Intelligent preparation process control method and system for compound lidocaine nanoparticle drug delivery

Abstract

The present invention relates to the technical field of intelligent process control, and discloses an intelligent preparation process control method and system for a compound lidocaine nano-drug delivery. The method comprises: performing coupled analysis on reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power, and reaction time, and calculating drug loading saturation of the compound lidocaine drug delivery; determining whether the current preparation moment is in a drug loading efficiency optimization stage, a particle size distribution control stage, or a comprehensive fine-tuning stage; when in the drug loading efficiency optimization stage, adjusting temperature control parameters and pH control parameters; when in the particle size distribution control stage, adjusting stirring speed control parameters and ultrasonic power control parameters; and when in the comprehensive fine-tuning stage, calculating a variable adjustment sequence and a corresponding variable adjustment amplitude. The present invention realizes predictive intelligent control of the compound lidocaine nano-drug delivery preparation process, thereby improving product quality stability and preparation process controllability.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent process control, and in particular to an intelligent preparation process control method and system for compound lidocaine nano-drug delivery. Background Art

[0002] Currently, the preparation process for compound lidocaine nanoparticles generally utilizes a static parameter setting model, which maintains process parameters unchanged throughout the entire process. This control approach is unable to adapt to the dynamic changes in key indicators such as drug saturation and particle size distribution during the drug delivery process. Existing technologies lack the ability to adjust control strategies in real time based on the preparation process, making it impossible to implement targeted control algorithms at different stages, such as optimizing drug delivery efficiency and regulating particle size distribution. This results in low product quality stability. Summary of the Invention

[0003] The present invention provides an intelligent preparation process control method and system for compound lidocaine nanoparticles, which realizes predictive intelligent control of the preparation process of compound lidocaine nanoparticles, thereby improving product quality stability and controllability of the preparation process.

[0004] In a first aspect, the present invention provides an intelligent preparation process control method for a compound lidocaine nanoparticle drug delivery system, the intelligent preparation process control method for a compound lidocaine nanoparticle drug delivery system comprising:

[0005] The reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time were coupled and analyzed to obtain a parameter variable coupling model.

[0006] Calculating the drug loading saturation of the compound lidocaine drug loading according to the parameter variable coupling model, and determining whether the current preparation moment is in the drug loading efficiency optimization stage, the particle size distribution control stage, or the comprehensive fine-tuning stage;

[0007] When in the drug loading efficiency optimization stage, temperature gradient adjustment and pH buffer adjustment are performed to obtain temperature control parameters and pH control parameters;

[0008] When in the particle size distribution control stage, particle size distribution control is performed to obtain stirring speed control parameters and ultrasonic power control parameters;

[0009] When in the comprehensive fine-tuning stage, variable partial derivative calculation and synchronous fine-tuning are performed to obtain the variable adjustment sequence and the corresponding variable adjustment amplitude.

[0010] In combination with the first aspect, in a first implementation of the first aspect of the present invention, the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time are coupled and analyzed to obtain a parameter variable coupling model, including:

[0011] The pH environment value was corrected for protonation state based on the lidocaine acid dissociation constant to obtain the protonation correction coefficient. At the same time, the reaction temperature value was calculated by Arrhenius diffusion to obtain the molecular diffusion coefficient.

[0012] Calculating a composite diffusion parameter based on the protonation correction coefficient and the molecular diffusion coefficient, and calculating the carrier crosslinking degree based on the carrier material concentration and the reaction time values ​​to obtain a carrier crosslinking degree parameter;

[0013] Establishing a coupled differential equation group of drug release behavior and carrier binding behavior based on the composite diffusion parameter and the carrier cross-linking degree parameter;

[0014] Based on the coupled differential equations, an orthogonal test is performed on the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time to obtain a parameter variable coupling model.

[0015] In combination with the first aspect, in a second implementation of the first aspect of the present invention, an orthogonal test is performed on the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time based on the coupled differential equations to obtain a parameter variable coupling model, including:

[0016] The three levels corresponding to the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time were set respectively, and the orthogonal experimental design was performed to obtain the orthogonal experimental design table;

[0017] The coupled differential equations are numerically solved according to the parameter values ​​of each group of experimental conditions in the orthogonal experimental design table to obtain the theoretical average particle size and theoretical encapsulation efficiency of the compound lidocaine nanoparticles under each group of experimental conditions;

[0018] Based on the theoretical average particle size and the theoretical encapsulation efficiency, variance analysis and range analysis were performed to obtain a first coupling effect coefficient between the reaction temperature and the lidocaine concentration and a second coupling effect coefficient between the pH value and the carrier material concentration;

[0019] The coefficients of the coupled differential equation group are adjusted according to the first coupling effect coefficient and the second coupling effect coefficient to obtain a parameter variable coupling model.

[0020] In combination with the first aspect, in a third implementation of the first aspect of the present invention, calculating the drug loading saturation of the compound lidocaine drug loading according to the parameter variable coupling model, and determining whether the current preparation time is in the drug loading efficiency optimization stage, the particle size distribution control stage, or the comprehensive fine-tuning stage, includes:

[0021] The drug loading amount and the maximum drug loading capacity at the current preparation moment are calculated based on the parameter variable coupling model to obtain the instantaneous drug loading amount and the theoretical maximum drug loading amount of the compound lidocaine drug loading;

[0022] Calculating a drug loading saturation ratio based on the instantaneous drug loading and the theoretical maximum drug loading to obtain a drug loading saturation;

[0023] A numerical interval judgment is performed on the drug loading saturation. When the drug loading saturation is less than the first target value, it is determined that the preparation process at the current preparation moment is in the drug loading efficiency optimization stage. When the drug loading saturation is greater than the second target value, it is determined that the preparation process at the current preparation moment is in the particle size distribution control stage. When the drug loading saturation is between the first target value and the second target value, it is determined that the preparation process at the current preparation moment is in the comprehensive fine-tuning stage.

[0024] In combination with the first aspect, in a fourth implementation of the first aspect of the present invention, the calculation of the drug loading amount and the maximum drug loading capacity at the current preparation moment based on the parameter variable coupling model to obtain the instantaneous drug loading amount value and the theoretical maximum drug loading amount value of the compound lidocaine drug loading includes:

[0025] The reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time at the current preparation moment are collected to obtain the process parameter combination at the current preparation moment;

[0026] Based on the process parameter combination, the protonation correction coefficient and the composite diffusion parameter in the parameter variable coupling model are dynamically updated and calculated to obtain the lidocaine protonation state parameter and the temperature-corrected molecular diffusion rate parameter under the current pH environment;

[0027] Solving the coupled differential equations according to the lidocaine protonation state parameter and the molecular diffusion rate parameter to obtain the instantaneous drug loading corresponding to the number of compound lidocaine molecules bound to the carrier at the current preparation moment;

[0028] The saturation upper limit of the maximum binding capacity of the carrier was calculated based on the carrier cross-linking degree parameters and the current carrier material concentration to obtain the theoretical maximum drug loading capacity.

[0029] In combination with the first aspect, in a fifth implementation of the first aspect of the present invention, when in the drug loading efficiency optimization stage, performing temperature gradient adjustment and pH buffer adjustment to obtain temperature control parameters and pH control parameters includes:

[0030] When in the drug loading efficiency optimization stage, the drug loading efficiency deviation is calculated based on the current drug loading saturation and the target drug loading saturation to obtain the drug loading efficiency deviation value. At the same time, the encapsulation deviation is calculated based on the current encapsulation efficiency and the target encapsulation efficiency to obtain the encapsulation efficiency deviation value;

[0031] Calculating the temperature gradient increment according to the drug loading efficiency deviation value and the preset temperature adjustment coefficient to obtain a temperature adjustment increment parameter, and performing temperature control on the current reaction temperature and the temperature adjustment increment parameter to obtain a temperature control parameter;

[0032] A pH buffer increment calculation is performed based on the encapsulation efficiency deviation value and a preset pH adjustment coefficient to obtain a pH adjustment increment parameter, and pH control is performed on the current pH value and the pH adjustment increment parameter to obtain a pH control parameter.

