A liquid preparation production system for oxytocin injection

Abstract

The present invention relates to the technical field of production scheduling control, and specifically to a liquid preparation production system for oxytocin injection, comprising: a multi-condition coupling modeling module that simulates the fluid flow and mixing in the liquid preparation tank and generates process scheduling instructions; a temperature-deviation adjustment module that uses an LSTM network to predict the oxytocin degradation rate and compensates for the refrigeration power deviation; a light control module that generates a light avoidance operation protocol based on a photosensitive feedback adjustment strategy and in combination with the light cumulative dose to reduce the risk of photodegradation; a pH value dynamic adjustment module that constructs a buffer capacity prediction model and adjusts the metering pump drive frequency through a fuzzy PID controller to achieve precise control of the pH value. The PLC control collaborative decision-making module calculates the QRI index using a dynamic weight allocation strategy, non-linearly compensates for the excess parameters, and generates a fault tree set to achieve risk prediction and optimal control. The present invention improves the stability, uniformity, and reliability of the liquid preparation process.

Description

Technical Field

[0001] The present invention relates to the technical field of production scheduling control, and particularly to a liquid preparation production system for oxytocin injection. Background Technique

[0002] The production of oxytocin injection involves multiple links such as raw material weighing, dissolution, liquid preparation, filtration, sterilization, filling, and sealing. In the existing automated liquid preparation production system, although programmed control technology based on programmable logic controller (PLC) and distributed control system (DCS) is adopted, there are still technical bottlenecks in the deep integration of industrial Internet of Things and advanced process control technology.

[0003] Firstly, there are limitations in the dynamic ratio accuracy control of highly active drug components. The existing system does not construct a dynamic compensation module based on mass flow meters and multivariable PID controllers. Especially during continuous feeding, the PLC system cannot adaptively adjust the motion curve of the servo motor through real-time viscosity sensor data, resulting in the accumulation of volume measurement deviation of temperature-sensitive raw materials and affecting the uniformity of the final product content. Secondly, the multi-parameter coupling control mechanism is not yet perfect. When synchronously regulating key quality attributes such as pH value, osmotic pressure, and aseptic conditions, there is an easy interaction interference between control loops, leading to process parameter drift beyond the scope specified in the pharmacopoeia. Thirdly, the adaptability of the existing program control architecture to complex production scenarios is insufficient. Especially when dealing with the switching of different specification batches, there is a lack of self-learning optimization algorithm based on real-time quality data, making it difficult to achieve dynamic correction of process parameters and intelligent matching of equipment operation modes. In addition, the integrity and real-time performance of the production data traceability system are defective, failing to fully meet the requirements of Good Manufacturing Practice (GMP) for continuous monitoring and audit tracking of key process parameters, and affecting the quality risk control efficiency. The above technical defects restrict the robustness and quality control level of the oxytocin injection production process and urgently need to be solved by an innovative system.

[0004] Therefore, a liquid preparation production system for oxytocin injection is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a liquid preparation production system for oxytocin injection to improve the stability, uniformity and reliability of the liquid preparation process. Through a multi-condition coupling modeling module, the fluid flow and mixing conditions in the liquid preparation tank are simulated to generate process scheduling instructions; the temperature-deviation adjustment module uses an LSTM network to predict the oxytocin degradation rate and compensate for the refrigeration power deviation; the light control module generates a light-shielding operation protocol based on a photosensitive feedback adjustment strategy and in combination with the cumulative light dose to reduce the risk of photodegradation; the pH value dynamic adjustment module constructs a buffer capacity prediction model and adjusts the driving frequency of the metering pump through a fuzzy PID controller to achieve precise control of the pH value. The PLC control collaborative decision-making module calculates the QRI index using a dynamic weight allocation strategy, non-linearly compensates for the excess parameters, and generates a fault tree set to achieve risk prediction and optimal control.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A liquid preparation production system for oxytocin injection, comprising:

[0008] A multi-condition coupling modeling module, which is used to simulate the fluid flow state of oxytocin and the material mixing situation in the liquid preparation tank based on a fluid dynamics model, and construct process scheduling instructions; calculate the uniformity index under the process scheduling instructions based on time-dependent simulation, and update the process scheduling instructions;

[0009] A temperature-deviation adjustment module, which is used to predict the oxytocin degradation rate using a long short-term memory network according to temperature time series data and compensate for the refrigeration power deviation of the process scheduling instructions;

[0010] A light control module, which is used to calculate the illuminance distribution in the liquid preparation tank using a photosensitive feedback adjustment strategy and generate a light-shielding operation protocol in combination with the cumulative light dose;

[0011] A pH value dynamic adjustment module, which is used to establish a buffer capacity prediction model to calculate the buffer capacity and adjust the driving frequency of the metering pump through a fuzzy PID controller;

[0012] A PLC control collaborative decision-making module, which is used to calculate the QRI index using a dynamic weight allocation strategy according to temperature control parameters, pH value, the cumulative light dose and liquid preparation time, generate a fault tree set, and non-linearly compensate for the excess parameters.

