A multi-functional sofa control method and system

Abstract

The present invention relates to a multifunctional sofa control method and system, belonging to the technical field of intelligent control. The method includes: parsing a control instruction to obtain specific operation parameters and a target state; calculating a dynamic adjustment factor according to historical usage data and current environmental information; determining a control signal according to the specific operation parameters, target state and dynamic adjustment factor; sending the control signal to a corresponding driving mechanism so that the driving mechanism adjusts each part of the sofa according to the specific control signal; monitoring the state of the sofa in real time through a sensor; and feeding back the monitored sofa state to the user in real time. The present invention can accurately obtain specific operation parameters and a target state, thereby realizing precise control of the adjustment of each part of the sofa and meeting the diverse needs of users.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and more specifically, to a control method and system for a multifunctional sofa. Background Art

[0002] Traditional sofa control systems are often relatively basic in design and function. They can generally receive and execute direct control instructions from users. However, such systems may not deeply analyze and optimize these instructions in detail. Therefore, in actual operation, when the sofa adjusts its form or configuration, there may sometimes be a lack of smoothness or inaccurate positioning, which will undoubtedly have a certain impact on the user experience.

[0003] More specifically, the deficiencies of existing sofa control systems at the data processing level can be elaborated from the following aspects:

[0004] For example, in terms of parsing control instructions, although traditional sofa control systems can handle basic instructions, they may not be good at extracting more detailed operation parameters and expected states. This to some extent restricts the accuracy and flexibility of sofa adjustment, making the adjustment results may not fully meet the actual needs of users.

[0005] For example, when a user hopes to adjust the sofa to a specific angle for reading, the traditional control system may only be able to adjust the sofa to a few preset angles and cannot accurately meet the specific angle desired by the user. Such a situation reflects the limitations of traditional sofa control systems in data processing, that is, they may not be able to fully analyze and execute more complex and refined control instructions. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a control method and system for a multifunctional sofa, which can accurately obtain specific operation parameters and target states, so as to achieve precise control of the adjustment of each part of the sofa and meet the diverse needs of users.

[0007] To solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0008] In a first aspect, a control method for a multifunctional sofa includes:

[0009] Parsing a control instruction to obtain specific operation parameters and a target state;

[0010] Calculating a dynamic adjustment factor according to historical usage data and current environmental information;

[0011] Determining a control signal according to the specific operation parameters, the target state, and the dynamic adjustment factor;

[0012] Send a control signal to the corresponding drive mechanism so that the drive mechanism adjusts each part of the sofa according to the specific control signal;

[0013] Monitor the status of the sofa in real time through sensors;

[0014] Feed back the monitored sofa status to the user in real time.

[0015] Furthermore, parse the control instruction to obtain specific operation parameters and target states, including:

[0016] Determine the number of clusters K according to the distribution of historical data and user habits;

[0017] Randomly determine K initial cluster centers, iteratively assign each data point to the corresponding cluster center, and update the position of the cluster center until the preset number of iterations is reached to obtain the clustering result;

[0018] According to the clustering result, analyze the characteristics of each cluster. When receiving a user input control instruction, extract the key information in the instruction, and judge the similar cluster corresponding to the current instruction according to the clustering result and the characteristics of each cluster;

[0019] Determine the specific operation parameters and target states of the current instruction according to the similar cluster.

[0020] Furthermore, the control instructions include adjusting the angle of the sofa backrest, adjusting the depth of the sofa seat cushion, starting or closing the built-in massage function, and adjusting the height and width of the sofa armrests.

[0021] Furthermore, sending a control signal to the corresponding drive mechanism includes:

[0022] Divide the control signal into multiple parts, each part corresponding to a drive mechanism of the sofa;

[0023] Set priorities for each drive mechanism according to the functional requirements of the sofa and the user's usage habits;

[0024] Sort the divided control signals according to the priorities, and send the control signals with corresponding priorities to each drive mechanism in turn.

[0025] Furthermore, the status of the sofa includes the angle of the sofa backrest, the depth of the seat cushion, the height and width of the armrests, and the on or off state of the massage function.

