Method, apparatus, and medium for optimizing intense pulsed light parameters based on heat transfer modeling

Abstract

The application discloses a kind of strong pulse light parameter simulation optimization method, device and medium based on heat transfer, method includes first obtaining the physiological characteristic data of skin tissue, then determine the thermal physical parameter data corresponding to physiological characteristic data;Again, the energy deposition equation of skin tissue is established, and a one-dimensional heat transfer model for the thickness direction of skin tissue is constructed;The one-dimensional heat transfer model is solved, and the score corresponding to each strong pulse light sequence is calculated according to the solving result and optimization target, and the strong pulse light sequence with the highest score is determined as the optimal strong pulse light sequence.The method model structure is simple, the physiological characteristics of skin tissue and the space-time distribution of light energy in skin tissue are considered simultaneously, the post-detection is changed into process prediction and feedforward control, the parameter optimization accuracy and parameter control ability are effectively improved, and the optimization strategy is more scientific and comprehensive.

Description

Technical Field

[0001] This invention relates to the field of dry eye treatment technology, and more specifically, to a method, apparatus, and medium for simulating and optimizing the parameters of intense pulsed light based on heat transfer. Background Technology

[0002] Meibomian gland dysfunction (MGD) is one of the main causes of dry eye, characterized by obstruction of the meibomian gland openings and / or abnormalities in the quality or quantity of meibomian gland secretion. Intense pulsed light (IPL) is currently one of the most widely used treatments for dry eye. However, some IPL devices on the market generally have fixed, non-adjustable parameters. These fixed, universal parameter settings are difficult to adapt to individualized treatment for different patients, making it difficult to guarantee treatment effectiveness. Some IPL devices on the market allow for manual parameter adjustment, but this manual adjustment method requires doctors to subjectively adjust based on feel, experience, and treatment results. Often, repeated testing is required to find the optimal treatment point, resulting in low efficiency and a lack of scientific basis. Chinese invention patent application number 202510931759X discloses a control system for an intense pulsed light therapy device, including a dynamic spectral energy adaptation module and a three-dimensional contact pressure field calibration module. It can calculate and optimize the energy parameters of the pulsed light in real time, including pulse width, spectral band and energy density, based on the patient's skin type, melanin content, hemoglobin distribution and stratum corneum thickness. It can also dynamically optimize the pulse parameters by combining multi-layer optical modeling of the skin and Monte Carlo photon transmission simulation with real-time feedback data, thereby improving the personalization of treatment.

[0003] However, the methods disclosed in the above patents have at least the following shortcomings: 1. The method disclosed in this patent uses a post-compensation control strategy, resulting in a delayed response: the method calculates and optimizes the energy parameters of the pulsed light in real time and then outputs it directly. After the output, it uses multi-layer optical modeling of the skin and Monte Carlo photon transmission simulation, then uses a mathematical and physical model to quantify the spatial distribution of light energy in the skin tissue, and then combines real-time feedback data to dynamically optimize the pulse parameters. The process is complex, and its control logic is post-braking rather than pre-planning. The control is relatively delayed, the treatment effect is difficult to guarantee, and there is also a certain risk of overheating and burning the skin.

[0004] 2. The method disclosed in this patent does not consider all factors when optimizing parameters: This patented method only quantifies the spatial distribution of light energy in skin tissue through mathematical and physical models, without considering the effects of heat conduction, accumulation, and dissipation in skin tissue over time. In other words, it does not consider the temporal distribution of light energy in skin tissue. In reality, the treatment of meibomian glands requires not only that the meibomian gland tissue reach a certain temperature (generally 40-44℃), but also that the temperature be maintained for a period of time to better melt the meibomian gland sebum. This is a requirement for gentle, continuous, and deep heating. Considering the distribution of temperature in different skin tissue layers over time is necessary and of great significance and importance.

[0005] 3. The method disclosed in this patent only provides the pulse width, spectral frequency band and energy density range when optimizing parameters, but does not propose or solve how to set the time interval between pulses to solve the core problem of keeping the deep meibomian glands from overheating while maintaining the temperature. This greatly affects the effect of melting meibomian fat, so it is necessary to improve it. Summary of the Invention

[0006] This invention provides a method, apparatus, and medium for simulating and optimizing the parameters of intense pulsed light based on heat transfer, which addresses the technical problems of low optimization efficiency, insufficient accuracy, and overly complex and unscientific optimization models for intense pulsed light parameters.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows: In a first aspect, embodiments of the present invention provide a method for simulating and optimizing the parameters of a high-intensity pulsed light based on heat transfer, characterized by comprising the following steps: S1. Obtain physiological characteristic data of skin tissue, including skin tissue thickness value, skin color grade value, and dry eye degree value; S2. Determine the thermophysical parameter data corresponding to the physiological characteristic data, wherein the thermophysical parameters include tissue density, tissue specific heat capacity, tissue thermal conductivity, blood perfusion rate, and absorption coefficient; S3. Based on Beer-Lambert's law and the aforementioned thermophysical parameter data, establish the energy deposition equation for skin tissue: ;in: ; ; in, High pulsed light energy deposition, The sequence is a high-intensity pulsed light sequence consisting of N pulses, the intensity of which varies with time t. N is an integer greater than 1, and k is the pulse number, where k is a positive integer. The high-intensity pulsed light sequence is defined by the peak intensity of the pulses. The absorption coefficient is... The coordinates of the skin group along the thickness direction; S4. Based on the energy deposition equation and the Pennes biothermal equation, establish a one-dimensional heat transfer model for the thickness direction of skin tissue: ; in, For tissue density, To organize specific heat capacity, The coordinates at time t are tissue temperature at that location, kJ b The thermal conductivity of the tissue is For blood perfusion rate, Blood density, For the specific heat capacity of blood, The core temperature of the blood; S5. Solve the one-dimensional heat transfer model to obtain the temperature change over time of each layer of skin tissue under different intense pulsed light sequences. Based on the solution results and the objective function, calculate the score corresponding to each intense pulsed light sequence and determine the intense pulsed light sequence with the highest score as the optimal intense pulsed light sequence.

