Effect analysis system and method for laser spot removal

Abstract

The present application relates to the field of data science and analysis, and discloses a laser freckle-removing effect analysis system and method. The system includes a freckle-removing demand label setting module, a collaborative adaptation value calculation module, a curative effect threshold determination module, a freckle-removing scheme planning module, and a freckle-removing effect analysis module. The freckle-removing demand label setting module is used to obtain time-series skin image data and freckle-removing demand description of a target user and set a freckle-removing demand label of the target user. The collaborative adaptation value calculation module is used to construct a color spot feature matrix of the target user and calculate a collaborative adaptation value of a laser parameter configuration corresponding to a skin physiological parameter. The curative effect threshold determination module is used to analyze a freckle-removing effect prediction trend of the target user, analyze a color spot evolution trend of the target user, and determine a curative effect threshold of the target user at different treatment stages. The freckle-removing scheme planning module is used to plan a laser freckle-removing scheme of the target user. The freckle-removing effect analysis module is used to execute laser freckle-removing processing on the target user and generate a freckle-removing effect analysis report of the target user. The present application can improve the accuracy of laser freckle-removing effect analysis.

Description

Technical Field

[0001] This invention relates to the field of beauty and skincare technology, and in particular to a system and method for analyzing the effects of laser freckle removal. Background Technology

[0002] In the current beauty and skincare field, as people's pursuit of beautiful skin continues to increase, the demand for freckle removal is growing. Laser freckle removal has become a widely used method for freckle removal due to its advantages such as high efficiency and convenience.

[0003] Currently, the analysis of the effectiveness of laser freckle removal mainly relies on subjective judgment. This involves using basic instruments such as skin testers to detect some basic skin parameters, such as moisture content, sebum secretion, and pigment density. Based on the doctor's personal experience and professional knowledge, a corresponding treatment plan is formulated, and the patient is treated according to the plan. Finally, the effectiveness of laser freckle removal is evaluated based on the skin effect after treatment. However, the instruments can only detect some parameters on the skin surface and cannot fully reflect the specific characteristics and changes of pigmentation. Furthermore, each doctor's level of understanding is different, which leads to a discrepancy between the formulated treatment plan and the patient's actual situation, ultimately resulting in low accuracy in the analysis of the effectiveness of laser freckle removal. Summary of the Invention

[0004] This invention provides a laser freckle removal effect analysis system, the main purpose of which is to improve the accuracy of laser freckle removal effect analysis.

[0005] To achieve the above objectives, the present invention provides a laser freckle removal effect analysis system, comprising: a freckle removal demand label setting module, a synergistic adaptation value calculation module, an efficacy threshold determination module, a freckle removal plan planning module, and a freckle removal effect analysis module;

[0006] The freckle removal demand tag setting module is used to acquire time-series skin image data and freckle removal demand description of the target user, extract the freckle distribution characteristics and freckle morphology parameters of the target user from the time-series skin image data, and set freckle removal demand tags for the target user based on the freckle removal demand description.

[0007] The collaborative adaptation value calculation module is used to construct a pigmentation feature matrix of the target user based on the pigmentation distribution characteristics and the pigmentation removal demand tag, associate and match the pigmentation feature matrix with historical clinical treatment data in a preset clinical case database to obtain a set of matching cases, extract laser parameter configuration and efficacy evaluation data from the set of matching cases, collect the skin physiological parameters of the target user, and calculate the collaborative adaptation value of the laser parameter configuration corresponding to the skin physiological parameters.

[0008] The efficacy threshold determination module is used to combine the synergistic adaptation value and the efficacy evaluation data to analyze the predicted trend of the freckle removal effect of the target user, analyze the freckle evolution trend of the target user based on the freckle morphology parameters, and determine the efficacy threshold of the target user at different treatment stages by combining the predicted trend of freckle removal effect and the freckle evolution trend.

[0009] The freckle removal plan planning module is used to plan a laser freckle removal plan for the target user based on the efficacy threshold and the freckle feature matrix, and to schedule the adverse effect avoidance criteria corresponding to the matching case set.

[0010] The freckle removal effect analysis module is used to combine the laser freckle removal plan and the adverse effect avoidance criteria to perform laser freckle removal treatment on the target user, and to collect freckle removal data corresponding to each stage of the freckle removal process in real time, and generate a freckle removal effect analysis report for the target user based on the stage freckle removal data.

[0011] Optionally, extracting the pigmentation distribution features and pigmentation morphology parameters of the target user from the time-series skin image data includes:

[0012] The temporal skin image data is subjected to image denoising processing to obtain denoised temporal skin images;

[0013] Extract the skin color features and skin texture features from the denoised temporal skin images respectively;

[0014] Based on the skin color features and the skin texture features, the blemish regions in the denoised temporal skin image are analyzed;

[0015] Based on the pigmented regions, the denoised temporal skin image is segmented to obtain a pigmented segmentation image;

[0016] The distribution features and morphological parameters of the target user's spots are extracted from the spotted segmentation image.

[0017] Optionally, setting freckle removal need tags for the target user based on the description of freckle removal needs includes:

[0018] The text cleaning process is performed on the description of the freckle removal needs to obtain the target freckle removal needs description;

[0019] Semantic parsing is performed on the description of the target freckle removal needs to obtain the semantics of freckle removal needs;

[0020] Based on the semantics of the freckle removal needs, determine the multidimensional freckle removal intent corresponding to the target freckle removal needs description;

[0021] Extract the blemish removal intent from the multidimensional blemish removal intent, and set blemish removal demand tags for the target user based on the blemish removal intent.

[0022] Optionally, constructing the pigmentation feature matrix of the target user based on the pigmentation distribution characteristics and the pigmentation removal demand tag includes:

[0023] The color spot distribution features are spatially encoded to obtain the color spot adjacency matrix;

[0024] Analyze the priority dimension corresponding to the freckle removal demand tag, and calculate the dimension weight corresponding to the priority dimension;

[0025] The dimensional weights are normalized to obtain the demand weight vector of the freckle removal demand tag;

[0026] Based on the color patch adjacency matrix and the demand weight vector, construct the composite feature matrix of the target user;

[0027] The composite feature matrix is ​​subjected to dimensionality reduction optimization to obtain the color spot feature matrix of the target user.

[0028] Optionally, calculating the co-fit value of the laser parameter configuration corresponding to the skin physiological parameters includes:

[0029] Extract the laser parameter values ​​from the laser parameter configuration and determine the physiological parameter values ​​corresponding to the skin physiological parameters;

[0030] Analyze the parametric correlation between the laser parameter configuration and the skin physiological parameters;

[0031] Based on the parameter correlation, the laser parameter configuration and the skin physiological parameters are correlated and combined to obtain the laser-skin parameter combination;

[0032] By combining the laser-skin parameter combination, the laser parameter value, and the physiological parameter value, the co-adaptation value of the laser parameter configuration corresponding to the skin physiological parameter is calculated.