[0033] In combination with the first aspect, in a sixth implementation of the first aspect of the present invention, when in the particle size distribution control stage, performing particle size distribution control to obtain stirring speed control parameters and ultrasonic power control parameters includes:

[0034] When in the particle size distribution control stage, the particle size deviation of the current average particle size of the compound lidocaine nanoparticles and the target average particle size is calculated to obtain the average particle size deviation value, and the dispersion deviation of the current polydispersity index and the target polydispersity index is calculated to obtain the polydispersity index deviation value;

[0035] Calculating a stirring shear force increment according to the average particle size deviation value and a preset shear force adjustment coefficient to obtain a stirring shear force adjustment parameter, and controlling the stirring speed based on the stirring shear force adjustment parameter and the current stirring speed to obtain a stirring speed control parameter;

[0036] An ultrasonic sound field increment is calculated based on the polydispersity index deviation value and the preset sound field intensity adjustment coefficient to obtain an ultrasonic sound field intensity adjustment parameter, and ultrasonic power control is performed according to the ultrasonic sound field intensity adjustment parameter and the current ultrasonic power to obtain an ultrasonic power control parameter.

[0037] In combination with the first aspect, in a seventh implementation of the first aspect of the present invention, when in the comprehensive fine-tuning stage, performing variable partial derivative calculation and synchronous fine-tuning to obtain a variable adjustment sequence and a corresponding variable adjustment amplitude includes:

[0038] When in the comprehensive fine-tuning stage, the average particle size, encapsulation efficiency and polydispersity index of the current compound lidocaine nanoparticles are weighted and a comprehensive optimization objective function including the average particle size objective function, the encapsulation efficiency objective function and the polydispersity index objective function is constructed;

[0039] Based on the comprehensive optimization objective function, numerical differential calculations are performed on the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time to obtain multiple objective function partial derivative values;

[0040] Sorting the multiple objective function partial derivative values ​​by absolute value to obtain a variable adjustment sequence, and allocating a learning rate to each variable in the variable adjustment sequence to obtain an adaptive learning rate parameter;

[0041] A gradient descent adjustment amplitude is calculated based on the partial derivative value of the objective function and the adaptive learning rate parameter to obtain a variable adjustment amplitude.

[0042] In combination with the first aspect, in an eighth implementation of the first aspect of the present invention, the absolute value sorting of the multiple objective function partial derivative values ​​to obtain a variable adjustment sequence, and performing a learning rate allocation on each variable in the variable adjustment sequence to obtain an adaptive learning rate parameter includes:

[0043] The absolute value calculation of the partial derivative values ​​of the objective function corresponding to the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time was performed respectively to obtain the absolute value data of the partial derivatives;

[0044] Sorting based on the absolute value data of the partial derivatives to obtain a variable adjustment sequence;

[0045] Performing initial learning rate weight allocation according to the adjustment priority of each process variable in the variable adjustment sequence to obtain an initial learning rate parameter;

[0046] The numerical change trend of the comprehensive optimization objective function within the continuous control period is monitored and analyzed. When the objective function value continuously decreases, the initial learning rate parameter is incrementally adjusted. When the objective function value oscillates, the initial learning rate parameter is decremented to obtain an adaptive learning rate parameter.

[0047] In a second aspect, the present invention provides an intelligent preparation process control system for a compound lidocaine nanoparticle drug delivery system, wherein the intelligent preparation process control system for the compound lidocaine nanoparticle drug delivery system comprises:

[0048] The coupling analysis module is used to perform coupling analysis on reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time to obtain a parameter variable coupling model;

[0049] a calculation module for calculating the drug loading saturation of the compound lidocaine drug loading according to the parameter variable coupling model, and determining whether the current preparation moment is in the drug loading efficiency optimization stage, the particle size distribution control stage, or the comprehensive fine-tuning stage;

[0050] An adjustment module is used to perform temperature gradient adjustment and pH buffer adjustment when in the drug loading efficiency optimization stage to obtain temperature control parameters and pH control parameters;

[0051] A control module is used to perform particle size distribution control when in the particle size distribution control stage to obtain stirring speed control parameters and ultrasonic power control parameters;

[0052] The synchronous fine-tuning module is used to perform variable partial derivative calculation and synchronous fine-tuning when in the comprehensive fine-tuning stage to obtain a variable adjustment sequence and a corresponding variable adjustment amplitude.

[0053] In the technical solution provided by the present invention, by introducing a protonation correction coefficient based on the dissociation constant of lidocaine acid, an improved coupled differential equation system that takes into account the change in the ionization state of the drug molecule is established, thereby solving the modeling defect of the prior art that ignores the protonation effect. A staged control strategy based on drug loading saturation is constructed, and the preparation process is divided into three stages: drug loading efficiency optimization, particle size distribution regulation and comprehensive fine-tuning, thereby achieving dynamic adaptive regulation and overcoming the limitations of traditional fixed parameter control. A dynamic coupling model covering eight process variables is established, and the nonlinear interaction relationship between variables is systematically identified through orthogonal experimental design to achieve precise multivariable coordinated control. A real-time optimization algorithm based on partial derivative numerical differential calculation is used to quantitatively determine the variable adjustment priority and adaptive learning rate parameters, thereby avoiding the subjective error of empirical regulation. The present invention realizes predictive intelligent control of the preparation process of compound lidocaine nano-drug loading, significantly improving product quality stability and preparation process controllability.

[0054] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1This is a schematic diagram of an embodiment of an intelligent preparation process control method for compound lidocaine nanoparticle drug delivery according to an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of an embodiment of an intelligent preparation process control system for compound lidocaine nano-drug delivery in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] The terms "including," "having," and any variations thereof, as used in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or device.

[0060] To facilitate understanding of this embodiment, firstly, a method for controlling the intelligent preparation process of a compound lidocaine nanoparticle drug delivery system disclosed in an embodiment of the present invention is described in detail. Figure 1 As shown, this method includes the following steps:

[0061] 101. Conduct coupling analysis on reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time to obtain a parameter variable coupling model;

[0062] It is understood that the execution subject of the present invention can be an intelligent preparation process control system for compound lidocaine nanoparticles, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking the server as the execution subject as an example.

[0063] Specifically, the pH environment value in the preparation system was corrected for biochemical reaction behavior based on the acid dissociation constant of lidocaine (pKa=7.9) to quantify the protonation degree of the drug molecule under different acid-base conditions. By constructing a protonation correction function to express the nonlinear relationship between the ionization state of the lidocaine molecule and the pH of the solution, a protonation correction coefficient reflecting the true reaction ability of the drug molecule was obtained. At the same time, the protonation state was introduced into the modeling calculation of the diffusion coefficient as a key factor affecting the diffusion activity. The Arrhenius diffusion mechanism was used to convert the reaction temperature into a physicochemical behavior. Based on the activation energy Ea and the gas constant R, the temperature parameter was converted into a temperature-dependent molecular diffusion coefficient, so that the diffusion behavior is not only affected by the physical temperature, but also includes the changes in the effective diffusion capacity caused by the electrical changes in the molecular structure under the pH environment, forming a composite diffusion parameter combining temperature and protonation state. The carrier material concentration and reaction time are regarded as important factors to determine the network strength of the nanocarrier structure. Time-concentration coupling calculation is performed based on the polynomial cross-linking model to extract the cross-linking behavior that gradually increases with time. A cross-linking degree function model is constructed to quantify the regulatory effect of the carrier structure on the encapsulation and release of drug molecules throughout the reaction process, and a quantifiable carrier cross-linking degree parameter is obtained. On this basis, the composite diffusion parameter and the carrier cross-linking degree parameter are used as coupling input variables to establish a differential dynamic model that describes the diffusion movement of drug molecules in the reaction system and the binding process of the carrier material, forming a dual-channel differential equation group that reflects the mutual coupling characteristics of drug release behavior and carrier binding behavior. The dual-channel differential equation group can dynamically simulate the comprehensive effect of drug concentration gradient and carrier cross-linking density on the final drug loading efficiency and particle size distribution under different preparation conditions. In order to extract the nonlinear interaction law between the various process variables, the experimental scheme containing eight core process variables was substituted into the above-mentioned coupled differential equation group, and L was used to calculate the total weight of the drug. 27 (3 8 ) An orthogonal experimental design strategy was adopted to set a three-level combination of reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time. The effects of each factor alone and its interaction on particle size and encapsulation efficiency were evaluated through batch simulation and variance analysis, thereby generating a parameter variable coupling model with engineering applicability and theoretical interpretability.

