An intelligent air wave optimization control method and system based on user feedback

Abstract

The application relates to the technical field of air wave physiotherapy, and specifically provides an intelligent air wave optimization control method and system based on user feedback, which comprises the following steps: constructing a physiotherapy feedback scene of an air wave physiotherapy instrument, designing a topological relationship diagram of the physiotherapy feedback scene under different feedback information; obtaining a running mechanism of the air wave physiotherapy instrument, and generating a control topological structure diagram; generating a correlation trigger model according to the topological relationship diagram and the control topological structure diagram, and based on the correlation trigger model, when receiving feedback information of a user, dynamically optimizing control parameters of the air wave physiotherapy instrument.

Description

Technical Field

[0001] This invention relates to the field of air wave therapy technology, and in particular to an intelligent air wave optimization control method and system based on user feedback. Background Technology

[0002] Currently, air wave pressure therapy devices, also known as circulatory pressure therapy devices, gradient pressure therapy devices, limb circulation devices, or pressure antithrombotic pumps, are a form of physical therapy. Air wave pressure therapy devices primarily work by sequentially and repeatedly inflating and deflating multi-sac air sacs to create circulatory pressure on various body tissues. This provides even and effective compression and relaxation from the distal to the proximal parts of the limbs, promoting overall blood circulation and lymphatic flow, improving microcirculation, and accelerating the return of body fluids. This helps prevent thrombosis and limb necrosis, and can directly or indirectly treat many diseases related to blood and lymphatic systems. After even massage, blood circulation gradually accelerates.

[0003] Existing air wave therapy devices, after integrating intelligent control functions, can perform corresponding therapy according to user-preset parameters. However, during the air wave therapy process, the air wave therapy device can only be dynamically adjusted by the user actively adjusting the parameters, and automatic feedback adjustment cannot be achieved.

[0004] For example, in CN114848427A, the method for controlling a physiotherapy device, the control device for a physiotherapy device, and the air wave pressure physiotherapy device propose that users can control the air wave pressure physiotherapy device by voice without moving their body, which makes it easier for users to operate the air wave pressure physiotherapy device during the physiotherapy process and gives users a more comfortable user experience.

[0005] However, this method can only achieve direct command control and cannot be adapted to various interactive environments or control different physiotherapy environments. Summary of the Invention

[0006] This invention provides a user-feedback-based intelligent air wave optimization control method and system to address the issue that existing air wave therapy devices, after integrating intelligent control functions, can perform corresponding therapy according to user-preset parameters. However, during air wave therapy, the device can only be dynamically adjusted by the user through active parameter adjustment, and automatic feedback adjustment is not possible.

[0007] This application proposes an intelligent air wave optimization control method based on user feedback, including:

[0008] Construct a physiotherapy feedback scenario for an air wave physiotherapy device, and design a topological relationship diagram of the physiotherapy feedback scenario under different feedback information;

[0009] Obtain the operating mechanism of the air wave therapy device and generate a control topology diagram;

[0010] Based on the topology diagram and control topology diagram, an associative triggering model is generated. Based on the associative triggering model, the control parameters of the air wave therapy device are dynamically optimized when user feedback is received.

[0011] Preferably, the physiotherapy feedback scenarios for constructing the air wave physiotherapy device include:

[0012] Obtain real-time operating parameters and feedback information from the air wave therapy device; among which,

[0013] Feedback information includes: voice information, active control commands, and muscle dynamic information;

[0014] Based on real-time operating parameters, the air wave therapy device is divided into functional components, and the control structure group of the functional components is determined.

[0015] Based on the control structure group, determine the control parameters corresponding to each control structure group with respect to different feedback information, and determine the corresponding interaction node;

[0016] Based on the interaction nodes, feedback information is matched with functional components to generate various different physical therapy feedback scenarios; among them,

[0017] Physiotherapy feedback scenarios include voice feedback control scenarios, command feedback control scenarios, and muscle dynamic information feedback scenarios.

[0018] Preferably, the topology diagram includes an instruction terminal and a feedback processing terminal;

[0019] The feedback processing unit is used to convert different feedback information into control commands for the air wave therapy device.