[0033] Optionally, the step of combining the laser-skin parameter combination, the laser parameter value, and the physiological parameter value to calculate the co-fit value of the laser parameter configuration corresponding to the skin physiological parameters includes:

[0034] Calculate the combination importance coefficient corresponding to the laser-skin parameter combination;

[0035] The laser parameter value and the physiological parameter value are normalized respectively to obtain the first parameter value and the second parameter value;

[0036] Combining the combined importance coefficient, the first parameter value, and the second parameter value, the co-fit value of the laser parameter configuration corresponding to the skin physiological parameters is calculated using the following formula:

[0037] ;

[0038] in, This indicates the compatibility value between the laser parameter configuration and the skin's physiological parameters. This represents the combination importance coefficient corresponding to the k-th combination in the laser-skin parameter combination. This represents the value of the i-th first parameter within the k-th combination of laser-skin parameter combinations. This represents the i-th second parameter value within the k-th combination of laser-skin parameter combinations, where k represents the starting value of the laser-skin parameter combination, m represents the number of laser-skin parameter combinations, i represents the sequence number corresponding to the first and second parameter values, and n represents the quantity corresponding to the first and second parameter values.

[0039] Optionally, the step of combining the co-adaptation value and the efficacy evaluation data to analyze the predicted trend of the freckle removal effect for the target user includes:

[0040] The efficacy evaluation data is cleaned to obtain purified efficacy evaluation data.

[0041] Analyze the evaluation tags corresponding to the pure efficacy evaluation data, and based on the evaluation tags, filter out the freckle removal effect evaluation data from the pure efficacy evaluation data;

[0042] Extract the freckle removal effect features from the freckle removal effect evaluation data, and perform feature selection processing on the freckle removal effect features to obtain key effect features;

[0043] Based on the aforementioned key performance characteristics, a trend prediction curve for the target user is generated;

[0044] Based on the collaborative adaptation value, the trend prediction curve is corrected to obtain the predicted trend of the spot removal effect for the target user.

[0045] Optionally, analyzing the color spot evolution trend of the target user based on the color spot morphology parameters includes:

[0046] Identify the morphological indicators corresponding to the color spot morphology parameters, and calculate the similarity between the morphological indicators;

[0047] Based on the index similarity, the color spot morphology parameters are subjected to parameter clustering to obtain clustered color spot morphology parameters;

[0048] Extract the time identifiers corresponding to the morphological parameters of the clustered color spots;

[0049] By combining the time identifier and the clustering spot morphology parameters, the indicator transition rate corresponding to the morphology index is calculated;

[0050] Based on the indicator change rate, analyze the indicator evolution trend corresponding to the morphological indicator;

[0051] Based on the evolution trend of the aforementioned indicators, the evolution pattern of pigmentation in the target user is analyzed.

[0052] Optionally, the step of combining the time identifier and the clustered color spot morphology parameters to calculate the index transition rate corresponding to the morphology index includes:

[0053] Based on the time identifier, the clustered color spot morphology parameters are sorted to obtain the sequence color spot morphology parameters;

[0054] The morphological parameters of the sequence of color spots are quantized to obtain morphological parameter values;

[0055] Based on the morphological parameter values, the indicator transition rate corresponding to the morphological indicator is calculated using the following formula:

[0056] ;

[0057] Where A represents the indicator transition rate corresponding to the morphological indicator. This indicates that the a-th indicator in the pattern indicator is in The morphological parameter values ​​corresponding to the time points, This indicates that the a-th indicator in the pattern indicator is in The morphological parameter values ​​corresponding to the time points, where 'a' represents the sequence number of the morphological indicator and 'r' represents the number of indicators corresponding to the morphological indicator. and These represent the (b+1)th and bth time points, respectively.

[0058] A method for analyzing the effect of laser freckle removal, characterized in that the method includes:

[0059] Acquire time-series skin image data and description of freckle removal needs of target users; extract the freckle distribution characteristics and freckle morphology parameters of target users from the time-series skin image data; and set freckle removal need tags for target users based on the description of freckle removal needs.

[0060] Based on the pigmentation distribution characteristics and the pigmentation removal demand tags, a pigmentation feature matrix of the target user is constructed. The pigmentation feature matrix is ​​then matched with historical clinical treatment data in a preset clinical case database to obtain a set of matched cases. Laser parameter configuration and efficacy evaluation data are extracted from the set of matched cases. Skin physiological parameters of the target user are collected, and the co-fit value of the laser parameter configuration corresponding to the skin physiological parameters is calculated.

[0061] By combining the synergistic adaptation value and the efficacy evaluation data, the predicted trend of the freckle removal effect for the target user is analyzed. Based on the freckle morphology parameters, the evolution trend of the freckles for the target user is analyzed. By combining the predicted trend of the freckle removal effect and the evolution trend of the freckles, the efficacy threshold for the target user at different treatment stages is determined.

[0062] Based on the efficacy threshold and the pigmentation feature matrix, a laser pigmentation removal plan is planned for the target user, and the adverse effect avoidance criteria corresponding to the matching case set are scheduled.

[0063] Combining the laser freckle removal scheme and the adverse effect avoidance criteria, laser freckle removal treatment is performed on the target user, and stage freckle removal data corresponding to each stage of the freckle removal process is collected in real time. Based on the stage freckle removal data, a freckle removal effect analysis report for the target user is generated.

[0064] This invention extracts the distribution features and morphological parameters of pigmentation spots from the time-series skin image data of the target user, thereby obtaining a precise quantitative description of the target user's pigmentation condition. This provides a solid and accurate data foundation for the subsequent formulation of personalized pigmentation removal plans and effect prediction. Optionally, this invention constructs a pigmentation feature matrix of the target user based on the pigmentation distribution features and the pigmentation removal demand tags. This transforms the user's complex pigmentation situation and personalized needs into a unified and easily analyzable mathematical structure, providing an important basis for subsequent related processing. This invention combines the synergistic adaptation value and the efficacy evaluation data to analyze the prediction trend of the target user's pigmentation removal effect. It can integrate the synergistic relationship between laser parameters and skin physiological parameters, as well as past efficacy data, thereby providing a quantitative and scientific... This invention provides a crucial basis for accurately understanding the trajectory of pigmentation removal effects in target users, enabling the optimization and adjustment of personalized pigmentation removal treatment plans. By planning a laser pigmentation removal plan for the target user based on the efficacy threshold and the pigmentation feature matrix, and then specifying the detailed treatment methods for the target user's pigmentation, and scheduling the adverse effect avoidance criteria corresponding to the matched case set, this invention can maximize the safety and effectiveness of pigmentation removal treatment and lay an important foundation for improving subsequent pigmentation removal effect analysis. By combining the laser pigmentation removal plan and the adverse effect avoidance criteria to perform laser pigmentation removal treatment on the target user, this invention can improve the accuracy of pigmentation removal treatment. Based on the pigmentation removal data at each stage, a pigmentation removal effect analysis report for the target user is generated, resulting in a highly accurate pigmentation removal effect analysis result. Therefore, this invention improves the accuracy of laser pigmentation removal effect analysis. Attached Figure Description

[0065] Figure 1 This is a functional module diagram of a laser freckle removal effect analysis system provided in an embodiment of the present invention;

[0066] Figure 2 This is a flowchart illustrating a method for analyzing the effects of laser freckle removal according to an embodiment of the present invention.