[0026] In a second aspect, a multifunctional sofa control system includes:

[0027] An acquisition module, configured to parse control instructions to obtain specific operation parameters and target states; calculate a dynamic adjustment factor according to historical usage data and current environmental information; and determine a control signal based on the specific operation parameters, target states, and dynamic adjustment factor.

[0028] A processing module, configured to send the control signal to a corresponding driving mechanism, so that the driving mechanism adjusts each part of the sofa according to the specific control signal; monitor the state of the sofa in real time through a sensor; and feedback the monitored state of the sofa to the user in real time.

[0029] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0030] Through in-depth parsing of control instructions, the present invention can accurately obtain specific operation parameters and target states, thereby realizing precise control of the adjustment of each part of the sofa and meeting the diverse needs of users.

[0031] The present invention combines historical usage data and current environmental information to calculate a dynamic adjustment factor, making the adjustment of the sofa more in line with the user's personal habits and the current environment, and providing a personalized comfortable experience. The present invention not only simply executes user instructions, but also through the introduction of a dynamic adjustment factor, enables the sofa to self-adjust according to historical data and environmental information, improving the intelligent level of the system.

[0032] By monitoring the state of the sofa in real time through a sensor and feedbacking the state to the user in real time, it not only ensures the safety of use, but also enables the user to always master the usage situation of the sofa, enhancing the user's usage experience and satisfaction. The present invention can quickly determine the control signal and send it to the driving mechanism by optimizing the data processing flow, reducing the delay in the adjustment process and improving the response speed and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. Some specific embodiments of the present application will be described in detail hereinafter with reference to the drawings in an exemplary rather than restrictive manner. The same reference numerals in the drawings denote the same or similar components or parts. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings:

[0034] Figure 1 is a schematic flow chart of the control method for the multifunctional sofa of the present invention.

[0035] Figure 2 is a schematic diagram of the control system for the multifunctional sofa of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] In order to enable those skilled in the art of this technology to better understand the solution of this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0037] The following embodiments of this application will take the multifunctional sofa control method and system as an example to elaborate on the solution of this application in detail, but these embodiments cannot limit the protection scope of this application.

[0038] As Figure 1 shown, the present invention provides a multifunctional sofa control method, and the method includes:

[0039] Step 11: Parse the control instruction to obtain specific operation parameters and target states. The control instruction includes adjusting the angle of the sofa backrest, adjusting the depth of the sofa seat cushion, starting or closing the built-in massage function, and adjusting the height and width of the sofa armrests;

[0040] Step 12: Calculate a dynamic adjustment factor based on historical usage data and current environmental information;

[0041] Step 13: Determine a control signal based on the specific operation parameters, target states, and dynamic adjustment factor;

[0042] Step 14: Send the control signal to the corresponding drive mechanism so that the drive mechanism adjusts each part of the sofa according to the specific control signal;

[0043] Step 15: Real-time monitor the state of the sofa through sensors. The state of the sofa includes the angle of the sofa backrest, the depth of the seat cushion, the height and width of the armrests, and the on / off state of the massage function;

[0044] Step 16: Real-time feedback the monitored state of the sofa to the user.

[0045] In an embodiment of the present invention, in step 11, this step can ensure that the system accurately understands the user's intention. By analyzing the control instructions in detail, the specific parameters and the desired state for the sofa adjustment can be precisely obtained. This provides the user with more detailed control options, meeting their different sitting postures and needs, thereby enhancing the usage experience. In step 12, by considering the user's historical usage habits and the current environment, the system can adjust the sofa state more intelligently, providing a personalized comfortable experience. This dynamic adjustment function enables the sofa to better adapt to different users and different scenarios, further enhancing the practicality and comfort of the sofa. In step 13, this step synthesizes the user instructions, historical data, and environmental information to generate accurate control signals. This ensures the accuracy and response speed of the sofa adjustment, avoiding misoperations or unnecessary adjustments, and improving the efficiency of the system and user satisfaction. In step 14, by sending accurate control signals to the drive mechanism, it can ensure that each part of the sofa is adjusted in the way expected by the user. This precise control not only improves the usage comfort of the sofa but also extends the service life of the sofa and its drive mechanism. In step 15, real-time monitoring of the sofa state can ensure that the system always knows the current configuration and functional state of the sofa. This helps to promptly detect and solve problems, improving the stability and security of the system. At the same time, this also provides the user with a higher level of information feedback, enabling them to more clearly understand the real-time state of the sofa. In step 16, by providing real-time feedback on the sofa state, the user can clearly understand the current configuration and functional state of the sofa, thereby better controlling and using the sofa. This feedback mechanism enhances the user's perception and control ability of the sofa, further improving the usage experience.

