Operation optimization method and system for hair removal instrument equipment

Abstract

The invention discloses an operation optimization method and system for hair removal instrument equipment. An operation time period is set, an analysis period is divided, parameters such as wavelength, pulse width and laser pulse repetition frequency are collected in real time, parameter correlation and changes are analyzed through Pearson's correlation coefficients, and an infrared monitoring unit is combined to obtain an infrared image of a target part for energy output analysis. And through weighted average of the parameter change consistency proportion and the energy output difference value, operation abnormity is evaluated, operation early warning information and an optimization scheme are generated, and intelligent operation optimization of the hair removal instrument equipment is realized.

Description

Technical Field

[0001] The present invention relates to the field of hair removal device analysis, and more specifically, to an operation optimization method and system for hair removal device. Background Art

[0002] As people's living standards improve, more and more people begin to pay attention to their personal image and quality of life, and hair removal has become a daily care need for many people. However, most existing hair removal devices use fixed parameter settings and operation methods, and are unable to effectively evaluate and locate abnormal parameters according to the operating status. They lack the ability to assess equipment operation abnormalities in combination with the energy dimension, making it difficult to locate abnormal parameters and adjust and optimize the equipment, resulting in difficulty in improving the user experience. Therefore, there is an urgent need for an operation optimization method for hair removal equipment to solve the above technical problems. Summary of the invention

[0003] The present invention overcomes the defects of the prior art and proposes an operation optimization method and system for hair removal equipment.

[0004] A first aspect of the present invention provides an operation optimization method for a hair removal device, comprising: Based on the operation plan of the hair removal device, an operation time period is set, and the operation time period is divided into multiple analysis cycles; In each analysis cycle, the real-time parameters of the hair removal device are collected, including wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are marked as the first parameter, the second parameter, and the third parameter respectively; In one analysis cycle, linear change analysis is performed on the first parameter, the second parameter, and the third parameter respectively. Based on the Pearson correlation coefficient, the correlation coefficients of the three are calculated, and the current parameter change data is generated. The simulation analysis of the equipment parameters is performed through the operation plan to generate comparative parameter change data. The current parameter change data and the comparative parameter change data are subjected to data anomaly analysis and consistency evaluation. The parameter proportion analysis is performed through the abnormal data points obtained through the evaluation, and the parameter change consistency ratio is calculated; Through the infrared monitoring unit, the target part corresponding to the hair removal device is monitored in real time, and multiple infrared images are obtained. The infrared images are used as energy output analysis images. Based on the color histogram, the color features of the infrared images are extracted, and the monitoring color feature data are obtained. The monitoring color feature data and the target infrared feature data are compared for feature vectorization and feature similarity, and a feature difference value is generated. The feature difference value is used as the energy output difference value; The weighted average is taken according to the parameter change consistency ratio and the energy output difference value to evaluate the operation abnormality. If there is an operation abnormality, the operation warning information is generated based on the monitored color feature data and the current parameter change data, and the operation optimization plan is generated through the operation warning information.

[0005] In this solution, the operation solution based on the hair removal device sets an operation time period, and divides the operation time period into multiple analysis cycles, specifically: Based on the operation plan of the hair removal device, an operation time period is set, wherein the operation plan includes target operation time, device parameter settings, and target hair removal effect information; Based on the equipment usage, the operation time period is divided into multiple analysis cycles, and the length of each analysis cycle is within the preset time period.

[0006] In this scheme, in each analysis cycle, the real-time parameter collection of the hair removal device is performed, and the parameters include wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are marked as the first parameter, the second parameter, and the third parameter, respectively, as follows: In each analysis cycle, the data acquisition unit collects real-time parameters of the hair removal device, including wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are marked as the first parameter, the second parameter, and the third parameter respectively; For each analysis cycle, the corresponding acquisition parameters are ordered and imported into a storage unit for storage.

[0007] In this scheme, in one analysis cycle, the linear change analysis is performed on the first parameter, the second parameter, and the third parameter respectively, and the correlation coefficient of the three is calculated based on the Pearson correlation coefficient, and the current parameter change data is generated. The simulation analysis of the equipment parameters is performed by running the scheme to generate comparative parameter change data, and the current parameter change data and the comparative parameter change data are subjected to data anomaly analysis and consistency evaluation. The parameter proportion analysis is performed through the abnormal data points obtained by the evaluation, and the parameter change consistency ratio is calculated, which is specifically: Linear change analysis and linear fitting were performed on the three parameters to form three change curves. Correlation analysis was performed on each parameter. The correlation calculation was based on the Pearson correlation coefficient to obtain three correlation coefficients. Integrate the three change curves and the three correlation coefficients into the current parameter change data; Obtain target parameter information of the hair removal device through the operation plan, perform parameter simulation change analysis based on the target parameter information, and generate comparative parameter change data; The current parameter change data is compared with the comparison parameter change data. Specifically, the parameter information comparison is to compare the values ​​of the three change curves with the three comparison fitting curves respectively, record the parameter points where the value difference between the two exceeds the preset range, mark them as the first abnormal data points, and perform difference analysis on the three correlation coefficients and the three comparison correlation coefficients to generate the second abnormal data points; The abnormal parameter ratio is calculated based on the first abnormal data point and the second abnormal data point to obtain the parameter change consistency ratio.

