Vacuum negative pressure auxiliary radio frequency slimming equipment and control method thereof

Abstract

The invention provides vacuum negative pressure auxiliary radio frequency slimming equipment and a control method thereof, and the method comprises the steps: carrying out the feature extraction of dynamic negative pressure values in dynamic negative pressure data in each radio frequency adjustment period, and obtaining a negative pressure accumulation feature sequence; temperature rise rate deviation degrees are extracted according to the negative pressure real-time temperature, and the negative pressure effect influence degree is determined based on the negative pressure cumulative feature sequence and correlation features between the temperature rise rate deviation degrees corresponding to all the radio frequency adjustment periods; when the negative pressure effect influence degree is higher than an influence threshold value, dynamic negative pressure influence prediction is carried out according to the negative pressure accumulation characteristic sequence, and a dynamic negative pressure influence index is obtained; and the radio frequency electromagnetic emission power of the radio frequency slimming equipment is dynamically adjusted based on the emotion score of the user and the dynamic negative pressure influence index, so that the power of the radio frequency slimming equipment can be dynamically adjusted according to the negative pressure influence degree and the emotion characteristics of the user. And the comfort of the user in the process of using the vacuum negative pressure auxiliary radio frequency slimming equipment is improved.

Description

Technical Field

[0001] The present application relates to the technical field of radio frequency slimming, and more particularly to a vacuum negative pressure assisted radio frequency slimming device and a control method thereof. Background Art

[0002] Radiofrequency slimming technology is an important means in the current field of non-invasive body management and local body shaping. It can emit low-intensity electromagnetic waves and use the electric field heat conduction function to directly heat the dermis and deep subcutaneous tissue, causing slight thermal damage to the layer, thereby inducing collagen regeneration and local fat tissue shrinkage, achieving the purpose of tightening the skin, reducing fat accumulation and improving body contours. A vacuum negative pressure adsorption head is used to assist radiofrequency slimming equipment in radiofrequency therapy. The target skin tissue is transferred upward through negative pressure adsorption, so that the radiofrequency electromagnetic energy acts more concentratedly on the target area, enhancing the deep heating effect, and promoting lymphatic return and tissue perfusion, which helps to improve fat metabolism and circulation efficiency.

[0003] However, in the existing technology, when the skin and subcutaneous tissue are sucked up by the negative pressure adsorption device, mechanical pulling stress is generated, which dilates the capillaries and tiny blood vessels. In order to meet the higher demand of local tissues for oxygen and nutrients, the blood flow rate will increase to adapt to the increased metabolic rate of the tissue after being stimulated by adsorption. Negative pressure adsorption usually causes the blood flow rate and local blood perfusion rate in the target area to increase, so that the user cannot achieve the expected slimming purpose when using the radio frequency slimming device, which interferes with the radio frequency slimming therapy process and reduces the user's comfort when using the radio frequency slimming device. Summary of the Invention

[0004] The present application provides a vacuum negative pressure assisted radio frequency slimming device and a control method thereof, which can dynamically adjust the power of the radio frequency slimming device according to the degree of negative pressure effect and the user's emotional characteristics, thereby improving the user's comfort during the process of using the vacuum negative pressure assisted radio frequency slimming device.

[0005] In a first aspect, the present application provides a control method for a vacuum negative pressure assisted radio frequency slimming device. The method can be executed by a network device, or can be executed by a chip configured in the network device, and the present application does not limit this.

[0006] Collect the dynamic negative pressure value of the negative pressure action area to obtain dynamic negative pressure data; Obtaining a preset radio frequency adjustment cycle, and performing feature extraction on the dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data to obtain a negative pressure cumulative feature sequence; Collecting the real-time temperature of the negative pressure, extracting the temperature rise rate deviation corresponding to each radio frequency adjustment cycle based on the real-time temperature of the negative pressure, and determining the influence of the negative pressure based on the correlation characteristics between the cumulative characteristic sequence of the negative pressure and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle; When the negative pressure effect is higher than the impact threshold, a dynamic negative pressure impact prediction is performed based on the negative pressure cumulative feature sequence to obtain a dynamic negative pressure impact index; The user's emotion score is obtained, and the radio frequency electromagnetic transmission power of the radio frequency slimming device is dynamically adjusted based on the user's emotion score and the dynamic negative pressure impact index.

[0007] In combination with the first aspect, in certain implementations of the first aspect, in the process of dynamically adjusting the radio frequency electromagnetic transmission power of the radio frequency slimming device based on the user's emotion score and the dynamic negative pressure impact index, the radio frequency electromagnetic transmission power of the radio frequency slimming device is dynamically adjusted based on a preset radio frequency power adjustment function.

[0008] In combination with the first aspect, in certain implementations of the first aspect, a thin film pressure sensor is used to collect dynamic negative pressure values in the negative pressure action area at equal intervals.

[0009] In combination with the first aspect, in certain implementations of the first aspect, when the negative pressure effect is lower than an effect threshold, the radio frequency slimming device performs radio frequency transmission according to a preset radio frequency electromagnetic transmission power.