[0046] In a preferred embodiment of the present invention, for the above step 11, analyzing the control instructions to obtain specific operation parameters and target states may include:

[0047] Step 111, determining the number of clusters K according to the distribution of historical data and user habits;

[0048] Step 112, randomly determining K initial cluster centers, iteratively assigning each data point to the corresponding cluster center, and updating the position of the cluster center until a preset number of iterations is reached to obtain the clustering result;

[0049] Step 113, according to the clustering result, analyzing the characteristics of each cluster. When receiving the control instructions input by the user, extracting the key information in the instructions, and judging the similar cluster corresponding to the current instruction according to the clustering result and the characteristics of each cluster;

[0050] Step 114, determining the specific operation parameters and target states of the current instruction according to the similar cluster.

[0051] In an embodiment of the present invention, in step 111, the number of clusters is determined by deeply analyzing historical data and user habits, which makes the subsequent clustering analysis more accurate and in line with the actual situation. In step 112, the historical data is effectively grouped through a clustering algorithm, revealing the hidden patterns and structures in the data, which helps to identify the regular operation habits of users. In step 113, by matching the user instructions with the previously formed clusters, the system can quickly identify the user's intention and correspond it to the known behavior patterns. This fast matching mechanism significantly improves the response speed and accuracy of the system to the user's needs. In step 114, by referring to the characteristics of similar clusters, the system can accurately analyze the specific operation parameters and target states in the user instructions. This ensures that the sofa control system can be precisely adjusted according to the actual intention of the user, thus greatly improving the user experience and the intelligent level of the system.

[0052] In another preferred embodiment of the present invention, in step 111, the historical usage data of the user is collected and analyzed, including the adjustment records, usage duration, usage frequency, etc. of the sofa; data visualization tools (such as scatter plots, histograms, etc.) are used to observe the distribution of the data and find possible natural groupings; statistical methods, such as the Elbow Method or Silhouette Analysis, are applied to determine the optimal number of clusters K. These methods can find a balance point, that is, the point at which increasing the number of clusters no longer significantly improves the clustering quality; in combination with user habits, such as the number of sofa configurations commonly used by the user, the number of clusters K is fine-tuned.

[0053] In step 112, randomly select K points in the dataset as the initial cluster centers. For each point in the dataset, calculate its distance from each cluster center and assign it to the cluster center with the closest distance; recalculate the center point of each cluster, that is, calculate the average value of all data points in the cluster; repeat the steps until the positions of the cluster centers no longer change significantly or reach the preset number of iterations; output the final clustering result, and each cluster represents a user habit or sofa usage pattern.

[0054] Step 113: For each cluster, analyze its features, such as the average value and distribution range of the sofa backrest angle, seat cushion depth, etc.; when receiving a control instruction input by the user, extract the key information in the instruction, such as the desired sofa backrest angle, seat cushion depth, etc., calculate the similarity between this key information and the features of each cluster, for example, by calculating the Euclidean distance; find the cluster that is most similar to the user instruction. Step 114: According to the similar cluster found in Step 113, obtain the typical characteristic values of this cluster, such as the average values of the sofa backrest angle, seat cushion depth, etc., and use these typical characteristic values as the specific operation parameters and target states of the current instruction. For example, if the user instruction is similar to a certain cluster, then the average backrest angle and seat cushion depth of this cluster can be used as the target states for adjusting the sofa; output these operation parameters and target states to the subsequent sofa control system for precise sofa adjustment.

