Method and system for process optimization of silicone gel preparation for acne scar repair

Abstract

This invention discloses an optimized method and system for preparing silicone gel for acne scar repair, relating to the field of silicone gel preparation technology. The method includes obtaining physicochemical indicators such as the viscosity coefficient, refractive index, and volatile content of silicone raw materials; using a rheological prediction model to calculate the expected viscosity change curve of the silicone raw material mixing process under the current reactor temperature field; comparing this curve with a process window threshold; if the curve exceeds the threshold, triggering a gradient temperature adjustment strategy and recalculating until the curve falls within the threshold; after determining the temperature adjustment strategy, introducing a state inversion algorithm to output the dispersity parameter of the initiator addition stage based on the crosslinking density index of the target product; and dynamically adjusting the stirring rate and the timing of dispersed phase addition according to the dispersity parameter to complete the preparation. This method enables precise control of the preparation process, improves product quality stability, and meets the performance requirements of silicone gel for acne scar repair.

Description

Technical Field

[0001] This invention belongs to the field of silicone gel preparation technology, specifically an optimized method and system for preparing silicone gel for acne scar repair. Background Technology

[0002] The core of preparing silicone gel for acne scar repair lies in controlling the mixing reaction and dispersing effect of silicone raw materials. In existing technologies, silicone gel preparation often employs a fixed reactor temperature field, fixed stirring rate, and fixed dispersing phase addition sequence. The preparation process simply references the basic physicochemical properties of the silicone raw materials, without accurately predicting and controlling viscosity changes during mixing. Furthermore, the dispersibility parameters during the initiator addition stage largely rely on operator experience, without specifically adjusting them based on the crosslinking density of the target product.

[0003] In existing technical solutions, the correlation between the physicochemical properties of silicone raw materials and viscosity changes during the mixing process is not established, making it impossible to predict viscosity change trends in advance. This often results in the viscosity at the mixing termination point exceeding the reasonable range, leading to uneven gel texture and poor molding effect. Furthermore, the dispersibility parameters during the initiator addition stage lack scientific basis, and the blind adjustment of stirring rate and dispersed phase addition sequence easily causes uneven raw material dispersion, which in turn affects the stability of the gel's cross-linking structure. This makes it unsuitable for the high precision requirements of texture and performance in silicone gels used for acne scar repair, and makes it difficult to guarantee the consistency and stability of product quality. Summary of the Invention

[0004] This invention aims to solve at least one of the technical problems existing in the prior art;

[0005] Therefore, this invention proposes an optimized method for preparing silicone gel for acne scar repair, including: Obtain the physicochemical properties of the target batch of silicone raw materials; Based on the physicochemical properties of the silicone raw material, a preset rheological prediction model is invoked to calculate the expected viscosity change curve of the silicone raw material during the mixing process under the current reactor temperature field setting. The endpoint viscosity value of the mixing termination point is obtained from the expected viscosity change curve. The endpoint viscosity value is compared with the preset process window threshold. If the control parameter of the expected viscosity change curve at the mixing termination point exceeds the process window threshold, the original gradient temperature adjustment strategy is triggered, and the rheological prediction model is called to recalculate the expected viscosity change curve until the expected viscosity change curve falls within the process window threshold. The corresponding original gradient temperature adjustment strategy is then determined as the target gradient temperature adjustment strategy. Based on the target gradient temperature adjustment strategy and combined with the state inversion algorithm, the dispersion parameter that the silicone raw material needs to achieve in the initiator addition stage is determined according to the crosslinking density index of the target product. Based on the dispersion parameters, the stirring rate of the silicone raw material and the timing of the addition of the dispersed phase are dynamically adjusted to complete the preparation of the silicone gel.

[0006] Furthermore, based on the physicochemical properties of the silicone raw material, the step of calling a preset rheological prediction model to calculate the expected viscosity change curve of the silicone raw material during the mixing process under the current reactor temperature field setting includes: The basic viscosity value and thixotropic index are extracted from the physicochemical properties of the silicone raw material and used as the input feature vector of the rheological prediction model. Read the real-time temperature field distribution data of the reactor and discretize the temperature field distribution data into several temperature control intervals, each temperature control interval corresponding to a thermal conductivity coefficient; The input feature vector and the thermal conductivity coefficient are input into the rheological prediction model to obtain the shear stress response of the silicone raw material at different shear rates; The shear stress response is integrated over time and converted into an apparent viscosity value. The apparent viscosity values ​​are then connected in a time series to generate the expected viscosity change curve.

[0007] Further, the step of comparing the expected viscosity change curve with a preset process window threshold, and if the control parameters of the expected viscosity change curve at the mixing termination point exceed the process window threshold, triggers the original gradient temperature adjustment strategy and calls the rheological prediction model to recalculate the expected viscosity change curve until the expected viscosity change curve falls within the process window threshold, and the corresponding original gradient temperature adjustment strategy is determined as the target gradient temperature adjustment strategy, including: Step S31: Based on the mixing termination time, extract the endpoint viscosity value corresponding to the mixing termination point from the expected viscosity change curve, and the slope corresponding to the endpoint viscosity value, as the control parameter to be corrected; Step S32: Determine whether the control parameter to be corrected is within the allowable range of the parameter defined by the process window threshold. If the control parameter is greater than the upper limit of the allowable range, it is determined that the viscosity is too high. If it is less than the lower limit of the allowable range, it is determined that the viscosity is too low. Step S33: When it is determined that the viscosity is too high, the initial heating temperature of the reactor is increased based on the original gradient temperature adjustment strategy; When the viscosity is determined to be too low, the heating power of the reactor is reduced based on the original gradient temperature adjustment strategy. Step S34: Using the initial heating temperature and the heating power as heating parameters of the reactor, calculate a new thermal conductivity coefficient, input the new thermal conductivity coefficient into the rheological prediction model, re-execute the calculation, and obtain a new expected viscosity change curve; Step S35: Extract the endpoint viscosity value and its slope from the new expected viscosity change curve as the new control parameter to be corrected, and return to step S32. Steps S31 to S35 are executed repeatedly until the control parameter to be corrected falls within the allowable range of the parameter, and the corresponding original gradient temperature adjustment strategy is determined as the target gradient temperature adjustment strategy.

[0008] Furthermore, based on the target gradient temperature adjustment strategy and combined with the state inversion algorithm, the determination of the dispersion parameter that the silicone raw material needs to achieve in the initiator addition stage according to the crosslinking density index of the target product includes: Retrieve the standard crosslinking density value corresponding to the target product from the production database, and use the standard crosslinking density value as the target state quantity of the state inversion algorithm; Establish the crosslinking reaction kinetic equation for the silicone raw material; The standard crosslinking density value is input into the crosslinking reaction kinetic equation, and the known variables of the crosslinking reaction kinetic equation are immobilized. The output is a nonlinear solution problem with initiator concentration and dispersion as unknowns. The nonlinear problem is solved using a numerical iterative method, and the minimum mechanical work required for uniform dispersion of the initiator that satisfies the standard crosslinking density value is output. The dispersion parameter is then obtained based on the minimum mechanical work required for uniform dispersion of the initiator.

