Data-driven massage bed function self-adaptive adjustment method

Abstract

The application relates to the technical field of data processing, and provides a data-driven massage bed function self-adaptive adjustment method.The method comprises the following steps: extracting a first massage mode of a massage bed; acquiring first experience feedback records of a user in the mode, including first physiological feedback information and subjective feedback information; matching first physiological index data of massage head data in the first physiological feedback information; performing correlation analysis on the massage head data and the physiological data to obtain a correlation coefficient, and combining subjective feedback information calibration to obtain an adaptability; judging whether the adaptability is within a predetermined threshold; if yes, adding the massage head data to a self-adaptive list, and generating a self-adaptive massage mode.The application solves the technical problem that a massage bed is difficult to provide individualized massage experience according to individual differences of users, and realizes the technical effect that a massage mode is dynamically adjusted according to physiological feedback and subjective feedback information of a user to improve the comfort and satisfaction of the user.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a data-driven adaptive adjustment method for massage bed functions. Background Technology

[0002] With the improvement of people's living standards and the enhancement of health awareness, massage equipment, especially smart massage beds, is gradually gaining popularity. Traditional massage beds typically offer multiple massage modes, providing various techniques such as kneading, tapping, and rubbing to meet users' basic comfort needs. However, current massage beds mainly rely on preset fixed modes, lacking the ability to adapt to individual user differences, thus failing to provide an optimal personalized experience. For users with different physical conditions, ages, and health statuses, a single massage mode often cannot fully meet their actual needs, and may even cause discomfort or physical strain. Therefore, due to the lack of sufficient consideration for individual user differences and the difficulty in effectively switching and optimizing between different modes, existing massage beds have significant shortcomings in personalization and comfort, making it difficult to achieve an ideal user experience. Summary of the Invention

[0003] This application provides a data-driven adaptive adjustment method for massage bed functions, aiming to solve the technical problem that massage beds are unable to provide personalized massage experiences based on individual user differences.

[0004] In view of the above problems, this application provides a data-driven adaptive adjustment method for massage bed functions.

[0005] This application provides a data-driven adaptive adjustment method for massage bed functions. The method includes: extracting a first massage mode from multiple massage modes of the massage bed; obtaining a first experience feedback record of a user in the first massage mode, the first experience feedback record including first physiological feedback information and first subjective feedback information; matching first physiological data of a first physiological indicator under first massage head data in the first physiological feedback information, the first massage head data referring to preset data of the first massage head in the first massage mode; performing correlation analysis on the first massage head data and the first physiological data to obtain a first correlation coefficient, and calibrating the first correlation coefficient in combination with the first subjective feedback information to obtain a first correlation fitness; determining whether the first correlation fitness is at a predetermined fitness threshold; if so, adding the first massage head data to an adaptive list, and generating an adaptive massage mode for the user using the massage bed based on the adaptive list.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The aforementioned data-driven adaptive adjustment method for massage bed functions first selects one of the massage bed's various massage modes as the primary massage mode and records the user's experience feedback under this mode, including physiological and subjective feedback. Among the collected physiological feedback, the preset parameters of the massage head under this mode are matched with the user's physiological indicators, ensuring the relevance of the data correlation analysis. Subsequently, a correlation analysis is performed on the preset parameters of the massage head and the user's physiological data to calculate the correlation coefficient. This coefficient is then calibrated using the user's subjective feedback to generate a correlation fitness level, reflecting the suitability of the massage mode to the user's needs. This process not only makes the analysis more accurate but also better aligns with the user's individual needs. Next, the correlation fitness level is evaluated to determine if the current massage parameters are appropriate. If the fitness threshold is reached, the massage head parameters are recorded in the adaptive list. Finally, a personalized massage mode is generated based on the adaptive list. In this way, the massage bed can automatically adjust to the massage mode most suitable for the user's needs during future use, making the massage experience more personalized and comfortable.

[0007] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a data-driven adaptive adjustment method for massage bed functions in one embodiment. Figure 2 This is a flowchart illustrating the process of obtaining the first relevant fitness in a data-driven adaptive adjustment method for massage bed functions in one embodiment. Detailed Implementation

[0010] This application provides a data-driven adaptive adjustment method for massage bed functions, thereby solving the technical problem that massage beds struggle to provide personalized massage experiences based on individual user differences.