[0008] In this solution, the infrared monitoring unit is used to monitor the target part corresponding to the hair removal device in real time, and multiple infrared images are obtained. The infrared image is used as the energy output analysis image. Based on the color histogram, the color feature of the infrared image is extracted, and the monitoring color feature data is obtained. The monitoring color feature data and the target infrared feature data are feature vectorized and feature similarity is compared to generate a feature difference value, and the feature difference value is used as the energy output difference value, specifically: Through the infrared monitoring unit, the target part corresponding to the hair removal device is monitored in real time, and multiple infrared images are obtained in one analysis cycle; Perform smoothing, noise reduction, and standardization preprocessing on multiple infrared images; According to the color histogram, color features of multiple infrared images are extracted to obtain multiple monitoring color feature data; Based on the operation plan, a target temperature control value of the target part is set, and infrared characteristic simulation is performed based on the target temperature control value to generate multiple target infrared characteristic data; The feature vectorization and feature similarity comparison of multiple monitoring color feature data and multiple target infrared feature data are performed, and the similarity is calculated based on the Euclidean distance method to obtain multiple difference values; An average value of the plurality of difference values ​​is taken as the energy output difference value.

[0009] In this scheme, the weighted average is performed based on the parameter change consistency ratio and the energy output difference value to evaluate the operation abnormality. If there is an operation abnormality, the operation warning information is generated based on the monitoring color feature data and the current parameter change data, and the operation optimization scheme is generated through the operation warning information, which is specifically: An abnormal operation is evaluated based on the parameter change consistency ratio and energy output difference value, and whether there is an abnormal operation state is determined in the form of weighted average; If the weighted mean is outside the preset abnormal value range, it is determined that there is an operation abnormality. The temperature control numerical analysis is performed based on the monitored color feature data to generate temperature distribution information. The operation warning information is generated in combination with the current parameter change data and abnormal data points, and an operation optimization plan is generated based on the operation warning information.

[0010] The second aspect of the present invention further provides an operation optimization system for a hair removal device, the system comprising: a memory, a processor, the memory comprising an operation optimization program for the hair removal device, the operation optimization program for the hair removal device being executed by the processor to implement the following steps: Based on the operation plan of the hair removal device, an operation time period is set, and the operation time period is divided into multiple analysis cycles; In each analysis cycle, the real-time parameters of the hair removal device are collected, including wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are marked as the first parameter, the second parameter, and the third parameter respectively; In one analysis cycle, linear change analysis is performed on the first parameter, the second parameter, and the third parameter respectively. Based on the Pearson correlation coefficient, the correlation coefficients of the three are calculated, and the current parameter change data is generated. The simulation analysis of the equipment parameters is performed through the operation plan to generate comparative parameter change data. The current parameter change data and the comparative parameter change data are subjected to data anomaly analysis and consistency evaluation. The parameter proportion analysis is performed through the abnormal data points obtained through the evaluation, and the parameter change consistency ratio is calculated; Through the infrared monitoring unit, the target part corresponding to the hair removal device is monitored in real time, and multiple infrared images are obtained. The infrared images are used as energy output analysis images. Based on the color histogram, the color features of the infrared images are extracted, and the monitoring color feature data are obtained. The monitoring color feature data and the target infrared feature data are compared for feature vectorization and feature similarity, and a feature difference value is generated. The feature difference value is used as the energy output difference value; The weighted average is taken according to the parameter change consistency ratio and the energy output difference value to evaluate the operation abnormality. If there is an operation abnormality, the operation warning information is generated based on the monitored color feature data and the current parameter change data, and the operation optimization plan is generated through the operation warning information.

[0011] The third aspect of the present invention also provides a computer-readable storage medium, which includes an operation optimization program for a hair removal device. When the operation optimization program for a hair removal device is executed by a processor, the steps of the operation optimization method for a hair removal device as described in any one of the above items are implemented.

[0012] The present invention discloses an operation optimization method and system for a hair removal device. By setting the operation time period and dividing the analysis cycle, parameters such as wavelength, pulse width, laser pulse repetition frequency, etc. are collected in real time, and the correlation and change of parameters are analyzed using the Pearson correlation coefficient. Infrared images of target parts are obtained in combination with an infrared monitoring unit to analyze energy output. Operation anomalies are evaluated by weighted average of parameter change consistency ratio and energy output difference value, and operation warning information and optimization scheme are generated to realize intelligent operation optimization of hair removal device. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flow chart showing an operation optimization method for a hair removal device according to the present invention is shown; Figure 2 A block diagram of an operation optimization system for a hair removal device according to the present invention is shown. DETAILED DESCRIPTION

[0014] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0015] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0016] Figure 1 A flow chart of an operation optimization method for a hair removal device according to the present invention is shown.

