Control method and device of depilation equipment, depilation equipment, readable storage medium and program product

Abstract

The invention relates to a control method and device of depilation equipment, the depilation equipment, a computer readable storage medium and a computer program product. The depilation equipment comprises an image acquisition module and a laser depilation module; the method comprises the following steps: acquiring a to-be-depilated area image of a to-be-depilated area, wherein the to-be-depilated area image is obtained by performing image acquisition on the to-be-depilated area by an image acquisition module; according to the image of the to-be-depilated area, detecting a target skin color grade, a target hair density and a target hair diameter of the to-be-depilated area; querying a target hair removal parameter set matched with the target skin color grade, the target hair density and the target hair diameter according to a preset hair removal parameter mapping relationship; and controlling the laser depilation module to perform laser depilation on the to-be-depilated area according to the target depilation parameter set. The method can improve the hair removal effect.

Description

Technical Field

[0001] This application relates to the field of laser hair removal technology, and in particular to a control method, apparatus, hair removal device, computer-readable storage medium, and computer program product for a hair removal device. Background Technology

[0002] With the development of semiconductor laser technology, hair removal devices are gradually becoming more widely used in the medical and beauty fields.

[0003] In traditional technologies, hair removal devices typically rely on a fixed laser wavelength and preset energy parameters to uniformly irradiate the treatment area.

[0004] However, traditional hair removal methods based on fixed parameters may cause thermal damage or even burns to the epidermis if the intensity is set too high, especially for those with darker skin or sensitive skin. Conversely, if the intensity is set too low, it may not provide enough energy to destroy the hair follicles for those with lighter skin or thicker, darker hair, resulting in incomplete hair removal and ultimately, poor hair removal results. Summary of the Invention

[0005] Therefore, it is necessary to provide a control method, apparatus, hair removal device, computer-readable storage medium, and computer program product for hair removal devices that can improve hair removal effects, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a control method for a hair removal device, the hair removal device including an image acquisition module and a laser hair removal module; the method includes:

[0007] The image of the area to be treated is acquired by the image acquisition module.

[0008] Based on the image of the area to be hair removed, the target skin color level, target hair density, and target hair diameter of the area to be hair removed are detected.

[0009] Based on the preset hair removal parameter mapping relationship, query the target hair removal parameter set that matches the target skin tone level, target hair density, and target hair diameter;

[0010] The laser hair removal module is controlled to perform laser hair removal on the area to be treated according to the target hair removal parameter set.

[0011] In one embodiment, detecting the target hair density in the area to be treated based on an image of the area to be treated includes:

[0012] Multi-dimensional grayscale features are extracted from the image of the area to be hair removed to obtain multi-dimensional initial grayscale features;

[0013] Based on the preset grayscale feature mean and preset grayscale feature standard deviation corresponding to each dimension, the initial grayscale features are standardized to obtain multi-dimensional target grayscale features. The preset grayscale feature mean and preset grayscale feature standard deviation are determined based on multiple sample grayscale features corresponding to each dimension. The multi-dimensional sample grayscale features are obtained by extracting multi-dimensional grayscale features from the sample image of the area to be hair removal.

[0014] Using a pre-defined linear regression network, the target hair density in the area to be treated is detected based on the grayscale features of each target. The weights and biases of the pre-defined linear regression network are obtained by training the linear regression network to be trained based on the grayscale features of each sample and the corresponding hair density labels of each sample grayscale feature.

[0015] In one embodiment, detecting the diameter of the target hair in the area to be treated, based on an image of the area to be treated, includes:

[0016] By performing hair stubble recognition on the image of the area to be treated, the number of hair stubble pixels and the number of hair stubbles in the area to be treated are determined.

[0017] The target hair diameter in the area to be treated is determined based on the number of hair follicle pixels and the number of hair follicles.

[0018] In one embodiment, the hair removal device further includes a semiconductor cooling module and a temperature sensor, the temperature sensor being disposed on the laser hair removal module; the method further includes, in the process of controlling the laser hair removal module to perform laser hair removal on the area to be treated according to the target hair removal parameter set:

[0019] The temperature sensor is controlled to collect the current temperature value of the area to be hair removed;

[0020] The target cooling power value of the semiconductor cooling module is determined based on the current temperature value, and the target cooling power value is positively correlated with the current temperature value;

[0021] The control semiconductor cooling module cools the area to be treated based on the target cooling power value.

[0022] In one embodiment, before the image acquisition module acquires an image of the area to be hair removed, the method further includes:

[0023] When the image acquisition module establishes a connection with the hair removal device, the mode selection information is displayed;

[0024] In response to a mode selection operation based on mode selection information, obtain the selected target operation mode;

[0025] When the target operation mode is expert mode, in response to the parameter setting operation, the user input set of self-selected parameters is obtained, the laser hair removal module is controlled, and laser hair removal is performed on the area to be treated according to the self-selected parameter set;

[0026] When the target operation mode is the simplified mode, the step of controlling the image acquisition module to acquire the image of the area to be hair removed is performed.

[0027] In one embodiment, based on an image of the area to be hair removed, the target skin tone level of the area to be hair removed is detected, including:

[0028] Extract multiple sets of initial luminance components and initial yellow-blue hue components from the image of the area to be hair removed;

[0029] Each initial luminance component is filtered according to its first mean and first standard deviation to obtain at least one target luminance component. Each initial yellow-blue luminance component is then filtered according to its second mean and second standard deviation to obtain at least one target yellow-blue luminance component. The difference between each target luminance component and its first mean is less than the first standard deviation, and the difference between each target yellow-blue luminance component and its second mean is less than the second standard deviation.

[0030] Based on the brightness component and the yellow-blue hue component of each target, the individual skin tone angle value of the area to be hair removed is detected.

[0031] Determine the target individual skin color angle range to which the individual skin color angle value belongs from multiple preset individual skin color angle ranges;

[0032] The skin tone grade corresponding to the angle range of the target individual's skin tone is determined as the target skin tone grade of the area to be treated.

[0033] Secondly, this application also provides a control device for a hair removal device, the hair removal device including an image acquisition module and a laser hair removal module; the device includes:

[0034] The acquisition module is used to acquire images of the area to be treated, which are obtained by the image acquisition module.

[0035] The detection module is used to detect the target skin tone level, target hair density, and target hair diameter of the area to be hair removed based on the image of the area to be hair removed.