[0008] In one possible embodiment, calculating the score corresponding to each strong pulse light sequence based on the solution results and the objective function specifically includes: The constraint condition is that no temperature data value in the solution result is greater than the first preset temperature threshold. For each solution that meets the constraints, the strong pulse light energy absorption rate, the effective temperature time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the temperature change smoothness are calculated according to the preset calculation formula. The energy absorption rate of intense pulsed light, the effective temperature-time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the smoothness of temperature change were used as optimization objectives. Weights were assigned to each objective and a weighted summation was performed to obtain a score for each intense pulsed light sequence.

[0009] In one possible embodiment, the weights are either preset fixed weights or weights that are adaptively adjusted according to actual working conditions.

[0010] In one possible embodiment, before weighted summation, the values ​​of intense pulsed light energy absorption rate, meibomian gland effective temperature time ratio, thermal damage risk coefficient, and temperature change smoothness are normalized.

[0011] In one possible embodiment, the heat transfer-based intense pulsed light parameter simulation optimization method further includes defining the skin tissue as having a layered structure.

[0012] In one possible embodiment, the heat transfer-based intense pulsed light parameter simulation optimization method further includes defining the skin tissue as having a layered structure along the thickness direction.

[0013] In one possible embodiment, the skin tissue is defined along the thickness direction from the surface to the innermost layer as the epidermis, dermis, subcutaneous tissue, orbicularis oculi muscle layer, meibomian gland layer, and deep tissue.

[0014] In one possible embodiment, the method for simulating and optimizing parameters of intense pulsed light based on heat transfer, specifically includes determining the thermophysical parameter data corresponding to the physiological feature data, according to a preset mapping relationship; or, converting the physiological feature data into corresponding thermophysical parameter data based on a preset conversion relationship.

[0015] In one possible embodiment, the high-pulse light parameter simulation optimization method based on heat transfer further includes: establishing the boundary control equations of the one-dimensional heat transfer model through the following steps: The first boundary governing equation is established based on the convective heat transfer coefficient between the skin surface and the environment: ; A second boundary governing equation is established based on the thermal conductivity and heat transfer coefficient of the innermost layer of skin tissue: ; in, The heat transfer coefficient between the skin surface and the environment. For ambient temperature, For the surface layer of skin tissue Temperature at any moment This represents the heat transfer coefficient at the innermost interface of the skin tissue. The total thickness of the skin tissue. For body core temperature, The innermost interface of skin tissue Temperature at any given time. The thickness direction of skin tissue is defined as the direction from the epidermis to the innermost layer, with coordinates ranging from 0 to L. Theoretically, the coordinate value of the innermost layer is equal to the skin tissue thickness value.

[0016] In one possible embodiment, the high-pulse light parameter simulation optimization method based on heat transfer further includes: discretizing the one-dimensional heat transfer model; The discretization process specifically includes using the implicit finite difference method to perform spatial and temporal discretization of the one-dimensional heat transfer model along the thickness direction of the skin tissue.

[0017] In one possible embodiment, the heat transfer-based high-pulse light parameter simulation optimization method further includes: using a search algorithm to select the highest score.

[0018] In one possible embodiment, the search algorithm employs a greedy algorithm or a dynamic programming algorithm.

[0019] Secondly, embodiments of the present invention also provide a high-intensity pulsed light parameter simulation and optimization device based on heat transfer, comprising: Memory, used to store program instructions; A processor is configured to invoke the program instructions stored in the memory to implement the high-intensity pulsed light parameter simulation optimization method based on heat transfer as described in any of the technical solutions of the first aspect.

[0020] Thirdly, embodiments of the present invention also provide a computer-readable medium storing program code for implementing the heat transfer-based high-pulse light parameter simulation optimization method as described in any of the technical solutions of the first aspect.

[0021] Compared with the prior art, the present invention has at least the following beneficial effects: On one hand, the present invention provides a method for simulating and optimizing high-intensity pulsed light parameters based on heat transfer. This method uses physiological characteristic data of skin tissue and a high-intensity pulsed light sequence defined by pulse peak intensity, pulse width, and pulse interval as input parameters to construct a one-dimensional heat transfer model. This method not only considers the physiological characteristics of skin tissue but also the spatial and temporal distribution of heat / temperature within the skin tissue, transforming post-event detection into pre-event simulation and prediction. This effectively improves the accuracy of parameter optimization and parameter control capabilities, avoiding the delays and lags caused by passive post-event responses. The optimization strategy is more scientific and comprehensive, with higher optimization efficiency and safety. The simulation optimization process provided by this method closely matches the needs of actual treatment and the heat transfer mechanism, effectively improving the prediction accuracy of temperature field and light energy distribution, and providing more reliable numerical basis for the optimization and selection of pulse parameters.

[0022] On the other hand, the technical solution provided by this invention can directly output a complete high-intensity pulsed light sequence by constructing and solving a one-dimensional heat transfer model and finally combining it with the set optimization target. This sequence not only gives the two important variables of pulse intensity and pulse width, but also gives the core optimization variable of pulse interval. The parameter optimization is more scientific, comprehensive and effective, which is conducive to quickly finding a set of optimal sequences that can maintain the effective temperature of deep glands for the longest time and without overheating throughout the process. This realizes a safe, long-lasting and effective heat transfer plan for meibomian glands, thereby improving the treatment effect. Attached Figure Description

[0023] Figure 1 This is a flowchart of a high-intensity pulsed light parameter simulation and optimization method based on heat transfer, as disclosed in an embodiment of the present invention. Figure 2 for Figure 1 A flowchart illustrating the process of determining the optimal high-intensity pulse light sequence in step S5. Figure 3 This is a schematic flowchart of a method for optimizing the intense pulse light sequence based on heat transfer, as disclosed in another embodiment of the present invention. Figure 4 This is a schematic diagram of the module composition of a high-intensity pulsed light parameter simulation and optimization device based on heat transfer, disclosed in another embodiment of the invention. Detailed Implementation

[0024] To better understand the inventive objectives, features, and advantages of this application, a further detailed description of the application is provided below in conjunction with the accompanying drawings and specific embodiments. Many specific details are set forth in the following description to provide a thorough understanding of the invention; however, this application may be implemented in other ways different from those described herein, and therefore, this application is not limited to the specific embodiments disclosed below.

