Dialect control intelligent bed system

Abstract

The invention discloses a dialect control intelligent bed system which comprises a dialect collecting and preprocessing module, a dialect recognition and instruction mapping module, a user state sensing module, a bed body motion control module, a safety protection and exception handling module and a man-machine interaction feedback module. According to the method, domestic mainstream dialects and pronunciation characteristics of users of different age groups are covered, multi-channel collection and accurate preprocessing are combined, the dialect recognition accuracy is greatly improved, and the use obstacles of non-mandarin users are solved; the bed state, posture and pressure distribution of a user are sensed through fusion of multiple sensors, invalid misoperation is avoided, instruction execution is more targeted, and use convenience is improved; a PID control algorithm and a parameter optimization mechanism are adopted, stable adjustment of lifting and massaging modes of the bed body is achieved, and body characteristics and using requirements of different users are met.

Description

Technical Field

[0001] This invention relates to the field of smart home and voice control technology, specifically a dialect-controlled smart bed system. Background Technology

[0002] With the popularization of smart home technology, smart beds, as core products for improving sleep quality and convenience, are increasingly diversifying their control methods. Currently, most mainstream smart beds use Mandarin voice control, mobile app control, or physical button control, which presents the following significant problems: The diverse dialects in China, especially among middle-aged and elderly users, often result in non-standard Mandarin pronunciation or unfamiliarity with smart device operation, leading to low voice control recognition rates and frequent false triggers. Existing voice control systems simply receive commands without considering the user's bed position or posture, increasing the risk of accidental operation when unattended or when the user is not ready. Bed motion control lacks algorithmic support, resulting in abrupt motion transitions and a failure to optimize execution parameters for different user body types and weights. Safety mechanisms are simplistic, relying solely on motor overload protection without addressing obstacle collisions or abnormal postures, posing usage risks. Insufficient human-computer interaction feedback prevents users from promptly confirming whether commands have been correctly recognized and executed, diminishing the user experience. Summary of the Invention

[0003] The purpose of this invention is to provide a dialect-controlled smart bed system to solve the problems existing in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a dialect-controlled intelligent bed system, comprising a dialect acquisition and preprocessing module, a dialect recognition and command mapping module, a user status perception module, a bed motion control module, a safety protection and anomaly handling module, and a human-computer interaction feedback module; The dialect acquisition and preprocessing module is used to acquire dialect speech signals from users in different regions, and output standardized dialect speech data through noise reduction, normalization, and endpoint detection. The dialect recognition and command mapping module is based on a multi-dialect training model library. It performs feature extraction and pattern matching on the preprocessed dialect speech data and maps the recognition results into control commands that can be executed by the smart bed. The user status perception module collects data on the user's bed status, body posture, and contact pressure through multi-sensor fusion to determine whether the user is within the effective control range and in a safe state. The bed motion control module receives control commands and, in conjunction with user status data, drives the bed motor and massage components to perform actions through a control algorithm. The safety protection and anomaly handling module monitors the bed's motion status, surrounding environmental obstacles, and equipment operating parameters in real time. When an anomaly is detected, it triggers braking, parameter adjustment, or alarm mechanisms. The human-computer interaction feedback module provides feedback to the user on command recognition results, action execution status, and abnormal prompts through voice, light, and touch.

[0005] Furthermore, the dialect acquisition and preprocessing module includes a multi-channel voice acquisition unit, a dialect adaptation unit, and a signal preprocessing unit; The multi-channel voice acquisition unit consists of four omnidirectional microphones, which are respectively arranged on both sides of the headboard and the footboard of the smart bed; The dialect adaptation unit pre-stores pronunciation feature libraries of dialects from different regions, and each dialect category contains pronunciation samples from users of different age groups; The signal preprocessing unit processes dialect speech signals using wavelet threshold denoising algorithm, normalization processing, and short-time energy and zero-crossing rate joint detection algorithm.

[0006] Furthermore, the wavelet threshold noise reduction algorithm formula of the signal preprocessing unit is as follows:

[0007] in This is the denoised dialect speech signal. It is the original dialect speech signal. These are the weighting coefficients of the wavelet basis functions. The wavelet decomposition level is denoted as . This is the index of the signal sampling points.

