Multi-dimensional somatosensory feedback graphene massager control method and system and medium

Abstract

The invention discloses a multi-dimensional somatosensory feedback graphene massager control method and system and a medium, and the method comprises the steps: obtaining the multi-dimensional features of the occupational information, work and rest information, diet information, massage position information and personal information of a current user, and combining the use data of other users in a database with the first user information of the current user, thereby achieving the multi-dimensional somatosensory feedback of the current user. And a personalized initial massage mode is generated, so that the intelligent level of the equipment is improved. In the massage process, physiological feedback information, manual adjustment information and feedback evaluation information of the user are collected, massage parameters are dynamically adjusted, massage experience better meeting personal requirements is provided for the current user, second reference massage information is dynamically optimized, and the intelligent sensing ability of the equipment is enhanced. The feedback information and the use mode of the current user in each massage process are recorded in the personal use database, data support is provided for subsequent massage mode optimization, and the overall use experience of the user is improved.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to a control method, system and medium for a graphene massager with multi-dimensional somatosensory feedback. Background Art

[0002] Graphene has high conductivity, high strength and high transparency. Taking advantage of this, massagers made of graphene have efficient thermal conductivity and can quickly transfer heat to the parts that need massage. Graphene also has good flexibility, which allows massagers made of graphene to better fit the skin, thereby improving the massage feel. The thermal conductivity and electrical conductivity of graphene can better control the temperature according to the control system, thereby improving the massage effect of the massager.

[0003] There are many existing graphene massage products, but most of them use built-in massage modes to massage users, and cannot achieve deeper dynamic adjustment, that is, fine-tuning of massage parameters and memory storage based on personal usage habits or usage experience, and cannot provide better customized massage functions. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a graphene massager control method, system and medium with multi-dimensional somatosensory feedback.

[0005] In order to achieve the above technical objectives, the technical solution adopted by the present invention is: In a first aspect, the present invention provides a control method for a graphene massager with multi-dimensional somatosensory feedback, comprising: Acquire first user information, where the first user information includes information features of different information categories; Generate first initial feature information according to the first user information, and match second feature information in the database using a cosine similarity function, where the second feature information is generated when other users use a graphene massager of the same massage device category; Acquire massage parameter information associated with the massage mode of the second characteristic information that matches successfully, and generate first reference massage information; Taking a weighted average of massage parameters of the same massage point category in a plurality of first reference massage information to generate second reference massage information; Obtaining an initial massage mode of the current user according to the second reference massage information and performing massage on the current user; Collecting user feedback information during the massage process, the feedback information includes physiological feedback information, manual adjustment information and feedback evaluation information; generating first usage characteristic information according to the feedback information, and adjusting the second reference massage information according to the first usage characteristic information to obtain third reference massage information; Generate a massage mode for use according to the third reference massage information, and replace the current initial massage mode in the next batch of massages; A personal use database is constructed, and feedback information of each batch of massage is stored in the personal use database in chronological order.

[0006] In some embodiments, generating the first initial feature information according to the first user information includes: The information features represented by non-numerical values ​​in the first user information are uniquely encoded, and the first user information is represented by formula (1), which is as follows: ; In formula (1), is the vector representation of the first user information, The first user information The numerical representation of the information characteristics of each information category, ; The first user information represented by the vector is normalized to obtain the first initial feature information, which is expressed by formula (2) to formula (4). Formula (2) is as follows: ; Formula (3) is as follows: ; Formula (4) is as follows: ; In formula (2) to formula (4), is the first initial feature information, for The average value of For the The eigenvalues ​​of the information features, For the The average value of the information features, for The standard deviation vector of For the The standard deviation vector of the information features, ; The second characteristic information is expressed by formula (5), which is as follows: ; In formula (5), is the second characteristic information, The second user information Numerical representation of information characteristics of each information category; Using a cosine similarity function to match second feature information similar to the first initial feature information in a database, obtaining massage parameter information associated with massage modes corresponding to a plurality of successfully matched second feature information, and arranging and generating first reference massage information according to the massage modes includes: The cosine similarity between each second feature information and the first initial feature information is calculated and expressed by formula (6). Formula (6) is as follows: ; In formula (6), is the cosine similarity function, for The Euclidean norm of , for The Euclidean norm of , is the cosine similarity value; Determine one by one whether the cosine similarity value corresponding to the second feature information is within a range of a preset similarity threshold; If yes, the second feature information is recorded as the first reference feature information, and the massage parameter information associated with the massage mode corresponding to the first reference feature information is obtained, and the massage parameter information is mapped and stored with the first reference feature information to obtain the first reference massage information; If not, it means that the current second feature information is not successfully matched with the first initial feature information.

[0007] In some embodiments, performing weighted averaging on massage parameters of the same massage point category in a plurality of first reference massage information, and generating second reference massage information according to the plurality of weighted averaged massage parameters comprises: The first reference massage information is expressed by formula (7), and the multiple massage parameters in the first reference massage information are recorded as first massage parameters. Formula (7) is as follows: ; In formula (7), For the The first reference massage information, For the The first reference massage information The first massage parameter of each massage point category; The first massage parameters of the same massage point category in the plurality of first reference massage information are weighted averaged to obtain a weighted average massage parameter, which is recorded as a second massage parameter and is expressed by formula (8). Formula (8) is as follows: ; In formula (8), For the The second massage parameter after weighted average, , For the The weight of the first reference massage information, For the The first reference massage information The first massage parameter of each massage point category; The second massage parameters are sorted to obtain the second reference massage information, which is expressed by formula (9). Formula (9) is as follows: ; In formula (9), For the second reference massage information.

[0008] In some embodiments, the first usage feature information includes a first dimension feature, a second dimension feature, and a third dimension feature, the first dimension feature is configured to be generated through physiological feedback information, the second dimension feature is configured to be generated through manual adjustment information, and the third dimension feature is configured to be generated through feedback evaluation information; Generating first usage characteristic information according to the feedback information, and adjusting the second reference massage information according to the first usage characteristic information to obtain third reference massage information includes: Collect feedback information of users of the current batch according to a first preset frequency to obtain first dimension features, second dimension features, and third dimension features; The first dimension features, the second dimension features and the third dimension features are input into the EKF model based on Kalman filtering for fusion to obtain a fused multi-dimensional feature vector; The multidimensional feature vector is input into the DQN algorithm model as an input feature to obtain the third reference massage information.

[0009] In some embodiments, the first dimension feature is configured to be generated by physiological feedback information including: Collecting pressure feedback information corresponding to massage position information within a preset time period, heart rate information of the current user within the preset time period, and skin conductivity information of the current user within the preset time period; Arranging the pressure feedback information in chronological order to obtain a pressure value change sequence, arranging the heart rate information in chronological order to obtain a heart rate value change sequence, and arranging the skin conductivity information in chronological order to obtain a skin conductivity change sequence; Calculate the pressure characteristics corresponding to the pressure feedback information of the current user according to the pressure value change sequence, where the pressure characteristics include the mean value of the pressure feedback information, the standard deviation of the pressure feedback information, and the peak value of the pressure feedback information; Calculate the heart rate characteristics corresponding to the heart rate information of the current user according to the heart rate value change sequence, where the heart rate characteristics include the average value of the heart rate information and the coefficient of variation of the heart rate information; Calculating skin conductivity features corresponding to the skin conductivity information of the current user according to the skin conductivity change sequence, where the skin conductivity features include an average value of the skin conductivity information and a fluctuation amplitude of the skin conductivity information; The pressure feature, heart rate feature and skin conductivity feature are concatenated to obtain the first dimension feature; The second dimension features are configured to be generated by manually adjusting information including: Collecting massage intensity adjustment information, massage temperature adjustment information and massage frequency adjustment information within a preset time segment, the massage intensity adjustment information including the number of massage intensity adjustments and the massage intensity adjustment value within the current preset time segment, the massage temperature adjustment information including the number of massage temperature adjustments and the massage temperature adjustment value within the current preset time segment, and the massage frequency adjustment information including the number of massage frequency adjustments and the massage frequency adjustment value within the current preset time segment; Constructing a massage intensity adjustment curve according to the massage intensity adjustment information, obtaining a first change slope value of the massage intensity in a unit time according to the massage intensity adjustment curve, and generating a massage intensity adjustment feature according to the first change slope value; Constructing a massage temperature adjustment curve according to the massage temperature adjustment information, obtaining a second change slope value of the massage temperature in a unit time according to the massage temperature adjustment curve, and generating a massage temperature adjustment feature according to the second change slope value; Constructing a massage frequency adjustment curve according to the massage frequency adjustment information, obtaining a third change slope value of the massage frequency in unit time according to the massage frequency adjustment curve, and generating a massage frequency adjustment feature according to the third change slope value; The massage intensity adjustment feature, the massage temperature adjustment feature and the massage frequency adjustment feature are concatenated to obtain the second dimension feature.

