Drug quality preservation drying method and device based on vacuum freeze drying technology

Abstract

The present application provides a method and device for drying medicines for quality preservation based on vacuum freeze drying technology, which relates to the field of medicine drying technology, obtains medicine drying requirements and basic medicine data; activates the multi-stage control decision channel for medicine freeze drying, and generates a vacuum freeze drying control plan; respectively performs drying control prediction and evaluation on the first, second and third stage freeze drying control plans, and obtains the drying control prediction and evaluation results of each stage; determines whether the drying control prediction and evaluation results of each stage meet the drying control prediction and evaluation constraints of each stage; if not, optimizes and adjusts the vacuum freeze drying control plan according to the drying control prediction and evaluation constraints of each stage, and generates a freeze drying control optimization plan. The present application solves the technical problem that traditional technology lacks specific control plans for different drying stages, resulting in uneven drying, realizes the refined control of the vacuum freeze drying process of medicines, and improves the stability and efficiency of the drying process.

Description

Technical Field

[0001] The present application relates to the field of drug drying technology, and in particular to a drug quality preservation drying method and device based on vacuum freeze drying technology. Background Art

[0002] Pharmaceutical vacuum freeze-drying technology is a widely used drug preservation and drying method in the modern pharmaceutical industry. It removes moisture by freezing the drug and sublimating ice crystals in a low-pressure environment. This process has significant advantages in maintaining the biological activity and chemical stability of drugs, and is particularly suitable for drying heat-sensitive drugs. However, traditional vacuum freeze-drying technology still has some shortcomings in its application. First, traditional technologies usually adopt a unified control strategy, perform simple parameter settings for the entire drying process, and lack flexible adjustments for different drug characteristics and drying stages. This method easily leads to uneven drying, affecting the quality and stability of the drug. Secondly, most existing drying processes rely on real-time adjustments, lacking predictive evaluation and optimization of drying schemes. When problems arise, the response is often untimely, resulting in drug losses and increased energy consumption. Summary of the invention

[0003] The present application provides a method and device for drying medicines to preserve quality based on vacuum freeze-drying technology, which solves the technical problems that the traditional vacuum freeze-drying technology has insufficient precision in the control process and lacks specific control schemes for different drying stages, resulting in uneven drying and thus affecting the quality and stability of the medicines. The application realizes refined control of the vacuum freeze-drying process of the medicines, improves the stability and efficiency of the drying process, and ensures the quality of the medicines.

[0004] In view of the above problems, on the one hand, the present application provides a drug quality preservation drying method based on vacuum freeze-drying technology, the method comprising: obtaining drug drying requirements of the drug to be dried; collecting basic information of the drug to be dried to obtain drug basic data; activating a multi-stage control decision channel for drug freeze drying, combining the drug drying requirements and the drug basic data to make drying control decisions on the vacuum freeze drying equipment, and generating a vacuum freeze drying control plan, wherein the vacuum freeze drying control plan includes a first-stage freeze drying control plan, a second-stage freeze drying control plan, and a third-stage freeze drying control plan; performing drying control prediction and evaluation on the first-stage freeze drying control plan, the second-stage freeze drying control plan, and the third-stage freeze drying control plan, respectively, to obtain drying control prediction and evaluation results for each stage; judging whether the drying control prediction and evaluation results for each stage meet the drying control prediction and evaluation constraints for each stage; if the drying control prediction and evaluation results for each stage do not meet the drying control prediction and evaluation constraints for each stage, optimizing the vacuum freeze drying control plan according to the drying control prediction and evaluation constraints for each stage, and generating a freeze drying control optimization plan; based on the vacuum freeze drying equipment, drying the drug to be dried according to the freeze drying control optimization plan.