[0064] 102. Calculate the drug loading saturation of the compound lidocaine drug loading according to the parameter variable coupling model, and determine whether the current preparation time is in the drug loading efficiency optimization stage, the particle size distribution control stage, or the comprehensive fine-tuning stage;

[0065] Specifically, the multivariable coupling model comprehensively considers the impact of eight key process parameters on the drug loading process, including reaction temperature, pH, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power, and reaction time. It also dynamically expresses the coupled relationship between drug molecular diffusion and binding behavior by introducing a lidocaine protonation correction factor and the molecular diffusion coefficient. Based on this, the current process conditions are input into the multivariable coupling model in real time for numerical solution. The actual total drug encapsulation at the current preparation moment, i.e., the instantaneous drug loading, is calculated, reflecting the degree of completion of the drug-nanocarrier binding reaction. Simultaneously, based on factors such as carrier concentration, degree of cross-linking, reaction time, and the theoretical maximum amount of lidocaine in the system, combined with known physicochemical capacity constraints, the same coupling model is used to determine the maximum drug loading capacity achievable under these physicochemical capacity constraints, yielding the theoretical maximum drug loading capacity. The ratio of the current actual drug loading capacity to the theoretical maximum drug loading capacity is calculated to determine the drug loading saturation of the current preparation process. This saturation serves as a unified metric that reflects the contribution of the drug binding process to the overall drug loading capacity. A logical judgment is made on the drug loading saturation value, and the first target value (for example, 0.6) and the second target value (for example, 0.8) are set as the stage division benchmarks. When the drug loading saturation is lower than the first target value, it indicates that the system is in the initial reaction stage and has not yet formed a sufficient drug coating structure. At this time, the control strategy prioritizes improving the reaction rate and binding efficiency, so the preparation process is judged to be in the "drug loading efficiency optimization stage"; if the drug loading saturation is higher than the second target value, it means that most of the drugs have completed binding and entered the late morphology stability range. At this time, particle size consistency and morphology uniformity become the main control targets, and the process switches to the "particle size distribution regulation stage"; if the drug loading saturation is between the first and second target values, the system is already in the intermediate transition zone, and it is necessary to balance the dual indicators of efficiency and morphology at the same time, so the preparation is judged to be in the "comprehensive fine-tuning stage".

[0066] 103. When in the drug loading efficiency optimization stage, temperature gradient adjustment and pH buffer adjustment are performed to obtain temperature control parameters and pH control parameters;

[0067] Specifically, when in the drug loading efficiency optimization stage, the drug loading efficiency deviation is quantitatively calculated based on the difference between the drug loading saturation value monitored during the current real-time preparation process and the set target drug loading saturation baseline value. That is, the degree of deviation between the current drug loading saturation and the target saturation is used as the core indicator to measure whether the current reaction kinetics is in the optimal state; at the same time, the difference calculation is performed between the current measured encapsulation efficiency and the preset target encapsulation efficiency to obtain the performance deviation of the current drug loading system in terms of structural stability and binding integrity. The drug loading efficiency deviation value is input into the temperature control path, and a temperature gradient increment calculation is performed in combination with a set of temperature adjustment coefficients obtained based on experience or model training. The temperature gradient increment reflects the magnitude and direction of the current temperature that needs to be adjusted upward or downward to increase the diffusion rate and reaction activity, thereby promoting the binding reaction between the drug and the carrier. The temperature adjustment increment is superimposed on the current reaction temperature value to form a new round of temperature control instructions, and the temperature control parameters for the current cycle are output accordingly. At the same time, in the pH control path, the buffer increment is calculated based on the encapsulation efficiency deviation value and the preset pH adjustment coefficient. Based on the high sensitivity of the drug protonation state to the binding efficiency, the system acid-base environment is precisely controlled through the incremental adjustment strategy, so that the ionization state of the lidocaine molecule is always in the range most conducive to carrier encapsulation. The resulting pH adjustment increment is applied to the current pH value to form the corrected buffer environment parameter, that is, the new pH control parameter. This process is continuously executed in a cycle within the control cycle and relies on a high-frequency feedback mechanism to dynamically track the target deviation. The two core variables of temperature and pH are respectively used to jointly adjust the overall drug loading behavior at the thermodynamic and chemical reaction levels. This ensures that in the drug loading efficiency optimization stage, the thermal field and acid-base environment are adaptively adjusted with the goal of maximizing the reaction advancement rate and optimizing the molecular binding state.

[0068] 104. When in the particle size distribution control stage, perform particle size distribution control to obtain stirring speed control parameters and ultrasonic power control parameters;

[0069] Specifically, during the particle size distribution control phase, the real-time detection module measures the average particle size of the compound lidocaine nanoparticles online. The deviation between the actual measured value and the preset target average particle size is calculated to obtain the average particle size deviation, reflecting the degree of deviation from the design target in terms of physical morphology control. Simultaneously, the current polydispersity index (PDI) is collected as a core indicator for evaluating particle size distribution uniformity. The current PDI is subtracted from the set target PDI to obtain the PDI deviation, which is used to determine whether the particle size consistency of the current particle system meets the standard or exhibits abnormal diffusion and expansion trends. The average particle size deviation is input into the stirring shear control module, where it is incrementally calculated based on a set of preset or model-trained shear force adjustment coefficients. The resulting stirring shear force adjustment parameter represents the required increase or decrease in energy input to the stirring unit. The stirring shear force adjustment parameter is then added to the current stirring speed value and, combined with the mechanical response delay model, corrected to derive the stirring speed control parameter for the current cycle. This ensures that when the nanoparticle morphology tends to expand or agglomerate, the increased shear action can achieve particle redispersion and size reduction. The polydispersity index deviation value is input into the acoustic field control path and combined with the acoustic field intensity adjustment coefficient to calculate the ultrasonic power increment, resulting in the ultrasonic field intensity adjustment parameter. This parameter is used to control the change in acoustic wave energy density required by the ultrasonic device during the current cycle, thereby improving or suppressing the proportion of particles of different sizes in the system and enhancing the uniformity and uniformity of the overall particle size distribution. The ultrasonic field intensity adjustment parameter is superimposed on the current ultrasonic power to form the ultrasonic power control parameter for the current cycle, which directly drives the ultrasonic source to output the adjusted energy waveform.

[0070] 105. When in the comprehensive fine-tuning stage, variable partial derivative calculation and synchronous fine-tuning are performed to obtain the variable adjustment sequence and the corresponding variable adjustment amplitude.

[0071] Specifically, when in the comprehensive fine-tuning stage, the average particle size, encapsulation efficiency, and polydispersity index of the current compound lidocaine nanoparticles are analyzed for their target contribution, and the weight factors between the three are set according to the formulation application or technical focus. A comprehensive objective function for multi-index collaborative optimization is constructed. The comprehensive objective function uses the average particle size objective function, encapsulation efficiency objective function, and polydispersity index objective function as weighted sub-items. The consistency of the dimensions of each physical quantity is ensured through normalization processing, and linear combinations are performed with w1, w2, and w3 as corresponding weight coefficients to form an evaluation expression with engineering adaptability. The reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power, and reaction time are substituted into the comprehensive objective function one by one. By setting a small perturbation, each variable is numerically differentiated, that is, the objective function values ​​before and after the positive and negative fine-tuning of the variable are calculated respectively, and the partial derivative approximation is solved accordingly to obtain the sensitivity response of the current objective function to each variable, forming eight partial derivative values. The eight partial derivatives are sorted by absolute value to form a variable adjustment sequence, which reflects the variable control path to which the current objective function is most sensitive. To ensure a balance between response stability and convergence speed during the control process, an adaptive learning rate parameter is assigned to each variable using the variable adjustment sequence as a benchmark. The learning rate assignment rule is based on the objective function's trend over the historical control cycle. When a variable has led to a monotonically decreasing objective function over the previous several cycles, its learning rate is increased to accelerate response efficiency. When oscillation or inverse changes occur, the learning rate is reduced to prevent overshoot. After completing the variable adjustment sequence and adaptive learning rate assignment, the partial derivative value corresponding to each variable is multiplied by the learning rate parameter and then multiplied by the control cycle. The gradient descent algorithm is then used to calculate the variable adjustment amplitude required for each variable in the current cycle, forming the final executable variable control amount.

[0072] In a specific embodiment, the process of executing step 101 may specifically include the following steps:

[0073] The pH environment value was corrected for protonation state based on the lidocaine acid dissociation constant to obtain the protonation correction coefficient. At the same time, the reaction temperature value was calculated by Arrhenius diffusion to obtain the molecular diffusion coefficient.

[0074] The composite diffusion parameter is calculated based on the protonation correction coefficient and the molecular diffusion coefficient, and the carrier cross-linking degree is calculated based on the carrier material concentration and reaction time values ​​to obtain the carrier cross-linking degree parameter;

[0075] A set of coupled differential equations for drug release and carrier binding behaviors was established based on the composite diffusion parameters and carrier cross-linking parameters.

[0076] Based on the coupled differential equations, orthogonal experiments were conducted on reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time to obtain a parameter variable coupling model.