[0013] Further, the construction process of the process scheduling instructions includes:

[0014] Introduce The turbulent model simulates the stirring turbulent behavior, and the Navier-Stokes equation is used to establish a model of the oxytocin fluid flow state for calculating the velocity field and pressure field in the liquid preparation tank; the convection-diffusion equation is used to model the material mixing situation for calculating the concentration distribution;

[0015] Based on the finite volume method, the liquid preparation tank is divided into grid cells, and local grid refinement is performed in the stirring area and the feeding port area; an adaptive grid adjustment strategy is used to calculate the division error and update the grid cells;

[0016] According to the velocity field, the pressure field and the concentration distribution, an integral equation is established for each grid cell based on the Gauss-Green theorem, and the SIMPLE algorithm is used for velocity-pressure coupling solution, and the velocity field, the pressure field and the concentration distribution are iteratively updated to generate the process scheduling instruction.

[0017] Furthermore, the update process of the process scheduling instruction includes:

[0018] At each time step, calculate the homogeneity index , expressed as:

[0019] ;

[0020] where is the concentration at grid cell , is the average concentration of the liquid preparation tank, is the actual mixing time, is the theoretical mixing time, is the dimensionless weight coefficient, is the total number of grid cells;

[0021] If is less than the preset threshold, the feeding order is adjusted using the genetic algorithm, and the process scheduling instruction is updated.

[0022] Furthermore, the implementation process of the genetic algorithm includes:

[0023] The oxytocin material order is set using the permutation coding method, and an initial population is generated according to the oxytocin material order and the process scheduling instruction;

[0024] Maximizing the homogeneity index is set as the fitness function, and roulette wheel selection operation, partial matching crossover and swap mutation are adopted. When the maximum number of iterations is reached, the updated process scheduling instruction is output.

[0025] Furthermore, the process of generating the light avoidance operation instruction includes:

[0026] Deploy virtual light sensors on grid cells, calculate light attenuation using the Beer-Lambert law, and obtain the illuminance distribution;

[0027] Multiply the illuminance distribution, exposure time, and photodegradation coefficient of the grid cell to obtain the light cumulative dose;

[0028] For each grid cell, compare the light cumulative dose with the safe light threshold. If the light cumulative dose is greater than the safe light threshold, generate the light avoidance operation instruction; otherwise, no additional operation is required;

[0029] Among them, the light avoidance operation instruction includes local light avoidance and global light avoidance.

[0030] Further, the process of adjusting the driving frequency of the metering pump includes:

[0031] Collect the pH value in the dispensing tank through a pH sensor and record the amount of alkali input;

[0032] Calculate the buffer capacity using the Henderson-Hasselbalch equation based on the pH value and the amount of alkali;

[0033] Input the pH error, the buffer capacity, and the error change rate into the fuzzy PID controller;

[0034] Design fuzzy rules based on expert experience. The fuzzy PID controller outputs PID parameters according to the fuzzy rules;

[0035] Calculate the control signal according to the PID parameters and convert the control signal into the driving frequency of the metering pump.

[0036] Further, the process of generating and analyzing the fault tree set includes:

[0037] Define the QRI index exceeding the standard as the top event of liquid dispensing, which is used to characterize the deviation of the QRI index from the final fault manifestation during the liquid dispensing process;

[0038] Define the temperature exceeding the standard, abnormal pH value, light cumulative dose exceeding the standard, and liquid dispensing time exceeding the limit as intermediate events of liquid dispensing, which are used to describe the key factors affecting the top event of liquid dispensing;

[0039] Define equipment failure, sensor error, and regulation strategy failure as basic events of liquid dispensing, which are used to describe the physical and system factors at the bottom layer that trigger the intermediate events of liquid dispensing;

[0040] Connect the top event of liquid dispensing, the intermediate events of liquid dispensing, and the basic events of liquid dispensing using logic gates to generate the fault tree set;

[0041] Through qualitative analysis, determine the key path and minimum cut set of the liquid preparation; through quantitative analysis, calculate the probability of the top event of the liquid preparation and identify the key risk points.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] 1. Through multi-condition coupling modeling, the present invention uses computational fluid dynamics to simulate the flow state of oxytocin fluid in the liquid preparation tank and the material mixing situation, and can accurately predict the fluid behavior. Based on the SIMPLE algorithm, velocity-pressure coupling solution is carried out, and the process scheduling instructions are dynamically optimized. At the same time, the feeding sequence is adaptively adjusted by combining the genetic algorithm, the uniformity index is improved, and the stability and consistency of the liquid preparation process are ensured, thereby reducing the efficacy fluctuation of oxytocin injection caused by uneven mixing.

[0044] 2. The present invention uses a long short-term memory network to predict the degradation rate of oxytocin, dynamically compensates the refrigeration power, optimizes the temperature control strategy, can accurately control the temperature change in the liquid preparation process, and ensures the stable preparation of the liquid medicine in a suitable temperature environment. In addition, through the photosensitive feedback adjustment strategy, the illuminance distribution in the liquid preparation tank is calculated, and the light avoidance operation instruction is generated based on the cumulative light dose. Combining the virtual light sensor and the Beer-Lambert law to simulate the light attenuation, local and global light avoidance control is realized, the light degradation risk is reduced, and the quality stability of oxytocin injection is improved.