[0005] On the other hand, the present application also provides a drug quality preservation drying device based on vacuum freeze-drying technology, the device comprising: a demand acquisition module, the demand acquisition module is used to obtain the drug drying requirements of the drug to be dried; a basic information acquisition module, the basic information acquisition module is used to collect the basic information of the drug to be dried and obtain the basic data of the drug; a control scheme generation module, the control scheme generation is used to activate the multi-stage control decision channel for drug freeze drying, combine the drug drying requirements and the basic data of the drug to make a drying control decision on the vacuum freeze drying equipment, and generate a vacuum freeze drying control scheme, wherein the vacuum freeze drying control scheme includes a first-stage freeze drying control scheme, a second-stage freeze drying control scheme and a third-stage freeze drying control scheme; a scheme prediction and evaluation module, the scheme prediction and evaluation module is used to The first stage freeze-drying control scheme, the second stage freeze-drying control scheme and the third stage freeze-drying control scheme are respectively subjected to drying control prediction evaluation to obtain drying control prediction evaluation results of each stage; a judgment module is used to judge whether the drying control prediction evaluation results of each stage meet the drying control prediction evaluation constraints of each stage; a scheme optimization module is used to optimize and adjust the vacuum freeze-drying control scheme according to the drying control prediction evaluation constraints of each stage if the drying control prediction evaluation results of each stage do not meet the drying control prediction evaluation constraints of each stage, and generate a freeze-drying control optimization scheme; a scheme execution module is used to dry the to-be-dried medicine according to the freeze-drying control optimization scheme based on the vacuum freeze-drying equipment.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] Obtain the drug drying requirements of the drug to be dried, clarify the final drying target of the drug, and provide a target basis for subsequent drying control decisions. Collect the basic information of the drug to be dried, obtain the basic data of the drug, provide necessary data support for the drying control decision, and ensure that the drying plan can be customized according to the characteristics of the drug, so as to protect the structure and activity of the drug. Activate the multi-stage control decision channel for drug freeze drying, combine the drug drying requirements and the basic data of the drug to make a drying control decision for the vacuum freeze drying equipment, and generate a vacuum freeze drying control plan, wherein the vacuum freeze drying control plan includes a first-stage freeze drying control plan, a second-stage freeze drying control plan, and a third-stage freeze drying control plan, so as to achieve refined management of the drying process, provide a preliminary, data-based control plan for the drying process, and ensure that each stage of the drying process can achieve the expected effect. Perform drying control prediction and evaluation on the first-stage freeze drying control plan, the second-stage freeze drying control plan, and the third-stage freeze drying control plan, respectively, obtain the drying control prediction and evaluation results of each stage, pre-discover and solve possible problems, and improve the success rate and efficiency of the drying process. Determine whether the prediction and evaluation results of the drying control at each stage meet the prediction and evaluation constraints of the drying control at each stage. If the prediction and evaluation results of the drying control at each stage do not meet the prediction and evaluation constraints of the drying control at each stage, optimize the vacuum freeze-drying control scheme according to the prediction and evaluation constraints of the drying control at each stage, generate a freeze-drying control optimization scheme, and generate a more effective control scheme through continuous optimization to improve the efficiency of the drying process and the quality of the medicine, and ensure that the final drying effect meets the requirements. Based on the vacuum freeze-drying equipment, the medicine to be dried is dried according to the freeze-drying control optimization scheme to achieve an efficient and high-quality drying process, maintain the active ingredients and stability of the medicine, and extend the shelf life of the medicine.

[0008] In summary, this application uses a multi-stage control decision channel to formulate specific control plans for the three drying stages according to the specific drying requirements of different drugs, and before actual drying, predicts, evaluates and dynamically optimizes the control plan for each stage to identify and solve potential problems in advance, thereby achieving refined control of the vacuum freeze-drying process of drugs, improving the stability and efficiency of the drying process, minimizing damage to drugs during the drying process, maintaining the molecular structure and performance of drugs, and thereby improving the stability and shelf life of drugs.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a process for drying a drug for quality preservation based on vacuum freeze drying technology provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a process for obtaining prediction and evaluation results of drying control at each stage in a drug quality preservation drying method based on vacuum freeze drying technology provided in an embodiment of the present application;

[0012] Figure 3 A schematic diagram of a process for obtaining a first-stage freeze-drying control optimization scheme in a drug quality-preserving drying method based on vacuum freeze-drying technology provided in an embodiment of the present application;

[0013] Figure 4 A schematic diagram of the structure of a drug preservation drying device based on vacuum freeze-drying technology provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: demand acquisition module 10 , basic information collection module 20 , control scheme generation module 30 , scheme prediction and evaluation module 40 , judgment module 50 , scheme optimization module 60 , scheme execution module 70 . DETAILED DESCRIPTION

[0015] The embodiments of the present application provide a method and device for drying medicines to preserve quality based on vacuum freeze-drying technology, thereby solving the technical problems that the traditional vacuum freeze-drying technology has an imprecise control process and lacks specific control schemes for different drying stages, resulting in uneven drying and thus affecting the quality and stability of the medicines. The embodiments of the present application achieve refined control of the vacuum freeze-drying process of the medicines, improve the stability and efficiency of the drying process, and ensure the quality of the medicines.