[0077] Specifically, lidocaine, a weakly basic drug, has its ionization behavior in solution directly regulated by the ambient pH. Its acid dissociation constant, pKa, is 7.9. Therefore, at pH levels below the pKa, lidocaine primarily exists as a protonated cation, while at pH levels above the pKa, it tends to be non-ionized. The protonation state not only affects its affinity with the carrier but also its diffusion rate and solubility. To address this issue, a functional transformation of pH was performed. By introducing a protonation correction function, the ambient pH value was mapped to the charge distribution probability of the drug molecule, thereby quantifying the protonation degree and forming a protonation correction factor. To account for the effect of temperature on molecular mobility, the Arrhenius diffusion model was used to thermodynamically correct the reaction temperature. A temperature-response function was constructed using the base diffusion coefficient, activation energy, and gas constant as inputs. The protonation correction factor was then embedded in the temperature-diffusion expression, forming a composite molecular diffusion coefficient influenced by both thermal energy and ionization state, reflecting the true effective diffusion capacity of the drug molecule under the prevailing conditions. Furthermore, as the core medium for drug loading and release, the carrier material's structural state and degree of cross-linking directly determine its encapsulation capacity and release rate. Therefore, an expression for the degree of cross-linking was constructed based on two key parameters: carrier concentration and reaction time, and the parameters were described using an empirical fitting function. This expression for the degree of cross-linking reflects the tendency of the carrier's molecular structure to evolve from a loose to a dense state as the reaction progresses, thereby generating a structural parameter, the carrier cross-linking degree. A coupled differential equation system for drug release and carrier binding was established based on the composite diffusion parameter and the carrier cross-linking degree parameter to describe the dynamic evolution of lidocaine release and binding in the preparation environment. The drug release behavior was described using a diffusion-guided equation, indicating that the spatial diffusion behavior of the drug is coupled to pH and temperature. Simultaneously, the binding behavior was represented as a dissipative reaction process, indicating that the reaction rate of the complex formation between the drug and carrier is controlled by the degree of cross-linking. By coupling these two kinetic expressions, a total differential equation for drug concentration was derived, describing the entire kinetic evolution of the drug from the free state to the carrier surface and into the encapsulated state. This equation is capable of simultaneously expressing both the spatial diffusion gradient and the reaction kinetics. The orthogonal experimental design framework is built based on the coupled differential equations. 27 (3 8) orthogonal table, with reaction temperature (55°C, 60°C, 65°C), pH value (6.5, 7.0, 7.5), stirring speed (800rpm, 1000rpm, 1200rpm), lidocaine concentration (2mg / mL, 4mg / mL, 6mg / mL), auxiliary drug concentration (1mg / mL, 2mg / mL, 3mg / mL), carrier material concentration (8mg / mL, 10mg / mL, 12mg / mL), ultrasonic power (200W, 250W, 300W) and reaction time (30min, 45min, 60min) as three-level variables, and 27 experimental plans covering the combination of various variables were designed. Under each set of experimental conditions, the response values ​​of indicators such as average particle size, encapsulation efficiency and polydispersity index in the drug loading process were predicted by numerically solving a set of coupled differential equations. The experimental results were input into the variance analysis model and the range analysis model to identify the most significant main effect variables and interaction terms, and establish a nonlinear coupling mapping model between parameter variables.

[0078] In a specific embodiment, the step of performing an orthogonal test on the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power, and reaction time based on the coupled differential equations to obtain a parameter variable coupling model may specifically include the following steps:

[0079] The three levels corresponding to the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time were set respectively, and the orthogonal experimental design was performed to obtain the orthogonal experimental design table;

[0080] The coupled differential equations were numerically solved according to the parameter values ​​of each experimental condition in the orthogonal experimental design table to obtain the theoretical average particle size and theoretical encapsulation efficiency of the compound lidocaine nanoparticles under each experimental condition.

[0081] Based on the theoretical average particle size and theoretical encapsulation efficiency, variance analysis and range analysis were performed to obtain the first coupling effect coefficient between reaction temperature and lidocaine concentration, and the second coupling effect coefficient between pH value and carrier material concentration.

[0082] The coefficients of the coupled differential equation group are adjusted according to the first coupling effect coefficient and the second coupling effect coefficient to obtain a parameter variable coupling model.

[0083] Specifically, for the eight core process variables in the preparation process of compound lidocaine nano-drug delivery, namely reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time, the experimental level setting was carried out, and each variable was set to three levels to capture its impact response under different conditions, among which the reaction temperature was set to 55°C, 60°C, and 65°C, the pH value was set to 6.5, 7.0, and 7.5, the stirring speed was set to 800rpm, 1000rpm, and 1200rpm, the lidocaine concentration was 2mg / mL, 4mg / mL, and 6mg / mL respectively, the auxiliary drug concentration was set to 1mg / mL, 2mg / mL, and 3mg / mL, the carrier material concentration was 8mg / mL, 10mg / mL, and 12mg / mL respectively, the ultrasonic power was 200W, 250W, and 300W, and the reaction time was set to 30min, 45min, and 60min. Based on the variable setting of three levels and eight factors, L 27 (3 8An orthogonal experimental design scheme was used to construct an experimental matrix, ensuring that each variable was evenly distributed across the three levels and combined with other variables to form independent sample points, thus generating an orthogonal experimental design table. For each set of experimental conditions in the orthogonal experimental design table, the set values ​​of the eight variables were sequentially input as initial conditions into a pre-established system of coupled differential equations. These equations are based on the bidirectional mechanism of lidocaine's diffusion behavior and its binding to the carrier material. The temperature-dependent Arrhenius diffusion factor and pH-dependent protonation correction coefficient were embedded, and dynamic variables such as the carrier cross-linking degree and reaction time were integrated to achieve a complete kinetic modeling of the drug from free diffusion to encapsulation reaction. During the solution process, numerical integration methods such as the fourth-order Runge-Kutta method were used to determine stability. By integrating the spatial concentration variation and the reaction rate expression, the theoretical drug loading kinetic response under these variable conditions was obtained. The theoretical average particle size and theoretical encapsulation efficiency of the composite lidocaine nanoparticles were then output. The average particle size was determined by the main peak of the particle size distribution in the system, while the encapsulation efficiency was calculated as the ratio of bound drug to total drug. The theoretical responses under 27 orthogonal experimental conditions were solved sequentially in this manner, constructing a complete data set containing the response results for all design variable combinations. The theoretical average particle size and encapsulation efficiency results were input into the statistical analysis module for analysis of variance and range analysis to identify the dominant effect and significance of each factor and its interaction terms on the response indicators. During the analysis, the mean effect of each factor on average particle size and encapsulation efficiency at different levels was calculated, and the range of the effect values ​​was calculated to preliminarily determine the importance of the variables. An analysis of variance was then performed, with dependent variable models set for both main effects and second-order interactions, and F tests were performed. Statistically significant variable combinations were identified, and their corresponding coupling effect coefficients were calculated. The interaction between reaction temperature and lidocaine concentration had the most significant effect on average particle size control. The interaction term had an F value of 15.8, with a P value less than 0.01, indicating high statistical significance and was defined as the primary coupling effect. The interaction between pH and carrier material concentration had the strongest influence on encapsulation efficiency, with an F value of 12.6, also with a P value less than 0.01, defining it as the secondary coupling effect. Based on the first and second coupling effect coefficients, the coupled differential equations were modified. Adjustment factors representing the strength of variable coupling were introduced into the original diffusion and binding terms. For example, the interaction term between temperature and lidocaine concentration was embedded in the diffusion coefficient expression as a product; the product term between pH and carrier concentration was also embedded in the cross-linking function. The statistical analysis results were converted into a parameter adjustment basis for the model's internal structure, enabling feedback coupling from experimental data to the kinetic model, and establishing a complete parameter-variable coupling model that reflects the nonlinear interactions between variables.

[0084] In a specific embodiment, the process of executing step 102 may specifically include the following steps:

[0085] The drug loading amount and the maximum drug loading capacity at the current preparation moment were calculated based on the parameter variable coupling model to obtain the instantaneous drug loading amount and the theoretical maximum drug loading amount of the compound lidocaine drug loading;

[0086] The drug loading saturation ratio is calculated based on the instantaneous drug loading and the theoretical maximum drug loading to obtain the drug loading saturation;

[0087] The drug loading saturation is judged by a numerical range. When the drug loading saturation is less than the first target value, it is determined that the preparation process at the current preparation moment is in the drug loading efficiency optimization stage. When the drug loading saturation is greater than the second target value, it is determined that the preparation process at the current preparation moment is in the particle size distribution control stage. When the drug loading saturation is between the first target value and the second target value, it is determined that the preparation process at the current preparation moment is in the comprehensive fine-tuning stage.