[0010] In conjunction with the first aspect, in certain implementations of the first aspect, feature extraction is performed on the dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data to obtain a negative pressure cumulative feature sequence, specifically including: Obtaining each dynamic negative pressure value in the dynamic negative pressure data and the corresponding collection time label; For any RF adjustment cycle, based on the collection tag corresponding to the dynamic negative pressure value, feature extraction is performed on the dynamic negative pressure value within the RF adjustment cycle to obtain the negative pressure cumulative feature vector corresponding to the RF adjustment cycle; The negative pressure cumulative feature vectors corresponding to each radio frequency adjustment period are combined into the negative pressure cumulative feature sequence according to a time sequence.

[0011] In conjunction with the first aspect, in certain implementations of the first aspect, determining the negative pressure effect based on the correlation characteristics between the negative pressure cumulative feature sequence and the temperature rise rate deviation corresponding to each RF adjustment cycle specifically includes: Obtaining each negative pressure cumulative feature vector in the negative pressure cumulative feature sequence; For any negative pressure cumulative feature vector, a neural network model is used to perform cluster scoring on the negative pressure cumulative feature vector to obtain the negative pressure intensity corresponding to each radio frequency adjustment cycle; The negative pressure effect during radio frequency transmission is determined based on the correlation characteristics between the negative pressure effect intensity and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle.

[0012] In combination with the first aspect, in certain implementations of the first aspect, extracting the temperature rise rate deviation corresponding to each RF adjustment cycle according to the real-time temperature of the negative pressure specifically includes: determining the temperature rise rate values corresponding to the negative pressure action area in different RF adjustment cycles according to the real-time temperature of the negative pressure, obtaining the standard temperature rise rates corresponding to different RF action times in the user's historical storage data, and comparing the temperature rise rates with the temperature rise rate values corresponding to different RF adjustment cycles to obtain the temperature rise rate deviation corresponding to each RF adjustment cycle.

[0013] In a second aspect, the present application provides a vacuum negative pressure assisted radiofrequency slimming device, which includes a power control unit, and the power control unit includes: A data acquisition module is used to collect dynamic negative pressure values in the negative pressure action area to obtain dynamic negative pressure data; A data processing module is used to obtain a preset radio frequency adjustment cycle, perform feature extraction on the dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data, and obtain a negative pressure cumulative feature sequence; The data processing module is further configured to collect the real-time temperature of the negative pressure, extract the temperature rise rate deviation corresponding to each radio frequency adjustment cycle based on the real-time temperature of the negative pressure, and determine the influence of the negative pressure based on the correlation characteristics between the cumulative characteristic sequence of the negative pressure and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle; an equipment decision module, configured to, when the negative pressure effect degree is higher than an influence threshold, perform a dynamic negative pressure influence prediction based on the negative pressure cumulative feature sequence to obtain a dynamic negative pressure influence index; The device control module is used to obtain the user's emotional score and dynamically adjust the radio frequency electromagnetic transmission power of the radio frequency slimming device based on the user's emotional score and the dynamic negative pressure impact index.

[0014] In a third aspect, the present application provides a computer terminal device, which includes a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned control method of a vacuum negative pressure assisted radio frequency slimming device.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to implement the operations performed by the above-mentioned control method of a vacuum negative pressure assisted radio frequency slimming device.

[0016] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In a vacuum negative pressure assisted radio frequency slimming device and a control method thereof provided by the present application, first, the dynamic negative pressure value of the negative pressure action area is collected to obtain dynamic negative pressure data; a preset radio frequency adjustment cycle is obtained, and the dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data are respectively feature extracted to obtain a negative pressure cumulative feature sequence; the real-time temperature of the negative pressure is collected, and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle is extracted according to the real-time temperature of the negative pressure, and the influence of the negative pressure effect is determined based on the correlation characteristics between the negative pressure cumulative feature sequence and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle; when the influence of the negative pressure effect is higher than the influence threshold, the dynamic negative pressure influence is predicted according to the negative pressure cumulative feature sequence to obtain a dynamic negative pressure influence index; the user's emotional score is obtained, and the radio frequency electromagnetic transmission power of the radio frequency slimming device is dynamically adjusted based on the user's emotional score and the dynamic negative pressure influence index.

[0017] Therefore, it can be seen that this application reflects the dynamic effect of the negative pressure field in different cycles by continuously collecting dynamic negative pressure values in the negative pressure action area and extracting features in combination with the preset RF adjustment cycle, providing dynamic negative pressure data support for subsequent adjustment strategies. During the RF heating process, the local temperature rise rate is a reflection of the energy coupling efficiency. By collecting real-time temperature, extracting the temperature rise rate deviation, and analyzing the correlation with the negative pressure characteristics, it is possible to determine the degree of interference of the negative pressure adsorption behavior on the RF thermal field distribution, thereby identifying when the negative pressure behavior produces a nonlinear disturbance on the heat output and triggering power adjustment. The negative pressure impact trend of future cycles is predicted to determine the dynamic negative pressure impact index, and the power can be actively adjusted before the impact occurs to avoid discomfort such as sudden changes in thermal sensation and excessive stimulation caused by adjustment lag. Finally, the emotional score and dynamic negative pressure impact index are introduced to dynamically adjust the RF power, which can adapt to the individual reaction differences of different users in different states, achieve more flexible and intelligent RF energy management, and improve the comfort of the user during use.

