Hair removal instrument multi-stage heat dissipation control method and system combined with intelligent sensor

Abstract

The application discloses a multi-stage heat dissipation control method and system of an epilator combined with intelligent sensors, and relates to the technical field of intelligent control. The method comprises the following steps: dividing the cooling surface of an epilating head into multiple independent temperature control areas, integrating intelligent sensors in each temperature control area to obtain skin monitoring information of the corresponding area; constructing a heat dissipation cluster according to the skin monitoring information; analyzing the cooperative heat dissipation execution target of each cluster according to the heat dissipation cluster, matching the preset multi-stage heat dissipation parameter list, generating a cooperative heat dissipation control strategy, and using the strategy for execution control of cooling and heat dissipation of each temperature control area. The technical problem of the prior art that the heat dissipation mode of the epilator is single and cannot be differentiated and regulated according to different skin areas, resulting in local overcooling or overheating and poor user experience is solved. The technical effect of dynamically sensing hair density and skin state through intelligent sensors and realizing adaptive regulation of multi-stage heat dissipation clusters is achieved, so that the heat dissipation uniformity and skin comfort are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and specifically to a multi-level heat dissipation control method and system for hair removal devices that incorporates intelligent sensors. Background Technology

[0002] Traditional hair removal devices often rely on a single heat dissipation module for overall temperature regulation, neglecting differences in skin tolerance and hair density across different areas. This can easily lead to localized overheating or cooling, affecting hair removal results and user experience. Existing heat dissipation methods lack precise control over the target area and cannot dynamically adjust based on real-time skin feedback. With the development of intelligent control and sensing technologies, combining sensor data with multi-level heat dissipation strategies to achieve precise matching of heat dissipation and energy consumption optimization during the hair removal process has become a key issue for the technological upgrade of hair removal devices. Summary of the Invention

[0003] This application provides a multi-level heat dissipation control method and system for hair removal devices that incorporates intelligent sensors, which solves the technical problem that existing hair removal devices have a single heat dissipation method and cannot be differentiated for different skin areas, resulting in local overcooling or overheating and a poor user experience.

[0004] A first aspect of this application provides a multi-stage heat dissipation control method for a hair removal device incorporating a smart sensor, the method comprising:

[0005] The cooling surface of the hair removal head is divided into multiple independent temperature control zones. Each temperature control zone integrates a smart sensor to acquire skin monitoring information for the corresponding area, including at least hair density data. Based on the skin monitoring information, the heat dissipation power demand distribution is identified, and a heat dissipation cluster is constructed by identifying the heat dissipation demand gradient. Based on the heat dissipation cluster, the collaborative heat dissipation execution target of each cluster is analyzed and matched with a preset multi-level heat dissipation parameter list to generate a collaborative heat dissipation control strategy for controlling the cooling and heat dissipation execution of each temperature control zone.

[0006] A second aspect of this application provides a multi-level heat dissipation control system for a hair removal device incorporating intelligent sensors, the system comprising:

[0007] Data acquisition module: Divides the cooling surface of the hair removal head into multiple independent temperature control zones. Each temperature control zone integrates a smart sensor to acquire skin monitoring information for that zone, including at least hair density data. Identification module: Identifies the heat dissipation power demand distribution based on the skin monitoring information and constructs a heat dissipation cluster based on the heat dissipation demand gradient. Control module: Analyzes the collaborative heat dissipation execution target of each cluster based on the heat dissipation cluster, matches it with a preset multi-level heat dissipation parameter list, and generates a collaborative heat dissipation control strategy for controlling the cooling and heat dissipation execution of each temperature control zone.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, the cooling surface of the hair removal head is divided into multiple independent temperature-controlled zones. Each zone integrates a smart sensor to acquire skin monitoring information, including at least hair density data. Then, based on the skin monitoring information, the heat dissipation power demand distribution is identified, and a heat dissipation cluster is constructed by identifying the heat dissipation demand gradient. Finally, based on the analysis of the heat dissipation clusters and the collaborative heat dissipation execution objectives of each cluster, a collaborative heat dissipation control strategy is generated and matched with a preset multi-level heat dissipation parameter list to control the cooling and heat dissipation execution of each temperature-controlled zone. This solves the technical problem of existing hair removal devices having a single heat dissipation method and being unable to differentiate and adjust for different skin areas, resulting in localized overcooling or overheating and a poor user experience. It achieves the technical effect of dynamically sensing hair density and skin condition through smart sensors, enabling adaptive adjustment of multi-level heat dissipation clusters, thereby improving heat dissipation uniformity and skin comfort. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic flowchart of a multi-stage heat dissipation control method for a hair removal device incorporating a smart sensor, provided in an embodiment of this application.

[0012] Figure 2 This is a schematic diagram of a multi-level heat dissipation control system for a hair removal device incorporating a smart sensor, provided in an embodiment of this application.

