Control method, system and equipment of massage equipment and storage medium

Abstract

The invention relates to a massage equipment control method and system, equipment and a storage medium, and belongs to the technical field of equipment control. The method comprises the steps of collecting input data of a user; inputting the input data into the generative AI model, and obtaining interaction data between the user and the generative AI model through the generative AI model; inputting the interaction data into an intelligent analysis module, analyzing the interaction data through the intelligent analysis module, and determining a first characteristic parameter; wherein the first feature parameter represents an emotion category extracted from the interaction data; and adjusting and controlling the massage equipment based on the first characteristic parameter. According to the method, intelligent control in the interaction process of the user and the massage equipment is provided, the personalized massage control scheme of the user is gradually formed along with the deep interaction process, so that the user experience is improved, different personalized experiences can be generated in the use process of different users, and the long-term use requirement of the user can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of device control, and in particular to a control method, system, device and storage medium for massage equipment. Background Art

[0002] Due to the accelerated pace of life, people's long-term desk work, exercise fatigue or accumulated stress can easily lead to muscle tension and poor blood circulation. Massage equipment, as a good helper for health therapy, can simulate massage through technological means to help relax muscles, relieve soreness, and promote blood circulation, allowing users to enjoy a professional-level comfortable experience at home.

[0003] However, the study found that current massagers mainly adjust the functions or parameters of massagers through preset programs or manual adjustments, such as through physical buttons or control through applications. Such products lack personalized adaptation capabilities, users have a single way of interacting with them, and the user experience is poor, which will reduce users' long-term use needs. Summary of the Invention

[0004] In order to solve the above-mentioned problems in the prior art, the present invention provides a control method, system, device and storage medium for a massage device.

[0005] In a first aspect, an embodiment of the present application provides a method for controlling a massage device, comprising: collecting input data from a user; inputting the input data into a generative AI model, and obtaining interaction data between the user and the generative AI model through the generative AI model; inputting the interaction data into an intelligent analysis module, analyzing the interaction data through the intelligent analysis module, and determining a first characteristic parameter; wherein the first characteristic parameter represents an emotion category extracted from the interaction data; and adjusting and controlling the massage device based on the first characteristic parameter.

[0006] Optionally, the input data includes at least one of input voice data, input text data, input picture data, input video data, input gesture data, and human body feature data.

[0007] Optionally, before inputting the input data into the generative AI model, the method further includes: identifying the input data, and determining whether there are any adjustment instructions for the massage device in the input data; if there are any adjustment instructions for the massage device in the input data, directly adjusting and controlling the massage device.

[0008] Optionally, the input data is input voice data; identifying the input data and determining whether there are any adjustment instructions for the massage device in the input data include: converting the input voice data into input text; identifying the input text and determining whether there are target keywords in the input text; wherein the target keywords represent the adjustment instructions for the massage device.

[0009] Optionally, the input data is input gesture data; identifying the input data and determining whether the input data contains adjustment instructions for the massage device includes: matching the input gesture data with a target gesture in a pre-built gesture library, and determining whether the input gesture data successfully matches the target gesture; wherein the target gesture represents the adjustment instructions for the massage device.

[0010] Optionally, different emotion categories match different working modes of the massage device; or, different emotion categories match different adjustment parameters of the massage device, or different emotion categories match different adjustment parameter ranges of the massage device.

[0011] Optionally, the adjustment parameters include at least one of vibration frequency, vibration amplitude, vibration duration, massage device temperature, and massage direction; or, the same emotion category corresponds to multiple groups of adjustment parameters; each group of adjustment parameters includes at least one of vibration frequency, vibration amplitude, vibration duration, massage device temperature, and massage direction.

[0012] Optionally, the interaction data includes at least one input data and at least one reply data; wherein, the reply data is generated by the generative AI model for the input data.

[0013] Optionally, the step of inputting the interaction data into an intelligent analysis module, analyzing the interaction data through the intelligent analysis module, and determining a first characteristic parameter includes: inputting the reply data into the intelligent analysis module, analyzing the interaction data through the intelligent analysis module, and determining the first characteristic parameter; wherein the first characteristic parameter represents the emotion category of the extracted generative AI model.

[0014] Optionally, the adjustment and control of the massage device based on the first characteristic parameter includes: determining a second characteristic parameter in combination with the historical adjustment and control data of the user when using the massage device; wherein the second characteristic parameter represents the satisfaction level of multiple adjustment mechanisms corresponding to the first characteristic parameter; based on the second characteristic parameter, determining the adjustment mechanism of the massage device, and adjusting and controlling the massage device based on the adjustment mechanism; wherein the adjustment mechanism of the massage device includes the working mode of the massage device, or the adjustment parameter of the massage device.

[0015] Optionally, the method further includes: inputting the historical data of the user in using the massage device into the intelligent analysis module; determining a third characteristic parameter through the intelligent analysis module; wherein the third characteristic parameter represents the behavioral preference of the user; and determining whether to adjust and control the massage device based on the third characteristic parameter.