[0067] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0070] In practice, the server-side equipment deployed in a laser freckle removal effect analysis system may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware devices. For example, it can be a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node, providing laser freckle removal effect analysis services to various users. Alternatively, it can be a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various users. Or, it can be a server composed of numerous identical or different types of hardware devices, with one or more devices configured to provide laser freckle removal effect analysis services to various users.

[0071] In terms of implementation, the laser freckle removal effect analysis system and the user terminal are mutually compatible. That is, if the laser freckle removal effect analysis system is an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the laser freckle removal effect analysis system is implemented as a website, then the user terminal is implemented as a webpage; or if the laser freckle removal effect analysis system is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0072] Reference Figure 1 The diagram shown is a functional block diagram of a laser freckle removal effect analysis system provided in an embodiment of the present invention.

[0073] The laser freckle removal effect analysis system 100 described in this invention can be set up in a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a server for analyzing the effect of laser freckle removal, a server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the laser freckle removal effect analysis system 100 includes a freckle removal demand tag setting module 101, a co-adaptation value calculation module 102, a therapeutic effect threshold determination module 103, a freckle removal plan planning module 104, and a freckle removal effect analysis module 105.

[0074] In this embodiment of the invention, in the tracking of laser freckle removal effect analysis, each of the above modules can be implemented independently and called upon other modules. This calling can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the laser freckle removal effect analysis system provided by this embodiment of the invention, the applicable scope of the laser freckle removal effect analysis architecture can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the laser freckle removal effect analysis system. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0075] The following describes the various components and specific workflow of the laser freckle removal effect analysis system, using specific embodiments as examples.

[0076] The freckle removal demand tag setting module 101 is used to acquire time-series skin image data and freckle removal demand description of the target user, extract the freckle distribution characteristics and freckle morphology parameters of the target user from the time-series skin image data, and set freckle removal demand tags for the target user based on the freckle removal demand description.

[0077] This invention extracts the pigmentation distribution characteristics and pigmentation morphology parameters of the target user from the time-series skin image data, thereby obtaining a precise quantitative description of the target user's pigmentation condition. This provides a solid and accurate data foundation for the subsequent formulation of personalized pigmentation removal plans and effect prediction. The time-series skin image data consists of skin images of the target user taken at different time points. The pigmentation removal requirement description is a textual expression of the target user's expectations regarding the pigmentation removal effect, method, and cycle. The pigmentation distribution characteristics describe the location, density, and other presentation of pigmentation on the target user's skin surface. The pigmentation morphology parameters are quantitative values ​​of the target user's pigmentation in terms of size, shape, and color depth.

[0078] As an embodiment of the present invention, the step of extracting the pigmentation distribution features and pigmentation morphology parameters of the target user from the time-series skin image data includes:

[0079] The temporal skin image data is subjected to image denoising processing to obtain denoised temporal skin images;

[0080] Extract the skin color features and skin texture features from the denoised temporal skin images respectively;

[0081] Based on the skin color features and the skin texture features, the blemish regions in the denoised temporal skin image are analyzed;

[0082] Based on the pigmented regions, the denoised temporal skin image is segmented to obtain a pigmented segmentation image;

[0083] The distribution features and morphological parameters of the target user's spots are extracted from the spotted segmentation image.

[0084] Wherein, the denoised temporal skin image is the image obtained after the temporal skin image data has been denoised to remove noise interference; the skin color feature and the skin texture feature are the characteristics of the denoised temporal skin image in terms of color and texture, respectively; the blemish region is the part of the denoised temporal skin image containing blemishes; the blemish segmentation image is the image obtained after the denoised temporal skin image has separated the blemish region from the normal skin region.

[0085] Optionally, the temporal skin image data can be denoised using a mean filtering denoising algorithm to obtain a denoised temporal skin image. Skin color and texture features of the denoised temporal skin image can be extracted from the HSV color space and gray-level co-occurrence matrix, respectively. A decision tree algorithm can be used to deeply mine the skin color and texture features, and based on the combination rules of feature parameters, a set of pixels with blemish features can be identified, thereby determining the blemish regions in the denoised temporal skin image. Based on the blemish regions, the denoised temporal skin image can be segmented using the Canny edge detection operator combined with region growing to obtain a blemish segmentation image. The blemish distribution features and blemish morphology parameters of the target user can be extracted from the blemish segmentation image using coordinate positioning and geometric measurement methods.

[0086] This invention, by setting freckle removal demand tags for the target user based on the description of the freckle removal demand, can obtain the user's personalized needs, laying a solid foundation for the subsequent development of a laser freckle removal plan that highly matches the user's expectations, and greatly improving the user experience. The freckle removal demand tags are refined identifiers of key information such as the target user's expectations for freckle removal effect, treatment cycle requirements, cost tolerance, and special preferences.

[0087] As an embodiment of the present invention, the step of setting the freckle removal need tag for the target user based on the description of the freckle removal need includes:

[0088] The text cleaning process is performed on the description of the freckle removal needs to obtain the target freckle removal needs description;

[0089] Semantic parsing is performed on the description of the target freckle removal needs to obtain the semantics of freckle removal needs;

[0090] Based on the semantics of the freckle removal needs, determine the multidimensional freckle removal intent corresponding to the target freckle removal needs description;

[0091] Extract the blemish removal intent from the multidimensional blemish removal intent, and set blemish removal demand tags for the target user based on the blemish removal intent.

[0092] The target freckle removal need description is a more standardized and accurate text description obtained after cleaning and removing noise and redundant information from the freckle removal need description. The freckle removal need semantics is the actual meaning and user's specific requirements and expectations for freckle removal related aspects implied by the target freckle removal need description through semantic parsing. The multidimensional freckle removal intention is a comprehensive portrayal of the user's freckle removal intention from multiple dimensions (such as freckle removal effect, treatment method, time requirements, etc.) corresponding to the target freckle removal need description. The freckle removal intention representation is the key part or specific direction of the multidimensional freckle removal intention that can represent and reflect the user's core freckle removal needs and characteristics.

[0093] Optionally, the text cleaning process can be performed on the description of the freckle removal needs by removing stop words, correcting typos, and standardizing the format, to obtain the target freckle removal needs description; semantic analysis can be used to perform semantic parsing on the target freckle removal needs description to obtain the freckle removal needs semantics; based on the freckle removal needs semantics, the multi-dimensional freckle removal intention corresponding to the target freckle removal needs description can be determined by using an intent recognition model or manually defined semantic rules; the freckle removal intention representation in the multi-dimensional freckle removal intention can be extracted by filtering key semantic elements and focusing on core appeal points; based on the freckle removal intention representation, freckle removal needs tags for the target user can be set, such as "fast freckle removal", "gentle treatment", "high cost-effective freckle removal", etc.