[0020] The command terminal is used to adjust the corresponding functional components of the air wave therapy device according to control commands.

[0021] Preferably, the feedback processing terminal includes a speech recognition conversion terminal, an input signal conversion terminal, and a muscle dynamic feedback information conversion terminal; wherein,

[0022] The voice recognition conversion terminal is used to convert the user's voice information into analog control commands for the air wave therapy device;

[0023] The input signal conversion terminal is used to convert the digital information of the user's real-time operation into analog control commands for the air wave therapy device;

[0024] The muscle dynamic information feedback terminal is used to convert the user's muscle sensing information into simulated control commands for the air wave therapy device.

[0025] Preferably, the step of acquiring the operating mechanism of the air wave therapy device and generating a control topology diagram includes:

[0026] The control signals of the functional components of the air wave physiotherapy device are decomposed to determine the instruction input and instruction execution terminals of different functional components;

[0027] Based on the command input terminal, determine the status trigger signals of different functional components;

[0028] Based on the instruction execution end, determine the status feedback signals of different functional components;

[0029] Based on the state trigger signal and state feedback signal, and according to the component information, a control topology diagram of the corresponding functional component is generated.

[0030] Preferably, the step of generating an associative triggering model based on the topology diagram and the control topology diagram includes:

[0031] Based on the topology diagram, set the trigger port and execution port;

[0032] Based on the control topology diagram, determine the corresponding functional components and triggering components;

[0033] The trigger port and trigger component are connected to generate the corresponding control signal tracking identifier and the first trigger model;

[0034] Data is interfaced between the execution port and functional components to generate a sensing identifier and a second trigger model for control signals;

[0035] An associative triggering model is constructed by using the first triggering model and the second triggering model.

[0036] Preferably, the dynamic optimization includes:

[0037] Based on the correlation triggering model, determine the user's real-time triggering feedback and obtain feedback control instructions within a preset time period;

[0038] Based on the feedback control commands, determine the functional components that will be controlled in real time;

[0039] Based on the current execution parameters of the functional components, determine the tuning parameters; among which,

[0040] The tuning parameter is the difference between the adjustment parameter corresponding to the feedback control command and the currently executed parameter;

[0041] Based on the difference parameters, the control of the functional components is optimized.

[0042] Preferably, the dynamic optimization further includes:

[0043] Receive real-time operating parameters from the air wave therapy device to determine the power parameters of different functional components;

[0044] Based on power parameters, calculate the real-time forward voltage of different functional components during operation;

[0045] Based on the real-time forward voltage, upon receiving feedback information, it is determined whether the difference between the estimated forward voltage and the real-time forward voltage exceeds a preset range when executing the tuning command corresponding to the feedback information; whereby...

[0046] When the preset range is exceeded, a voice prompt message is generated.

[0047] If the preset range is not exceeded, the corresponding tuning command is executed.

[0048] Preferably, the feedback information received from the user also includes:

[0049] In response to receiving an information feedback request, the real-time execution parameters of the target object corresponding to the information feedback request are obtained, wherein the target object is a functional component of the air wave therapy device;

[0050] By detecting real-time execution parameters, the corresponding physiotherapy area of ​​the target object is obtained; among which,

[0051] The physiotherapy area refers to the body part that is being treated.

[0052] Obtain the tuning parameters for the target object and verify the tuning parameters based on the treatment area; among them,

[0053] The verification is used to determine whether the target object in the optimization parameters is located in the physiotherapy area;

[0054] In response to the target object being located in the physiotherapy area in the tuning parameters, the corresponding tuning parameters are executed using an associative triggering model.

[0055] A user-feedback-based intelligent air wave optimization control system includes:

[0056] Scene Relationship Processing Module: Used to construct the physiotherapy feedback scene of the air wave physiotherapy device and design the topological relationship diagram of the physiotherapy feedback scene under different feedback information;

[0057] Control relationship processing module: used to acquire the operating mechanism of the air wave therapy device and generate a control topology diagram;

[0058] Correlational optimization model: It is used to generate a correlational triggering model based on the topology diagram and control topology diagram, and to dynamically optimize the control parameters of the air wave therapy device when user feedback is received based on the correlational triggering model.