[0017] like Figure 1 As shown, the first aspect of the present invention provides an operation optimization method for a hair removal device, comprising: S102, based on the operation plan of the hair removal device, setting an operation time period, and dividing the operation time period into multiple analysis cycles; S104, in each analysis cycle, real-time parameter collection of the hair removal device is performed, the parameters including wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are respectively marked as a first parameter, a second parameter, and a third parameter; S106, in one analysis cycle, linear change analysis is performed on the first parameter, the second parameter, and the third parameter respectively, and the correlation coefficient of the three is calculated based on the Pearson correlation coefficient, and current parameter change data is generated, and simulation analysis of equipment parameters is performed through the operation plan to generate comparative parameter change data, and data anomaly analysis and consistency evaluation are performed on the current parameter change data and the comparative parameter change data, and parameter proportion analysis is performed through the abnormal data points obtained through the evaluation, and the parameter change consistency ratio is calculated; S108, using the infrared monitoring unit, real-time monitoring of the target part corresponding to the hair removal device is performed, and multiple infrared images are obtained, the infrared images are used as energy output analysis images, color features of the infrared images are extracted based on the color histogram, and monitoring color feature data is obtained, the monitoring color feature data is quantized and feature similarity is compared with the target infrared feature data, a feature difference value is generated, and the feature difference value is used as the energy output difference value; S110, weighted average is performed based on the parameter change consistency ratio and the energy output difference value to evaluate the operation abnormality. If there is an operation abnormality, operation warning information is generated based on the monitored color feature data and the current parameter change data, and an operation optimization plan is generated through the operation warning information.

[0018] According to an embodiment of the present invention, the operation scheme based on the hair removal device sets an operation time period, and divides the operation time period into multiple analysis cycles, specifically: Based on the operation plan of the hair removal device, an operation time period is set, wherein the operation plan includes target operation time, device parameter settings, and target hair removal effect information; Based on the equipment usage, the operation time period is divided into multiple analysis cycles, and the length of each analysis cycle is within the preset time period.

[0019] It should be noted that the equipment parameters include wavelength, pulse width, laser pulse repetition frequency, irradiation mode and other hair removal equipment operating parameters. The purpose of dividing the analysis period is to finely analyze the changes in equipment parameters, energy output and other aspects of multiple equipment usage periods. The hair removal equipment is specifically a laser type of hair removal equipment.

[0020] According to an embodiment of the present invention, in each analysis cycle, real-time parameter collection is performed on the hair removal device, and the parameters include wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are respectively marked as the first parameter, the second parameter, and the third parameter, specifically: In each analysis cycle, the data acquisition unit collects real-time parameters of the hair removal device, including wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are marked as the first parameter, the second parameter, and the third parameter respectively; For each analysis cycle, the corresponding acquisition parameters are ordered and imported into a storage unit for storage.

[0021] It should be noted that the ordering is to sort and store the parameter data based on the time dimension.

[0022] According to an embodiment of the present invention, in one analysis cycle, linear change analysis is performed on the first parameter, the second parameter, and the third parameter, respectively. Based on the Pearson correlation coefficient, the correlation coefficient of the three is calculated, and current parameter change data is generated. The simulation analysis of the equipment parameters is performed through the operation scheme to generate comparative parameter change data. The current parameter change data and the comparative parameter change data are subjected to data anomaly analysis and consistency evaluation. The parameter proportion analysis is performed through the abnormal data points obtained through the evaluation, and the parameter change consistency ratio is calculated, specifically: Linear change analysis and linear fitting were performed on the three parameters to form three change curves. Correlation analysis was performed on each parameter. The correlation calculation was based on the Pearson correlation coefficient to obtain three correlation coefficients. Integrate the three change curves and the three correlation coefficients into the current parameter change data; Obtain target parameter information of the hair removal device through the operation plan, perform parameter simulation change analysis based on the target parameter information, and generate comparative parameter change data; The current parameter change data is compared with the comparison parameter change data. Specifically, the parameter information comparison is to compare the values ​​of the three change curves with the three comparison fitting curves respectively, record the parameter points where the value difference between the two exceeds the preset range, mark them as the first abnormal data points, and perform difference analysis on the three correlation coefficients and the three comparison correlation coefficients to generate the second abnormal data points; The abnormal parameter ratio is calculated based on the first abnormal data point and the second abnormal data point to obtain the parameter change consistency ratio.

[0023] It should be noted that the comparison parameter change data is the data obtained by simulation analysis and parameter fitting based on the target operating parameters (i.e., the set parameters of the ideal state), including the comparison fitting curves of the first, second, and third parameters, and the corresponding three comparison correlation coefficients. A correlation analysis is performed on each two parameters, specifically the first and second parameters, the second and third parameters, and the correlation fitting between the first and third parameters. During the operation of the hair removal device, there are certain real-time changes in parameters, and the device abnormalities during the operation process have a large correlation with the parameters. The correlation analysis between the parameters can realize the rapid and accurate screening of abnormal parameter states, and analyze the correlation between parameter changes and abnormalities, effectively assisting users to correct the operating problems and abnormal parameters of the positioning device, and further assisting users to optimize the device operation plan and improve the hair removal experience. The three correlation coefficients and the three comparison correlation coefficients are analyzed to specifically evaluate whether the absolute difference between the correlation coefficient and the comparison correlation coefficient is within the preset range. If not, the second abnormal data point is obtained by marking the abnormal data point based on the coefficients of the two. If there is no abnormality, no abnormal data point is generated.