[0009] Further, the step of dynamically adjusting the stirring rate of the silicone raw material and the timing of the addition of the dispersed phase according to the dispersion parameter includes: By analyzing the dispersion parameters, we can obtain the mixing efficiency index and the homogenization degree index. Based on the mixing efficiency index, a step-by-step stirring rate growth scheme is set to adjust the stirring rate. The stirring rate growth scheme includes controlling the initial feeding process to run at a low rate to prevent material splashing, and switching to a high rate after the material melts to form a fluid vortex. Based on the homogenization index, the addition sequence of the dispersed phase is set to adjust the addition sequence of the dispersed phase. The addition sequence includes adding the difficult-to-disperse additives in batches and at intervals to the vortex center of the silicone raw material, and using fluid shear force to achieve crushing.

[0010] Furthermore, the optimized preparation process method for the silicone gel used for acne scar repair also includes online monitoring and parameter correction of the silicone gel curing process; the online monitoring and parameter correction of the silicone gel curing process includes: Near-infrared spectral data of silicone gel were continuously collected during the curing reaction of silicone gel; The collected near-infrared spectral data is input into a soft measurement model to estimate the actual degree of curing at the current moment in real time; The difference between the actual degree of curing and the theoretical degree of curing calculated by the rheological prediction model is used to obtain the curing deviation. If the curing deviation is not within the preset tolerance range, the actual degree of curing is input as a new target state quantity into the state inversion algorithm to obtain the abnormal factors that cause the deviation, and the cooling medium flow rate of the reactor is corrected.

[0011] Furthermore, the step of inputting the collected near-infrared spectral data into a soft measurement model to estimate the actual degree of curing at the current moment in real time includes: The near-infrared spectral data were preprocessed to remove water peak interference and baseline drift, and characteristic bands related to siloxane bond stretching vibrations were extracted. Several characteristic wavelength points sensitive to the curing reaction are selected from the characteristic wavelength bands, and the absorbance ratio of the characteristic wavelength points is calculated as the input variable of the soft measurement model. The soft measurement model is trained using near-infrared spectral data from historical batches and the actual curing degree obtained from offline testing, thereby establishing a mapping relationship between the input variables and the actual curing degree. During the online operation phase, the ratio to be identified is calculated based on the absorbance of the currently collected characteristic wavelength points, and the ratio to be identified is input into the trained soft measurement model to output the actual degree of curing.

[0012] Furthermore, if the curing deviation is not within the preset tolerance range, the actual degree of curing is input as a new target state quantity into the state inversion algorithm to obtain the abnormal factors causing the deviation, and the cooling medium flow rate of the reactor is corrected, including: When the detected curing deviation is positive, it indicates that the actual curing speed is slower than the theoretical value. Based on the inversion target of shortening the curing time, the abnormal factor causing the deviation is determined to be the temperature of the reaction system. When the detected curing deviation is negative, it indicates that the actual curing speed is faster than the theoretical value, and there is a risk of explosive polymerization. Based on the inversion target of reducing the reaction rate, the abnormal factors causing the deviation are determined to be the local concentration of the initiator or the increased heat dissipation capacity of the system. Based on the obtained reaction system temperature, the local concentration of the initiator, and the increased heat dissipation capacity of the system, the jacket heating power of the reactor or the addition rate of the dispersed phase is adjusted to correct the cooling medium flow rate of the reactor.

[0013] Furthermore, the present invention also includes a silicone gel preparation process optimization system for acne scar repair, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements the steps of the silicone gel preparation process optimization method for acne scar repair as described above.

[0014] Compared with the prior art, the beneficial effects of the present invention are: Based on the obtained physicochemical properties of the silicone raw material, including viscosity coefficient, refractive index, and volatile content, a pre-set rheological prediction model is invoked to calculate the expected viscosity change curve of the silicone raw material during the mixing process under the current reactor temperature setting. This expected viscosity change curve is compared with a preset process window threshold. If the mixing termination point exceeds the process window threshold, a gradient temperature adjustment strategy is triggered, and the expected viscosity change curve is recalculated until it falls within the process window threshold. This technology enables precise prediction of viscosity changes during mixing and achieves dynamic control of viscosity through gradient temperature adjustment. It avoids problems such as uneven gel texture and poor molding effect caused by viscosity exceeding a reasonable range, resulting in more thorough mixing and more stable reaction of the silicone raw material. Compared with conventional preparation methods with fixed temperature settings, it can effectively avoid insufficient or excessive reaction of raw materials due to unreasonable temperature, improving the controllability of the mixing process.

[0015] Based on the target gradient temperature adjustment strategy and combined with the state inversion algorithm, the required dispersion parameter of the silicone raw material during the initiator addition stage is determined according to the crosslinking density index of the target product. Based on this dispersion parameter, the stirring rate of the silicone raw material and the timing of the dispersed phase addition are dynamically adjusted. This technology achieves precise matching between the dispersion parameter and the crosslinking density index. By dynamically adjusting the stirring rate and the addition timing, the silicone raw material is more evenly dispersed during the initiator addition stage, avoiding problems such as uneven dispersion and poor crosslinking effect caused by conventional fixed stirring rates and addition timings. This results in a more stable crosslinked structure and more uniform texture in the prepared silicone gel, thereby improving the product's performance and meeting the needs of acne scar repair. Compared to conventional empirical dispersion control methods, this technology effectively improves the consistency of product quality. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the optimized preparation process of the silicone gel for acne scar repair described in this invention. Figure 2A flowchart for outputting dispersion parameters; Figure 3 The crosslinking density iterative convergence curve of the silicone gel; Figure 4 The curve showing the change in the ratio of absorbance to near-infrared characteristic wavelengths during the silicone gel curing process; Figure 5 A comparison chart of viscosity coefficient distribution between excellent and ordinary silicone gel preparation processes. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0018] See Figure 1 The physicochemical properties of the target batch of silicone raw materials are obtained, including viscosity coefficient, refractive index, and volatile content. Based on these properties, a pre-defined rheological prediction model is used to calculate the expected viscosity change curve of the silicone raw materials during the entire mixing process under the current reactor temperature setting. Subsequently, the endpoint viscosity value at the mixing termination point is obtained from the expected viscosity change curve. This endpoint viscosity value is compared with a preset process window threshold. If the endpoint viscosity value exceeds the process window threshold, an initial gradient temperature adjustment strategy is triggered, and the expected viscosity change curve is recalculated based on the adjusted temperature parameters. This process is repeated until the curve falls completely within the process window threshold, at which point the corresponding initial gradient temperature adjustment strategy is determined as the target gradient temperature adjustment strategy. Based on the target gradient temperature adjustment strategy and a state inversion algorithm, which determines the required dispersion parameters of the silicone raw materials during the initiator addition stage according to the crosslinking density value required by the target product, the stirring rate and the timing of the dispersed phase addition are dynamically adjusted according to the output dispersion parameters to complete the preparation of the final silicone gel product.