[0094] The collaborative adaptation value calculation module 102 is used to construct a pigmentation feature matrix of the target user based on the pigmentation distribution characteristics and the pigmentation removal demand tag, associate and match the pigmentation feature matrix with historical clinical treatment data in a preset clinical case database to obtain a set of matching cases, extract laser parameter configuration and efficacy evaluation data from the set of matching cases, collect the skin physiological parameters of the target user, and calculate the collaborative adaptation value of the laser parameter configuration corresponding to the skin physiological parameters.

[0095] This invention constructs a pigmentation feature matrix for the target user based on the pigmentation distribution characteristics and the pigmentation removal demand tags. This transforms the user's complex pigmentation situation and personalized needs into a unified mathematical structure that is easy to analyze and calculate, providing an important basis for subsequent related processing. The pigmentation feature matrix is ​​a two-dimensional data array constructed based on the pigmentation distribution characteristics and the pigmentation removal demand tags to comprehensively, quantitatively, and structurally present the target user's pigmentation status and personalized pigmentation removal needs.

[0096] As an embodiment of the present invention, the step of constructing the pigmentation feature matrix of the target user based on the pigmentation distribution characteristics and the pigmentation removal demand tag includes:

[0097] The color spot distribution features are spatially encoded to obtain the color spot adjacency matrix;

[0098] Analyze the priority dimension corresponding to the freckle removal demand tag, and calculate the dimension weight corresponding to the priority dimension;

[0099] The dimensional weights are normalized to obtain the demand weight vector of the freckle removal demand tag;

[0100] Based on the color patch adjacency matrix and the demand weight vector, construct the composite feature matrix of the target user;

[0101] The composite feature matrix is ​​subjected to dimensionality reduction optimization to obtain the color spot feature matrix of the target user.

[0102] Wherein, the blemish adjacency matrix is ​​a matrix that spatially encodes the blemish distribution features and represents the spatial distribution of blemishes through node adjacency relationships; the priority dimension is the demand category with different levels of importance corresponding to the blemish removal demand tag; the dimension weight represents the relative importance value corresponding to the priority dimension; the demand weight vector is a vector that reflects the proportion of demand in each dimension after the dimension weights have been normalized and summed to 1; the composite feature matrix is ​​a matrix that combines the blemish distribution features reflected by the blemish adjacency matrix and the blemish removal demand features reflected by the demand weight vector for the target user.

[0103] Furthermore, the distribution features of the pigmentation spots can be spatially encoded using a custom spatial location analysis algorithm to obtain a pigmentation spot adjacency matrix; the priority dimension corresponding to the pigmentation removal demand tag can be analyzed by combining semantic analysis with user demand surveys; the dimension weight corresponding to the priority dimension can be calculated using the analytic hierarchy process (AHP); the dimension weight can be normalized using a standard normalization formula to obtain the demand weight vector of the pigmentation removal demand tag; based on the pigmentation spot adjacency matrix and the demand weight vector, a composite feature matrix of the target user can be constructed using a matrix concatenation algorithm; and the composite feature matrix can be dimensionality-reduced and optimized using Principal Component Analysis (PCA) to obtain the pigmentation spot feature matrix of the target user.

[0104] This invention, by collecting the skin physiological parameters of the target user, can accurately grasp the individual characteristics of the user's skin, providing a key basis for evaluating the suitability of laser parameter configuration with skin physiological condition. It calculates the synergistic adaptation value between the laser parameter configuration and the skin physiological parameters. This synergistic adaptation value allows for understanding the degree of compatibility between the laser treatment plan and the target user's skin, thereby scientifically adjusting the treatment plan, improving the pigmentation removal effect, and reducing the risk of adverse reactions. The pre-set clinical case database stores a large amount of historical clinical treatment data (including pigmentation characteristics, laser parameter configuration, efficacy evaluation data, etc.). The matching case set is a data set where the pigmentation feature matrix and the historical clinical treatment data in the pre-set clinical case database have correlation and matching. The laser parameter configuration and the efficacy... The evaluation data includes parameter settings for laser treatment and data evaluating treatment effects from the matched case set. The skin physiological parameters are relevant indicators reflecting the target user's skin physiological characteristics (such as skin texture, skin color, and pigmentation). The co-fit value represents the quantitative value of the degree of fit between the laser parameter configuration and the skin physiological parameters. Furthermore, the pigmentation feature matrix can be correlated and matched with historical clinical treatment data in a preset clinical case database using a matching algorithm, including a cosine similarity algorithm, to obtain a matched case set. Laser parameter configurations and efficacy evaluation data from the matched case set can be extracted using an extraction function compiled by a programming language. The target user's skin physiological parameters can be collected using a professional skin testing instrument.

[0105] As an embodiment of the present invention, the calculation of the co-fit value of the laser parameter configuration corresponding to the skin physiological parameters includes:

[0106] Extract the laser parameter values ​​from the laser parameter configuration and determine the physiological parameter values ​​corresponding to the skin physiological parameters;

[0107] Analyze the parametric correlation between the laser parameter configuration and the skin physiological parameters;

[0108] Based on the parameter correlation, the laser parameter configuration and the skin physiological parameters are correlated and combined to obtain the laser-skin parameter combination;

[0109] By combining the laser-skin parameter combination, the laser parameter value, and the physiological parameter value, the co-adaptation value of the laser parameter configuration corresponding to the skin physiological parameter is calculated.

[0110] Wherein, the laser parameter value is the specific parameter value in the laser parameter configuration (such as the specific parameter values ​​of the laser wavelength, energy density, pulse width, etc.); the physiological parameter value is the specific measurement value corresponding to the skin physiological parameter (such as the measurement value of physiological characteristics such as skin moisture content, pH, elasticity, etc.); the parameter correlation relationship is the mutual influence and interaction relationship between the laser parameter configuration and the skin physiological parameter (for example, a specific laser wavelength may affect skin pigmentation, which is a parameter correlation relationship).

[0111] Optionally, the laser parameter values ​​in the laser parameter configuration can be extracted through the device data reading module, and the physiological parameter values ​​corresponding to the skin physiological parameters can be determined through skin detection instruments and supporting analysis software. The parameter correlation between the laser parameter configuration and the skin physiological parameters can be analyzed through statistical analysis algorithms and professional knowledge databases. Based on the parameter correlation, the laser parameter configuration and the skin physiological parameters can be associated and combined through data fusion algorithms to obtain laser-skin parameter combinations. This allows for the precise integration of multi-source data, providing a comprehensive and orderly data foundation for in-depth analysis of the synergistic adaptability between laser and skin physiological characteristics, and effectively improving the scientific nature and accuracy of laser treatment plan formulation.