[0024] It is worth mentioning that when the hair removal device is running in real time, there is a certain amount of floating change between its set parameters and the actual values ​​of the running state. Affected by factors such as the environment, user skin characteristics, and device operation mode, it is difficult for the existing technology to accurately evaluate its real-time abnormal state. During actual operation, parameters such as wavelength, pulse width, and laser pulse repetition frequency have a certain amount of abnormal fluctuation, but the existing technology is difficult to assess whether an abnormal state will eventually occur, whether it affects the actual user experience, etc. At the same time, it is difficult to locate abnormal parameters and adjust and optimize the device. Based on this, the embodiment of the present invention analyzes the parameters of different analysis cycles of the device, uses core parameters to evaluate periodic changes, combines correlation to compare changes, and screens out abnormal data points for initial evaluation. Further, based on the infrared monitoring unit, infrared images of the user's target parts are monitored, and energy output changes are evaluated in combination with infrared features. Finally, the parameter change consistency ratio and energy output difference value are combined to evaluate abnormal parameters and abnormal time nodes. This method can effectively improve the evaluation accuracy and efficiency of abnormal parameters, achieve accurate abnormal positioning of device parameter settings, effectively improve device operation efficiency, and thus improve user experience. The method of the present invention can be used for real-time adjustment of equipment operation and can also be used for equipment testing in a production process, effectively assisting users in real-time adjustment and optimization of equipment parameters.

[0025] According to an embodiment of the present invention, the infrared monitoring unit is used to monitor the target part corresponding to the hair removal device in real time, and multiple infrared images are obtained. The infrared image is used as an energy output analysis image, and the color feature of the infrared image is extracted based on the color histogram, and the monitoring color feature data is obtained. The monitoring color feature data and the target infrared feature data are feature vectorized and feature similarity is compared to generate a feature difference value, and the feature difference value is used as the energy output difference value, specifically: Through the infrared monitoring unit, the target part corresponding to the hair removal device is monitored in real time, and multiple infrared images are obtained in one analysis cycle; Perform smoothing, noise reduction, and standardization preprocessing on multiple infrared images; According to the color histogram, color features of multiple infrared images are extracted to obtain multiple monitoring color feature data; Based on the operation plan, a target temperature control value of the target part is set, and infrared characteristic simulation is performed based on the target temperature control value to generate multiple target infrared characteristic data; The feature vectorization and feature similarity comparison of multiple monitoring color feature data and multiple target infrared feature data are performed, and the similarity is calculated based on the Euclidean distance method to obtain multiple difference values; An average value of the plurality of difference values ​​is taken as the energy output difference value.

[0026] It should be noted that the multiple infrared images are images collected at different time points within an analysis cycle, and the focus of the infrared images is their color feature data, which is used to analyze their temperature distribution and value. The target infrared feature data is the infrared feature data under ideal conditions, and the temperature data corresponding to its color features is consistent with the target temperature control value. The temperature control parameters of the hair removal device can effectively reflect the energy output of the device, and the energy output is an important indicator of the corresponding hair removal effect of the hair removal device.

[0027] According to an embodiment of the present invention, the weighted average is performed based on the parameter change consistency ratio and the energy output difference value to evaluate the operation abnormality. If there is an operation abnormality, operation warning information is generated based on the monitoring color feature data and the current parameter change data, and an operation optimization plan is generated through the operation warning information, specifically: An abnormal operation is evaluated based on the parameter change consistency ratio and energy output difference value, and whether there is an abnormal operation state is determined in the form of weighted average; If the weighted mean is outside the preset abnormal value range, it is determined that there is an operation abnormality. The temperature control numerical analysis is performed based on the monitored color feature data to generate temperature distribution information. The operation warning information is generated in combination with the current parameter change data and abnormal data points, and an operation optimization plan is generated based on the operation warning information.

[0028] It should be noted that the weights in the weighted process can be set based on the user. The parameter change consistency ratio and the energy output difference value are used for operation abnormality assessment and weighted average calculation, which is specifically calculated as follows: ; Among them, Q1 and Q2 are preset weights, P1 and P2 are parameter change consistency ratios and energy output difference values, and K is the weighted average.

[0029] According to an embodiment of the present invention, the method further includes: sending the operation optimization plan to a preset terminal device.

[0030] It should be noted that the preset terminal devices include computers and mobile terminals.