[0019] In one embodiment of the present invention, the basic viscosity and thixotropic index are extracted from the physicochemical properties of the silicone raw material. These two parameters constitute the input feature vector of the rheological prediction model. Real-time temperature field distribution data of the reactor is read and discretized into several independent temperature control intervals, each corresponding to a calculated thermal conductivity coefficient. The input feature vector and the thermal conductivity coefficients of each temperature control interval are input into the rheological prediction model. By solving a set of partial differential equations describing the behavior of non-Newtonian fluids under varying temperature fields, the shear stress response of the silicone raw material at different shear rates is simulated. The simulated shear stress response is integrated over time and converted into an apparent viscosity value. These apparent viscosity values ​​are then connected according to the time series to form a complete expected viscosity change curve.

[0020] In the specific implementation, the method involves a target batch of silicone raw materials, whose physicochemical properties, after testing, include a viscosity coefficient of 5500 mPa·s, a refractive index of 1.403, and a volatile content of 0.12%. The basic viscosity value of 5500 mPa·s and the thixotropic index of 0.85 are extracted from these physicochemical properties of the silicone raw materials. The basic viscosity value and the thixotropic index together form the input feature vector [5500, 0.85]. Real-time temperature field distribution data of the reactor are read. The reactor is divided into upper, middle, and lower regions. The real-time temperature field distribution data fed back by the temperature sensor are 75℃, 78℃, and 72℃, respectively. The real-time temperature field distribution data of the reactor is discretized into three corresponding temperature control intervals. The heat transfer coefficient is calculated for each temperature control interval using Fourier's law of heat conduction. The calculated heat transfer coefficients are 2.1 W / (m·K), 2.3 W / (m·K), and 1.9 W / (m·K), respectively. In practice, the input feature vector [5500, 0.85] and the thermal conductivity coefficients [2.1, 2.3, 1.9] for the three temperature control zones are input into a pre-defined rheological prediction model. The rheological prediction model is a mathematical model based on viscoelastic fluid dynamics, its core being the solution of a set of partial differential equations describing the flow behavior of silicone raw materials under non-isothermal, non-uniform shear fields. It can be understood that the equations of this rheological prediction model consider the inherent material properties represented by the input feature vector and the heat transfer boundary conditions represented by the thermal conductivity coefficients.

[0021] In some embodiments, the process of solving the partial differential equations is performed in numerical computation software, using the finite volume method to discretize the space and the implicit Euler method to advance the time. At each discrete time step, for each temperature control interval, the equations are solved to obtain the distribution of the shear stress response values ​​of the silicone raw material within that temperature control interval. The simulation process covers the complete time period from the start to the end of mixing, and records the silicone raw material at different shear rates (e.g., 10 s). -1 50s-1 100s -1 The corresponding shear stress response value is given. Optionally, the shear stress response value can be calculated and correlated using a formula of the following form:

[0022] in: This represents the shear stress response value at time t and spatial point (i,j). Indicates dependence on shear rate The apparent viscosity function of temperature T This represents the corresponding shear rate tensor component. Indicates the thixotropic index The time-dependent yield stress term. Apparent viscosity function. The specific form is determined by the parameterization of the basic viscosity value and thixotropic index in the input feature vector, and its temperature dependence is obtained by solving the thermal conductivity coefficient of each temperature control range through the coupling of the energy equation.

[0023] In practical implementation, the simulated time-varying shear stress response value will be used. Numerical integration is performed according to the time series. At each time point, the shear stress response value is converted to the corresponding shear rate. The conversion relationship is based on the definition of apparent viscosity; the shear stress response value is divided by a set reference shear rate to obtain the apparent viscosity value at that moment. It can be understood that the selection of the reference shear rate corresponds to the typical shear rate of the impeller tip in actual processes. In some embodiments, 300 apparent viscosity values ​​are recorded at 1-second intervals during the 300-second simulation period of the entire mixing process. These apparent viscosity values ​​are plotted on a coordinate system according to their corresponding time points. A smooth curve is used to connect all these data points sequentially, ultimately forming a curve showing the expected viscosity change, starting from the initial viscosity and decreasing over time before stabilizing. The expected viscosity change curve is output in graphical form, with the horizontal axis representing mixing time and the vertical axis representing apparent viscosity.

[0024] In one embodiment of the present invention, the endpoint viscosity value at the mixing termination time and the slope of the curve at that point are extracted from the calculated expected viscosity change curve. These two parameters are used as control parameters to be corrected. It is determined whether the control parameter to be corrected is within the allowable range defined by the process window threshold. If the control parameter is greater than the upper limit of the allowable range, the viscosity is determined to be too high; if it is less than the lower limit, the viscosity is determined to be too low. When the viscosity is determined to be too high, the original gradient temperature adjustment strategy will increase the initial heating temperature of the reactor and appropriately extend the holding time of the high-temperature zone, thereby reducing the apparent viscosity of the silicone raw material during the mixing process. When the viscosity is determined to be too low, the original gradient temperature adjustment strategy will reduce the heating power of the reactor and shorten the time of the high-temperature zone, using a relatively lower temperature to increase the internal frictional resistance between silicone raw material molecules. The initial heating temperature and heating power are used as the heating parameters of the reactor. After adjusting the heating parameters of the reactor, the calculated new thermal conductivity coefficient is input into the rheological prediction model, the model calculation is re-executed, a new expected viscosity change curve is obtained, and the control parameters are compared with the process window threshold again. This process is repeated until the control parameters finally fall into the parameter allowable range, and the corresponding original gradient temperature adjustment strategy is determined as the target gradient temperature adjustment strategy.

[0025] In specific implementation, the implementation method follows the previously calculated expected viscosity change curve. A specific expected viscosity change curve shows that the final viscosity value at the 300-second mixing termination time is 3200 mPa·s, and the instantaneous slope of the curve at this point is 8 mPa·s / s. The final viscosity value and the slope are used together as the control parameters to be corrected [3200, 8]. The preset process window threshold defines the parameter allowable range as follows: the final viscosity value range is 2500-2800 mPa·s, and the slope range is 0-5 mPa·s / s. It is determined whether the control parameters [3200, 8] are within the parameter allowable range. If the final viscosity value of 3200 mPa·s is greater than the upper limit of 2800 mPa·s, and the slope of 8 mPa·s / s is greater than the upper limit of 5 mPa·s / s, the system determines that the viscosity is too high and changes too rapidly. In some embodiments, when the viscosity is determined to be too high, the original gradient temperature adjustment strategy will execute the temperature parameter modification command. The original gradient temperature adjustment strategy increases the initial heating temperature of the reactor from 75°C to 80°C and extends the holding time of the high-temperature zone within the reactor from 100 seconds to 120 seconds. Increasing the initial heating temperature and extending the holding time of the high-temperature zone aims to accelerate the thermal motion of the silicone raw material molecular chains, thereby reducing the apparent viscosity of the silicone raw material during the mixing process. It can be understood that the initial heating temperature and the holding time of the high-temperature zone are the two core operating variables in the original gradient temperature adjustment strategy that directly affect the heat transfer process.