[0010] In some embodiments, the third dimension feature is configured to generate the following through feedback evaluation information: Collect multiple review texts within a preset time period, and perform the following steps for each review text: Perform sentence segment recognition on the review text to obtain multiple basic sentence segments; Inputting the basic sentence segment into the language recognition model to obtain the sentiment feature, the sentiment feature includes sentiment polarity and sentiment intensity, the sentiment polarity includes positive sentiment and negative sentiment, and the sentiment intensity includes the intensity value of positive sentiment and the intensity value of negative sentiment; Grouping the multiple basic segments according to relevance to obtain at least one first segment group; Arranging the plurality of emotion features in the first sentence group one by one in chronological order to obtain a first emotion feature sequence of the current user, and constructing an emotion fluctuation curve according to the first emotion feature sequence; Calculating a fourth change slope value corresponding to each emotion fluctuation curve; splicing a plurality of fourth change slope values ​​to obtain a second emotion feature sequence corresponding to the current review text; Multiple second emotion feature sequences are concatenated to obtain the third dimension feature.

[0011] In some embodiments, the first dimension feature, the second dimension feature, and the third dimension feature are input into an EKF model based on Kalman filtering for fusion, and the fused multi-dimensional feature vector obtained includes: The first dimension feature, the second dimension feature and the third dimension feature are expressed by formula (10), and formula (10) is as follows: ; In formula (10), The first dimension feature, the second dimension feature, and the third dimension feature are used as the first vector expression of the input feature of the EKF model. is the first dimension feature, is the second dimension feature, It is the third dimension feature; Will The acquisition time in the preset time segment is split into multiple second vector expressions, and the second vector expression is expressed by formula (11), which is as follows: ; In formula (11), The preset time period The second vector expression of the moment, For the The first dimension feature of the moment, For the The second dimension feature of the moment, For the The third dimension characteristics of each moment; Construct an EKF model based on Kalman filtering, take the second vector expression as the state vector, define the state equation and observation equation, and the state equation is expressed by formula (12), which is as follows: ; The observation equation is expressed by formula (13), which is as follows:

[0012] In formula (12) and formula (13), For the The state vector at the moment, For the The parameter vector at each moment, For the The state noise vector at the moment is is the state transfer function, is the observation function, For the The observation noise vector at time instant, No. The observation vector at each moment; The EKF model is initialized and the prediction step is performed. The prediction step is expressed by formula (14), which is as follows: ; In formula (14), is the state transfer Jacobian matrix, is the transposed matrix of the state transfer Jacobian matrix, For the current Based on the previous moment The state noise vector predicted by the information, For Based on the previous moment The state noise vector predicted by the information, For the current Based on the previous moment The state estimate covariance obtained by the state estimate and input prediction is, exist Based on the previous moment The state estimate covariance between the state estimate of and the state estimate obtained by input prediction; The update step is performed, which is expressed by formula (15), which is as follows: ; In formula (15), is the Kalman gain matrix, For the current The state estimation covariance obtained by the state estimation at time and the input prediction is, is the observation matrix, is the transposed matrix of the observation matrix, is the observation noise covariance matrix, For the current The state noise vector of the information prediction at the moment, is the identity matrix; After the acquisition time iteration within the preset time segment is completed, the final estimated state vector of EKF is obtained, which is recorded as the fused multidimensional feature vector .

[0013] In some embodiments, the DQN algorithm model includes a DQN online network model and a DQN target network model; The multi-dimensional feature vector is input into the DQN algorithm model as an input feature, and the third reference massage information is obtained including: The multi-dimensional feature vector is input into the DQN online network model and expressed by formula (16). Formula (16) is as follows: ; In formula (16), is the DQN online network model, is the DQN online network model function, is the first The corresponding moment status, For the Action value prediction of the second massage parameter for each massage point category; The output result of the DQN online network model is input into the DQN target network model, and the prediction result of the maximum action value prediction of the second massage parameter in the future state is calculated as the third reference massage parameter, which is expressed by formula (17). Formula (17) is as follows: ; In formula (17), For the The prediction result of the maximum action value prediction of the second massage parameter of the massage point category, that is, The third massage parameter of each massage point category; Repeat the above steps until the generated The third massage parameters corresponding to the massage point categories are sorted to form the third reference massage information, which is expressed by formula (18). Formula (18) is as follows: ; In formula (18), For third reference massage information.

[0014] In a second aspect, the present invention further provides a graphene massager control system with multi-dimensional somatosensory feedback, which is applicable to the graphene massager control method with multi-dimensional somatosensory feedback described in the first aspect. The system includes an information acquisition module, a massage parameter configuration module, a feedback analysis module, and a personal data management module. The information acquisition module is used to obtain first user information, and the first user information includes information features of five information categories: occupation information, work and rest information, diet information, massage position information, and personal information of the current user; the massage parameter configuration module is used to generate first initial feature information according to the first user information, and use a cosine similarity function in a database to match second feature information similar to the first initial feature information, where the second feature information is second user information generated by other users when using a graphene massager of the same massage device category; massage parameter information associated with massage modes corresponding to multiple successfully matched second feature information is obtained, and first reference massage information is generated according to the massage mode; massage parameters of the same massage point category in multiple first reference massage information are weighted averaged, and second reference massage information is generated according to multiple weighted averaged massage parameters; the initial massage mode of the current user is obtained according to the second reference massage information and the current user is massaged; The feedback analysis module is used to collect user feedback information during the massage process, and the feedback information includes physiological feedback information, manual adjustment information and feedback evaluation information. The physiological feedback information includes at least one of pressure feedback information, heart rate information and skin conductivity information. The manual adjustment information includes massage intensity adjustment information, massage temperature adjustment information and massage frequency adjustment information. The feedback evaluation information includes the comment text and comment score entered by the user during the massage process; the massage parameter configuration module is also used to generate first usage characteristic information according to the feedback information, and adjust the second reference massage information according to the first usage characteristic information to obtain third reference massage information; generate a massage mode according to the third reference massage information, and replace the current initial massage mode in the next batch of massages; the personal data management module is used to build a personal usage database, map and store the feedback information of each batch of massages with the massage mode, and store them in the personal usage database in chronological order.

[0015] In a third aspect, the present invention further provides a computer-readable storage medium storing computer program instructions, which implement the method described in the first aspect when executed by a processor.

[0016] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: Different from the existing technology, the above technical solution obtains the multi-dimensional characteristics of the current user's occupation information, work and rest information, diet information, massage position information and personal information, and matches it with the usage data of other users. Through the usage data of other users in the database, combined with the first user information of the current user, a personalized initial massage mode is generated, thereby improving the intelligence level of the device. At the same time, during the massage process, the user's physiological feedback information, manual adjustment information and feedback evaluation information are collected, and the massage parameters are dynamically adjusted to provide the current user with a massage experience that better meets personal needs, dynamically optimize the second reference massage information, and further enhance the intelligent perception ability of the device. In addition, the feedback information and usage mode of each massage process of the current user are recorded in the personal usage database to provide data support for the subsequent optimization of the massage mode and enhance the overall user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 is a schematic diagram of steps S101 to S107 of a graphene massager control method; Figure 2 is a schematic diagram of steps S201 to S206 of the graphene massager control method; Figure 3 is a schematic diagram of steps S301 to S305 of the graphene massager control method; Figure 4 is a schematic diagram of steps S401 to S407 of the graphene massager control method; Figure 5 It is a schematic diagram of the graphene massager control system.