[0013] Explanation of reference numerals in the attached diagram: Data acquisition module 11, identification module 12, control module 13. Detailed Implementation

[0014] This application provides a multi-level heat dissipation control method and system for hair removal devices that incorporates intelligent sensors. This solves the technical problem in the prior art where hair removal devices have a single heat dissipation method and cannot be differentiated for different skin areas, resulting in local overcooling or overheating and a poor user experience.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, this application provides a multi-level heat dissipation control method for a hair removal device incorporating a smart sensor, wherein the method includes:

[0018] The cooling surface of the hair removal head is divided into multiple independent temperature control zones. Each temperature control zone integrates a smart sensor to obtain skin monitoring information for the corresponding area, including at least hair density data.

[0019] In this embodiment, the cooling surface of the hair removal head is spatially divided into several independent temperature control zones according to a preset energy distribution model. Each temperature control zone has a relatively independent heat dissipation control unit in its physical structure. In addition, each temperature control zone integrates at least one smart sensor for real-time acquisition of skin monitoring information corresponding to that zone. The smart sensor includes, but is not limited to, an optical sensor, a temperature sensor, and a moisture sensor. The optical sensor can be a miniature CMOS image sensor to acquire images of the surface of the cooling surface in contact with the skin. By performing texture feature and contrast analysis on the acquired skin images, the pixel ratio of the hair-covered area is identified, thereby calculating the hair density data of that area. The temperature sensor is used to detect the instantaneous temperature value at the contact point between the skin and the cooling surface in that area. The moisture sensor is used to monitor the moisture content of the skin surface.

[0020] Furthermore, the cooling surface of the hair removal head is divided into multiple independent temperature-controlled zones, including:

[0021] An energy distribution model of the light spot emitted from the treatment head is obtained to represent the energy intensity at different locations within the light spot area. Based on the energy distribution model, the cooling surface of the treatment head is divided into multiple independent temperature control zones. The boundary of each temperature control zone is determined according to the contour lines of the light spot energy intensity or a preset energy intensity threshold range.

[0022] The energy distribution model of the light spot emitted from the treatment head is obtained through experimental measurement or numerical simulation. The energy distribution model is used to characterize the energy intensity distribution at various locations within the light spot area. For example, a contour map of the light spot energy changing with location is drawn in a two-dimensional coordinate system.

[0023] Based on the energy distribution model, the cooling surface of the treatment head is spatially divided. Specifically, according to the isopleths of the energy intensity of the light spot, or according to the preset energy intensity threshold range, the cooling surface positions with energy intensities in the same range are divided into independent temperature control areas, thus obtaining multiple independent temperature control areas. The boundary of each temperature control area is based on the isopleths or threshold ranges of energy intensity to ensure that the energy intensity in the same area is basically consistent.

[0024] Furthermore, based on the energy distribution model, the cooling surface of the treatment head is divided into multiple independent temperature control zones, including:

[0025] When the cooling surface of the treatment head is circular, based on the energy distribution model, the cooling surface is divided into a core central circle and at least one annular outer region surrounding the core central circle, according to the distribution of energy intensity using concentric rings. The core central circle has the highest energy intensity, and the annular outer region diffuses outward from the core central circle from strong to weak.

[0026] Optionally, when the cooling surface of the treatment head is a circular structure, it can be regionalized using concentric rings based on the energy distribution model of the light spot and the distribution of energy intensity. Specifically, firstly, the energy intensity at the center of the light spot is obtained, and this is determined as the highest energy region. Then, with this center point as the center, energy contour lines are drawn radially according to the attenuation trend of energy intensity, and the cooling surface is divided into several concentric ring regions based on the contour lines or a preset energy threshold interval. The division result includes at least a core central circle region and an annular outer region surrounding the core central circle. The core central circle corresponds to the region with the highest energy distribution, which usually requires higher power heat dissipation control; the annular outer region shows a diffusion distribution with gradually decreasing energy outwards, and different annular regions correspond to different energy intensity ranges.

[0027] Furthermore, based on the energy distribution model, the cooling surface of the treatment head is divided into multiple independent temperature control zones, including:

[0028] When the cooling surface of the treatment head is not circular, the energy-distance function is used to perform region clustering based on the distance from each point on the cooling surface to the energy center of the light spot, and the regions are divided into different temperature control areas.

[0029] Optionally, when the cooling surface of the treatment head is a non-circular structure, the cooling surface can be divided into regions based on the energy distribution model of the light spot, combined with the energy-distance function. Specifically, first, the energy center point of the light spot is determined, and the geometric distance from any point on the cooling surface to this energy center point is calculated. Then, the geometric distance is mapped to the energy intensity value of that point to obtain the energy-distance function value. Based on the similarity of the energy-distance function values, a clustering algorithm is used to group the points on the cooling surface, assigning points with similar function values ​​and adjacent spatial locations to the same temperature control region.