[0018] In summary, the present application can dynamically adjust the power of the radio frequency slimming device according to the degree of negative pressure effect and the user's emotional characteristics, thereby improving the user's comfort during the process of using the vacuum negative pressure assisted radio frequency slimming device. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1is an exemplary flow chart of a control method of a vacuum negative pressure assisted radio frequency slimming device according to some embodiments of the present application; Figure 2 is a schematic structural diagram of a power control unit according to some embodiments of the present application; Figure 3 This is a structural diagram of a computer terminal device for implementing a control method for a vacuum negative pressure assisted radio frequency slimming device according to some embodiments of the present application. DETAILED DESCRIPTION

[0020] The present application obtains dynamic negative pressure data by collecting dynamic negative pressure values in the negative pressure action area; obtains a preset radio frequency adjustment cycle, and performs feature extraction on the dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data to obtain a negative pressure cumulative feature sequence; collects the real-time temperature of the negative pressure, extracts the temperature rise rate deviation corresponding to each radio frequency adjustment cycle according to the real-time temperature of the negative pressure, and determines the negative pressure effect based on the correlation characteristics between the negative pressure cumulative feature sequence and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle; when the negative pressure effect is higher than the impact threshold, a dynamic negative pressure effect prediction is performed according to the negative pressure cumulative feature sequence to obtain a dynamic negative pressure effect index; obtains the user's emotional score, and dynamically adjusts the radio frequency electromagnetic transmission power of the radio frequency slimming device based on the user's emotional score and the dynamic negative pressure effect index, so that the power of the radio frequency slimming device can be dynamically adjusted according to the degree of negative pressure effect and the user's emotional characteristics, thereby improving the user's comfort in using the vacuum negative pressure assisted radio frequency slimming device.

[0021] In order to better understand the above technical solution, the following will be combined with the accompanying drawings and specific implementation methods to describe the above technical solution in detail. Figure 1 , which is an exemplary flow chart of a control method for a vacuum negative pressure assisted radio frequency slimming device according to some embodiments of the present application. The control method 100 for a vacuum negative pressure assisted radio frequency slimming device mainly includes the following steps: In step S101 , the dynamic negative pressure value of the negative pressure action area is collected to obtain dynamic negative pressure data.

[0022] It should be noted that the negative pressure action area described in this application refers to the local contact range in which the vacuum negative pressure adsorption head is tightly attached to the user's skin, producing an adsorption and pulling effect on the skin and the soft tissue underneath in a closed space. The spatial boundary of the negative pressure action area is defined by the sealing ring or silicone cover ring of the negative pressure adsorption head, and the shape may be circular, elliptical or rectangular. Preferably, in some embodiments, the negative pressure action area is a closed cavity formed between the vacuum negative pressure adsorption head and the skin surface. The cavity generates an adsorption pressure lower than atmospheric pressure when driven by a negative pressure pump, so as to pull the target skin area. The dynamic pressure value is the cavity pressure value that changes continuously with time in the negative pressure action area, expressed as negative pressure relative to atmospheric pressure, in kilopascals.

[0023] Preferably, in some embodiments, a MEMS pressure sensor (thin film pressure sensor) can be used to collect the dynamic negative pressure values of the negative pressure action area at equal intervals. In specific implementation, the sampling frequency of the dynamic negative pressure value is calibrated to 50Hz. In some other embodiments, other devices or equipment that can realize negative pressure collection can also be used, and this application does not limit this.

[0024] Optionally, in some embodiments, the dynamic negative pressure data described in this application includes multiple dynamic negative pressure values and corresponding collection time labels.

[0025] In step S102, a preset radio frequency adjustment cycle is obtained, and feature extraction is performed on the dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data to obtain a negative pressure cumulative feature sequence.

[0026] Preferably, in some embodiments, the RF adjustment period can be calibrated to 5s based on historical experience. In some other embodiments, the RF adjustment period can also be adjusted in stages based on the user's historical verification information or the device gear knob. This application does not limit this.

[0027] Preferably, in some embodiments, feature extraction is performed on the dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data to obtain a negative pressure cumulative feature sequence, specifically comprising: Obtaining each dynamic negative pressure value in the dynamic negative pressure data and the corresponding collection time label; For any RF adjustment cycle, based on the collection tag corresponding to the dynamic negative pressure value, feature extraction is performed on the dynamic negative pressure value within the RF adjustment cycle to obtain the negative pressure cumulative feature vector corresponding to the RF adjustment cycle; The negative pressure cumulative feature vectors corresponding to each radio frequency adjustment period are combined into the negative pressure cumulative feature sequence according to a time sequence.

[0028] It should be noted that the negative pressure cumulative characteristic vector in this application is used to characterize the cumulative effect trend characteristics of the negative pressure adsorption behavior on the negative pressure action area. Preferably, in some embodiments, the negative pressure cumulative characteristics contained in the negative pressure cumulative characteristic vector include: negative pressure cumulative time, maximum dynamic negative pressure value, minimum dynamic negative pressure value, average dynamic negative pressure value, negative pressure fluctuation amplitude difference, negative pressure standard deviation and maximum dynamic change rate. In some other embodiments, the negative pressure cumulative characteristics can also be characterized by other parameters that can express the intensity of the negative pressure effect, which will not be elaborated in this application.