[0016] Optionally, the method also includes: obtaining human characteristic data of the user when using the massage device; inputting the human characteristic data into the intelligent analysis module to determine a fourth characteristic parameter; wherein the fourth characteristic parameter represents the physiological state of the user; based on the fourth characteristic parameter, determining whether to adjust and control the massage device, and / or, obtaining behavioral data of the user when using the massage device; inputting the behavioral data into the intelligent analysis module to determine a fifth characteristic parameter; wherein the fifth characteristic parameter represents the behavioral state of the user; based on the fifth characteristic parameter, determining whether to adjust and control the massage device.

[0017] Optionally, the generative AI model is a local generative AI model; or, the generative AI model is a cloud-based generative AI model.

[0018] Optionally, the adjusting and controlling the massage device based on the first characteristic parameter includes: adjusting and controlling the massage device based on the first characteristic parameter, and the adjustment and control time is a preset duration.

[0019] In second aspect, the present application provides a control system for a massage device, comprising: an acquisition module for acquiring user input data; an interaction module for inputting the input data into a generative AI model, and obtaining interaction data between the user and the generative AI model through the generative AI model; an intelligent analysis module for inputting the interaction data into the intelligent analysis module, analyzing the interaction data through the intelligent analysis module, and determining a first characteristic parameter; wherein the first characteristic parameter represents an emotion category extracted from the interaction data; and an adjustment and control module for adjusting and controlling the massage device based on the first characteristic parameter.

[0020] In a third aspect, the present application provides an electronic device, comprising: a processor; the processor is used to execute a computer program to implement any optional method as described in the first aspect above.

[0021] In a fourth aspect, the present application provides a massage device comprising: a massage device body and a controller; the controller is arranged inside the massage device body; the controller is used to execute a computer program to implement any optional method as described in the first aspect above.

[0022] In a fifth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, it implements any optional method as described in the first aspect above.

[0023] The beneficial effects of the present invention include: The present application provides a method for interacting through a generative AI model, performing emotion recognition on the interaction data, and then adjusting and controlling the massage equipment based on the emotion category. The cleverness of this method is that it can design a dialogue path through a generative AI model, extract emotions during the interaction process, and implement intelligent control based on emotions, thereby solving the problem of undifferentiated control of traditional massage equipment. In other words, the method provided by the present application provides an intelligent control in the interaction process between the user and the massage equipment. As the interaction process deepens, a personalized massage control plan for the user is gradually formed, thereby improving the user experience. Different users will have different personalized experiences during use, which can meet the user's long-term use needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flowchart of a method for controlling a massage device according to an embodiment of the present invention; Figure 2 A flowchart of another method for controlling a massage device according to an embodiment of the present invention; Figure 3 A flowchart of the steps of a control method for a massage device provided by an embodiment of the present invention; Figure 4 A block diagram of a control system for a massage device provided by an embodiment of the present invention; Figure 5 This is a module block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, circuits, and methods are omitted to avoid obstructing the description of the present application with unnecessary details.

[0026] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0027] The study found that current massagers mainly adjust their functions or parameters through preset programs or manual adjustments, such as through physical buttons or control through applications. These products lack personalized adaptation capabilities, users have a single way to interact with them, and the user experience is poor, which in turn reduces users' demand for long-term use.

[0028] In view of the above problems, the present application proposes the following embodiments to solve the above technical problems.

[0029] See also Figure 1 , an embodiment of the present application provides a control method for a massage device, the method specifically comprising: steps 101 to 104.

[0030] In the embodiments of the present application, the massage device may include but is not limited to a full-body massager, such as a massage chair; a local massager, such as a head massager, a neck massager, a shoulder massager, and a leg massager.

[0031] Step 101: Collect user input data.

[0032] When a user uses the massage device, input data of the user's interaction with the massage device can be collected.

[0033] The input data may be collected by the massage device itself, or may be collected by the user's communication device, such as the user's mobile phone or tablet.

[0034] Step 102: Input the input data into the generative AI model, and obtain the interaction data between the user and the generative AI model through the generative AI model.

[0035] That is, the generative AI model is configured to receive input data from the user and is able to interact with the user to generate interaction data.

[0036] Step 103: Input the interaction data into the intelligent analysis module, analyze it through the intelligent analysis module, and determine the first characteristic parameter.

[0037] The first feature parameter represents the emotion category extracted from the interaction data.

[0038] That is, after the interaction data is generated, intelligent analysis is performed based on the intelligent analysis module to extract the emotion categories in the interaction process.

[0039] Among them, the emotion categories can be but are not limited to: happiness, tension, fatigue, pain, comfort, excitement, etc.

[0040] The above-mentioned intelligent analysis module can be but is not limited to an AI model, a machine learning model, a deep learning model, a reinforcement learning model, etc.