[0036] The query module is used to query the target hair removal parameter set that matches the target skin tone level, target hair density, and target hair diameter based on the preset hair removal parameter mapping relationship;

[0037] The hair removal module is also used to control the laser hair removal module to perform laser hair removal on the area to be treated according to the target hair removal parameter set.

[0038] Thirdly, this application also provides a hair removal device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0039] The image of the area to be treated is acquired by the image acquisition module.

[0040] Based on the image of the area to be hair removed, the target skin color level, target hair density, and target hair diameter of the area to be hair removed are detected.

[0041] Based on the preset hair removal parameter mapping relationship, query the target hair removal parameter set that matches the target skin tone level, target hair density, and target hair diameter;

[0042] The laser hair removal module is controlled to perform laser hair removal on the area to be treated according to the target hair removal parameter set.

[0043] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0044] The image of the area to be treated is acquired by the image acquisition module.

[0045] Based on the image of the area to be hair removed, the target skin color level, target hair density, and target hair diameter of the area to be hair removed are detected.

[0046] Based on the preset hair removal parameter mapping relationship, query the target hair removal parameter set that matches the target skin tone level, target hair density, and target hair diameter;

[0047] The laser hair removal module is controlled to perform laser hair removal on the area to be treated according to the target hair removal parameter set.

[0048] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0049] The image of the area to be treated is acquired by the image acquisition module.

[0050] Based on the image of the area to be hair removed, the target skin color level, target hair density, and target hair diameter of the area to be hair removed are detected.

[0051] Based on the preset hair removal parameter mapping relationship, query the target hair removal parameter set that matches the target skin tone level, target hair density, and target hair diameter;

[0052] The laser hair removal module is controlled to perform laser hair removal on the area to be treated according to the target hair removal parameter set.

[0053] The aforementioned hair removal device control method, apparatus, equipment, computer-readable storage medium, and computer program product first utilize an image acquisition module to acquire images of the area to be treated, obtaining images of the area containing skin and hair feature information. Then, based on the image, three key physiological parameters of the area to be treated are automatically detected and quantified: target skin tone, target hair density, and target hair diameter. These parameters are closely related to the actual hair removal intensity requirements of different individuals and body parts. Furthermore, according to a preset hair removal parameter mapping relationship, a corresponding set of target hair removal parameters is specifically matched to the area to be treated, achieving personalized customization of hair removal parameters. Finally, the laser hair removal module is controlled to irradiate the area to be treated according to the target hair removal parameter set. In this way, for individuals with darker skin or sensitive skin, lower energy parameters can be automatically matched to effectively control the epidermis's absorption of laser energy, reducing the risk of thermal damage and burns. For individuals with lighter skin or thicker, darker hair, higher energy parameters sufficient to penetrate the epidermis and reach the hair follicle can be matched to ensure that the hair follicle structure is fully destroyed. Throughout the process, all hair removal parameters are adjusted strictly based on objective data obtained from image analysis and are automatically completed through preset mapping relationships, which can significantly improve the hair removal effect while ensuring the safety of the hair removal process. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a structural block diagram of the hardware system of a hair removal device in one embodiment of this application;

[0056] Figure 2 This is a structural block diagram of the software system of a hair removal device in one embodiment of this application;

[0057] Figure 3 This is a schematic diagram illustrating the interaction between the various software systems of the hair removal device in one embodiment of this application;

[0058] Figure 4This is a flowchart illustrating the control method of a hair removal device in one embodiment of this application;

[0059] Figure 5 This is a structural block diagram of the control device of a hair removal device in one embodiment of this application;

[0060] Figure 6 This is a diagram showing the internal structure of a hair removal device in one embodiment of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] With the development of semiconductor laser technology, hair removal devices are becoming increasingly popular in the medical and cosmetic fields. In traditional technologies, hair removal devices typically rely on a fixed laser wavelength and preset energy parameters to uniformly irradiate the treatment area.

[0063] However, fixed parameters cannot dynamically adapt to different skin tones and hair types. In people with darker skin, epidermal melanin competes with hair follicle melanin for laser energy absorption. If the parameters do not reduce the energy peak, it can easily lead to epidermal burns; if the energy is too low, it cannot effectively destroy the hair follicle.

[0064] Individual differences exist in skin's tolerance to heat stimulation, and fixed parameters cannot adjust energy output based on real-time feedback. For example, some users may experience thermal damage reactions such as erythema and blisters under fixed high energy, while low energy is ineffective for those with coarse hair. Studies have shown that the pain score of laser hair removal is directly related to energy density, and fixed parameters make it difficult to control pain below a safe threshold.

[0065] Manual parameter setting is highly subjective, requiring operators to manually select parameters such as energy density and pulse width based on experience, lacking objective guidance. For example, lip hair removal requires low energy (20-25 J / cm²) and a short pulse width (1-3 ms), but beginners might mistakenly set high energy parameters for limb hair removal, leading to burns in sensitive areas. Clinical data shows that approximately 30% of hair removal failures are directly related to incorrect parameter settings.

[0066] Fixed-parameter devices lack real-time temperature monitoring and dynamic cooling systems, making it impossible to balance photothermal effects and epidermal protection. For example, if ice-point hair removal does not simultaneously reduce peak energy and extend cooling intervals, skin temperature may exceed a safe threshold (e.g., 45°C), causing irreversible thermal damage. Clinical cases show that the incidence of skin burns due to improper parameter settings is approximately 5%-8%.

[0067] In one exemplary embodiment, a control method for a hair removal device is provided. This embodiment illustrates the application of this method to a control terminal of the hair removal device. The control terminal can be, but is not limited to, the host computer of the hair removal device, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. It is understood that this method can also be applied to a server, and can also be applied to a system including a control terminal and a server, and implemented through the interaction between the control terminal and the server.

[0068] Among them, hair removal equipment can refer to beauty devices that use semiconductors as laser working materials to generate lasers of specific wavelengths.

[0069] This solution includes at least an image acquisition module and a laser hair removal module.

[0070] An image acquisition module refers to a component with optical imaging capabilities, and its structure may include a lens, an image sensor, and driving circuitry. The image acquisition module can acquire optical information and convert it into digital image signals.

[0071] The image acquisition module can be integrated into the hair removal device or used as a standalone accessory. During use, it establishes a communication connection with the hair removal device via wired or wireless means and exchanges data.

[0072] In some feasible embodiments, such as Figure 1 As shown, the hardware system of the hair removal device includes a main unit hardware system and a hair removal handle hardware system.

