Intelligent sofa control method based on mobile terminal

Abstract

The invention relates to the technical field of intelligent sofa control, in particular to an intelligent sofa control method based on a mobile terminal. The method comprises the steps of collecting a historical state adjustment mode of a user, marking an adjustment instruction corresponding to the historical state adjustment mode, extracting a data bit corresponding to the adjustment instruction, and generating an adjustment data bit packet; and forming an adjustment control instruction according to the adjustment data bit packet. According to the method, different state feature point types are divided, adaptive response starting is carried out, sofa states are matched in advance according to user demands, instant demands of users are ensured, the use effect is improved, meanwhile, user historical state adjustment modes are collected, and the user experience is improved. The adjustment data bit packet of the adjustment control instruction is divided in advance according to the use habit of the user to serve as the predetermined control instruction after the state point is changed, the user state feature point mapping adjustment control instruction database is established, the adaptive sofa state change route is matched in advance according to the state change of the user, and the adaptation effect of the intelligent sofa is further improved.

Description

Technical Field

[0001] This invention relates to the field of smart sofa control technology, and more specifically, to a smart sofa control method based on a mobile terminal. Background Technology

[0002] Smart home systems represent an ideal living environment. These systems, installed within a residence, enhance home life by making it safer, more energy-efficient, smarter, more convenient, and more comfortable. Smart homes not only provide users with a safe, healthy, and comfortable living environment, but also allow them to remotely monitor their home's status and control home appliances.

[0003] As the industry develops, intelligent control functions are becoming more and more numerous, the objects being controlled are constantly expanding, and the requirements for control linkage scenarios are becoming higher. With the continuous improvement of technology, users' demands for furniture functions are gradually increasing, especially the demand for control of smart sofas.

[0004] Nowadays, the needs of smart sofas are no longer limited to daily sitting and reclining. They also include auxiliary needs such as massage, physiotherapy, and posture correction. They can also be customized according to the user's usage habits. Traditional control solutions mainly rely on users to adjust according to their own needs and behavioral habits. For complex usage needs, the adjustment steps are relatively complicated and inefficient. Moreover, for some auxiliary functions, they need to be activated in advance to meet the user's needs in actual use.

[0005] To address the aforementioned issues, there is an urgent need for a smart sofa control method based on mobile terminals. Summary of the Invention

[0006] The purpose of this invention is to provide a smart sofa control method based on a mobile terminal. This method categorizes different state feature point types and initiates adaptive responses, pre-matching the sofa state according to user needs to ensure immediate user requirements. Simultaneously, by collecting historical user state adjustment methods, it pre-classifies adjustment control command data packets based on user habits as predetermined control commands for subsequent state point changes. Furthermore, it establishes a user state feature point mapping adjustment control command database, updating the mapping adjustment weights in real time based on the adjustment frequency of each adjustment control command at specified state feature points. This pre-matches an appropriate sofa state change route based on user state changes, thereby solving the problems mentioned in the background art, namely: Traditional control schemes rely primarily on user adjustments to meet their needs, making it impossible to anticipate complex user requirements in advance.

[0007] To achieve the above objectives, a smart sofa control method based on a mobile terminal is provided, including collecting user status information, obtaining user status feature points, wherein the user status feature points include regular features and changing features, using regular features to identify user identity information, matching control commands in advance for regular features, collecting user changing features in real time, predicting and responding to control commands based on changing features, and using this as the basis for subsequent control command generation, transmission and response initiation, so as to pre-deploy the sofa status according to user needs.