[0088] Specifically, the dynamic state of the reaction system is continuously simulated and periodically analyzed based on a parameter-variable coupling model. This model systematically integrates multiple key variables that influence the compound lidocaine nanoparticle drug delivery process, including reaction temperature, pH, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power, and reaction time. The model then uses a set of coupled differential equations to describe the entire process of drug molecules diffusing to the carrier surface and gradually achieving binding, encapsulation, or release. Based on this, the model is invoked and numerically solved based on the current combination of process parameters input, obtaining the actual total amount of drug molecules entering the carrier structure at that moment, i.e., the instantaneous drug loading. This metric is expressed as the total molar number or mass value of bound lidocaine, reflecting the actual degree of drug encapsulation. Simultaneously, the theoretical maximum drug loading capacity is estimated based on boundary conditions such as the current carrier material concentration, reaction time, carrier cross-linking function value, and the upper limit of lidocaine concentration. This estimation is independent of the current state and instead simulates the maximum encapsulation level achievable under the same system configuration, assuming optimal encapsulation of all lidocaine and carrier capacity saturation. Further adjustments are made to the carrier structure's pore size, surface affinity parameters, and cross-linking density to determine the theoretical maximum drug loading capacity. The drug saturation ratio is calculated based on the instantaneous drug loading and the theoretical maximum drug loading, yielding the drug loading saturation. The drug loading saturation varies continuously between 0 and 1, uniformly representing the progress of the current drug loading process within the theoretically achievable state. By comparing the saturation value with a preset stage boundary threshold, process state determination and control strategy switching are guided. To implement segmented control logic, two static thresholds are set as decision boundaries: a first target value S1 (set to 0.6) and a second target value S2 (set to 0.8). Segmented logic decisions are then made based on the actual drug loading saturation value.If the calculated drug loading saturation is less than the first target value S1, it means that the drug molecules are still in the rapid binding stage and have not yet been saturated. The binding rate is much higher than the morphology stabilization rate. It is necessary to prioritize improving the encapsulation efficiency and drug binding ability. At this time, it is automatically determined that the current preparation process is in the "drug loading efficiency optimization stage", and a parameter scheduling strategy with temperature regulation and pH regulation as the core is enabled in the control logic to maximize the binding reaction rate of the drug and the carrier; on the contrary, if the saturation exceeds the second target value S2, it means that the drug has basically completed the binding reaction, the system has tended to the drug loading saturation upper limit, and entered the structural stability and morphology convergence stage. At this time, further drug binding has little effect on the total amount increase, and particle size control becomes the main optimization goal. The system automatically switches to the "particle size distribution control stage" and starts a distribution adjustment mechanism based on stirring speed and ultrasonic power to improve particle uniformity and size consistency; if the drug loading saturation is between the first target value and the second target value, that is, 0.6 ≤ S ≤ If the value is 0.8, it indicates that the system is in the transition zone from rapid response to stable generation. In the transition zone, multiple indicators such as particle size, encapsulation rate and dispersion are changing dynamically and influencing each other. It is necessary to control diffusivity, binding and morphology stability at the same time. Therefore, the current state is determined to be the "comprehensive fine-tuning stage" and the full-variable collaborative control strategy is started in this stage. The adjustment path priority is sorted through the comprehensive objective function construction and variable partial derivative calculation mechanism, and the gradient descent update is performed in combination with the adaptive learning rate to ensure that each variable converges between stability and performance.

[0089] In a specific embodiment, the step of calculating the drug loading amount and the maximum drug loading capacity at the current preparation moment based on the parameter variable coupling model to obtain the instantaneous drug loading amount value and the theoretical maximum drug loading amount value of the compound lidocaine drug loading may specifically include the following steps:

[0090] The reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time at the current preparation moment are collected to obtain the process parameter combination at the current preparation moment;

[0091] Based on the process parameter combination, the protonation correction coefficient and composite diffusion parameter in the parameter variable coupling model are dynamically updated and calculated to obtain the lidocaine protonation state parameter and the temperature-corrected molecular diffusion rate parameter under the current pH environment;

[0092] The coupled differential equations are solved according to the protonation state parameters and molecular diffusion rate parameters of lidocaine to obtain the instantaneous drug loading corresponding to the number of compound lidocaine molecules bound to the carrier at the current preparation moment.

[0093] The saturation upper limit of the maximum binding capacity of the carrier was calculated based on the carrier cross-linking degree parameters and the current carrier material concentration to obtain the theoretical maximum drug loading capacity.

[0094] Specifically, during the preparation process, multiple sensors deployed within the reactor collect high-frequency data on core variables, including reaction temperature, solution pH, stirring speed, initial lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power, and reaction time. This real-time, multi-dimensional data constitutes the current process parameter combination. This real-time process parameter combination is input into a parameter-variable coupling model, dynamically correcting the ionization state of the lidocaine molecule based on the current pH and lidocaine's acid dissociation constant (pKa = 7.9) to construct a protonation correction factor. The protonation correction factor reflects the actual protonation ratio of the lidocaine molecule under the current acid-base environment, directly influencing the binding rate between the drug molecule and the carrier. A higher degree of protonation increases the proportion of positively charged molecular surface charge and enhances electrostatic adsorption to charged areas on the carrier surface. Simultaneously, the real-time monitored reaction temperature is substituted into the Arrhenius diffusion model. Using the exponential relationship between temperature and diffusion coefficient, the temperature-corrected molecular diffusion rate parameter is calculated. The protonation correction factor is then incorporated into the diffusion coefficient calculation expression to obtain the current composite diffusion parameter. The protonation state parameters under the current pH environment and the temperature-corrected molecular diffusion rate parameters, along with the current stirring speed, ultrasonic power, and concentration conditions, are input into a coupled differential equation system for solution. By simultaneously describing the diffusion of drug molecules within the system and their binding reactions with the carrier material, this coupled differential equation system captures the dynamic transformation of the drug from the free state to the carrier-bound state. By numerically solving the coupled differential equation system, the number of lidocaine molecules bound to the carrier structure is determined at each time step and converted to the instantaneous drug loading. Simultaneously, the theoretical maximum binding capacity of the carrier is evaluated. This calculation is based on the carrier cross-linking parameter and the current carrier material concentration. The carrier cross-linking parameter is determined by the carrier structural characteristics and reaction time. A higher cross-linking degree results in a denser three-dimensional network within the carrier, providing more effective binding sites but also reducing the accessibility of diffusion channels. Therefore, the optimal cross-linking function is determined through model fitting. The effective cross-linking state of the current carrier is calculated using the current carrier material concentration and reaction time parameters. Combined with the carrier structure's surface area, porosity, and material properties, the maximum number of drug molecules that the current carrier can accommodate under theoretically optimal conditions—the theoretical maximum drug loading—is derived. The theoretical maximum drug loading reflects the maximum drug encapsulation limit achievable under current preparation conditions, assuming a complete reaction and saturated binding behavior.

[0095] In a specific embodiment, the process of executing step 103 may specifically include the following steps:

[0096] When in the drug loading efficiency optimization stage, the drug loading efficiency deviation is calculated based on the current drug loading saturation and the target drug loading saturation to obtain the drug loading efficiency deviation value. At the same time, the encapsulation deviation is calculated based on the current encapsulation efficiency and the target encapsulation efficiency to obtain the encapsulation efficiency deviation value;

[0097] Calculating the temperature gradient increment based on the drug loading efficiency deviation value and the preset temperature adjustment coefficient to obtain a temperature adjustment increment parameter, and performing temperature control on the current reaction temperature and the temperature adjustment increment parameter to obtain a temperature control parameter;

[0098] The pH buffer increment is calculated based on the encapsulation efficiency deviation value and the preset pH adjustment coefficient to obtain the pH adjustment increment parameter, and the pH control is performed on the current pH value and the pH adjustment increment parameter to obtain the pH control parameter.