[0112] Furthermore, as an optional embodiment of the present invention, the step of calculating the co-fit value of the laser parameter configuration corresponding to the skin physiological parameters by combining the laser-skin parameter combination, the laser parameter value, and the physiological parameter value includes:

[0113] Calculate the combination importance coefficient corresponding to the laser-skin parameter combination;

[0114] The laser parameter value and the physiological parameter value are normalized respectively to obtain the first parameter value and the second parameter value;

[0115] Combining the combined importance coefficient, the first parameter value, and the second parameter value, the co-fit value of the laser parameter configuration corresponding to the skin physiological parameters is calculated using the following formula:

[0116] ;

[0117] in, This indicates the compatibility value between the laser parameter configuration and the skin's physiological parameters. This represents the combination importance coefficient corresponding to the k-th combination in the laser-skin parameter combination. This represents the value of the i-th first parameter within the k-th combination of laser-skin parameter combinations. This represents the i-th second parameter value within the k-th combination of laser-skin parameter combinations, where k represents the starting value of the laser-skin parameter combination, m represents the number of laser-skin parameter combinations, i represents the sequence number corresponding to the first and second parameter values, and n represents the quantity corresponding to the first and second parameter values.

[0118] Wherein, the combination importance coefficient represents the relative importance of the laser-skin parameter combination in evaluating the synergistic fit value; the first parameter value and the second parameter value are the actual values ​​used by the laser parameter value and the physiological parameter value in the specific calculation of the synergistic fit value, respectively.

[0119] Optionally, the combination importance coefficient corresponding to the laser-skin parameter combination can be calculated by combining expert experience with a large amount of clinical data through the analytic hierarchy process (AHP); the laser parameter value and the physiological parameter value can be normalized by the z-score standardization formula to obtain the first parameter value and the second parameter value.

[0120] The efficacy threshold determination module 103 is used to combine the synergistic adaptation value and the efficacy evaluation data to analyze the predicted trend of the freckle removal effect of the target user, analyze the freckle evolution trend of the target user based on the freckle morphology parameters, and determine the efficacy threshold of the target user at different treatment stages by combining the predicted trend of freckle removal effect and the freckle evolution trend.

[0121] This invention combines the synergistic adaptation value and the efficacy evaluation data to analyze the predicted trend of the pigmentation removal effect for the target user. It can integrate the synergistic relationship between laser parameters and skin physiological parameters, as well as past efficacy data, thereby accurately understanding the trend of the pigmentation removal effect for the target user in a quantitative and scientific way. This provides a key basis for optimizing and adjusting personalized pigmentation removal treatment plans. The predicted trend of pigmentation removal effect is the expected direction and degree of change in the improvement of pigmentation in the target user's pigmentation over a future period of time after receiving a specific laser pigmentation removal treatment plan.

[0122] As an embodiment of the present invention, the step of combining the synergistic adaptation value and the efficacy evaluation data to analyze the predicted trend of the freckle removal effect for the target user includes:

[0123] The efficacy evaluation data is cleaned to obtain purified efficacy evaluation data.

[0124] Analyze the evaluation tags corresponding to the pure efficacy evaluation data, and based on the evaluation tags, filter out the freckle removal effect evaluation data from the pure efficacy evaluation data;

[0125] Extract the freckle removal effect features from the freckle removal effect evaluation data, and perform feature selection processing on the freckle removal effect features to obtain key effect features;

[0126] Based on the aforementioned key performance characteristics, a trend prediction curve for the target user is generated;

[0127] Based on the collaborative adaptation value, the trend prediction curve is corrected to obtain the predicted trend of the spot removal effect for the target user.

[0128] The pure efficacy evaluation data refers to clean and accurate data obtained after cleaning operations such as removing noise, errors, duplicates, and filling missing values ​​from the efficacy evaluation data; the evaluation label is an identifier corresponding to the pure efficacy evaluation data to mark the category, characteristics, or evaluation result of the data; the freckle removal effect evaluation data is a subset of data directly related to the freckle removal treatment effect from the pure efficacy evaluation data; the freckle removal effect features are various quantitative indicators (such as changes in freckle area, changes in color depth, etc.) in the freckle removal effect evaluation data that can reflect the freckle removal effect; the key effect features are the features that are most representative and influential for evaluating the freckle removal effect after screening to remove redundant and irrelevant features; the trend prediction curve is a curve constructed by the target user based on the key effect features to intuitively present the trend of freckle removal effect changes over time or in the treatment process.

[0129] Furthermore, text classification algorithms (such as Naive Bayes and Support Vector Machines) can be used to analyze the evaluation labels corresponding to the pure efficacy evaluation data. Based on the evaluation labels, freckle removal effect evaluation data can be filtered from the pure efficacy evaluation data by writing filtering rules based on the label content (such as filtering corresponding data when the label contains words related to "freckle removal effect"). Freckle removal effect features in the freckle removal effect evaluation data can be extracted by combining domain knowledge with data analysis tools (such as based on dermatologists' evaluation points for freckle removal effect and data mining algorithms). Statistical tests (such as correlation analysis and chi-square test) and machine learning can also be used to further analyze the data. The algorithm (such as recursive feature elimination) is combined with feature selection processing on the freckle removal effect features to obtain key effect features. Based on the key effect features, a trend prediction curve for the target user can be generated by a time series analysis model (such as the ARIMA model) or a curve fitting algorithm (such as polynomial fitting). According to the co-fit value, the trend prediction curve can be corrected by constructing a weighted adjustment function (the adjustment amplitude and direction are determined according to the magnitude of the co-fit value) to obtain the freckle removal effect prediction trend for the target user. This series of methods can systematically and scientifically generate accurate freckle removal effect prediction results from the original data step by step.

[0130] This invention analyzes the evolution of pigmentation in the target user based on the pigmentation morphology parameters, which can grasp the changes in size, shape, and boundary clarity of pigmentation over time. This provides doctors with key information to predict the treatment effect of pigmentation removal in advance and adjust the treatment plan in a timely manner. The pigmentation evolution trend refers to the changes and trends in the size, shape, color, and distribution of the target user's pigmentation over time or during the treatment process.

[0131] As an embodiment of the present invention, the step of analyzing the color spot evolution trend of the target user based on the color spot morphology parameters includes:

[0132] Identify the morphological indicators corresponding to the color spot morphology parameters, and calculate the similarity between the morphological indicators;

[0133] Based on the index similarity, the color spot morphology parameters are subjected to parameter clustering to obtain clustered color spot morphology parameters;

[0134] Extract the time identifiers corresponding to the morphological parameters of the clustered color spots;

[0135] By combining the time identifier and the clustering spot morphology parameters, the indicator transition rate corresponding to the morphology index is calculated;

[0136] Based on the indicator change rate, analyze the indicator evolution trend corresponding to the morphological indicator;

[0137] Based on the evolution trend of the aforementioned indicators, the evolution pattern of pigmentation in the target user is analyzed.