[0055] In a preferred embodiment of the present invention, the above Step 12, calculating the dynamic adjustment factor according to the historical usage data and the current environmental information, may include:

[0056] Step 121: According to the historical usage data and the current environmental information, through calculate the dynamic adjustment factor;

[0057] where D represents the dynamic adjustment factor; α, β, γ, and δ represent weight coefficients; N represents the number of the user's historical adjustment records; θ i represents the backrest angle in the i-th adjustment record; w i represents the weight of the i-th adjustment record; T represents the current indoor temperature; T ref represents the reference temperature; T max represents the maximum possible value of the indoor temperature, used to normalize the temperature between 0 and 1; H represents the current indoor humidity; H ref represents the reference humidity; H max represents the maximum possible value of the indoor humidity, used to normalize the humidity between 0 and 1; m and n represent exponential parameters.

[0058] In an embodiment of the present invention, by considering the user's historical usage data, the system can learn the user's preferences and usage habits. This means that the sofa can be automatically adjusted according to the user's personalized needs, providing a more comfortable experience. This method not only considers user habits but also combines environmental factors such as the current indoor temperature and humidity. This enables the sofa to be intelligently adjusted according to different environmental conditions and maintain the best comfort level. The calculation method of the dynamic adjustment factor takes into account the weighted combination of multiple factors, including historical adjustment records and the current environmental state, making the adjustment of the sofa more flexible and dynamic. This dynamic nature ensures that the sofa is always in the most suitable state for the user. Through formula calculation, the system can more precisely control the adjustment parameters of the sofa. This precise control not only improves user comfort but also extends the service life of the sofa. The implementation of this method transforms traditional sofas into intelligent ones, which can automatically respond to environmental changes and user habits, improving the intelligent level of the home.

[0059] In a preferred embodiment of the present invention, step 13, determining the control signal according to specific operation parameters, target states, and dynamic adjustment factors, may include:

[0060] Step 131, according to specific operation parameters, target states, and dynamic adjustment factors, calculate the control signal through u(t) = Calculate the control signal;

[0061] where u(t) is the control signal, representing the control amount to be applied to the actuator at time t; Kp represents the proportional coefficient; e(t) is the error, representing the difference between the target state and the current operation parameters at time t; Ki represents the integral coefficient; is the integral of the error, representing the accumulation of the error from the initial time to the current time; Kd represents the differential coefficient; is the differential of the error, representing the rate of change of the error over time; Kb represents the bias coefficient; bias(t) is the bias, representing the long-term bias amount existing at time t.

[0062] In the embodiments of the present invention, by comprehensively considering the errors in three aspects of proportional, integral, and differential, this method can calculate the control signal more precisely. This PID (Proportional-Integral-Differential) control method can significantly improve the stability and response speed of the system, ensuring that the operating parameters can reach the target state quickly and smoothly. Due to the introduction of a dynamic adjustment factor, the control signal can be dynamically adjusted according to the actual situation. This enables the system to adapt and respond more quickly when facing different environments and user requirements. Through the integral term, the system can consider the accumulation of historical errors, thereby more effectively eliminating the steady-state error. At the same time, the differential term helps to predict the change trend of future errors, further improving the accuracy and response speed of control. By introducing a deviation coefficient and a deviation term, this method can handle the possible long-term deviation in the system, ensuring that the control signal is more accurate and improving the robustness of the system.

[0063] In a preferred embodiment of the present invention, step 14, sending a control signal to the corresponding driving mechanism includes:

[0064] Step 141, splitting the control signal into multiple parts, each part corresponding to a driving mechanism of the sofa;

[0065] Step 142, setting priorities for each driving mechanism according to the functional requirements of the sofa and the user's usage habits;

[0066] Step 143, sorting the split control signals according to the priorities, and sequentially sending the control signals corresponding to the priorities to each driving mechanism.