[0030] Determine the energy center point (x) of the light spot c ,y c ), calculate any point (x) on the cooling surface i ,y i Geometric distance to the energy center point: Among them, (x i ,y i (x) represents the coordinate position of any point on the cooling surface. c ,y c ) represents the coordinate position of the energy center of the light spot, d(x) i ,y i () represents the geometric distance from the point to the energy center.

[0031] At the same time, obtain the point (x) i ,y i The corresponding energy intensity value E(x) i ,y i And construct the energy-distance function: f(x) i ,y i )=α·d(x i ,y i )+β·E(x i ,y i ), where f(x) i ,y i ) represents point (x) i ,y i The energy-distance function value of d(x) is used for clustering determination; i ,y i ) represents point (x) i ,y i The geometric distance from the energy center of the light spot to E(x) i ,y i ) represents the energy intensity value at that point, which can be obtained through calculation by an energy distribution model or experimental measurement; α is the distance weighting coefficient, used to adjust the influence of distance factors in the function; β is the energy weighting coefficient, used to adjust the influence of energy intensity in the function.

[0032] By using the function value f(x) at all sampling pointsi ,y i Cluster analysis is performed to group points with similar function values ​​and adjacent physical locations into the same temperature control zone.

[0033] Furthermore, skin monitoring information for the corresponding area is obtained, including at least hair density data, including:

[0034] The smart sensor includes at least an optical sensor, wherein the optical sensor is a miniature CMOS image sensor. Texture and contrast analysis is performed on the skin surface image captured by the optical sensor to calculate the hair coverage ratio and obtain the hair density data.

[0035] The optical sensor is preferably a miniature CMOS image sensor, which is small in size and suitable for embedding in the area corresponding to the cooling surface of the treatment head. Specifically, the optical sensor acquires real-time images of the skin surface when in contact with the skin, obtaining skin image data including hair distribution characteristics. Subsequently, texture feature extraction and contrast analysis are performed on the image data. By identifying the grayscale or color difference between hairy and non-hairy areas in the image, the proportion of hair pixels to total pixels in the target area is calculated, thereby obtaining the hair coverage ratio. Based on the hair coverage ratio, the hair density data of the temperature-controlled area can be obtained.

[0036] Furthermore, the smart sensor also includes a temperature sensor and a moisture sensor.

[0037] Smart sensors include not only optical sensors but also temperature and moisture sensors. Temperature sensors can be embedded below the cooling surface of each temperature-controlled zone or close to the skin's contact interface. They are used to detect the temperature at the skin-cooling contact point in real time and convert temperature changes into digital signals that are transmitted to the control unit. The data from the temperature sensors can determine whether there is a risk of localized overheating or overcooling of the skin, thus serving as an important input parameter for heat dissipation regulation. Moisture sensors monitor the water content of the skin's surface layer. They can detect the skin's moisture level through capacitance, resistance, or infrared absorption principles and output corresponding moisture parameters.

[0038] A multimodal monitoring system composed of optical sensors, temperature sensors, and moisture sensors can obtain multi-dimensional skin monitoring information, including hair density, temperature, and moisture.

[0039] Based on the skin monitoring information, the heat dissipation power demand distribution is identified, and the heat dissipation demand gradient is identified to construct a heat dissipation cluster.

[0040] In this embodiment, the acquired skin monitoring information corresponding to each temperature-controlled area is processed and its features are extracted, including parameters such as hair density, skin surface temperature, and skin moisture content. Based on a preset heat dissipation power requirement mapping relationship, the skin monitoring information is converted into corresponding heat dissipation power requirement values, thus obtaining the distribution of heat dissipation power requirements for each temperature-controlled area. For example, when a certain area has a high hair density and the skin temperature rises rapidly, the system determines that the area requires a higher level of heat dissipation power; while when the skin moisture content is high, the heat dissipation intensity can be appropriately reduced to avoid overcooling.

[0041] After obtaining the heat dissipation power demand distribution, the heat dissipation demand gradient between adjacent temperature control zones is further calculated, i.e., the degree of difference between the heat dissipation demand levels of different zones. Based on the gradient values ​​and spatial relationships, multiple temperature control zones with similar heat dissipation power demand levels and physical proximity are aggregated into the same heat dissipation cluster. Each heat dissipation cluster therefore represents a group of temperature control zones with consistent or similar heat dissipation demands, facilitating the subsequent unified allocation of heat dissipation control strategies.