[0029] In step S103, the real-time temperature of the negative pressure is collected, and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle is extracted according to the real-time temperature of the negative pressure. The influence of the negative pressure effect is determined based on the correlation characteristics between the negative pressure cumulative feature sequence and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle.

[0030] It should be noted that when the skin and subcutaneous tissue are continuously subjected to negative pressure adsorption, the local tissue will be pulled and lifted, forming mechanical stress stimulation, which will trigger a series of vasodilation and capillary opening reactions. Its physiological mechanisms include: capillary dilation: in order to cope with the pressure changes in the adsorption area, local small blood vessels dilate and increase blood inflow; accelerated microcirculation: tissue metabolic needs increase, and the blood circulation rate increases to replenish more oxygen and nutrients; local tissue temperature increases: increased perfusion leads to enhanced heat exchange, and local temperature changes that are not caused by radio frequency. The increase in blood perfusion rate induced by negative pressure will form a physiological interference background, which will interfere with the subsequent radio frequency heating temperature rise judgment. Radio frequency technology heats tissues through electromagnetic waves, and its thermal effect mainly depends on: tissue resistance characteristics (high-impedance tissues such as fat are more likely to accumulate heat); local absorption and conversion efficiency of electromagnetic energy; tissue heat conduction and heat dissipation. When negative pressure causes a significant increase in local perfusion rate: blood flow quickly carries away heat; the area where heat should have accumulated dissipates heat prematurely, resulting in insufficient temperature rise; the use of ordinary temperature feedback will cause the system to mistakenly believe that heat conduction is insufficient. Some radio frequency devices dynamically adjust power through real-time temperature control feedback or impedance detection mechanisms: if negative pressure causes a sudden increase in blood perfusion → tissue temperature changes nonlinearly → the feedback temperature control mechanism is mistakenly triggered; the system may mistakenly reduce power output, or maintain a low power state, making it difficult to achieve the expected slimming intensity.

[0031] Preferably, in some embodiments, a temperature sensor can be used to collect the real-time temperature of negative pressure, and the real-time temperature of negative pressure is the temperature change value obtained by continuous collection of the negative pressure action area. In some other embodiments, an infrared camera can also be used to collect infrared images of the deep temperature of the negative pressure action area to obtain the real-time temperature of negative pressure. This application does not limit this.

[0032] Preferably, in some embodiments, extracting the temperature rise rate deviation corresponding to each RF adjustment cycle according to the real-time temperature of negative pressure specifically includes: determining the temperature rise rate values corresponding to the negative pressure action area in different RF adjustment cycles according to the real-time temperature of negative pressure, obtaining the standard temperature rise rates corresponding to different RF action times in the user's historical storage data, and comparing the temperature rise rates with the temperature rise rate values corresponding to different RF adjustment cycles to obtain the temperature rise rate deviation corresponding to each RF adjustment cycle.

[0033] In specific implementation, the user's historical storage data can be modeled according to the typical temperature rise rate under the factory calibration of the equipment or a large amount of historical user sample data, and divided into different parts, skin types, negative pressure levels, radio frequency power and other conditions. In some other embodiments, the temperature rise rate of the negative pressure action area of the same user before negative pressure adsorption assistance can also be recorded and stored, wherein the ratio between the temperature rise rate values corresponding to different radio frequency adjustment cycles and the standard temperature rise rate of the corresponding radio frequency action time is used as the temperature rise rate deviation corresponding to different radio frequency adjustment cycles.

[0034] Preferably, in some embodiments, determining the negative pressure effect based on the correlation characteristics between the negative pressure cumulative characteristic sequence and the temperature rise rate deviation corresponding to each radio frequency adjustment period specifically includes: Obtaining each negative pressure cumulative feature vector in the negative pressure cumulative feature sequence; For any negative pressure cumulative feature vector, a neural network model is used to perform cluster scoring on the negative pressure cumulative feature vector to obtain the negative pressure intensity corresponding to each radio frequency adjustment cycle; The negative pressure effect during radio frequency transmission is determined based on the correlation characteristics between the negative pressure effect intensity and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle.