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0012] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0013] Examples, such as Figure 1 As shown, this application provides a data-driven adaptive adjustment method for massage bed functions, the method comprising: Extract the first massage mode from the various massage modes available on the massage bed.

[0014] In this embodiment, the system terminal selects one of the various preset massage modes of the massage bed as the first massage mode. Massage beds typically have multiple modes, each including different massage areas, techniques, intensities, and rhythms, designed to meet the diverse massage needs of users.

[0015] Obtain the user's first experience feedback record in the first massage mode, the first experience feedback record includes first physiological feedback information and first subjective feedback information.

[0016] In one embodiment, during the user's first massage mode experience, the system terminal records the user's feedback information, which is divided into physiological feedback information and subjective feedback information. Physiological feedback information includes objective physiological responses generated by the user's body during the massage, such as heart rate, muscle tension, and skin temperature. This data can be monitored and recorded in real time by sensors. Subjective feedback information is the user's personal evaluation of the massage experience, such as whether the massage was comfortable and whether the pressure was appropriate. This feedback is usually collected through user input or via a smart interface. The purpose of collecting these two types of feedback information is to gain a more comprehensive understanding of the user's experience in the current massage mode, thereby providing a basis for subsequent personalized adjustments.

[0017] The first physiological data of the first physiological indicator is matched with the first massage head data in the first physiological feedback information. The first massage head data refers to the preset data of the first massage head in the first massage mode.

[0018] In one embodiment, after obtaining the user's physiological feedback information, the system terminal searches for physiological indicator data related to the currently used massage head from the first physiological feedback information. Specifically, the massage head in the first massage mode has some preset data parameters, namely the first massage head data, including but not limited to pressure, position, angle, frequency, etc. The system terminal searches for corresponding physiological indicator data in the user's physiological feedback by using matching benchmark constraints set according to the first massage head data, such as muscle reactions, blood flow changes, local temperature, etc. at the contact point with the massage head. Subsequently, this user's physiological data is matched with the preset parameters of the massage head. This matching is to confirm the user's specific physiological response under the current massage head settings, thereby providing a basis for subsequent correlation analysis and mode optimization.

[0019] Furthermore, the first physiological data of the first physiological indicator under the first massage head data is matched in the first physiological feedback information, which previously included: Obtain the massage head set of the massage bed, the massage head set including multiple massage heads with body part identifiers; extract any massage head from the multiple massage heads with body part identifiers, the arbitrary massage head corresponding to any body part; construct an arbitrary set of physiological indicators for the arbitrary body part; establish an arbitrary mapping relationship between the arbitrary massage head and the arbitrary set of physiological indicators, and use the arbitrary mapping relationship as a matching benchmark constraint for the first physiological indicator under the first massage head data in the first physiological feedback information.

[0020] Preferably, the system terminal acquires a set of all massage heads on the massage bed. Each massage head has an identifier corresponding to a body part, such as shoulder, waist, or leg, so that the system terminal understands which body area each massage head corresponds to. Then, from these massage heads labeled with body parts, one massage head is randomly selected for analysis; this massage head corresponds to a specific body part, such as shoulder or back. The purpose of this step is to focus on a specific massage head to more accurately monitor the physiological responses of that specific area. Next, a set of physiological indicators is constructed for this body part, consisting of multiple physiological parameters such as muscle tension, blood flow, and skin temperature. This set encompasses possible physiological response indicators for that area, laying the foundation for subsequent analysis. Then, the system terminal establishes a mapping relationship between the massage head and the set of physiological indicators. This mapping relationship allows the system terminal to find user physiological response data (e.g., pressure, position) that matches the massage head data in the initial physiological feedback information. These mapping relationships become the benchmark constraints for subsequent matching of physiological feedback, ensuring that accurate physiological data related to massage head operation can be found during analysis. The purpose of this process is to establish a framework for matching physiological indicators for different massage heads and body parts, thereby providing a scientific data basis for subsequent personalized adjustments.