[0031] According to an embodiment of the present invention, it also includes: In one analysis cycle, based on the operation warning information, the first abnormal data point and the second abnormal data point are extracted and the operation time nodes where the abnormal data points are located are analyzed to obtain the first abnormal time node; Using the temperature distribution information, the abnormal temperature control data point is marked, and the running time node where the abnormal temperature control data point is located is analyzed to obtain the second abnormal time node; By means of the hair removal device, corresponding control instructions are extracted based on the first abnormal time node and the second abnormal time node, and the obtained control instructions are stored in a list form to obtain a device control instruction record table; For multiple analysis cycles, multiple equipment control instruction record tables are generated, and based on the multiple equipment control instruction record tables, maintenance needs of the equipment are evaluated and equipment maintenance plans are generated.

[0032] It should be noted that when the hair removal device is in long-term operation, it is affected by use and the environment, and a corresponding maintenance plan needs to be set. However, traditional technology is often based on manual settings, and the maintenance effect is low. The present invention evaluates the abnormal data points of each analysis cycle, including abnormal evaluations of parameters and temperature control, and extracts the corresponding control parameter instruction table. For each instruction table, it can accurately locate the abnormality and perform maintenance analysis with the device control instructions, and generate a maintenance plan to improve equipment utilization and work efficiency.

[0033] Figure 2 A block diagram of an operation optimization system for a hair removal device according to the present invention is shown.

[0034] The second aspect of the present invention further provides an operation optimization system 2 for a hair removal device, the system comprising: a memory 21, a processor 22, the memory 21 comprising an operation optimization program for a hair removal device, the operation optimization program for a hair removal device being executed by the processor 22 to implement the following steps: Based on the operation plan of the hair removal device, an operation time period is set, and the operation time period is divided into multiple analysis cycles; In each analysis cycle, the real-time parameters of the hair removal device are collected, including wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are marked as the first parameter, the second parameter, and the third parameter respectively; In one analysis cycle, linear change analysis is performed on the first parameter, the second parameter, and the third parameter respectively. Based on the Pearson correlation coefficient, the correlation coefficients of the three are calculated, and the current parameter change data is generated. The simulation analysis of the equipment parameters is performed through the operation plan to generate comparative parameter change data. The current parameter change data and the comparative parameter change data are subjected to data anomaly analysis and consistency evaluation. The parameter proportion analysis is performed through the abnormal data points obtained through the evaluation, and the parameter change consistency ratio is calculated; Through the infrared monitoring unit, the target part corresponding to the hair removal device is monitored in real time, and multiple infrared images are obtained. The infrared images are used as energy output analysis images. Based on the color histogram, the color features of the infrared images are extracted, and the monitoring color feature data are obtained. The monitoring color feature data and the target infrared feature data are compared for feature vectorization and feature similarity, and a feature difference value is generated. The feature difference value is used as the energy output difference value; The weighted average is taken according to the parameter change consistency ratio and the energy output difference value to evaluate the operation abnormality. If there is an operation abnormality, the operation warning information is generated based on the monitored color feature data and the current parameter change data, and the operation optimization plan is generated through the operation warning information.

[0035] According to an embodiment of the present invention, the operation scheme based on the hair removal device sets an operation time period, and divides the operation time period into multiple analysis cycles, specifically: Based on the operation plan of the hair removal device, an operation time period is set, wherein the operation plan includes target operation time, device parameter settings, and target hair removal effect information; Based on the equipment usage, the operation time period is divided into multiple analysis cycles, and the length of each analysis cycle is within the preset time period.

[0036] It should be noted that the equipment parameters include wavelength, pulse width, laser pulse repetition frequency, irradiation mode and other hair removal equipment operating parameters. The purpose of dividing the analysis period is to finely analyze the changes in equipment parameters, energy output and other aspects of multiple equipment usage periods. The hair removal equipment is specifically a laser type of hair removal equipment.

[0037] According to an embodiment of the present invention, in each analysis cycle, real-time parameter collection is performed on the hair removal device, and the parameters include wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are respectively marked as the first parameter, the second parameter, and the third parameter, specifically: In each analysis cycle, the data acquisition unit collects real-time parameters of the hair removal device, including wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are marked as the first parameter, the second parameter, and the third parameter respectively; For each analysis cycle, the corresponding acquisition parameters are ordered and imported into a storage unit for storage.

[0038] It should be noted that the ordering is to sort and store the parameter data based on the time dimension.