[0138] Wherein, the morphological index is the morphological type corresponding to the color spot morphological parameter, such as area, perimeter, and roundness; the index similarity represents the degree of similarity between the morphological indices; the clustered color spot morphological parameter is a set of parameters obtained after classifying the color spot morphological parameters based on the index similarity; the time identifier is the information of the clustered color spot morphological parameter in the time dimension, such as the collection time point; the index change rate represents the numerical representation of the rate of change of the morphological index within a unit of time; and the index evolution trend is a description of the development trend and change pattern of the morphological index under a period of time or a series of changing conditions.

[0139] Furthermore, morphological indicators corresponding to the morphological parameters of the pigmentation can be identified using machine learning-based classification algorithms (such as decision trees, random forests, etc.); the similarity between the morphological indicators can be calculated using the Euclidean distance algorithm; when the similarity is greater than a similarity threshold, the morphological parameters of the pigmentation are clustered using a classification function to obtain clustered pigmentation morphological parameters, such as the K-Means clustering function. The similarity threshold is a pre-set reference value, which is set according to the actual application scenario; the time identifiers corresponding to the clustered pigmentation morphological parameters can be extracted from the time field information of the data records or specific time stamping rules; based on the indicator change rate, the evolution trend of the indicators corresponding to the morphological indicators can be analyzed using time series analysis methods (such as the ARIMA model); based on the indicator evolution trend, combined with medical expertise, the evolution trend of the pigmentation of the target user can be analyzed, such as judging that the pigmentation is in a benign improvement state based on the continuous shrinkage of the pigmentation area and the gradual lightening of the color in the indicator evolution trend, combined with the medical understanding of the principle of pigmentation fading; or based on the abnormal fluctuation of the clarity of the pigmentation boundary in the indicator, combined with medical knowledge, it can be inferred that there may be treatment interference factors causing the pigmentation evolution to be unstable.

[0140] Furthermore, as an optional embodiment of the present invention, the step of calculating the index transition rate corresponding to the morphological index by combining the time identifier and the clustered color spot morphology parameters includes:

[0141] Based on the time identifier, the clustered color spot morphology parameters are sorted to obtain the sequence color spot morphology parameters;

[0142] The morphological parameters of the sequence of color spots are quantized to obtain morphological parameter values;

[0143] Based on the morphological parameter values, the indicator transition rate corresponding to the morphological indicator is calculated using the following formula:

[0144] ;

[0145] Where A represents the indicator transition rate corresponding to the morphological indicator. This indicates that the a-th indicator in the pattern indicator is in The morphological parameter values ​​corresponding to the time points, This indicates that the a-th indicator in the pattern indicator is in The morphological parameter values ​​corresponding to the time points, where 'a' represents the sequence number of the morphological indicator and 'r' represents the number of indicators corresponding to the morphological indicator. and These represent the (b+1)th and bth time points, respectively.

[0146] The sequence of color spot morphology parameters is an ordered set of parameters obtained by sorting the clustered color spot morphology parameters according to the time identifier. The morphology parameter value is the quantized value corresponding to the sequence of color spot morphology parameters. Furthermore, the sorting of the clustered color spot morphology parameters can be achieved by the bubble sort algorithm. The quantization of the sequence of color spot morphology parameters can be achieved by edge detection combined with the method of moments. For example, the Canny edge detection algorithm can be used to accurately delineate the color spot outline, and the outline coordinate points can be obtained based on this. Then, the zero-order moment can be calculated to obtain the color spot area, the first-order moment can be used to determine the centroid position, and the second-order and above moments can be used to calculate ellipticity, eccentricity, etc., thereby completing the comprehensive quantization of the sequence of color spot morphology parameters.

[0147] This invention, by combining the predicted trend of pigmentation removal effect and the evolution of pigmentation, determines the efficacy threshold for the target user at different treatment stages. This provides doctors with precise treatment references and important basis for planning the subsequent laser pigmentation removal program for the target user. The efficacy threshold is a quantitative standard for the degree of pigmentation improvement at different treatment stages, used to determine whether the current treatment stage has achieved the expected effective treatment results. Furthermore, by combining the predicted trend of pigmentation removal effect and the evolution of pigmentation, the efficacy threshold for the target user at different treatment stages is determined. For example, by comprehensively analyzing the expected degree of pigmentation fading at different time points in the predicted trend of pigmentation removal effect, and the changes in parameters such as the actual pigmentation area, color, and shape in the evolution of pigmentation, specific efficacy thresholds such as the area reduction ratio and color fading value required for pigmentation improvement at each treatment stage are determined through data fitting and medical experience.

[0148] The freckle removal plan planning module 104 is used to plan a laser freckle removal plan for the target user based on the efficacy threshold and the freckle feature matrix, and to schedule the adverse effect avoidance criteria corresponding to the matching case set.

[0149] This invention plans a laser freckle removal program for a target user based on the efficacy threshold and the pigmentation feature matrix. It then details the target user's treatment method for pigmentation and schedules adverse effect avoidance criteria corresponding to the matched case set. This maximizes the safety and effectiveness of freckle removal treatment and lays an important foundation for subsequent analysis of freckle removal effects. The laser freckle removal program is a personalized treatment plan for the target user, including laser type, energy parameters, number of treatments, and intervals. The adverse effect avoidance criteria are a series of methods and measures corresponding to the matched case set to prevent and address potential adverse reactions and complications during laser freckle removal. Furthermore, the specific planning steps for the laser freckle removal program based on the efficacy threshold and the pigmentation feature matrix are as follows: First, determine the key characteristics of the pigmentation, such as type, area, and depth, based on the pigmentation feature matrix. Then, clarify the expected treatment effect at each stage based on the efficacy threshold. Next, select suitable laser equipment and parameters based on this information, and then determine the number of treatments, intervals, and treatment schedule. Simultaneously, adjust and optimize the program considering individual patient differences. The scheduling of the adverse effect avoidance criteria can be obtained from the case precautions in the matched case set.

[0150] The freckle removal effect analysis module 105 is used to combine the laser freckle removal plan and the adverse effect avoidance criteria to perform laser freckle removal treatment on the target user, and collect the freckle removal data corresponding to each stage of the freckle removal process in real time, and generate a freckle removal effect analysis report for the target user based on the stage freckle removal data.

[0151] This invention, by combining the aforementioned laser freckle removal scheme and the adverse effect avoidance criteria, performs laser freckle removal treatment on the target user, thereby improving the treatment accuracy. Based on the stage-specific freckle removal data, a freckle removal effect analysis report is generated for the target user, resulting in a highly accurate freckle removal effect analysis. The stage-specific freckle removal data refers to the freckle removal treatment records corresponding to each stage of the freckle removal process, and the freckle removal effect analysis report is a visual display of the freckle removal effect on the target user. Furthermore, the laser freckle removal treatment for the target user can be achieved using appropriate laser treatment equipment; it can be performed with high precision. Skin imaging equipment, intelligent sensors, and professional medical monitoring software collect real-time data on each stage of the freckle removal process. Based on this data, a freckle removal effect analysis report is generated for the target user. First, the data is categorized and organized, with data such as freckle area, color, and texture arranged in an orderly manner according to the treatment stage. Then, statistical analysis methods are used to calculate key indicators such as the trend and degree of improvement of the data at each stage. Finally, combined with medical expertise, the freckle removal effect is evaluated based on these indicators, and a freckle removal effect analysis report is generated in a graphic and textual format, including comparisons of each stage, a summary of the effect, and follow-up suggestions.