[0059] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0060] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0061] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0062] Figure 1 This is a flowchart of a smart air wave optimization control method based on user feedback in an embodiment of the present invention;

[0063] Figure 2 This is a flowchart illustrating the construction of the associative triggering model in an embodiment of the present invention;

[0064] Figure 3 This is a system composition diagram of an intelligent air wave optimization control system based on user feedback, as described in an embodiment of the present invention. Detailed Implementation

[0065] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0066] This application proposes an intelligent air wave optimization control method based on user feedback, including:

[0067] Construct a physiotherapy feedback scenario for an air wave physiotherapy device, and design a topological relationship diagram of the physiotherapy feedback scenario under different feedback information;

[0068] Obtain the operating mechanism of the air wave therapy device and generate a control topology diagram;

[0069] Based on the topology diagram and control topology diagram, an associative triggering model is generated. Based on the associative triggering model, the control parameters of the air wave therapy device are dynamically optimized when user feedback is received.

[0070] The principle behind the above technical solution is as follows:

[0071] As attached Figure 1As shown, this application mainly describes a method for automatically optimizing control parameters of an air wave therapy device by receiving user information during the control process. In its implementation, this application first designs therapy feedback scenarios, including but not limited to voice feedback control scenarios, command feedback optimization control scenarios, and optimization control scenarios based on dynamic changes in the device head. Simultaneously, this application sets different therapy scenarios based on the user's age, physical condition, and other factors. For example, it sets relaxation scenarios (playing music) for young people and pain relief scenarios (acupoint stimulation for pain relief) for the elderly. Furthermore, different topology diagrams can be designed based on these different scenarios to facilitate the classification and processing of user needs.

[0072] Then, based on these scenarios, a control relationship topology diagram is established, linking corresponding feedback information and control components. By acquiring the operating components of the air wave therapy device, the hardware structure and software algorithm of the controller can be further determined and transformed into a control topology diagram. This facilitates the design and optimization of the controller, enabling it to better meet user needs.

[0073] The operating mechanism of the air wave physiotherapy device includes a heating mechanism, a pressure control mechanism, an electromagnetic control decoupling mechanism, and a vibration control mechanism. By determining the control components of these operating mechanisms and the signal adjustment port of the microprocessor, the corresponding control information is determined, as well as the specific data conversion nodes that these operating mechanisms pass through when executing an instruction, and the corresponding control topology is determined, thus achieving structural positioning.

[0074] Based on the topology diagram and control topology diagram, scene feedback information is interconnected with the specific control components of the airwave therapy device. When feedback information occurs, the components of the airwave therapy device automatically respond, achieving automatic optimization control. That is, by analyzing user behavior data during use, combined with the topology diagram and control topology diagram, an associative triggering model is generated. Thus, when a user uses the airwave therapy device, only the corresponding feedback information is received, and the control parameters are automatically adjusted to better meet the user's needs. This dynamic optimization not only improves the user experience but also reduces energy consumption and extends device lifespan.

[0075] In actual implementation, control parameters are monitored based on different feedback information to determine the precision, recall, and F1 score of the feedback information. Based on the preset thresholds for precision, recall, and F1 score, the correlation triggering model is improved and optimized to determine the performance indicators for different physiotherapy feedback scenarios.

[0076] The beneficial effects of the above technical solution are as follows:

[0077] (1) This application builds a physiotherapy feedback scenario, which can identify the user's use of the air wave physiotherapy device according to the scenario information and generate the corresponding scenario, so as to classify and process different feedback information according to the user's needs.

[0078] (2) This application generates a control topology diagram by using the operating structure of the air wave therapy device, such as heating components, massage components, and air pressure components, and can determine the direct control method of different control components;

[0079] (3) Through the correlation triggering model, user feedback information can be directly converted into control and adjustment information of the corresponding operating mechanism, thereby adjusting the user feedback information in the first instance. Since this application requires heating and pressure adjustment to achieve physiotherapy, this instantaneous feedback adjustment control mechanism can improve equipment safety, extend equipment life, and reduce energy consumption.