[0045] 3. The present invention calculates the QRI index through the dynamic weight task strategy, and combines the nonlinear compensation technology to optimize the excess parameters, improving the stability of the oxytocin injection liquid preparation process. Using the fault tree set analysis method, a fault tree set model with an excessive QRI index is constructed, key risk factors such as temperature, pH value, cumulative light dose, and liquid preparation time are identified, and the probability of the top event of the liquid preparation is calculated. Through qualitative and quantitative analysis, the liquid preparation process parameters are optimized, the system safety and fault diagnosis ability are improved, and the reliability of the oxytocin injection production process is thus enhanced. Description of the Drawings

[0046] Figure 1 It is a schematic structural diagram of a liquid preparation production system for an oxytocin injection provided by the present invention;

[0047] Figure 2 It is a schematic flow diagram of generating and analyzing a fault tree set of the present invention. Detailed Embodiments

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Please refer to Figures 1 to 2 , the present invention provides a liquid preparation production system for oxytocin injection, and the technical solution is as follows:

[0050] Embodiment 1:

[0051] Oxytocin is an important bioactive polypeptide drug, which is widely used in obstetric clinics to promote uterine contractions. However, during the liquid preparation process of oxytocin injection, it is easily affected by factors such as temperature, pH value, light, and the feeding order, resulting in a decrease in biological activity, a decline in the stability of the preparation, and it is difficult to ensure production consistency.

[0052] Currently, traditional liquid preparation systems mainly rely on fixed feeding orders and empirical parameters for regulation, and it is difficult to adapt to various working conditions. In addition, the existing temperature control methods do not fully consider the degradation characteristics of oxytocin, the pH value adjustment lacks accurate prediction, and the management of the light environment is also relatively rough, resulting in fluctuations in the quality of the liquid medicine and affecting the safety and effectiveness of the final product. Therefore, it is necessary to develop an intelligent and multi-variable coupled control liquid preparation production system for oxytocin injection to optimize the feeding strategy, improve product stability, and ensure the consistency of production quality.

[0053] In order to optimize the feeding strategy and improve the stability of product quality, as Figure 1 shown, a liquid preparation production system for oxytocin injection is used, including:

[0054] Refer to Figure 1 the multi-condition coupling modeling module, which is used to simulate the fluid flow state and material mixing situation of oxytocin in the liquid preparation tank based on the hydrodynamic model, and construct a process scheduling instruction; calculate the uniformity index under the process scheduling instruction based on time-dependent simulation, and update the process scheduling parameters.

[0055] Among them, the process scheduling instruction refers to the specific material feeding order formulated according to the optimization goal during the liquid preparation process, which is used to guide key links such as equipment operation and time arrangement.

[0056] Furthermore, the construction process of the process scheduling instruction includes:

[0057] Introduce the turbulence model to simulate the stirring turbulence behavior. This model calculates the turbulent viscosity by solving the transport equations of turbulent kinetic energy and dissipation rate , so as to describe the influence of turbulence on the convective flow field. Then, turbulent viscosity is introduced into the Navier-Stokes equations to establish a model of the oxytocin fluid flow state for calculating the velocity field in the liquid preparation tank and the pressure field , expressed as:

[0058] ;

[0059] wherein, is the fluid density, is the dynamic viscosity of the fluid, is the external force, is the gradient operator, is the time variable, is the partial derivative symbol.

[0060] The convective-diffusion equation is used to model the material mixing situation for calculating the concentration distribution , expressed as:

[0061] ;

[0062] wherein, is the diffusion coefficient, and the convective term depends on the velocity field solved by the Navier-Stokes equations , realizing the coupling of the flow field and the concentration field.

[0063] For numerical solution, the liquid preparation tank is divided into grid cells based on the finite volume method, and local grid refinement is performed in the stirring area and the feed port area to improve the calculation accuracy of the key areas; an adaptive grid adjustment strategy is used to calculate the division error and update the grid cells.

[0064] Among them, the present invention adopts an adaptive grid adjustment strategy, effectively solving the limitations of static grids in traditional fluid simulation. Traditional methods often lead to insufficient accuracy or computational redundancy because they cannot adapt to the dynamic changes in flow characteristics. To this end, the present invention introduces the concept of flow gradient, which reflects the rate of change of fluid physical quantities (such as velocity, pressure, and concentration, etc.) in space. Common flow gradients include velocity gradient and pressure gradient, etc. Based on the flow gradient or residual, the system can dynamically adjust the grid density. For example, in areas with complex flow such as near the stirrer, the grid resolution is increased by 2 times to capture fine flow characteristics. For areas that need to be refined, by subdividing the existing grids, larger grid cells are divided into multiple smaller cells, thereby improving local accuracy. On the contrary, in the stable area far from the stirring, the number of grids can be reduced by 50%. By merging adjacent grid cells (such as merging four small square grids into a larger square, or eight cubic grids into a larger cube), the computational burden is reduced. This dynamic adjustment strategy significantly improves efficiency while ensuring computational accuracy. Experiments show that compared with static grids, the computational time is reduced by about 22.35%, achieving a balance between accuracy and efficiency and providing a better solution for complex fluid simulation.