[0026] In practical implementation, when the viscosity is determined to be too low, the original gradient temperature adjustment strategy executes another set of temperature parameter modification instructions. For example, for a calculated expected viscosity change curve, if the endpoint viscosity value is 2200 mPa·s, which is lower than the lower limit of the parameter allowable range of 2500 mPa·s, then the viscosity is determined to be too low. The original gradient temperature adjustment strategy reduces the heating power of the reactor from 90% to 70% of the rated power and shortens the holding time of the high-temperature zone in the reactor from 100 seconds to 80 seconds. The operation of reducing the heating power of the reactor and shortening the high-temperature zone time aims to slow down the heating rate of the silicone raw material, increase the internal frictional resistance between silicone raw material molecules by utilizing a relatively lower temperature environment, thereby increasing the apparent viscosity. In some embodiments, after adjusting the heating parameters of the reactor, the system automatically recalculates the thermal conductivity coefficient of each discrete section of the reactor based on the new initial heating temperature, high-temperature zone holding time, and other parameters. The new thermal conductivity coefficient is input into the rheological prediction model as a new boundary condition for model solution. It can be understood that the input operation is a necessary data link connecting the original gradient temperature adjustment strategy and the rheological prediction model.

[0027] In practice, the rheological prediction model re-performs the simulation calculation based on the new thermal conductivity coefficient to obtain a new expected viscosity change curve. The control parameters for the mixing termination time are extracted again from this new expected viscosity change curve; for example, the new control parameters are [2700, 3]. The new control parameters [2700, 3] are then compared again with the parameter allowable range [2500-2800, 0-5] defined by the process window threshold. The endpoint viscosity value of 2700 mPa·s in the new control parameters falls between 2500-2800 mPa·s, and the slope of 3 mPa·s / s falls between 0-5 mPa·s / s. Therefore, the control parameters fall within the allowable range, and the cycle terminates. If the new control parameters still do not fall within the allowable range, the original gradient temperature adjustment strategy is triggered again, the temperature parameters are modified, and the "recalculation-comparison" cycle is repeated until the control parameters meet the requirements. The corresponding original gradient temperature adjustment strategy is then determined as the target gradient temperature adjustment strategy. Optionally, the adjustments to temperature and time in the original gradient temperature adjustment strategy can be quantified based on the degree to which the control parameters deviate from their allowable range. In one embodiment, the relationship between the adjustment amount ΔT and the endpoint viscosity deviation Δη can be initially estimated using the following formula:

[0028] in: This indicates the amount of initial heating temperature change that needs to be adjusted. It is a positive proportionality coefficient related to the thermal properties of silicone raw materials. It is the median viscosity within the allowable range of the process window threshold parameter. This is the endpoint viscosity value of the current expected viscosity change curve. It is a viscosity offset constant introduced to prevent the logarithmic parameter from becoming too small. When Greater than hour, A positive value indicates that the initial heating temperature needs to be increased; conversely, a negative value indicates that the initial heating temperature needs to be decreased. The adjustment amount for the high-temperature zone holding time is proportional to the slope deviation. Optionally, a maximum number of iterations can be set for the cyclic comparison process, for example, 10 times. When the maximum number of iterations is reached and the control parameters still do not fall within the allowable range, the system terminates the cycle and issues a process parameter abnormality alarm, prompting a check of the input silicone raw material physicochemical properties or the parameter settings of the rheological prediction model.

[0029] In one embodiment of the present invention, see [reference] Figure 2 The standard crosslinking density value corresponding to the target product is retrieved from the production database and used as the target state variable for the state inversion algorithm. A crosslinking reaction kinetic equation for the silicone raw material is established, with initiator concentration, dispersion, temperature, and reaction time as variables. Temperature and reaction time are provided by a target gradient temperature adjustment strategy. The standard crosslinking density value is input into the crosslinking reaction kinetic equation, and all known variables except for initiator concentration and dispersion are fixed, thus transforming the original kinetic equation into a nonlinear problem concerning initiator concentration and dispersion. This nonlinear problem is solved using a numerical iteration method to obtain the minimum mechanical work required for the initiator to be uniformly dispersed in the system under the premise of satisfying the standard crosslinking density value, and then the specific dispersion parameter is calculated. The obtained dispersion parameter is analyzed and decomposed into specific indicators of mixing efficiency and homogenization degree. Based on the requirements for mixing efficiency, a stepped stirring rate increase scheme is set. A low rate is used in the initial stage of feeding to prevent splashing, and a high rate is immediately switched after the material is completely melted to form a strong vortex in the reactor. Based on the required homogenization level, a dispersive phase addition sequence was established. This sequence involves adding the difficult-to-disperse auxiliary components in batches and intermittently to the center of the vortex formed by the silicone raw material, utilizing the shear force generated by the fluid to break them down and disperse them. After adjusting the stirring rate and the dispersive phase addition sequence, real-time torque data from the stirring system was collected. If the torque feedback data remained stable within the rated range, it was confirmed that the current physical operation met the dispersibility parameter requirements.

[0030] In specific implementation, the implementation method is carried out after the gradient temperature adjustment strategy is finally determined. The standard crosslinking density value corresponding to the target product "Acne Scar Repair Silicone Gel - Standard Type" is retrieved from the production database as 0.85. This standard crosslinking density value serves as the target state variable for the state inversion algorithm. A crosslinking reaction kinetic equation for the silicone raw material is established, using initiator concentration, dispersion, temperature, and reaction time as key variables. Temperature and reaction time are provided by the target gradient temperature adjustment strategy, specifically maintaining the reaction system temperature at 80°C and the reaction time during the initiator addition stage at 120 seconds. In some embodiments, the standard crosslinking density value of 0.85 is input into the crosslinking reaction kinetic equation, and all known variables except for initiator concentration and dispersion are fixed. These known variables include the target temperature of 80°C, the reaction time of 120 seconds, and the inherent reaction rate constant of the raw material. The original kinetic equation is transformed into a nonlinear problem of solving for the initiator concentration c and dispersion ψ. Mathematically, this involves finding a set of (c, ψ) values ​​such that, under fixed temperature and time conditions, the error between the theoretical crosslinking density value calculated by the equation and the target value of 0.85 is less than a set tolerance.