[0080] Specifically, the therapeutic feedback scenarios for constructing the air wave therapy device include:

[0081] Obtain real-time operating parameters and feedback information from the air wave therapy device; among which,

[0082] Feedback information includes: voice information, active control commands, and muscle dynamic information;

[0083] Based on real-time operating parameters, the air wave therapy device is divided into functional components, and the control structure group of the functional components is determined.

[0084] Based on the control structure group, determine the control parameters corresponding to each control structure group with respect to different feedback information, and determine the corresponding interaction node;

[0085] Based on the interaction nodes, feedback information is matched with functional components to generate various different physical therapy feedback scenarios; among them,

[0086] Physiotherapy feedback scenarios include voice feedback control scenarios, command feedback control scenarios, and muscle dynamic information feedback scenarios.

[0087] The principle behind the above technical solution is as follows:

[0088] In the implementation of this application, the main physiotherapy feedback scenario is a physiotherapy feedback scenario based on control information; under the control information as the scenario foundation, a personalized single-person physiotherapy scenario will also be generated based on the user's age, gender and physical condition.

[0089] In practice, various data will be collected from the equipment, such as its operating current, operating voltage, and power, as well as various feedback behaviors generated by the user during use, such as voice, movement, and muscle dynamics. The acquisition of this data is the foundation for subsequent analysis.

[0090] Real-time operating parameters include parameters of heating equipment, pressure equipment, and massage vibration equipment, etc. These devices are different functional components of the air wave therapy device. By dividing these functional components, the control method of each functional component can be determined, and the control structure group can be determined. The control structure group is the structure group of different components in the process of control signal conversion and transmission during the control of each functional component, as well as the feedback device structure group of the feedback information acquisition device corresponding to the feedback information.

[0091] By using these control structure groups and the corresponding feedback information, the control flow and corresponding control parameters of the control structure groups can be determined, and the interaction nodes in the control process can be identified. The interaction nodes include signal transmission nodes, signal conversion nodes, and command execution nodes.

[0092] Finally, through interactive nodes, feedback information can be correlated with physiotherapy control components, thereby enabling the construction of different physiotherapy feedback scenarios and achieving direct feedback control based on these scenarios.

[0093] The beneficial effects of the above technical solution are as follows:

[0094] By establishing a connection between feedback information and feedback scenarios, we can more accurately identify and understand user needs and feedback.

[0095] Based on voice, user commands, and real-time muscle sensing, scene control is achieved, and user commands are analyzed and executed more quickly.

[0096] This application can improve the accuracy and effectiveness of feedback information. During the control optimization process, more precise feedback control strategies and signals can be set, thereby better meeting user needs and improving the product service experience, achieving intelligent physiotherapy tailored to Huahe.

[0097] Specifically, the topology diagram includes an instruction terminal and a feedback processing terminal;

[0098] The feedback processing unit is used to convert different feedback information into control commands for the air wave therapy device.

[0099] The command terminal is used to adjust the corresponding functional components of the air wave therapy device according to control commands.

[0100] The principle behind the above technical solution is as follows:

[0101] To enable rapid conversion of feedback information and generation of control commands, this application includes a feedback processing port for direct information conversion. Based on the received feedback information, it can generate corresponding control commands for the air wave therapy device, such as increasing the intensity of heat application or decreasing the vibration frequency. It also receives and processes device status information sent back from the command terminal, such as temperature changes and user feedback. The command terminal can then directly implement the control commands to control the corresponding functional components.

[0102] The beneficial effects of the above technical solution are as follows:

[0103] The control commands of the air wave therapy device can be dynamically adjusted in real time according to the user's needs, enabling direct control and thus achieving intelligent and optimized control of the equipment.

[0104] Preferably, the feedback processing terminal includes a speech recognition conversion terminal, an input signal conversion terminal, and a muscle dynamic feedback information conversion terminal; wherein,

[0105] The voice recognition conversion terminal is used to convert the user's voice information into analog control commands for the air wave therapy device;

[0106] The input signal conversion terminal is used to convert the digital information of the user's real-time operation into analog control commands for the air wave therapy device;

[0107] The muscle dynamic information feedback terminal is used to convert the user's muscle sensing information into simulated control commands for the air wave therapy device.

