or more. The module also features a built-in PCB antenna (3dBi gain) and supports Bluetooth Mesh networking, enabling simultaneous connection to multiple smart bed devices for parallel debugging.

[0041] Specifically, the wireless connection and data transmission of the Bluetooth communication module uses Bluetooth Low Energy (BLE) technology to achieve wireless connection between the debugging device and the smart bed control unit. The connection establishment time is ≤3 seconds. A dedicated data transmission protocol is defined, and the data packet format includes a frame header (2 bytes), command type (1 byte), data length (2 bytes), data content (0-256 bytes), and a checksum (2 bytes). Data encryption transmission is supported, and the AES-128 encryption algorithm is used to encrypt the transmitted data to ensure data security. A heartbeat mechanism is implemented, sending a heartbeat packet every 500ms to detect the connection status, and the timeout period is set to 3 seconds.

[0042] like Figure 1 As shown in the figure, the current detection module is used to collect the motor running current in real time. It uses the INA219 high-precision current detection chip (measurement range ±32A, resolution 0.1mA, accuracy ±0.5%) and realizes real-time detection of motor current through a shunt resistor (0.01Ω, 1% accuracy). The sampling frequency is up to 1kHz and supports I 2 The module is connected to the main control unit through the C communication interface. The module has a built-in programmable gain amplifier that can automatically adjust the amplification factor according to the current range of different motors, ensuring high-precision measurement results in both low and high current situations.

[0043] like Figure 1 As shown, the motor driver module adopts an H-bridge circuit topology and utilizes the L298N full-bridge driver chip. It integrates four high-power MOSFET switches and a PWM modulator, capable of outputting PWM waveforms with a frequency range of 1-20kHz and an adjustable duty cycle of 0-100%, enabling bidirectional motor speed control. The built-in PWM modulator outputs PWM waveforms with a frequency range of 1-20kHz and an adjustable duty cycle of 0-100%. The driver module also features overtemperature protection (threshold 175°C), overcurrent protection (threshold 35A), and undervoltage protection (threshold 7V) to ensure safe and reliable system operation.

[0044] like Figure 5 As shown in the figure, the power stage design of the H-bridge circuit topology is as follows: four 30N06 MOSFETs (Q1-Q4) are used to form the H-bridge, and the parameters are optimized as follows:

[0045] On-resistance: typical value 8mΩ, reducing conduction loss; gate charge: Qg = 72nC, matching the drive capability of EG2131; avalanche energy: EAS = 150mJ, enhancing shock resistance.

[0046] The driver stage is designed as follows: using two EG2131 driver chips (U1 and U2) to control the left and right half bridges respectively, with the following characteristics:

[0047] Dead time: Built-in 200ns dead time to prevent shoot-through; Bootstrap circuit: Each channel is equipped with 0.47μF / 25V bootstrap capacitors (C1, C2); Undervoltage lockout: Automatically shuts down the output when VCC < 10V.

[0048] The current sensing network includes: sampling resistor: 20mΩ / 10W four-terminal resistor (RS1), temperature coefficient <50ppm / °C, differential amplifier: INA213, CMRR>110dB, gain 50 times, filtering network: RC low-pass filter (R1 = 10kΩ, C3 = 10nF, cutoff frequency 1.6kHz).

[0049] It is worth noting that the basic control mode of the H-bridge circuit topology is:

[0050] Forward mode: U1_HIN high level → Q1 is turned on, U2_LIN high level → Q4 is turned on;

[0051] Current path: VCC→Q1→motor→Q4→GND.

[0052] Inversion mode: U2_HIN high level → Q2 is turned on, U1_LIN high level → Q3 is turned on;

[0053] Current path: VCC→Q2→motor→Q3→GND.

[0054] Braking mode: Q1+Q3 are turned on at the same time or Q2+Q4 are turned on at the same time, and the motor winding is short-circuited to achieve energy-consuming braking.

[0055] Speed ​​control: The on-time of Q1 / Q4 or Q2 / Q3 is controlled by PWM signal, with a typical frequency of 5-20kHz.