[0035] In specific implementation, a single hidden layer neural network model can be used to cluster and score the negative pressure cumulative feature vectors to obtain the negative pressure effect intensity corresponding to each radio frequency adjustment cycle; the following is a specific embodiment of the present application using a single hidden layer neural network model to cluster and score the negative pressure cumulative feature vectors: each negative pressure cumulative feature vector in the negative pressure cumulative feature sequence is input into the single hidden layer neural network in the form of a multidimensional data vector. Each dimension of the data vector may include: the hidden layer of the single hidden layer neural network contains multiple activation function nodes for classification training, and the classification result is output through the output layer of the single hidden layer neural network, and the negative pressure effect intensity score mapping is performed according to the classification result to obtain the corresponding mapping value as the negative pressure effect intensity corresponding to the negative pressure cumulative feature vector, wherein the different dimensions of the data vector include: negative pressure accumulation time, maximum dynamic negative pressure value, minimum dynamic negative pressure value, average dynamic negative pressure value, negative pressure fluctuation amplitude difference, negative pressure standard deviation and maximum dynamic change rate, wherein a plurality of pre-prepared negative pressure cumulative feature vector samples and the corresponding negative pressure effect intensity manual scoring results are used as the training set, and a plurality of negative pressure cumulative feature vector samples are input The single hidden layer neural network performs classification training, and the hidden layer of the single hidden layer neural network includes multiple activation function nodes for performing classification training on input samples, and outputs the classification results through the output layer of the single hidden layer neural network. During the training process, the training samples are input into the neural network to obtain the output classification results, and the correlation between the output classification results and the corresponding manual scoring results is compared. When the matching degree between the classification results and the manual scoring (correlation coefficient can be used) is lower than a preset threshold, the activation parameters of the activation function in the hidden layer are iteratively optimized through the back propagation mechanism until the correlation between the classification results and the manual scoring results of the negative pressure effect intensity reaches a preset standard, and the mapping relationship between the classification results and the manual scoring of the negative pressure effect intensity is obtained.

[0036] It should be noted that during RF heating, the local temperature rise rate reflects the energy coupling efficiency. By collecting real-time temperature, extracting the temperature rise rate deviation, and correlating it with the negative pressure characteristics for analysis, the degree of interference of negative pressure adsorption behavior on the RF thermal field distribution can be determined. The introduction of the negative pressure effect described in this application is essentially a dynamic feedback mechanism that identifies when negative pressure behavior produces nonlinear perturbations in the heat output and triggers power regulation. Preferably, in some embodiments, the Pearson correlation coefficient between the temperature rise rate value and the negative pressure intensity value is used as the negative pressure effect.

[0037] In step S104, when the negative pressure effect degree is higher than the influence threshold, a dynamic negative pressure influence prediction is performed according to the negative pressure cumulative feature sequence to obtain a dynamic negative pressure influence index.

[0038] Preferably, in some embodiments, the impact threshold is calibrated as a constant threshold based on multiple tests. This application does not go into details about this. In specific implementation, when the impact of the negative pressure effect is lower than the impact threshold, the radio frequency slimming device performs radio frequency transmission according to the preset radio frequency electromagnetic transmission power.

[0039] Preferably, in some embodiments, the dynamic negative pressure impact prediction is performed based on the negative pressure cumulative feature sequence to obtain the dynamic negative pressure impact index, which specifically includes: Determining the negative pressure intensity corresponding to each radio frequency adjustment cycle according to the negative pressure cumulative characteristic sequence; According to the negative pressure intensity corresponding to each radio frequency adjustment cycle, the moving average autoregressive model is used to dynamically predict the negative pressure intensity of the next radio frequency adjustment cycle to obtain the predicted value of the negative pressure intensity; A dynamic negative pressure impact index corresponding to the next radio frequency adjustment cycle is determined based on the predicted value of the negative pressure effect intensity.

[0040] The following describes a preferred embodiment of the present application, using a moving average autoregressive model to dynamically predict the negative pressure intensity during the next RF regulation cycle: First, a prediction period is preset to 15 RF regulation cycles, and the system records the corresponding historical negative pressure intensity values within this period, forming a set of historical negative pressure intensity sequences. In other embodiments, the prediction period can also be set to a different number of cycles based on actual needs.

[0041] Next, a time series graph is plotted for the historical negative pressure intensity series, with the radio frequency regulation period as the horizontal axis. To eliminate the variance trend caused by the temporal evolution of the data, exponential smoothing or variance stabilization transformation can be performed on the time series.

[0042] Next, plot the autocorrelation coefficient (ACF) and partial autocorrelation coefficient (PACF) of the stabilized time series, with the horizontal axis representing the lag order and the vertical axis representing the corresponding autocorrelation or partial autocorrelation coefficient, respectively. By observing these plots, we can initially determine the order setting for the ARMA model. For example, if the autocorrelation coefficient decays rapidly after order 3 and approaches 0, the order of the AR (autoregressive) term can be considered 3; if the partial autocorrelation coefficient exhibits truncation characteristics after order 2, the order of the MA (moving average) term can be set to 2.

[0043] During the modeling phase, a preliminary moving average autoregressive (p, q) model can be constructed using the initial order (e.g., p=3, q=2) determined from the autocorrelation and partial autocorrelation coefficient plots. The model parameters are then estimated using the least squares method, and the rationality of each order parameter is verified using significance tests (e.g., with a test level set at 0.05). Finally, the Schwarz Bayesian Criterion (BIC) or Akaike Information Criterion (AIC) is used to optimize the different order combinations and determine the optimal autoregressive moving average model structure.

[0044] After the establishment is completed, the negative pressure intensity sequence in the current prediction cycle is input into the model to predict the negative pressure intensity value at the end of the next RF adjustment cycle, and it is used as the target prediction result for the dynamic feedback basis in the subsequent control strategy or adjustment model.