[0021] A first correlation coefficient is obtained by performing a correlation analysis between the first massage head data and the first physiological data, and the first correlation coefficient is calibrated by combining the first subjective feedback information to obtain the first correlation fitness.

[0022] In one embodiment, the system terminal formats the first massage head data and the first physiological data, including time alignment and noise filtering. This ensures that the data stream from the massage head and the user's physiological data stream have the same time reference and eliminates interference factors. Then, the first massage head data and the first physiological data are standardized using a range standardization method, giving the data from different dimensions the same dimensions to facilitate correlation analysis. Subsequently, based on the established mapping relationship between the set of physiological indicators and the massage head, the first massage head data is associated with the corresponding first physiological data. The associated first massage head data is then used as the independent variable, and the first physiological data as the dependent variable, and input into the correlation calculation formula for correlation calculation. The specific correlation calculation formula is as follows: Where r is the first correlation coefficient, with a value range of [−1, 1]. When r > 0, it indicates a positive correlation between the first massage head data and the first physiological data, meaning the first massage parameter has a positive effect on physiological feedback. When r < 0, it indicates a negative correlation between the two, meaning the first massage parameter may have an adverse effect on physiological feedback. When r = 0, it indicates no correlation between the two. This is the i-th parameter of the first massage parameter. is the i-th parameter of the first physiological data. and These are the mean values ​​of the first massage head data and the first physiological data, respectively. Using this correlation calculation formula, the system terminal calculates the first correlation coefficient between the first massage head data and the first physiological data. This first correlation coefficient reflects the degree of association between the first massage head parameters and the user's physiological response, i.e., whether the massage head settings can effectively induce the expected physiological effect. After obtaining the first correlation coefficient, the system terminal calibrates the correlation coefficient in conjunction with the user's subjective feedback. The purpose of calibration is to incorporate the user's subjective feelings into the overall evaluation, because even if the physiological response is good, if the user's subjective feeling is poor, the massage mode needs to be adjusted. Through correlation analysis and subjective feedback calibration, a correlation fitness is finally obtained. This fitness integrates the user's physiological and subjective responses, enabling a more comprehensive evaluation of whether the current massage head parameters are suitable for the user's needs. The entire process ensures the scientific and personalized nature of the massage mode adjustment, allowing the massage bed to provide effective massage effects based on objective data and optimize the experience based on the user's subjective feelings.

[0023] Furthermore, such as Figure 2As shown, a first correlation coefficient is obtained by performing correlation analysis on the first massage head data and the first physiological data, and the first correlation coefficient is calibrated by combining the first subjective feedback information to obtain the first correlation fitness, including: Match the first body part corresponding to the first massage head; filter the first subjective comfort data corresponding to the first body part from the first subjective feedback information; calibrate the first correlation coefficient based on the first subjective comfort data to obtain the first correlation fitness.

[0024] Optionally, based on the body part identification configuration of the first massage head on the massage bed, the system terminal determines the specific location of the first massage head, i.e., the corresponding first body part. For example, the first massage head may be located on the user's shoulder, waist, or leg. This matching is accomplished through the massage bed's hardware configuration or sensor information, ensuring a one-to-one correspondence between the physical location of the first massage head and the body part identification. When the physical location of the first massage head and the body part identification are the same, the system terminal extracts data related to that body part from the user's first subjective feedback information, such as the user's comfort rating for the shoulder in the feedback interface. The system terminal filters out the first subjective comfort data that is only related to the first body part (such as the shoulder), ignoring information unrelated to other parts. Subsequently, the filtered first subjective comfort data of the first body part is combined with the calculated first correlation coefficient for calibration, i.e., the first correlation coefficient is directly multiplied by the first subjective comfort data. After the first correlation coefficient calibration is completed, the system terminal outputs the calibrated first correlation coefficient as the first correlation fitness. The first correlation fitness integrates the user's physiological data and subjective feedback information, and is a comprehensive assessment of the adaptability of the massage mode. This result is used to determine whether the current massage parameters meet the user's needs and serves as the basis for subsequent optimization and adjustment.