[0056] The current detection principle of the H-bridge circuit topology is:

[0057] Sampling resistor voltage division: Current I passing through RS1 generates voltage V = I × 0.02Ω. Example: 10A current corresponds to a 0.2V voltage drop.

[0058] Signal amplification: INA213 amplifies the differential signal by 50 times, and the output voltage Vout = I × 1V / A. For example, 10A current corresponds to 10V output.

[0059] ADC acquisition: The output signal is filtered and connected to the ADC channel of the MCU, with a resolution of ≥12 bits.

[0060] The characteristics of the H-bridge circuit are: dynamic dead-zone optimization circuit, adaptive current sampling technology and integrated protection system.

[0061] Among them, the dynamic dead zone optimization circuit has a dynamic adjustment network composed of R5, C5, and D1, which forms a cascade structure with the built-in dead zone of EG2131. It can avoid the direct pass caused by insufficient dead zone at high frequencies and reduce the dead zone loss at low frequencies.

[0062] The working principle of the dynamic dead zone optimization circuit is: when the PWM frequency changes, the charge and discharge time of C5 changes, and the dead zone width is dynamically adjusted; the dead zone time is automatically increased to 250ns at high frequencies and restored to 200ns at low frequencies.

[0063] Adaptive current sampling technology includes multi-range switching: the sampling resistor (20mΩ / / 100mΩ) is switched through relay K1 to achieve wide-range detection; switching logic: when the detection current is <5A, a 20mΩ resistor is used (high sensitivity); when the detection current is ≥5A, it switches to a parallel 100mΩ resistor (extending the range to 30A), maintaining a detection accuracy of ±0.5% within the full range.

[0064] The integrated protection system includes: overcurrent protection: The LM393 comparator monitors the sampled voltage in real time. When the threshold (corresponding to 15A) is exceeded, an RS trigger is triggered to shut down all drive signals; overtemperature protection: The NTC thermistor monitors the heat sink temperature. When it exceeds 85°C, the comparator outputs a high level, shutting down the PWM output; undervoltage protection: When VCC is less than 9V, the TL431 outputs a low level, blocking the driver chip input. The H-bridge circuit test data and performance indicators are shown in the following table:

[0065]

[0066]

[0067] The current detection accuracy parameters are as follows:

[0068]

[0069] The power loss and efficiency parameters are as follows:

[0070]

[0071] The protection function response time parameters are as follows:

[0072] Protection Type Trigger threshold Response time Reset method Overcurrent protection 15A±0.5A ≤10μs Automatic reset Overtemperature protection 85℃±2℃ ≤50ms Manual reset Undervoltage protection 9V±0.3V ≤200μs Automatic reset

[0073] In summary, the H-bridge circuit has:

[0074] High-performance switch control and dynamic dead-zone optimization technology reduce shoot-through risk and improve reliability; optimize drive parameters and reduce switching losses by 25%;

[0075] High-precision current detection, multi-range adaptive sampling technology achieves full range accuracy of ±0.5%, and temperature compensation algorithm reduces the impact of temperature drift;

[0076] Improved protection mechanism, multi-level protection system (overcurrent / overtemperature / undervoltage) improves system safety, protection response time <10μs, effectively protects power devices;

[0077] EMI suppression design and power circuit minimization design reduce radiation interference, and differential signal transmission improves anti-interference ability.

[0078] like Figure 1As shown, the voice processing module includes a WM8960 microphone array and an LD3320 voice recognition chip, supports far-field voice recognition, and integrates a WT588D speech synthesis chip, supporting Chinese and English voice broadcasting.

[0079] The voice processing module includes a 4-channel digital microphone with a 48kHz sampling rate and a 94dB signal-to-noise ratio (SNR) and an LD3320 voice recognition chip. It supports far-field voice recognition (effective range ≥ 5 meters) and has a built-in 32MB Flash memory for storing voice model parameters. It also integrates the WT588D speech synthesis chip, supports Chinese and English voice broadcasts, and has an output volume range of 60-90dB. The voice processing module supports 360° omnidirectional sound pickup and uses beamforming technology to suppress ambient noise. Its voice recognition accuracy is ≥ 98% in quiet environments and ≥ 95% in noisy environments.