[0019] Reference numerals: 1. Graphene massager control system; 11. Information collection module; 12. Massage parameter configuration module; 13. Feedback analysis module; 14. Personal data management module. DETAILED DESCRIPTION

[0020] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. It is particularly noted that the following examples are only used to illustrate the present invention, but are not intended to limit the scope of the present invention. Similarly, the following examples are only partial embodiments of the present invention rather than all embodiments, and all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 In a first aspect, this embodiment provides a control method for a graphene massager with multi-dimensional somatosensory feedback, comprising: S101, obtaining first user information, where the first user information includes information features of different information categories; the information categories include occupation information, work and rest information, diet information, massage location information, and personal information; S102, generating first initial feature information according to the first user information, and matching second feature information in the database using a cosine similarity function, where the second feature information is generated when other users use a graphene massager of the same massage device category; S103, obtaining massage parameter information associated with the massage mode of the successfully matched second characteristic information, and generating first reference massage information; S104, performing weighted averaging on massage parameters of the same massage point category in a plurality of first reference massage information to generate second reference massage information; S105, obtaining the initial massage mode of the current user according to the second reference massage information and massaging the current user; and collecting feedback information of the user during the massage process, wherein the feedback information includes physiological feedback information, manual adjustment information and feedback evaluation information; the physiological feedback information includes at least one of pressure feedback information, heart rate information and skin conductivity information, the manual adjustment information includes massage intensity adjustment information, massage temperature adjustment information and massage frequency adjustment information, and the feedback evaluation information includes a comment text and a comment score input by the user during the massage process; S106, generating first usage characteristic information according to the feedback information, and adjusting the second reference massage information according to the first usage characteristic information to obtain third reference massage information; S107, generating a massage mode according to the third reference massage information, and replacing the current initial massage mode in the next batch of massages; and constructing a personal use database, storing the feedback information of each batch of massages in the personal use database in chronological order.

[0022] In step S101, the first user information is the information of the user currently using the massager. It is understandable that the first user information can be imported by manual input or third-party application software input when the user first uses the current massager. Specifically, the first user information includes occupational information, work and rest information, dietary information, massage location information and personal information. Among them, the occupational information includes occupational name, occupational place and main position. The occupational information can be used as one of the reference indicators for the distribution of the user's whole body muscle state. For example, users who are responsible for writing work in the office are more likely to have problems with lumbar muscle strain, lumbar disc herniation and cervical vertebrae herniation. In the express delivery profession, users are more likely to have problems with leg muscle soreness and arm muscle soreness. Users of different occupations have different muscle strain groups and different massage needs; the work and rest information includes the user's nap information and the night sleep time segment. The time node when the user uses the massager can be more in line with the user's work and rest information. For example, before falling asleep at night, the user's massage demand tends to be more inclined to a soothing massage mode, which helps to fall asleep. For example, after waking up from a nap, the user's massage demand tends to be more inclined to a targeted muscle massage mode, and so on. As one of the data of the user's living habits, dietary information can be used to infer the current user's dietary category, and then serve as a reference indicator for the healthiness of the user's living habits. For example, users whose dietary information contains rich protein and fiber can reflect that the current user's living habits are healthier, and on this basis, the muscle content in the user's body can be further reasonably predicted and inferred. That is, fitness people have higher muscle content, and the massage parameters in the adapted massage mode may be stronger; obese people have higher fat content, and the massage parameters in the adapted massage mode will be adjusted back and forth as time goes by.

[0023] The massage position information specifically includes the body parts of the current user during the massage. It is understandable that when constructing the current user's personal use database, a rough classification can be made according to the massage position information, and then the massage data of each batch of the user can be stored in the same massage position information. Specifically, the massage position information can include the waist and back, waist and abdomen, upper buttocks, lower buttocks, thighs, calves, neck and back, shoulders, etc. The specific noun setting can refer to special medical terms, or image indications of body parts, as an auxiliary input method for the user's massage position information. Personal information includes the user's name, contact number, age, and medical history information. The input of personal information can also be used as one of the reference indicators for setting subsequent massage parameters. That is, the massage needs of the elderly and middle-aged people are different, and similarly, the massage needs of middle-aged people and young people are also different. The input of personal information also helps to establish and allocate a personal use database.

[0024] In step S102, the first initial feature information is generated according to the first user information. Specifically, it can be understood that the numerical information and non-numerical information contained in the first user information are uniformly represented by vectors, and on this basis, the second feature information is matched in the database. In this embodiment, each person will generate usage data when using the massager. On this basis, a cloud database is built, and each person's usage data is uploaded to this database. The usage data includes the second feature information generated according to the second user information, and the fine-tuning data of other users during use, etc. These data can be used as further reference data for subsequent massager product improvement or research. On this basis, this embodiment uses the second feature information of the database to match user groups similar to the current user. This similarity is calculated using the cosine similarity function.

[0025] Further, in step S103, the massage parameter information recorded in the massage mode used by the second feature information that successfully matches is organized into the first reference massage information. For example, the number of second feature information that matches the first initial feature information is three, that is, there are three other users who are very similar to the current user, then the massage modes corresponding to these three other users are obtained, there are three massage modes in total, and the massage parameters in these three massage modes are organized into three first reference massage information. The specific organization method can be to organize the massage parameters in the massage mode into a set and record the set as the first reference massage information, or to organize the massage parameters in the massage mode into a vector expression and record it as the first reference massage information.

[0026] Further, in step S104, after obtaining a plurality of first reference massage information, a weighted average is performed on the massage parameters of the same massage point category in the plurality of first reference massage information. In this embodiment, the massage point category can be understood as follows: in the same massager, the massage execution end has temperature control parameters, strength control parameters and frequency control parameters, and each first reference massage information has temperature control parameters, strength control parameters and frequency control parameters. In more detail, the three types of parameters are subdivided according to the massage points, that is, a massage execution end can have multiple massage points, and each massage point can be equipped with temperature control parameters, strength control parameters, frequency control parameters, etc. In other optional embodiments, one of the temperature control parameters, strength control parameters and frequency control parameters can be retained, which meets the settings of multiple massage execution ends. On this basis, the same massage point category, that is, the massage parameters that are also temperature-type control parameters in multiple first reference massage information are weighted averaged, and the strength-type control parameters and frequency-type control parameters are understood with reference to this method. The massage parameters obtained are the weighted average massage parameters, which are recorded as the massage parameters in the second reference massage information. This method can eliminate the individual differences of other users who are similar to the current user, thereby providing a rough reference data, that is, the second reference massage information.

[0027] In step S105, the initial massage mode of the massager used by the current user is initialized according to the second reference massage information, and the current user is massaged. This method utilizes the screening, classification and similarity matching of big data, so that in the initial stage and taking into account the individual needs of the current user, an initial massage mode that is more suitable for the current user is generated to massage the current user, giving the user a more comfortable user experience and happiness index. Furthermore, during the massage process, the user's feedback information is collected, and the feedback information can be collected by arranging sensors, interacting with the user through voice or text, and other methods. Specifically, the feedback information includes physiological feedback information, manual adjustment information and feedback evaluation information, wherein the physiological feedback information is the physiological information naturally generated by the current user during the massage process, which can also be understood as somatosensory feedback, and the physiological feedback information specifically includes pressure feedback information, heart rate information and skin conductivity information, and the pressure feedback information specifically includes the pressure feedback information of the massage execution end of the massager, and the heart rate information is the heart rate information of the user during the massage process. The relaxation degree and stress level of the user due to the massage effect of the massager will be reflected by the change of heart rate. Similarly, the skin conductivity information can reflect the skin moisture and blood circulation state of the human body. The different massage degrees will cause the changes of skin moisture and blood circulation state. This embodiment obtains the physiological information of these three categories as physiological feedback information. The manual adjustment information is the adjustment data generated by the current user through the third-party application, the button or screen on the massager during the massage process. For example, the massage parameters of a certain massage point category in the current massage mode are numerically adjusted, which can more intuitively reflect the current user's adjustment needs for massage, and the aforementioned physiological feedback parameters quantify the user's adjustment needs for the current massage mode from the perspective of somatosensory. Furthermore, the feedback evaluation information includes the comment text and comment score input by the user during the massage process. Specifically, the user can interact with the user in the form of a pop-up window to input the comment text and comment score, or a voice inquiry can be used. The final output feedback evaluation information is the comment text and comment score.