[0042] Furthermore, based on the skin monitoring information, the heat dissipation power demand distribution is identified, and the heat dissipation demand gradient is identified to construct a heat dissipation cluster, including:

[0043] Based on the hair density data, as well as the monitored skin temperature and skin moisture data, a heat dissipation demand analysis is performed to obtain the heat dissipation power demand distribution of each temperature control area; according to the physical location relationship of each temperature control area, the heat dissipation demand gradient of adjacent temperature control areas is calculated; based on the aggregation gradient threshold, the heat dissipation demand gradient of the temperature control areas is aggregated to construct the heat dissipation cluster, wherein the areas within the heat dissipation cluster have the same or similar heat dissipation power demand level and are physically adjacent.

[0044] Specifically, the system performs multi-source fusion analysis on hair density data, skin temperature data, and skin moisture data acquired in each temperature control area. This includes normalizing different parameters and performing weighted calculations according to preset weights to obtain the comprehensive heat dissipation demand index for that area. Based on the comprehensive heat dissipation demand index and the heat dissipation power level threshold table (as shown in Table 1), the heat dissipation power demand distribution for each temperature control area is determined.

[0045] Calculate the comprehensive heat dissipation demand index for each temperature-controlled zone:

[0046] D i =w1·H i +w2·T i +w3·M i , where D i H represents the overall heat dissipation demand index of the i-th temperature control zone; i This represents the hair density data (normalized hair coverage ratio) for this region; Ti This represents the skin temperature data (normalized temperature values) for this region; M i This represents the skin moisture data (normalized moisture content) for that area; w1, w2, and w3 are preset weighting coefficients that sum to 1 and can be adjusted based on actual heat dissipation experiments and user comfort feedback.

[0047] Table 1: Threshold Table for Heat Dissipation Power Level

[0048]

[0049]

[0050] Secondly, based on the physical distribution of each temperature control area on the cooling surface, the heat dissipation demand gradient between adjacent temperature control areas is calculated. This gradient is used to characterize the degree of difference in heat dissipation power demand between different areas. For example, when the difference in heat dissipation power level between two adjacent areas is greater than a preset threshold, it is determined that there is a significant heat dissipation demand gradient at that location.

[0051] Based on a preset aggregation gradient threshold, the system clusters and aggregates the heat dissipation demand gradient, dividing multiple adjacent temperature control zones with the same or similar heat dissipation power demand levels into a heat dissipation cluster.

[0052] Based on the analysis of the collaborative heat dissipation execution target of each heat dissipation cluster, and matching it with the preset multi-level heat dissipation parameter list, a collaborative heat dissipation control strategy is generated for the cooling and heat dissipation execution control of each temperature control area.

[0053] In this embodiment, the system analyzes the collaborative heat dissipation execution objectives of each heat dissipation cluster and matches them with a preset multi-level heat dissipation parameter list to generate a collaborative heat dissipation control strategy to guide the cooling and heat dissipation execution control of each temperature-controlled area. Specifically, the system first analyzes the attribute information of each heat dissipation cluster, which includes at least the heat dissipation power demand level within the cluster, the physical location range covered by the cluster, and the distribution relationship of adjacent clusters. Based on this attribute information, the system determines the collaborative heat dissipation execution objective of each heat dissipation cluster, such as ensuring that the temperature of the temperature-controlled area within the cluster is maintained within a safe and comfortable range, or achieving rapid heat dissipation under optimal energy consumption conditions. Subsequently, the system matches the collaborative heat dissipation execution objective with a preset multi-level heat dissipation parameter list. The multi-level heat dissipation parameter list is obtained through experimental calibration or simulation optimization and predefines optimal operating parameter combinations under multiple heat dissipation demand levels. The parameters include, but are not limited to, the driving current and voltage of the semiconductor cooling chip and the speed of the cooling fan. Through the matching process, the system can select control parameters corresponding to the heat dissipation demand level for each heat dissipation cluster. After obtaining the target heat dissipation control parameters for each cluster, the system further considers the spatial relationship between different heat dissipation clusters and performs smoothing transition processing on parameter differences between adjacent clusters to avoid significant temperature abrupt changes on the cooling surface. Finally, the system allocates the matched and smoothed control parameters to each temperature control zone, thereby generating an overall collaborative heat dissipation control strategy.

[0054] Furthermore, based on the collaborative heat dissipation execution objectives of each cluster analyzed by the heat dissipation cluster, and matched with a preset multi-level heat dissipation parameter list, a collaborative heat dissipation control strategy is generated, including:

[0055] The cluster attribute information of each heat dissipation cluster is parsed to generate a collaborative heat dissipation execution target for the cluster. The collaborative heat dissipation execution target is matched with a preset multi-level heat dissipation parameter list to obtain the heat dissipation control parameters corresponding to the predefined level parameters in the list. Based on the location distribution relationship of the heat dissipation clusters and the heat dissipation control parameters, a smooth transition process is performed. Specifically, the heat dissipation control parameters of two or more adjacent clusters are parsed, the transition heat dissipation control parameters between adjacent clusters are calculated, and the transition heat dissipation control parameters are assigned to the boundary region. The collaborative heat dissipation control strategy is generated based on the heat dissipation control parameters or transition heat dissipation control parameters assigned to all temperature control regions.