[0067] In the embodiments of the present invention, by splitting the control signal into multiple parts, each part dedicated to controlling a driving mechanism of the sofa, this segmented control method can significantly improve the control accuracy. Each driving mechanism can receive a control instruction customized for it, thereby performing actions more precisely and meeting the personalized needs of users. Setting priorities for each driving mechanism according to the functional requirements of the sofa and the user's usage habits can ensure that important functions or adjustments that users care most about are executed first. This priority sorting not only improves the efficiency of system response but also makes the user experience smoother and more comfortable. The priority setting allows the system to be adjusted according to different user needs and habits, providing higher flexibility and customized services. For example, for users who often need to adjust the backrest angle, the system can set the driving mechanism for controlling the backrest angle to a high priority. By sorting the control signals according to the priorities, the system can control each driving mechanism in an orderly manner, avoiding conflicts and interferences that may occur when multiple mechanisms act simultaneously. This orderly control method also helps to extend the service life of the sofa and its driving mechanisms.

[0068] Step 141: Analyze the structure and content of the control signal to determine which parts need to be segmented. According to the design of the sofa drive mechanism, segment the control signal according to different functional areas. For example, backrest angle adjustment, seat depth adjustment, footrest adjustment, etc. Ensure that each segmented part of the control signal can be clearly mapped to a specific drive mechanism for accurate instruction sending in the subsequent steps.

[0069] Step 142: Analyze the various functions of the sofa and evaluate the importance of each function to the user experience. For example, the adjustment of the backrest angle may have a greater impact on the user's comfort, so a higher priority can be set. Collect and analyze the user's usage habit data to understand the most frequently adjusted functional areas when the user uses the sofa. Based on the function importance and user usage habits, set reasonable priorities for each drive mechanism. The drive mechanism with a higher priority will receive the control signal first in the subsequent steps.

[0070] Step 143: Sort the segmented control signals according to the priorities set in Step 142. The control signals with higher priorities will be ranked first. Traverse the sorted control signal list in sequence, and perform the following operations for each control signal:

[0071] Identify the drive mechanism corresponding to the control signal, and send the control signal to the corresponding drive mechanism through an appropriate communication protocol (such as Bluetooth, Wi-Fi, etc.). Wait for the response of the drive mechanism to confirm that it has correctly received the control signal and started to execute the corresponding action. After all control signals are sent, check the execution status of each drive mechanism to ensure that all instructions have been correctly executed. If necessary, retry the unexecuted instructions or report an error.

[0072] The specific implementation process of Step 15:

[0073] Install sensors at key parts of the sofa, such as the backrest, seat cushion, armrests, etc., to monitor the state of the sofa; set corresponding parameters for the sensors according to the states to be monitored, such as angle range, depth range, height and width ranges, etc.; for the monitoring of the massage function, the on or off state of the massage device can be detected through current or voltage sensors; continuously collect various state data of the sofa through the sensors, such as backrest angle, seat cushion depth, armrest height and width, etc., preprocess the collected data, including filtering, denoising and formatting, to ensure the accuracy and usability of the data, and transmit the processed data to the sofa control system or the central processing unit by wired or wireless means. In the control system or the processing unit, further process and store the data for subsequent analysis and feedback; by analyzing the sensor data, detect whether there are abnormal conditions in the sofa, such as excessive tilting, too deep or too shallow seat cushion, etc. If an abnormal condition is found, trigger the alarm mechanism in time to notify the user or the maintenance personnel for handling.

[0074] The specific implementation process of step 16:

[0075] Display the monitored sofa state data to the user in a graphical or digital way, such as through an APP, an LED display screen or voice broadcast, etc.; when the sofa state changes, immediately update the data displayed to the user to ensure that the user can obtain the latest state of the sofa in real time; conduct in-depth analysis of the historical data to discover the user's usage habits and preferences, so as to optimize the design and function of the sofa. According to the data analysis results, provide more personalized services and suggestions for the user to improve the user experience.

[0076] Such as Figure 2 As shown, a multifunctional sofa control system 20 includes:

[0077] An acquisition module 21, which is used to parse the control instruction to obtain specific operation parameters and target states; calculate a dynamic adjustment factor according to the historical usage data and the current environmental information; determine a control signal according to the specific operation parameters, target states and dynamic adjustment factor;

[0078] A processing module 22, which is used to send a control signal to the corresponding driving mechanism so that the driving mechanism adjusts each part of the sofa according to the specific control signal; monitor the state of the sofa in real time through the sensor; and feedback the monitored sofa state to the user in real time.