[0025] Determine whether the first relevant fitness is at a predetermined fitness threshold; if so, add the first massage head data to the adaptive list, and generate an adaptive massage mode for the user to use the massage bed based on the adaptive list.

[0026] In one embodiment, the system terminal compares the calculated first relevance fitness with a predetermined fitness threshold, which is used to assess whether the current massage mode meets the user's needs, such as 0.6. If the first relevance fitness is less than the predetermined fitness threshold, it means that the current massage head parameters are insufficient to meet the user's needs, and the massage mode needs to be switched. Conversely, it means that the current massage head data and user feedback have a high degree of matching and are suitable for the current user. At this time, the system terminal records the parameters of the first massage head (such as intensity, frequency, position, etc.) into the adaptive list. The adaptive list is a dynamically updated data structure that stores the massage head parameters that provide a good user experience. The newly added data includes massage head parameters and associated fitness information, which are used to generate personalized massage modes later. The system terminal filters and combines all the massage head data in the adaptive list, extracts the parameters with the highest fitness, and generates an adaptive massage mode for the user. For example, the shoulders may use parameters with higher intensity, while the waist may choose a mode with a faster frequency. In this way, it can be ensured that the massage bed can dynamically adjust according to the user's physiological and subjective feedback, improving the user's overall massage experience.

[0027] Furthermore, the first massage head data is added to the adaptive list, which previously included: The user information of the user is obtained; the user information is traversed and analyzed according to the read predetermined user indicators to obtain user indicator data; the user indicator data after weighted normalization is used to obtain the user feedback coefficient; the first massage head data is adjusted according to the user feedback coefficient; wherein, the predetermined user indicators include at least age, gender, weight, height, occupation, and health status.

[0028] Preferably, the system terminal collects user information through preset interaction methods or sensor devices. This includes, but is not limited to, basic information and health-related information. Data can be obtained through user registration input, connection to health devices (such as wearable devices), or uploading of medical reports. Subsequently, predetermined user indicators are read, including age, gender, weight, height, occupation, and health status. The system terminal analyzes each indicator in the user information individually, extracting data that conforms to the predetermined user indicators to form user indicator data. Then, the data for each indicator is normalized to a standard range (e.g., 0 to 1). For numerical variables such as age and weight, normalization can be performed using range standardization. For example, for age, the ratio of the difference between the current age and the minimum age to the difference between the maximum age and the minimum age can be calculated. For categorical variables such as occupation and gender, normalization can be performed by combining Label Encoding and range standardization. For example, for occupation, occupation is directly mapped to an integer value, such as 0 for sedentary office workers, 1 for standing occupations, and 2 for manual laborers. Then, range standardization is used to normalize the categorical values. Then, weights are assigned to different indicators, based on professional experience in fields such as medicine and massage therapy. A weighted calculation is then performed using these weights and normalized values ​​to obtain the user feedback coefficient. After obtaining the user feedback coefficient, it is used to adjust the parameters of the first massage head (such as intensity, frequency, and position). The adjustment method involves directly multiplying the parameters of the first massage head by the user feedback coefficient. By acquiring user information, analyzing predetermined indicators, calculating feedback coefficients, and adjusting massage head parameters, the system terminal achieves optimized adjustments based on the user's personalized needs. This method can provide a more scientific and precise massage mode based on the user's age, health condition, and other personalized information, improving user comfort and satisfaction.

[0029] Further, determining whether the first relevant fitness is at a predetermined fitness threshold, followed by: If not, extract the second massage mode from the multiple massage modes; analyze the user's second experience feedback record in the second massage mode to obtain the second correlation fitness of the second massage head data, wherein the second massage head data refers to the preset data of the first massage head in the second massage mode; determine whether the second correlation fitness is at the predetermined fitness threshold; if it is, add the second massage head data to the adaptive list.