[0028] In step S106, the first usage characteristic information is generated using the feedback information. It should be noted that the collection time of the feedback information can be further refined and standardized. Specifically, in this embodiment, a complete massage cycle performed by the user is recorded as a batch. In a batch, there can be multiple feedback information. For example, if the user massages a batch for 30 minutes, the collection frequency of the feedback information is 10 minutes / time or 5 minutes / time. By frequently collecting feedback information, more detailed feedback content of the user during the massage process can be obtained, thereby optimizing the massage parameters of subsequent batches. Optionally, in some embodiments, after the massage parameters of multiple batches are set, the collection frequency of each batch of feedback information can be reduced, so as to achieve a more comfortable massage process and improve the user's usage experience.

[0029] In this embodiment, the first usage characteristic information is generated by using feedback information, which can be understood as unifying the numerical data and non-numerical data in the feedback information in the form of vectors or matrices. On this basis, it is converted into an adjustment value of the second reference massage information, and the massage parameters in the second reference massage information are adjusted and modified by this adjustment value, and finally the modified second reference massage information is obtained, which is recorded as the third reference massage information.

[0030] In step S107, a massage mode is generated based on the third reference massage information. It should be noted that the massage mode is used to distinguish it from the initial massage mode, and step S106 has the effect of multiple iterations in the massage process of a single batch, that is, the massage mode is updated once after each feedback information is collected, and the last massage mode is used as the initial massage mode for the next batch of massages. On this basis, when the user first uses this massager, a personal use database is constructed, and after each batch of massage operations is completed, the massage data generated by the batch will be stored, including multiple feedback information corresponding to each batch of massage operations and the massage mode used in each iteration, which are stored in the personal use database according to the user's use time sequence of the current massager. This method is convenient for the subsequent product upgrade of the massager and the characterization of the user's use portrait. Furthermore, the data in the personal use database can be used to synchronously analyze the changes in the user's use needs over a period of time, which serves as the basis for evaluating the user's personal health.

[0031] This embodiment makes full use of big data and machine learning technology to generate a personalized initial massage plan based on the first user information (occupation information, work and rest information, dietary information, etc.) of the current user and the usage data of other users, so as to provide users with a massage experience that is more suitable for their own needs. On this basis, various feedback information from users is collected in real time during actual use, and the massage parameters are continuously optimized and adjusted using the feedback information, thereby further improving the user's satisfaction and experience. This embodiment also establishes a personal use database to record the current user's usage history and feedback information, which can not only provide a more accurate personalized plan for the next use, but also can be used to analyze the current user's physical health status and demand change trends. This embodiment improves the intelligent adjustment level of existing massagers, realizes dynamic adjustment during the massage process, and improves the user's comfort.

[0032] In some embodiments, generating the first initial feature information according to the first user information includes: The non-numerical information features in the first user information are uniquely encoded, and the first user information is represented by formula (1). Formula (1) is as follows: ; In formula (1), is the vector representation of the first user information, The first user information The numerical representation of the information characteristics of each information category, ; The first user information represented by the vector is normalized to obtain the first initial feature information, which is expressed by formula (2) to formula (4). Formula (2) is as follows: ; Formula (3) is as follows: ; Formula (4) is as follows: ; In formula (2) to formula (4), is the first initial feature information, for The average value of For the The eigenvalues ​​of the information features, For the The average value of the information features, for The standard deviation vector of For the The standard deviation vector of the information features, ; The second characteristic information is expressed by formula (5), which is as follows: ; In formula (5), is the second characteristic information, The second user information Numerical representation of information characteristics of each information category; Using a cosine similarity function to match second feature information similar to the first initial feature information in a database, obtaining massage parameter information associated with massage modes corresponding to a plurality of successfully matched second feature information, and arranging and generating first reference massage information according to the massage modes includes: The cosine similarity between each second feature information and the first initial feature information is calculated and expressed by formula (6). Formula (6) is as follows: ; In formula (6), is the cosine similarity function, for The Euclidean norm of , for The Euclidean norm of , is the cosine similarity value; Determine one by one whether the cosine similarity value corresponding to the second feature information is within a range of a preset similarity threshold; If yes, the second feature information is recorded as the first reference feature information, and the massage parameter information associated with the massage mode corresponding to the first reference feature information is obtained, and the massage parameter information is mapped and stored with the first reference feature information to obtain the first reference massage information; If not, it means that the current second feature information is not successfully matched with the first initial feature information.

[0033] In this embodiment, the non-numerical information features in the first user information are one-hot encoded. For example, if the first user information contains non-numerical information of sleep time, deep sleep, light sleep, and nap sleep, then one-hot encoding can be used to represent the binary vector, thereby converting it into a numerical feature. For some quantifiable indicators, one-hot encoding is not required, and it can be directly used as a vector in the first user information. After one-hot encoding, the first user information can be represented by formula (1). It can be understood that: That is, the aforementioned information categories include five information categories: occupational information, work and rest information, dietary information, massage location information, and personal information. For the convenience of description, occupational information is recorded as the first information category in this order, corresponding to , record the work and rest information as the second information category, corresponding to , dietary information is recorded as the third information category, corresponding to , massage position information is recorded as the fourth information category, corresponding to , personal information is recorded as the fifth information category, corresponding to .

[0034] Furthermore, the first user information is standardized and recorded as the first initial feature information. Specifically, a standardized formula is used. Each information category has multiple feature values. After accumulating and averaging the feature values ​​in each information category, the average value of the feature values ​​in each information category is obtained. The standard deviation vector corresponding to each information category is further obtained. Finally, the first initial feature information is obtained. The first initial feature information includes the standardized features corresponding to each information category, which is the meaning referred to by formula (2).

[0035] Furthermore, the second feature information is represented by formula (5), which is similar to the vector representation of the first user information. The difference is that the vector representation of the second user information is the version uploaded to the database, that is, the second user information here is the standardized feature. Therefore, the second feature information is directly represented by the vector representation of the second user information.

[0036] In this embodiment, the cosine similarity function is used to calculate the similarity between the first initial feature information and the second feature information. By calculating the similarity between the second feature information and the first initial feature information one by one, the corresponding cosine similarity value between each second feature information and the first initial feature information is obtained.

[0037] It is determined whether the cosine similarity value is within the range of the preset similarity threshold value. It should be noted that the range of the preset similarity threshold value can be realized in a form of artificial presetting. In this embodiment, if the cosine similarity value is within the range of the preset similarity threshold value, it means that the current second feature information has a certain similarity with the first initial feature information, and the second feature information is recorded as the first reference feature information, and then the massage parameter information associated with the first reference feature information is obtained, and it is mapped and stored with the first reference feature information to obtain the first reference massage information.

[0038] By repeating the above steps, the second characteristic information within the preset similarity threshold range can be screened out from the database, thereby obtaining a plurality of first reference massage information.

[0039] In this embodiment, the non-numerical features in the first user information are uniquely encoded to obtain the first initial feature information, and the second user information is represented as the second feature information. The cosine similarity between the two is calculated by the cosine similarity function to determine whether the cosine similarity is within a preset threshold range. If so, the second feature information is filtered out from the database, and the corresponding massage parameter information is obtained to form the first reference massage information. In this embodiment, the dimensional difference between different feature values ​​in the same information category is eliminated through standardization processing. The cosine similarity can more accurately measure the similarity between the first initial feature information and the second feature information. The first reference feature information and the massage parameter information are associated and stored to facilitate the generation of the first reference massage information.

[0040] In some embodiments, performing weighted averaging on massage parameters of the same massage point category in a plurality of first reference massage information, and generating second reference massage information according to the plurality of weighted averaged massage parameters comprises: The first reference massage information is expressed by formula (7), and the multiple massage parameters in the first reference massage information are recorded as first massage parameters. Formula (7) is as follows: ; In formula (7), For the The first reference massage information, For the The first reference massage information The first massage parameter of each massage point category; The first massage parameters of the same massage point category in the plurality of first reference massage information are weighted averaged to obtain a weighted average massage parameter, which is recorded as a second massage parameter and is expressed by formula (8). Formula (8) is as follows: ; In formula (8), For the The second massage parameter after weighted average, , For the The weight of the first reference massage information, For the The first reference massage information The first massage parameter of each massage point category; The second massage parameters are sorted to obtain the second reference massage information, which is expressed by formula (9). Formula (9) is as follows: ; In formula (9), For the second reference massage information.