[0008] Furthermore, to improve the sofa control response efficiency, this solution collects users' historical state adjustment methods, marks the corresponding adjustment commands for these methods, extracts the data bits corresponding to these commands, generates adjustment data bit packets, pre-calculates users' historical usage needs as the direction for subsequent adaptive adjustments, and assembles adjustment control commands based on the adjustment data bit packets. The solution adjusts the corresponding control commands by converting the corresponding adjustment data bits within the packets, establishes a user state feature point mapping adjustment control command database, updates the mapping adjustment weights based on the adjustment frequency of each control command at specified state feature points, updates the corresponding state adjustment needs under different state characteristics based on user habits, and promptly matches and adapts control commands. This pre-matching of appropriate sofa state change routes based on user state changes further improves the smart sofa's adaptability.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: This mobile terminal-based smart sofa control method classifies different state feature point types and initiates adaptive responses. It pre-matches the sofa state according to user needs, ensuring immediate user requirements and improving usability. Simultaneously, by collecting historical user state adjustment methods, it pre-divides adjustment control command data packets based on user habits, serving as predetermined control commands for subsequent state point changes. Furthermore, it establishes a user state feature point mapping adjustment control command database, updating the mapping adjustment weights in real-time based on the adjustment frequency of each control command at specified state feature points. This pre-matches appropriate sofa state change routes based on user state changes, further enhancing the smart sofa's adaptability. Attached Figure Description

[0010] Figure 1 This is a diagram illustrating the method steps of the present invention; Figure 2 This is a schematic diagram of a single character of the present invention; Figure 3 This is a schematic diagram of the overall instruction string structure of the present invention; Figure 4 This is a schematic diagram of the data packet division for the present invention. Detailed Implementation

[0011] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] Please see Figure 1 As shown, a smart sofa control method based on a mobile terminal is provided, including the following steps: S1. Collect user status information and obtain user status feature points, which include regular features and changing features. S2. Response to control commands for normal features, and predictive sequential response to control commands for changing features; S3. Perform character matching and combination on each control command to generate the overall control command string corresponding to the user status feature point; S4. Collect the user's historical status adjustment methods, mark the adjustment instructions corresponding to the historical status adjustment methods, extract the data bits corresponding to the adjustment instructions, and generate adjustment data bit packets; S5. Build adjustment control instructions based on the adjustment data packets; S6. Collect various adjustment and control commands, establish a database of user state feature points mapped to adjustment and control commands, and update the mapping adjustment weights according to the adjustment frequency of each adjustment and control command in the specified state feature points.

[0013] The details are as follows: First, in order to perform proactive response control, this solution collects user status information in advance and obtains user status feature points. That is, the user's status is collected through the mobile terminal. This includes fixed and unchanging routine features, such as the user's height and weight range in a short period of time. Of course, this weight range will be updated periodically. It also includes changing features, such as the user's real-time biorhythm, including real-time heart rate, respiratory rate and blood glucose concentration. These features are all used as the basis for subsequent sofa pre-adjustment.

[0014] When collecting status feature points, routine features, such as the user's height and weight, are collected periodically at regular intervals and entered into the user's mobile terminal (phone or other electronic device). Changing features are collected in real time. The specific collection method includes the following steps: First, based on user behavior habits, corresponding change feature monitoring devices are configured, such as smartwatches equipped with real-time monitoring tools, which can collect changes in the user's heart rate and respiratory rate in real time and establish interval upload time nodes. In this solution, the mobile terminal and the smart sofa communicate via Bluetooth to realize short-range data exchange between fixed devices, mobile devices and building personal area networks. When the user is a certain distance away from the smart sofa (for example, when they are about to arrive at their doorstep), the mobile terminal establishes a data transmission channel with the smart sofa. The mobile terminal uploads regular features and change features to the smart sofa. The smart sofa identifies the current user's identity through regular features and obtains the current user's status feature points.