[0025] This application embodiment is applied to the adaptive optimization of intense pulsed light parameters. In order to determine the optimal intense pulsed light sequence more scientifically, comprehensively, accurately and quickly, the parameters of intense pulsed light can be comprehensively predicted and simulated in advance, taking into account the physiological characteristics of skin tissue and the spatial and temporal distribution of light energy in skin tissue. The parameter optimization is more scientific and accurate.

[0026] The embodiments of this application will be further described in detail below with reference to the accompanying drawings and specific implementation methods. It should be understood that the embodiments described herein are only for illustration and explanation of this application and are not intended to limit this application. Furthermore, the embodiments of this application and the features in the embodiments can be combined with each other without conflict.

[0027] like Figure 1 The image shows a method for simulating and optimizing high-intensity pulsed light parameters based on heat transfer, provided in an embodiment of this application. The method includes the following steps: Step S1: Obtain physiological characteristic data of skin tissue, including skin tissue thickness value, skin color grade value, and dry eye degree value.

[0028] In some specific implementations of this step, physiological characteristics of the patient's orbital (i.e., periocular) skin tissue are collected, including periocular skin thickness, skin color grade, and degree of dry eye, for example: (1) Skin thickness can be measured using ultrasound or OCT (Optical Coherence Tomography) imaging equipment to obtain skin thickness values. In some specific implementations, the orbital skin can be divided into six tissue layers, from the surface to the deep layer: epidermis, dermis, subcutaneous tissue, orbicularis oculi muscle layer, meibomian gland layer, and deep tissue. After obtaining the skin thickness value, the measured total thickness of the eyelid can be allocated to each tissue layer according to the thickness ratio of different layers. The thickness ratio of different layers can be predetermined and is not specifically limited here. (2) Based on the Fitzpatrick skin color grading method, combined with skin colorimetric measurement data and subjective evaluation, skin color grades are determined. The skin color grading method is shown in Table 1: Table 1 Skin Color Grading Table

[0029] (3) Combine clinical diagnosis with existing methods such as tear secretion test or meibomian gland imaging to assess the degree of dry eye. Dry eye is divided into four levels from 0 to 3 according to severity, corresponding to no dry eye, mild, moderate and severe.

[0030] In some specific implementations, after obtaining the physiological characteristic data of skin tissue, standardized preprocessing such as data filtering, outlier removal, consistency verification, and data normalization can be performed on the physiological characteristic data.

[0031] Step S2: Determine the thermophysical parameter data corresponding to the physiological characteristic data, wherein the thermophysical parameters include tissue density, tissue specific heat capacity, tissue thermal conductivity, blood perfusion rate, and absorption coefficient; In one possible embodiment, the method for simulating and optimizing parameters of intense pulsed light based on heat transfer, specifically includes determining the thermophysical parameter data corresponding to the physiological feature data, according to a preset mapping relationship; or, converting the physiological feature data into corresponding thermophysical parameter data based on a preset conversion relationship.

[0032] In some specific implementations, a mapping table between physiological characteristics and thermophysical parameters can be pre-established. Based on this pre-established mapping table, the thermophysical parameter data corresponding to the physiological characteristic data can be obtained by looking up the table. For example, parameter mapping tables can be pre-established for different skin color types. Each parameter mapping table is a two-dimensional table, where rows represent ocular skin thickness and columns represent dry eye level. The values ​​of the data area, i.e., thermophysical parameters, are determined by the rows and columns. For example, if the obtained physiological characteristic data is (skin thickness 5.5mm, skin color type II, moderate dry eye level), the table to be looked up can be determined according to the first pre-established mapping relationship, based on skin color type II, such as the second table. In the second table, the rows and columns can be skin thickness and dry eye level, respectively. The values ​​of the data area determined by the rows and columns are the corresponding thermophysical parameters (which can be a predefined set of thermophysical parameter data).

[0033] In some specific implementations of step S2, the orbital skin is defined as a layered structure consisting of the epidermis, dermis, subcutaneous tissue, orbicularis oculi muscle layer, meibomian gland layer, and deep tissue. A reference value table of thermal properties for each layer of tissue, as shown in Table 2, is pre-established. After obtaining the physiological characteristic data of the skin tissue, the reference values ​​of each parameter shown in Table 2, along with thickness scaling factors, skin color adjustment factors, thermal conductivity adjustment factors, and blood perfusion rate adjustment factors, are combined to obtain the model calculation values ​​of the thermal properties of each layer. The thickness scaling factor is determined based on the measured thickness of the skin tissue and the total reference thickness (i.e., the sum of the reference values ​​for each layer in Table 2). The thickness reference value of each layer is multiplied by the thickness scaling factor to obtain the thickness value used for model calculation of each layer. The skin color adjustment factor is determined based on the skin color type and is used to adjust the absorption coefficient reference value; the darker the skin color, the higher the absorption coefficient. The thermal conductivity adjustment factor is determined based on the dry eye level and is used to adjust the thermal conductivity; the higher the dry eye level (i.e., the more severe the dry eye), the higher the thermal conductivity adjustment factor. The blood perfusion rate adjustment factor is determined based on the dry eye grade. The higher the dry eye grade (i.e., the more severe the dry eye), the higher the blood perfusion rate adjustment factor.

[0034] As an example, as shown in Table 2, the reference thickness of each layer is preset, and the formula is used. The reference total thickness is calculated, where, Let i be the reference thickness of the i-th layer, i = 1, 2, 3... 6. The measured total thickness will be... Compared with the reference total thickness The ratio is determined as the thickness scaling factor. Using the formula The model thickness value (i.e., calculated value) of each layer of the structure is obtained, where, For the first The model thickness of the layer, This is the thickness scaling factor. It should be noted that if the measured thickness is obtained directly from optical coherence tomography (OCT), then the measured layer thicknesses are used directly without scaling.

[0035] Table 2 Reference values ​​for thermophysical properties of layered structures

[0036] It should be noted that the data in Table 2 are only exemplary parameter data and do not represent the only settings; other thermophysical parameters can be assigned uniform standard values, such as tissue density, tissue specific heat capacity, blood density, blood specific heat capacity, blood core temperature, etc.