[0041] In this embodiment, for the convenience of description, the multiple massage parameters in the first reference massage information are recorded as the first massage parameters, and the parameters in the second reference massage information are recorded as the second massage parameters. The weighted average of the first massage parameters in the massage point category shown in the aforementioned step S104 can be specifically understood by combining formula (7) and formula (8). By weighted averaging the first massage parameters in the same massage point category, the weight of the first reference massage information can be set according to actual needs. After weighted averaging, the second massage parameters corresponding to the first massage parameters in the same massage point category are obtained, and then the second massage parameters are sorted according to the massage point category to form the second reference massage information.

[0042] In some embodiments, the first usage feature information includes a first dimension feature, a second dimension feature, and a third dimension feature, the first dimension feature is configured to be generated through physiological feedback information, the second dimension feature is configured to be generated through manual adjustment information, and the third dimension feature is configured to be generated through feedback evaluation information; Generating first usage characteristic information according to the feedback information, and adjusting the second reference massage information according to the first usage characteristic information to obtain third reference massage information includes: Collect feedback information of users of the current batch according to a first preset frequency to obtain first dimension features, second dimension features, and third dimension features; The first dimension features, the second dimension features and the third dimension features are input into the EKF model based on Kalman filtering for fusion to obtain a fused multi-dimensional feature vector; The multidimensional feature vector is input into the DQN algorithm model as an input feature to obtain the third reference massage information.

[0043] In this embodiment, the feedback information of the users of the current batch is collected according to the first preset frequency. It can be understood that during the massage process of the current batch, the feedback information is collected at intervals, and the adjacent interval time periods can be calculated by the first preset frequency, which is the preset time segment described below. That is, the collection time of the feedback information has a certain interval, so as to realize the regular feedback and adjustment in the massage operation of the same batch, and realize the dynamic adjustment and change of the massage intensity.

[0044] Furthermore, this embodiment uses the EKF model to fuse the first dimension features, the second dimension features, and the third dimension features in the feedback information, and after obtaining a multi-dimensional feature vector, it is input into the DQN algorithm model to obtain the third reference massage information. For ease of description, the massage parameters in the third reference massage information can be recorded as the third massage parameters.

[0045] For further information, see Figure 2In some embodiments, the first dimension feature is configured to be generated by physiological feedback information and includes: S201, collecting pressure feedback information corresponding to massage position information within a preset time segment, heart rate information of the current user within the preset time segment, and skin conductivity information of the current user within the preset time segment; S202, arranging the pressure feedback information in chronological order to obtain a pressure value change sequence, arranging the heart rate information in chronological order to obtain a heart rate value change sequence, and arranging the skin conductivity information in chronological order to obtain a skin conductivity change sequence; S203, calculating the pressure characteristics corresponding to the pressure feedback information of the current user according to the pressure value change sequence, where the pressure characteristics include the mean value of the pressure feedback information, the standard deviation of the pressure feedback information, and the peak value of the pressure feedback information; S204, calculating the heart rate characteristics corresponding to the heart rate information of the current user according to the heart rate value change sequence, where the heart rate characteristics include the average value of the heart rate information and the coefficient of variation of the heart rate information; S205, calculating skin conductivity features corresponding to the skin conductivity information of the current user according to the skin conductivity change sequence, where the skin conductivity features include an average value of the skin conductivity information and a fluctuation amplitude of the skin conductivity information; S206, concatenating the pressure feature, the heart rate feature, and the skin conductivity feature to obtain a first dimension feature.

[0046] In step S201, the preset time segment is the time interval segment in the aforementioned first preset collection frequency. The first dimension features include three features: pressure feedback information, heart rate information, and skin conductivity information. Specifically, the user's heart rate value, pressure value, and skin conductivity in this preset time segment are collected in real time, and the heart rate value is sorted into heart rate information, the pressure value is sorted into pressure feedback information, and the skin conductivity is sorted into skin conductivity information.

[0047] In step S202, the pressure feedback information is arranged in chronological order to obtain a pressure value change sequence. Specifically, the pressure value change sequence can be used to construct a two-dimensional curve graph for visualization, or the pressure value change sequence can be subjected to logical operations and data processing to obtain the mean, standard deviation, and peak value corresponding to the pressure value change sequence shown in step S203, which are recorded as the mean, standard deviation, and peak value of the pressure feedback information, i.e., the pressure feature.

[0048] In step S202, the heart rate values ​​are arranged in chronological order to obtain a heart rate value change sequence. In step S204, the average heart rate value and the coefficient of variation of the heart rate value of the current user are further calculated through the heart rate value change sequence and recorded as the average heart rate information and the coefficient of variation, which are the heart rate characteristics.

[0049] In step S202, the skin conductivity change sequence is obtained by arranging the skin conductivity in chronological order. In step S205, the average value and fluctuation range of the skin conductivity are further calculated and recorded as the average value and fluctuation range of the skin conductivity information, that is, the skin conductivity feature.

[0050] In step S206, the pressure feature, heart rate feature and skin conductivity feature are concatenated to obtain the first dimensional feature. The first dimensional feature reflects the user's physiological feeling of the massage mode within a preset time period, that is, the body sensation, which will serve as one of the input features for adjusting the massage parameters of the subsequent massage mode.

[0051] For further information, see Figure 3 , the second dimension features are configured to be generated by manually adjusting information including: S301, collecting massage intensity adjustment information, massage temperature adjustment information and massage frequency adjustment information within a preset time segment, the massage intensity adjustment information including the number of massage intensity adjustments and the massage intensity adjustment value within the current preset time segment, the massage temperature adjustment information including the number of massage temperature adjustments and the massage temperature adjustment value within the current preset time segment, and the massage frequency adjustment information including the number of massage frequency adjustments and the massage frequency adjustment value within the current preset time segment; S302, constructing a massage intensity adjustment curve according to the massage intensity adjustment information, obtaining a first change slope value of the massage intensity in unit time according to the massage intensity adjustment curve, and generating a massage intensity adjustment feature according to the first change slope value; S303, constructing a massage temperature adjustment curve according to the massage temperature adjustment information, obtaining a second change slope value of the massage temperature in unit time according to the massage temperature adjustment curve, and generating a massage temperature adjustment feature according to the second change slope value; S304, constructing a massage frequency adjustment curve according to the massage frequency adjustment information, obtaining a third change slope value of the massage frequency in unit time according to the massage frequency adjustment curve, and generating a massage frequency adjustment feature according to the third change slope value; S305, performing feature splicing on the massage intensity adjustment feature, the massage temperature adjustment feature, and the massage frequency adjustment feature to obtain a second dimension feature.

[0052] In step S301, the manual adjustment information is divided into massage intensity adjustment information, massage temperature adjustment information and massage frequency adjustment information, wherein the massage intensity adjustment information is the number of massage intensity adjustment and the adjustment value of the adjustment intensity input by the current user during the massage process, the massage temperature adjustment information is the number of massage temperature adjustment and the adjustment value of the adjustment temperature input by the current user during the massage process, and the massage frequency adjustment information is the number of massage frequency adjustment and the adjustment value of the massage frequency input by the current user during the massage process.

[0053] In step S302, a massage intensity adjustment curve is constructed, which can be formed by performing hyperbolic fitting on the discretely distributed massage intensity adjustment times and massage intensity adjustment values. On this basis, the first change slope value of the massage intensity in a unit time is obtained according to the massage intensity adjustment curve, that is, the user's adjustment trend of the massage intensity, and then the first change slope value is vectorized to form a massage intensity adjustment feature. It should be noted that the unit time is the minimum recording time. For example, when the preset time segment is 5 minutes, the unit time can be refined into 10 seconds or 1 second. When the preset time segment is 1 hour, the unit time can be refined into 1 minute, etc. This embodiment is not limited to this, and the order of magnitude of the unit time can be defined according to time requirements.

[0054] In step S303, a massage temperature adjustment curve is constructed, which can be formed by performing hyperbola fitting on the discretely distributed massage temperature adjustment times and the massage temperature adjustment values. On this basis, the second change slope value of the massage temperature in unit time is obtained according to the massage temperature adjustment curve, that is, the user's adjustment trend of the massage temperature, and then the second change slope value is represented by a vector to form a massage temperature adjustment feature.

[0055] In step S304, a massage frequency adjustment curve is constructed, which can be formed by performing hyperbola fitting on the discretely distributed adjustment times of the massage frequency and the adjustment value of the massage frequency. On this basis, the third change slope value of the massage frequency in unit time is obtained according to the massage frequency adjustment curve, that is, the user's adjustment trend of the massage frequency, and then the third change slope value is represented by a vector to form a massage frequency adjustment feature.