[0080] In addition, the remote control adapter module features an infrared receiver and a radio frequency receiver, supporting multiple signal transmission methods. It also includes a built-in signal decoding chip capable of parsing the signal protocols of mainstream remote controls on the market, such as NEC, RC-5, and Sony SIRC. The module also comes with a 16MB EEPROM that stores signal characteristics and button function mappings for remote controls from different manufacturers, supporting online updates and expansion.

[0081] Among them, the infrared connector model is HS0038B, the receiving frequency is 38kHz, the RF receiving module model is nRF24L01, the operating frequency is 2.4GHz, and it can support multiple signal transmission methods.

[0082] Specifically, the display and input module is used to display the operating parameters of the smart bed. It uses a 4.3-inch TFT color touch screen (resolution 480×272) and supports capacitive touch operation. Five physical buttons (power button, confirmation button, return button, up / down button) are configured below the screen to provide physical operation. The display module supports 16-bit true color display and a brightness range of 200-800nits, which can be automatically adjusted according to the ambient light.

[0083] like Figure 1 As shown, the power management module uses the TPS65910 power management chip. The input power supports a wide AC100-240V voltage range, and a built-in switching power supply (efficiency ≥90%) converts AC power to DC12V and 5V. The TPS65910 power management chip distributes and manages power across each module. It also supports battery backup (using an 18650 lithium battery with a capacity of 2600mAh), maintaining system operation for ≥30 minutes in the event of a main power outage.

[0084] The software architecture consists of an operating system, a drive system, a middleware system and an application system. The middleware system includes a Bluetooth communication management module, a current detection and control algorithm module, a PWM speed control algorithm module, a voice control module, a manufacturer-defined configuration module, a remote control adaptation module, an intelligent anti-snoring control module and a sleep-inducing dynamic control module.

[0085] The operating system adopts FreeRTOS real-time operating system, which supports task priority scheduling, time slice rotation and interrupt management. The system clock is configured to 480MHz, and the task switching time is ≤1μs, ensuring real-time response of key control tasks.

[0086] The driving system includes Bluetooth driver, I 2 C driver, SPI driver, PWM driver, ADC driver, UART driver.

[0087] The Bluetooth communication management module is used to implement Bluetooth device discovery, pairing, connection and data transmission management, and supports concurrent communication among multiple devices.

[0088] like Figures 2 to 4 As shown, the current sensing and control algorithm module establishes a motor load model based on current sensing data. Using a Kalman filter algorithm, it integrates current and time information to accurately calculate the bed frame angle and ascent speed. It also includes an adaptive PID controller that automatically adjusts control parameters based on real-time current feedback to ensure system stability and responsiveness.

[0089] Among them, the motor stroke control method based on current detection includes:

[0090] ① Establish a mathematical model of motor current and stroke position: θ = a × I 2 +b×I+c, where θ is the bed frame angle, I is the detection current, and a, b, and c are fitting coefficients (calibrated for different motor models);

[0091] ② Determine the model parameters by fitting experimental data with a fitting accuracy of ≥99%. Set a current threshold to determine the end of the motor's travel. When the current value exceeds the threshold (such as 1.5 times the rated current) and lasts for ≥200ms, the motor is determined to have reached the end of its travel and stops running.

[0092] ③ Implement current fluctuation compensation algorithm to eliminate current detection errors caused by power supply fluctuations, load changes and other factors, with a compensation accuracy of ±0.05A.

[0093] like Figure 6As shown in the figure, the PWM speed control algorithm module is based on fuzzy PID control theory and automatically adjusts the PWM frequency and duty cycle in combination with the motor load characteristics. It uses low-frequency PWM (1-5kHz) to increase torque output when running at low speed, and uses high-frequency PWM (15-20kHz) to reduce motor heating and noise when running at high speed.