[0015] After acquiring the state feature points, since the user's routine characteristics are within a certain range over a short period, control commands are responded to based on these routine characteristics. For example, the positions of auxiliary devices such as footrests, lumbar supports, and neck supports are adjusted according to the user's height and weight to adapt to these routine characteristics. Control commands adapted to routine characteristics are directly initiated. However, for changing characteristics, due to their varying numerical states—for example, after exercise, the user's heart rate and respiratory rate will change significantly—the routine settings are difficult to meet. Therefore, real-time prediction and adjustment are required. Thus, control command prediction and sequential response are performed for changing characteristics, as detailed below: First, the actual values ​​of various change characteristics of the current user are collected, and the mapping relationship between historical change characteristics and control commands is collected. The numerical range intervals of various change characteristics are divided, and the response probability of each control command within the current numerical range interval is obtained. Calculate the mapping score of each control command in different combinations of numerical variation characteristics. ,and ,in to These are the mapping scores of the current control command in different combinations of numerical variation characteristics. to The mapping score threshold is determined by defining the interval constants of the numerical range to which the mapped change feature values ​​belong. When the mapping score of the current control command Exceeding the mapping score threshold When it is true, it is marked as responding to the start control command; otherwise, when the mapping score of the current control command is not true, it is marked as not responding to the start control command. Not exceeding the mapping score threshold If the current control command is not initiated, then the current control command will not be activated. For example, a complete response activation cycle includes three changing characteristics, namely A, B, and C. The corresponding characteristic A includes three sets of numerical range intervals, namely... , as well as The interval constants for each data range are as follows: , as well as Feature B includes two sets of numerical ranges, namely... as well as The interval constants for each data range are as follows: as well as The C feature includes two sets of numerical ranges, namely... as well as The interval constants for each data range are as follows: as well as It is worth noting that the interval constants of each interval are related to the influence trend of the change characteristics on the control commands. That is, the higher the current change characteristic data, the easier it is to trigger the corresponding control command. Therefore, the higher the numerical range of the current control command in that change characteristic, and the larger the corresponding interval constant. The actual collected value of change characteristic A is... The collected change feature B value is The collected change feature C has the following value: ,and , , Then the corresponding control command mapping fraction at this time and the corresponding mapping score threshold The comparison is performed to determine whether the currently predicted control command should be initiated.

[0016] Furthermore, in this solution, the smart sofa control terminal and the mobile terminal transmit commands asynchronously. Therefore, different control commands consist of corresponding characters, such as... Figure 2 As shown, a complete character consists of a front-end header, a space, a start bit, data bits, a parity bit, and a stop bit. The start bit, data bits, parity bit, and stop bit form a character frame, and the corresponding data bits are the areas that carry information, such as... Figure 3 As shown, different characters are arranged in sequence to form control instructions. The length of the instruction and the number of characters involved will change the control content of the instruction. Therefore, after the control instruction is predicted, character matching and combination are needed based on the transmitted content of the current control instruction to generate the overall control instruction string corresponding to the user state feature points, such as... Figure 3As shown, instruction ① consists of a single character 0, instruction ② consists of characters 1-4, instruction ③ consists of characters 5-10, and instruction ④ consists of characters 11-12. Instructions ①, ②, ③, and ④ together constitute the overall control instruction string, which serves as the instruction to control the smart sofa. The instructions are sent sequentially, and the smart sofa responds by executing them. For example, instruction ① indicates turning on the smart sofa switch, instruction ② indicates adjusting the sofa backrest angle, instruction ③ indicates turning on the seat heating, and instruction ④ indicates adjusting the footrest position. When the overall control instruction string is transmitted to the smart sofa control terminal, the sofa completes the instructions in the following order: first, turning on the smart sofa switch; second, adjusting the sofa backrest angle; third, turning on the seat heating; and finally, adjusting the footrest position.