[0039] According to an embodiment of the present invention, in one analysis cycle, linear change analysis is performed on the first parameter, the second parameter, and the third parameter, respectively. Based on the Pearson correlation coefficient, the correlation coefficient of the three is calculated, and current parameter change data is generated. The simulation analysis of the equipment parameters is performed through the operation scheme to generate comparative parameter change data. The current parameter change data and the comparative parameter change data are subjected to data anomaly analysis and consistency evaluation. The parameter proportion analysis is performed through the abnormal data points obtained through the evaluation, and the parameter change consistency ratio is calculated, specifically: Linear change analysis and linear fitting were performed on the three parameters to form three change curves. Correlation analysis was performed on each parameter. The correlation calculation was based on the Pearson correlation coefficient to obtain three correlation coefficients. Integrate the three change curves and the three correlation coefficients into the current parameter change data; Obtain target parameter information of the hair removal device through the operation plan, perform parameter simulation change analysis based on the target parameter information, and generate comparative parameter change data; The current parameter change data is compared with the comparison parameter change data. Specifically, the parameter information comparison is to compare the values ​​of the three change curves with the three comparison fitting curves respectively, record the parameter points where the value difference between the two exceeds the preset range, mark them as the first abnormal data points, and perform difference analysis on the three correlation coefficients and the three comparison correlation coefficients to generate the second abnormal data points; The abnormal parameter ratio is calculated based on the first abnormal data point and the second abnormal data point to obtain the parameter change consistency ratio.

[0040] It should be noted that the comparison parameter change data is the data obtained by simulation analysis and parameter fitting based on the target operating parameters (i.e., the set parameters of the ideal state), including the comparison fitting curves of the first, second, and third parameters, and the corresponding three comparison correlation coefficients. A correlation analysis is performed on each two parameters, specifically the first and second parameters, the second and third parameters, and the correlation fitting between the first and third parameters. During the operation of the hair removal device, there are certain real-time changes in parameters, and the device abnormalities during the operation process have a large correlation with the parameters. The correlation analysis between the parameters can realize the rapid and accurate screening of abnormal parameter states, and analyze the correlation between parameter changes and abnormalities, effectively assisting users to correct the operating problems and abnormal parameters of the positioning device, and further assisting users to optimize the device operation plan and improve the hair removal experience. The three correlation coefficients and the three comparison correlation coefficients are analyzed to specifically evaluate whether the absolute difference between the correlation coefficient and the comparison correlation coefficient is within the preset range. If not, the second abnormal data point is obtained by marking the abnormal data point based on the coefficients of the two. If there is no abnormality, no abnormal data point is generated.

[0041] It is worth mentioning that when the hair removal device is running in real time, there is a certain amount of floating change between its set parameters and the actual values ​​of the running state. Affected by factors such as the environment, user skin characteristics, and device operation mode, it is difficult for the existing technology to accurately evaluate its real-time abnormal state. During actual operation, parameters such as wavelength, pulse width, and laser pulse repetition frequency have a certain amount of abnormal fluctuation, but the existing technology is difficult to assess whether an abnormal state will eventually occur, whether it affects the actual user experience, etc. At the same time, it is difficult to locate abnormal parameters and adjust and optimize the device. Based on this, the embodiment of the present invention analyzes the parameters of different analysis cycles of the device, uses core parameters to evaluate periodic changes, combines correlation to compare changes, and screens out abnormal data points for initial evaluation. Further, based on the infrared monitoring unit, infrared images of the user's target parts are monitored, and energy output changes are evaluated in combination with infrared features. Finally, the parameter change consistency ratio and energy output difference value are combined to evaluate abnormal parameters and abnormal time nodes. This method can effectively improve the evaluation accuracy and efficiency of abnormal parameters, achieve accurate abnormal positioning of device parameter settings, effectively improve device operation efficiency, and thus improve user experience. The method of the present invention can be used for real-time adjustment of equipment operation and can also be used for equipment testing in a production process, effectively assisting users in real-time adjustment and optimization of equipment parameters.

[0042] According to an embodiment of the present invention, the infrared monitoring unit is used to monitor the target part corresponding to the hair removal device in real time, and multiple infrared images are obtained. The infrared image is used as an energy output analysis image, and the color feature of the infrared image is extracted based on the color histogram, and the monitoring color feature data is obtained. The monitoring color feature data and the target infrared feature data are feature vectorized and feature similarity is compared to generate a feature difference value, and the feature difference value is used as the energy output difference value, specifically: Through the infrared monitoring unit, the target part corresponding to the hair removal device is monitored in real time, and multiple infrared images are obtained in one analysis cycle; Perform smoothing, noise reduction, and standardization preprocessing on multiple infrared images; According to the color histogram, color features of multiple infrared images are extracted to obtain multiple monitoring color feature data; Based on the operation plan, a target temperature control value of the target part is set, and infrared characteristic simulation is performed based on the target temperature control value to generate multiple target infrared characteristic data; The feature vectorization and feature similarity comparison of multiple monitoring color feature data and multiple target infrared feature data are performed, and the similarity is calculated based on the Euclidean distance method to obtain multiple difference values; An average value of the plurality of difference values ​​is taken as the energy output difference value.

[0043] It should be noted that the multiple infrared images are images collected at different time points within an analysis cycle, and the focus of the infrared images is their color feature data, which is used to analyze their temperature distribution and value. The target infrared feature data is the infrared feature data under ideal conditions, and the temperature data corresponding to its color features is consistent with the target temperature control value. The temperature control parameters of the hair removal device can effectively reflect the energy output of the device, and the energy output is an important indicator of the corresponding hair removal effect of the hair removal device.