[0030] Preferably, if the first relevant fitness is not at a threshold value, i.e., the first relevant fitness is less than a predetermined fitness threshold, the system terminal will consider that the current massage mode cannot effectively meet the user's needs and needs to switch to another massage mode. In this case, the system terminal extracts a massage mode different from the first massage mode from among multiple massage modes as the second massage mode. After determining the second massage mode, the system terminal performs the same process as described above, including obtaining the second experience feedback record under the second massage mode, performing second physiological data matching on the second physiological feedback information in the second experience feedback record, and calculating the second relevant fitness of the second massage head data based on the second subjective feedback information and second physiological data in the second experience feedback record. Here, the second massage head data refers to the preset mode parameters of the first massage head under the second massage mode, such as intensity, frequency, and position. Subsequently, the second relevant fitness is compared with the predetermined fitness threshold. If it is still not within the threshold range, the system terminal further switches to other massage modes. Conversely, if it is within the threshold range, it means that the second massage mode is suitable for the user's needs. In this case, the data of the second massage head (such as intensity, frequency, and position) is recorded in the adaptive list as the basis for generating an adaptive massage mode.

[0031] Furthermore, this application provides an adaptive massage mode for the user to use the massage bed based on the adaptive list, and then further includes: The process involves: matching a target massage head set corresponding to the target body part within the massage head set; matching target adaptive data of the target massage head set within the adaptive massage mode; obtaining an adaptive massage log, which refers to the user's massage record information under the adaptive massage mode; extracting target subjective comfort data of the target body part from the adaptive massage log; constructing a target training dataset based on the target adaptive data and the target subjective comfort data; performing machine learning validation on the target training dataset to obtain a target comfort prediction model; and optimizing the massage for the target body part based on the target comfort prediction model to obtain the target optimal massage scheme for the target body part.

[0032] Optionally, the system terminal selects massage heads from the massage head set that are applied to the target body part, forming a target massage head set. Then, it extracts parameters corresponding to the target massage head set from the adaptive massage mode as target adaptive data. For example, the data for the target massage head set (shoulder) includes intensity (20N), frequency (5Hz), and location (left shoulder area). This data will be used for subsequent training and optimization. It also selects adaptive massage logs from the massage logs, which are detailed data recorded for each massage when the user uses the adaptive massage mode. The system terminal extracts subjective comfort data related to the target body part from the adaptive massage logs as target subjective comfort data, which will be used as label values ​​(target variables) for subsequent model training. Subsequently, the target adaptive data is used as input features, and the target subjective comfort data is used as target variables to construct a target training dataset, which is then used for machine learning validation. Specifically, the system terminal trains the target training dataset using a multilayer perceptron (MLP) neural network. The target adaptive data, as input features, enters the neural network through the input layer and propagates layer by layer to the hidden and output layers. The hidden layer employs the ReLU activation function to enhance nonlinear mapping capabilities, while the output layer generates predicted comfort data as a single node. After each forward propagation, the difference between the predicted value and the target subjective comfort data is calculated, and the mean squared error (MSE) is used as the loss function to quantify this difference. After calculating the loss, the network weights and biases are adjusted using the backpropagation algorithm, and the parameters are updated using the Adam optimizer to minimize the loss. During training, the system terminal performs multiple iterations primarily using the training set data, while simultaneously using the validation set data to monitor the model's generalization performance. By recording the validation loss in each round, it ensures that the model does not exhibit overfitting. After training is complete, the round with the lowest validation loss is selected as the final target comfort prediction model. After model training is complete, the system terminal randomly generates several combinations of massage parameters and inputs them into the prediction model, calculating the predicted comfort data for these combinations. The parameter combination with the highest predicted comfort data is selected as the target optimal massage scheme for the target body part. Finally, this target optimal massage scheme is updated to an adaptive massage mode, ensuring that users receive a more accurate and comfortable massage experience in subsequent use.

[0033] Furthermore, this application optimizes the massage of the target body part based on the target comfort prediction model to obtain the target optimal massage scheme for the target body part, and then further includes: First experimental data of the target massage head set is randomly generated; the first experimental data is used as input information of the target comfort prediction model to obtain the model output result, wherein the model output result includes first predicted comfort data; when the first predicted comfort data is greater than the target subjective comfort data, the first experimental data is used to replace the target adaptive data as the target optimal massage scheme; the target optimal massage scheme is updated to the adaptive massage mode.