[0056] Specifically, the cluster attribute information of each heat dissipation cluster is analyzed. This cluster attribute information includes at least the cluster's heat dissipation power requirement level, the number and spatial distribution of temperature-controlled areas it covers, and the boundary relationships between the cluster and adjacent clusters. Based on the cluster attribute information, corresponding collaborative heat dissipation execution objectives are generated, such as maintaining overall cluster temperature stability within a limited power consumption range, or prioritizing rapid cooling in high-energy-density areas. The collaborative heat dissipation execution objectives are matched with a pre-defined multi-level heat dissipation parameter list. This list is obtained through system experiments and simulation calibration, and predefines several heat dissipation requirement levels and their corresponding optimal operating parameter combinations. Operating parameters include the driving current and voltage of the semiconductor cooling chip, and the speed of the cooling fan, etc. The matching process retrieves the corresponding optimal parameter combination based on the target heat dissipation level, thereby obtaining specific heat dissipation control parameters. Next, a smooth transition processing is performed on the matched heat dissipation control parameters. Specifically, when there are significant differences in heat dissipation control parameters between two or more adjacent clusters, a transitional heat dissipation control parameter between adjacent clusters is calculated using interpolation or a weighted average method, and this transitional parameter is allocated to the boundary region to avoid abrupt changes in cooling surface temperature. Finally, the heat dissipation control parameters or transitional heat dissipation control parameters allocated to all temperature control zones are summarized to generate an overall coordinated heat dissipation control strategy for dynamic heat dissipation management of each temperature control zone.

[0057] When performing smooth transition processing on the matched heat dissipation control parameters, a calculation method based on interpolation of adjacent cluster parameters can be used. Specifically, when two adjacent heat dissipation clusters correspond to heat dissipation control parameters P... i With P j At that time, for the temperature control unit located in the boundary region between the two clusters, the control unit calculates its transition heat dissipation control parameter P. b The formula is: P b =λ·P i +(1-λ)·P j , where P i P represents the heat dissipation control parameters for the i-th heat dissipation cluster; j P represents the heat dissipation control parameters of the j-th heat dissipation cluster adjacent to the i-th heat dissipation cluster; b λ represents the transitional heat dissipation control parameter in the boundary region between cluster i and cluster j; λ represents the weighting coefficient, used to characterize the spatial positional relationship between the boundary region point and the two adjacent clusters. Optionally, λ takes a value greater than 0.5 for boundary points closer to cluster i; a value less than 0.5 for boundary points closer to cluster j; and λ takes a value of 0.5 when the boundary point is at the midpoint between the two clusters.

[0058] Furthermore, it matches against a preset multi-level heat dissipation parameter list, which previously included:

[0059] The system obtains the operating parameter ranges of the heat dissipation component and the cooling component, where the operating parameters of the cooling component include the driving current and voltage of the semiconductor cooler, and the operating parameters of the heat dissipation component include the speed of the cooling fan. Based on the controllable operating parameter ranges, a test matrix with multiple parameter combinations is constructed. Based on the test matrix, the hair removal device is tested under each parameter combination, and corresponding system performance data is collected. The system performance data includes the treatment head temperature change rate, system power consumption, operating noise, and the hot end temperature of the cooler. According to a preset optimization goal, a multi-objective trade-off analysis is performed on all system performance data to select the optimal parameter combination for each heat dissipation requirement level. All heat dissipation requirement levels and their corresponding optimal parameter combinations are mapped to construct the preset multi-level heat dissipation parameter list. The heat dissipation requirement level is determined by using a collaborative heat dissipation execution goal to match and map the heat dissipation parameter combinations, which is used to generate heat dissipation control parameters.

[0060] The operating parameter ranges of the cooling and heat dissipation components were calibrated. The operating parameters of the cooling components mainly include the driving current and voltage of the semiconductor cooling chip, while the operating parameters of the heat dissipation components mainly include the speed of the cooling fan. By testing the controllable range of each operating component under experimental conditions, their safe operating range and critical thresholds were determined.

[0061] After obtaining the above operating parameter range, a test matrix is ​​constructed based on multi-dimensional combinations of current, voltage, and rotation speed. This test matrix covers the possible operating states of each component, comprehensively characterizing the system performance under different control combinations. Subsequently, the hair removal device is run under each parameter combination for actual testing, and system performance data is collected. This system performance data includes at least the treatment head temperature change rate (reflecting heat dissipation response speed), system power consumption (reflecting energy efficiency level), operating noise (reflecting comfort), and the hot end temperature of the cooling pad (reflecting cooling stability).