[0008] Furthermore, the dialect recognition and instruction mapping module includes a dialect model training unit, a feature extraction unit, a pattern matching unit, and an instruction mapping unit; The dialect model training unit uses a hybrid CNN and LSTM network structure to construct a multi-dialect recognition model; The feature extraction unit extracts the Mel frequency cepstral coefficients, linear prediction cepstral coefficients, and fundamental frequency of the dialect speech signal to form a three-dimensional feature vector; The pattern matching unit calculates the semantic matching probability using the Softmax function to determine the recognition validity; The instruction mapping unit pre-stores a mapping table between dialect semantics and control instructions, and maps the recognized valid dialect semantics into control instruction codes.

[0009] Furthermore, the user status perception module includes a bed detection unit, a posture recognition unit, and a pressure acquisition unit; the bed detection unit consists of 8 pressure sensors, evenly distributed in the head, shoulder, waist, hip, and leg areas of the mattress; the posture recognition unit uses a 6-axis IMU inertial measurement unit, installed in the middle of the mattress; the pressure acquisition unit collects the contact pressure values ​​of each area through pressure sensors to construct a pressure distribution matrix.

[0010] Furthermore, the bed motion control module includes a motion analysis unit, a parameter optimization unit, and a drive control unit; The action parsing unit parses the action type, target parameters, and execution speed of the control command; The parameter optimization unit combines the user pressure distribution matrix and attitude angle data to optimize the target parameters; The drive control unit uses a PID control algorithm to drive the motor. The PID control formula is as follows:

[0011] in, For motor drive control, This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients. This represents the deviation between the target parameter and the actual parameter. To control time, It is the integral variable.

[0012] Furthermore, the safety protection and anomaly handling module includes an obstacle detection unit, a status monitoring unit, and an anomaly handling unit. The obstacle detection unit consists of two infrared distance sensors and one ultrasonic sensor, which are respectively arranged above the head of the bed, below the foot of the bed, and on the side of the middle of the bed. The status monitoring unit collects the motor operating current, voltage, temperature, and the rate of change of the bed's attitude angle. The anomaly handling unit adopts a hierarchical processing mechanism, which triggers parameter adjustment, emergency braking, and audible and visual alarms respectively.

[0013] Furthermore, the dynamic safe distance threshold calculation formula for the obstacle detection unit is as follows:

[0014] in For dynamic safety distance threshold, The current speed of the bed. For system response time, This refers to the braking acceleration of the bed.

[0015] Furthermore, the human-computer interaction feedback module includes a voice feedback unit, a light feedback unit, and a tactile feedback unit; the voice feedback unit has a built-in dialect speech synthesis module, which supports converting feedback information into the dialect currently used by the user for broadcast; the light feedback unit consists of 3 LED indicator lights, which indicate different working states of the system through different on / off states; the tactile feedback unit has a vibration motor installed at the headboard armrest, which provides feedback through vibration at different frequencies.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention covers the pronunciation characteristics of mainstream Chinese dialects and users of different age groups. Combining multi-channel acquisition and precise preprocessing, it significantly improves dialect recognition accuracy, overcoming usage barriers for non-Mandarin speakers. Through multi-sensor fusion, it perceives the user's bed position, posture, and pressure distribution, avoiding invalid misoperations and making command execution more targeted, thus improving ease of use. Employing a PID control algorithm and parameter optimization mechanism, it achieves smooth adjustment of bed height and massage modes, adapting to the physical characteristics and usage needs of different users. A dynamic safety distance calculation and graded anomaly handling mechanism take into account obstacle collision protection, equipment overload protection, and fault alarms, comprehensively ensuring user safety and equipment stability. Multi-dimensional feedback from dialect voice, light, and touch ensures users promptly obtain command execution status and anomaly information, enhancing user experience and operational confidence. Attached Figure Description