[0152] This invention extracts the distribution features and morphological parameters of pigmentation spots from the time-series skin image data of the target user, thereby obtaining a precise quantitative description of the target user's pigmentation condition. This provides a solid and accurate data foundation for the subsequent formulation of personalized pigmentation removal plans and effect prediction. Optionally, this invention constructs a pigmentation feature matrix of the target user based on the pigmentation distribution features and the pigmentation removal demand tags. This transforms the user's complex pigmentation situation and personalized needs into a unified and easily analyzable mathematical structure, providing an important basis for subsequent related processing. This invention combines the synergistic adaptation value and the efficacy evaluation data to analyze the prediction trend of the target user's pigmentation removal effect. It can integrate the synergistic relationship between laser parameters and skin physiological parameters, as well as past efficacy data, thereby providing a quantitative and scientific... This invention provides a crucial basis for accurately understanding the trajectory of pigmentation removal effects in target users, enabling the optimization and adjustment of personalized pigmentation removal treatment plans. By planning a laser pigmentation removal plan for the target user based on the efficacy threshold and the pigmentation feature matrix, and then specifying the detailed treatment methods for the target user's pigmentation, and scheduling the adverse effect avoidance criteria corresponding to the matched case set, this invention can maximize the safety and effectiveness of pigmentation removal treatment and lay an important foundation for improving subsequent pigmentation removal effect analysis. By combining the laser pigmentation removal plan and the adverse effect avoidance criteria to perform laser pigmentation removal treatment on the target user, this invention can improve the accuracy of pigmentation removal treatment. Based on the pigmentation removal data at each stage, a pigmentation removal effect analysis report for the target user is generated, resulting in a highly accurate pigmentation removal effect analysis result. Therefore, this invention improves the accuracy of laser pigmentation removal effect analysis.

[0153] like Figure 2 The diagram shown is a flowchart illustrating a laser freckle removal effect analysis method according to an embodiment of the present invention. In this embodiment, the laser freckle removal effect analysis method includes:

[0154] Acquire time-series skin image data and description of freckle removal needs of target users; extract the freckle distribution characteristics and freckle morphology parameters of target users from the time-series skin image data; and set freckle removal need tags for target users based on the description of freckle removal needs.

[0155] Based on the pigmentation distribution characteristics and the pigmentation removal demand tags, a pigmentation feature matrix of the target user is constructed. The pigmentation feature matrix is ​​then matched with historical clinical treatment data in a preset clinical case database to obtain a set of matched cases. Laser parameter configuration and efficacy evaluation data are extracted from the set of matched cases. Skin physiological parameters of the target user are collected, and the co-fit value of the laser parameter configuration corresponding to the skin physiological parameters is calculated.

[0156] By combining the synergistic adaptation value and the efficacy evaluation data, the predicted trend of the freckle removal effect for the target user is analyzed. Based on the freckle morphology parameters, the evolution trend of the freckles for the target user is analyzed. By combining the predicted trend of the freckle removal effect and the evolution trend of the freckles, the efficacy threshold for the target user at different treatment stages is determined.

[0157] Based on the efficacy threshold and the pigmentation feature matrix, a laser pigmentation removal plan is planned for the target user, and the adverse effect avoidance criteria corresponding to the matching case set are scheduled.

[0158] Combining the laser freckle removal scheme and the adverse effect avoidance criteria, laser freckle removal treatment is performed on the target user, and stage freckle removal data corresponding to each stage of the freckle removal process is collected in real time. Based on the stage freckle removal data, a freckle removal effect analysis report for the target user is generated.

[0159] In the several embodiments provided by this invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0160] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A laser freckle removal effect analysis system, characterized in that, The laser freckle-removing effect analysis system comprises a freckle-removing demand label setting module, a synergic adaptation value calculation module, a curative effect threshold value determination module, a freckle-removing scheme planning module and a freckle-removing effect analysis module. The freckle-removing demand label setting module is used for acquiring time-series skin image data and a freckle-removing demand description of a target user, extracting a freckle distribution feature and a freckle morphology parameter of the target user from the time-series skin image data, and setting a freckle-removing demand label of the target user based on the freckle-removing demand description. The synergic adaptation value calculation module is used for constructing a freckle feature matrix of the target user based on the freckle distribution feature and the freckle-removing demand label, associating and matching the freckle feature matrix with historical clinical treatment data in a preset clinical case database to obtain a matching case set, extracting laser parameter configuration and curative effect evaluation data in the matching case set, collecting skin physiological parameters of the target user, and calculating a synergic adaptation value of the laser parameter configuration corresponding to the skin physiological parameters. The curative effect threshold value determination module is used for analyzing a freckle-removing effect prediction trend of the target user in combination with the synergic adaptation value and the curative effect evaluation data, analyzing a freckle evolution trend of the target user based on the freckle morphology parameter, and determining curative effect threshold values of the target user at different treatment stages in combination with the freckle-removing effect prediction trend and the freckle evolution trend. The combination of the synergic adaptation value and the curative effect evaluation data for analyzing the freckle-removing effect prediction trend of the target user comprises: performing data cleaning processing on the curative effect evaluation data to obtain pure curative effect evaluation data; analyzing evaluation labels corresponding to the pure curative effect evaluation data, screening freckle-removing effect evaluation data from the pure curative effect evaluation data based on the evaluation labels; extracting freckle-removing effect features in the freckle-removing effect evaluation data, performing feature selection processing on the freckle-removing effect features to obtain key effect features; generating a trend prediction curve of the target user based on the key effect features; correcting the trend prediction curve according to the synergic adaptation value to obtain a freckle-removing effect prediction trend of the target user. The freckle-removing scheme planning module is used for planning a laser freckle-removing scheme of the target user based on the curative effect threshold values and the freckle feature matrix, and scheduling adverse effect avoidance criteria corresponding to the matching case set. The freckle-removing effect analysis module is used for executing laser freckle-removing processing of the target user in combination with the laser freckle-removing scheme and the adverse effect avoidance criteria, collecting stage freckle data corresponding to each stage of the freckle-removing processing process in real time, and generating a freckle-removing effect analysis report of the target user based on the stage freckle data.