[0108] The principle behind the above technical solution is as follows:

[0109] The speech recognition and conversion unit can receive the user's raw audio, perform speech analysis, determine the speech information contained within, and then use the speech information to determine the functional components that need to be adjusted. Voice commands can be simple words such as "increase," "decrease," or "change," or more complex commands such as "generate air waves at a fixed speed per hour."

[0110] The input signal conversion port can receive user control commands, including those from apps, WeChat mini-programs, web applications, or buttons on the host device, enabling comprehensive control. Alternatively, it can collect motion data from sensors (infrared motion capture sensors) such as hand movements, foot movements, and arm swings, transmitting this data to the controller. By analyzing the user's movement patterns and frequencies, the controller can determine user feedback and control parameters to improve the user experience.

[0111] The muscle dynamic information feedback terminal can collect the electrical signals of the muscle and convert them into digital or analog signals for processing and application.

[0112] These signals can include information such as changes in the electric field during muscle contraction and relaxation, and the frequency and amplitude of muscle activity, thereby determining whether the pressure and temperature are suitable for the user and making adaptive adjustments.

[0113] Specifically, the acquisition of the operating mechanism of the air wave therapy device and the generation of a control topology diagram include:

[0114] The control signals of the functional components of the air wave physiotherapy device are decomposed to determine the instruction input and instruction execution terminals of different functional components;

[0115] Based on the command input terminal, determine the status trigger signals of different functional components;

[0116] Based on the instruction execution end, determine the status feedback signals of different functional components;

[0117] Based on the state trigger signal and state feedback signal, and according to the component information, a control topology diagram of the corresponding functional component is generated.

[0118] The principle behind the above technical solution is as follows:

[0119] The process of generating the control topology diagram involves determining the command generation and input terminals of functional components based on signal decomposition. In practice, the output signal of each functional component is sampled to obtain a discrete time series. This time series is then analyzed to determine the value at each discrete point, which represents the command input terminal of that component. Next, a controller signal diagram is plotted based on these values ​​to determine the command execution terminal of each component. According to the controller signal diagram, each component has a specific response when receiving a command; this response is the state trigger signal. Simultaneously, each component has a specific response when executing a command; this response is the state feedback signal. By combining the state trigger and state feedback signals with the information from each component, a control topology diagram can be obtained. This diagram contains the control logic for each component throughout the process, allowing the component's behavior to be controlled.

[0120] The beneficial effects of the above technical solution are as follows:

[0121] In this way, the direct control topology of the functional components can be determined, and the control mode of the functional components can be better adjusted.

[0122] Specifically, the step of generating an associative triggering model based on the topology diagram and the control topology diagram includes:

[0123] Based on the topology diagram, set the trigger port and execution port;

[0124] Based on the control topology diagram, determine the corresponding functional components and triggering components;

[0125] The trigger port and trigger component are connected to generate the corresponding control signal tracking identifier and the first trigger model;

[0126] Data is interfaced between the execution port and functional components to generate a sensing identifier and a second trigger model for control signals;

[0127] An associative triggering model is constructed by using the first triggering model and the second triggering model.

[0128] The principle behind the above technical solution is as follows:

[0129] As attached Figure 2 As shown, in the process of building the associated triggering model, this application configures the direct control endpoints of different functional components in the control process through the topology diagram, that is, sets the direct control ports of different functional components.

[0130] By matching and combining control signal tracking identifiers with trigger ports and trigger components, a specific trigger model is generated. Matching and combining execution ports and functional components can also generate a specific trigger model. Tracking identifiers can track control commands and determine whether the transmission path is accurate during command execution. Sensing identifiers can determine whether control commands have been implemented. The first trigger model controls the adjustment of specific parameters in the control signal and the timing of changes. The second trigger model controls how functional components respond to the control signals of the first trigger model and the timing of response changes.

[0131] When the two interact with each other, they can achieve interconnected control.

[0132] The beneficial effects of the above technical solution are as follows:

[0133] As attached Figure 2 As shown, the correlation-based triggering model built in this application can quickly locate and identify the physiotherapy areas and parameters that need to be optimized and controlled because it can trigger based on the correlation between data.

[0134] Once feedback is generated, the model can quickly trigger the corresponding processing flow, thereby responding to the user's needs in a timely manner.