[0017] Figure 1 This is a system module diagram of the present invention; Figure 2 This is a schematic diagram of the dialect acquisition and preprocessing module of the present invention; Figure 3 This is a schematic diagram of the dialect recognition and instruction mapping module of the present invention; Figure 4 This is a schematic diagram of the user status perception module of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1-4 The present invention provides a dialect-controlled smart bed system, including a dialect acquisition and preprocessing module, a dialect recognition and command mapping module, a user status perception module, a bed motion control module, a safety protection and anomaly handling module, and a human-computer interaction feedback module; The dialect acquisition and preprocessing module is used to collect dialect speech signals from users in different regions. Through noise reduction, normalization, endpoint detection and other processing, it outputs clear and standardized dialect speech data, which is compatible with mainstream dialects in China such as Northern dialects, Wu dialect, Cantonese, Sichuan-Chongqing dialect, and Min dialect. The dialect recognition and command mapping module is based on a multi-dialect training model library. It performs feature extraction and pattern matching on the pre-processed dialect speech data and maps the recognition results into control commands that the smart bed can execute, including bed lifting, angle adjustment, massage mode switching, massage intensity adjustment, wake-up function, etc. The user status perception module collects data such as the user's bed status, body posture, and contact pressure through multi-sensor fusion to determine whether the user is within the effective control range and in a safe state, and provides triggering conditions for command execution. The bed motion control module receives the mapped control commands, combines them with user status data, and drives the bed motor and massage components to perform actions through a precise control algorithm, thereby achieving fine adjustment of the bed posture and massage parameters. The safety protection and anomaly handling module monitors the bed's movement status, surrounding environmental obstacles, and equipment operating parameters in real time. When an anomaly is detected, it triggers emergency braking, parameter adjustment, or alarm mechanisms to ensure user safety and equipment stability. The human-computer interaction feedback module provides users with information such as command recognition results, action execution status, and abnormal prompts through voice broadcasts, light indicators, and tactile feedback, enabling two-way interaction.

[0020] The dialect acquisition and preprocessing module includes a multi-channel speech acquisition unit, a dialect adaptation unit, and a signal preprocessing unit; Multi-channel voice acquisition unit: Composed of 4 omnidirectional microphones, respectively arranged on both sides of the headboard and footboard of the smart bed, forming a 360° voice acquisition range, adapting to the dialect pronunciation of users in different positions; Dialect adaptation unit: Pre-stores pronunciation feature databases of mainstream Chinese dialects, including Northern dialects, Wu dialect, Cantonese, Sichuan-Chongqing dialect, and Min dialect. Northern dialects include Northeastern Mandarin, North China Mandarin, and Northwest Mandarin; Wu dialects include Suzhou dialect, Shanghai dialect, and Hangzhou dialect; Cantonese includes Guangzhou dialect, Foshan dialect, and Dongguan dialect; Sichuan-Chongqing dialects include Chengdu dialect, Chongqing dialect, and Mianyang dialect; and Min dialects include Fuzhou dialect, Xiamen dialect, and Quanzhou dialect. Each dialect category contains pronunciation samples from users of different age groups (18-30 years old, 31-50 years old, and 51 years and older). Signal preprocessing unit: Preprocesses the acquired dialect speech signals sequentially. First, it removes environmental noise, such as background human voices and electrical appliance noise, using a wavelet threshold denoising algorithm. The denoising formula is: ,in, This is the denoised dialect speech signal. It is the original dialect speech signal. Weighting coefficients of wavelet basis functions The wavelet decomposition level is denoted as . The signal sampling points are indexed; then, speech normalization processing is performed to adjust the signal amplitude to the [-1,1] range to eliminate the influence of differences in the volume of different users' speech; finally, the start and end points of the speech signal are accurately segmented by a joint detection algorithm of short-time energy and zero-crossing rate, and invalid silent segments are eliminated.

[0021] The dialect recognition and instruction mapping module includes a dialect model training unit, a feature extraction unit, a pattern matching unit, and an instruction mapping unit; Dialect Model Training Unit: A multi-dialect recognition model is built based on a deep learning framework. It adopts a hybrid network structure of CNN and LSTM and is trained with a large number of dialect speech samples (≥100,000 samples for each dialect category). The input of the model is the preprocessed speech signal, and the output is the dialect category and semantic probability. Feature extraction unit: Extracts Mel frequency cepstral coefficients (MFCC), linear predictive cepstral coefficients (LPCC), and fundamental frequency (F0) of dialect speech signals to form a three-dimensional feature vector, in which the MFCC dimension is set to 13 dimensions, the LPCC dimension is set to 12 dimensions, and F0 is 1 dimension, comprehensively representing the spectral and prosodic features of dialect pronunciation; Pattern matching unit: Inputs the extracted feature vector into the trained multi-dialect recognition model, calculates the semantic matching probability through the Softmax function, and determines the recognition to be valid when the maximum probability value is greater than or equal to the preset threshold; Command mapping unit: Pre-stores a mapping table between dialect semantics and smart bed control commands. Each control command corresponds to multiple sets of dialect expressions, such as "raise the head of the bed" corresponding to Cantonese "raise the foot of the bed", Sichuan and Chongqing dialect "raise the head of the bed a little", Wu dialect "raise the head of the bed", etc. After the recognition is valid, the dialect semantics are accurately mapped to the corresponding control command code. The command code contains information such as action type, execution parameters, and priority.