[0017] Furthermore, when users remain in the same posture or control state for an extended period, discomfort can easily arise. In such cases, users will need to adjust their posture, and the smart sofa's state must adapt accordingly. To improve adaptability, it is necessary to collect data on users' historical posture adjustment methods, mark the corresponding adjustment commands, extract the data bits corresponding to the adjustment commands, and generate an adjustment data bit packet. The specific generation method is as follows: First, obtain the overall control command string between two adjacent control cycles. The control cycle is the time interval from when the sofa responds to the first control command to when it fully executes the last sequential control command. Extract the associated control commands, i.e., control commands that control the same state. For example, two control commands controlling the backrest angle are associated control commands, linked by specific data bits within specific characters. Adjusting these data bits completes the overall control command conversion. Therefore, it's necessary to extract the specific conversion data bits from the associated control commands and mark them as adjustment data bits in the associated control commands. Figure 4 As shown, the current character includes four adjustment data bits: adjustment data bit I, adjustment data bit II, adjustment data bit III, and adjustment data bit IV. The order of each adjustment data bit is positively correlated with the corresponding adjustment frequency; that is, the higher the adjustment frequency, the higher the corresponding adjustment order, reflecting the user's usage habits. It is worth noting that the associated control command here collects adjustment data from the user's manual operation and the mobile terminal. That is, the user automatically adjusts a certain state of the sofa on the mobile terminal, such as the neck support position, or manually adjusts the footrest angle by adjusting the control buttons on the smart sofa. According to the user's adjustment results, the corresponding single adjustment data packet or combination of adjustment data packets is matched, and the adjustment order of each adjustment data bit in the corresponding adjustment data bit packet is updated in real time.

[0018] Furthermore, after completing the change of the adjustment data bits, it is necessary to compose adjustment control instructions based on the adjustment data bit packets. The adjustment control instructions include single adjustment control instructions and multiple adjustment control instructions. The single adjustment control instruction only needs to change the data bits in one character, while the multiple adjustment control instruction needs to change the data bits in multiple characters.

[0019] Finally, to further enhance the intelligence of the smart sofa and perform predictive control in advance, this solution collects various adjustment control commands, establishes a database of user state feature points mapped to adjustment control commands, and updates the mapping adjustment weights based on the adjustment frequency of each adjustment control command at a specified state feature point. In establishing this database, the first step is to obtain the range of changes in user state feature points. This is achieved by collecting user state data via a mobile terminal, capturing the changes in various user state feature points, and marking the range of these changes. For example, if a user's breathing rate slows down after using the sofa, it indicates that the user is about to fall asleep. The corresponding breathing rate change range is the characteristic of the sofa's state response control. When the breathing rate drops to a certain range, the sofa backrest angle is adjusted to allow the upper limbs to lie flat, or the footrest angle is adjusted to allow the lower limbs to lie flat. Adjusting the sofa backrest angle and footrest angle is related to the user's usage habits, i.e., positively correlated with the corresponding adjustment frequency. When the breathing rate value changes to the corresponding range, the adjustment frequency of each adjustment control command within the current range of feature point changes is compared, and the adjustment control command with the highest adjustment frequency is extracted and activated.

[0020] This invention categorizes different state feature point types and initiates adaptive responses, pre-matching sofa states according to user needs to ensure immediate user requirements and improve usability. Simultaneously, by collecting historical user state adjustment methods, it pre-divides adjustment control command data packets based on user habits, serving as predetermined control commands for subsequent state point changes. Furthermore, it establishes a user state feature point mapping adjustment control command database, updating the mapping adjustment weights in real-time based on the adjustment frequency of each control command at designated state feature points. This pre-matching of appropriate sofa state change routes according to user state changes further enhances the adaptability of the smart sofa.

[0021] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart sofa control method based on a mobile terminal, characterized in that: Includes the following steps: S1. Collect user status information and obtain user status feature points, which include regular features and changing features. S2. Response to control commands for normal features, and predictive sequential response to control commands for changing features; S3. Perform character matching and combination on each control command to generate the overall control command string corresponding to the user status feature point; S4. Collect the user's historical status adjustment methods, mark the adjustment instructions corresponding to the historical status adjustment methods, extract the data bits corresponding to the adjustment instructions, and generate adjustment data bit packets; S5. Build adjustment control instructions based on the adjustment data packets; S6. Collect various adjustment and control commands, establish a database of user state feature points mapped to adjustment and control commands, and update the mapping adjustment weights according to the adjustment frequency of each adjustment and control command in the specified state feature points.