[0037] Based on the examples above, the darker the skin tone, the higher the melanin content, and the greater the light absorption coefficient. The specific process of adjusting the absorption coefficient based on skin tone type can include the following steps: defining a skin tone adjustment factor. (Linear interpolation or table lookup methods can be used), specifically: Type I:

[0038] Type II:

[0039] Type III: (Benchmark) Type IV:

[0040] V-shape:

[0041] Type VI:

[0042] Then the absorption coefficient of each layer Adjusted to:

[0043] in The values ​​are reference values ​​for the absorption coefficients of the corresponding layers in Table 2.

[0044] The degree of dry eye reflects the functional status of the meibomian glands and the local metabolic level, affecting heat transfer characteristics. The adjustment of thermal conductivity and blood perfusion rate by dry eye level can specifically include the following steps: Thermal conductivity (kbi): Changes in the lipid composition of the meibomian gland layer and tissue inflammation can lead to changes in thermal conductivity when dry eye worsens. Definition of dry eye adjustment factor (fcond): No dry eye (Grade 0): fcond=1.0 Mild (Level 1) 0: fcond=1.05 Moderate (Level 2): ​​fcond=1.12 Severe (Level 3): fcond=1.20 This factor can act only on the meibomian gland layer, or on multiple layers depending on the tissue characteristics.

[0045] Using formula The adjusted parameters are obtained, which are the thermal conductivity values ​​used for model calculations. ,in, The values ​​are reference values ​​for the thermal conductivity of the layered structure in Table 2.

[0046] Blood perfusion rate (wi): Dry eye is often accompanied by local inflammation, which may increase blood flow. Definition of perfusion adjustment factor (fperf): No dry eye (Grade 0): fperf=1.0 Mild (Level 1): fperf=1.2 Moderate (Level 2): ​​fperf=1.5 Severe (Level 3): fperf=2.0 Adjusted parameters: Using formula The adjusted parameters are obtained, which are the blood perfusion rate values ​​used for model calculation. ,in, The values ​​represent reference values ​​for blood perfusion rates in the stratified structures shown in Table 2. To simplify calculations, considering that this factor acts on layers rich in blood vessels or with active metabolism, the values ​​can be adjusted for the dermis, meibomian glands, etc., without specific restrictions.

[0047] Other thermophysical parameters can be uniformly assigned values, and those skilled in the art can flexibly select, define, and adjust them, which will not be elaborated here.

[0048] To facilitate understanding, specific examples are provided below.

[0049] Assuming the physiological characteristics data of the skin tissue (i.e., the input) are obtained as follows: Eyelid thickness ; Skin color grade: Type IV; Dry eye severity: Moderate; The thermal property parameters in the model are then determined according to the following steps: First, calculate the reference total thickness in Table 2: Then calculate the thickness scaling factor (denoted as hou): hou The thickness reference values ​​for each layer in Table 2 are multiplied by this factor to obtain the thickness value of each layer used for model calculation (e.g., the reference value for the meibomian gland layer thickness is 0.5 * 1.035). 0.518mm); Skin color factor The absorption coefficient of each layer is multiplied by 1.3.

[0050] Dry eye tolerance: Thermal conductivity adjustment factor It is only used on the meibomian gland layer; blood perfusion rate adjustment factor. It is used for the dermis and meibomian glands. All other parameters are taken from a standardized value.

[0051] Step S3: Based on Beer-Lambert's law and the aforementioned thermophysical parameter data, establish the energy deposition equation for skin tissue: ;in: ; ; in, For high pulsed light energy deposition, This is a sequence of N pulses whose intensity varies with time, where N is an integer greater than 1, and k is the pulse number, which is an integer greater than or equal to 1. The sequence of pulses is defined by the peak intensity of the pulses. The absorption coefficient is... The coordinates of the skin group along the thickness direction; In some specific implementations of this step, the IPL pulse parameter combination sequence (pulse peak intensity) Pulse width Pulse interval The data can be derived from historical and empirical data. To obtain a more accurate personalized recommendation, in one implementation, the IPL pulse parameter combination sequence can also adopt an automated parameter combination generation strategy. This involves setting the search range and search step size for pulse width, pulse interval, and pulse peak value, while setting parameter constraints to automatically eliminate invalid parameter combinations (such as pulse width greater than pulse interval not meeting heat dissipation requirements, pulse peak value not exceeding a safety threshold, etc.).

[0052] Step S4: Based on the energy deposition equation and the Pennes biothermal equation, establish a one-dimensional heat transfer model for the thickness direction of skin tissue: ; in, For tissue density, To organize specific heat capacity, The coordinates at time t are tissue temperature at that location, kJ b The thermal conductivity of the tissue is For blood perfusion rate, Blood density, For the specific heat capacity of blood, The core temperature of the blood; Substituting the energy deposition equation established in step S2 into the Pennes biothermal equation yields a one-dimensional heat transfer model for the thickness direction of skin tissue. Since the heat transfer process of intense pulsed light heating tissue is a continuous physical field, to facilitate computer solution, in some specific embodiments of this application, the intense pulsed light parameter simulation and optimization method based on heat transfer further includes: discretizing the heat transfer model using an implicit finite difference method, specifically including spatial and temporal discretization, to form discrete numerical points that can be processed by a computer.

[0053] After time and space discretization, the discretized expression of the heat transfer model (i.e., the first...) The grid, the first The discrete equations for each time step are: ; in:

[0054] Assuming skin tissue Since the absorption coefficient is uniform, the integral simplifies to an exponential form:

[0055] To facilitate iterative solutions, the discretized heat transfer model for all grid points is transformed into implicit matrix equations: ; Wherein, A is a coefficient matrix with a tridiagonal structure, which can be adaptively generated based on the thermophysical parameter data determined in step S2; The right-hand term vector, Explicitly dependent on time step ,because As it varies with the pulse sequence, its expression is: .