[0016] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a method for drying medicines based on vacuum freeze drying technology, the method comprising:

[0017] Step S1: Obtaining the drug drying requirements of the drug to be dried.

[0018] Specifically, drug drying requirements refer to the specific requirements that drugs need to meet during the drying process. These requirements include, but are not limited to, the physical properties of the drug, chemical stability, target residual moisture content, drying speed, and temperature conditions. These requirements are usually determined by the nature and intended use of the drug. For example, for an antibiotic, it is required to maintain 95% activity after drying, and the residual moisture should not exceed 2%. In this step, it is necessary to understand these requirements of the drug to be dried in detail to provide clear goals and references for the subsequent drying control plan, so that the drying process can accurately match the specific needs of the drug and ensure the quality of the drug.

[0019] Step S2: Collect basic information of the medicine to be dried to obtain basic data of the medicine.

[0020] Specifically, basic information refers to various initial data and property information of drugs, including the composition of drugs, initial moisture content, particle size, thermal stability, etc. These data are collectively referred to as basic drug data, which are important bases for formulating drying control plans.

[0021] The basic information of the drug to be dried can be collected using laboratory equipment, such as high-performance liquid chromatography, thermogravimetric analysis, particle size analyzer, moisture analyzer and other tools to measure the physical and chemical properties of the drug. For example, for an antibiotic preparation that needs to be dried, high-performance liquid chromatography is used to analyze its main components and content, and thermogravimetric analysis is used to determine that its initial moisture content is 10%, and its thermal stability is poor. At the same time, a particle size analyzer is used to determine that its particle size is 10 to 50 microns. Collect the initial state and characteristic data of the drug so that the drying process can be optimized and adjusted according to the specific characteristics of the drug, avoiding possible damage or quality degradation of the drug during the drying process.

[0022] Step S3: Activate the multi-stage control decision channel for drug freeze-drying, make drying control decisions for the vacuum freeze-drying equipment based on the drug drying requirements and the drug basic data, and generate a vacuum freeze-drying control plan, wherein the vacuum freeze-drying control plan includes a first-stage freeze-drying control plan, a second-stage freeze-drying control plan, and a third-stage freeze-drying control plan.

[0023] Specifically, in the process of vacuum freeze drying equipment to dry the medicine for quality preservation, it can usually be divided into three key drying stages: the first stage is the pre-freezing stage, which is the starting step of the vacuum freeze drying process, and its purpose is to condense the free water in the medicine into ice crystals to prepare for the subsequent sublimation drying; the second stage is the sublimation drying stage, which is the core step of vacuum freeze drying, in which the ice crystals directly sublimate into water vapor under vacuum and appropriate heating conditions and are pumped away by the vacuum system; the third stage is the analytical drying stage, which is carried out after the sublimation drying is completed, and aims to remove the residual unfrozen water in the medicine to further improve the drying effect. Each stage has different temperature, pressure and time control strategies.

[0024] Activate the constructed multi-stage control decision channel for drug freeze drying, which is an algorithm or model for managing and controlling the freeze drying process, and can formulate and optimize the drying plan in stages. It combines the drying requirements and basic data of the drug, makes drying control decisions for the vacuum freeze drying equipment, determines the parameters of each stage of the drying process, and generates a vacuum freeze drying control plan to ensure that the drug can achieve the best effect in each stage of the drying process. Among them, the vacuum freeze drying equipment is a device used for the freeze drying process, which freezes and sublimates the water in the drug by lowering the temperature and pressure to achieve drying. The vacuum freeze drying control plan is a specific control plan formulated for the drug drying process, including the first stage freeze drying control plan, the second stage freeze drying control plan and the third stage freeze drying control plan. The first stage freeze drying control plan is the control plan for the pre-freezing stage, focusing on rapid freezing; the second stage freeze drying control plan is the control plan for the sublimation stage, which performs slow sublimation; the third stage freeze drying control plan is the control plan for the analytical drying stage, which performs the final drying and stabilization.