[0073] The control of the hair removal handle hardware system mainly includes operation control, hair removal parameter adjustment, and status monitoring. Operation control primarily includes palladium strip light emission control, i.e., the conversion control of electrical energy to light energy; hair removal parameter adjustment is mainly achieved through a touchscreen; status monitoring mainly includes palladium strip temperature monitoring, skin contact detection at the light emission port, and button detection. The control of the hair removal handle hardware system can also include indication control of the hair removal handle's working status, which can be achieved through indicator modules such as LED strips and buzzers.

[0074] The control of the host hardware system mainly includes power supply control, operation control, and status monitoring. Power supply control mainly includes voltage conversion, input distribution and isolation control, key switch, emergency stop button, etc.; operation control mainly includes touch screen display control, palladium strip light emission control, palladium strip cooling control, liquid circuit circulation control, liquid circuit cooling control, host working status indication control, hair removal handle communication control, host internal heat dissipation control, handle insertion detection, and handle identification (e.g., handle ID (identity document)) detection; status monitoring mainly includes liquid circuit flow detection, liquid circuit temperature detection, light emission current detection, liquid level detection, foot switch detection, interlock switch detection, and hair removal handle presence monitoring, etc.

[0075] In some feasible embodiments, such as Figure 2 As shown, the software system of the hair removal device includes main control software, anomaly monitoring software, human-machine interface software, hair removal handle control software, photo-taking handle software, and image processing software.

[0076] The main control software is primarily used to perform power-on self-test, fault handling, energy calibration, laser output control, light emission trigger detection, hair removal handle insertion detection, LED strip control, and voice output.

[0077] The anomaly monitoring software is mainly used to implement functions such as liquid flow monitoring, liquid temperature monitoring, palladium bar current monitoring, liquid level monitoring, interlock switch monitoring, back cover removal switch, laser power supply monitoring, flow monitoring, fan speed monitoring, and energy meter display interface.

[0078] Human-computer interface software is mainly used to realize human-computer interaction functions, advanced system rental functions, image and video display functions, mode switching functions, parameter setting functions, real-time data display functions, interface element replacement functions, system debugging functions, logging functions, aging test setting functions, etc.

[0079] The hair removal handle control software is mainly used to realize the functions of palladium head temperature monitoring, skin contact detection, human-computer interaction, laser parameter adjustment, and board temperature detection.

[0080] The camera handle software is mainly used for video capture and transmission, and camera control.

[0081] Image processing software is mainly used to implement functions such as skin color detection, hair density detection, and hair thickness detection.

[0082] In some feasible embodiments, the interaction methods between the software are as follows: Figure 3As shown. The HMI software communicates with the photo-taking handle software via USB (Universal Serial Bus) transmission protocol to control video acquisition, data transmission, and photo-taking functions. The HMI software also communicates with the image processing software via USB transmission protocol to transmit photo data to the image recognition software, control image recognition enabling and disabling, and receive image recognition results. The main control software communicates with the HMI software via RS232 (recommended standard 232) communication protocol to upload system parameters and real-time status. The main control software also communicates with the hair removal handle control software via RS232 communication protocol to receive the working status of the hair removal handle control module, the hair removal handle temperature value, user-set laser parameter values, and control commands. Finally, the main control software communicates with the anomaly monitoring software via UART (Universal Asynchronous Receiver and Transmitter) communication protocol to read the values ​​and anomaly states of each sensor used for anomaly monitoring.

[0083] In this embodiment, as Figure 4 As shown, the control method for the hair removal device includes the following steps S10-S40. Wherein:

[0084] Step S10: Obtain an image of the area to be hair removed. The image of the area to be hair removed is obtained by the image acquisition module.

[0085] The area to be treated for hair removal can refer to the specific skin area on the body of the person seeking laser hair removal that requires laser hair removal treatment.

[0086] In some feasible embodiments, the area to be treated can refer to a continuous skin area that can be completely covered by the light output window of the laser hair removal module and receive a complete pulse or scan during a single laser irradiation. When the area of ​​skin to be treated with laser hair removal is large, the light output window of the laser hair removal module may not be able to cover the entire area. Therefore, the large area needs to be divided into multiple areas to be treated for sequential or multiple irradiations. Given that there may be natural differences in skin color, hair density, and hair diameter between different areas of the same body, to ensure that the parameters for each irradiation are accurately matched with the physiological characteristics of the corresponding area to be treated, image acquisition and physiological parameter detection can be performed on each area before laser irradiation. Based on the latest detected target skin color level, target hair density, and target hair diameter, the corresponding target hair removal parameter set can be matched to the area by querying a preset hair removal parameter mapping relationship, thereby achieving real-time, local adaptive adjustment of hair removal parameters.

[0087] In some feasible embodiments, in response to the start of hair removal operation for the laser hair removal module, an image acquisition command can be sent to the image acquisition module first, instructing the image acquisition module to acquire images of the area to be hair removed; then, the step of acquiring the image of the area to be hair removed is performed until the laser hair removal of the area to be hair removed is completed.

[0088] In other feasible embodiments, the laser hair removal module can also be equipped with a displacement sensor to monitor the displacement of the laser hair removal module relative to the previous acquisition position in real time. When the displacement detected by the displacement sensor exceeds a preset displacement threshold, it is determined that the laser hair removal module has moved to a new area to be treated. At this time, an image acquisition command is automatically sent to the image acquisition module, instructing the image acquisition module to re-acquire images of the area to be treated. Subsequently, based on the newly acquired image of the area to be treated, the target skin tone level, target hair density, and target hair diameter are re-detected, and a preset hair removal parameter mapping relationship is queried accordingly to generate a new set of target hair removal parameters that matches the physiological characteristics of the new area to be treated. Finally, the laser hair removal module will irradiate the area to be treated after the position has changed according to the new set of target hair removal parameters. Compared to the mode where the image acquisition and parameter calculation are only started after the user manually starts hair removal each time, this embodiment can quickly complete the parameter preparation for the new area to be treated while the laser hair removal module is moving. This significantly shortens the operation interval and waiting time when continuously irradiating multiple areas to be treated, improving the smoothness and overall efficiency of hair removal.

[0089] The image of the area to be treated can refer to a digital image file obtained through an image acquisition module, which contains visual information such as the color, texture, and hair distribution of the skin surface in the area to be treated.