[0094] Specifically, the PWM speed control algorithm module adopts 16-bit PWM modulation with a resolution of 0.0015%, which can achieve precise control of the motor speed.

[0095] Automatically adjusts the PWM frequency based on motor load characteristics: high-frequency PWM (15-20kHz) is used under light loads to reduce motor heating, while low-frequency PWM (1-5kHz) is used under heavy loads to increase torque output. Soft start and soft stop functions are implemented, with configurable start / stop times (default 0.5-2 seconds) to avoid motor startup shock and mechanical vibration. Supports closed-loop speed control, using current sensing feedback to adjust the PWM duty cycle in real time to ensure stable motor speed within ±2% of the set value.

[0096] like Figure 7 As shown, the voice control module supports command word customization, uses a deep neural network (DNN) algorithm to achieve voice recognition, combines natural language processing (NLP) technology to understand user intent, supports multi-round conversations and context understanding, and can recognize dialects and accents.

[0097] Among them, the voice control module can provide a graphical command word management interface, supporting the addition, deletion, and modification of voice control command words. Each command word can be associated with multiple synonymous expressions, such as "level the bed", "lie flat", "return to original position", etc., which can all trigger the same operation.

[0098] In addition, the voice control module can also support command word group management, and can organize command words according to functional modules (such as angle adjustment, massage control, mode switching, etc.) to implement command word priority management. When there are semantic conflicts between multiple command words, the priority can be set to determine the execution order.

[0099] The manufacturer-defined configuration module provides a graphical configuration interface, allowing manufacturers to set various parameters of the smart bed, including the massage motor's PWM voltage regulation parameters, intensity level mapping table, voice control command words, dynamic mode operation logic, etc. It supports parameter storage, modification, import, and export.

[0100] The manufacturer's custom configuration module supports custom massage modes, including settings for intensity (1-16), massage area (back, waist, legs, etc.), massage technique (kneading, beating, rolling, etc.), and massage time (1-60 minutes). It also supports custom dynamic modes, allowing users to set parameters such as the bed frame angle curve, speed, and hold time to create a personalized sleep experience. It also supports custom smart scenes, linking smart home devices such as lights, curtains, and air conditioners to achieve scenario-based control. For example, setting a "sleep scene" automatically dims the lights, closes the curtains, and adjusts the air conditioner temperature when the user lies down.

[0101] The remote control adaptation module stores signal protocols and button function mapping tables for remote controls from different manufacturers, enabling automatic recognition and adaptation of remote control signals. It also supports learning programming, allowing users to learn new button functions by manually operating the remote control.

[0102] Specifically, the remote control adaptation module has a built-in library of common remote control signal protocols, which supports rapid identification of remote controls of mainstream brands on the market; it implements a learning programming function: when encountering a remote control with an unknown protocol, the user can press the remote control button, and the system will automatically learn and record the button signal characteristics; it supports button function customization: the user can map any button on the remote control to any function of the smart bed to achieve personalized operation; storage capacity: it can store the configuration information of ≥100 different remote controls, and each remote control supports ≥32 button learning.

[0103] like Figure 8 As shown in the figure, the intelligent anti-snoring control module integrates a snoring recognition algorithm, collects ambient sounds through a microphone array, and uses Mel-frequency cepstral coefficient (MFCC) feature extraction and convolutional neural network (CNN) classifier to identify snoring signals. When snoring is detected, the preset anti-snoring action sequence is automatically triggered.

[0104] It is worth noting that the snoring recognition accuracy of the intelligent anti-snoring control module is ≥95%, and it can distinguish between true snoring and environmental noise (such as TV sound, talking sound, etc.).

[0105] The intelligent anti-snoring control module has a multi-level response mechanism: when snoring is detected, the first level action is executed first (such as slowly raising the head of the bed 5 degrees); if the snoring persists, the second level action is executed (such as adding a light back massage); automatic reset function: when the snoring stops for more than the set time (the default is 5 minutes), the bed frame is automatically restored to its initial position; learning optimization function: by recording the user's anti-snoring effect data, the anti-snoring action parameters are gradually optimized to improve the anti-snoring effect.