[0025] In this step, a phased freeze-drying control plan is formulated based on the drug drying requirements and basic data to ensure an orderly drying process. Each stage can be optimized according to the specific needs of the drug to ensure the overall drying effect and drug quality.

[0026] Step S4: performing drying control prediction and evaluation on the first stage freeze-drying control scheme, the second stage freeze-drying control scheme and the third stage freeze-drying control scheme respectively to obtain drying control prediction and evaluation results for each stage.

[0027] Specifically, the drying control prediction evaluation results are the output of the drying control prediction evaluation, which shows the expected effect and feasibility of the control schemes at each stage. Use drying process simulation software, such as the freeze-drying process simulator, to simulate the first-stage freeze-drying control scheme, the second-stage freeze-drying control scheme, and the third-stage freeze-drying control scheme. These software can predict the impact of temperature, pressure, and time on the drug during the drying process based on the input parameters. Then, evaluate whether the parameters of each stage can meet the drying requirements of the drug, and output the prediction results of each stage, that is, the drying control prediction evaluation results, which include key indicators such as drying rate and final moisture content of the drug. Through predictive evaluation, possible problems and deficiencies can be identified in advance, providing a basis for the adjustment and optimization of the scheme. This helps to ensure that the final drying scheme can meet the quality requirements of the drug and improve the efficiency and effectiveness of the drying process.

[0028] Step S5: determining whether the prediction and evaluation results of the drying control at each stage satisfy the prediction and evaluation constraints of the drying control at each stage.

[0029] Specifically, the drying control prediction evaluation constraints are the expected standards and restrictions for each stage of the drying process, including specific parameters such as temperature, pressure, time, and moisture content. These constraints are used to ensure the effectiveness of the drying process and the quality of the drug.

[0030] According to the drying requirements and basic data of the drugs, through laboratory tests, industry standards and literature, specific evaluation constraints are set for each drying stage, and the drying control prediction evaluation results of each stage are compared with the preset constraints to determine whether these constraints are met. If the evaluation results of a certain stage do not meet the preset constraints, these results are recorded and provide a basis for the next step of optimization and adjustment. For example, according to the drying requirements of antibiotic preparations, specific constraints are set for each stage. First stage constraints: The temperature must reach -40°C and the freezing time must be no less than 2 hours. Second stage constraints: During the sublimation process, the temperature is maintained at -20°C and the pressure is maintained at 10Pa for no less than 12 hours. Third stage constraints: The temperature is raised to 25°C, the pressure is maintained at 10Pa, and the final moisture content does not exceed 2%.

[0031] By judging the prediction and evaluation results of drying control at each stage, it can be ensured that the final drying plan can meet the quality requirements of the drugs and avoid non-compliance with the standards during the drying process.

[0032] Step S6: If the prediction and evaluation results of the drying control at each stage do not satisfy the prediction and evaluation constraints of the drying control at each stage, the vacuum freeze-drying control scheme is optimized and adjusted according to the prediction and evaluation constraints of the drying control at each stage to generate a freeze-drying control optimization scheme.

[0033] Specifically, when the prediction and evaluation results of the drying control at each stage do not meet the preset constraints, it is necessary to optimize and adjust the vacuum freeze-drying control scheme to generate a new freeze-drying control optimization scheme. Among them, the freeze-drying control optimization scheme is a new control scheme generated after optimization and adjustment, which aims to ensure that the drying process at each stage can meet the expected standards and effects.