[0090] In some feasible embodiments, a fixed spatial relationship between the image acquisition module and the laser hair removal module can be established through one or more methods such as mechanical design, optical alignment, or coordinate calibration. This ensures that, under this spatial relationship, the effective image acquisition area of ​​the image acquisition module, and its image acquisition range on the preset hair removal plane, can follow and cover the effective irradiation area formed by the light emission window of the laser hair removal module on the preset hair removal plane. The preset hair removal plane can refer to the skin surface on which the user wishes to have hair removed.

[0091] For example, when the operator aligns the laser hair removal module with the area to be treated, the image acquisition module simultaneously aligns with the area and acquires an image of the area. The acquired image can be directly sent to the control terminal, or it can be stored in the memory first, and the control terminal can retrieve it from the memory as needed.

[0092] Step S20: Based on the image of the area to be hair removed, detect the target skin tone level, target hair density, and target hair diameter of the area to be hair removed.

[0093] Skin tone grades refer to a quantitative classification of skin color depth. For example, the Fitzpatrick Skin Tone Classification Scale can be used to classify skin tones into types I to VI, with type I being the lightest and type VI the darkest.

[0094] The target skin tone grade can refer to the skin tone grade that the area to be treated with hair should match.

[0095] Hair density can refer to the number of hairs on a unit area of ​​skin.

[0096] In some feasible embodiments, the average hair density of the area to be treated, or a hair density level that matches the average hair density, can be used as the target hair density.

[0097] In some feasible embodiments, hair density ranges corresponding to different hair density levels can be predefined, and then the target hair density range to which the average hair density of the area to be hair removed belongs can be determined from each hair density range. The hair density level corresponding to the target hair density range can then be determined as the target hair density.

[0098] Hair diameter can refer to the thickness of the main shaft of a single hair.

[0099] In some feasible embodiments, the average hair diameter of some or all the hair in the area to be treated, or a hair diameter class that matches the average hair diameter, can be used as the target hair diameter.

[0100] In some feasible embodiments, hair diameter ranges corresponding to different hair diameter grades can be predefined, and then the target hair diameter range to which the average hair diameter of some or all hairs in the area to be hair removed belongs can be determined from each hair diameter range. The hair diameter grade corresponding to the target hair diameter range is then determined as the target hair diameter.

[0101] For example, after acquiring an image of the area to be hair removed, the control terminal calls the built-in image analysis algorithm to process the image of the area to be hair removed, and calculates and infers the target skin color level, target hair density, and target hair diameter.

[0102] Step S30: Based on the preset hair removal parameter mapping relationship, query the target hair removal parameter set that matches the target skin tone level, target hair density, and target hair diameter.

[0103] The target hair removal parameter set refers to the operating parameters of the laser hair removal module matched to the area to be treated. The target hair removal parameter set may include at least one of the following: laser energy density, pulse width, pulse frequency, laser wavelength, and cooling parameters.

[0104] Hair removal parameter mapping can refer to the correspondence between pre-stored physiological characteristics and recommended hair removal parameters. This mapping can take the form of a lookup table, database, or mathematical model. For example, a hair removal parameter mapping can be a three-dimensional lookup table where rows, columns, and layers correspond to skin tone level, hair density, and hair diameter, respectively, with each cell storing a set of laser parameters.

[0105] For example, the target skin tone level, target hair density, and target hair diameter can be used as query conditions to find the hair removal parameter set that matches the target skin tone level, target hair density, and target hair diameter in the preset hair removal parameter mapping relationship, and then use it as the target hair removal parameter set.

[0106] Step S40: Control the laser hair removal module to perform laser hair removal on the area to be treated according to the target hair removal parameter set.

[0107] For example, the control terminal generates a corresponding drive command based on the queried target hair removal parameter set and sends it to the laser hair removal module. After receiving the drive command, the laser hair removal module's internal components, such as the laser, energy control circuit, and cooling system, work together to accurately emit the corresponding laser towards the area to be treated according to the target hair removal parameter set carried in the drive command, thereby achieving hair removal treatment of the area to be treated.

[0108] In the control method of the aforementioned hair removal device, firstly, an image acquisition module is used to acquire images of the area to be treated, obtaining images of the area containing skin and hair feature information. Then, based on the image of the area to be treated, three key physiological parameters are automatically detected and quantified: target skin tone, target hair density, and target hair diameter. These parameters are closely related to the actual hair removal intensity requirements of different individuals and body parts. Furthermore, according to a preset hair removal parameter mapping relationship, a corresponding set of target hair removal parameters is matched specifically to the area to be treated, achieving personalized customization of hair removal parameters. Finally, the laser hair removal module is controlled to irradiate the area to be treated according to the target hair removal parameter set. In this way, for individuals with darker skin or sensitive skin, lower energy parameters can be automatically matched to effectively control the epidermis's absorption of laser energy, reducing the risk of thermal damage and burns. For individuals with lighter skin or thicker, darker hair, higher energy parameters sufficient to penetrate the epidermis and reach the hair follicle can be matched to ensure that the hair follicle structure is fully destroyed. Throughout the process, all hair removal parameters are adjusted strictly based on objective data obtained from image analysis and are automatically completed through preset mapping relationships, which can significantly improve the hair removal effect while ensuring the safety of the hair removal process.

[0109] In one exemplary embodiment, detecting the target hair density in the area to be treated based on an image of the area to be treated includes:

[0110] Multi-dimensional grayscale features are extracted from the image of the area to be treated, resulting in multi-dimensional initial grayscale features. Based on the preset grayscale feature mean and standard deviation for each dimension, the initial grayscale features are standardized to obtain multi-dimensional target grayscale features. The preset grayscale feature mean and standard deviation are determined based on multiple sample grayscale features corresponding to each dimension. These multi-dimensional sample grayscale features are obtained by extracting multi-dimensional grayscale features from sample images of the area to be treated. A preset linear regression network is used to detect the target hair density in the area to be treated based on each target grayscale feature. The weights and biases of the preset linear regression network are obtained by training the linear regression network based on each sample grayscale feature and the corresponding hair density label.

[0111] Among them, multi-dimensional grayscale features can refer to feature data used to characterize the grayscale distribution characteristics of hair and skin, including but not limited to the lowest grayscale value of the hair region, the highest grayscale value of the hair region, the lowest grayscale value of the skin region, the highest grayscale value of the skin region, the median grayscale value of the entire image, and the mean grayscale value of the entire image.