[0062] After obtaining performance data for all test combinations, the system performs a multi-objective trade-off analysis based on preset optimization goals. These optimization goals may include achieving rapid cooling, reducing energy consumption, and suppressing noise while ensuring user comfort. Through a multi-objective optimization method, performance data is filtered and weighted for evaluation, selecting the optimal parameter combination for each heat dissipation requirement level. Specifically, a weighted comprehensive evaluation function can be used to uniformly quantify different performance indicators; let the temperature change rate be R. T (A larger value indicates a faster heat dissipation response), system power consumption is P (a smaller value indicates higher energy efficiency), operating noise is N (a smaller value indicates a better user experience), and the hot-end temperature of the cooler is T. h (The smaller the value, the higher the cooling stability). Therefore, the comprehensive evaluation function can be expressed as: Here, α, β, γ, and δ are preset weighting coefficients, and their sum is 1. By adjusting the weighting coefficients, the heat dissipation performance, energy efficiency, noise reduction, and stability can be optimized according to different application scenarios or user needs.

[0063] During the analysis, the comprehensive evaluation value F corresponding to each parameter combination is calculated, and the parameter combination with the largest comprehensive evaluation value is selected as the optimal parameter for that level under the same heat dissipation requirement level. By traversing all heat dissipation requirement levels and completing the optimal parameter selection, a complete list of preset multi-level heat dissipation parameters is finally constructed.

[0064] In summary, the embodiments of this application have at least the following technical effects:

[0065] First, the cooling surface of the hair removal head is divided into multiple independent temperature-controlled zones. Each zone integrates a smart sensor to acquire skin monitoring information, including at least hair density data. Then, based on the skin monitoring information, the heat dissipation power demand distribution is identified, and a heat dissipation cluster is constructed by identifying the heat dissipation demand gradient. Finally, based on the analysis of the heat dissipation clusters and the collaborative heat dissipation execution objectives of each cluster, a collaborative heat dissipation control strategy is generated and matched with a preset multi-level heat dissipation parameter list to control the cooling and heat dissipation execution of each temperature-controlled zone. This solves the technical problem of existing hair removal devices having a single heat dissipation method and being unable to differentiate and adjust for different skin areas, resulting in localized overcooling or overheating and a poor user experience. It achieves the technical effect of dynamically sensing hair density and skin condition through smart sensors, enabling adaptive adjustment of multi-level heat dissipation clusters, thereby improving heat dissipation uniformity and skin comfort.

[0066] Example 2, based on the same inventive concept as the multi-stage heat dissipation control method for hair removal devices combined with intelligent sensors in the foregoing examples, such as... Figure 2 As shown, this application provides a multi-level heat dissipation control system for a hair removal device incorporating intelligent sensors, wherein the system includes:

[0067] Data acquisition module 11: Divides the cooling surface of the hair removal head into multiple independent temperature control zones. By integrating intelligent sensors in each temperature control zone, it acquires skin monitoring information of the corresponding area, including at least hair density data. Identification module 12: Identifies the heat dissipation power demand distribution based on the skin monitoring information and identifies the heat dissipation demand gradient to construct a heat dissipation cluster. Control module 13: Analyzes the collaborative heat dissipation execution target of each cluster based on the heat dissipation cluster, matches it with a preset multi-level heat dissipation parameter list, and generates a collaborative heat dissipation control strategy for controlling the cooling and heat dissipation execution of each temperature control zone.

[0068] Furthermore, the data acquisition module 11 is used to perform the following methods:

[0069] An energy distribution model of the light spot emitted from the treatment head is obtained to represent the energy intensity at different locations within the light spot area. Based on the energy distribution model, the cooling surface of the treatment head is divided into multiple independent temperature control zones. The boundary of each temperature control zone is determined according to the contour lines of the light spot energy intensity or a preset energy intensity threshold range.

[0070] Furthermore, the data acquisition module 11 is used to perform the following methods:

[0071] When the cooling surface of the treatment head is circular, based on the energy distribution model, the cooling surface is divided into a core central circle and at least one annular outer region surrounding the core central circle, according to the distribution of energy intensity using concentric rings. The core central circle has the highest energy intensity, and the annular outer region diffuses outward from the core central circle from strong to weak.

[0072] Furthermore, the data acquisition module 11 is used to perform the following methods:

[0073] When the cooling surface of the treatment head is not circular, the energy-distance function is used to perform region clustering based on the distance from each point on the cooling surface to the energy center of the light spot, and the regions are divided into different temperature control areas.

[0074] Furthermore, the data acquisition module 11 is used to perform the following methods:

[0075] The smart sensor includes at least an optical sensor, wherein the optical sensor is a miniature CMOS image sensor. Texture and contrast analysis is performed on the skin surface image captured by the optical sensor to calculate the hair coverage ratio and obtain the hair density data.