[0034] First, according to the unsatisfied constraints, find out the reasons and make preliminary adjustments to the parameters in the drying control scheme. For example, if the freezing temperature in the first stage is too high and the drug stability is insufficient, the temperature setting is lowered. Then, the parameters are further adjusted using optimization algorithms, such as genetic algorithms or particle swarm optimization, to find the best drying parameter combination. These algorithms simulate different parameter combinations, predict their effects on the drug drying effect, and select the optimal parameter settings. Then, according to step S4, the adjusted scheme is predicted and evaluated to verify whether the new parameter combination can meet the drug drying requirements. If the new parameter combination still does not meet the constraints, parameter adjustment and simulation will continue until the best parameter combination is found. Finally, through continuous optimization, a new control scheme is generated to ensure that the parameters in each stage can meet the specific needs of the drug, thereby improving the efficiency of the drying process and the quality of the drug. This process relies on precise simulation software and optimization algorithms to ensure that the final freeze-drying control scheme can achieve the best drying effect.

[0035] Step S7: Based on the vacuum freeze-drying equipment, the to-be-dried medicine is dried according to the freeze-drying control optimization scheme.

[0036] Specifically, first, ensure that the equipment is operating normally, set the initial parameters, and prepare the medicines to be dried. Input the optimized control scheme into the control system of the equipment, and set the temperature, pressure, and time parameters for each stage. Then, according to the input optimization scheme, start the drying equipment and perform the actual vacuum freeze-drying operation. Monitor the temperature, pressure, time and other parameters of the drying process in real time to ensure that the equipment operates according to the set scheme, and record the data of the drying process. Through this step, it can be ensured that the medicines are dried according to the optimal parameters, thereby improving the quality and drying efficiency of the medicines.

[0037] Furthermore, step S3 of the embodiment of the present application also includes:

[0038] Step S31: The multi-stage control decision channel for drug freeze drying includes a pre-freezing stage drying control decision channel, a sublimation drying stage control decision channel and an analytical drying stage control decision channel.

[0039] Step S32: Based on the drug drying requirements and the drug basic data, and according to the pre-freezing stage drying control decision channel, the first stage freeze-drying control plan is generated.

[0040] Step S33: Based on the drug drying requirements and the drug basic data, the second-stage freeze-drying control plan is generated according to the sublimation drying stage control decision channel.

[0041] Step S34: Based on the drug drying requirements and the drug basic data, the third stage freeze-drying control plan is generated according to the analytical drying stage control decision channel.

[0042] Step S35: generating the vacuum freeze-drying control plan according to the first-stage freeze-drying control plan, the second-stage freeze-drying control plan and the third-stage freeze-drying control plan.

[0043] Specifically, the multi-stage control decision channel for drug freeze drying includes the pre-freezing stage drying control decision channel, the sublimation drying stage control decision channel, and the analytical drying stage control decision channel. Each channel is responsible for formulating the drying strategy for the corresponding stage.

[0044] First, the drug drying requirements and drug basic data obtained in steps S1 and S2 are input into the drug freeze-drying multi-stage control decision channel. Then, the pre-freezing stage drying control decision channel is responsible for formulating the drying strategy of the pre-freezing stage, and generating the first stage freeze-drying control plan according to the drug drying requirements and drug basic data, by rapidly cooling the drug to form ice crystals to ensure that the water is frozen. The sublimation drying stage control decision channel is responsible for formulating the drying strategy of the sublimation drying stage, and generating the second stage freeze-drying control plan according to the drug drying requirements and drug basic data, by reducing the pressure to make the ice crystals sublimate into water vapor, and removing most of the water in the drug. The analytical drying stage control decision channel is responsible for formulating the drying strategy of the analytical drying stage, and generating the third stage freeze-drying control plan according to the drug drying requirements and drug basic data, by slightly increasing the temperature to remove the remaining water, so that the drug reaches the required degree of dryness. Then, the freeze-drying control plans of the first, second and third stages are integrated to generate a vacuum freeze-drying control plan. This plan includes all stages and parameters of the entire drying process to ensure that the quality and activity of the drug are maintained to the greatest extent during the drying process.

[0045] Through the above steps, it can be ensured that the drugs are properly controlled at each drying stage, thereby improving the efficiency of the entire drying process and the quality of the drugs.

[0046] Preferably, in step S3 of the embodiment of the present application, the step of constructing the multi-stage control decision channel for drug freeze drying includes:

[0047] Step 1: Retrieve drug drying control records based on the vacuum freeze-drying equipment to obtain a drug drying requirement record set, a drug basic data record set, and a drug drying control plan record set.