[0022] The user status perception module includes a bed detection unit, a posture recognition unit, and a pressure acquisition unit; In-bed detection unit: It consists of 8 pressure sensors distributed inside the mattress, evenly arranged in the head, shoulder, waist, hip and leg areas. It determines whether the user is on the bed by detecting whether the pressure value is greater than the preset reference value. Attitude recognition unit: A 6-axis IMU inertial measurement unit is installed in the middle of the mattress to collect the attitude angles (pitch angle, roll angle) and angular velocity data of the bed and the user. Combined with the distributed pressure value of the pressure sensor, it determines the user's current posture (such as lying flat, lying on the side, sitting, semi-reclining). Pressure acquisition unit: Collects contact pressure values ​​in each area using pressure sensors, and constructs a pressure distribution matrix, as shown in the formula:

[0023] in, The pressure distribution matrix is... For the first Line number The values ​​collected by the pressure sensor. This represents the row number of the pressure sensor. This matrix represents the number of columns for the pressure sensors; it is used to analyze the user's center of gravity position and contact state, providing parameters for action execution.

[0024] The bed motion control module includes a motion analysis unit, a parameter optimization unit, and a drive control unit. The motion analysis unit receives the control command code output by the command mapping unit and analyzes the motion type (such as headrest lifting, footrest lifting, back massage, leg massage, etc.), target parameters (such as lifting angle, massage frequency, massage intensity, etc.) and execution speed. Parameter optimization unit: Combines the pressure distribution matrix and posture angle data output by the user status perception module to optimize and adjust the target parameters, such as adjusting the massage intensity according to the user's weight and adjusting the lifting speed according to the center of gravity position; Drive control unit: The system uses a PID control algorithm to drive the bed lifting motors (4 in total, controlling the left side of the headboard, right side of the headboard, left side of the footboard, and right side of the footboard) and the massage motors (6 in total, distributed for the back, waist, and legs). The PID control formula is as follows:

[0025] in, For motor drive control, This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients. This represents the deviation between the target parameter and the actual parameter. To control time, The variable is the integral variable; this algorithm enables precise control of motor speed and torque, ensuring smooth bed posture adjustment and uniform massage intensity.

[0026] The safety protection and anomaly handling module includes an obstacle detection unit, a status monitoring unit, and an anomaly handling unit; Obstacle detection unit: Composed of two infrared distance sensors (located above the headboard and below the footboard) and one ultrasonic sensor (located on the side of the middle of the bed), it detects the distance to obstacles within the bed's movement range in real time. When the detected distance is less than a safe distance threshold, a braking command is triggered. The safe distance threshold is dynamically adjusted according to the bed's movement direction, and the calculation formula is as follows:

[0027] in, For dynamic safety distance threshold, The current speed of the bed. For system response time, This refers to the braking acceleration of the bed. Status monitoring unit: Real-time acquisition of motor operating current, voltage, temperature and bed attitude angle change rate. When the current exceeds the rated value, the temperature exceeds the preset upper limit or the attitude angle change rate is abnormal, it is determined that the equipment is operating abnormally. Anomaly Handling Unit: Adopts a hierarchical processing mechanism. Level 1 anomalies (such as minor obstacle obstruction or motor current slightly exceeding the threshold) trigger parameter adjustments (reducing movement speed and torque); Level 2 anomalies (such as severe obstacle collision or excessively high motor temperature) trigger emergency braking and simultaneously cut off the motor power supply; Level 3 anomalies (such as sensor failure or control module failure) trigger audible and visual alarms, emitting an alarm sound through a buzzer, flashing the bedside indicator light, and storing the anomaly log.