[0031] In practical implementation, the nonlinear problem is solved using a numerical iteration method, specifically the Newton-Raphson method. The initial guess for initiator concentration is set to 0.5 wt%, and the initial guess for dispersity is 0.6. These are input into the crosslinking reaction kinetic equation to calculate an initial predicted crosslinking density of 0.72. The difference between the predicted value of 0.72 and the target value of 0.85 is compared. Based on the difference and the Jacobian matrix information at the current position of the equation, the guessed initiator concentration and dispersity values ​​are adjusted simultaneously, and iterative calculations are performed. After several iterations, when the absolute error between the predicted crosslinking density value and the target value of 0.85 is less than 0.001, the iteration converges, yielding an initiator concentration solution of 0.65 wt% and a dispersity solution of 0.78 that satisfies the standard crosslinking density value. It can be understood that a dispersity solution of 0.78 represents the quantitative index corresponding to the uniform dispersion state that the initiator must achieve in the system. Optionally, a specific expression of the crosslinking reaction kinetic equation is as follows:

[0032] in: This represents the theoretical crosslinking density value at time t. It is the proportionality coefficient of the maximum possible crosslinking density. It is a frequency factor. Indicates the initiator concentration. It is the reaction order of the initiator concentration. Representation and Dispersion The relevant dispersion efficiency function, It is the reaction time. It is the activation energy of the reaction. It is the ideal gas constant. It is absolute temperature. In this embodiment, The target state variable is set to 0.85. It lasts for 120 seconds. It is 353.15K (80℃). , , , Given the material constants, the solution variables are: and implied in In The dispersion parameter is ultimately obtained by solving the problem. Value (0.78) and its corresponding The function value was converted into a specific index of mechanical work, and the minimum mechanical work was calculated to be 850J.

[0033] In some embodiments, the dispersion parameter 0.78 is analyzed to extract specific indicators for mixing efficiency and homogenization. The requirement for mixing efficiency is that the system viscosity needs to be reduced to a set value within 60 seconds, and the requirement for homogenization is that the volume fraction of agglomerates with a particle size greater than 50 micrometers needs to be less than 0.1%. Based on the requirement for mixing efficiency, a stepped stirring rate increase scheme is set: in the initial feeding stage (0-30 seconds), a low stirring rate of 200 rpm is used to prevent splashing; in the 30-120 seconds after the material melts, the stirring rate linearly increases to 800 rpm within 10 seconds, and is maintained at a high stirring rate of 800 rpm for the following 80 seconds to form a strong vortex in the reactor. It can be understood that the stirring rate increase scheme is executed by programming into the controller of the stirring motor.

[0034] In the specific implementation, based on the requirements for homogenization, the timing of adding the dispersed phase was planned. The dispersed phase includes a solid peroxide initiator and a liquid thickening resin. The plan is as follows: 40 seconds after the stirring speed reaches 800 rpm and a stable vortex is formed, 50% of the total mass of the peroxide initiator is added from the feed port to the center of the vortex formed by the silicone raw material; after a 20-second interval, at the 60th second, all the liquid thickening resin is added to the center of the vortex at once; after another 20-second interval, at the 80th second, the remaining 50% of the peroxide initiator is added. The high shear force generated by the fluid at the center of the vortex is used to break up the difficult-to-disperse solid particles and rapidly mix the liquid material. After adjusting the stirring speed and the timing of adding the dispersed phase, real-time torque feedback data is collected from the stirring motor controller. The real-time torque feedback data shows that during the high-speed stirring stage, the torque value is stable within the range of 85-95 N·m, while the rated range is 80-100 N·m. The torque feedback data is stable within the rated range, confirming that the current physical operation meets the requirements for the dispersion parameters.

[0035] See Figure 3 In the iterative solution of the crosslinking density value of silicone gel, the convergence trend of the number of iterations and the calculated crosslinking density value intuitively reflects the optimization efficiency of the state inversion algorithm. In the figure, the solid line represents the change of the calculated crosslinking density value with the number of iterations, and the dashed line is the preset target crosslinking density (0.85). Starting from the initial iteration value of 0.72, the algorithm adjusts the initiator concentration and dispersion simultaneously to make the calculated crosslinking density value gradually approach the target crosslinking density: iterations 0 to 3: the calculated crosslinking density value increases rapidly, from 0.72 to 0.81, reflecting the efficient convergence characteristics of the Newton-Raphson method in nonlinear solutions; iterations 4 to 6: the growth rate of the calculated crosslinking density value slows down, from 0.84 to 0.85, and the error gradually shrinks to the set tolerance (<0.001); iterations 6 to 7: the calculated crosslinking density value stabilizes at around 0.85, indicating that the iterative process has converged and the optimal combination of process parameters that meets the standard crosslinking density value (i.e., crosslinking density index) of the target product has been obtained. The solid line not only verifies the effectiveness of the state inversion algorithm in crosslinking density control, but also provides a visual basis for the iterative optimization of process parameters: by monitoring the changing trend of crosslinking density during the iteration process, the convergence state of the algorithm can be judged in time, avoiding excessive iteration or premature termination, and ultimately ensuring the precise control of crosslinking density of silicone gel products.

[0036] In one embodiment of the present invention, during the curing reaction of the silicone gel, near-infrared spectral data of the system are continuously acquired using a near-infrared spectral probe. This data reflects the transformation of chemical bonds within the system. The acquired near-infrared spectral data is preprocessed to remove interference from water peaks and baseline drift, and characteristic bands related to the stretching vibrations of siloxane bonds are extracted. Several characteristic wavelengths sensitive to the curing reaction are selected from these characteristic bands, and the absorbance ratios of these characteristic wavelengths are calculated as input variables for the soft measurement model. The soft measurement model is trained using near-infrared spectral data from historical batches and actual curing degree data obtained through offline testing to establish a mapping relationship between the input variables and the actual curing degree. During the online operation phase, the currently acquired input variables are input into the trained soft measurement model, which directly outputs the actual curing degree corresponding to the current moment. The difference between the calculated actual curing degree and the theoretical curing degree derived from the rheological prediction model is calculated to obtain the curing deviation. If the curing deviation exceeds the preset tolerance range, the actual degree of curing is input into the state inversion algorithm as a new target state variable to identify the abnormal factors causing the deviation and correct the cooling medium flow rate of the reactor. When a positive curing deviation is detected, it indicates that the actual curing rate is slower than the theoretical value. Based on the inversion target of shortening the curing time, the required increase in the reaction system temperature is determined. When a negative curing deviation is detected, it indicates that the actual curing rate is faster than the theoretical value, posing a risk of explosive polymerization. Based on the inversion target of reducing the reaction rate, the required reduction in the local initiator concentration or the need to increase the system's heat dissipation capacity is determined. Based on the obtained reaction system temperature or local initiator concentration, the heating power of the reactor jacket or the rate of dispersive phase addition is adjusted to suppress further expansion of the curing deviation.