[0048] Step 2: Perform feature sorting according to the drug drying control scheme record set to obtain a pre-freezing stage drying control scheme record set, a sublimation drying stage control scheme record set, and a parsing drying stage control scheme record set.

[0049] Step three: construct the pre-freezing stage drying control decision channel according to the drug drying demand record set, the drug basic data record set and the pre-freezing stage drying control scheme record set.

[0050] Step 4: Building the sublimation drying stage control decision channel according to the drug drying demand record set, the drug basic data record set and the sublimation drying stage control plan record set.

[0051] Step 5: Building the control decision channel for the analytical drying stage according to the drug drying demand record set, the drug basic data record set and the analytical drying stage control plan record set.

[0052] Step six: connecting the pre-freezing stage drying control decision channel, the sublimation drying stage control decision channel and the analytical drying stage control decision channel to generate the multi-stage control decision channel for drug freeze drying.

[0053] Specifically, the drug drying requirements record set refers to a collection of data records related to drug drying requirements, including requirements for drying parameters such as temperature, pressure, and time. The drug basic data record set refers to a collection of data on the initial state and characteristics of the drug, including composition, initial moisture content, particle size, thermal stability, etc. The drug drying control scheme record set refers to a collection of all drying control schemes that have been used in history. The pre-freezing stage drying control scheme record set, the sublimation drying stage control scheme record set, and the parsing drying stage control scheme record set are control scheme record sets classified according to different drying stages, corresponding to the three stages of pre-freezing, sublimation, and parsing, respectively.

[0054] First, by querying and retrieving the history records and database of vacuum freeze drying equipment, relevant information on drug drying requirements, drug basic data, and drying control plans is collected. This information includes the types, characteristics, drying history records, and previous control plans of drugs, namely, drug drying requirements record set, drug basic data record set, and drug drying control plan record set.

[0055] Next, the collected historical control scheme records are analyzed, and the drying control schemes are classified according to the pre-freezing stage, sublimation drying stage, and analytical drying stage, and the pre-freezing stage drying control scheme record set, the sublimation drying stage control scheme record set, and the analytical drying stage control scheme record set are obtained respectively. This process can be completed by data analysis software, such as Python's Pandas library.

[0056] Then, according to the drying requirements and basic data of the drugs, as well as the control plan records of each stage, the control decision channels for each stage are built. These channels predict and determine the best control strategy based on historical data and drug characteristics. For example, for the pre-freezing stage, the decision channel will consider the freezing temperature and time of the drug; for the sublimation drying stage, the decision channel will consider temperature and pressure; for the analytical drying stage, the decision channel will consider pressure and drying time. The building process can be implemented using simulation software and optimization algorithms, such as COMSOL Multiphysics and MATLAB Optimization Toolbox.

[0057] Finally, the control decision channels of these three stages are connected to form a complete multi-stage control decision channel for drug freeze drying, which can provide a complete control strategy from pre-freezing to analytical drying.

[0058] Through the above steps, a multi-stage control decision channel can be constructed that comprehensively considers the characteristics of the drug and the drying requirements, ensuring that the best control strategy can be provided in each drying stage, thereby achieving fine control of the entire drying process and improving the efficiency and stability of the drying process.

[0059] Further, such as Figure 2 As shown, step S4 of the embodiment of the present application also includes:

[0060] Step S41: establishing a first-stage expected drug moisture change curve, a second-stage expected drug moisture change curve, and a third-stage expected drug moisture change curve for the drug to be dried.

[0061] Step S42: performing simulated drying of the to-be-dried medicine according to the first-stage freeze-drying control scheme, the second-stage freeze-drying control scheme, and the third-stage freeze-drying control scheme, and generating a first-stage simulated medicine moisture change curve, a second-stage simulated medicine moisture change curve, and a third-stage simulated medicine moisture change curve.

[0062] Step S43: Based on the first-stage expected drug moisture change curve and the first-stage simulated drug moisture change curve, a similarity evaluation is performed to generate a first-stage drying control prediction evaluation coefficient.