[0099] Specifically, when in the drug loading efficiency optimization stage, the current drug loading saturation is numerically subtracted from the preset target saturation value to obtain a drug loading efficiency deviation value with directionality and magnitude, reflecting the degree of deviation of the current drug loading behavior compared to the target state. At the same time, the encapsulation efficiency is analyzed. The encapsulation efficiency is a structural parameter that measures the tightness of the drug-carrier binding. Its calculation is based on the ratio of the mass of the bound drug to the total amount of the original feed, reflecting the changing trend of the material utilization rate during the preparation process. The current encapsulation efficiency is subtracted from the target encapsulation efficiency value to obtain the encapsulation efficiency deviation value, forming a joint regulation signal for both efficiency and structural performance. The drug loading efficiency deviation value is input into the temperature control module. The temperature control module is embedded with a set of temperature regulation coefficients obtained based on empirical regression or control optimization training. The temperature regulation coefficient is used to convert the drug loading efficiency deviation value into a temperature regulation response amplitude. Specifically, the temperature gradient increment is calculated through a linear or nonlinear gradient algorithm. The temperature gradient increment value represents the degree of correction that the system currently needs to apply to the thermal field parameters. The temperature adjustment increment parameter is numerically combined with the current reaction temperature to form an updated temperature control target. This temperature control target is then transmitted to the thermal field controller via a closed-loop PID control or fuzzy logic control module, driving the heating module to respond immediately. This allows for rapid compensation and gradual convergence of the temperature parameter when drug loading efficiency deviates, restoring the reaction rate to the optimal range for drug loading binding. The encapsulation efficiency deviation is input into the pH control channel. Considering that pH has a direct influence on the protonation state of lidocaine molecules, thereby affecting the electrostatic adsorption between lidocaine and the carrier, a preset pH adjustment coefficient is used. This pH adjustment coefficient reflects the buffering strength required for pH adjustment per unit encapsulation efficiency deviation. Based on this, the encapsulation efficiency deviation is converted into a pH buffer increment parameter, which expresses how the system should adjust the current acid-base environment to improve binding efficiency. The pH adjustment increment parameter can be positive or negative, corresponding to correction requirements in the alkaline or acidic direction, respectively. The pH adjustment increment parameter is superimposed on the real-time pH reading of the current solution to form a target pH control value. With the help of an automatic titration device, a weak acid and weak base solution injection pump or an online buffer system, the target pH value is stably achieved within the control period, thereby changing the charge state of lidocaine, optimizing its spatial structure matching ability, increasing its chance of entering the carrier pore structure, and promoting the formation of a more stable drug-carrier composite structure.

[0100] In a specific embodiment, the process of executing step 104 may specifically include the following steps:

[0101] When in the particle size distribution control stage, the particle size deviation of the current average particle size of the compound lidocaine nanoparticles and the target average particle size is calculated to obtain the average particle size deviation value, and the dispersion deviation of the current polydispersity index and the target polydispersity index is calculated to obtain the polydispersity index deviation value;

[0102] Calculating the stirring shear force increment according to the average particle size deviation value and the preset shear force adjustment coefficient to obtain the stirring shear force adjustment parameter, and controlling the stirring speed based on the stirring shear force adjustment parameter and the current stirring speed to obtain the stirring speed control parameter;

[0103] The ultrasonic sound field increment is calculated based on the polydispersity index deviation value and the preset sound field intensity adjustment coefficient to obtain the ultrasonic sound field intensity adjustment parameter, and the ultrasonic power is controlled according to the ultrasonic sound field intensity adjustment parameter and the current ultrasonic power to obtain the ultrasonic power control parameter.

[0104] Specifically, during the particle size distribution control phase, key particle size information for the compound lidocaine drug-loaded nanoparticles is acquired in real time. The average particle size (D) and polydispersity index (PDI) are two key indicators for assessing particle morphology quality and stability. Using an online particle size analyzer or dynamic light scattering system, the average particle size of the nanoparticles in the current preparation system is collected in real time and compared with the process target (e.g., 92.0 nm). The average particle size deviation is calculated, reflecting the absolute difference between the current particle size and the target size, with clear directionality and response strength. Simultaneously, the PDI value of the current particle system is acquired and compared with the set polydispersity control target (e.g., <0.2). The PDI deviation, or polydispersity index deviation, indicates whether the current particle size distribution uniformity meets the target. A large deviation indicates particle aggregation or inter-particle structural instability. The average particle size deviation is input into the shear force adjustment module, which internally sets a set of preset shear force adjustment coefficients to map the particle size deviation to the shear energy adjustment path, thereby quantifying the required shear strength increment for the current system. The shear force adjustment coefficient is given by historical experience, fitting regression model or process optimization results. Its physical meaning is the amount of stirring shear energy correction required to adjust the unit particle size deviation. The stirring shear force adjustment parameter is calculated through the shear force adjustment algorithm. The stirring shear force adjustment parameter is superimposed on the current stirring speed value, combined with the stirring device response inertia and system inertia, and the corrected stirring speed control parameter is generated through feedback adjustment. At the same time, the polydispersity index deviation value is input into the sound field adjustment module. The control variable corresponding to the sound field adjustment module is ultrasonic power, which intervenes in the distribution state of particles at the microscopic scale by adjusting the sound field energy density. A set of sound field intensity adjustment coefficients are introduced as the energy compensation coefficient corresponding to the unit PDI deviation. Its essence is the conversion coefficient of the PDI deviation to the sound field intervention response. The sound field intensity adjustment coefficient is used to convert the PDI deviation value into the required sound field adjustment amplitude, that is, the ultrasonic sound field intensity adjustment parameter. If the PDI deviation is positive and large, it indicates that the system has problems with a wide particle size distribution, particle agglomeration, or uneven formation. In this case, the ultrasonic power is increased to accelerate the dispersion process and promote the uniformity of particle size. If the PDI deviation is small or negative, it means that the system particle size distribution has stabilized and the acoustic field effect is appropriately weakened to maintain structural integrity and prevent structural damage. The ultrasonic sound field intensity adjustment parameter is superimposed on the current ultrasonic power to form the ultrasonic power control parameter. The ultrasonic control unit acts on the ultrasonic oscillator in an automatic closed-loop manner to complete the acoustic energy adjustment within the cycle, thereby gradually optimizing the particle size distribution structure.

[0105] In a specific embodiment, the process of executing step 105 may specifically include the following steps:

[0106] When in the comprehensive fine-tuning stage, the average particle size, encapsulation efficiency and polydispersity index of the current compound lidocaine nanoparticles are weighted and a comprehensive optimization objective function including the average particle size objective function, the encapsulation efficiency objective function and the polydispersity index objective function is constructed;

[0107] Based on the comprehensive optimization objective function, numerical differential calculations were performed on the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time, and multiple partial derivative values ​​of the objective function were obtained.

[0108] Sort the absolute values ​​of the partial derivatives of multiple objective functions to obtain a variable adjustment sequence, and assign a learning rate to each variable in the variable adjustment sequence to obtain an adaptive learning rate parameter;

[0109] The gradient descent adjustment amplitude is calculated based on the partial derivative value of the objective function and the adaptive learning rate parameter to obtain the variable adjustment amplitude.

[0110] Specifically, when in the comprehensive fine-tuning stage, the main quality indicators in the current preparation state are aggregated and modeled, including three key indicators: average particle size, encapsulation efficiency, and polydispersity index. These three indicators represent the structural size, drug binding ability, and particle size distribution uniformity of the product, respectively. In actual applications, they restrict each other. Therefore, through weighted integration of the objective function, the system has a unified optimization path. During the modeling process, a weight value is assigned to each indicator. The weight setting is pre-configured based on the product application focus or adaptively adjusted through historical process data. For example, in the nano-drug delivery system for nerve blockade, more attention is paid to drug release stability. At this time, the weight of encapsulation efficiency is appropriately increased; if it is used for rapid absorption, particle size uniformity and small particle size are more important, and the weight of average particle size and PDI needs to be increased. After setting the weight, the normalized objective function of each indicator is constructed separately, and the three are combined into an overall comprehensive optimization objective function through weighted linear superposition. Based on the comprehensive optimization objective function, the numerical partial derivatives of all eight process control variables are solved one by one. That is, the variables such as reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time are perturbed and interpolated. The central difference method is used to calculate the degree of influence of the variable change on the objective function, that is, the partial derivative value. The larger the partial derivative, the stronger the influence of the variable on the overall preparation performance, and vice versa. In this calculation process, to ensure that the variable perturbation is within the physical constraints of the system, the perturbation amplitude is set according to the ratio of the current value of the variable. At the same time, the influence of cross-interference between variables on the accuracy of the partial derivative is considered. The high-order coupling error is reduced by independent variable perturbation and fixed cross control strategy to obtain the partial derivative response vector. All partial derivative values ​​are sorted by absolute value, and the eight variables are arranged from high to low according to the degree of influence on the objective function to form a variable adjustment priority sequence. Each variable in the variable adjustment priority sequence is assigned an adaptive learning rate parameter. Each variable's learning rate is updated based on its historical response performance over the control cycle. If a variable leads to a continuous decrease in the objective function during previous adjustments, the learning rate is adjusted upward to accelerate convergence. If the objective function oscillates or rebounds after adjustment, the learning rate is reduced to prevent system overshoot or uncontrolled oscillation, thus achieving dynamic and precise step-size control. Based on the partial derivatives of the objective function and the adaptive learning rate parameter, a gradient descent method is applied to each variable to calculate the adjustment amplitude and output a set of variable adjustment instructions. These instructions act synchronously on all variable channels, finely adjusting the reaction temperature, implementing precise buffering of the pH value, performing dynamic shear compensation on the stirring speed, performing micro-reconfiguration of lidocaine and auxiliary drug concentrations, adjusting the crosslinking strength curve of the carrier concentration, performing acoustic energy density correction on the ultrasound power, and compensating the reaction time window through delay or schedule adjustments.