[0112] The preset grayscale feature mean can refer to the average value calculated in advance across various feature dimensions based on a large number of sample images of the hair removal area, used for standardization processing. As an example, before practical application, a large number of sample images of the hair removal area can be collected first. Multi-dimensional grayscale features can be extracted from each sample image of the hair removal area to obtain the multi-dimensional initial grayscale features of each sample image. Then, for each feature dimension, the initial grayscale features of all sample images of the hair removal area in that feature dimension are averaged, and the calculated average value is used as the preset grayscale feature mean.

[0113] The preset grayscale feature standard deviation refers to the standard deviation value calculated in advance based on a large number of sample images of the hair removal area in each feature dimension, used for standardization processing. As an example, before practical application, a large number of sample images of the hair removal area can be collected first. Multi-dimensional grayscale features are extracted for each sample image of the hair removal area to obtain the multi-dimensional initial grayscale features of each sample image of the hair removal area. Then, for each feature dimension, the standard deviation of the initial grayscale features of all sample images of the hair removal area in that feature dimension is calculated, and the calculated standard deviation value is used as the preset grayscale feature mean.

[0114] A linear regression network can refer to a prediction model composed of linear functions. The output of a linear regression network is obtained by weighting the input features with weights and adding bias values. In this embodiment, the output of the linear regression network is the target hair density, and the input is a feature vector composed of concatenated target grayscale features.

[0115] In some feasible embodiments, the weights and biases of the linear regression network can be pre-trained using collected sample images of the hair removal area and their hair density annotations. Specifically, a batch of sample images of the hair removal area can be acquired, and multi-dimensional grayscale features can be extracted from each sample image to obtain multi-dimensional sample grayscale features. These multi-dimensional sample grayscale features are concatenated into a sample grayscale feature vector, and the hair density annotation value of each sample image is obtained. The sample grayscale feature vector is input into the linear regression network to be trained, and the training hair density is obtained through forward propagation. Based on the difference between the training hair density and the hair density annotation value, the model loss of the linear regression network is calculated, and the weights and biases of the linear regression network are updated through backpropagation. Subsequently, a new batch of sample images of the hair removal area is acquired to perform a new round of iterative optimization on the linear regression network until the model loss converges or the number of iterations reaches a preset optimization threshold, thus obtaining a trained linear regression network. The weights and biases of the trained linear regression network are loaded into the control terminal of the hair removal device for application.

[0116] For example, after acquiring the image of the area to be hair removed, the image can be converted into a grayscale image. Then, the grayscale image can be segmented into hair and skin regions by means of grayscale thresholding or edge detection. Statistical analysis can be performed on the entire grayscale image, the hair region, and the skin region respectively to obtain multi-dimensional initial grayscale features.

[0117] Subsequently, for each feature dimension, the initial grayscale feature of that dimension is standardized based on the preset grayscale feature mean and preset grayscale feature standard deviation to obtain the target grayscale feature for that dimension. After performing standardization on the initial grayscale features of all dimensions in sequence, the multi-dimensional target grayscale features can be obtained.

[0118] Then, the multi-dimensional target grayscale features are spliced ​​into a target grayscale feature vector, and the target grayscale feature vector is input into a preset linear regression network. The target hair density of the area to be treated is calculated through the preset linear regression network.

[0119] In some feasible embodiments, the target grayscale feature = (initial grayscale feature - preset grayscale feature mean) / preset grayscale feature standard deviation.

[0120] In this embodiment, by extracting multi-dimensional grayscale features, the complex information related to hair distribution in the image of the area to be treated can be comprehensively and quantitatively described from different perspectives, providing rich evidence beyond a single feature for accurate estimation. Standardization reduces the impact of differences in the original dimensions and value ranges of different feature dimensions, improving the numerical stability and generalization ability of the linear regression network. The linear regression network, trained on a large number of samples, learns the mapping relationship between multi-dimensional grayscale features and actual hair density, thereby achieving objective, fast, and repeatable density estimation. The entire scheme reduces reliance on operator experience, significantly improving the automation, accuracy, and consistency of hair density detection, and providing reliable input for subsequent density-based personalized laser parameter matching.

[0121] In one exemplary embodiment, detecting the diameter of the target hair in the area to be treated, based on an image of the area to be treated, includes:

[0122] By identifying hair stubble in the image of the area to be treated, the number of hair stubble pixels and the total number of hair stubbles in the area to be treated are determined; based on the number of hair stubble pixels and the total number of hair stubbles, the diameter of the target hair in the area to be treated is determined.

[0123] Among them, hair stumps can refer to the cross-sectional portion of hair that is still embedded in the skin before it is fully exposed from the skin surface.

[0124] The number of hair stubble can refer to the number of hair stubble identified in the area to be treated.

[0125] The number of hair stubble pixels refers to the number of pixels in the area to be treated that are identified as hair stubble.

[0126] In some feasible embodiments, hair stumps are intertwined with skin tissue in the image and are identified as skin areas during hair and skin segmentation, but their brightness is higher compared to the surrounding skin areas. Therefore, hair stumps in the area to be hair removed can be identified by setting a hair stump brightness threshold.

[0127] As an example, the image of the area to be hair removed can be converted into a grayscale image first. Then, the brightness value of each pixel in the grayscale image can be extracted. By traversing each pixel in the grayscale image, pixels with brightness values ​​higher than the preset hair pile brightness threshold and that are consecutive can be classified into the same hair pile area, thereby realizing hair pile recognition in the area to be hair removed.

[0128] For example, after acquiring an image of the area to be treated, a preset image recognition algorithm can be called to identify hair stubble in the image, locating and marking each hair stubble. Then, the number of hair stubbles identified from the image and the number of pixels identified as hair stubbles are counted. The ratio of the number of hair stubble pixels to the total number of hair stubbles is determined as the target hair diameter for the area to be treated.

[0129] In this embodiment, the average diameter of the region can be indirectly calculated using image information through simple statistical calculations. Compared with the method of accurately tracking or measuring the outline of a single hair, the calculation efficiency of this embodiment is higher.

[0130] In one exemplary embodiment, detecting the target skin tone level of the area to be hair removed based on an image of the area to be removed includes:

[0131] Multiple initial luminance components and initial yellow-blue hue components are extracted from the image of the area to be hair removed. Each initial luminance component is filtered based on its first mean and first standard deviation to obtain at least one target luminance component. Similarly, each initial yellow-blue hue component is filtered based on its second mean and second standard deviation to obtain at least one target yellow-blue hue component. The difference between each target luminance component and its first mean is less than the first standard deviation, and the difference between each target yellow-blue hue component and its second mean is less than the second standard deviation. Based on each target luminance component and its target yellow-blue hue component, the individual skin tone angle value of the area to be hair removed is detected. From multiple preset individual skin tone angle ranges, the target individual skin tone angle range to which the individual skin tone angle value belongs is determined. The skin tone level corresponding to the target individual skin tone angle range is determined as the target skin tone level of the area to be hair removed.