[0106] The dynamic sleep control module includes multiple built-in sleep-inducing curves, such as wave-like rocking and progressive vibration. It supports user-defined parameters for rocking motion, including rocking amplitude (0-15°), frequency (0.1-1Hz), vibration intensity (levels 1-10), and duration (10-90 minutes). It uses an adaptive control algorithm to dynamically adjust the parameters based on user motion feedback.

[0107] In addition, the dynamic control module for coaxing to sleep has the following functions: body movement feedback adjustment: the user's body movement is detected through the pressure sensor. When the body movement frequency decreases, the swing amplitude and vibration intensity are automatically reduced; when the body movement frequency increases, the stimulation intensity is appropriately increased; sleep monitoring linkage: linked with the sleep monitoring sensor, automatically adjust the coaxing to sleep parameters according to the user's sleep stage (light sleep, deep sleep, REM).

[0108] The application system is used to provide a user interface and application logic, including functional modules such as device management, debugging tools, mode configuration, and data analysis. It supports multi-language interface switching (Chinese, English, Japanese, etc.) to meet the needs of users in different regions.

[0109] A debugging method for an intelligent bed debugging system with a multi-mode adaptive control function comprises the following steps:

[0110] S1. Establish a wireless connection with the smart bed through the Bluetooth communication module.

[0111] The specific steps are as follows: power on the debugging device, perform a self-test and initialization, load the necessary drivers and configuration files, activate the Bluetooth communication module, enter device search mode, scan for nearby smart bed devices, list the searched smart bed devices on the display interface, and select the target device for pairing and connection. After a successful connection, the device status information (such as battery level and firmware version) is displayed.

[0112] S2. The motor operating current is collected through the current detection module, the bed frame angle and rising speed are calculated based on the current data, the PWM speed control algorithm module is used to control the motor operation, and the PWM parameters are automatically adjusted according to the motor load characteristics.

[0113] The specific steps are:

[0114] S21, current detection module calibration: in the no-load state, collect the motor current baseline value and set the zero point calibration parameters;

[0115] S22, PWM speed control parameter configuration: Set the initial PWM frequency to 10kHz and the duty cycle to 50%, drive the push rod motor to drive the simulated load, monitor the motor current in real time through the current detection module, gradually adjust the PWM frequency and duty cycle, and record the motor's operating status and performance under different parameters;

[0116] S23, speed curve optimization: Collect the speed data of the motor under different PWM parameters, draw the speed curve, and optimize the speed response time and stability by adjusting the PID parameters (such as proportional coefficient Kp = 0.8, integral coefficient Ki = 0.2, differential coefficient Kd = 0.1) so that the speed fluctuation of the motor within the speed range of 0-100% does not exceed ±5%;

[0117] S24, travel limit setting: control the push rod motor to run to the maximum stroke endpoint, record the current value at this time as the upper threshold; similarly, record the current value at the minimum stroke endpoint as the lower threshold, set a reasonable safety margin (such as 1.1 times the threshold) to ensure that the motor runs within a safe travel range.

[0118] The steps of calculating the bed frame angle include: establishing a mathematical model of the motor current and the bed frame angle, determining the model parameters by fitting the motor current data at different travel positions, and calculating the bed frame angle by substituting the real-time detected current value into the model, with the angle calculation error ≤±0.5°;

[0119] The steps of adjusting PWM parameters include: dynamically adjusting PWM frequency and duty cycle according to motor load characteristics, reducing PWM frequency to increase torque output when motor load increases, and increasing PWM frequency to reduce heat generation when motor load decreases, thereby achieving stepless speed regulation of motor speed within the range of 0-100%, with a speed control accuracy of ±2%;

[0120] S3. Configure the anti-snoring mode, collect and identify snoring signals through the intelligent anti-snoring control module, and trigger the bedside angle adjustment action after identification;

[0121] The steps for configuring the smart anti-snoring mode include setting up a multi-level response mechanism. When snoring is continuously detected for more than a first time threshold, a first action is triggered. When it exceeds a second time threshold, a second action is triggered. When the snoring stops for more than a set time, the bed frame is automatically restored to its original position.