[0037] In the specific implementation, the method involves online monitoring of the silicone gel curing process. During the 1800 seconds of the curing reaction, near-infrared spectral data of the system are continuously acquired using a near-infrared fiber optic probe installed on the sight glass of the reactor. The sampling interval is 30 seconds, and a total of 60 sets of spectra are acquired. The near-infrared spectral data reflects the transformation of chemical bonds such as siloxane bonds (Si-O-Si) and silanol bonds (Si-OH) within the system. Each set of near-infrared spectral data is preprocessed. The preprocessing includes using standard normal variable transformation (SNV) to eliminate optical path differences, using first derivative combined with Savitzky-Golay smoothing to filter out interference and baseline drift of water peaks near 1450 nm and 1900 nm, and extracting the 1100-1250 nm characteristic band related to the stretching vibration of siloxane bonds from the processed spectrum. The difference between the calculated actual degree of curing and the theoretical degree of curing at the same time calculated by the rheological prediction model is calculated to obtain the curing deviation. For example, at 900 seconds, the actual degree of curing is 0.46, and the theoretical degree of curing is 0.50, so the curing deviation is -0.04. The preset tolerance range is ±0.02. Since the absolute value of the curing deviation -0.04 is greater than 0.02, exceeding the preset tolerance range, the system triggers correction logic. Using a state inversion algorithm, with the current actual degree of curing of 0.46 as the new target state variable, the abnormal factors causing the deviation are output, and the cooling water flow rate of the reactor jacket is corrected in real time based on the output results. Optional, some data from online monitoring and calculation are shown in Table 1.

[0038] Table 1: Online Monitoring Data of Silicone Gel Curing Process

[0039] In some embodiments, when a positive curing deviation is detected, it indicates that the actual curing rate is slower than the theoretical rate. For example, at a certain time point, the actual degree of curing is 0.35, the theoretical degree of curing is 0.30, and the curing deviation is +0.05. In this case, the state inversion algorithm sets an inversion target to shorten the curing time, reducing the remaining curing time by 10%, and based on the corrected curing time target, obtains the required increase in the reaction system temperature. The state inversion algorithm solves the inverse problem involving the heat transfer equation to determine that the reaction system temperature needs to be increased from the current 80°C to 83°C. In specific implementations, when a negative curing deviation is detected, it indicates that the actual curing rate is faster than the theoretical rate, posing a risk of explosive polymerization. As shown in the data at the 900th second in Table 1, the curing deviation is -0.04. In this case, the state inversion algorithm sets an inversion target to reduce the reaction rate, reducing the target average reaction rate by 15%, and based on this target, obtains the required reduction in the local initiator concentration or the need to increase the system's heat dissipation capacity. It can be understood that the state inversion algorithm performs inversion calculations by coupling the material conservation and energy conservation equations. In the state inversion algorithm, a relationship between the actual degree of solidification and the operational variable can be expressed as:

[0040] in: This represents the actual degree of cure estimated online by the soft measurement model. This indicates the initial degree of cure at the start of the curing reaction. It is a kinetic coefficient that integrates the frequency factor, reaction order, and reference concentration. It is the apparent activation energy of the curing reaction. It is the universal gas constant. It is over time The temperature of the changing reaction system It is over time The relative concentration of the initiator changes. These are time-varying coefficients characterizing the mixing effect. All factors in the integral term on the right-hand side of the formula are dimensionless, and the integral result is consistent with... Adding them together yields a dimensionless number. , with consistent dimensions. When When the value deviates from the theoretical value, the algorithm adjusts... or The preset function form makes the calculated value fit the actual measured value. This allows us to obtain the temperature or concentration parameters that need to be adjusted.

[0041] It is understandable that the system executes parameter adjustment commands based on the obtained reaction system temperature or local initiator concentration. For example, when the output indicates a need to reduce the local initiator concentration by 5%, the adjustment command increases the addition rate of the dispersed phase, injecting the subsequently planned diluent phase in advance to reduce the local concentration. When the output indicates a need to increase the system's heat dissipation capacity, the adjustment command immediately increases the cooling water flow rate of the reactor jacket from 5 m³ / h to 6.5 m³ / h to suppress further expansion of the curing deviation.

[0042] See Figure 4 In the online near-infrared spectroscopy monitoring of the silicone gel curing process, the absorbance ratio at characteristic wavelengths exhibits a regular decreasing trend with curing time. This trend directly reflects the degree of conversion between siloxane bonds and silanol groups within the system and is the core input variable for the soft measurement model to estimate the actual degree of curing. Specifically, the three curves correspond to absorbance ratios at different characteristic wavelengths: R1 (1100nm / 1200nm): Initially the highest value (1.50 at 600s), it continuously decreases to 0.95 within 1800s, exhibiting the largest overall decrease and the strongest sensitivity to the curing reaction, making it a suitable main characteristic variable for degree of curing estimation. R2 (1150nm / 1250nm): Initially 1.25, it gradually decreases to 0.75 as curing progresses, with a decreasing trend highly synchronized with R1, making it a suitable auxiliary characteristic variable to improve model robustness. R3 (1125nm / 1225nm): The initial value was 1.10, and it eventually decreased to 0.65, with a decay rate consistent with the previous two, further verifying the strong correlation between the absorbance ratio of the characteristic wavelength band and the curing reaction process. From a kinetic perspective, the monotonic decay of the absorbance ratio originates from the transformation of silanol groups (Si-OH) to siloxane bonds (Si-O-Si) during the curing process. The absorbance difference at the characteristic wavelength points decreases with the chemical bond transformation, thus the ratio decreases over time. This trend completely corresponds to the change in the actual degree of curing from 0.28 (600s) to 0.92 (1800s) in Table 1, proving that the near-infrared spectral characteristic ratio can effectively characterize the degree of curing of silicone gel, providing a reliable online monitoring basis for subsequent curing deviation calculation and process parameter correction.

[0043] In one embodiment of the present invention, after each batch of silicone gel preparation is completed, the physicochemical properties of the silicone raw materials used in this batch, the final determined gradient temperature adjustment strategy, the calculated dispersibility parameters, and the actual quality test results of the final product are packaged and stored as a process case. Periodically, process cases that perform well in quality test results are selected from all stored process cases, and the key process parameter combinations common to these excellent process cases are extracted. Machine learning algorithms are used to explore the potential relationship between these key process parameter combinations and the final product quality, thereby generating a new and more optimized set of process rules. When the next batch of production tasks is initiated, this new set of process rules is used as initial constraints and embedded into the calculation process of the rheological prediction model and the state inversion algorithm to achieve automatic accumulation and evolution of process knowledge.