[0135] By defining different association rules and triggering conditions, the model can adapt to different physical therapy scenarios and changing needs. This allows the model to flexibly handle various complex situations.

[0136] Specifically, the dynamic optimization includes:

[0137] Based on the correlation triggering model, determine the user's real-time triggering feedback and obtain feedback control instructions within a preset time period;

[0138] Based on the feedback control commands, determine the functional components that will be controlled in real time;

[0139] Based on the current execution parameters of the functional components, determine the tuning parameters; among which,

[0140] The tuning parameter is the difference between the adjustment parameter corresponding to the feedback control command and the currently executed parameter;

[0141] Based on the difference parameters, the control of the functional components is optimized.

[0142] The principle behind the above technical solution is as follows:

[0143] During the dynamic optimization process, the associative triggering model in this application can process user feedback information in real time, directly analyze feedback control commands and controllable functional components, specifically by analyzing user behavior data to identify various triggering behaviors that users may generate, such as setting timers for voice commands, manually controlling the air wave therapy device, and feedback signals of muscle changes, etc., as the basis for real-time triggering.

[0144] Then, by using the currently executed parameters and the parameters adjusted in the feedback control command, the tuning parameters are determined. The tuning parameters are the difference between the adjustment parameters corresponding to the feedback control command and the currently executed parameters. For example, if a temperature threshold is set in the feedback control command, and the current actual temperature differs significantly from the threshold, then this difference can be considered as the tuning parameter. The tuning parameter is then applied to the functional components that are controlled in real time, thereby achieving control tuning of the functional components.

[0145] The beneficial effects of the above technical solution are as follows:

[0146] This application enables refined user feedback analysis, achieving precise control of the air wave therapy device and optimizing commands.

[0147] Specifically, the dynamic optimization also includes:

[0148] Receive real-time operating parameters from the air wave therapy device to determine the power parameters of different functional components;

[0149] Based on power parameters, calculate the real-time forward voltage of different functional components during operation;

[0150] Based on the real-time forward voltage, upon receiving feedback information, it is determined whether the difference between the estimated forward voltage and the real-time forward voltage exceeds a preset range when executing the tuning command corresponding to the feedback information; whereby...

[0151] When the preset range is exceeded, a voice prompt message is generated.

[0152] If the preset range is not exceeded, the corresponding tuning command is executed.

[0153] The principle behind the above technical solution is as follows:

[0154] The application also includes determining the power parameters of different functional components at the current moment through the real-time operating parameters of air wave therapy, as well as information such as working status, working time, and usage frequency during the dynamic optimization process.

[0155] Power parameters can determine the real-time operating voltage of functional components. The real-time forward voltage can be obtained by comprehensively calculating the power parameters and parameters such as current and voltage. Then, by estimating the difference between the forward voltage and the real-time forward voltage, it can be determined whether the power parameters of the air wave need to be adjusted.

[0156] The preset range is the adjustment range of the power parameters of the functional components, which determines the adjustment power parameters. If the adjusted information exceeds the preset range, it indicates a possible equipment malfunction or abnormality, and a voice prompt will be issued. If the information does not exceed the preset range, the tuning command can be executed.

[0157] The beneficial effects of the above technical solution are as follows:

[0158] This application, by adjusting the power parameters of the air wave therapy device, can determine whether the air wave therapy device will malfunction or be adjusted beyond the limit during adjustment, and thus provide voice prompts.

[0159] Specifically, the feedback information received from users also includes:

[0160] In response to receiving an information feedback request, the real-time execution parameters of the target object corresponding to the information feedback request are obtained, wherein the target object is a functional component of the air wave therapy device;

[0161] By detecting real-time execution parameters, the corresponding physiotherapy area of ​​the target object is obtained; among which,

[0162] The physiotherapy area refers to the body part that is being treated.

[0163] Obtain the tuning parameters for the target object and verify the tuning parameters based on the treatment area; among them,

[0164] The verification is used to determine whether the target object in the optimization parameters is located in the physiotherapy area;

[0165] In response to the target object being located in the physiotherapy area in the tuning parameters, the corresponding tuning parameters are executed using an associative triggering model.