[0122] The specific steps for debugging the intelligent anti-snoring function are as follows:

[0123] S31. Snoring recognition training: Record background noise samples in a quiet environment and snoring samples in a simulated snoring environment. Input the sample data into the debugging system, train the snoring recognition model using the CNN algorithm, adjust the MFCC feature parameters and CNN network structure, and achieve a snoring recognition accuracy of ≥95%;

[0124] S32, Action Sequence Customization: Set parameters such as the head of bed lifting angle range (5-15°), lifting speed (1-5° / second), and holding time (1-10 minutes). Configure a multi-level response mechanism. When snoring is detected for more than 30 seconds, the first action is triggered, and when it exceeds 2 minutes, the second action (such as adding a light massage) is triggered.

[0125] S33. Effect Verification: Test the effectiveness of the intelligent anti-snoring function by simulating snoring, record changes in snoring intensity, frequency, and duration, evaluate the anti-snoring effect, and adjust the anti-snoring parameters based on the test results until a satisfactory effect is achieved;

[0126] S4. Configure the dynamic mode of coaxing to sleep, use the dynamic control module to set the swing amplitude, frequency and vibration intensity of the smart bed, and dynamically optimize it according to the user's status.

[0127] The specific steps for dynamic debugging of coaxing to sleep are:

[0128] S41. Basic parameter settings: Set basic parameters such as swing amplitude (e.g., 8°), swing frequency (e.g., 0.3Hz), vibration intensity (e.g., level 5), and duration (e.g., 45 minutes);

[0129] S42. Dynamic Adjustment Strategy Configuration: Configure an adaptive adjustment algorithm to automatically reduce the sway amplitude and vibration intensity when the user's body movement frequency decreases. When the body movement frequency increases, restore the initial parameters or appropriately increase the stimulation intensity. Set the adjustment threshold and step size. For example, if the body movement frequency decreases by 20%, the sway amplitude decreases by 1°.

[0130] S43. User experience testing: Invite testers to experience the dynamic mode of coaxing to sleep, collect subjective feedback, and adjust parameters based on the feedback, such as optimizing the swing waveform, adjusting the vibration frequency, etc., to improve user comfort and sleep efficiency.

[0131] S5, remote control matching, uses the remote control adapter module to parse the remote control signal protocol through automatic recognition or manual learning to achieve button function mapping.

[0132] The specific remote control adaptation and debugging steps include the following steps:

[0133] S51. Automatic identification and adaptation: Align the remote control to be adapted with the debugging system and press any button. The system automatically collects the remote control signal and matches it in the built-in protocol library. If a matching protocol is found, the adaptation is automatically completed and the button function mapping table is displayed.

[0134] S52, manual learning and adaptation: If automatic recognition fails, enter manual learning mode and press each button on the remote control in sequence according to the system prompts. The system records the signal characteristics of each button. After learning is completed, the user can customize the smart bed function corresponding to each button;

[0135] S53, Function test: Use the remote control to test whether the functions of each button are normal. If there is any abnormality, re-learn or adjust the button function mapping.

[0136] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0137] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A smart bed debugging system with multi-mode adaptive control function, characterized in that: Including hardware architecture and software architecture; The hardware architecture consists of a main control unit, a Bluetooth communication module, a current detection module, a motor drive module, a voice processing module, a remote control adapter module, a display and input module, and a power management module; The software architecture consists of an operating system, a driver system, a middleware system, and an application system; Among them, the middleware system includes Bluetooth communication management module, current detection and control algorithm module, PWM speed control algorithm module, voice control module, manufacturer-customized configuration module, remote control adaptation module, intelligent anti-snoring control module and sleep-inducing dynamic control module.

2. The intelligent bed debugging system with multi-mode adaptive control function according to claim 1 is characterized in that: The main control unit is used to coordinate the work of various modules of the system, process Bluetooth communication data, current detection signals, voice commands and remote control signals, and integrates DSP and FPU units to support real-time processing of complex algorithms.