[0045] It should be noted that the dynamic negative pressure influence index in the present application is a quantitative parameter used to measure the dynamic influence of the negative pressure adsorption effect on the temperature rise rate of the target area during the RF regulation process. Preferably, in some embodiments, the process of determining the dynamic negative pressure influence index corresponding to the next RF regulation cycle based on the negative pressure effect intensity prediction value can use the negative pressure effect intensity corresponding to the historical RF regulation cycles as classification samples, and use K-means clustering to cluster the negative pressure effect intensity corresponding to the historical RF regulation cycles and the negative pressure effect intensity prediction value, and use the average value of the temperature rise rate deviation of the historical RF regulation cycles corresponding to each negative pressure effect intensity in the cluster where the negative pressure effect intensity prediction value is located as the dynamic negative pressure influence index.

[0046] In the specific implementation, first, the negative pressure intensity values corresponding to the historical RF adjustment cycles are used as sample inputs to construct a sample set for cluster analysis; wherein, each sample and its corresponding RF adjustment cycle are associated with the actual measured temperature rise rate deviation value; secondly, the K-means clustering algorithm is used to perform cluster analysis on the above sample set. Specifically, the historical negative pressure intensity values and the currently predicted negative pressure intensity prediction values are input into the clustering model for processing to complete the classification of the samples. Furthermore, by identifying the cluster to which the negative pressure intensity prediction value belongs and retrieving the temperature rise rate deviation corresponding to the historical samples in the cluster, the average value of the temperature rise rate deviation of all samples in the cluster is calculated as the dynamic negative pressure impact index.

[0047] In step S105, the user's emotion score is obtained, and the radio frequency electromagnetic transmission power of the radio frequency slimming device is dynamically adjusted based on the user's emotion score and the dynamic negative pressure impact index.

[0048] Preferably, in some embodiments, obtaining the user's emotion score specifically includes: collecting the user's multimodal physiological data for deep learning to obtain the user's emotion score.

[0049] In specific implementation, the multimodal physiological data is processed using an emotional state classification model based on machine learning. In this embodiment, a three-category model is used to classify and identify the user's emotional state, and the model output includes three emotional state labels, wherein the input features of the model may include: RMSSD (root mean square of the difference between consecutive heartbeats) of heart rate variability, heart rate low-frequency / high-frequency energy ratio, skin electrical responses per minute, and skin conductivity level average in the multimodal physiological data, wherein the multimodal physiological data has a preset reasonable range, for example, if the RMSSD data of heart rate variability is less than 20ms, or the heart rate low-frequency / high-frequency energy ratio is higher than 2.0, it can be judged that the user has entered a highly nervous state, and the model output is a highly nervous classification result, and the three-category model is used to log the number In the process of classification based on features, the input features of the emotion classification model are the aforementioned multimodal physiological parameters. Feature mapping or threshold judgment is performed through the trained classifier, and different multimodal physiological data are mapped to different emotion classification results based on the multi-threshold mapping table. In specific implementation, the user emotion features may include three emotion classification features: calm, nervous and highly nervous. When the RMSSD is less than 20ms, or the LF / HF ratio is greater than 2.0, it can be judged that the user is in a highly nervous state, and the model output label is "highly nervous". The other situations are mapped according to the multimodal parameters and the multi-threshold mapping table built into the model to obtain the final emotion score classification result.

[0050] It should be noted that the three-classification model described in this application can be trained and constructed based on, for example, decision trees, support vector machines (SVMs), random forests, or deep neural networks. Its classification rules or mapping functions can be automatically adjusted based on the correlation between physiological features and known emotional states in the training samples. The variability of multimodal physiological data center frequency reflects the activity of the user's sympathetic and parasympathetic nerves, while electrical skin signals are highly sensitive to psychological stress or anxiety and can effectively reveal changes in sweat gland activity. Accurately modeling and classifying emotional states based on multimodal physiological signals can comprehensively reflect the user's physiological sensitivity and tolerance threshold under different energy output conditions. By dynamically adjusting the RF energy based on the emotional score results, the user can maintain a relatively comfortable experience range, effectively relieve muscle tension, control blood flow changes, and reduce pain perception, significantly improving user adaptability and acceptance during use.

[0051] Preferably, in some embodiments, in the process of dynamically adjusting the radio frequency electromagnetic transmission power of the radio frequency slimming device based on the user's emotion score and the dynamic negative pressure impact index, the radio frequency electromagnetic transmission power of the radio frequency slimming device is dynamically adjusted based on a preset radio frequency power adjustment function.

[0052] The radio frequency power adjustment function is defined as: ; in, is the adjusted radio frequency electromagnetic transmission power, is the radio frequency electromagnetic transmission power before adjustment, is the emotion regulation weight coefficient, is the negative pressure adjustment coefficient, is the deviation between the real-time user's current sentiment score and the standard sentiment score. is the real-time dynamic negative pressure impact index, and t is the current moment.

[0053] Preferably, in some embodiments, 4 W / kg is set as the average specific absorption rate of the human body based on safety regulations, and is used as a limiting condition for the radio frequency electromagnetic transmission power of the radio frequency slimming device.