[0044] According to an embodiment of the present invention, the weighted average is performed based on the parameter change consistency ratio and the energy output difference value to evaluate the operation abnormality. If there is an operation abnormality, operation warning information is generated based on the monitoring color feature data and the current parameter change data, and an operation optimization plan is generated through the operation warning information, specifically: An abnormal operation is evaluated based on the parameter change consistency ratio and energy output difference value, and whether there is an abnormal operation state is determined in the form of weighted average; If the weighted mean is outside the preset abnormal value range, it is determined that there is an operation abnormality. The temperature control numerical analysis is performed based on the monitored color feature data to generate temperature distribution information. The operation warning information is generated in combination with the current parameter change data and abnormal data points, and an operation optimization plan is generated based on the operation warning information.

[0045] It should be noted that the weights in the weighted process can be set based on the user. The parameter change consistency ratio and the energy output difference value are used for operation abnormality assessment and weighted average calculation, which is specifically calculated as follows: ; Among them, Q1 and Q2 are preset weights, P1 and P2 are parameter change consistency ratios and energy output difference values, and K is the weighted average.

[0046] According to an embodiment of the present invention, the method further includes: sending the operation optimization plan to a preset terminal device.

[0047] It should be noted that the preset terminal devices include computers and mobile terminals.

[0048] The third aspect of the present invention also provides a computer-readable storage medium, which includes an operation optimization program for a hair removal device. When the operation optimization program for a hair removal device is executed by a processor, the steps of the operation optimization method for a hair removal device as described in any one of the above items are implemented.

[0049] The present invention discloses an operation optimization method and system for a hair removal device. By setting the operation time period and dividing the analysis cycle, parameters such as wavelength, pulse width, laser pulse repetition frequency, etc. are collected in real time, and the correlation and change of parameters are analyzed using the Pearson correlation coefficient. Infrared images of target parts are obtained in combination with an infrared monitoring unit to analyze energy output. Operation anomalies are evaluated by weighted average of parameter change consistency ratio and energy output difference value, and operation warning information and optimization scheme are generated to realize intelligent operation optimization of hair removal device.

[0050] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0051] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0052] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0053] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0054] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0055] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the operation of a hair removal device, characterized in that: include: Based on the operation plan of the hair removal device, an operation time period is set, and the operation time period is divided into multiple analysis cycles; In each analysis cycle, the real-time parameters of the hair removal device are collected, including wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are marked as the first parameter, the second parameter, and the third parameter respectively; In one analysis cycle, linear change analysis is performed on the first parameter, the second parameter, and the third parameter respectively. Based on the Pearson correlation coefficient, the correlation coefficients of the three are calculated, and the current parameter change data is generated. The simulation analysis of the equipment parameters is performed through the operation plan to generate comparative parameter change data. The current parameter change data and the comparative parameter change data are subjected to data anomaly analysis and consistency evaluation. The parameter proportion analysis is performed through the abnormal data points obtained through the evaluation, and the parameter change consistency ratio is calculated; Through the infrared monitoring unit, the target part corresponding to the hair removal device is monitored in real time, and multiple infrared images are obtained. The infrared images are used as energy output analysis images. Based on the color histogram, the color features of the infrared images are extracted, and the monitoring color feature data are obtained. The monitoring color feature data and the target infrared feature data are compared for feature vectorization and feature similarity, and a feature difference value is generated. The feature difference value is used as the energy output difference value; The weighted average is taken according to the parameter change consistency ratio and the energy output difference value to evaluate the operation abnormality. If there is an operation abnormality, the operation warning information is generated based on the monitored color feature data and the current parameter change data, and the operation optimization plan is generated through the operation warning information.

2. The operation optimization method for a hair removal device according to claim 1, characterized in that: The operation scheme based on the hair removal device sets an operation time period, and divides the operation time period into multiple analysis cycles, specifically: Based on the operation plan of the hair removal device, an operation time period is set, wherein the operation plan includes target operation time, device parameter settings, and target hair removal effect information; Based on the equipment usage, the operation time period is divided into multiple analysis cycles, and the length of each analysis cycle is within the preset time period.

3. The operation optimization method for a hair removal device according to claim 1, characterized in that: In each analysis cycle, the real-time parameter collection of the hair removal device is performed, and the parameters include wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are respectively marked as the first parameter, the second parameter, and the third parameter, specifically: In each analysis cycle, the data acquisition unit collects real-time parameters of the hair removal device, including wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are marked as the first parameter, the second parameter, and the third parameter respectively; For each analysis cycle, the corresponding acquisition parameters are ordered and imported into a storage unit for storage.