[0041] Step 104: Based on the first characteristic parameter, the massage device is adjusted and controlled.

[0042] That is, the massage equipment can ultimately be adjusted and controlled according to the identified emotion category.

[0043] In summary, the embodiments of the present application provide a method for interacting through a generative AI model, performing emotion recognition on the interaction data, and then adjusting and controlling the massage equipment based on the emotion category. The cleverness of this method is that it can design a dialogue path through a generative AI model, extract emotions during the interaction process, and implement intelligent control based on emotions, thereby solving the problem of undifferentiated control of traditional massage equipment. In other words, the method provided by the present application provides an intelligent control in the interaction process between the user and the massage equipment. As the interaction process deepens, a personalized massage control plan for the user is gradually formed, thereby improving the user experience. Different users will have different personalized experiences during use, which can meet the user's long-term use needs.

[0044] Optionally, the above-mentioned input data includes at least one of input voice data, input text data, input picture data, input video data, input gesture data, and human body feature data.

[0045] For example, when the input data is input voice data, it indicates that the user desires to have a voice conversation. At this time, the input voice data can be converted into input text, and then the input text can be recognized and an answer text can be generated. The answer text can then be played voice-wise to form an interactive data.

[0046] For example, if the user's input text is "I am so tired today", the answer text automatically generated by the AI ​​model corresponding to the input text "I am so tired today" can be "It seems that you are a little tired today, so you need to relax."

[0047] Similarly, input text data, input image data, and input video data can be input directly by the user through the application. When the generative AI model receives the above three types of data, it recognizes them and generates response data. The response data in this case can correspond to the input data. For example, the response data corresponding to the input text data can be text data, the response data corresponding to the input image data can be image data, and the response data corresponding to the input video data can be video data. Of course, the response data can also be unified as text data, or other data such as voice data, etc., which is not limited at this time.

[0048] The input gesture data can be detected and collected by the camera. The generative AI model can generate corresponding response data by recognizing the input gesture data.

[0049] For example, the user's gesture is clenching fists, indicating that the user may have a nervous reaction during the massage, and the answer data of the generative AI model may be "You can relax."

[0050] The above-mentioned human body characteristic data may be, but is not limited to, brain wave data, electrocardiogram signal, heart rate data, respiratory rate, electromyography signal, etc.

[0051] For example, the generative AI model can generate answer data based on different human body feature data. For example, if it is detected that the user's heart rate data is gradually increasing, the answer data of the generative AI model may be "You seem a little nervous."

[0052] It should be noted that the interaction data in the embodiment of the present application includes at least one input data and at least one reply data, wherein the reply data is generated by the generative AI model for the input data.

[0053] In one application scenario, the input data may be input into an intelligent analysis module, and the intelligent analysis module performs analysis to determine a first characteristic parameter. In this case, the first characteristic parameter represents the emotion category of the user.

[0054] The input data is taken as input text data for example.

[0055] In example 1, the user's input data is "I am very tired today". The intelligent analysis module can perform emotion recognition on the content of the input data "I am very tired today", such as determining that the user's emotion category is: tired.

[0056] In example 2, the user's input data is "Not bad, today's massage is very comfortable". The intelligent analysis module can perform emotion recognition on the content of the input data "Not bad, today's massage is very comfortable", such as determining that the user's emotion category is: pleasure.

[0057] In example three, the user's input data is "a little pain, a little discomfort". The intelligent analysis module can perform emotion recognition on the content of the input data "a little pain, a little discomfort", such as determining that the user's emotion category is: pain.

[0058] Then, based on the user's determined emotional categories, different adjustments and controls can be applied to the massage device. In other words, the aforementioned application scenario can be represented as triggering different massage device adjustments based on the user's emotional category changes while using the massage device. In this process, the generative AI model can guide the user's emotional exposure through a designed dialogue path, achieving personalized adjustments and controls tailored to the user's emotional changes.

[0059] In another application scenario, the reply data can be input into the intelligent analysis module, which analyzes the response data and determines the first characteristic parameter. In this case, the first characteristic parameter represents the emotion category of the extracted generative AI model.

[0060] The input data is taken as input text data for example.

[0061] In example 1, the user input data is "It's nice to chat with you," and the generative AI model's response data is "I'm glad to have your approval." The intelligent analysis module can perform emotion recognition on the response data "I'm glad to have your approval" and determine that the generative AI model's emotion category is "happy."

[0062] In Example 2, the user input is "Let's play idiom chain games together," and the generative AI model responds with "That's great! I've been wanting to play this for a while." The intelligent analysis module can perform emotion recognition on the response "That's great! I've been wanting to play this for a while," and determine that the generative AI model's emotion category is excitement.