[0065] After the grid cell division is completed, based on the velocity field, the pressure field, and the concentration distribution, an integral equation is established for each grid cell according to the Gauss-Green theorem, expressed as:

[0066] ;

[0067] Among them, is a conserved quantity (such as velocity or concentration distribution ), is the operation of integrating the conserved quantity over the volume , is the flux, representing the flow rate of the conserved quantity through a unit area, is the unit normal vector, is the operation of performing an area integral of the flux over the surface of the grid cell, is the source term, representing the factor that generates or consumes the conserved quantity inside the grid cell, is the operation of integrating the source term over the volume of the grid cell.

[0068] To solve the velocity field and the pressure field For the coupling relationship, the SIMPLE algorithm is used to solve the velocity-pressure coupling, and the velocity field, the pressure field, and the concentration distribution are iteratively updated until the convergence condition is satisfied (such as the residual being less than ), forming a closed-loop coupling solution to generate the process scheduling instruction.

[0069] Among them, based on the obtained flow field (velocity field and pressure field ), and the concentration field (concentration distribution ), the material mixing uniformity is analyzed. For example, if the concentration deviation is less than 5%, it is considered that the mixing is uniform. If the requirement is not met, the process scheduling instruction is readjusted, such as adjusting the stirring speed or changing the feeding order.

[0070] By accurately simulating fluid flow and material mixing, it ensures that the concentration distribution of oxytocin injection is uniform, reduces the local concentration deviation, and thus improves the product quality. Based on the real-time calculation results, the system can dynamically adjust the stirring speed and feeding strategy. At the same time, the adaptive grid adjustment strategy reduces the number of grids in the stable area, effectively reducing the computational resource requirements. In addition, the SIMPLE algorithm and iterative solution ensure that a stable numerical solution can still be obtained under complex turbulent conditions, improving the system reliability.

[0071] Furthermore, the update process of the process scheduling instruction includes:

[0072] At each time step of the liquid preparation production, the system obtains the concentration distribution of each grid unit in the liquid preparation tank through numerical simulation and calculates the uniformity index to quantify the degree of material mixing uniformity. At each time step, the uniformity index under the process scheduling instruction is calculated, expressed as:

[0073] ;

[0074] Among them, is the concentration at grid unit , is the average concentration of the liquid preparation tank, is the actual mixing time, is the theoretical mixing time (predicted based on experience or model, for example, set to 10 minutes), is a dimensionless weight coefficient used to adjust the influence of time on (for example ), is the total number of grid units;

[0075] A preset threshold is set, for example, 0.95, representing the goal of mixing uniformity. If If it is less than the preset threshold, the genetic algorithm is used to adjust the feeding order and update the process scheduling instruction.

[0076] The update process of the process scheduling instruction ensures a uniform distribution of the material concentration in the liquid dispensing tank and reduces the local concentration deviation by calculating in real time and dynamically adjusting the feeding order, thereby improving the quality of oxytocin injection. The application of the genetic algorithm avoids the inefficiency of traditional trial and error, quickly finds the optimal feeding order, and the system automatically adjusts the process parameters based on real-time data to form a closed-loop control, which can adapt to the dynamic changes in the production process and improve the robustness and flexibility of the system. In addition, the UI formula comprehensively evaluates the concentration deviation and mixing time, and through the flexible adjustment of the weight coefficient ensures the accuracy and scientificity of the mixing effect evaluation.

[0077] Furthermore, the genetic algorithm is used to optimize the feeding order of oxytocin materials to maximize the uniformity index , and the implementation process of the genetic algorithm includes:

[0078] The permutation coding method is used to set the order of oxytocin materials. For example, if there are three materials A, B, and C, the feeding order can be expressed as [A, B, C], that is, material A is fed first, then material B, and finally material C. According to the oxytocin material order and the process scheduling instruction (such as [A, C, B]), a group of feeding orders is randomly generated as the initial population.

[0079] Maximizing the uniformity index is set as the fitness function, The higher it is, the better the mixing uniformity. And the roulette wheel selection operation, partial matching crossover, and swap mutation are adopted. When the maximum number of iterations is reached, such as 100 generations, the best feeding order [B, C, A] is found, and its is the largest, and the updated process scheduling instruction [B, C, A] is output. Through steps such as permutation coding, initial population generation, fitness function, roulette wheel selection, partial matching crossover, and swap mutation, the genetic algorithm successfully optimizes the feeding order of oxytocin materials. The system uses these steps for iterative optimization, can dynamically adapt to changes in the production process, such as fluctuations in material properties, so as to maintain the optimization effect and ensure that the feeding order of oxytocin injection is always in the optimal state.