[0044] In specific implementation, the method involves closed-loop optimization of preparation data from multiple batches. After the preparation of the first batch of silicone gel "acne scar repair silicone gel - standard type", the physicochemical indicators of the silicone raw materials, the final determined gradient temperature adjustment strategy, the calculated dispersibility parameters, and the actual quality test results of the final product are packaged and stored as a process case. The process case is assigned a unique identifier "Case_001" and stored in the process database. In some embodiments, after the preparation of the 100th batch of silicone gel, 100 independent process cases have been accumulated in the process database. The system periodically initiates optimization analysis to screen out those process cases that perform well in quality test results from all 100 stored process cases. The screening criteria are set as follows: transmittance > 90%, adhesion between 1.8-2.5N, and crosslinking density between 0.82-0.88. After screening, 15 process cases were identified as having excellent performance. The key process parameter combinations common to these 15 process cases were extracted. The key process parameter combinations include: the viscosity coefficient range of silicone raw materials, the initial heating temperature range in the gradient temperature adjustment strategy, and the dispersion parameter range.

[0045] In practical implementation, machine learning algorithms are used to explore the potential relationship between key process parameter combinations and product quality. The machine learning algorithm used is the random forest regression algorithm. Before building the model, all process parameters are preprocessed using dimensionless standardization, transforming the values ​​of viscosity coefficient, initial heating temperature, and dispersion parameter to a distribution with a mean of 0 and a standard deviation of 1. The preprocessed process parameters are used as feature variables, and the product quality index is used as the target variable to train the random forest regression model. The trained model reveals the nonlinear relationship between the feature variables and the target variable and generates a new set of process rules, which are expressed in the form of "IF-THEN", for example: "IF: standardized viscosity coefficient between -0.5 and +0.5 AND standardized initial heating temperature between -0.8 and +0.8, THEN: the standardized dispersion parameter target value is set to -0.3 to +0.3, and the expected transmittance is >91%". Optionally, the similarity between process cases and the summarization of key parameters can be analyzed using a quantitative formula. Before calculation, all process parameters have undergone the aforementioned dimensionless standardization process.

[0046] in: This represents the overall similarity score between the i-th process case and the j-th process case. It refers to the number of key process parameters considered. It is the weighting coefficient assigned to the k-th process parameter. and Let represent the specific values ​​of the k-th process parameter in the i-th and j-th process cases after dimensionless standardization, respectively. This formula is used to quantify the degree of distribution of each case in the process parameter space before selecting excellent cases. Cases in high-similarity clusters are more likely to be jointly selected for extracting key process parameter combinations.

[0047] It is understandable that the generated new process rules are a pattern summary of historical successful experiences by the machine learning algorithm. When the next batch of production tasks is initiated, the system uses this set of new process rules as constraints for the initial calculation process, embedding them into the rheological prediction model and the state inversion algorithm. The new process rules provide prior ranges for "the standardized initial heating temperature is between -0.8 and +0.8" and "the standardized dispersion parameter target value is between -0.3 and +0.3". Before calling the rheological prediction model and the state inversion algorithm, these standardized constraints are converted back into specific ranges in the original parameter space, serving as the initial search interval for model calculation, thereby narrowing the solution space. In some embodiments, the embedding of the new process rules changes the initial behavior of the model. In the calculation of the 101st batch, the rheological prediction model suggests an initial heating temperature of 80.5°C based on the initial constraints, and the state inversion algorithm suggests a dispersion parameter target of 0.79 based on the initial constraints. After the entire preparation process is completed, the new data of the 101st batch will be packaged again into a new process case "Case_101" and stored in the database. Understandably, the addition of new cases provides new data sources for the next round of periodic analysis, thereby enabling the automatic accumulation and evolution of process knowledge. The closed-loop optimization process allows the system to continuously self-correct and improve its internal process parameter recommendation logic based on historical production data.

[0048] See Figure 5 In a comparative analysis of superior and ordinary cases of closed-loop optimization in multiple batches of silicone gel preparation processes, the distribution characteristics of viscosity coefficients can intuitively reflect the differences in process parameters between the two types of cases. Specifically, the viscosity coefficients were visualized using box plots, grouped by process case type: the median viscosity coefficient of ordinary cases was approximately 490, distributed between 340 and 650, with an abnormally low value below 300; the median viscosity coefficient of superior cases was approximately 475, distributed between 360 and 620, with a more concentrated distribution and no obvious outliers. Statistically, the core distribution ranges of viscosity coefficients overlap between the two types of cases, but the box plot of superior cases is narrower and the interquartile range is smaller, indicating better stability of its process parameters; the viscosity coefficient of ordinary cases fluctuates more widely and has extreme low values, reflecting stronger dispersion in the control of raw material physicochemical indicators or process execution. This distribution difference provides an intuitive basis for subsequent machine learning to mine key process parameters: the narrower viscosity coefficient distribution range in excellent cases can be used as a constraint to embed into rheological prediction models and state inversion algorithms to improve the stability of the silicone gel preparation process and the consistency of product quality.

[0049] The above embodiments are only used to illustrate the technical methods 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 methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An optimized method for preparing silicone gel for acne scar repair, characterized in that, include: Obtain the physicochemical properties of the target batch of silicone raw materials; Based on the physicochemical properties of the silicone raw material, a preset rheological prediction model is invoked to calculate the expected viscosity change curve of the silicone raw material during the mixing process under the current reactor temperature field setting. The endpoint viscosity value of the mixing termination point is obtained from the expected viscosity change curve. The endpoint viscosity value is compared with the preset process window threshold. If the endpoint viscosity value exceeds the process window threshold, the original gradient temperature adjustment strategy is triggered, and the rheological prediction model is called to recalculate the expected viscosity change curve until the endpoint viscosity value of the mixing termination point in the expected viscosity change curve falls within the process window threshold. Then, the corresponding original gradient temperature adjustment strategy is determined as the target gradient temperature adjustment strategy. Based on the target gradient temperature adjustment strategy and combined with the state inversion algorithm, the dispersion parameter that the silicone raw material needs to achieve in the initiator addition stage is determined according to the crosslinking density index of the target product. Based on the dispersion parameters, the stirring rate of the silicone raw material and the timing of the addition of the dispersed phase are dynamically adjusted to complete the preparation of the silicone gel.

2. The optimized preparation process of the silicone gel for acne scar repair as described in claim 1, characterized in that, Based on the physicochemical properties of the silicone raw material, a preset rheological prediction model is invoked to calculate the expected viscosity change curve of the silicone raw material during the mixing process under the current reactor temperature field setting, including: The basic viscosity value and thixotropic index are extracted from the physicochemical properties of the silicone raw material and used as the input feature vector of the rheological prediction model. Read the real-time temperature field distribution data of the reactor and discretize the read real-time temperature field distribution data into several temperature control intervals, each temperature control interval corresponding to a thermal conductivity coefficient; The input feature vector and the thermal conductivity coefficient are input into the rheological prediction model to obtain the shear stress response values ​​of the silicone raw material at different shear rates; The shear stress response value is integrated over time and converted into an apparent viscosity value. The apparent viscosity values ​​are then connected in a time series to generate the expected viscosity change curve.