[0063] Then, different adjustment controls can be performed on the massage device based on the different emotion categories of the generative AI model determined. For example, if the generative AI model is happy, the massage device can be controlled to increase the vibration amplitude; if the generative AI model is depressed, the massage device can be controlled to reduce the vibration amplitude. That is, the above application scenario can be expressed as triggering different adjustment controls of the massage device based on the change of the emotion category of the generative AI model during the interaction process when the user is using the massage device. In this process, a new interactive control mechanism is provided, which takes the generative AI model as the main body and can use the interactive emotion of the generative AI model to feedback control the adjustment of the massage device, that is, making the massage device a physical extension carrier of AI, converting the emotion of the AI ​​model into physical feedback of the massage device, and enhancing the human-computer interaction experience.

[0064] In the first embodiment, different emotion categories can match different working modes of the massage device.

[0065] Among them, the working mode can be differentiated according to the massage technique, for example, the working mode can be divided into kneading mode, percussion mode, vibration mode, massage mode, etc. For example, when the emotion category is determined to be fatigue, the working mode of the matching massage device can be kneading mode and massage mode.

[0066] The working mode can also be differentiated according to the intensity level. For example, the working mode can be divided into low intensity mode, medium intensity mode, and high intensity mode. When the emotion category is determined to be pain, the working mode can be switched from high intensity mode to medium intensity mode.

[0067] Working modes can also be differentiated based on functional characteristics. For example, the working modes are divided into Level 1, Level 2, and Level 3. Level 1 corresponds to low-frequency vibration + heat compress. Level 2 corresponds to high-frequency shock + cold compress. Level 3 corresponds to slow-paced kneading. If the emotion category is determined to be pain, the working mode can be switched from Level 2 to Level 3.

[0068] The above working modes can also be customized or classified according to other methods, which are not limited here.

[0069] In a second embodiment, different emotion categories are matched with different adjustment parameters of the massage device.

[0070] In one embodiment, the adjustment parameters may include at least one of vibration frequency, vibration amplitude, vibration duration, massage device temperature, and massage direction.

[0071] In other words, after determining the emotion category, one or more corresponding adjustment parameters may be determined for the emotion category.

[0072] For example, when the emotion category is determined to be pain, the corresponding adjustment parameter may be to reduce the vibration frequency.

[0073] When the emotion category is determined to be fatigue, the corresponding adjustment parameters may be to extend the vibration duration and increase the temperature of the massage device.

[0074] In another case, the same emotion category corresponds to multiple sets of adjustment parameters; each set of adjustment parameters includes at least one of vibration frequency, vibration amplitude, vibration duration, massage device temperature, and massage direction.

[0075] That is, the same emotion category can correspond to multiple sets of different adjustment parameters. In each matching process, one set of adjustment parameters can be randomly selected for adjustment control.

[0076] For example, for the emotion category of pain, there are three groups of adjustment parameters, namely adjustment parameter group A, adjustment parameter group B and adjustment parameter group C.

[0077] Adjustment parameter group A corresponds to reducing the vibration frequency. Adjustment parameter group B corresponds to reducing the vibration frequency and vibration amplitude; the value of the vibration frequency reduction in adjustment parameter group B can be different from the value of the vibration frequency reduction in adjustment parameter group A. Adjustment parameter group C corresponds to changing the massage direction and increasing the temperature of the massage device. Then, when the emotion category is determined to be pain, a random selection can be made from the three groups of adjustment parameters.

[0078] This approach can optimize the adjustment process, allowing users to experience different massage adjustment controls during use. It can also provide a theoretical basis for the subsequent development of personalized adjustment control mechanisms.

[0079] In a third embodiment, different emotion categories are matched with different adjustment parameter ranges of the massage device.

[0080] For example, when the adjustment parameters may include at least one of vibration frequency, vibration amplitude, vibration duration, massage device temperature, and massage direction, the adjustment parameter range corresponds to the interval of each adjustment parameter.

[0081] For example, when the emotion category is happy, the vibration duration can be in the range of 10s to 20s. The specific vibration duration can be randomly selected within this range. For example, when the emotion category is happy, the vibration duration can be randomly determined to be 15s.

[0082] For another example, when the emotion category is pleasure, the vibration frequency may range from 20 Hz to 30 Hz.

[0083] Of course, in this embodiment, the level of each emotion category can be further refined. For each emotion category, different levels of the emotion category correspond to different values ​​within the adjustment parameter range.

[0084] For example, when the emotion category is happy, it can be further refined into happy level 1, happy level 2, and happy level 3. The smaller the level value, the higher the degree of happiness. When the emotion category is happy, if the vibration duration range is 10s to 20s, happy level 1 can correspond to a vibration duration of any value between 16s and 20s, happy level 2 can correspond to a vibration duration of any value between 13s and 16s, and happy level 3 can correspond to a vibration duration of any value between 10s and 13s.

[0085] See also Figure 2 In one embodiment, before inputting the input data into the generative AI model, the method further includes: steps 201 to 202.