[0080] Refer to Figure 1 's temperature-deviation adjustment module, which is used to predict the oxytocin degradation rate using a long short-term memory network based on temperature time series data and compensate for the cooling power deviation of the process scheduling instruction;

[0081] Specifically, by deploying temperature sensors at multiple key positions in the liquid preparation tank, temperature time-series data is collected in real time. After being collected by the PLC controller, these data are stored in the database, and the corresponding actual oxytocin degradation rate is recorded simultaneously to form a training data set. Using this data set to construct a long short-term memory network model, the temperature time-series data of the past n seconds is input into the network to predict the future temperature change trend and the future oxytocin degradation rate. By comparing the actual oxytocin degradation rate with the future oxytocin degradation rate, the loss is calculated using the mean square error to optimize the model parameters. Then, the current temperature time-series data is input into the trained LSTM model to predict the oxytocin degradation rate within the future time interval The oxytocin degradation rate within is predicted. Based on the prediction result, the PID control algorithm is used to calculate the cooling power deviation and output a new cooling power instruction. The PLC controller adjusts the cooling system accordingly to dynamically increase or decrease the cooling power to ensure the stability of the temperature during the liquid preparation process, thereby reducing the risk of oxytocin degradation and improving the stability of the liquid medicine quality.

[0082] Reference Figure 1 The light control module is used to calculate the illuminance distribution in the liquid preparation tank using the photosensitive feedback regulation strategy and generate a light avoidance operation protocol in combination with the light cumulative dose;

[0083] Furthermore, the process of generating the light avoidance operation instruction includes:

[0084] Virtual light sensors are deployed on the grid cells, and the Beer-Lambert law is used to calculate the light attenuation to obtain the illuminance distribution, expressed as:

[0085] ;

[0086] where is the illuminance distribution at depth , is the incident light intensity, is the absorption coefficient of the oxytocin liquid, is the natural base.

[0087] Multiplying the illuminance distribution, exposure time, and photodegradation coefficient of the grid cell to obtain the light cumulative dose , expressed as:

[0088] ;

[0089] where is the exposure time of this grid cell, is the photodegradation coefficient of oxytocin.

[0090] Sample and calculate the cumulative light dose at different time points, and accumulate it to the final cumulative light dose. Set a safety light threshold, and for each of the grid cells, compare the cumulative light dose with the safety light threshold. If the cumulative light dose is greater than the safety light threshold, generate the light avoidance operation instruction; otherwise, no additional operation is performed;

[0091] The photosensitive feedback regulation strategy is a control method that dynamically adjusts the light avoidance operation instruction based on the light measurement results. The light avoidance operation instruction includes local light avoidance and global light avoidance. If only a few grid cells exceed the standard (for example, the cumulative light dose of 5 grid cells is 550 Lux·hr), generate a local light avoidance instruction, such as installing light-shielding plates in the area where the standard is exceeded. If multiple or most grid cells exceed the standard (for example, the cumulative light dose of 50 grid cells is greater than 500 Lux·hr), generate a global light avoidance instruction, such as turning off the light source or pulling down the light-shielding curtain.

[0092] The system accurately calculates the cumulative light dose of each grid cell through virtual light sensors and the Beer-Lambert law, realizes precise light avoidance operation, and avoids unnecessary intervention of traditional global light avoidance methods. The system monitors the illuminance distribution in real time and dynamically feeds back, quickly responding to light changes. For example, when the light source intensity suddenly increases, it can detect the exceeding of the standard and generate a light avoidance instruction within a few minutes, timely preventing drug degradation, thereby improving the quality of oxytocin injection products.

[0093] Reference Figure 1 The pH value dynamic regulation module is used to establish a buffer capacity prediction model to calculate the buffer capacity and adjust the driving frequency of the metering pump through a fuzzy PID controller;

[0094] Further, the process of adjusting the driving frequency of the metering pump includes:

[0095] Collect the pH value in the liquid dispensing tank through a pH sensor and record the amount of alkali input;

[0096] According to the pH value and the amount of alkali, use the Henderson-Hasselbalch equation to calculate the buffer capacity, expressed as:

[0097] ;

[0098] ;

[0099] Among them, is the acidity of the solution, is the negative logarithm of the acid dissociation constant, reflecting the strength of the acid, is the concentration of the base (i.e., the concentration of the conjugate base after acid dissociation), is the concentration of the acid (the concentration of undissociated acid molecules), is the logarithmic function, is the buffer capacity, which measures the ability of oxytocin injection to resist changes in pH value. is the change in the amount of added base. is the change amount of oxytocin injection after adding base (or acid).

[0100] Input the pH error , the buffer capacity and the error change rate into the fuzzy PID controller; the pH error is the difference between the target pH value (such as 7.00) and the actual pH value, and the error change rate is the difference between the current error and the error at the previous moment.

[0101] Design fuzzy rules according to expert experience. The fuzzy PID controller outputs PID parameters according to the fuzzy rules.