[0034] Optionally, the system terminal randomly generates several sets of experimental data as the first set of experimental data based on the parameter range of the target massage head set. Each set of experimental data includes parameter values ​​such as the intensity, frequency, and position of the target massage head. The generation of these experimental data is based on a predetermined parameter range and distribution. For example, the intensity range is 10N to 30N, the frequency range is 4Hz to 8Hz, and the position is selectable areas such as the shoulder and waist. The system terminal generates experimental data using a uniform or normal distribution to ensure coverage of possible parameter combinations. The generated first set of experimental data is input into the target comfort prediction model for evaluation. The model outputs the predicted comfort data corresponding to each set of experimental data as the first predicted comfort data based on the relationship between massage parameters and comfort scores learned during training. The system terminal compares the first predicted comfort data with the target subjective comfort data provided by the user in the adaptive massage log. When a set of first predicted comfort data is higher than the target subjective comfort data, the system terminal determines that this set of experimental data is more suitable for the user's needs than the current target adaptive data. For example, if the target subjective comfort data is 85%, and the predicted comfort data of a set of experimental data is 92%, then this set of experimental data will be selected to replace the current target adaptive data. The first selected trial data is considered the optimal massage plan for the target body part. The system terminal updates this optimal plan to the adaptive massage mode and records the relevant parameters for the user to use in subsequent massages. The updated adaptive massage mode can more accurately meet the user's personalized needs, thereby improving the overall massage experience.

[0035] Furthermore, this application provides an iterative prediction process that continues until a predetermined number of iterations is reached, outputting the target optimal massage scheme.

[0036] Optionally, after generating the first set of experimental data for the target massage head set and evaluating it through the target comfort prediction model, the system terminal will initiate an iterative optimization process. In each iteration, the range or parameter distribution of the experimental data will be adjusted based on the prediction results of the previous round, generating new experimental data. The newly generated experimental data will be input into the target comfort prediction model again to calculate the corresponding predicted comfort data. In each iteration, the system terminal will compare the predicted comfort data of the new experimental data with the currently recorded highest comfort data. If the predicted comfort score of the new experimental data is higher, it will be replaced with the current target optimal massage scheme, and the corresponding intensity, frequency, and position parameters will be recorded. At the same time, the strategy for generating experimental data will be dynamically adjusted to make subsequent experimental data more concentrated in the high-scoring parameter range, thereby accelerating the optimization speed. This process will continue until a preset iteration threshold is reached. For example, if the system terminal sets the iteration count to 100 rounds, after reaching 100 rounds, regardless of whether the comfort score continues to improve, the massage parameters corresponding to the currently recorded highest score will be output as the final target optimal massage scheme. The final optimal massage plan will be updated into the adaptive massage mode to ensure that users can enjoy a more precise and optimized massage experience in subsequent use. Through iterative optimization, the parameter space can be fully explored to improve the reliability and personalization of the optimal plan.

[0037] In summary, the embodiments of this application have at least the following technical effects: This application embodiment extracts an initial massage mode from multiple massage modes of a massage bed and obtains the user's physiological and subjective feedback information. It establishes a matching relationship between massage head data and physiological indicators, performs correlation analysis on the two, and combines subjective feedback to calibrate and generate relevant fitness. When the fitness meets a predetermined threshold, the massage head data is added to an adaptive list, and an adaptive massage mode for the user is generated based on this list; if it does not meet the threshold, it switches to another massage mode and re-analyzes the fitness. Furthermore, by constructing a mapping relationship between massage heads and physiological indicators, data related to the target body part is filtered, and a target comfort prediction model is generated using machine learning methods. The optimal massage scheme for the target body part is then achieved through iterative optimization. Finally, this optimal scheme is updated to the adaptive massage mode, providing the user with a highly personalized massage experience. These technical effects collectively solve the technical problem that massage beds struggle to provide personalized massage experiences based on individual user differences, achieving the technical effect of dynamically adjusting the massage mode based on the user's physiological and subjective feedback information to improve user comfort and satisfaction.