[0056] In step S305, the massage intensity adjustment feature, the massage temperature adjustment feature and the massage frequency adjustment feature are concatenated to obtain a second dimensional feature. The second dimensional feature reflects the user's intuitive demand for the massage mode within a preset time period and will serve as one of the input features for adjusting the massage parameters of the subsequent massage mode.

[0057] See also Figure 4In some embodiments, the third dimension feature is configured to be generated by feedback evaluation information including: Collect multiple review texts within a preset time period, and perform the following steps for each review text: S401, performing sentence segment recognition on the review text to obtain multiple basic sentence segments; S402, inputting the basic sentence segment into the language recognition model to obtain sentiment features, where the sentiment features include sentiment polarity and sentiment intensity, where the sentiment polarity includes positive sentiment and negative sentiment, and where the sentiment intensity includes the intensity value of the positive sentiment and the intensity value of the negative sentiment; S403, grouping the multiple basic segments according to their relevance to obtain at least one first segment group; S404, arranging the plurality of emotion features in the first sentence group one by one in chronological order to obtain a first emotion feature sequence of the current user, and constructing an emotion fluctuation curve according to the first emotion feature sequence; S405, calculating the fourth change slope value corresponding to each emotion fluctuation curve; S406, splicing a plurality of fourth change slope values ​​to obtain a second emotion feature sequence corresponding to the current review text; S407, concatenating multiple second emotion feature sequences to obtain third dimensional features.

[0058] In this embodiment, the third dimension feature is the generation of feedback evaluation information. The feedback evaluation information is collected in the form of manual input or voice input by the user. Based on this, the feedback evaluation information needs to be text-processed.

[0059] Specifically, multiple review texts are collected within a preset time period, that is, the feedback evaluation information includes multiple review texts within the preset time period. On this basis, in step S401, the review text is segmented to obtain multiple basic segments, which can be specifically achieved by identifying punctuation marks on the segments.

[0060] In step S402, the basic sentence segment is input into the language recognition model. The language recognition model can be a pre-trained language model such as BERT and RoBERTa. This language recognition model is also connected to the sentiment classifier. After encoding the sentence segment, it will further perform sentiment classification to obtain sentiment features. The sentiment features include sentiment polarity and sentiment intensity, that is, each basic sentence segment has a sentiment feature.

[0061] In step S403, multiple basic sentence segments are grouped according to their relevance. The relevance shown in this embodiment can be understood as the relevance of the basic sentence segments in sentence expression, that is, the distribution of primary and secondary sentence segments in a review text. Specifically, the relevance grouping of the basic sentence segments can be performed by constructing a neural network or a dendrogram. This method can obtain at least one first sentence segment group, that is, the number of first sentence segment groups can be multiple.

[0062] In step S404, the plurality of emotion features in the first sentence group are arranged in chronological order to construct a first emotion feature sequence, and then the emotion features in the discrete first emotion feature sequence are fitted to obtain an emotion fluctuation curve corresponding to the first sentence group.

[0063] In step S405, the fourth change slope value of each emotion fluctuation curve is calculated, and in step S406, multiple fourth change slope values ​​are concatenated to obtain a second emotion feature sequence.

[0064] Furthermore, in step S407, multiple second emotion feature sequences are concatenated to obtain third dimensional features.

[0065] The above embodiment shows the specific generation process of the first dimensional features, the second dimensional features and the third dimensional features, which is convenient for subsequent further calculation and analysis.

[0066] In some embodiments, the first dimension feature, the second dimension feature, and the third dimension feature are input into an EKF model based on Kalman filtering for fusion, and the fused multi-dimensional feature vector obtained includes: The first dimension feature, the second dimension feature and the third dimension feature are expressed by formula (10), and formula (10) is as follows: ; In formula (10), The first dimension feature, the second dimension feature, and the third dimension feature are used as the first vector expression of the input feature of the EKF model. is the first dimension feature, is the second dimension feature, It is the third dimension feature; Will The acquisition time in the preset time segment is split into multiple second vector expressions, and the second vector expression is expressed by formula (11), which is as follows: ; In formula (11), The preset time period The second vector expression of the moment, For the The first dimension feature of the moment, For the The second dimension feature of the moment, For the The third dimension characteristics of each moment; Construct an EKF model based on Kalman filtering, take the second vector expression as the state vector, define the state equation and observation equation, and the state equation is expressed by formula (12), which is as follows: ; The observation equation is expressed by formula (13), which is as follows:

[0067] In formula (12) and formula (13), For the The state vector at the moment, For the The parameter vector at each moment, For the The state noise vector at the moment is is the state transfer function, is the observation function, For the The observation noise vector at time instant, No. The observation vector at each moment; The EKF model is initialized and the prediction step is performed. The prediction step is expressed by formula (14), which is as follows: ; In formula (14), is the state transfer Jacobian matrix, is the transposed matrix of the state transfer Jacobian matrix, For the current Based on the previous moment The state noise vector predicted by the information, For Based on the previous moment The state noise vector predicted by the information, For the current Based on the previous moment The state estimate covariance obtained by the state estimate and input prediction is, exist Based on the previous moment The state estimate covariance between the state estimate of and the state estimate obtained by input prediction; The update step is performed, which is expressed by formula (15), which is as follows: ; In formula (15), is the Kalman gain matrix, For the current The state estimation covariance obtained by the state estimation at time and the input prediction is, is the observation matrix, is the transposed matrix of the observation matrix, is the observation noise covariance matrix, For the current The state noise vector of the information prediction at the moment, is the identity matrix; After the acquisition time iteration within the preset time segment is completed, the final estimated state vector of EKF is obtained, which is recorded as the fused multidimensional feature vector .

[0068] In this embodiment, the first dimension features, the second dimension features and the third dimension features are used as the input features of the EKF model, making full use of the complementary information of various sensory data to improve the richness expressed by the input features. Through the prediction and update mechanism of EKF, the state information of the historical moment is used to better estimate the fusion features of the current moment. In addition, the EKF model introduces noise terms in the state equation and the observation equation, which can effectively suppress the noise interference in the features of each dimension and improve the accuracy of the fusion results.

[0069] In some embodiments, the DQN algorithm model includes a DQN online network model and a DQN target network model; The multi-dimensional feature vector is input into the DQN algorithm model as an input feature, and the third reference massage information is obtained including: The multi-dimensional feature vector is input into the DQN online network model and expressed by formula (16). Formula (16) is as follows: ; In formula (16), is the DQN online network model, is the DQN online network model function, is the first The corresponding moment status, For the Action value prediction of the second massage parameter for each massage point category; The output result of the DQN online network model is input into the DQN target network model, and the prediction result of the maximum action value prediction of the second massage parameter in the future state is calculated as the third reference massage parameter, which is expressed by formula (17). Formula (17) is as follows: ; In formula (17), For the The prediction result of the maximum action value prediction of the second massage parameter of the massage point category, that is, The third massage parameter of each massage point category; Repeat the above steps until the generated The third massage parameters corresponding to the massage point categories are sorted to form the third reference massage information, which is expressed by formula (18). Formula (18) is as follows: ; In formula (18), For third reference massage information.

[0070] In this embodiment, the DQN model can dynamically learn and predict the best massage parameter solution through interactive updates of the online network and the target network, rather than simply making decisions based on static rules or experience, so as to better adapt to the complexity and dynamic nature of user needs. The DQN model has good learning transfer capabilities, and can continuously improve the accuracy and robustness of decisions by continuously optimizing the online network and the target network. The third reference massage information finally obtained specifically gives the optimal massage parameters for each massage point category, which can provide users with more personalized massage solutions and improve user experience satisfaction.