[0102] Calculate the control signal according to the PID parameters, and convert the control signal into the driving frequency of the metering pump, which is expressed as:

[0103] ;

[0104] where is the driving frequency, is the reference frequency, is the conversion coefficient, is the control signal at time, and the control signal is an instruction output by the control system for adjusting the operation of the metering pump, which is calculated by the fuzzy PID controller according to the pH error, the error change rate and the buffer capacity.

[0105] The pH value dynamic regulation module calculates the buffer capacity by using the Henderson-Hasselbalch equation through real-time collecting the pH value and the amount of base, and dynamically regulates the driving frequency of the metering pump in combination with the fuzzy PID controller, so as to realize the precise control of the pH value in the liquid preparation process, and can adjust the acid-base addition amount in real time according to the acid-base balance characteristics of the solution, ensuring the stability and consistency of the oxytocin injection liquid preparation process.

[0106] Reference Figure 1 's PLC control collaborative decision-making module is used to calculate the QRI index by using the dynamic weight allocation strategy according to the temperature control parameters, the pH value, the cumulative light dose and the liquid preparation time, generate a fault tree set, and perform non-linear compensation on the excess parameters.

[0107] where the excess parameter refers to the key parameter that exceeds the set threshold during the liquid preparation process. Calculating the QRI index is expressed as:

[0108] ​ ;

[0109] Wherein, is the QRI index, is the temperature deviation at time t, is the cumulative light dose at time t, is the liquid preparation time at , , and are the temperature weight, pH weight, light weight and time weight respectively.

[0110] Wherein, , , and The weights are allocated according to the dynamic weight allocation strategy. As shown in Table 1, the weights corresponding to different stages are given. In the dissolution stage, the pH value has the greatest impact on QRI because the dissolution efficiency is most affected by the pH value. In the mixing stage, the pH value and temperature jointly affect QRI, but the impact of light is relatively small. In the holding stage, the weight of temperature is the largest because the stability of oxytocin is mainly affected by temperature. This phased weight allocation strategy can more accurately reflect the impact of various factors on QRI at different stages, thus optimizing the control of the liquid preparation process.

[0111] Table 1 Dynamic weights

[0112] Process stage Temperature weight pH weight Light weight Time weight Dissolution stage 0.4 0.5 0.0 0.1 Mixing stage 0.3 0.4 0.1 0.2 Retention stage 0.5 0.3 0.1 0.1

[0113] Furthermore, as Figure 2 shown, the process of generating and analyzing the fault tree set includes:

[0114] Defining the QRI index exceeding the standard as the top event of liquid preparation, which is used to characterize the final fault manifestation of the QRI index deviation (QRI > 2.0) during the liquid preparation process;

[0115] Defining the temperature exceeding the standard, abnormal pH value, cumulative light dose exceeding the standard and liquid preparation time exceeding the time limit as the intermediate events of liquid preparation, which are used to describe the key factors affecting the top event of liquid preparation;

[0116] Defining equipment failures (mixer failure and refrigeration system failure), sensor errors (thermometer drift and pH sensor aging) and control strategy failures (PLC adjustment lag and parameter setting error) as the basic events of liquid preparation, which are used to describe the physical factors and system factors that trigger the intermediate events of liquid preparation at the bottom layer;

[0117] Connecting the top event of liquid preparation, the intermediate events of liquid preparation and the basic events of liquid preparation with logic gates to generate the fault tree set;

[0118] Among them, an "OR gate" is used between the top event of liquid preparation (QRI exceeding the standard) and the intermediate events of liquid preparation (each parameter exceeding the standard), indicating that any parameter exceeding the standard may lead to QRI exceeding the standard. The connection between the intermediate events of liquid preparation and the basic events of liquid preparation selects a logic gate according to the specific situation. For example, the temperature exceeding the standard may be caused by a refrigeration system failure or a sensor error, and an "OR gate" is used for connection; if a certain event needs multiple basic events to occur simultaneously to trigger, an "AND gate" is used.

[0119] Through qualitative analysis, determine the critical path of liquid preparation and the minimal cut sets of liquid preparation; through quantitative analysis, calculate the probability of the top event of liquid preparation and identify the key risk points.

[0120] Among them, in the fault tree set analysis, qualitative analysis identifies the minimal cut sets, that is, the minimum combination of basic events that cause the top event of liquid preparation to occur. For example, a "refrigeration system failure" alone can cause the temperature to exceed the standard and lead to QRI exceeding the standard, so it constitutes a minimal cut set. The critical path of liquid preparation is used to identify the paths with higher occurrence probabilities in the fault tree set, and these high-risk paths are given priority attention. For example, historical data shows that "pH sensor drift" occurs frequently, so its path needs special attention.

[0121] In quantitative analysis, a failure probability is assigned to each basic event of liquid preparation, which can be based on historical data or expert evaluation. According to the fault tree set structure and logical gate relationship, calculate the probability of QRI exceeding the standard. For example, if the top event is multiple intermediate events of liquid preparation connected by an "OR gate", the probability of the top event of liquid preparation is where is the probability of each intermediate event of liquid preparation, is the product. Then, calculate the influence degree of each basic event of liquid preparation on the top event (such as Fussell-Vesely importance), and identify the key risk points. For example, if a "metering pump failure" contributes the most to QRI exceeding the standard, its importance is the highest.