[0166] The principle behind the above technical solution is as follows:

[0167] When the system receives a user's feedback request, it acquires the real-time execution parameters of the target object. These real-time execution parameters are specific to certain functional components of the airwave therapy device, such as temperature, vibration frequency, and pressure level. The system detects and analyzes the acquired real-time execution parameters to determine the corresponding treatment area for the target object. A key aspect of this process is determining the range and location of the treatment area, as it directly affects the user experience and treatment effectiveness. Optimization parameters are set based on different user needs and environmental conditions, aiming to improve user comfort and treatment outcomes when using the airwave therapy device. Verification ensures the effectiveness and rationality of the optimization parameters, guaranteeing optimal treatment results for the user. The verification process typically involves comparative analysis and actual testing to ensure the optimization parameters match the current environment and usage. Finally, the operating parameters of the airwave therapy device are adjusted based on the target in the optimization parameters and the detected treatment area.

[0168] The beneficial effects of the above technical solution are as follows:

[0169] This application receives user feedback, submits corresponding feedback requests, determines corresponding real-time execution parameters based on the feedback requests, determines the control area of ​​physiotherapy based on the real-time execution parameters, and thus determines whether the location of real-time optimization is the area where the user needs physiotherapy optimization, thereby achieving point-to-point control optimization.

[0170] A user-feedback-based intelligent air wave optimization control system includes:

[0171] Scene Relationship Processing Module: Used to construct the physiotherapy feedback scene of the air wave physiotherapy device and design the topological relationship diagram of the physiotherapy feedback scene under different feedback information;

[0172] Control relationship processing module: used to acquire the operating mechanism of the air wave therapy device and generate a control topology diagram;

[0173] Correlational optimization model: It is used to generate a correlational triggering model based on the topology diagram and control topology diagram, and to dynamically optimize the control parameters of the air wave therapy device when user feedback is received based on the correlational triggering model.

[0174] The principle behind the above technical solution is as follows:

[0175] As attached Figure 3As shown, this application mainly describes a method for automatically optimizing control parameters of an air wave therapy device by receiving user information during the control process. In specific implementation, this application first designs therapy feedback scenarios, including but not limited to voice feedback control scenarios, command feedback optimization control scenarios, and optimization control scenarios based on dynamic changes in the device head. Then, through these scenarios, a control relationship topology diagram is set that associates the corresponding feedback information with the corresponding control components.

[0176] The operating mechanism of the air wave physiotherapy device includes a heating mechanism, a pressure control mechanism, an electromagnetic control decoupling mechanism, and a vibration control mechanism. By determining the control components of these operating mechanisms and the signal adjustment port of the microprocessor, the corresponding control information is determined, as well as the specific data conversion nodes that these operating mechanisms pass through when executing an instruction, and the corresponding control topology is determined, thus achieving structural positioning.

[0177] Based on the topology diagram and control topology diagram, the scene feedback information and the specific control components of the air wave therapy device are interconnected. When the feedback information appears, the components of the air wave therapy device respond automatically to achieve automatic optimization control.

[0178] The beneficial effects of the above technical solution are as follows:

[0179] When receiving user feedback, this application can understand the user's needs and comfort level during real-time physiotherapy by collecting and analyzing user feedback, and then optimize the control of air therapy waves based on user needs and feedback to improve the user experience.

[0180] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A smart air wave optimization control system based on user feedback, characterized in that, include: Scene Relationship Processing Module: Used to construct the physiotherapy feedback scene of the air wave physiotherapy device and design the topological relationship diagram of the physiotherapy feedback scene under different feedback information; Control relationship processing module: used to acquire the operating mechanism of the air wave therapy device and generate a control topology diagram; Correlational optimization model: used to generate a correlational triggering model based on the topology diagram and control topology diagram, and to dynamically optimize the control parameters of the air wave therapy device when user feedback is received based on the correlational triggering model; The step of generating an associative triggering model based on the topology diagram and control topology diagram includes: Based on the topology diagram, set the trigger port and execution port; Based on the control topology diagram, determine the corresponding functional components and triggering components; The trigger port and trigger component are connected to generate the corresponding control signal tracking identifier and the first trigger model; Data is interfaced between the execution port and functional components to generate a sensing identifier and a second trigger model for control signals; An associative triggering model is constructed by using the first triggering model and the second triggering model.