[0086] Step 201: Identify input data and determine whether the input data contains an instruction representing adjustment of the massage device.

[0087] Step 202: If the input data contains an instruction for adjusting the massage device, the massage device is directly adjusted and controlled.

[0088] The above process can be understood as directly identifying the user's control instructions and using them to adjust and control the massage equipment in a timely manner.

[0089] This application takes into account the problem that massage equipment connected to the generative AI model may have response delays during use and cannot respond to the user's adjustment needs in a timely manner. Therefore, this application proposes a judgment and response mechanism, which first identifies the input data locally. If there are instructions representing the adjustment of the massage equipment in the input data, the massage equipment is directly adjusted and controlled. If there are no instructions representing the adjustment of the massage equipment, the generative AI model is then connected for processing. This method has the following advantages: First, it can provide a quick response channel for user needs. Specifically, when the user needs to control the massage equipment, it can respond directly, thereby improving the timeliness of the response to the user's control needs.

[0090] Second, if the user currently has no direct control needs and may only want to communicate with the massage device, the connected generative AI model can provide an intelligent interaction process with the user. During this process, it can be determined whether the user still has the need to adjust and control the massage device, and then determine whether the massage device needs to be adjusted and controlled, thereby improving the user experience.

[0091] In one embodiment, if the input data is input voice data; then the above-mentioned identification of the input data and determination of whether the input data contains instructions representing adjustment instructions for the massage device include: converting the input voice data into input text; identifying the input text and determining whether the input text contains target keywords; wherein the target keywords represent the adjustment instructions for the massage device.

[0092] That is, after the input voice is converted into text form (input text), keyword detection is performed on the input text to determine whether there is a target keyword representing an adjustment instruction for controlling the massage device.

[0093] The target keyword may be, but is not limited to, “increase frequency”, “switch mode”, “reduce intensity”, “lower temperature” and the like.

[0094] For example, assuming that the user's corresponding input text is "My legs are sore today, increase the massage intensity", then through this step, it can be identified that there is a target keyword in the input text, and the target keyword is "increase the massage intensity".

[0095] It is understandable that a target keyword library for massage equipment may be pre-built, and then the input text may be segmented, and each segmented word may be matched with each target keyword in the target keyword library for similarity.

[0096] The similarity matching can be performed using, but not limited to, the Jaccard similarity algorithm and word vector statistical methods. The threshold for the similarity matching can be set to be above 80%.

[0097] When the target keyword is present in the input text, the massage device is directly adjusted and controlled based on the adjustment instructions corresponding to the target keyword. For example, when the target keyword "increase massage intensity" is recognized, the massage device is directly controlled, such as adjusting the motor speed, the movement amplitude of the transmission component, the airbag inflation volume, and so on.

[0098] The above process can be understood as directly recognizing the user's voice control instructions and using them to adjust and control the massage equipment in a timely manner.

[0099] In another embodiment, if the input data is input gesture data; then the above-mentioned identification of the input data and determination of whether the input data contains adjustment instructions for the massage device include: matching the input gesture data with a target gesture in a pre-built gesture library, and determining whether the input gesture data successfully matches the target gesture; wherein the target gesture represents the adjustment instructions for the massage device.

[0100] For example, the target gesture may be, but is not limited to, raising the hand, lowering the hand, etc. When the target gesture is raising the hand, it may indicate that the user wants to increase the intensity of the massage device, and when the target gesture is lowering the hand, it may indicate that the user wants to lower the intensity of the massage device.

[0101] The above process can be understood as directly recognizing the user's gesture control instructions and using them to adjust and control the massage equipment in a timely manner.

[0102] Optionally, in one embodiment, the above-mentioned adjustment and control of the massage device based on the first characteristic parameter may further specifically include: determining the second characteristic parameter in combination with the historical adjustment and control data of the user when using the massage device; determining the adjustment mechanism of the massage device based on the second characteristic parameter, and adjusting and controlling the massage device based on the adjustment mechanism.

[0103] The massage device's adjustment mechanism includes an operating mode or adjustment parameter. The second characteristic parameter represents a user's satisfaction with the various adjustment mechanisms corresponding to the first characteristic parameter; that is, the second characteristic parameter represents a user's satisfaction with the adjustment control process under different emotional states. This satisfaction level can be determined based on the number of adjustments performed by the user.

[0104] For example, when it is determined that the user's emotional category is pain, there are matching adjustment control data of two time nodes in the historical adjustment control data. At the first time node, the vibration frequency is lowered for regulation, and there is no other adjustment control afterwards, which means that the user is highly satisfied with the adjustment mechanism of lowering the vibration frequency. At the second time node, after lowering the vibration amplitude, the massage device still actively or passively adjusts other parameters multiple times, such as continuing to lower the vibration frequency, which means that the user is less satisfied with the adjustment mechanism of lowering the vibration amplitude. Therefore, for the current adjustment, the adjustment mechanism of lowering the vibration frequency can be adopted when it is determined that the user's emotional category is pain. That is, the method can be to select the adjustment mechanism with the highest satisfaction for different emotional categories and apply it to the current scene.