[0028] The human-computer interaction feedback module includes a voice feedback unit, a light feedback unit, and a tactile feedback unit; Voice feedback unit: Built-in dialect speech synthesis module, which supports converting feedback information into the user's current dialect for broadcast. Feedback content includes command confirmation (e.g., Cantonese "Received, raising the headboard"), execution completion (e.g., Sichuan and Chongqing dialect "Massage mode has been switched to gentle mode"), and error prompts (e.g., Wu dialect "There is an obstacle at the foot of the bed, please move it"). Light feedback unit: consists of 3 LED indicator lights, namely green, yellow and red. Solid green indicates normal system standby, flashing green indicates command execution, flashing yellow indicates parameter adjustment, and flashing red indicates abnormal alarm. Tactile feedback unit: A vibration motor is installed at the headboard armrest. When a command is successfully recognized, executed, or an abnormality occurs, different frequencies of vibration feedback are provided. Successful recognition: 1 short vibration; Execution completed: 1 long vibration; Abnormality: 3 consecutive vibrations. This is suitable for users with hearing impairments or in noisy environments.

[0029] Example: The multi-channel speech acquisition unit of the dialect acquisition and preprocessing module acquires user dialect speech signals through four omnidirectional microphones. For example, when a user speaking Sichuan or Chongqing dialect issues the instruction "raise the head of the bed a little higher", the microphones capture the signal. The dialect adaptation unit calls the Sichuan or Chongqing dialect pronunciation feature library, and the signal preprocessing unit removes air conditioning noise in the environment through wavelet threshold noise reduction algorithm. After normalization processing, the effective speech segments are segmented. The feature extraction unit of the dialect recognition and command mapping module extracts the MFCC, LPCC and F0 features of the speech signal and inputs them into the CNN and LSTM multi-dialect recognition model. The pattern matching unit calculates the semantic matching probability as 92% ≥ the preset threshold. The command mapping unit maps the dialect semantics to the "bedside lifting" control command with a target angle of 30° and a medium execution speed. The pressure sensor in the user status perception module detects pressure values ​​in the head and shoulder areas of the mattress, determines that the user is in a lying position, the IMU inertial measurement unit collects attitude angle data, and the pressure acquisition unit constructs a pressure distribution matrix to confirm that the user is within the effective control range. The motion analysis unit of the bed motion control module analyzes the command parameters, the parameter optimization unit determines the user's weight to be approximately 65kg based on the user pressure distribution matrix, optimizes the lifting speed, and the drive control unit outputs control quantity through the PID control algorithm to drive the motors on both sides of the headboard to start and smoothly raise the headboard to 30°. The infrared distance sensor of the safety protection and anomaly handling module detects obstacles above the head of the bed in real time. No anomalies are detected, and the status monitoring unit collects normal motor current and temperature. The voice feedback unit of the human-computer interaction feedback module announces "Received, raising the headboard" in Sichuan and Chongqing dialect, the green LED indicator flashes, and the headboard armrest vibration motor vibrates once briefly; when the headboard is raised to the target angle, the voice announces "The headboard has been raised to the appropriate position", the green indicator light stays on, and the vibration motor vibrates once for a long time.

[0030] If the user issues the command "There is an obstacle at the foot of the bed" in Cantonese at the foot of the bed, the ultrasonic sensor of the obstacle detection unit detects an obstacle 0.2m below the foot of the bed. According to the dynamic safety distance formula, the current safety distance threshold is calculated to be 0.3m. Since the detection distance is less than the threshold, the safety protection module triggers emergency braking. At the same time, the voice feedback unit announces in Cantonese "An obstacle has been detected. Action has stopped. Please move the obstacle." The red LED indicator flashes, the vibration motor vibrates three times continuously, and the anomaly handling unit stores the anomaly log.

[0031] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dialect-controlled intelligent bed system, characterized in that: It includes a dialect acquisition and preprocessing module, a dialect recognition and command mapping module, a user status perception module, a bed motion control module, a safety protection and anomaly handling module, and a human-computer interaction feedback module; The dialect acquisition and preprocessing module is used to acquire dialect speech signals from users in different regions, and output standardized dialect speech data through noise reduction, normalization, and endpoint detection. The dialect recognition and command mapping module is based on a multi-dialect training model library. It performs feature extraction and pattern matching on the preprocessed dialect speech data and maps the recognition results into control commands that can be executed by the smart bed. The user status perception module collects data on the user's bed status, body posture, and contact pressure through multi-sensor fusion to determine whether the user is within the effective control range and in a safe state. The bed motion control module receives control commands and, in conjunction with user status data, drives the bed motor and massage components to perform actions through a control algorithm. The safety protection and anomaly handling module monitors the bed's motion status, surrounding environmental obstacles, and equipment operating parameters in real time. When an anomaly is detected, it triggers braking, parameter adjustment, or alarm mechanisms. The human-computer interaction feedback module provides feedback to the user on command recognition results, action execution status, and abnormal prompts through voice, light, and touch.