[0056] Through steps S3 and S4, the high-intensity pulsed light sequence, defined by the three pulse parameters of pulse peak intensity, pulse width, and pulse interval, and the physiological characteristic data of skin tissue are simultaneously introduced as input parameters into the heat source term (i.e., The definition of heat transfer is incorporated into the established one-dimensional heat transfer model, enabling the optimization strategy to not only consider the physiological characteristics of skin tissue but also to anticipate the effects of heat accumulation and dissipation under different pulse sequences, making its considerations more scientific and comprehensive. The effective combination of these two approaches also allows for early intervention simulation of intense pulsed light parameter optimization, effectively avoiding the delays and hysteresis caused by reactive responses, thus improving optimization efficiency and safety.

[0057] Step S5: Solve the one-dimensional heat transfer model to obtain the temperature change over time of each layer of skin tissue under different intense pulsed light sequences. Based on the solution results and optimization objectives, calculate the score corresponding to each intense pulsed light sequence and determine the intense pulsed light sequence with the highest score as the optimal intense pulsed light sequence.

[0058] In some specific implementations of this step, the calculation of the score corresponding to each strong pulse light sequence based on the solution results and optimization objectives specifically includes: The constraint condition is that no temperature data value in the solution result is greater than the first preset temperature threshold. For each solution that meets the constraints, the strong pulse light energy absorption rate, the effective temperature time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the temperature change smoothness are calculated according to the preset calculation formula. The energy absorption rate of intense pulsed light, the effective temperature-time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the smoothness of temperature change were used as optimization objectives. Weights were assigned to each objective and a weighted summation was performed to obtain a score for each intense pulsed light sequence.

[0059] By setting constraints, parameters in the model solution are filtered out to remove those exceeding the temperature safety threshold, thus avoiding overheating and improving parameter safety. As an example, the first preset temperature threshold can be 45 degrees Celsius.

[0060] This application proposes to use the energy absorption rate of intense pulsed light (IPL), the effective temperature-time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the smoothness of temperature changes as optimization objectives. Compared to the traditional single optimization objective of maximizing energy absorption rate, this application's optimization objective considers the combined contribution of different indicators to subsequent actual treatment, providing a more comprehensive and scientifically sound approach. Furthermore, this application proposes to assign different weights to the IPL energy absorption rate, the effective temperature-time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the smoothness of temperature changes to reflect the varying degrees of influence of each indicator in their combined contribution, making the parameter optimization results more accurate and reliable.

[0061] The high-intensity pulsed light parameter simulation and optimization method based on heat transfer provided in this application defines skin tissue as a layered structure. By associating physiological characteristic data, it configures corresponding thermophysical parameters for skin tissue of different thicknesses. Then, based on Beer-Lambert's law, Pennes' biothermal equation, etc., it constructs a one-dimensional heat transfer model along the thickness direction of skin tissue, solves the model, and finally determines the optimal high-intensity pulsed light sequence based on the solution results and optimization objectives.

[0062] This method transforms post-event detection into early prediction, effectively improving the accuracy of parameter optimization and parameter control capabilities. The simulated optimization process closely matches the actual treatment needs and heat transfer mechanisms, effectively improving the prediction accuracy of temperature field and light energy distribution, and providing more reliable numerical basis for the optimization and screening of pulse parameters.

[0063] The laser energy deposition rate is integrally calculated over the target treatment layer (i.e., the meibomian gland layer) during the runtime to obtain the intense pulsed light energy absorption value of the target treatment layer. The ratio of the intense pulsed light energy absorption value to the total incident light energy is then calculated, which is the intense pulsed light energy absorption rate. In some specific implementations, the intense pulsed light energy absorption rate (denoted as...) The ratio of energy absorbed by the target treatment layer (meibomian gland layer) to the total incident energy can be calculated using the following formula: ; in, This represents the area of ​​the intense pulsed light spot. The starting depth of the target treatment layer (meibomian gland layer), which is the distance from the skin surface to the upper boundary of this layer. The final depth of the target treatment layer (meibomian gland layer), which is the distance from the skin surface to the lower boundary of this layer. This refers to the total duration of an intense pulsed light (IPL) sequence, i.e., the duration of the entire treatment pulse sequence.

[0064] In some specific implementation methods, the effective temperature of the meibomian gland layer is generally 40℃-44℃, and the percentage of time with effective temperature of the meibomian gland layer (denoted as...) The percentage of time the meibomian gland layer is within the effective temperature range of 40-44℃ is the ratio of the time the meibomian gland layer is within the effective temperature range to the total operating time. The formula for this is: ; in, The total number of time steps. For the first Temperature of each tissue layer at each time step.

[0065] In some specific implementation methods, the thermal damage risk coefficient (denoted as...) The probability that the temperature of a tissue layer exceeds a safe temperature threshold (generally set to less than 45℃) can be calculated using the following formula: ; in, For organizational layers, The total number of time steps. For indicator functions, For the first Layered organization in the first The temperature at each time step, j=1,2,3....n, This is the safe temperature threshold (usually set to 45℃).

[0066] In some specific implementations, the smoothness of temperature changes (denoted as...) This is configured to characterize the stationarity of the temperature change curve of the meibomian gland layer. Specifically, the stationarity of the temperature change curve can be quantified by the amplitude of the curve's fluctuations, the slope variance, the first-order difference, or the second-order difference. For example, the calculation of temperature change smoothness may include: First, the temperature change curve Perform least-squares fitting to obtain a smooth fitting curve. ; Then, calculate the sum of squared residuals: ; in, The total number of time steps. The closer the value is to 1, the more stable the temperature change.

[0067] In one possible embodiment, each of the weights adopts a preset fixed weight or a weight that is adaptively adjusted according to the actual working conditions.

[0068] In some specific implementations, considering the different impacts on subsequent actual treatment, different weights are assigned to the intense pulsed light energy absorption rate, the effective temperature-time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the smoothness of temperature changes. As an example, a weighted summation function (denoted as...) is constructed. ) can be: ; The value of the weighted summation function is the score of the intense pulsed light sequence; maximizing the weighted summation function is the objective function. The objective function is used to evaluate the temperature change over time in different tissue layers, and the optimal sequence is searched based on the score. , , Combinations. It should be noted that the weight values ​​given in this example are for reference only, and the specific weight values ​​for each item are not set.

[0069] In one possible embodiment, before assigning weights to the strong pulsed light energy absorption rate, the effective temperature time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the temperature change smoothness and performing a weighted summation calculation, the method further includes: normalizing the values ​​of the strong pulsed light energy absorption rate, the effective temperature time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the temperature change smoothness.