[0105] The degree of satisfaction can be determined inversely with the number of adjustments. For example, for emotion category A, if the user does not change the first adjustment mechanism after using it, the satisfaction with the first adjustment mechanism is 100. If the user continues to adjust once after using the second adjustment mechanism, the satisfaction with the second adjustment mechanism is 80. If the user continues to adjust twice after using the third adjustment mechanism, the satisfaction with the third adjustment mechanism is 60, and so on.

[0106] It can be seen that this method realizes the personalized formulation of adjustment mechanisms for users, can integrate emotion recognition and driving decisions, and improve the user experience.

[0107] Optionally, the method further includes: inputting the historical data of the user using the massage device into an intelligent analysis module; determining a third characteristic parameter through the intelligent analysis module; wherein the third characteristic parameter represents the user's behavioral preference; and determining whether to adjust and control the massage device based on the third characteristic parameter.

[0108] It should be noted that the above historical data may be data of the user using the massage device in different time periods, that is, the historical data includes time information of the user's use process.

[0109] For example, it can be determined from historical data that users typically use massage devices for low-intensity heat and light kneading between 6:00 and 9:00, such as to relieve stiff muscles in the morning.

[0110] When the time period is 18:00~20:00, users usually use massage devices for high-frequency vibration, such as when users want to relax tense muscles after doing fitness.

[0111] After determining the user's behavioral preferences, the massage device can then determine whether adjustments are needed at the current time. For example, if the current time is 8:00, the adjustment strategy might be to adjust the massage device's mode to "low-intensity heat and gentle kneading." If the current time is 19:00, the adjustment strategy might be to adjust the massage device's mode to "high-frequency vibration."

[0112] Of course, the user's behavioral preferences can be combined with the user's emotional category to jointly determine whether the massage device needs to be adjusted and controlled. The two can be weighted to determine the final adjustment mechanism. Of course, if the adjustment mechanism determined by the user's behavioral preferences conflicts with the adjustment mechanism determined by the user's emotional category, the adjustment mechanism determined by the user's emotional category can be used as the primary control.

[0113] In summary, the embodiments of the present application provide a personalized adjustment method for massage equipment that drives decisions through historical data, which can intelligently implement adjustment control based on the user's behavioral preferences and improve the user experience.

[0114] Optionally, the method also includes: obtaining human body characteristic data of the user when using the massage device; inputting the human body characteristic data into the intelligent analysis module to determine a fourth characteristic parameter; wherein the fourth characteristic parameter represents the physiological state of the user; and based on the fourth characteristic parameter, determining whether to adjust and control the massage device.

[0115] The user's body characteristic data may include, but is not limited to, brain wave data, electrocardiogram (ECG) signals, heart rate data, respiratory rate, myoelectric signals, etc. The user's body characteristic data may be detected by a sensor, such as a heart rate detection sensor.

[0116] Identifying the user's emotion category has been described in the previous embodiments. The user's body characteristic data can be analyzed to identify the user's current physiological changes (i.e., changes in physiological state). For example, a rapidly increasing heart rate, indicating a high massage intensity, or feelings of anxiety or discomfort, can be used to provide feedback and adjust the massage device based on the determined physiological state.

[0117] Optionally, the method also includes: obtaining behavioral data of the user when using the massage device; inputting the behavioral data into an intelligent analysis module to determine a fifth characteristic parameter; wherein the fifth characteristic parameter represents the behavioral state of the user; and determining whether to adjust and control the massage device based on the fifth characteristic parameter.

[0118] The user's behavior data includes but is not limited to whether the user removes or moves the massage device away from the body (which can be determined based on the pressure value detected by the massage head). The user's behavior data can be detected by a sensor, such as a pressure sensor.

[0119] It's important to note that user behavior data can also reflect usage. For example, if a user moves the massage device away from the massage area, it indicates that the user is not adapting to the current massage intensity. Therefore, based on analyzed user behavior data, adjustments can be made to achieve more precise response control.

[0120] Optionally, the generative AI model is a local generative AI model; or, the generative AI model is a cloud-based generative AI model.

[0121] In other words, in one implementation, the generative AI model can be deployed directly on the massage device or the user's electronic device, making it a lightweight model. Local deployment of the generative AI model can further improve response speed, eliminating the need for network transmission and achieving lower latency.

[0122] In another implementation, the generative AI model can be a cloud-based model, in which case user data needs to be uploaded to the cloud. This approach can enable more complex conversation and sentiment analysis.

[0123] Optionally, in one embodiment, the above-mentioned adjusting and controlling the massage device based on the first characteristic parameter may further specifically include: adjusting and controlling the massage device based on the first characteristic parameter, and the adjustment and control time is a preset duration.