2. The dialect-controlled intelligent bed system according to claim 1, characterized in that: The dialect acquisition and preprocessing module includes a multi-channel speech acquisition unit, a dialect adaptation unit, and a signal preprocessing unit. The multi-channel voice acquisition unit consists of four omnidirectional microphones, which are respectively arranged on both sides of the headboard and the footboard of the smart bed; The dialect adaptation unit pre-stores pronunciation feature libraries of dialects from different regions, and each dialect category contains pronunciation samples from users of different age groups; The signal preprocessing unit processes dialect speech signals using wavelet threshold denoising algorithm, normalization processing, and short-time energy and zero-crossing rate joint detection algorithm.

3. The dialect-controlled intelligent bed system according to claim 2, characterized in that: The wavelet threshold noise reduction algorithm formula of the signal preprocessing unit is as follows: ； in This is the denoised dialect speech signal. It is the original dialect speech signal. These are the weighting coefficients of the wavelet basis functions. The wavelet decomposition level is denoted as . This is the index of the signal sampling points.

4. The dialect-controlled intelligent bed system according to claim 1, characterized in that: The dialect recognition and instruction mapping module includes a dialect model training unit, a feature extraction unit, a pattern matching unit, and an instruction mapping unit; The dialect model training unit uses a hybrid CNN and LSTM network structure to construct a multi-dialect recognition model; The feature extraction unit extracts the Mel frequency cepstral coefficients, linear prediction cepstral coefficients, and fundamental frequency of the dialect speech signal to form a three-dimensional feature vector; The pattern matching unit calculates the semantic matching probability using the Softmax function to determine the recognition validity; The instruction mapping unit pre-stores a mapping table between dialect semantics and control instructions, and maps the recognized valid dialect semantics into control instruction codes.

5. The dialect-controlled intelligent bed system according to claim 1, characterized in that: The user status perception module includes a bed detection unit, a posture recognition unit, and a pressure acquisition unit. The bed detection unit consists of eight pressure sensors, evenly distributed in the head, shoulder, waist, hip, and leg areas of the mattress. The posture recognition unit uses a 6-axis IMU inertial measurement unit, installed in the middle of the mattress. The pressure acquisition unit collects the contact pressure values ​​of each area through pressure sensors to construct a pressure distribution matrix.

6. The dialect-controlled intelligent bed system according to claim 1, characterized in that: The bed motion control module includes a motion analysis unit, a parameter optimization unit, and a drive control unit; The action parsing unit parses the action type, target parameters, and execution speed of the control command; The parameter optimization unit combines the user pressure distribution matrix and attitude angle data to optimize the target parameters; The drive control unit uses a PID control algorithm to drive the motor. The PID control formula is as follows: ； in, For motor drive control, This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients. This represents the deviation between the target parameter and the actual parameter. To control time, It is the integral variable.

7. The dialect-controlled intelligent bed system according to claim 1, characterized in that: The security protection and anomaly handling module includes an obstacle detection unit, a status monitoring unit, and an anomaly handling unit. The obstacle detection unit consists of two infrared distance sensors and one ultrasonic sensor, which are respectively located above the head of the bed, below the foot of the bed, and on the side of the middle of the bed. The status monitoring unit collects motor operating current, voltage, temperature, and bed attitude angle change rate. The anomaly handling unit adopts a hierarchical processing mechanism, which triggers parameter adjustment, emergency braking, and audible and visual alarms respectively.

8. The dialect-controlled intelligent bed system according to claim 7, characterized in that: The dynamic safe distance threshold calculation formula for the obstacle detection unit is as follows: ； in For dynamic safety distance threshold, The current speed of the bed. For system response time, This refers to the braking acceleration of the bed.

9. The dialect-controlled intelligent bed system according to claim 1, characterized in that: The human-computer interaction feedback module includes a voice feedback unit, a light feedback unit, and a tactile feedback unit; the voice feedback unit has a built-in dialect speech synthesis module, which supports converting feedback information into the dialect currently used by the user for broadcast; the light feedback unit consists of 3 LED indicator lights, which indicate different working states of the system through different on / off states; The tactile feedback unit has a vibration motor installed on the headboard armrest, which provides feedback through vibrations at different frequencies.

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