[0070] In some specific implementations, all evaluation indicators are normalized to the [0,1] interval.

[0071] In one possible embodiment, the heat transfer-based high-pulse light parameter simulation optimization method further includes: using a search algorithm to select the highest score.

[0072] In one possible embodiment, the search algorithm employs a grid search algorithm, a genetic algorithm, a particle swarm optimization algorithm, or a Bayesian optimization algorithm.

[0073] Grid search: Discretizes the feasible range of each parameter into several value points, enumerates all parameter combinations and calculates the corresponding scores, and selects the combination with the best score. This algorithm is suitable for nonlinear, high-dimensional optimization problems and can effectively handle the coupling between parameters. (Scores of each strong pulse light sequence at each time point and position) Genetic algorithms encode each set of parameters as an individual and simulate the natural evolutionary process through operations such as selection, crossover, and mutation, iteratively updating the population until it converges to the optimal solution. This algorithm is suitable for nonlinear, high-dimensional optimization problems and can effectively handle the coupling between parameters.

[0074] Particle Swarm Optimization (PSO): Initialize a swarm of random particles (with parameter combinations). Each particle adjusts its flight speed based on its own historical best position and the swarm's historical best position, gradually approaching the global optimum. This algorithm has fast convergence speed, is simple to implement, and is suitable for continuous parameter spaces.

[0075] Bayesian optimization: This approach establishes a probabilistic surrogate model (such as a Gaussian process) of parameters and scores, dynamically selects the next evaluation point using a sampling function (such as desired improvement), and balances exploration and utilization in each iteration to find the optimal solution with the fewest evaluations. It is suitable for scenarios with high evaluation costs (such as those requiring time-consuming simulations).

[0076] Considering the strong coupling effect between impulse parameters, the above-described global optimization method can comprehensively explore the parameter space and avoid getting trapped in local optima. This embodiment preferably uses the Bayesian optimization method.

[0077] It should be noted that the embodiments of this application do not involve any improvement to the above search algorithm. Those skilled in the art can use any suitable algorithm to select the highest score value from several scores as needed, and no specific restrictions are made here.

[0078] In one possible embodiment, the heat transfer-based intense pulsed light parameter simulation optimization method further includes defining the skin tissue along the thickness direction from the surface to the innermost layer as the epidermis, dermis, subcutaneous tissue, orbicularis oculi muscle layer, meibomian gland layer, and deep tissue.

[0079] In one possible embodiment, the high-pulse light parameter simulation optimization method based on heat transfer further includes establishing the boundary control equations of the one-dimensional heat transfer model through the following steps: The left boundary governing equation is established based on the convective heat transfer coefficient between the skin tissue surface and the environment: ; The right boundary governing equation is established based on the heat transfer coefficient of the innermost layer of skin tissue: ; in, The heat transfer coefficient between the skin surface and the environment. For ambient temperature, The temperature of the skin surface at time t. This represents the heat transfer coefficient at the innermost interface of the skin tissue. The total thickness of the skin tissue. For body core temperature, The innermost interface of skin tissue The temperature of a moment.

[0080] By establishing boundary control equations for skin tissue, the heat exchange rules between the epidermis and the external environment, and between deep tissues and the body core, are defined, thereby solving for the steady-state temperature field under resting conditions. This steady-state temperature field serves as the initial condition, providing a foundation for subsequent dynamic heat transfer simulations under intense pulsed light irradiation, enabling more precise parameter optimization.

[0081] In some specific embodiments of this application, the high-intensity pulsed light parameter simulation optimization method based on heat transfer further includes: initializing a one-dimensional heat transfer model.

[0082] In some specific implementations, the initialization process includes: first, setting the intensity of the strong pulsed light to zero for a sufficient time to allow the model to reach a steady state at rest. In some specific implementations, based on a preset initial temperature field at rest (approximately 34℃, or the system default value can be used), an initial light intensity of 0, rest time, and a convergence threshold for rest iteration, iterative calculations are performed using a built-in heat transfer model until the change in temperature field is less than the preset convergence threshold, satisfying the initial resting steady state where the temperature field does not change over time and heat input and heat output are balanced, and the resting temperature value is saved. In some specific implementations, model initialization also includes: updating the one-dimensional heat transfer model based on the thermophysical parameters corresponding to the collected physiological characteristic data of skin tissue.

[0083] In some further embodiments of this application, the method for simulating and optimizing the parameters of intense pulsed light based on heat transfer further includes, before constructing a one-dimensional heat transfer model for the thickness direction of skin tissue, making model assumptions, specifically including: 1. Differences in patient conditions include three factors: eyelid thickness, skin color, and degree of dry eye.

[0084] 2. One-dimensional heat transfer: Heat is conducted only along the depth direction, ignoring temperature changes in the horizontal direction.

[0085] 3. Uniform structure parameters: The thermal properties (thermal conductivity, density, specific heat capacity, etc.) of each layer of the structure remain constant during the simulation.

[0086] 4. Uniform irradiation by intense pulsed light: It is assumed that the intense pulsed light is uniformly distributed within the irradiated area.

[0087] 5. Uniform blood perfusion: It is assumed that the blood perfusion rate is uniformly distributed within the tissue.

[0088] 6. Boundary conditions: The left boundary (skin surface) is for convective heat transfer, and the right boundary (deep tissue) is for conductive heat transfer.

[0089] In some further embodiments of this application, the heat transfer-based high-intensity pulsed light parameter simulation optimization method further includes: outputting the pulse parameter combination of the optimal high-intensity pulsed light sequence.

[0090] The output data is available for review by the diagnosing physician and includes the raw data, corresponding scores, and weighted total scores for each assessment indicator.

[0091] In one possible embodiment, the high-pulse light parameter simulation optimization method based on heat transfer further includes: training the model after establishing a one-dimensional heat transfer model for the thickness direction of skin tissue.