[0071] See also Figure 5In the second aspect, the present embodiment further provides a graphene massager control system 1 with multi-dimensional somatosensory feedback, which is applicable to the graphene massager control method with multi-dimensional somatosensory feedback described in the first aspect. The system includes an information acquisition module 11, a massage parameter configuration module 12, a feedback analysis module 13 and a personal data management module 14. The information acquisition module 11 is used to obtain first user information, and the first user information includes information features of five information categories, namely, occupation information, work and rest information, diet information, massage position information and personal information of the current user; the massage parameter configuration module 12 is used to generate first initial feature information according to the first user information, and use the cosine similarity function to match the second feature information similar to the first initial feature information in the database, and the second feature information is the second user information generated by other users when using the graphene massager of the same massage device category; the massage parameter information associated with the massage mode corresponding to the multiple second feature information that are successfully matched is obtained, and the first reference massage information is generated according to the massage mode; the massage parameters of the same massage point category in the multiple first reference massage information are weighted averaged, and the second reference massage information is generated according to the multiple weighted averaged massage parameters; the initial massage mode of the current user is obtained according to the second reference massage information and the current user is massaged; The feedback analysis module 13 is used to collect user feedback information during the massage process, and the feedback information includes physiological feedback information, manual adjustment information and feedback evaluation information. The physiological feedback information includes at least one of pressure feedback information, heart rate information and skin conductivity information. The manual adjustment information includes massage intensity adjustment information, massage temperature adjustment information and massage frequency adjustment information. The feedback evaluation information includes the comment text and comment score entered by the user during the massage process; the massage parameter configuration module 12 is also used to generate first usage characteristic information according to the feedback information, and adjust the second reference massage information according to the first usage characteristic information to obtain third reference massage information; generate a massage mode according to the third reference massage information, and replace the current initial massage mode in the next batch of massages; the personal data management module 14 is used to build a personal usage database, map the feedback information of each batch of massages with the massage mode, and store them in the personal usage database in chronological order.

[0072] Corresponding to the aforementioned text, the system provided in this embodiment is used to implement the method recorded in the aforementioned text. The specific steps involved can be understood by referring to the aforementioned text, and this embodiment will not be described in detail here.

[0073] In a third aspect, this embodiment further provides a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described in the first aspect is implemented.

[0074] The computer program involved in this embodiment can be stored in a computer device readable storage medium, which includes but is not limited to a disk, a tape, a magnetic card, a floppy disk, a flash memory, an optical disk, an optical card, a read-only memory (ROM), a random access memory (RAM), an erasable programmable ROM (EPROM) and an electrically erasable programmable ROM (EEPROM), etc., and also includes other biological, physical or chemical structures that can achieve the same or equivalent functions as the storage media listed above, such as DNA, RNA, protein and other units with information storage capabilities. In a specific embodiment, the storage medium involved can be one of the above-mentioned media types, or a combination of the above-mentioned media types. In different embodiments, the computer program involved in the embodiment can be stored in a single medium in a centralized manner, or it can be stored in multiple media in a distributed manner. The memory containing the computer device readable storage medium can be a non-volatile memory or a random access memory. These computer device readable storage media can be built into the device, or they can be connected to the device involved in the embodiment as an external device or a part of an external device. In some embodiments, a memory with a computer device readable storage medium is deployed locally; in other embodiments, a solution of deploying the memory away from the processor may also be adopted, such as a network attached memory accessed via an RF circuit or an external port and a communication network, wherein the communication network may be the Internet, one or more intranets, a local area network (LAN), a wide area network (WLAN), a storage area network (SAN), etc., or a suitable combination thereof, as long as the computer device can access the memory. In addition, the computer program involved in the embodiment may be stored in plaintext / ciphertext form, or may be designed as training data, which may be integrated and reorganized through model training and implicitly stored in the parameter state of a deep neural network or other machine learning model.

[0075] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: Different from the existing technology, the above technical solution obtains the multi-dimensional characteristics of the current user's occupation information, work and rest information, diet information, massage position information and personal information, and matches it with the usage data of other users. Through the usage data of other users in the database, combined with the first user information of the current user, a personalized initial massage mode is generated, thereby improving the intelligence level of the device. At the same time, during the massage process, the user's physiological feedback information, manual adjustment information and feedback evaluation information are collected, and the massage parameters are dynamically adjusted to provide the current user with a massage experience that better meets personal needs, dynamically optimize the second reference massage information, and further enhance the intelligent perception ability of the device. In addition, the feedback information and usage mode of each massage process of the current user are recorded in the personal usage database to provide data support for the subsequent optimization of the massage mode and enhance the overall user experience.

[0076] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0077] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0078] The above descriptions are only some embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A control method for a graphene massager with multi-dimensional somatosensory feedback, characterized in that: include: Acquire first user information, where the first user information includes information features of different information categories; Generate first initial feature information according to the first user information, and match second feature information in the database using a cosine similarity function, where the second feature information is generated when other users use a graphene massager of the same massage device category; Acquire massage parameter information associated with the massage mode of the second characteristic information that matches successfully, and generate first reference massage information; Taking a weighted average of massage parameters of the same massage point category in a plurality of first reference massage information to generate second reference massage information; obtaining an initial massage mode of the current user according to the second reference massage information and performing massage on the current user; Collecting user feedback information during the massage process, wherein the feedback information includes physiological feedback information, manual adjustment information, and feedback evaluation information; generating first usage characteristic information according to the feedback information, and adjusting the second reference massage information according to the first usage characteristic information to obtain third reference massage information; Generate a massage mode for use according to the third reference massage information, and replace the current initial massage mode in the next batch of massages; A personal use database is constructed, and feedback information of each batch of massage is stored in the personal use database in chronological order.

2. The control method of the graphene massager with multi-dimensional somatosensory feedback according to claim 1, characterized in that: Generating first initial characteristic information according to the first user information includes: The non-numerical information features in the first user information are one-hot encoded, and the first user information is represented by formula (1), which is as follows: ； In formula (1), is the vector representation of the first user information, The first user information The numerical representation of the information characteristics of each information category, ; The first user information represented by the vector is normalized to obtain the first initial feature information, which is expressed by formula (2) to formula (4). Formula (2) is as follows: ； Formula (3) is as follows: ； Formula (4) is as follows: ； In formula (2) to formula (4), is the first initial feature information, for The average value of For the The eigenvalues ​​of the information features, For the The average value of the information features, for The standard deviation vector of For the The standard deviation vector of the information features, ; The second characteristic information is expressed by formula (5), and the formula (5) is as follows: ； In formula (5), is the second characteristic information, The second user information Numerical representation of information characteristics of each information category; Using a cosine similarity function to match second feature information similar to the first initial feature information in a database, obtaining massage parameter information associated with massage modes corresponding to a plurality of successfully matched second feature information, and arranging and generating first reference massage information according to the massage modes includes: The cosine similarity between each of the second feature information and the first initial feature information is calculated and expressed by formula (6). Formula (6) is as follows: ； In formula (6), is the cosine similarity function, for The Euclidean norm of , for The Euclidean norm of , is the cosine similarity value; Determining one by one whether the cosine similarity values ​​corresponding to the second feature information are within a range of a preset similarity threshold; If yes, the second feature information is recorded as the first reference feature information, massage parameter information associated with the massage mode corresponding to the first reference feature information is obtained, the massage parameter information is mapped and stored with the first reference feature information, and the first reference massage information is obtained; If not, it means that the second feature information is currently unsuccessful in matching with the first initial feature information.

3. The control method of the graphene massager with multi-dimensional somatosensory feedback according to claim 1, characterized in that: Performing weighted averaging on massage parameters of the same massage point category in a plurality of first reference massage information, and generating second reference massage information according to the plurality of weighted averaged massage parameters comprises: The first reference massage information is represented by formula (7), and the multiple massage parameters in the first reference massage information are recorded as first massage parameters. The formula (7) is as follows: ； In formula (7), For the The first reference massage information, For the The first reference massage information The first massage parameter of each massage point category; The first massage parameters of the same massage point category in the plurality of first reference massage information are weighted averaged to obtain a weighted average massage parameter, which is recorded as a second massage parameter and is expressed by formula (8). Formula (8) is as follows: ； In formula (8), For the The second massage parameter after weighted average, , For the The weight of the first reference massage information, For the The first reference massage information The first massage parameter of each massage point category; The second massage parameters are sorted to obtain the second reference massage information, which is expressed by formula (9). The formula (9) is as follows: ； In formula (9), Massage information for the second reference.

4. The control method of the graphene massager with multi-dimensional somatosensory feedback according to claim 3, characterized in that: The first usage feature information includes a first dimension feature, a second dimension feature, and a third dimension feature, the first dimension feature is configured to be generated by the physiological feedback information, the second dimension feature is configured to be generated by the manual adjustment information, and the third dimension feature is configured to be generated by the feedback evaluation information; Generating first usage characteristic information according to the feedback information, and adjusting the second reference massage information according to the first usage characteristic information to obtain third reference massage information includes: Collect feedback information of users of the current batch according to a first preset frequency to obtain the first dimensional features, the second dimensional features, and the third dimensional features; Inputting the first dimensional features, the second dimensional features and the third dimensional features into an EKF model based on Kalman filtering for fusion, to obtain a fused multi-dimensional feature vector; The multidimensional feature vector is input as an input feature into the DQN algorithm model to obtain the third reference massage information.