[0122] For the case of severe overage, use exponential decay compensation, expressed as:

[0123] ;

[0124] where is the compensation term, is the compensation coefficient, is the adjustment coefficient, is the overage duration, is the natural base.

[0125] For the case of slight overage, the compensation value increases slowly, expressed as:

[0126] ;

[0127] where is a logarithmic function.

[0128] Fault tree set analysis supports rapid fault diagnosis and the formulation of preventive measures by systematically identifying potential causes of quality risks, thus ensuring the quality and stability of the oxytocin injection dispensing process.

[0129] Through the multi-condition coupling modeling module, the present invention optimizes the process scheduling instructions based on fluid dynamics simulation to improve the uniformity of the dispensing process. The temperature-deviation adjustment module uses a neural network to predict the degradation rate and compensates for the deviation of the refrigeration power to ensure temperature stability. The light control module adopts a photosensitive feedback adjustment strategy to generate a light-shielding operation protocol and improve light stability. The pH value dynamic adjustment module combines a buffer capacity prediction model and fuzzy PID control to achieve precise adjustment. The PLC control collaborative decision-making module calculates the QRI index through a dynamic weight allocation strategy, identifies key risk points using fault tree set analysis, and optimizes parameter non-linear compensation to enhance the fault diagnosis ability. These modules work together to achieve precise control of the oxytocin injection dispensing process, improve the stability, consistency, and safety of the process, reduce process deviations, and provide a reliable guarantee for high-quality drug production.

[0130] Example 2:

[0131] Based on Example 1, a pharmaceutical enterprise applied a dispensing production system for oxytocin injection in a small-batch production line. The enterprise used a stainless steel stirring tank for dispensing. Due to changes in the material flow state, mixing uniformity, and environmental factors in the production line, adaptive adjustments were required. A dispensing production system for oxytocin injection includes:

[0132] A multi-condition coupling modeling module for simulating the fluid flow state and material mixing situation of oxytocin in the dispensing tank based on a fluid dynamics model and constructing process scheduling instructions; calculating the uniformity index under the process scheduling instructions based on time-dependent simulation and updating the process scheduling instructions;

[0133] A temperature-deviation adjustment module for predicting the degradation rate of oxytocin using a long short-term memory network based on temperature time-series data and compensating for the deviation of the refrigeration power of the process scheduling instructions;

[0134] A light control module for calculating the illuminance distribution in the dispensing tank using a photosensitive feedback adjustment strategy and generating a light-shielding operation protocol in combination with the cumulative light dose;

[0135] A pH value dynamic adjustment module for establishing a buffer capacity prediction model to calculate the buffer capacity and adjusting the driving frequency of the metering pump through a fuzzy PID controller.

[0136] Table 2 Fuzzy Sets of Input Variables

[0137]

[0138] Table 3 Output Variable Fuzzy Sets

[0139]

[0140] Specifically, according to expert experience and process requirements, fuzzy rules are formulated. The fuzzy sets are shown in Table 2 and Table 3, and the input variables are pH error , the buffer capacity and the error change rate . and take 7 fuzzy subsets, and the output variable proportional gain , integral gain and derivative gain all take 7 fuzzy subsets. Among them, NB is negative large, NM is negative medium, NS is negative small, Z is zero, PS is positive small, PM is positive medium, PB is positive large, S is small, M is medium, and L is large.

[0141] According to the number of fuzzy rules, the possible combination number of input variables is calculated as 7×7×3 = 147 rules. Therefore, this fuzzy PID controller has a total of 147 fuzzy control rules. Of course, unnecessary rules can also be trimmed according to specific situations to make it more concise. The fuzzy rules are shown in Table 4. If the pH error is positive large and the buffer capacity is small, then increase the proportional gain and derivative gain for fast response; if the error change rate is large and the buffer capacity is large, then decrease the derivative gain to avoid overshoot. According to the fuzzy rules, calculate the adjustment amounts of PID parameters ( , and ), and update the PID parameters. The fuzzy PID adaptively adjusts the acid-base addition rate in the liquid preparation process, avoiding over-adjustment or delayed reaction.

[0142] Table 4 Example of Fuzzy Rules

[0143]

[0144] The PLC control collaborative decision-making module is used to calculate the QRI index using the dynamic weight allocation strategy according to the temperature control parameters, pH value, the cumulative light dose, and the liquid preparation time, generate a fault tree set, and perform non-linear compensation for the excess parameters.