[0124] Among them, the preset time length can be set according to needs, such as 20 seconds, 30 seconds, 1 minute, etc.

[0125] The above process can be understood as follows: when the emotion category is identified as happiness, the vibration frequency can be increased for 20 seconds, and then the vibration frequency can be automatically reduced to the previous frequency after 20 seconds.

[0126] Of course, it is also possible to increase the frequency to a fixed value when the emotion category is identified as happiness, and then slowly attenuate the frequency. The whole process lasts for 20 seconds, and after 20 seconds, the vibration frequency is reduced to the previous frequency.

[0127] Taking the emotion category of the generative AI model extracted by the first characteristic parameter as an example, when the emotion category of the generative AI model is determined to be happy based on the response data of the generative AI model, the massage device can be actively triggered to increase the vibration frequency for 20 seconds.

[0128] The control method of the massage device provided in the embodiment of the present application is described below with a specific example. Figure 3 .

[0129] First, when using the massage device, the user can have a voice conversation. After the user inputs the voice, the massage device detects the input voice in real time, converts it into input text, and then recognizes the input text and extracts keywords. If the target keyword exists in the input text, that is, it is determined that there are adjustment instructions for the massage device in the input text, then the relevant instructions for the massage device are directly executed based on the target keyword. If there are no adjustment instructions for the massage device, it is connected to the AI ​​model (generative AI model), and the AI ​​model automatically generates an answer text corresponding to the input text, and the answer text is played voice. At the same time, the answer text is emotionally recognized, and the massage device is adjusted and controlled through the identified emotion category of the generative AI model.

[0130] See also Figure 4 Based on the same inventive concept, the present embodiment further provides a control system 400 for a massage device, including: The collection module 401 is used to collect user input data; An interaction module 402 is configured to input the input data into a generative AI model and obtain interaction data between the user and the generative AI model through the generative AI model; An intelligent analysis module 403 is configured to input the interaction data into the intelligent analysis module, analyze the interaction data through the intelligent analysis module, and determine a first characteristic parameter; wherein the first characteristic parameter represents an emotion category extracted from the interaction data; The adjustment control module 404 is used to adjust and control the massage device based on the first characteristic parameter.

[0131] See also Figure 5 Based on the same inventive concept, the embodiment of the present application provides an electronic device 500 that applies the above method. The electronic device 500 includes: at least one processor 501 ( Figure 5 Only one is shown in the figure), when the processor 501 executes the computer program, the steps of the method in any of the aforementioned embodiments are implemented.

[0132] Those skilled in the art will understand that Figure 5 This is merely an example of the electronic device 500 and does not constitute a limitation on the electronic device 500 . The electronic device 500 may include more or fewer components than shown in the figure, or may combine certain components, or may include different components.

[0133] The processor 501 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0134] Based on the same inventive concept, an embodiment of the present application provides a massage device that applies the above method.

[0135] The massage device includes a massage device body and a controller. The controller is disposed inside the massage device body and is configured to execute a computer program to implement the steps of the method provided in the aforementioned embodiment.

[0136] The controller may refer to the description of the processor in the aforementioned embodiment.

[0137] In the embodiments of the present application, the massage device may include but is not limited to a full-body massager, such as a massage chair; a local massager, such as a head massager, a neck massager, a shoulder massager, and a leg massager.

[0138] The hardware structure of the massage equipment may also include but is not limited to a drive motor, transmission components (such as gears, connecting rods, massage heads), air pumps, sensors, power supply systems, etc.

[0139] It should be noted that the above-mentioned systems, equipment, etc. are based on the same concept as the method embodiments of this application. The modules designed for the systems, the steps performed by the equipment, and the technical effects brought about can all be found in the method embodiment section and will not be repeated here.

[0140] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0141] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0142] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device, a recording medium, computer memory, read-only memory (ROM), random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Examples include a USB flash drive, a removable hard drive, a magnetic disk, or an optical disk.

[0144] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0145] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0146] In the embodiments provided in this application, it should be understood that the disclosed systems / devices and methods can be implemented in other ways. For example, the system / device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0148] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for controlling a massage device, characterized in that: include: Collect user input data; Inputting the input data into a generative AI model, and obtaining interaction data between the user and the generative AI model through the generative AI model; Inputting the interaction data into an intelligent analysis module, analyzing the interaction data through the intelligent analysis module to determine a first characteristic parameter; wherein the first characteristic parameter represents an emotion category extracted from the interaction data; Based on the first characteristic parameter, the massage device is adjusted and controlled.

2. The control method of the massage device according to claim 1, characterized in that: The input data includes at least one of input voice data, input text data, input picture data, input video data, input gesture data, and human body feature data.