3. The intelligent bed debugging system with multi-mode adaptive control function according to claim 2, characterized in that: The Bluetooth communication module is used to establish a wireless connection with the smart bed and supports the Bluetooth low energy protocol. The current detection module is used to collect the motor running current in real time. The motor drive module adopts an H-bridge circuit topology, includes four high-power MOSFET switch tubes and a PWM modulator, and can output a PWM waveform with a frequency range of 1-20kHz and an adjustable duty cycle of 0-100%, thereby realizing bidirectional speed control of the motor.

4. The intelligent bed debugging system with multi-mode adaptive control function according to claim 3, characterized in that: The voice processing module includes a WM8960 microphone array and an LD3320 voice recognition chip, supports far-field voice recognition, and integrates a voice synthesis chip WT588D, supporting Chinese and English voice broadcasting. The remote control adapter module is equipped with an infrared receiving head and a radio frequency receiving module, supports multiple signal transmission methods, and has a built-in signal decoding chip that can parse the signal protocols of mainstream remote controls on the market.

5. The intelligent bed debugging system with multi-mode adaptive control function according to claim 4 is characterized in that: The display and input module is used to display the operating parameter information of the smart bed. The power management module adopts the TPS65910 power management chip to realize power distribution and management of each module and support battery backup function.

6. The intelligent bed debugging system with multi-mode adaptive control function according to claim 5, characterized in that: The operating system adopts FreeRTOS real-time operating system, which supports task priority scheduling, time slice rotation and interrupt management. The driving system includes Bluetooth driver, I 2 C driver, SPI driver, PWM driver, ADC driver, UART driver.

7. The intelligent bed debugging system with multi-mode adaptive control function according to claim 6, characterized in that: The Bluetooth communication management module is used to realize Bluetooth device discovery, pairing, connection and data transmission management, and supports concurrent communication of multiple devices. The current detection and control algorithm module establishes a motor load model based on current detection data, and integrates current and time information through the Kalman filter algorithm to achieve accurate calculation of the bed frame angle and rising speed.

8. The intelligent bed debugging system with multi-mode adaptive control function according to claim 7, characterized in that: The PWM speed control algorithm module is based on fuzzy PID control theory and automatically adjusts the PWM frequency and duty cycle in combination with the motor load characteristics. The voice control module supports command word customization, adopts a deep neural network (DNN) algorithm to achieve voice recognition, and combines natural language processing (NLP) technology to understand user intentions.

9. The intelligent bed debugging system with multi-mode adaptive control function according to claim 8, characterized in that: The intelligent anti-snoring control module integrates a snoring recognition algorithm, collects ambient sounds through a microphone array, and uses Mel-frequency cepstral coefficient (MFCC) feature extraction and convolutional neural network (CNN) classifier to identify snoring signals. The dynamic sleep-coaxing control module has built-in multiple sleep-coaxing mode curves and adopts an adaptive control algorithm to dynamically adjust the sleep-coaxing parameters based on user body movement feedback.

10. A debugging method for an intelligent bed debugging system with multi-mode adaptive control function according to claim 9, characterized in that: The following steps are involved: S1. Establish a wireless connection with the smart bed through the Bluetooth communication module; S2. The motor operating current is collected through the current detection module, the bed frame angle and rising speed are calculated based on the current data, the PWM speed control algorithm module is used to control the motor operation, and the PWM parameters are automatically adjusted according to the motor load characteristics; S3. Configure the anti-snoring mode, collect and identify snoring signals through the intelligent anti-snoring control module, and trigger the bedside angle adjustment action after identification; S4. Configure the dynamic mode of coaxing to sleep. Use the dynamic control module to set the swing amplitude, frequency and vibration intensity of the smart bed, and dynamically optimize it according to the user's status. S5, remote control matching, uses the remote control adapter module to parse the remote control signal protocol through automatic recognition or manual learning to achieve button function mapping.

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