[0079] It should be noted that this system corresponds to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

[0080] An embodiment of the present invention further provides a computing device, including: a processor and a memory storing a computer program. When the computer program is run by the processor, it executes the method described above. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0081] An embodiment of the present invention further provides a computer-readable storage medium storing instructions. When the instructions are run on a computer, the computer is made to execute the method described above. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0082] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A multifunctional sofa control method, characterized in that: include: S11 parses the control instruction to obtain specific operation parameters and target states; including determining the number of clusters K according to the distribution of historical data and user habits; randomly determining K initial cluster centers, iteratively assigning each data point to the corresponding cluster center, and updating the position of the cluster center until a preset number of iterations is reached to obtain a clustering result; According to the clustering results, the characteristics of each cluster are analyzed. When a control instruction input by the user is received, the key information in the instruction is extracted. According to the clustering results and the characteristics of each cluster, the similar cluster corresponding to the current instruction is determined; according to the similar clusters, the specific operation parameters and target state of the current instruction are determined; S12 calculates a dynamic adjustment factor based on historical usage data and current environmental information; This includes: Based on historical usage data and current environment information, the dynamic adjustment factor is calculated using the following formula: Where D represents the dynamic adjustment factor; α, β, γ and δ represent weight coefficients; N represents the number of historical adjustment records of the user; θ i represents the backrest angle in the i-th adjustment record; w i represents the weight of the i-th adjustment record; T represents the current indoor temperature; T ref Indicates reference temperature; T max Indicates the maximum indoor temperature, which is used to standardize the temperature between 0 and 1; H indicates the current indoor humidity; H ref Indicates reference humidity; H max Indicates the maximum value of indoor humidity, used to standardize humidity to between 0 and 1; m and n represent index parameters; S13 determines the control signal according to the specific operating parameters, target state and dynamic adjustment factor; which includes: calculating the control signal according to the specific operating parameters, target state and dynamic adjustment factor by the following formula: Wherein, u(t) represents the control amount to be applied to the actuator at time t; Kp represents the proportional coefficient; e(t) is the difference between the target state and the current operating parameters at time t; Ki is the integral coefficient; is the error integral, which represents the accumulation of errors from the initial moment to the current moment; Kd represents the differential coefficient; represents the rate of change of error over time; Kb represents the bias coefficient; bias(t) is the bias, which represents the long-term bias at time t; S14 sends a control signal to the corresponding driving mechanism, so that the driving mechanism adjusts various parts of the sofa according to the specific control signal; S15 monitors the status of the sofa in real time through sensors; S16 will provide real-time feedback of the sofa status to the user.

2. The multifunctional sofa control method according to claim 1, characterized in that: Control commands include adjusting the sofa backrest angle, adjusting the sofa cushion depth, starting or turning off the built-in massage function, and adjusting the height and width of the sofa armrests.

3. The multifunctional sofa control method according to claim 1, characterized in that: Sending control signals to corresponding drive mechanisms, including: Split the control signal into multiple parts, each part corresponds to a driving mechanism of the sofa; Set priorities for each drive mechanism based on the functional requirements of the sofa and the user's usage habits; The divided control signals are prioritized, and control signals of corresponding priorities are sent to each driving mechanism in sequence according to the priority ranking.

4. The multifunctional sofa control method according to claim 1, characterized in that: The status of the sofa includes the sofa backrest angle, seat cushion depth, armrest height and width, and whether the massage function is on or off.

5. A multifunctional sofa control system, the system implements the method according to claim 1, characterized in that: include: The acquisition module is used to parse the control instructions to obtain specific operating parameters and target states; calculate the dynamic adjustment factor based on historical usage data and current environmental information; and determine the control signal based on the specific operating parameters, target states and dynamic adjustment factors; The processing module is used to send control signals to the corresponding driving mechanisms so that the driving mechanisms adjust various parts of the sofa according to the specific control signals; monitor the state of the sofa in real time through sensors; and feed back the monitored sofa state to the user in real time.

6. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 4 when executed by a processor.

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