[0145] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A liquid preparation production system for oxytocin injection, characterized in that, Including: A multi-condition coupling modeling module, which is used to simulate the fluid flow state and material mixing situation of oxytocin in the liquid distribution tank based on the hydrodynamic model, and construct a process scheduling instruction; Calculating the uniformity index under the process scheduling instruction based on time-dependent simulation, and updating the process scheduling instruction; A temperature-deviation adjustment module, which is used to predict the oxytocin degradation rate using a long short-term memory network according to the temperature time-series data, and compensate for the refrigeration power deviation of the process scheduling instruction; A light control module, which is used to calculate the illuminance distribution in the liquid distribution tank using a photosensitive feedback adjustment strategy, and generate a light avoidance operation protocol in combination with the light cumulative dose; A pH value dynamic adjustment module, which is used to establish a buffer capacity prediction model to calculate the buffer capacity, and adjust the driving frequency of the metering pump through a fuzzy PID controller; A PLC control collaborative decision-making module, which is used to calculate the QRI index using a dynamic weight allocation strategy according to the temperature control parameters, pH value, the light cumulative dose, and the liquid distribution time, generate a fault tree set, and perform non-linear compensation for the excess parameters.

2. The liquid preparation production system of an oxytocin injection according to claim 1, wherein The construction process of the process scheduling instruction includes: Introduction The turbulent model simulates the stirring turbulent behavior, and the Navier-Stokes equation is used to establish a model of the oxytocin fluid flow state for calculating the velocity field and pressure field in the dispensing tank; the convection-diffusion equation is used to model the material mixing situation for calculating the concentration distribution; Dividing the liquid distribution tank into grid cells based on the finite volume method, and performing local grid refinement in the stirring area and the feed port area; calculating the division error using an adaptive grid adjustment strategy, and updating the grid cells; According to the velocity field, the pressure field, and the concentration distribution, establishing an integral equation for each grid cell based on the Gauss-Green theorem, and using the SIMPLE algorithm for velocity-pressure coupling solution, iteratively updating the velocity field, the pressure field, and the concentration distribution, and generating the process scheduling instruction.

3. The liquid preparation production system of an oxytocin injection according to claim 1, characterized in that, The update process of the process scheduling instruction includes: At each time step, calculate the uniformity index , which is expressed as: ； Among them, is the concentration at the grid cell, is the average concentration of the liquid preparation tank, is the actual mixing time, is the theoretical mixing time, is the dimensionless weight coefficient, is the total number of grid cells; If is less than a preset threshold value, the feeding order is adjusted using a genetic algorithm, and the process scheduling instruction is updated.

4. The liquid preparation production system of an oxytocin injection according to claim 3, characterized in that, The implementation process of the genetic algorithm includes: Setting the oxytocin material order using the permutation coding method, and generating an initial population according to the oxytocin material order and the process scheduling instruction; Setting the maximization of the uniformity index as the fitness function, and adopting roulette wheel selection operation, partial matching crossover, and swap mutation. When the maximum number of iterations is reached, output the updated process scheduling instruction.

5. The liquid preparation production system of an oxytocin injection according to claim 1, characterized in that, The process of generating a light avoidance operation instruction includes: Deploying virtual light sensors on the grid cells, calculating the light attenuation using the Beer-Lambert law, and obtaining the illuminance distribution; Multiplying the illuminance distribution, exposure time, and photodegradation coefficient of the grid cells to obtain the light cumulative dose; For each grid cell, comparing the light cumulative dose with the safe light threshold. If the light cumulative dose is greater than the safe light threshold, generating the light avoidance operation instruction; otherwise, no additional operation; Among them, the light avoidance operation instruction includes local light avoidance and global light avoidance.

6. The liquid preparation production system of an oxytocin injection according to claim 1, characterized in that, The process of adjusting the driving frequency of the metering pump includes: Collecting the pH value in the liquid distribution tank through a pH sensor, and recording the amount of alkali input; Calculating the buffer capacity using the Henderson-Hasselbalch equation according to the pH value and the amount of alkali; Define the difference between the target pH value and the actual pH value as the pH error, and define the difference between the current error and the error at the previous moment as the error change rate; input the pH error, the buffer capacity, and the error change rate into the fuzzy PID controller; Design fuzzy rules based on expert experience, and the fuzzy PID controller outputs PID parameters according to the fuzzy rules; Calculate the control signal according to the PID parameters, and convert the control signal into the driving frequency of the metering pump.

7. The liquid preparation production system of an oxytocin injection according to claim 1, wherein, The process of generating and analyzing the fault tree set includes: Define the QRI index exceeding the standard as the top event of liquid preparation, which is used to characterize the deviation of the QRI index from the final fault performance during the liquid preparation process; Define the temperature exceeding the standard, the abnormal pH value, the light cumulative dose exceeding the standard, and the liquid preparation time exceeding the limit as the intermediate events of liquid preparation, which are used to describe the key factors affecting the top event of liquid preparation; Define equipment failure, sensor error, and regulation strategy failure as the basic events of liquid preparation, which are used to describe the physical factors and system factors that cause the intermediate events of liquid preparation at the bottom layer; Connect the top event of liquid preparation, the intermediate events of liquid preparation, and the basic events of liquid preparation using logic gates to generate the fault tree set; through qualitative analysis, determine the key path of liquid preparation and the minimum cut set of liquid preparation; through quantitative analysis, calculate the probability of the top event of liquid preparation and identify the key risk points.

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