[0111] In a specific embodiment, the execution step sorts the absolute values ​​of the partial derivatives of the multiple objective functions to obtain a variable adjustment sequence, and assigns a learning rate to each variable in the variable adjustment sequence to obtain an adaptive learning rate parameter. The process may specifically include the following steps:

[0112] The absolute value calculation of the partial derivative values ​​of the objective function corresponding to the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time was performed respectively to obtain the absolute value data of the partial derivatives;

[0113] Sorting is performed based on the absolute value data of partial derivatives to obtain the variable adjustment sequence;

[0114] The initial learning rate weight is allocated according to the adjustment priority of each process variable in the variable adjustment sequence to obtain the initial learning rate parameter;

[0115] The numerical change trend of the comprehensive optimization objective function within the continuous control period is monitored and analyzed. When the objective function value continuously decreases, the initial learning rate parameter is incrementally adjusted. When the objective function value oscillates, the initial learning rate parameter is decremented to obtain an adaptive learning rate parameter.

[0116] Specifically, the absolute values ​​of the partial derivatives of the objective function corresponding to reaction temperature, pH, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power, and reaction time are calculated to eliminate interference from directional signs and retain only the response intensity of each variable's impact on the objective function. This produces a data set of partial derivative absolute values. This data set is sorted from largest to smallest to form a variable adjustment priority queue. This sorting result reflects which variables are most sensitive to the regulation of the comprehensive performance indicator within the current fine-tuning cycle, i.e., those with the greatest regulatory potential and the widest range of influence. After the sorting is completed, a variable adjustment sequence is obtained. Based on the variable adjustment priority, an initial learning rate weight is assigned to each variable, resulting in an initial learning rate parameter set. The learning rate represents the adjustment step size of each variable during the gradient descent adjustment process. A larger value indicates a greater change in the variable corresponding to a unit partial derivative, thus playing an amplifying or suppressing role in system regulation. The initial learning rate allocation strategy follows the principle of matching the influence of the variables. Specifically, variables with larger absolute values ​​of partial derivatives and greater influence are assigned higher initial learning rates to accelerate convergence. Conversely, variables with weaker influence are assigned smaller learning rates to reduce misadjustments or system perturbations. Learning rates are normalized to ensure that the adjustment range of all variables in their initial states is constrained within a safe margin. To ensure the dynamic adaptability of the learning rate during system operation, an adaptive learning rate update mechanism is established to monitor and identify the numerical trend of the objective function within consecutive control cycles in real time. After each control cycle, the combined objective function values ​​of the current and previous cycles are recorded, and the direction of change is determined based on the trend. If the objective function value shows a monotonically decreasing trend over multiple consecutive cycles (e.g., three consecutive cycles) with fluctuations less than a preset threshold, indicating that the current learning rate setting is conservative and the system's adjustment response can tolerate a faster step size, the incremental adjustment mechanism is triggered, increasing the initial learning rate parameter by a fixed percentage (e.g., 10%) and updating it to the new adaptive learning rate parameter. This accelerates system adjustment in the next cycle and improves convergence efficiency. On the contrary, when the system detects that the objective function oscillates in two or more cycles, that is, the value fluctuates up and down and does not show a monotonically decreasing trend, it means that the current learning rate is too high, causing the system to repeatedly adjust without convergence. At this time, the reduction adjustment strategy is triggered, and the learning rate parameter is lowered by a fixed ratio (such as 20%), so that the variable adjustment in the next cycle is more gentle, which helps to improve system stability and avoid over-adjustment.

[0117] The above describes the intelligent preparation process control method of the compound lidocaine nanoparticle drug delivery system in the embodiment of the present invention. The following describes the intelligent preparation process control system of the compound lidocaine nanoparticle drug delivery system in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, an intelligent control system for the preparation of compound lidocaine nanoparticles includes:

[0118] A coupling analysis module 201 is used to perform coupling analysis on reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time to obtain a parameter variable coupling model;

[0119] Calculation module 202, for calculating the drug loading saturation of the compound lidocaine drug loading according to the parameter variable coupling model, and determining whether the current preparation time is in the drug loading efficiency optimization stage, the particle size distribution control stage, or the comprehensive fine-tuning stage;

[0120] The adjustment module 203 is used to perform temperature gradient adjustment and pH buffer adjustment to obtain temperature control parameters and pH control parameters when in the drug loading efficiency optimization stage;

[0121] The control module 204 is used to perform particle size distribution control when in the particle size distribution control stage to obtain stirring speed control parameters and ultrasonic power control parameters;

[0122] The synchronous fine-tuning module 205 is used to perform variable partial derivative calculation and synchronous fine-tuning when in the comprehensive fine-tuning stage to obtain a variable adjustment sequence and a corresponding variable adjustment amplitude.

[0123] Through the synergistic cooperation of the above-mentioned components, the present invention incorporates the changes in the ionization state of drug molecules under different pH environments into mathematical modeling by introducing a protonation correction coefficient based on the lidocaine acid dissociation constant. This establishes a system of coupled differential equations including protonation correction, overcoming the technical defect of the prior art that ignores the influence of the protonation state of drug molecules on drug loading behavior. The preparation process is divided into a drug loading efficiency optimization stage, a particle size distribution control stage, and a comprehensive fine-tuning stage according to the drug loading saturation value. Specific control algorithms are used to meet the technical requirements of different stages, overcoming the limitations of the prior art's fixed parameter control throughout the process, achieving dynamic adaptive adjustment of the preparation process, and significantly improving the level of intelligent control of the nano-drug delivery preparation process. An eight-variable coupled model covering reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power, and reaction time is established. Through orthogonal experimental design, the nonlinear interaction relationship between variables is systematically identified, achieving precise coordinated control of multiple variables, and solving the technical problem that the existing single-factor or simple multi-factor control methods cannot accurately describe complex coupling relationships. The partial derivatives of the objective function with respect to each process variable are calculated in real time through the numerical differentiation method, the variable adjustment priority and adjustment amplitude are quantitatively determined, an adaptive learning rate adjustment mechanism is established, and precise control based on mathematical principles is achieved. This avoids the subjective errors of the prior art that rely on experience to adjust parameters, and achieves predictive control of the preparation process of the compound lidocaine nano-drug delivery. Compared with the passive parameter adjustment of the prior art, the present invention can actively predict and adjust the preparation process, significantly improving the stability and consistency of product quality.

[0124] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0126] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent preparation process control method for compound lidocaine nanoparticle drug delivery, characterized in that: include: The reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time were coupled and analyzed to obtain a parameter variable coupling model. Calculating the drug loading saturation of the compound lidocaine drug loading according to the parameter variable coupling model, and determining whether the current preparation moment is in the drug loading efficiency optimization stage, the particle size distribution control stage, or the comprehensive fine-tuning stage; When in the drug loading efficiency optimization stage, temperature gradient adjustment and pH buffer adjustment are performed to obtain temperature control parameters and pH control parameters; When in the particle size distribution control stage, particle size distribution control is performed to obtain stirring speed control parameters and ultrasonic power control parameters; When in the comprehensive fine-tuning stage, variable partial derivative calculation and synchronous fine-tuning are performed to obtain the variable adjustment sequence and the corresponding variable adjustment amplitude.

2. The intelligent preparation process control method of compound lidocaine nano-drug delivery according to claim 1, characterized in that: The coupling analysis of the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time is performed to obtain a parameter variable coupling model, including: The pH environment value was corrected for protonation state based on the lidocaine acid dissociation constant to obtain the protonation correction coefficient. At the same time, the reaction temperature value was calculated by Arrhenius diffusion to obtain the molecular diffusion coefficient. Calculating a composite diffusion parameter based on the protonation correction coefficient and the molecular diffusion coefficient, and calculating the carrier crosslinking degree based on the carrier material concentration and the reaction time values ​​to obtain a carrier crosslinking degree parameter; Establishing a coupled differential equation group of drug release behavior and carrier binding behavior based on the composite diffusion parameter and the carrier cross-linking degree parameter; Based on the coupled differential equations, an orthogonal test is performed on the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time to obtain a parameter variable coupling model.