2. The intelligent air wave optimization control system based on user feedback as described in claim 1, characterized in that, The therapeutic feedback scenarios for constructing the air wave therapy device include: Obtain real-time operating parameters and feedback information from the air wave therapy device; among which, Feedback information includes: voice information, active control commands, and muscle dynamic information; Based on real-time operating parameters, the air wave therapy device is divided into functional components, and the control structure group of the functional components is determined. Based on the control structure group, determine the control parameters corresponding to each control structure group with respect to different feedback information, and determine the corresponding interaction node; Based on the interaction nodes, feedback information is matched with functional components to generate various different physical therapy feedback scenarios; among them, Physiotherapy feedback scenarios include voice feedback control scenarios, command feedback control scenarios, and muscle dynamic information feedback scenarios.

3. The intelligent air wave optimization control system based on user feedback as described in claim 1, characterized in that, The topology diagram includes a command end and a feedback processing end; The feedback processing unit is used to convert different feedback information into control commands for the air wave therapy device. The command terminal is used to adjust the corresponding functional components of the air wave therapy device according to control commands.

4. The intelligent air wave optimization control system based on user feedback as described in claim 3, characterized in that, The feedback processing terminal includes a speech recognition conversion terminal, an input signal conversion terminal, and a muscle dynamic feedback information conversion terminal; wherein... The voice recognition conversion terminal is used to convert the user's voice information into analog control commands for the air wave therapy device; The input signal conversion terminal is used to convert the digital information of the user's real-time operation into analog control commands for the air wave therapy device; The muscle dynamic information feedback terminal is used to convert the user's muscle sensing information into simulated control commands for the air wave therapy device.

5. The intelligent air wave optimization control system based on user feedback as described in claim 1, characterized in that, The acquisition of the operating mechanism of the air wave therapy device, generating a control topology diagram, includes: The control signals of the functional components of the air wave physiotherapy device are decomposed to determine the instruction input and instruction execution terminals of different functional components; Based on the command input terminal, determine the status trigger signals of different functional components; Based on the instruction execution end, determine the status feedback signals of different functional components; Based on the state trigger signal and state feedback signal, and according to the component information, a control topology diagram of the corresponding functional component is generated.

6. The intelligent air wave optimization control system based on user feedback as described in claim 1, characterized in that, The dynamic optimization includes: Based on the correlation triggering model, determine the user's real-time triggering feedback and obtain feedback control instructions within a preset time period; Based on the feedback control commands, determine the functional components that will be controlled in real time; Based on the current execution parameters of the functional components, determine the tuning parameters; among which, The tuning parameter is the difference between the adjustment parameter corresponding to the feedback control command and the currently executed parameter; Based on the difference parameters, the control of the functional components is optimized.

7. The intelligent air wave optimization control system based on user feedback as described in claim 1, characterized in that, The dynamic optimization also includes: Receive real-time operating parameters from the air wave therapy device to determine the power parameters of different functional components; Based on power parameters, calculate the real-time forward voltage of different functional components during operation; Based on the real-time forward voltage, upon receiving feedback information, it is determined whether the difference between the estimated forward voltage and the real-time forward voltage exceeds a preset range when executing the tuning command corresponding to the feedback information; whereby... When the preset range is exceeded, a voice prompt message is generated; If the preset range is not exceeded, the corresponding tuning command is executed.

8. The intelligent air wave optimization control system based on user feedback as described in claim 1, characterized in that, The feedback information received from users also includes: In response to receiving an information feedback request, the real-time execution parameters of the target object corresponding to the information feedback request are obtained, wherein the target object is a functional component of the air wave therapy device; By detecting real-time execution parameters, the corresponding physiotherapy area of ​​the target object is obtained; among which, The physiotherapy area refers to the body part that is being treated. Obtain the tuning parameters for the target object and verify the tuning parameters based on the treatment area; among them, The verification is used to determine whether the target object in the optimization parameters is located in the physiotherapy area; In response to the target object being located in the physiotherapy area in the tuning parameters, the corresponding tuning parameters are executed using an associative triggering model.

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