3. The optimized preparation process of the silicone gel for acne scar repair as described in claim 2, characterized in that, The process involves obtaining the endpoint viscosity value of the mixing termination point from the expected viscosity change curve, comparing the endpoint viscosity value with a preset process window threshold, and if the endpoint viscosity value exceeds the process window threshold, triggering the original gradient temperature adjustment strategy and recalculating the expected viscosity change curve using the rheological prediction model until the endpoint viscosity value of the mixing termination point in the expected viscosity change curve falls within the process window threshold. Then, the corresponding original gradient temperature adjustment strategy is determined as the target gradient temperature adjustment strategy, including: Step S31: Based on the mixing termination time, extract the endpoint viscosity value corresponding to the mixing termination point from the expected viscosity change curve, and the slope corresponding to the endpoint viscosity value, as the control parameter to be corrected; Step S32: Determine whether the control parameter to be corrected is within the allowable range of the parameter defined by the process window threshold. If the control parameter is greater than the upper limit of the allowable range, it is determined that the viscosity is too high. If it is less than the lower limit of the allowable range, it is determined that the viscosity is too low. Step S33: When it is determined that the viscosity is too high, the initial heating temperature of the reactor is increased based on the original gradient temperature adjustment strategy; When the viscosity is determined to be too low, the heating power of the reactor is reduced based on the original gradient temperature adjustment strategy. Step S34: Using the initial heating temperature and the heating power as heating parameters of the reactor, calculate a new thermal conductivity coefficient, input the new thermal conductivity coefficient into the rheological prediction model, re-execute the calculation, and obtain a new expected viscosity change curve; Step S35: Extract the endpoint viscosity value and its slope from the new expected viscosity change curve as the new control parameter to be corrected, and return to step S32. Steps S31 to S35 are executed repeatedly until the control parameter to be corrected falls within the allowable range of the parameter, and the corresponding original gradient temperature adjustment strategy is determined as the target gradient temperature adjustment strategy.

4. The optimized preparation process of the silicone gel for acne scar repair as described in claim 3, characterized in that, The method based on the target gradient temperature adjustment strategy, combined with the state inversion algorithm, determines the dispersion parameters that the silicone raw material needs to achieve during the initiator addition stage according to the crosslinking density index of the target product, including: The standard crosslinking density value corresponding to the target product is retrieved from the production database as the crosslinking density index, and the crosslinking density index is used as the target state quantity of the state inversion algorithm. Establish the crosslinking reaction kinetic equation for the silicone raw material; The standard crosslinking density value is input into the crosslinking reaction kinetic equation, and the crosslinking reaction kinetic equation is subjected to known variable immobilization processing to output a nonlinear solution problem with initiator concentration and dispersion as unknowns. The nonlinear problem is solved using a numerical iterative method, and the minimum mechanical work required for uniform dispersion of the initiator that satisfies the standard crosslinking density value is output. The dispersion parameter is then obtained based on the minimum mechanical work required for uniform dispersion of the initiator.

5. The optimized preparation process of the silicone gel for acne scar repair as described in claim 4, characterized in that, The step of dynamically adjusting the stirring rate of the silicone raw material and the timing of the addition of the dispersed phase according to the dispersion parameter includes: By analyzing the dispersion parameters, we can obtain the mixing efficiency index and the homogenization degree index. Based on the mixing efficiency index, a step-by-step stirring rate growth scheme is set to adjust the stirring rate. The stirring rate growth scheme includes controlling the initial feeding process to run at a low rate to prevent material splashing, and switching to a high rate after the material melts to form a fluid vortex. Based on the homogenization index, the addition sequence of the dispersed phase is set to adjust the addition sequence of the dispersed phase. The addition sequence includes adding the difficult-to-disperse additives in batches and at intervals to the vortex center of the silicone raw material, and using fluid shear force to achieve crushing.

6. The optimized preparation process of the silicone gel for acne scar repair as described in claim 5, characterized in that, The optimized preparation process method for the silicone gel used for acne scar repair further includes online monitoring and parameter correction of the silicone gel curing process; the online monitoring and parameter correction of the silicone gel curing process includes: Near-infrared spectral data of the silicone gel were continuously collected during the curing reaction. The collected near-infrared spectral data is input into the soft measurement model to estimate the actual degree of curing at the current moment in real time; The difference between the actual degree of curing and the theoretical degree of curing calculated by the rheological prediction model is used to obtain the curing deviation. If the curing deviation is not within the preset tolerance range, the actual degree of curing is input as a new target state quantity into the state inversion algorithm to obtain the abnormal factors causing the deviation, and the cooling medium flow rate of the reactor is corrected.

7. The optimized preparation process of the silicone gel for acne scar repair as described in claim 6, characterized in that, The step of inputting the collected near-infrared spectral data into the soft measurement model to estimate the actual degree of curing at the current moment in real time includes: The near-infrared spectral data were preprocessed to remove water peak interference and baseline drift, and characteristic bands related to siloxane bond stretching vibrations were extracted. Several characteristic wavelengths sensitive to the curing reaction are selected from the characteristic wavelength bands, and the absorbance ratio of the characteristic wavelengths is calculated as the input variable of the soft measurement model. The soft measurement model is trained using near-infrared spectral data from historical batches and the actual curing degree obtained from offline testing, thereby establishing a mapping relationship between the input variables and the actual curing degree. During the online operation phase, the ratio to be identified is calculated based on the absorbance of the currently collected characteristic wavelength points, and the ratio to be identified is input into the trained soft measurement model to output the actual degree of curing at the current moment.

8. The optimized preparation process of the silicone gel for acne scar repair as described in claim 6, characterized in that, If the curing deviation is not within the preset tolerance range, the actual degree of curing is input as a new target state quantity into the state inversion algorithm to obtain the abnormal factors causing the deviation, and the cooling medium flow rate of the reactor is corrected, including: When the detected curing deviation is positive, it indicates that the actual curing speed is slower than the theoretical value. Based on the inversion target of shortening the curing time, the abnormal factor causing the deviation is determined to be the temperature of the reaction system. When the detected curing deviation is negative, it indicates that the actual curing speed is faster than the theoretical value, and there is a risk of explosive polymerization. Based on the inversion target of reducing the reaction rate, the abnormal factors causing the deviation are determined to be the local concentration of the initiator or the increased heat dissipation capacity of the system. Based on the obtained reaction system temperature, the local concentration of the initiator, and the increased heat dissipation capacity of the system, the jacket heating power of the reactor or the addition rate of the dispersed phase is adjusted to correct the cooling medium flow rate of the reactor.

9. A silicone gel preparation process optimization system for acne scar repair, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the optimized method for preparing silicone gel for acne scar repair as described in any one of claims 1 to 8.

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