[0063] Step S44: Based on the second-stage expected drug moisture change curve and the second-stage simulated drug moisture change curve, a similarity evaluation is performed to generate a second-stage drying control prediction evaluation coefficient.

[0064] Step S45: Based on the third-stage expected drug moisture change curve and the third-stage simulated drug moisture change curve, a similarity evaluation is performed to generate a third-stage drying control prediction evaluation coefficient.

[0065] Step S46: adding the first-stage drying control prediction evaluation coefficient, the second-stage drying control prediction evaluation coefficient and the third-stage drying control prediction evaluation coefficient to the drying control prediction evaluation results of each stage.

[0066] Specifically, the expected drug moisture change curve is an ideal curve of the expected change of drug moisture content over time at each drying stage. The simulated drug moisture change curve is an actual curve of the change of drug moisture content over time at each stage obtained by simulating the drying process. Similarity evaluation is to evaluate the similarity between the expected curve and the simulated curve to judge the effectiveness of the drying control scheme. The drying control prediction evaluation coefficient is the result of the similarity evaluation, which is used to quantify the degree of consistency between the expected effect of the drying control scheme and the actual effect.

[0067] First, establish the expected moisture change curve of the drug at each stage of the drug to be dried. The specific process includes: obtaining the moisture change data of the drug under different drying conditions. These data can come from previous experimental records, literature or small-scale experiments. Through experimental data analysis and literature review, clarify the target moisture content and time node of each drying stage. For example, in the pre-freezing stage, the drug temperature needs to be quickly reduced to -40°C and maintained for a certain period of time to freeze the water into ice crystals. Using the collected experimental data, mathematical methods are used to fit the expected moisture change curve for each stage. These curves should accurately reflect the trend of the moisture content of the drug over time. According to the actual situation and drying requirements, the fitted curve is optimized to ensure that the curve can represent the best drying effect. The fitting process can be completed with the help of data analysis software such as MATLAB and Excel.

[0068] Exemplarily, in the laboratory, a pre-freezing experiment is carried out on the antibiotic preparation, and the moisture content at different time points is recorded. For example, under the condition of -40°C, the moisture content is recorded every 30 minutes. The data points obtained are: 0 hour (100% moisture), 0.5 hour (80% moisture), 1 hour (60% moisture), 1.5 hours (40% moisture), 2 hours (20% moisture). The goal of the pre-freezing stage is to reduce the moisture content to 20% and keep it at -40°C for 2 hours. Using the curve fitting function of MATLAB or Excel, a function curve representing the moisture change can be obtained by the polynomial fitting method. Adjust the smoothness and slope of the curve to ensure that the trend of moisture change conforms to the actual drying process and obtain the expected moisture change curve in the first stage.

[0069] Next, computer simulation software is used to simulate the drying of the drugs to be dried according to the first stage freeze drying control scheme, the second stage freeze drying control scheme, and the third stage freeze drying control scheme. These simulation software, such as Aspen Plus or COMSOL Multiphysics, are usually based on physical models, such as heat transfer and mass transfer models, to simulate the drying process of drugs under different conditions, obtain the actual moisture change data of each stage, and generate the first stage simulated drug moisture change curve, the second stage simulated drug moisture change curve, and the third stage simulated drug moisture change curve.

[0070] Then, the similarity between the first-stage expected curve and the first-stage simulated drug moisture change curve is evaluated by a similarity calculation tool to quantify the degree of fit of the drying effect. The root mean square error or correlation coefficient, such as the Pearson correlation coefficient, can be used to evaluate the similarity between the two and generate the first-stage drying control prediction evaluation coefficient. Similarly, the similarity between the second-stage expected curve and the second-stage simulated drug moisture change curve, as well as the similarity between the third-stage expected curve and the third-stage simulated drug moisture change curve, are evaluated to generate the corresponding second and third-stage drying control prediction evaluation coefficients. If the simulated curve is very close to the expected curve, the prediction evaluation coefficient will be high; if there is a large deviation, the prediction evaluation coefficient will be low.

[0071] Finally, the similarity evaluation results are integrated into the overall evaluation, that is, the above-mentioned drying control prediction evaluation coefficients of each stage are added to the drying control prediction evaluation results of each stage, so as to comprehensively evaluate the effectiveness of the drying control scheme.