[0076] Furthermore, the data acquisition module 11 is used to perform the following methods:

[0077] The smart sensors also include: a temperature sensor and a moisture sensor.

[0078] Furthermore, the identification module 12 is used to perform the following method:

[0079] Based on the hair density data, as well as the monitored skin temperature and skin moisture data, a heat dissipation demand analysis is performed to obtain the heat dissipation power demand distribution of each temperature control area; according to the physical location relationship of each temperature control area, the heat dissipation demand gradient of adjacent temperature control areas is calculated; based on the aggregation gradient threshold, the heat dissipation demand gradient of the temperature control areas is aggregated to construct the heat dissipation cluster, wherein the areas within the heat dissipation cluster have the same or similar heat dissipation power demand level and are physically adjacent.

[0080] Furthermore, the control module 12 is used to perform the following methods:

[0081] The cluster attribute information of each heat dissipation cluster is parsed to generate a collaborative heat dissipation execution target for the cluster. The collaborative heat dissipation execution target is matched with a preset multi-level heat dissipation parameter list to obtain the heat dissipation control parameters corresponding to the predefined level parameters in the list. Based on the location distribution relationship of the heat dissipation clusters and the heat dissipation control parameters, a smooth transition process is performed. Specifically, the heat dissipation control parameters of two or more adjacent clusters are parsed, the transition heat dissipation control parameters between adjacent clusters are calculated, and the transition heat dissipation control parameters are assigned to the boundary region. The collaborative heat dissipation control strategy is generated based on the heat dissipation control parameters or transition heat dissipation control parameters assigned to all temperature control regions.

[0082] Furthermore, the control module 13 is used to perform the following methods:

[0083] The system obtains the operating parameter ranges of the heat dissipation component and the cooling component, where the operating parameters of the cooling component include the driving current and voltage of the semiconductor cooler, and the operating parameters of the heat dissipation component include the speed of the cooling fan. Based on the controllable operating parameter ranges, a test matrix with multiple parameter combinations is constructed. Based on the test matrix, the hair removal device is tested under each parameter combination, and corresponding system performance data is collected. The system performance data includes the treatment head temperature change rate, system power consumption, operating noise, and the hot end temperature of the cooler. According to a preset optimization goal, a multi-objective trade-off analysis is performed on all system performance data to select the optimal parameter combination for each heat dissipation requirement level. All heat dissipation requirement levels and their corresponding optimal parameter combinations are mapped to construct the preset multi-level heat dissipation parameter list. The heat dissipation requirement level is determined by using a collaborative heat dissipation execution goal to match and map the heat dissipation parameter combinations, which is used to generate heat dissipation control parameters.

[0084] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0085] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0086] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A multi-stage heat dissipation control method for hair removal devices incorporating intelligent sensors, characterized in that: The method includes: The cooling surface of the hair removal head is divided into multiple independent temperature control zones. Each temperature control zone integrates a smart sensor to obtain skin monitoring information for the corresponding area, including at least hair density data. Based on the skin monitoring information, the heat dissipation power demand distribution is identified, and the heat dissipation demand gradient is identified to construct a heat dissipation cluster. Based on the analysis of the collaborative heat dissipation execution target of each heat dissipation cluster, and matching it with the preset multi-level heat dissipation parameter list, a collaborative heat dissipation control strategy is generated for the cooling and heat dissipation execution control of each temperature control area. The cooling surface of the hair removal head is divided into multiple independent temperature-controlled zones, including: Obtain an energy distribution model of the light spot emitted from the treatment head to demonstrate the energy intensity at different locations within the light spot area; Based on the energy distribution model, the cooling surface of the treatment head is divided into multiple independent temperature control zones. The boundary of each temperature control zone is determined based on the contour lines of the light spot energy intensity or a preset energy intensity threshold range. Based on the skin monitoring information, the heat dissipation power demand distribution is identified, and the heat dissipation demand gradient is identified to construct a heat dissipation cluster, including: Based on the hair density data, as well as the monitored skin temperature and skin moisture data, a heat dissipation demand analysis is performed to obtain the heat dissipation power demand distribution of each temperature control area. Based on the physical location relationship of each temperature control zone, the heat dissipation demand gradient of adjacent temperature control zones is calculated; Based on the aggregation gradient threshold, the heat dissipation demand gradient of the temperature control area is aggregated to construct the heat dissipation cluster, wherein the areas within the heat dissipation cluster have the same or similar heat dissipation power demand level and are physically adjacent. Based on the collaborative heat dissipation execution objectives of each cluster as analyzed by the heat dissipation cluster, and matched with a preset multi-level heat dissipation parameter list, a collaborative heat dissipation control strategy is generated, including: The cluster attribute information of each heat dissipation cluster is analyzed to generate the collaborative heat dissipation execution target of the cluster. The cluster attribute information includes at least the heat dissipation power requirement level of the cluster, the number and spatial distribution of the temperature control area covered, and the boundary relationship between the cluster and adjacent clusters. The collaborative heat dissipation execution target is matched with a preset multi-level heat dissipation parameter list. The matching process retrieves the corresponding optimal parameter combination according to the target heat dissipation level to obtain the heat dissipation control parameters corresponding to the predefined level parameters in the list. Smooth transition processing is performed based on the location distribution relationship of heat dissipation clusters and heat dissipation control parameters. Specifically, the heat dissipation control parameters of two or more adjacent clusters are analyzed, the transition heat dissipation control parameters between adjacent clusters are calculated, and the transition heat dissipation control parameters are assigned to the boundary region. When there are large differences in the heat dissipation control parameters of two or more adjacent clusters, the transition heat dissipation control parameters between adjacent clusters are calculated by interpolation or weighted average method. The collaborative heat dissipation control strategy is generated based on the heat dissipation control parameters or transitional heat dissipation control parameters allocated to all temperature control zones.