2. The smart sofa control method based on a mobile terminal according to claim 1, characterized in that: The conventional features in S1 are collected periodically, while the changing features are collected in real time.

3. The intelligent sofa control method based on a mobile terminal according to claim 2, characterized in that: The method for obtaining user state feature points in S1 includes the following steps: S1.1 Configure corresponding change feature monitoring devices according to user behavior habits; S1.2 Establish interval upload time nodes; S1.

3. Identify the current user's identity through conventional features, and obtain the current user's status feature points by cooperating with monitoring equipment to provide corresponding change feature values ​​based on the upload time node.

4. The intelligent sofa control method based on a mobile terminal according to claim 1, characterized in that: The method for predicting the sequential response of control commands based on changing characteristics in S2 includes the following steps: S2.1 Collect the actual values ​​of various changing characteristics of the current user; S2.2 Collect the mapping relationship between historical change characteristics and control commands, divide the numerical range intervals of various change characteristics, and obtain the response probability of each control command within the current numerical range interval. ; S2.3 Calculate the mapping score of each control command in different combinations of numerical variation characteristics. ; S2.4, Determine the mapping score threshold and the mapping fraction of control commands. Compare: When the mapping score of the current control command Exceeding the mapping score threshold When this occurs, it is marked as a response to the start control command; When the mapping score of the current control command Not exceeding the mapping score threshold If this occurs, the current control command will not be initiated.

5. The intelligent sofa control method based on a mobile terminal according to claim 4, characterized in that: The mapping fraction is calculated in S2.

3. The algorithm is as follows: ； in to These are the mapping scores of the current control command in different combinations of numerical variation characteristics. to These are the interval constants for the range of values ​​to which the changing characteristic values ​​of the mapping belong.

6. The intelligent sofa control method based on a mobile terminal according to claim 1, characterized in that: The method for generating the overall control instruction string corresponding to the user state feature points in S3 includes the following steps: S3.1 A character frame consists of a start bit, data bits, parity bits, and a stop bit. S3.2 Obtain the content carried by the data bits in each character frame, and construct the control instructions for the control content according to the order of composition; S3.

3. Combine the predicted control commands sequentially to generate a complete control command string.

7. The intelligent sofa control method based on a mobile terminal according to claim 1, characterized in that: The method for generating the adjustment data bit packet in S4 includes the following steps: S4.1 Obtain the overall control instruction string between two adjacent control cycles; S4.2 Extract the associated control instructions, and extract the specific data bits that are converted in the associated control instructions, and mark them as the adjustment data bits in the associated control instructions; S4.3 Match the corresponding single adjustment data packet or combination of adjustment data packets, and update the adjustment order of each adjustment data bit in the corresponding adjustment data bit packet in real time.

8. The intelligent sofa control method based on a mobile terminal according to claim 7, characterized in that: The control cycle in S4.1 is the time period from when the sofa responds to the first control command to when it fully executes the last control command.

9. The intelligent sofa control method based on a mobile terminal according to claim 1, characterized in that: The adjustment control commands in S5 include single adjustment control commands and multiple adjustment control commands. The single-item adjustment control command requires only changing the data bits of one character in the current adjustment control command. Multiple adjustment control commands require changes to data bits in multiple characters.

10. The intelligent sofa control method based on a mobile terminal according to claim 1, characterized in that: The method for establishing a user state feature point mapping adjustment control command database in S6 includes the following steps: S6.1 Obtain the range of changes in user status feature points and mark the range of changes in feature points; S6.2 Compare the adjustment frequency of each adjustment control command within the range of various changes of the feature points; S6.

3. Bind the adjustment frequency of each adjustment control command in different ranges of the feature points, and establish a database of user state feature point mapping adjustment control commands.

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