3. The control method of the massage device according to claim 1, characterized in that: Before inputting the input data into the generative AI model, the method further includes: identifying the input data and determining whether the input data contains an instruction representing adjustment of the massage device; If the input data contains an instruction for adjusting the massage device, the massage device is directly adjusted and controlled.

4. The control method of the massage device according to claim 3, characterized in that: The input data is input voice data; and the identifying the input data and determining whether the input data contains an instruction representing adjustment of the massage device includes: Converting the input speech data into input text; Identify the input text and determine whether a target keyword exists in the input text; wherein the target keyword represents an adjustment instruction of the massage device.

5. The control method of the massage device according to claim 3, characterized in that: The input data is input gesture data; The step of identifying the input data and determining whether the input data contains an instruction representing adjustment of the massage device includes: Matching the input gesture data with a target gesture in a pre-built gesture library to determine whether the input gesture data successfully matches the target gesture; The target gesture represents an adjustment instruction for the massage device.

6. The control method of the massage device according to claim 1, characterized in that: Different emotion categories match different working modes of the massage device; or, Different emotion categories match different adjustment parameters of the massage device, or Different emotion categories match different adjustment parameter ranges of the massage device.

7. The control method of the massage device according to claim 6, characterized in that: The adjustment parameters include at least one of vibration frequency, vibration amplitude, vibration duration, massage device temperature, and massage direction; or, The same emotion category corresponds to multiple groups of adjustment parameters; each group of adjustment parameters includes at least one of vibration frequency, vibration amplitude, vibration duration, massage device temperature, and massage direction.

8. The control method of the massage device according to claim 1, characterized in that: The interaction data includes at least one input data and at least one reply data; wherein the reply data is generated by the generative AI model for the input data.

9. The control method of the massage device according to claim 8, characterized in that: Inputting the interaction data into an intelligent analysis module, analyzing the interaction data by the intelligent analysis module, and determining the first characteristic parameter includes: Inputting the reply data into the intelligent analysis module, and analyzing the response data by the intelligent analysis module to determine the first characteristic parameter; Among them, the first feature parameter represents the emotion category of the extracted generative AI model.

10. The control method of the massage device according to claim 1, characterized in that: The adjusting and controlling the massage device based on the first characteristic parameter includes: Determining a second characteristic parameter based on historical adjustment control data of the user when using the massage device; wherein the second characteristic parameter represents a satisfaction level of multiple adjustment mechanisms corresponding to the first characteristic parameter; determining an adjustment mechanism for the massage device based on the second characteristic parameter, and adjusting and controlling the massage device based on the adjustment mechanism; Wherein, the adjustment mechanism of the massage device includes the working mode of the massage device, or the adjustment parameters of the massage device.

11. The control method of the massage device according to claim 1, characterized in that: The method further comprises: Inputting the historical data of the user using the massage device into the intelligent analysis module; Determining a third characteristic parameter through the intelligent analysis module; wherein the third characteristic parameter represents the user's behavioral preference; Based on the third characteristic parameter, it is determined whether to perform adjustment control on the massage device.

12. The control method of the massage device according to claim 1, characterized in that: The method further comprises: Acquire human body feature data of the user when using the massage device; Inputting the human body characteristic data into the intelligent analysis module to determine a fourth characteristic parameter; wherein the fourth characteristic parameter represents the physiological state of the user; Based on the fourth characteristic parameter, determining whether to adjust and control the massage device, and / or, Obtaining behavioral data of the user using the massage device; Inputting the behavior data into the intelligent analysis module to determine a fifth characteristic parameter; wherein the fifth characteristic parameter represents the behavior state of the user; Based on the fifth characteristic parameter, it is determined whether to adjust and control the massage device.

13. The control method of the massage device according to claim 1, characterized in that: The generative AI model is a local generative AI model; Alternatively, the generative AI model is a cloud-based generative AI model.

14. The control method of the massage device according to claim 1, characterized in that: The adjusting and controlling the massage device based on the first characteristic parameter includes: Based on the first characteristic parameter, the massage device is adjusted and controlled, and the adjustment control time is a preset duration.

15. A control system for a massage device, characterized in that: include: The acquisition module is used to collect user input data; An interaction module, configured to input the input data into a generative AI model and obtain interaction data between the user and the generative AI model through the generative AI model; An intelligent analysis module, configured to input the interaction data into the intelligent analysis module, analyze the interaction data through the intelligent analysis module, and determine a first characteristic parameter; wherein the first characteristic parameter represents an emotion category extracted from the interaction data; An adjustment and control module is used to adjust and control the massage device based on the first characteristic parameter.

16. An electronic device, characterized in that: include: processor; The processor is configured to execute a computer program to implement the method according to any one of claims 1 to 14.

17. A massage device, characterized in that: include: Massage device body and controller; The controller is arranged inside the massage device body; The controller is configured to execute a computer program to implement the method according to any one of claims 1 to 14.

18. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 14 is implemented.

Images