[0054] In addition, in another aspect of the present application, in some embodiments, the present application provides a vacuum negative pressure assisted radio frequency slimming device, the system includes a power control unit, reference Figure 2 , which is a schematic diagram of exemplary hardware and / or software structure of a power control unit according to some embodiments of the present application. The power control unit 200 includes: a data acquisition module 201, a data processing module 202, a device decision module 203, and a device control module 204, which are described as follows: The data acquisition module 201 is used to collect the dynamic negative pressure value of the negative pressure action area to obtain dynamic negative pressure data; The data processing module 202 is configured to obtain a preset radio frequency adjustment cycle, perform feature extraction on the dynamic negative pressure values within each radio frequency adjustment cycle in the dynamic negative pressure data, and obtain a negative pressure cumulative feature sequence; The data processing module 202 is further configured to collect the real-time negative pressure temperature, extract the temperature rise rate deviation corresponding to each RF adjustment cycle based on the real-time negative pressure temperature, and determine the negative pressure effect based on the correlation characteristics between the negative pressure cumulative feature sequence and the temperature rise rate deviation corresponding to each RF adjustment cycle. The equipment decision module 203 is configured to, when the negative pressure effect degree is higher than the impact threshold, perform dynamic negative pressure impact prediction based on the negative pressure cumulative feature sequence to obtain a dynamic negative pressure impact index; The device control module 204 is configured to obtain the user's emotion score and dynamically adjust the radio frequency electromagnetic transmission power of the radio frequency slimming device based on the user's emotion score and the dynamic negative pressure impact index.

[0055] The above describes in detail an example of a vacuum negative pressure assisted radio frequency slimming device and its control method provided in an embodiment of the present application. It can be understood that in order to achieve the above functions, the corresponding device includes a hardware structure and / or software module corresponding to each function.

[0056] Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function in the application is executed in hardware or in a computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Therefore, professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0057] In addition, the present application also provides a computer terminal device, which includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned control method of the vacuum negative pressure assisted radio frequency slimming device.

[0058] In some embodiments, reference Figure 3 , which is a schematic diagram of the structure of a computer terminal device for implementing a control method of a vacuum negative pressure assisted radio frequency slimming device according to some embodiments of the present application. A control method of a vacuum negative pressure assisted radio frequency slimming device in the above embodiment can be achieved by Figure 3 The computer terminal device 300 shown in FIG. 1 is implemented as shown in FIG. 1 , and the computer terminal device 300 includes at least one communication bus 301 , a communication interface 302 , a processor 303 and a memory 304 .

[0059] The processor 303 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more processors for controlling the execution of the control method of the vacuum negative pressure assisted radio frequency slimming device in the present application.

[0060] The communication bus 301 may include a path for transmitting information between the aforementioned components.

[0061] Memory 304 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, an optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer. Memory 304 may be independent and connected to processor 303 via communication bus 301. Memory 304 may also be integrated with processor 303.

[0062] Memory 304 is used to store program code for executing the solution of the present application, and is controlled by processor 303 for execution. Processor 303 is used to execute the program code stored in memory 304. The program code may include one or more software modules. In the above embodiment, the determination of the negative pressure effect can be implemented by processor 303 and one or more software modules in the program code in memory 304.

[0063] The communication interface 302 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0064] Optionally, the computer terminal device 300 may further include a power supply 305 for providing power to various devices or circuits in the real-time computer terminal device.

[0065] In a specific implementation, as an example, a computer terminal device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0066] The aforementioned computer terminal device can be a general-purpose computer terminal device or a dedicated computer terminal device. In a specific implementation, the computer terminal device can be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of this application do not limit the type of computer terminal device.

[0067] In addition, in other aspects of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the control method of the above-mentioned vacuum negative pressure assisted radio frequency slimming device.

[0068] In summary, in a vacuum negative pressure assisted radio frequency slimming device and a control method thereof disclosed in an embodiment of the present application, first, the dynamic negative pressure value of the negative pressure action area is collected to obtain dynamic negative pressure data; a preset radio frequency adjustment cycle is obtained, and the dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data are respectively feature extracted to obtain a negative pressure cumulative feature sequence; the real-time temperature of the negative pressure is collected, and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle is extracted according to the real-time temperature of the negative pressure, and the negative pressure effect is determined based on the correlation characteristics between the negative pressure cumulative feature sequence and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle; when the negative pressure effect is higher than the effect threshold, the dynamic negative pressure effect is predicted according to the negative pressure cumulative feature sequence to obtain a dynamic negative pressure effect index; the user's emotional score is obtained, and the radio frequency electromagnetic transmission power of the radio frequency slimming device is dynamically adjusted based on the user's emotional score and the dynamic negative pressure effect index, so that the power of the radio frequency slimming device can be dynamically adjusted according to the degree of negative pressure effect and the user's emotional characteristics, thereby improving the user's comfort during the process of using the vacuum negative pressure assisted radio frequency slimming device.

[0069] The above description is merely an embodiment of the present application. Common knowledge such as the specific technical solutions or features of the solutions is not described in detail herein. It should be noted that those skilled in the art may make various modifications and improvements without departing from the technical solution of the present application, and these modifications and improvements should also be considered within the scope of protection of the present application. These modifications and improvements will not affect the effectiveness of the implementation of the present application or the practical application of the patent.

[0070] The scope of protection claimed by this application shall be determined by the content of the claims. The specific embodiments and other descriptions in the specification may be used to interpret the content of the claims. Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of the invention. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application is intended to include such modifications and variations.