[0038] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0039] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0040] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A data-driven adaptive adjustment method for massage bed functions, characterized in that, include: Extract the first massage mode from the various massage modes of the massage bed; Obtain the user's first experience feedback record in the first massage mode, the first experience feedback record including first physiological feedback information and first subjective feedback information; The first physiological data of the first physiological indicator under the first massage head data is matched in the first physiological feedback information. The first massage head data refers to the preset data of the first massage head under the first massage mode. A first correlation coefficient is obtained by performing a correlation analysis between the first massage head data and the first physiological data, and the first correlation coefficient is calibrated by combining the first subjective feedback information to obtain the first correlation fitness. Determine whether the first relevant fitness is at a predetermined fitness threshold; If so, add the first massage head data to the adaptive list, and generate an adaptive massage mode for the user using the massage bed based on the adaptive list; The first physiological data of the first physiological indicator matched with the first massage head data in the first physiological feedback information includes: Obtain the massage head set of the massage bed, the massage head set including multiple massage heads with body part markings; Extract any one of the multiple massage heads labeled with body parts, wherein the arbitrary massage head corresponds to any body part; Construct a set of arbitrary physiological indicators for any body part; Establish an arbitrary mapping relationship between the arbitrary massage head and the arbitrary set of physiological indicators, and use the arbitrary mapping relationship as a matching benchmark constraint for the first physiological indicator under the first massage head data in the first physiological feedback information. A first correlation coefficient is obtained by performing correlation analysis between the first massage head data and the first physiological data, and the first correlation coefficient is calibrated by combining the first subjective feedback information to obtain a first correlation fitness, including: Match the first body part corresponding to the first massage head; Filter the first subjective comfort data corresponding to the first body part from the first subjective feedback information; The first correlation coefficient is calibrated based on the first subjective comfort data to obtain the first correlation fitness. Add the first massage head data to the adaptive list, which previously included: Obtain the user information of the user; The user information is traversed and analyzed based on the read predetermined user metrics to obtain user metric data; The user feedback coefficient is obtained by weighted normalization of the user indicator data. The data of the first massage head is adjusted based on the user feedback coefficient; The pre-booked user indicators include at least age, gender, weight, height, occupation, and health status. The process includes determining whether the first relevant fitness is at a predetermined fitness threshold, followed by: If not, extract the second massage mode from the multiple massage modes; Analyze the user's second experience feedback record in the second massage mode to obtain the second correlation fitness of the second massage head data, wherein the second massage head data refers to the preset data of the first massage head in the second massage mode; Determine whether the second relevant fitness is within the predetermined fitness threshold; If so, add the second massage head data to the adaptive list.

2. The data-driven adaptive adjustment method for massage bed functions according to claim 1, characterized in that, The process further includes generating an adaptive massage mode for the user using the massage bed based on the adaptive list, followed by: Match the target massage head set corresponding to the target body part in the set of massage heads; Match the target adaptive data of the target massage head set in the adaptive massage mode; Obtain the adaptive massage log, which refers to the massage record information of the user in the adaptive massage mode; Extract the target subjective comfort data of the target body part from the adaptive massage log; A target training dataset is constructed based on the target adaptive data and the target subjective comfort data; Machine learning tests are performed on the target training dataset to obtain a target comfort prediction model; The target body part is massaged according to the target comfort prediction model to obtain the target optimal massage scheme for the target body part.

3. The data-driven adaptive adjustment method for massage bed functions according to claim 2, characterized in that, Based on the target comfort prediction model, the massage of the target body part is optimized to obtain the target optimal massage scheme for the target body part, and then the following steps are included: First experimental data for the target massage head set were randomly generated; The first experimental data is used as input information for the target comfort prediction model to obtain the model output results, wherein the model output results include the first predicted comfort data; When the first predicted comfort data is greater than the target subjective comfort data, the first experimental data is used to replace the target adaptive data as the target optimal massage scheme; Update the target optimal massage scheme to the adaptive massage mode.

4. The data-driven adaptive adjustment method for massage bed functions according to claim 3, characterized in that, The prediction is iterated until a predetermined number of iterations is reached, and then the target optimal massage plan is output.

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