3. The intelligent preparation process control method of compound lidocaine nano-drug delivery according to claim 2, characterized in that: The orthogonal test is performed on the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time based on the coupled differential equations to obtain a parameter variable coupling model, including: The three levels corresponding to the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time were set respectively, and the orthogonal experimental design was performed to obtain the orthogonal experimental design table; The coupled differential equations are numerically solved according to the parameter values ​​of each group of experimental conditions in the orthogonal experimental design table to obtain the theoretical average particle size and theoretical encapsulation efficiency of the compound lidocaine nanoparticles under each group of experimental conditions; Based on the theoretical average particle size and the theoretical encapsulation efficiency, variance analysis and range analysis were performed to obtain a first coupling effect coefficient between the reaction temperature and the lidocaine concentration and a second coupling effect coefficient between the pH value and the carrier material concentration; The coefficients of the coupled differential equation group are adjusted according to the first coupling effect coefficient and the second coupling effect coefficient to obtain a parameter variable coupling model.

4. The intelligent preparation process control method of compound lidocaine nano-drug delivery according to claim 1, characterized in that: The method of calculating the drug loading saturation of the compound lidocaine drug loading according to the parameter variable coupling model and determining whether the current preparation moment is in the drug loading efficiency optimization stage, the particle size distribution control stage, or the comprehensive fine-tuning stage includes: The drug loading amount and the maximum drug loading capacity at the current preparation moment are calculated based on the parameter variable coupling model to obtain the instantaneous drug loading amount and the theoretical maximum drug loading amount of the compound lidocaine drug loading; Calculating a drug loading saturation ratio based on the instantaneous drug loading and the theoretical maximum drug loading to obtain a drug loading saturation; A numerical interval judgment is performed on the drug loading saturation. When the drug loading saturation is less than the first target value, it is determined that the preparation process at the current preparation moment is in the drug loading efficiency optimization stage. When the drug loading saturation is greater than the second target value, it is determined that the preparation process at the current preparation moment is in the particle size distribution control stage. When the drug loading saturation is between the first target value and the second target value, it is determined that the preparation process at the current preparation moment is in the comprehensive fine-tuning stage.

5. The intelligent preparation process control method of compound lidocaine nano-drug delivery according to claim 4, characterized in that: The drug loading amount and the maximum drug loading capacity at the current preparation moment are calculated based on the parameter variable coupling model to obtain the instantaneous drug loading amount value and the theoretical maximum drug loading amount value of the compound lidocaine drug loading, including: The reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time at the current preparation moment are collected to obtain the process parameter combination at the current preparation moment; Based on the process parameter combination, the protonation correction coefficient and the composite diffusion parameter in the parameter variable coupling model are dynamically updated and calculated to obtain the lidocaine protonation state parameter and the temperature-corrected molecular diffusion rate parameter under the current pH environment; Solving the coupled differential equations according to the lidocaine protonation state parameter and the molecular diffusion rate parameter to obtain the instantaneous drug loading corresponding to the number of compound lidocaine molecules bound to the carrier at the current preparation moment; The saturation upper limit of the maximum binding capacity of the carrier was calculated based on the carrier cross-linking degree parameters and the current carrier material concentration to obtain the theoretical maximum drug loading capacity.

6. The intelligent preparation process control method of compound lidocaine nano-drug delivery according to claim 1, characterized in that: When in the drug loading efficiency optimization stage, performing temperature gradient adjustment and pH buffer adjustment to obtain temperature control parameters and pH control parameters includes: When in the drug loading efficiency optimization stage, the drug loading efficiency deviation is calculated based on the current drug loading saturation and the target drug loading saturation to obtain the drug loading efficiency deviation value. At the same time, the encapsulation deviation is calculated based on the current encapsulation efficiency and the target encapsulation efficiency to obtain the encapsulation efficiency deviation value; Calculating the temperature gradient increment according to the drug loading efficiency deviation value and the preset temperature adjustment coefficient to obtain a temperature adjustment increment parameter, and performing temperature control on the current reaction temperature and the temperature adjustment increment parameter to obtain a temperature control parameter; A pH buffer increment calculation is performed based on the encapsulation efficiency deviation value and a preset pH adjustment coefficient to obtain a pH adjustment increment parameter, and pH control is performed on the current pH value and the pH adjustment increment parameter to obtain a pH control parameter.

7. The intelligent preparation process control method of compound lidocaine nano-drug delivery according to claim 1, characterized in that: When in the particle size distribution control stage, performing particle size distribution control to obtain stirring speed control parameters and ultrasonic power control parameters includes: When in the particle size distribution control stage, the particle size deviation of the current average particle size of the compound lidocaine nanoparticles and the target average particle size is calculated to obtain the average particle size deviation value, and the dispersion deviation of the current polydispersity index and the target polydispersity index is calculated to obtain the polydispersity index deviation value; Calculating a stirring shear force increment according to the average particle size deviation value and a preset shear force adjustment coefficient to obtain a stirring shear force adjustment parameter, and controlling the stirring speed based on the stirring shear force adjustment parameter and the current stirring speed to obtain a stirring speed control parameter; An ultrasonic sound field increment is calculated based on the polydispersity index deviation value and the preset sound field intensity adjustment coefficient to obtain an ultrasonic sound field intensity adjustment parameter, and ultrasonic power control is performed according to the ultrasonic sound field intensity adjustment parameter and the current ultrasonic power to obtain an ultrasonic power control parameter.

8. The intelligent preparation process control method of compound lidocaine nano-drug delivery according to claim 1, characterized in that: When in the comprehensive fine-tuning stage, variable partial derivative calculation and synchronous fine-tuning are performed to obtain a variable adjustment sequence and a corresponding variable adjustment amplitude, including: When in the comprehensive fine-tuning stage, the average particle size, encapsulation efficiency and polydispersity index of the current compound lidocaine nanoparticles are weighted and a comprehensive optimization objective function including the average particle size objective function, the encapsulation efficiency objective function and the polydispersity index objective function is constructed; Based on the comprehensive optimization objective function, numerical differential calculations are performed on the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time to obtain multiple objective function partial derivative values; Sorting the multiple objective function partial derivative values ​​by absolute value to obtain a variable adjustment sequence, and allocating a learning rate to each variable in the variable adjustment sequence to obtain an adaptive learning rate parameter; A gradient descent adjustment amplitude is calculated based on the partial derivative value of the objective function and the adaptive learning rate parameter to obtain a variable adjustment amplitude.

9. The intelligent preparation process control method of compound lidocaine nano-drug delivery according to claim 8, characterized in that: The step of sorting the absolute values ​​of the partial derivatives of the multiple objective functions to obtain a variable adjustment sequence, and allocating a learning rate to each variable in the variable adjustment sequence to obtain an adaptive learning rate parameter includes: The absolute value calculation of the partial derivative values ​​of the objective function corresponding to the reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time was performed respectively to obtain the absolute value data of the partial derivatives; Sorting based on the absolute value data of the partial derivatives to obtain a variable adjustment sequence; Performing initial learning rate weight allocation according to the adjustment priority of each process variable in the variable adjustment sequence to obtain an initial learning rate parameter; The numerical change trend of the comprehensive optimization objective function within the continuous control period is monitored and analyzed. When the objective function value continuously decreases, the initial learning rate parameter is incrementally adjusted. When the objective function value oscillates, the initial learning rate parameter is decremented to obtain an adaptive learning rate parameter.

10. An intelligent preparation process control system for compound lidocaine nano-drug delivery, characterized in that: The intelligent preparation process control method for executing the compound lidocaine nano-drug delivery method according to any one of claims 1 to 9, wherein the intelligent preparation process control system for the compound lidocaine nano-drug delivery method comprises: The coupling analysis module is used to perform coupling analysis on reaction temperature, pH value, stirring speed, lidocaine concentration, auxiliary drug concentration, carrier material concentration, ultrasonic power and reaction time to obtain a parameter variable coupling model; a calculation module for calculating the drug loading saturation of the compound lidocaine drug loading according to the parameter variable coupling model, and determining whether the current preparation moment is in the drug loading efficiency optimization stage, the particle size distribution control stage, or the comprehensive fine-tuning stage; An adjustment module is used to perform temperature gradient adjustment and pH buffer adjustment when in the drug loading efficiency optimization stage to obtain temperature control parameters and pH control parameters; A control module is used to perform particle size distribution control when in the particle size distribution control stage to obtain stirring speed control parameters and ultrasonic power control parameters; The synchronous fine-tuning module is used to perform variable partial derivative calculation and synchronous fine-tuning when in the comprehensive fine-tuning stage to obtain a variable adjustment sequence and a corresponding variable adjustment amplitude.

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