[0132] The luminance component refers to a numerical value that describes the lightness or darkness of a color. As an example, the L component in the Lab color space can be used as the luminance component, with values ​​ranging from 0 (black) to 100 (white), representing brightness.

[0133] The yellow-blue tint component refers to a numerical value that describes the tendency of a color between yellow and blue. As an example, the b component in the Lab color space can be used as the yellow-blue tint component, with a positive value indicating a yellow tint and a negative value indicating a blue tint.

[0134] Individual skin tone angle values ​​refer to angular scalars calculated from the lightness and yellow-blue hue components, used to characterize the combined hue and lightness attributes of skin color. For example, a larger angle value may correspond to a yellowish, lighter skin tone, while a smaller angle value may correspond to a bluish, darker skin tone.

[0135] For example, after obtaining the image of the area to be hair removed, each pixel in the image can be converted to a color space such as CIE Lab or YCbCr that contains independent brightness and chromaticity information, so as to obtain the initial brightness component and the initial yellow-blue component of each pixel in the image.

[0136] Then, the average of all initial luminance components is calculated to obtain the first mean, and the standard deviation of all initial luminance components is calculated to obtain the first standard deviation. The average of all initial yellow-blue luminance components is calculated to obtain the second mean, and the standard deviation of all initial yellow-blue luminance components is calculated to obtain the second standard deviation. Next, each initial luminance component is checked to see if it falls within the interval formed by the first mean plus or minus the first standard deviation. Initial luminance components that do not fall within this interval, along with their corresponding initial yellow-blue luminance components, are filtered out. Similarly, each initial yellow-blue luminance component is checked to see if it falls within the interval formed by the second mean plus or minus the second standard deviation. Initial yellow-blue luminance components that do not fall within this interval, along with their corresponding initial luminance components, are filtered out. The final retained initial luminance components are used as the target luminance components, and the final retained initial yellow-blue luminance components are used as the target yellow-blue luminance components. By filtering out initial luminance and initial yellow-blue luminance components that deviate significantly from the mean, abnormal color values ​​caused by skin surface reflection, local spots, and uneven lighting can be eliminated, retaining the data that best represents the base color of the skin and hair.

[0137] Then, each pair of target luminance components and target yellow-blue hue components are substituted into a preset formula for calculating individual skin tone angle values ​​to obtain the individual skin tone angle value of a single pixel. The average of these individual skin tone angle values ​​is then calculated to obtain the individual skin tone angle value of the area to be treated. This individual skin tone angle value is compared with multiple pre-stored individual skin tone angle ranges to identify the target individual skin tone angle range into which the value falls. Based on the preset mapping relationship between individual skin tone angle ranges and skin tone levels, the target skin tone level corresponding to the target individual skin tone angle range is determined.

[0138] In this embodiment, the filtering mechanism can reduce interference factors such as skin surface reflection, local spots, and uneven lighting, accurately capture the user's true skin color characteristics, and map them into standardized skin color levels, providing a reliable basis for the personalized matching of subsequent laser parameters.

[0139] In one exemplary embodiment, the hair removal device further includes a semiconductor cooling module and a temperature sensor, the temperature sensor being disposed on the laser hair removal module; in the process of controlling the laser hair removal module to perform laser hair removal on the area to be treated according to the target hair removal parameter set, the method further includes:

[0140] The system controls the temperature sensor to collect the current temperature value of the area to be treated; it then determines the target cooling power value of the semiconductor cooling module based on the current temperature value, which is positively correlated with the current temperature value; finally, it controls the semiconductor cooling module to cool the area to be treated based on the target cooling power value.

[0141] The semiconductor refrigeration module can refer to a solid-state heat pump device that utilizes the Peltier effect and achieves active heat absorption or release through direct current control. In this embodiment, the semiconductor refrigeration module operates in cooling mode.

[0142] The cooling power value refers to the electrical power setting required to drive the semiconductor cooling module. The higher the cooling power value, the more heat the semiconductor cooling module removes from the skin surface per unit time.

[0143] For example, during the laser hair removal process where the semiconductor laser hair removal module emits laser light according to the target hair removal parameter set, the control terminal can send a data acquisition command to the temperature sensor installed on the laser hair removal module. Upon receiving the command, the temperature sensor measures the temperature of the skin surface of the area to be treated, which is in contact with or directly facing its probe, in real time, obtaining the current temperature value, and then transmits the acquired current temperature value back to the control terminal. The control terminal calculates or queries the target cooling power value corresponding to the current temperature value based on a preset mapping relationship between temperature and cooling power; the target cooling power value is positively correlated with the current temperature value. Subsequently, the control terminal converts the target cooling power value into a specific control signal and sends this control signal to the drive circuit of the semiconductor cooling module to drive the semiconductor cooling module to operate according to the target cooling power value, thereby cooling the area to be treated.

[0144] In this embodiment, by acquiring the temperature of the area to be treated and adaptively adjusting the cooling power of the semiconductor cooling module based on the acquired current temperature value, an adaptive match between the cooling intensity and the heat load generated by laser irradiation is achieved, along with the adaptive adjustment of the semiconductor cooling module. Therefore, when the laser energy is high and the skin temperature rises too quickly, cooling can be automatically enhanced to prevent overheating damage to the epidermis and improve the safety of the hair removal process; when the skin temperature is low, cooling can be automatically weakened to avoid patient discomfort caused by excessive cooling and to ensure sufficient epidermal temperature to facilitate the transfer of target laser energy to the hair follicle. This optimizes the comfort and final therapeutic effect of the hair removal process while ensuring its safety.

[0145] In an exemplary embodiment, before the image acquisition module acquires an image of the area to be hair removed, the method further includes:

[0146] When the image acquisition module establishes a connection with the hair removal device, mode selection information is displayed; in response to the mode selection operation based on the mode selection information, the selected target operation mode is obtained; if the target operation mode is expert mode, in response to the parameter setting operation, the user-inputted set of parameters is obtained, and the laser hair removal module is controlled to perform laser hair removal on the area to be treated according to the set of parameters; if the target operation mode is simple mode, the step of controlling the image acquisition module to acquire the image of the area to be treated is executed.