[0092] In one possible embodiment, the high-pulse light parameter simulation optimization method based on heat transfer further includes: To obtain physiological characteristic data of skin tissue after new standard pretreatment; Based on the processed physiological characteristic data and the correspondence between physiological characteristics and thermophysical parameters, the parameters of the trained one-dimensional heat transfer model are updated. Set the initial intensity of the strong pulse light to zero for a sufficient time to allow the model to reach a steady state at rest (for example, if the temperature change of each layer does not exceed 0.1 degrees within 1 second, it is considered to have reached a steady state). Define the parameter space for intense pulsed light, including the possible ranges of pulse width, pulse interval, and pulse peak intensity. Use optimization algorithms (such as grid search, genetic algorithm, particle swarm optimization, or Bayesian optimization) to traverse or iteratively search the parameter space. For each set of candidate parameters, simulate the irradiation process for a preset duration and execute the following sub-steps: Calculate optical energy deposition; Construct the right-hand vector b; Solve the system of linear equations A =b, obtain and store the temperature distribution data; Based on the temperature distribution at each time point, the highest temperature of each tissue layer, and the average temperature of the meibomian gland layer, the maximum temperature rise and temperature gradient are calculated. The maximum temperature of each layer is compared to see if it exceeds the safety threshold (e.g., not greater than 45 degrees). The overall absorption efficiency, the proportion of time the meibomian gland temperature is above 40 degrees to the total laser time, and the risk of thermal damage are assessed. Screen out strong pulse sequence parameter combinations whose highest temperature exceeds the safety threshold; Using a greedy algorithm or dynamic programming, find a set of high-intensity pulsed light sequences defined by pulse width, pulse interval, and pulse peak value as the optimal solution, such that: during a single irradiation, the temperature of each tissue (especially the epidermis) does not exceed the safe temperature threshold (45℃); the absorption rate of the high-intensity pulsed light is as high as possible, and the transmittance is as low as possible (i.e., the absorption rate reaches more than 90%); the energy of the high-intensity pulsed light mainly acts on the meibomian glands; and the meibomian gland area is maintained at a temperature above 40℃ for as long as possible.

[0093] For ease of understanding, such as Figure 3 The diagram shown is a flowchart illustrating a method for simulating and optimizing high-intensity pulsed light parameters based on heat transfer, according to another embodiment of this application. The method specifically includes the following steps: Step S100: Input the patient's basic parameters (including physiological characteristic data), for example: Physicians (or other operators) obtain the following three patient characteristics through measurement or assessment: Eyelid thickness: 1.5 mm (measured by ultrasound or optical imaging equipment); Skin type: Type IV (Fitzpatrick classification, corresponding to higher melanin content); Dry eye severity: Moderate (assessed through tear secretion test, meibomian gland imaging, etc.); Step S200: Map the parameters to the model input, for example: Based on the patient characteristics obtained in step S100, the following model parameters are automatically mapped or calculated according to the adjustment rules in Table 3: Table 3 Parameter Mapping Adjustment Table

[0094] Step S300: Construct and initialize the heat transfer model through the following steps: 1. Update the layer thickness and absorption coefficient in the model based on the results of step S200. Blood perfusion rate ; 2. Set the initial temperature field to a resting state (approximately 34°C); 3. Set safety thresholds: epidermal layer ≤ 45℃, meibomian gland layer target temperature 40-44℃.

[0095] Step S400, define the optimization objective: The system goal is to find a set of IPL parameters for this patient, including pulse width, pulse interval, and peak power, such that: Maximize the time for the epidermal layer to have a maximum temperature ≤ 45℃ and the meibomian gland layer to have an average temperature ≥ 40℃, maximize the laser energy absorption rate, and make the temperature curve as stable as possible (avoid drastic fluctuations). Step S500: Perform parameter optimization calculations. Referring to Table 3, the system uses a greedy algorithm or dynamic programming to search for the optimal combination within the following ranges as shown in Table 4 (the search range and step size are only examples): Table 4 Search Reference Table

[0096] Example of calculation process: The system iterates through all parameter combinations. For each parameter set, it runs a heat transfer model to simulate the temperature changes over time in different layers of skin tissue when a strong pulsed light sequence defined by that parameter set is applied.

[0097] Calculate and evaluate: the duration of peak epidermal temperature, meibomian gland temperature > 40°C, and overall absorption efficiency; Filter the parameter combination with the highest score.

[0098] Step S600: Output recommended parameters (i.e., output the optimal parameter combination): The system outputs the recommended IPL parameters for this patient, for example: Patient ID: 001 Recommended IPL parameters: - Pulse width: 8 ms - Pulse interval: 25 ms - Pulse peak value: 18 W Expected results: - Highest skin temperature: 44.2℃ - Meibomian gland temperature > 40℃ Duration: 120 ms - Energy absorption rate: 92% - Temperature curve smoothness: High In some specific implementations, this simulation optimization method also includes outputting and displaying the model solution results (i.e., data values ​​showing the temperature changes over time in each layer of skin tissue under each intense pulsed light sequence) and / or optimization results (i.e., the peak pulse intensity, pulse width, and pulse interval of the determined optimal intense pulsed light sequence). The output recommended parameters can be confirmed by the doctor for treatment, and the doctor can view the simulated temperature curves and safety assessments.

[0099] The high-intensity pulsed light parameter simulation and optimization method based on heat transfer provided in this invention constructs and solves a one-dimensional heat transfer model. Finally, combined with the set optimization objective, it can directly output a complete high-intensity pulsed light sequence. This sequence not only provides the two important variables of pulse intensity and pulse width, but also the core optimization variable of pulse interval. The parameter optimization is more scientific, comprehensive and effective, which is conducive to quickly finding a set of optimal sequences that can maintain the effective temperature of deep glands for the longest time without overheating. This realizes a safe, long-lasting and effective heat transfer plan for meibomian glands, thereby improving the treatment effect.

[0100] The high-intensity pulsed light (HIPL) parameter simulation and optimization method based on heat transfer provided in this invention constructs a one-dimensional heat transfer model along the thickness of the skin tissue by analyzing the physiological characteristics of skin tissue, blood perfusion heat dissipation, and tissue heat transfer mechanisms. This model can simulate the distribution of light energy and temperature changes within the patient's skin tissue under different pulse parameters online. A multi-dimensional evaluation system is set up to comprehensively score the treatment quality, efficacy, and safety of the HIPL pulse sequence, determining the pulse parameter combination suitable for the patient's meibomian gland targeted therapy. The offline device is then manually confirmed and triggered for treatment. This method can fully consider the differences in the patient's skin structure and tissue thermophysical properties to provide personalized treatment plans, intelligently adjusting the treatment laser pulse parameters to achieve optimal treatment results while significantly reducing the safety risks of tissue thermal damage.