2. The effect analysis system for laser spot removal according to claim 1, wherein The extraction of the freckle distribution feature and the freckle morphology parameter of the target user from the time-series skin image data comprises: performing image noise reduction processing on the time-series skin image data to obtain a noise-reduced time-series skin image; respectively extracting skin color features and skin texture features of the noise-reduced time-series skin image; Based on the skin color feature and the skin texture feature, a color spot area in the noise-reduced time sequence skin image is analyzed; Based on the color spot area, image segmentation processing is performed on the noise-reduced time sequence skin image to obtain a color spot segmentation image; From the color spot segmentation image, a color spot distribution feature and a color spot morphological parameter of the target user are extracted.

3. The effect analysis system for laser speckle removal according to claim 1, wherein Based on the color spot distribution feature and the color spot morphological parameter, a color spot feature matrix of the target user is constructed, including: The color spot distribution feature is subjected to spatial coding processing to obtain a color spot adjacency matrix; The priority dimension corresponding to the color spot morphological parameter is analyzed, and a dimension weight corresponding to the priority dimension is calculated; The dimension weight is subjected to normalization processing to obtain a demand weight vector of the color spot morphological parameter; Based on the color spot adjacency matrix and the demand weight vector, a composite feature matrix of the target user is constructed; 4. The effect analysis system for laser spot removal according to claim 1, wherein The composite feature matrix is subjected to dimension reduction optimization processing to obtain the color spot feature matrix of the target user. The synergistic adaptation value of the laser parameter configuration corresponding to the skin physiological parameter is calculated, including: The laser parameter value in the laser parameter configuration is extracted, and a physiological parameter value corresponding to the skin physiological parameter is determined; The parameter correlation between the laser parameter configuration and the skin physiological parameter is analyzed; Based on the parameter correlation, the laser parameter configuration and the skin physiological parameter are combined to obtain a laser-skin parameter combination; The synergistic adaptation value of the laser parameter configuration corresponding to the skin physiological parameter is calculated by combining the laser-skin parameter combination, the laser parameter value, and the physiological parameter value.

5. The effect analysis system for laser speckle removal according to claim 1, wherein The synergistic adaptation value of the laser parameter configuration corresponding to the skin physiological parameter is calculated by combining the laser-skin parameter combination, the laser parameter value, and the physiological parameter value, including: The combination importance coefficient corresponding to the laser-skin parameter combination is calculated; The laser parameter value and the physiological parameter value are respectively subjected to normalization processing to obtain a first parameter value and a second parameter value; The synergistic adaptation value of the laser parameter configuration corresponding to the skin physiological parameter is calculated by combining the combination importance coefficient, the first parameter value, and the second parameter value through the following formula: Based on the color spot morphological parameter, the color spot evolution trend of the target user is analyzed, including:

6. The effect analysis system for laser speckle removal according to claim 5, wherein The morphological index corresponding to the color spot morphological parameter is identified, and the index similarity between the morphological indexes is calculated; Based on the index similarity, the color spot morphological parameter is subjected to parameter clustering processing to obtain a clustered color spot morphological parameter; ​ ​ ； wherein, represents a synergistic adaptation value of the laser parameter configuration corresponding to the skin physiological parameter, represents a combination importance coefficient corresponding to the kth combination in the laser-skin parameter combination, represents the ith first parameter value in the kth combination in the laser-skin parameter combination, represents the ith second parameter value in the kth combination in the laser-skin parameter combination, k represents a starting value of the laser-skin parameter combination, m represents a number of the laser-skin parameter combination, i represents a sequence number corresponding to the first parameter value and the second parameter value, and n represents a number corresponding to the first parameter value and the second parameter value.

7. The effect analysis system for laser speckle removal according to claim 1, wherein ​ ​ ​ extract a time identifier corresponding to the cluster speckle morphology parameter; combine the time identifier and the cluster speckle morphology parameter to calculate an index transition rate corresponding to the morphology index; analyze an index evolution trend corresponding to the morphology index based on the index transition rate; analyze a speckle evolution trend of the target user based on the index evolution trend.

8. The effect analysis system for laser speckle removal according to claim 7, wherein The combining the time identifier and the cluster speckle morphology parameter to calculate the index transition rate corresponding to the morphology index comprises: sorting the cluster speckle morphology parameter based on the time identifier to obtain a sequence speckle morphology parameter; quantizing the sequence speckle morphology parameter to obtain a morphology parameter value; calculating the index transition rate corresponding to the morphology index based on the morphology parameter value through the following formula: ； wherein A represents an index transition rate corresponding to the modality index, represents a modality parameter value corresponding to the a-th index in the modality index at the b-th time point, represents a modality parameter value corresponding to the a-th index in the modality index at the b-th time point, represents a modality parameter value corresponding to the a-th index in the modality index at the b-th time point, represents a modality parameter value corresponding to the a-th index in the modality index at the b-th time point, a represents a sequence number corresponding to the modality index, and r represents an index number corresponding to the modality index, and represent the b+1-th and b-th time points, respectively.

9. A method of analyzing the effect of laser spot removal, characterized by, The method comprises: obtaining time sequence skin image data and speckle removal requirement description of a target user, extracting speckle distribution features and speckle morphology parameters of the target user from the time sequence skin image data, and setting speckle removal requirement labels of the target user based on the speckle removal requirement description; based on the speckle distribution features and the speckle removal requirement labels, constructing a speckle feature matrix of the target user, associating and matching the speckle feature matrix with historical clinical treatment data in a preset clinical case database to obtain a matching case set, extracting laser parameter configurations and efficacy evaluation data in the matching case set, collecting skin physiological parameters of the target user, and calculating a synergistic adaptation value of the skin physiological parameters corresponding to the laser parameter configurations; combining the synergistic adaptation value and the efficacy evaluation data to analyze a speckle removal effect prediction trend of the target user, analyzing a speckle evolution trend of the target user based on the speckle morphology parameters, and determining an efficacy threshold of the target user at different treatment stages in combination with the speckle removal effect prediction trend and the speckle evolution trend; wherein the combining the synergistic adaptation value and the efficacy evaluation data to analyze the speckle removal effect prediction trend of the target user comprises: performing data cleaning processing on the efficacy evaluation data to obtain pure efficacy evaluation data; analyzing evaluation labels corresponding to the pure efficacy evaluation data, and filtering speckle removal effect evaluation data from the pure efficacy evaluation data based on the evaluation labels; extracting speckle removal effect features in the speckle removal effect evaluation data, performing feature selection processing on the speckle removal effect features to obtain key effect features; generating a trend prediction curve of the target user based on the key effect features; modifying the trend prediction curve according to the synergistic adaptation value to obtain a speckle removal effect prediction trend of the target user; based on the efficacy threshold and the speckle feature matrix, planning a laser speckle removal scheme for the target user, and scheduling adverse effect avoidance criteria corresponding to the matching case set; In combination with the laser spot-removing scheme and the adverse effect avoidance criterion, laser spot-removing treatment is performed on the target user, and stage spot-removing data corresponding to each stage of the spot-removing treatment process is collected in real time. Based on the stage spot-removing data, a spot-removing effect analysis report of the target user is generated.

Images