[0147] The mode selection information can refer to a set of visual information used to prompt and guide users to select a working mode. For example, a menu interface containing two options, "Expert Mode" and "Easy Mode"; or a pop-up dialog box displaying the text "Please select operating mode: Expert Mode / Easy Mode" and the corresponding button.

[0148] Expert mode refers to a working mode that allows operators with professional knowledge to manually set and control all key laser hair removal parameters.

[0149] The simplified mode refers to the working mode that automatically determines the laser hair removal parameters based on image analysis.

[0150] For example, when the control terminal detects that the image acquisition module has established a communication connection with the hair removal device through a physical interface or wireless protocol, it can display mode selection information on the display screen of the hair removal device or the interactive interface associated with the hair removal device, allowing the user to select the appropriate working mode according to their actual needs. After viewing the mode selection information, the user can perform a mode selection operation on the working mode option through input devices such as a touch screen or clicking a physical button. The control terminal continuously listens for input events, and when it detects a mode selection operation event, it extracts the target operating mode selected by the user from the mode selection operation event.

[0151] When the extracted target operation mode is expert mode, the control terminal can display the parameter setting interface on the screen of the hair removal device or the interactive interface associated with the hair removal device. Users can set or adjust laser hair removal parameters item by item on the parameter setting interface. After completing the settings, the user can trigger a parameter setting operation event by clicking the confirmation button. The control terminal parses the parameter setting operation event to obtain the parameter values ​​set by the user, forming a user-selected parameter set. Subsequently, the control terminal generates corresponding drive commands based on the user-selected parameter set and sends them to the laser hair removal module. After receiving the drive commands, the laser hair removal module's internal laser, energy control circuit, cooling system, and other components work together to accurately emit the corresponding laser towards the area to be treated according to the user-selected parameter set carried by the drive commands, thus achieving hair removal treatment.

[0152] When the extracted target operation mode is the simplified mode, the control terminal can activate the image acquisition module, perform image acquisition, physiological parameter detection, and hair removal parameter matching processes, automatically adjust the hair removal parameters, and achieve hair removal treatment on the hair removal area.

[0153] In this embodiment, in professional medical settings, experienced doctors can choose expert mode, directly inputting parameters to reduce potential delays or errors from automatic analysis, thus improving hair removal efficiency and accuracy. In home or beauty salon environments, ordinary users can choose simple mode, where the hair removal device automatically completes the complex image analysis and parameter matching process without requiring knowledge of technical details, reducing operational difficulty and the risk of misoperation. This dual-mode design allows the same device to adapt to various usage scenarios and user groups, providing ultimate ease of operation while maintaining professional-grade hair removal results.

[0154] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0155] Based on the same inventive concept, this application also provides a control device for a hair removal device to implement the control method of the hair removal device described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more control device embodiments of hair removal devices provided below can be found in the limitations of the control method of hair removal device above, and will not be repeated here.

[0156] In one exemplary embodiment, such as Figure 5 As shown, a control device for a hair removal equipment is provided, including: an acquisition module 502, a detection module 504, a query module 506, and a hair removal module 508, wherein:

[0157] The acquisition module 502 is used to acquire an image of the area to be hair removed, which is obtained by the image acquisition module acquiring an image of the area to be hair removed.

[0158] The detection module 504 is used to detect the target skin color level, target hair density, and target hair diameter of the area to be hair removed based on the image of the area to be hair removed.

[0159] The query module 506 is used to query the target hair removal parameter set that matches the target skin tone level, target hair density, and target hair diameter according to the preset hair removal parameter mapping relationship;

[0160] The hair removal module 508 is also used to control the laser hair removal module to perform laser hair removal on the area to be treated according to the target hair removal parameter set.

[0161] In one exemplary embodiment, the detection module 504 is further configured to:

[0162] Multi-dimensional grayscale features are extracted from the image of the area to be hair removed to obtain multi-dimensional initial grayscale features;

[0163] Based on the preset grayscale feature mean and preset grayscale feature standard deviation corresponding to each dimension, the initial grayscale features are standardized to obtain multi-dimensional target grayscale features. The preset grayscale feature mean and preset grayscale feature standard deviation are determined based on multiple sample grayscale features corresponding to each dimension. The multi-dimensional sample grayscale features are obtained by extracting multi-dimensional grayscale features from the sample image of the area to be hair removal.

[0164] Using a pre-defined linear regression network, the target hair density in the area to be treated is detected based on the grayscale features of each target. The weights and biases of the pre-defined linear regression network are obtained by training the linear regression network to be trained based on the grayscale features of each sample and the corresponding hair density labels of each sample grayscale feature.

[0165] In one exemplary embodiment, the detection module 504 is further configured to:

[0166] By performing hair stubble recognition on the image of the area to be treated, the number of hair stubble pixels and the number of hair stubbles in the area to be treated are determined.

[0167] The target hair diameter in the area to be treated is determined based on the number of hair follicle pixels and the number of hair follicles.

[0168] In one exemplary embodiment, the control device of the hair removal equipment further includes a cooling module; the hair removal equipment also includes a semiconductor cooling module and a temperature sensor, the temperature sensor being disposed on the laser hair removal module; during the process of controlling the laser hair removal module to perform laser hair removal on the area to be treated according to the target hair removal parameter set, the cooling module is used to:

[0169] The temperature sensor is controlled to collect the current temperature value of the area to be hair removed;

[0170] The target cooling power value of the semiconductor cooling module is determined based on the current temperature value, and the target cooling power value is positively correlated with the current temperature value;

[0171] The control semiconductor cooling module cools the area to be treated based on the target cooling power value.

[0172] In one exemplary embodiment, the control device of the hair removal device further includes a mode selection module; before the control image acquisition module acquires an image of the area to be hair removed, the mode selection is used for:

[0173] When the image acquisition module establishes a connection with the hair removal device, the mode selection information is displayed;

[0174] In response to a mode selection operation based on mode selection information, obtain the selected target operation mode;

[0175] When the target operation mode is expert mode, in response to the parameter setting operation, the user input set of self-selected parameters is obtained, the laser hair removal module is controlled, and laser hair removal is performed on the area to be treated according to the self-selected parameter set;

[0176] When the target operation mode is the simplified mode, the step of controlling the image acquisition module to acquire the image of the area to be hair removed is performed.