2. The multi-stage heat dissipation control method for a hair removal device incorporating intelligent sensors according to claim 1, characterized in that, Based on the energy distribution model, the cooling surface of the treatment head is divided into multiple independent temperature control zones, including: When the cooling surface of the treatment head is circular, based on the energy distribution model, according to the distribution of energy intensity, the cooling surface is divided into a core central circle and at least one annular outer area surrounding the core central circle. The core central circle has the highest energy intensity, while the energy intensity in the outer ring area diffuses outward from the core central circle from the central circle to the outer ring.

3. The multi-stage heat dissipation control method for a hair removal device combined with an intelligent sensor according to claim 1, characterized in that, Based on the energy distribution model, the cooling surface of the treatment head is divided into multiple independent temperature control zones, including: When the cooling surface of the treatment head is not circular, the energy-distance function is used to perform region clustering based on the distance from each point on the cooling surface to the energy center of the light spot, and the regions are divided into different temperature control areas.

4. The multi-stage heat dissipation control method for a hair removal device incorporating intelligent sensors according to claim 1, characterized in that, The acquisition of skin monitoring information for the corresponding area includes at least hair density data, including: The smart sensor includes at least an optical sensor, wherein the optical sensor is a miniature CMOS image sensor. Texture and contrast analysis is performed on the skin surface image captured by the optical sensor to calculate the hair coverage ratio and obtain the hair density data.

5. The multi-stage heat dissipation control method for a hair removal device incorporating intelligent sensors according to claim 4, characterized in that, The smart sensors also include: a temperature sensor and a moisture sensor.

6. The multi-stage heat dissipation control method for a hair removal device incorporating intelligent sensors according to claim 1, characterized in that, Matched against a pre-defined multi-level heat dissipation parameter list, previously including: The operating parameter ranges of the heat dissipation component and the cooling component are obtained, wherein the operating parameters of the cooling component include the driving current and voltage of the semiconductor cooling chip, and the operating parameters of the heat dissipation component include the speed of the cooling fan. Based on the controllable operating parameter range, a test matrix with multiple parameter combinations is constructed; Based on the test matrix, the hair removal device was tested under each parameter combination, and the corresponding system performance data was collected. The system performance data included the treatment head temperature change rate, system power consumption, operating noise, and the temperature of the hot end of the cooling plate. Based on the preset optimization goals, a multi-objective trade-off analysis is performed on all system performance data to select the optimal parameter combination for each heat dissipation requirement level. All heat dissipation requirement levels and their corresponding optimal parameter combinations are mapped to construct the preset multi-level heat dissipation parameter list. The heat dissipation requirement level is determined by the collaborative heat dissipation execution target, and the heat dissipation parameter combination is matched and mapped to generate heat dissipation control parameters.

7. A multi-level heat dissipation control system for a hair removal device incorporating intelligent sensors, characterized in that: For implementing the multi-stage heat dissipation control method for a hair removal device incorporating a smart sensor as described in any one of claims 1-6, the system comprises: Data acquisition module: The cooling surface of the hair removal head is divided into multiple independent temperature control zones. Each temperature control zone integrates a smart sensor to acquire skin monitoring information of the corresponding area, including at least hair density data. Identification module: Identifies the heat dissipation power demand distribution based on the skin monitoring information, identifies the heat dissipation demand gradient, and constructs a heat dissipation cluster; Control module: Based on the analysis of the collaborative heat dissipation execution target of each heat dissipation cluster, it matches the target with a preset multi-level heat dissipation parameter list to generate a collaborative heat dissipation control strategy for the cooling and heat dissipation execution control of each temperature control area.

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