Claims

1. A control method for a vacuum negative pressure assisted radio frequency slimming device, characterized in that: include: Collect the dynamic negative pressure value of the negative pressure action area to obtain dynamic negative pressure data; Obtaining a preset radio frequency adjustment cycle, and performing feature extraction on the dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data to obtain a negative pressure cumulative feature sequence; Collecting the real-time temperature of the negative pressure, extracting the temperature rise rate deviation corresponding to each radio frequency adjustment cycle based on the real-time temperature of the negative pressure, and determining the influence of the negative pressure based on the correlation characteristics between the cumulative characteristic sequence of the negative pressure and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle; When the negative pressure effect is higher than the impact threshold, a dynamic negative pressure impact prediction is performed based on the negative pressure cumulative feature sequence to obtain a dynamic negative pressure impact index; The user's emotion score is obtained, and the radio frequency electromagnetic transmission power of the radio frequency slimming device is dynamically adjusted based on the user's emotion score and the dynamic negative pressure impact index.

2. The method according to claim 1, wherein In the process of dynamically adjusting the radio frequency electromagnetic transmission power of the radio frequency slimming device based on the user's emotion score and the dynamic negative pressure impact index, the radio frequency electromagnetic transmission power of the radio frequency slimming device is dynamically adjusted based on a preset radio frequency power adjustment function.

3. The method according to claim 1, wherein A thin film pressure sensor is used to collect the dynamic negative pressure values in the negative pressure action area at equal intervals.

4. The method according to claim 1, wherein When the negative pressure effect is lower than the impact threshold, the radio frequency slimming device performs radio frequency transmission according to the preset radio frequency electromagnetic transmission power.

5. The method according to claim 1, wherein The dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data are respectively subjected to feature extraction to obtain a negative pressure cumulative feature sequence, specifically including: Obtaining each dynamic negative pressure value in the dynamic negative pressure data and the corresponding collection time label; For any RF adjustment cycle, based on the collection tag corresponding to the dynamic negative pressure value, feature extraction is performed on the dynamic negative pressure value within the RF adjustment cycle to obtain the negative pressure cumulative feature vector corresponding to the RF adjustment cycle; The negative pressure cumulative feature vectors corresponding to each radio frequency adjustment period are combined into the negative pressure cumulative feature sequence according to a time sequence.

6. The method according to claim 1, wherein Determining the negative pressure effect based on the correlation characteristics between the negative pressure cumulative characteristic sequence and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle specifically includes: Obtaining each negative pressure cumulative feature vector in the negative pressure cumulative feature sequence; For any negative pressure cumulative feature vector, a neural network model is used to perform cluster scoring on the negative pressure cumulative feature vector to obtain the negative pressure intensity corresponding to each radio frequency adjustment cycle; The negative pressure effect during radio frequency transmission is determined based on the correlation characteristics between the negative pressure effect intensity and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle.

7. The method according to claim 1, wherein Extracting the temperature rise rate deviation corresponding to each RF adjustment cycle according to the real-time temperature of negative pressure specifically includes: determining the temperature rise rate values corresponding to the negative pressure action area in different RF adjustment cycles according to the real-time temperature of negative pressure, obtaining the standard temperature rise rates corresponding to different RF action times in the user's historical storage data, and comparing the temperature rise rates with the temperature rise rate values corresponding to different RF adjustment cycles to obtain the temperature rise rate deviation corresponding to each RF adjustment cycle.

8. A vacuum negative pressure assisted radio frequency slimming device, comprising a power control unit, wherein the power control unit is configured to execute the control method of the vacuum negative pressure assisted radio frequency slimming device according to any one of claims 1 to 7, characterized in that: The power control unit comprises: A data acquisition module is used to collect dynamic negative pressure values in the negative pressure action area to obtain dynamic negative pressure data; A data processing module is used to obtain a preset radio frequency adjustment cycle, perform feature extraction on the dynamic negative pressure values in each radio frequency adjustment cycle in the dynamic negative pressure data, and obtain a negative pressure cumulative feature sequence; The data processing module is further configured to collect the real-time temperature of the negative pressure, extract the temperature rise rate deviation corresponding to each radio frequency adjustment cycle based on the real-time temperature of the negative pressure, and determine the influence of the negative pressure based on the correlation characteristics between the cumulative characteristic sequence of the negative pressure and the temperature rise rate deviation corresponding to each radio frequency adjustment cycle; an equipment decision module, configured to, when the negative pressure effect degree is higher than an influence threshold, perform a dynamic negative pressure influence prediction based on the negative pressure cumulative feature sequence to obtain a dynamic negative pressure influence index; The device control module is used to obtain the user's emotional score and dynamically adjust the radio frequency electromagnetic transmission power of the radio frequency slimming device based on the user's emotional score and the dynamic negative pressure impact index.

9. A computer terminal device, characterized in that: The computer terminal device includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the control method of the vacuum negative pressure assisted radio frequency slimming device according to any one of claims 1 to 7.

10. A computer-readable storage medium storing at least one computer program, characterized in that: The computer program is loaded and executed by a processor to implement the operations performed by the control method of a vacuum negative pressure assisted radio frequency slimming device according to any one of claims 1 to 7.

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