[0177] In one exemplary embodiment, the detection module 504 is further configured to:

[0178] Extract multiple sets of initial luminance components and initial yellow-blue hue components from the image of the area to be hair removed;

[0179] Each initial luminance component is filtered according to its first mean and first standard deviation to obtain at least one target luminance component. Each initial yellow-blue luminance component is then filtered according to its second mean and second standard deviation to obtain at least one target yellow-blue luminance component. The difference between each target luminance component and its first mean is less than the first standard deviation, and the difference between each target yellow-blue luminance component and its second mean is less than the second standard deviation.

[0180] Based on the brightness component and the yellow-blue hue component of each target, the individual skin tone angle value of the area to be hair removed is detected.

[0181] Determine the target individual skin color angle range to which the individual skin color angle value belongs from multiple preset individual skin color angle ranges;

[0182] The skin tone grade corresponding to the angle range of the target individual's skin tone is determined as the target skin tone grade of the area to be treated.

[0183] Each module in the control device of the aforementioned hair removal equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the hair removal equipment in hardware form or independent of it, or stored in the memory of the hair removal equipment in software form, so that the processor can call and execute the corresponding operations of each module.

[0184] In one exemplary embodiment, a hair removal device is provided, which may be a terminal, and its internal structure diagram may be as follows. Figure 6 As shown, the hair removal device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a control method for the hair removal device. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the hair removal device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the outer shell of the hair removal device, or an external keyboard, touchpad, or mouse, etc.

[0185] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the hair removal device to which the present application is applied. A specific hair removal device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0186] In one embodiment, a hair removal device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0187] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0188] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0189] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0190] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0191] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0192] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A control method of an epilating apparatus, characterized by, The hair removal device comprises an image acquisition module and a laser hair removal module; the method comprises: acquiring a to-be-removed region image of a to-be-removed region, the to-be-removed region image being obtained by the image acquisition module acquiring an image of the to-be-removed region; detecting a target skin color level, a target hair density and a target hair diameter of the to-be-removed region according to the to-be-removed region image; querying a target hair removal parameter set matched with the target skin color level, the target hair density and the target hair diameter according to a preset hair removal parameter mapping relationship; controlling the laser hair removal module to perform laser hair removal on the to-be-removed region according to the target hair removal parameter set.

2. The method of claim 1, wherein, detecting the target hair density of the to-be-removed region according to the to-be-removed region image, comprising: performing multi-dimensional gray feature extraction on the to-be-removed region image to obtain initial gray features in multiple dimensions; performing standardization processing on each of the initial gray features according to a preset gray feature mean value and a preset gray feature standard deviation corresponding to each dimension to obtain target gray features in multiple dimensions, wherein the preset gray feature mean value and the preset gray feature standard deviation are determined according to a plurality of sample gray features corresponding to each dimension, and the sample gray features in multiple dimensions are obtained by performing multi-dimensional gray feature extraction on a to-be-removed region sample image; detecting the target hair density of the to-be-removed region based on each of the target gray features through a preset linear regression network, wherein the weight value and the bias value of the preset linear regression network are obtained by training a to-be-trained linear regression network based on each of the sample gray features and a hair density label value corresponding to each of the sample gray features.

3. The method of claim 1, wherein, detecting the target hair diameter of the to-be-removed region according to the to-be-removed region image, comprising: determining the number of hair follicle pixels and the number of hair follicles of the to-be-removed region by performing hair follicle recognition on the to-be-removed region image; determining the target hair diameter of the to-be-removed region according to the number of hair follicle pixels and the number of hair follicles.

4. The method of claim 1, wherein, The hair removal device further comprises a semiconductor refrigeration module and a temperature sensor, and the temperature sensor is arranged on the laser hair removal module; during the process of controlling the laser hair removal module to perform laser hair removal on the to-be-removed region according to the target hair removal parameter set, the method further comprises: controlling the temperature sensor to collect a current temperature value of the to-be-removed region; determining a target refrigeration power value of the semiconductor refrigeration module according to the current temperature value, wherein the target refrigeration power value is positively correlated with the current temperature value; controlling the semiconductor refrigeration module to cool the to-be-removed region based on the target refrigeration power value.

5. The method according to any one of claims 1 to 4, characterized in that, Before the control of the image acquisition module to acquire the to-be-removed region image of the to-be-removed region, the method further comprises: when the image acquisition module is connected with the hair removal device, displaying mode selection information; in response to a mode selection operation on the mode selection information, acquiring a selected target operation mode; In a case that the target operation mode is the expert mode, in response to a parameter setting operation, a user-inputted self-selected parameter set is acquired, and the laser hair removal module is controlled to perform laser hair removal on the hair removal area according to the self-selected parameter set; In a case that the target operation mode is the simple mode, the step of controlling the image acquisition module to acquire the hair removal area image of the hair removal area is performed.

6. The method according to any one of claims 1 to 4, characterized in that, According to the hair removal area image, a target skin color grade of the hair removal area is detected, including: A plurality of groups of initial brightness components and initial yellow-blue chroma components are extracted from the hair removal area image; According to a first mean value and a first standard deviation value of each of the initial brightness components, each of the initial brightness components is filtered to obtain at least one target brightness component, and according to a second mean value and a second standard deviation value of each of the initial yellow-blue chroma components, each of the initial yellow-blue chroma components is filtered to obtain at least one target yellow-blue chroma component, wherein a difference between each of the target brightness components and the first mean value is less than the first standard deviation value, and a difference between each of the target yellow-blue chroma components and the second mean value is less than the second standard deviation value; According to each of the target brightness components and each of the target yellow-blue chroma components, an individual skin color angle value of the hair removal area is detected; A target individual skin color angle range to which the individual skin color angle value belongs is determined from a plurality of preset individual skin color angle ranges; A skin color grade corresponding to the target individual skin color angle range is determined as the target skin color grade of the hair removal area.

7. A control device for an epilating apparatus, characterized in that The hair removal device includes an image acquisition module and a laser hair removal module; and the apparatus includes: An acquisition module configured to acquire a hair removal area image of a hair removal area, the hair removal area image being obtained by the image acquisition module performing image acquisition on the hair removal area; A detection module configured to detect, according to the hair removal area image, a target skin color grade, a target hair density, and a target hair diameter of the hair removal area; A query module configured to query, according to a preset hair removal parameter mapping relationship, a target hair removal parameter set matched with the target skin color grade, the target hair density, and the target hair diameter; A hair removal module configured to control the laser hair removal module to perform laser hair removal on the hair removal area according to the target hair removal parameter set.

8. An epilating device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

Images