[0101] like Figure 4 As shown, in some other embodiments of this application, a high-intensity pulsed light parameter simulation and optimization device 400 based on heat transfer is also provided, comprising: Memory 401 is used to store program instructions; Processor 402 is used to call the program instructions stored in the memory to implement the high-intensity pulsed light parameter simulation optimization method based on heat transfer as described in any of the above embodiments.

[0102] In some other embodiments of this application, a computer-readable medium is also provided, the computer-readable storage medium storing program code for implementing the heat transfer-based high-pulse light parameter simulation optimization method as described in any of the above embodiments.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that the various embodiments of this application can be implemented by means of software or software combined with necessary general-purpose hardware platforms, and of course, they can also be implemented by hardware functions. Based on this understanding, the technical solution of this application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions to cause a computer device, such as including but not limited to a personal computer, server, or network device, to execute all or part of the steps of the method described in any embodiment of this application.

[0104] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for simulating and optimizing the parameters of a high-intensity pulsed light based on heat transfer, characterized in that, Includes the following steps: S1. Obtain physiological characteristic data of skin tissue, including skin tissue thickness value, skin color grade value, and dry eye degree value; S2. Determine the thermophysical parameter data corresponding to the physiological characteristic data, wherein the thermophysical parameters include tissue density, tissue specific heat capacity, tissue thermal conductivity, blood perfusion rate, and absorption coefficient; S3. Based on Beer-Lambert's law and the aforementioned thermophysical parameter data, establish the energy deposition equation for skin tissue: ;in, ， ， For high pulsed light energy deposition, For a by A sequence of strong pulsed light whose intensity varies with time t, consisting of several pulses. It is an integer greater than 1. The pulse number represents the sequence of high-intensity pulses, which is determined by the pulse peak intensity. Pulse width Pulse interval Common definition, The absorption coefficient is... The coordinates of the skin group along the thickness direction; S4. Based on the energy deposition equation and the Pennes biothermal equation, establish a one-dimensional heat transfer model for the thickness direction of skin tissue: ； in, For tissue density, To organize specific heat capacity, The coordinates at time t are The tissue temperature at that location, The thermal conductivity of the tissue is For blood perfusion rate, Blood density, For the specific heat capacity of blood, The core temperature of the blood; S5. Solve the one-dimensional heat transfer model to obtain the data values ​​of temperature change over time in each layer of skin tissue under different intense pulsed light sequences. Based on the solution results and optimization objectives, calculate the score corresponding to each intense pulsed light sequence and determine the intense pulsed light sequence with the highest score as the optimal intense pulsed light sequence.

2. The method for simulating and optimizing high-intensity pulsed light parameters based on a heat transfer model according to claim 1, characterized in that, The process of calculating the score corresponding to each strong pulse light sequence based on the solution results and optimization objectives specifically includes: The constraint condition is that no temperature data value in the solution result is greater than the first preset temperature threshold. For each solution that meets the constraints, the strong pulse light energy absorption rate, the effective temperature time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the temperature change smoothness are calculated according to the preset calculation formula. The energy absorption rate of intense pulsed light, the effective temperature-time ratio of the meibomian gland layer, the thermal damage risk coefficient, and the smoothness of temperature change were used as optimization objectives. Weights were assigned to each objective and a weighted summation was performed to obtain a score for each intense pulsed light sequence.

3. The method for simulating and optimizing high-intensity pulsed light parameters based on heat transfer according to claim 2, characterized in that, The weights are preset fixed weights.

4. The method for simulating and optimizing high-intensity pulsed light parameters based on heat transfer according to claim 3, characterized in that, Also includes: The skin tissue is defined as having a layered structure, the layered structure including the meibomian gland layer.

5. The method for simulating and optimizing high-intensity pulsed light parameters based on heat transfer according to claim 1, characterized in that, The step of determining the thermophysical parameter data corresponding to the physiological feature data specifically includes: determining the thermophysical parameter data corresponding to the physiological feature data according to a preset mapping relationship; or, converting the physiological feature data into the corresponding thermophysical parameter data based on a preset conversion relationship.

6. The method for simulating and optimizing high-intensity pulsed light parameters based on heat transfer according to claim 1, characterized in that, It also includes establishing the boundary control equations of the one-dimensional heat transfer model through the following steps: The first boundary governing equation is established based on the convective heat transfer coefficient between the skin surface and the environment: ； A second boundary governing equation is established based on the thermal conductivity and heat transfer coefficient of the innermost layer of skin tissue: ； in, The heat transfer coefficient between the skin surface and the environment. For ambient temperature, For the surface layer of skin tissue Temperature at any moment This represents the heat transfer coefficient at the innermost interface of the skin tissue. The total thickness of the skin tissue. For body core temperature, The innermost interface of skin tissue The temperature of a moment.

7. The method for simulating and optimizing high-intensity pulsed light parameters based on heat transfer according to claim 6, characterized in that, Also includes: The one-dimensional heat transfer model is discretized. The discretization process specifically includes using the implicit finite difference method to perform spatial and temporal discretization of the one-dimensional heat transfer model along the thickness direction of the skin tissue.

8. The method for simulating and optimizing high-intensity pulsed light parameters based on heat transfer according to claim 7, characterized in that, Also includes: The highest score is selected using a search algorithm.

9. A device for simulating and optimizing the parameters of a high-intensity pulsed light based on heat transfer, characterized in that, include: Memory, used to store program instructions; A processor is configured to invoke the program instructions stored in the memory to implement the high-intensity pulsed light parameter simulation optimization method based on heat transfer as described in any one of claims 1 to 8.

10. A computer-readable medium, characterized in that, The computer-readable storage medium stores program code for implementing the high-intensity pulsed light parameter simulation optimization method based on heat transfer as described in any one of claims 1 to 8.

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