4. The operation optimization method for a hair removal device according to claim 1, characterized in that: In one analysis cycle, linear change analysis is performed on the first parameter, the second parameter, and the third parameter respectively. Based on the Pearson correlation coefficient, the correlation coefficient of the three is calculated, and current parameter change data is generated. The simulation analysis of the equipment parameters is performed through the operation plan to generate comparative parameter change data. The current parameter change data and the comparative parameter change data are subjected to data anomaly analysis and consistency evaluation. The parameter proportion analysis is performed through the abnormal data points obtained through the evaluation, and the parameter change consistency ratio is calculated, which is specifically: Linear change analysis and linear fitting were performed on the three parameters to form three change curves. Correlation analysis was performed on each parameter. The correlation calculation was based on the Pearson correlation coefficient to obtain three correlation coefficients. Integrate the three change curves and the three correlation coefficients into the current parameter change data; Obtain target parameter information of the hair removal device through the operation plan, perform parameter simulation change analysis based on the target parameter information, and generate comparative parameter change data; The current parameter change data is compared with the comparison parameter change data. Specifically, the parameter information comparison is to compare the values ​​of the three change curves with the three comparison fitting curves respectively, record the parameter points where the value difference between the two exceeds the preset range, mark them as the first abnormal data points, and perform difference analysis on the three correlation coefficients and the three comparison correlation coefficients to generate the second abnormal data points; The abnormal parameter ratio is calculated based on the first abnormal data point and the second abnormal data point to obtain the parameter change consistency ratio.

5. The operation optimization method for a hair removal device according to claim 4, characterized in that: The infrared monitoring unit is used to monitor the target part corresponding to the hair removal device in real time, and multiple infrared images are obtained. The infrared image is used as the energy output analysis image. Based on the color histogram, the color feature of the infrared image is extracted, and the monitoring color feature data is obtained. The monitoring color feature data and the target infrared feature data are subjected to feature vectorization and feature similarity comparison to generate a feature difference value, and the feature difference value is used as the energy output difference value, specifically: Through the infrared monitoring unit, the target part corresponding to the hair removal device is monitored in real time, and multiple infrared images are obtained in one analysis cycle; Perform smoothing, noise reduction, and standardization preprocessing on multiple infrared images; According to the color histogram, color features of multiple infrared images are extracted to obtain multiple monitoring color feature data; Based on the operation plan, a target temperature control value of the target part is set, and infrared characteristic simulation is performed based on the target temperature control value to generate multiple target infrared characteristic data; The feature vectorization and feature similarity comparison of multiple monitoring color feature data and multiple target infrared feature data are performed, and the similarity is calculated based on the Euclidean distance method to obtain multiple difference values; An average value of the plurality of difference values ​​is taken as the energy output difference value.

6. The operation optimization method for a hair removal device according to claim 5, characterized in that: The weighted average is performed based on the parameter change consistency ratio and the energy output difference value to evaluate the operation abnormality. If there is an operation abnormality, the operation warning information is generated based on the monitored color feature data and the current parameter change data, and the operation optimization plan is generated through the operation warning information, specifically: An abnormal operation is evaluated based on the parameter change consistency ratio and energy output difference value, and whether there is an abnormal operation state is determined in the form of weighted average; If the weighted mean is outside the preset abnormal value range, it is determined that there is an operation abnormality. The temperature control numerical analysis is performed based on the monitored color feature data to generate temperature distribution information. The operation warning information is generated in combination with the current parameter change data and abnormal data points, and an operation optimization plan is generated based on the operation warning information.

7. An operation optimization system for a hair removal device, characterized in that: The system includes: a memory and a processor, wherein the memory includes an operation optimization program for a hair removal device, and when the operation optimization program for a hair removal device is executed by the processor, the following steps are implemented: Based on the operation plan of the hair removal device, an operation time period is set, and the operation time period is divided into multiple analysis cycles; In each analysis cycle, the real-time parameters of the hair removal device are collected, including wavelength, pulse width, and laser pulse repetition frequency, and the three parameters are marked as the first parameter, the second parameter, and the third parameter respectively; In one analysis cycle, linear change analysis is performed on the first parameter, the second parameter, and the third parameter respectively. Based on the Pearson correlation coefficient, the correlation coefficients of the three are calculated, and the current parameter change data is generated. The simulation analysis of the equipment parameters is performed through the operation plan to generate comparative parameter change data. The current parameter change data and the comparative parameter change data are subjected to data anomaly analysis and consistency evaluation. The parameter proportion analysis is performed through the abnormal data points obtained through the evaluation, and the parameter change consistency ratio is calculated; Through the infrared monitoring unit, the target part corresponding to the hair removal device is monitored in real time, and multiple infrared images are obtained. The infrared images are used as energy output analysis images. Based on the color histogram, the color features of the infrared images are extracted, and the monitoring color feature data are obtained. The monitoring color feature data and the target infrared feature data are compared for feature vectorization and feature similarity, and a feature difference value is generated. The feature difference value is used as the energy output difference value; The weighted average is taken according to the parameter change consistency ratio and the energy output difference value to evaluate the operation abnormality. If there is an operation abnormality, the operation warning information is generated based on the monitored color feature data and the current parameter change data, and the operation optimization plan is generated through the operation warning information.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes an operation optimization program for a hair removal device. When the operation optimization program for a hair removal device is executed by a processor, the steps of the operation optimization method for a hair removal device as described in any one of claims 1 to 6 are implemented.

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