5. The control method of the graphene massager with multi-dimensional somatosensory feedback according to claim 4, characterized in that: The first dimension feature is configured to be generated by the physiological feedback information and includes: Collecting pressure feedback information corresponding to massage position information within a preset time period, heart rate information of the current user within a preset time period, and skin conductivity information of the current user within a preset time period; Arranging the pressure feedback information in chronological order to obtain a pressure value change sequence, arranging the heart rate information in chronological order to obtain a heart rate value change sequence, and arranging the skin conductivity information in chronological order to obtain a skin conductivity change sequence; Calculating the pressure characteristics corresponding to the pressure feedback information of the current user according to the pressure value change sequence; Calculating the heart rate characteristics corresponding to the heart rate information of the current user according to the heart rate value change sequence; Calculating a skin conductivity feature corresponding to the skin conductivity information of the current user according to the skin conductivity change sequence; Concatenating the pressure feature, the heart rate feature, and the skin conductivity feature to obtain the first dimensional feature; The second dimension feature is configured to be generated by the manual adjustment information and includes: Collecting massage intensity adjustment information, massage temperature adjustment information and massage frequency adjustment information within a preset time period; Constructing a massage intensity adjustment curve according to the massage intensity adjustment information, obtaining a first change slope value of the massage intensity within a unit time according to the massage intensity adjustment curve, and generating a massage intensity adjustment feature according to the first change slope value; Constructing a massage temperature adjustment curve according to the massage temperature adjustment information, obtaining a second change slope value of the massage temperature in a unit time according to the massage temperature adjustment curve, and generating a massage temperature adjustment feature according to the second change slope value; Constructing a massage frequency adjustment curve according to the massage frequency adjustment information, obtaining a third change slope value of the massage frequency in unit time according to the massage frequency adjustment curve, and generating a massage frequency adjustment feature according to the third change slope value; The massage intensity adjustment feature, the massage temperature adjustment feature and the massage frequency adjustment feature are concatenated to obtain the second dimension feature.

6. The control method of the graphene massager with multi-dimensional somatosensory feedback according to claim 5, characterized in that: The third dimension feature is configured to be generated through the feedback evaluation information and includes: Collect multiple review texts within a preset time period, and perform the following steps on each review text: Performing sentence segment recognition on the review text to obtain a plurality of basic sentence segments; Inputting the basic sentence segment into a language recognition model to obtain a sentiment feature, wherein the sentiment feature includes sentiment polarity and sentiment intensity, wherein the sentiment polarity includes positive sentiment and negative sentiment, and the sentiment intensity includes a positive sentiment intensity value and a negative sentiment intensity value; Grouping the plurality of basic segments according to relevance to obtain at least one first segment group; Arranging the plurality of emotion features in the first sentence group one by one in chronological order to obtain a first emotion feature sequence of the current user, and constructing an emotion fluctuation curve according to the first emotion feature sequence; Calculating a fourth change slope value corresponding to each of the emotion fluctuation curves; splicing a plurality of the fourth change slope values ​​to obtain a second emotion feature sequence corresponding to the current review text; The third dimension feature is obtained by concatenating multiple second emotion feature sequences.

7. The control method of the graphene massager with multi-dimensional somatosensory feedback according to claim 6, characterized in that: The first dimensional features, the second dimensional features and the third dimensional features are input into the EKF model based on Kalman filtering for fusion, and the fused multi-dimensional feature vector includes: The first dimensional feature, the second dimensional feature and the third dimensional feature are expressed by formula (10), and the formula (10) is as follows: ； In formula (10), The first dimension feature, the second dimension feature, and the third dimension feature are used as the first vector expression of the input feature of the EKF model. is the first dimension feature, is the second dimension feature, It is the third dimension feature; Will The acquisition time in the preset time segment is divided into multiple second vector expressions, and the second vector expressions are expressed by formula (11), which is as follows: ； In formula (11), The preset time period The second vector expression of the moment, For the The first dimension feature of the moment, For the The second dimension feature of the moment, For the The third dimension characteristics of each moment; Construct an EKF model based on Kalman filtering, use the second vector expression as the state vector, define the state equation and the observation equation, and the state equation is expressed by formula (12), which is as follows: ； The observation equation is expressed by formula (13), which is as follows: In formula (12) and formula (13), For the The state vector at the moment, For the The parameter vector at each moment, For the The state noise vector at the moment is is the state transfer function, is the observation function, For the The observation noise vector at time instant, No. The observation vector at each moment; The EKF model is initialized and a prediction step is performed. The prediction step is represented by formula (14), which is as follows: ； In formula (14), is the state transfer Jacobian matrix, is the transposed matrix of the state transfer Jacobian matrix, For the current Based on the previous moment The state noise vector predicted by the information, For Based on the previous moment The state noise vector predicted by the information, For the current Based on the previous moment The state estimate covariance obtained by the state estimate and input prediction is, exist Based on the previous moment The state estimate covariance between the state estimate of and the state estimate obtained by input prediction; The update step is performed, which is expressed by formula (15), which is as follows: ； In formula (15), is the Kalman gain matrix, For the current The state estimation covariance obtained by the state estimation at time and the input prediction is, is the observation matrix, is the transposed matrix of the observation matrix, is the observation noise covariance matrix, For the current The state noise vector of the information prediction at the moment, is the identity matrix; After the acquisition time iteration within the preset time segment is completed, the final estimated state vector of the EKF is obtained, which is recorded as the fused multidimensional feature vector .

8. The control method of the graphene massager with multi-dimensional somatosensory feedback according to claim 7, characterized in that: The DQN algorithm model includes a DQN online network model and a DQN target network model; Inputting the multidimensional feature vector as an input feature into the DQN algorithm model, and obtaining the third reference massage information includes: The multi-dimensional feature vector is input into the DQN online network model and expressed by formula (16), which is as follows: ； In formula (16), is the DQN online network model, is the DQN online network model function, is the first The corresponding moment status, For the Action value prediction of the second massage parameter for each massage point category; The output result of the DQN online network model is input into the DQN target network model, and the prediction result of the maximum action value prediction of the second massage parameter in the future state is calculated as the third reference massage parameter, which is expressed by formula (17). The formula (17) is as follows: ； In formula (17), For the The prediction result of the maximum action value prediction of the second massage parameter of the massage point category, that is, The third massage parameter of each massage point category; Repeat the above steps until the generated The third massage parameters corresponding to the massage point categories are sorted to form the third reference massage information, which is expressed by formula (18). The formula (18) is as follows: ； In formula (18), The third reference massage information.

9. A graphene massager control system with multi-dimensional somatosensory feedback, characterized in that: The method according to any one of claims 1 to 8, comprising: An information collection module, used to obtain first user information, wherein the first user information includes information characteristics of five information categories: occupation information, work and rest information, diet information, massage location information and personal information of the current user; A massage parameter configuration module is used to generate first initial feature information according to the first user information, match it with second feature information in a database using a cosine similarity function, wherein the second feature information is generated when other users use a graphene massager of the same massage device category; obtain massage parameter information associated with massage modes corresponding to multiple successfully matched second feature information, sort and generate first reference massage information according to the massage mode; perform weighted averaging on massage parameters of the same massage point category in multiple first reference massage information, and generate second reference massage information according to multiple weighted averaged massage parameters; obtain the initial massage mode of the current user according to the second reference massage information and massage the current user; A feedback analysis module, used to collect user feedback information during the massage process, wherein the feedback information includes physiological feedback information, manual adjustment information and feedback evaluation information; The massage parameter configuration module is further used to generate first usage characteristic information according to the feedback information, and adjust the second reference massage information according to the first usage characteristic information to obtain third reference massage information; generate a usage massage mode according to the third reference massage information, and replace the current initial massage mode in the next batch of massages; The personal data management module is used to construct a personal usage database and store the feedback information of each batch of massages in the personal usage database in chronological order.

10. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method of any one of claims 1 to 8 when executed by a processor.

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