[0072] Furthermore, step S6 of the embodiment of the present application also includes:

[0073] Step S61: the drying control prediction and evaluation constraints at each stage include the first stage drying control prediction and evaluation constraints, the second stage drying control prediction and evaluation constraints and the third stage drying control prediction and evaluation constraints.

[0074] Step S62: If the first-stage drying control prediction evaluation coefficient does not satisfy the first-stage drying control prediction evaluation constraint, the first-stage freeze-drying control scheme is optimized and adjusted according to the first-stage drying control prediction evaluation constraint to obtain the first-stage freeze-drying control optimization scheme.

[0075] Step S63: If the second-stage drying control prediction evaluation coefficient does not satisfy the second-stage drying control prediction evaluation constraint, the second-stage freeze-drying control scheme is optimized and adjusted according to the second-stage drying control prediction evaluation constraint to obtain the second-stage freeze-drying control optimization scheme.

[0076] Step S64: If the third-stage drying control prediction evaluation coefficient does not satisfy the third-stage drying control prediction evaluation constraint, the third-stage freeze-drying control scheme is optimized and adjusted according to the third-stage drying control prediction evaluation constraint to obtain the third-stage freeze-drying control optimization scheme.

[0077] Step S65: generating the freeze-drying control optimization scheme according to the first-stage freeze-drying control optimization scheme, the second-stage freeze-drying control optimization scheme and the third-stage freeze-drying control optimization scheme.

[0078] Specifically, the first stage drying control prediction and evaluation constraints, the second stage drying control prediction and evaluation constraints, and the third stage drying control prediction and evaluation constraints correspond to the specific evaluation criteria of the pre-freezing, sublimation and decomposition stages respectively. The freeze-drying control optimization scheme is an optimized and adjusted drying control scheme to ensure that the parameter settings of each drying stage can meet the preset constraints and drying requirements.

[0079] Set the prediction and evaluation constraints of drying control in each stage, including the specific evaluation criteria of the first stage, the second stage and the third stage. Check whether the prediction and evaluation coefficients of drying control in the first stage, the second stage and the third stage meet the corresponding prediction and evaluation constraints of drying control. If the coefficient of a certain stage does not meet the constraint conditions, the corresponding freeze-drying control scheme is optimized and adjusted according to the constraint conditions of that stage to obtain a new control optimization scheme. Then, the freeze-drying control optimization schemes of the first stage, the second stage and the third stage are integrated to generate a freeze-drying control optimization scheme. This scheme is adjusted and optimized according to the constraints and prediction and evaluation results of each stage to ensure that the entire drying process meets the quality requirements of all stages.

[0080] Preferably, Figure 3 As shown, step S62 of the embodiment of the present application also includes:

[0081] Step S621: performing global mining of drying control in the pre-freezing stage according to the medicine to be dried, and establishing a first-stage optimization adjustment constraint space.

[0082] Step S622: Randomly adjust the first-stage freeze-drying control plan according to the first-stage optimal adjustment constraint space to generate a first-stage drying control adjustment plan.

[0083] Step S623: performing drying control prediction evaluation according to the first-stage drying control adjustment scheme to obtain a drying control evaluation coefficient of the first adjustment scheme.

[0084] Step S624: determining whether the drying control evaluation coefficient of the first adjustment scheme satisfies the first-stage drying control prediction evaluation constraint.

[0085] Step S625: If the drying control evaluation coefficient of the first adjustment scheme satisfies the first-stage drying control prediction evaluation constraint, the first-stage drying control adjustment scheme is added to the first-stage freeze-drying control optimization scheme.

[0086] Step S626: If the drying control evaluation coefficient of the first adjustment scheme does not satisfy the first-stage drying control prediction evaluation constraint, continue to adjust the first-stage freeze-drying control scheme according to the first-stage optimization adjustment constraint space until the first-stage freeze-drying control optimization scheme is generated.

[0087] Specifically, global mining of drying control in the pre-freezing stage refers to a comprehensive analysis and exploration of the drying control parameters in the pre-freezing stage to find out the key factors that affect the drying effect. The first-stage optimal adjustment constraint space refers to the space composed of all possible adjustable parameters and conditions during the optimization of the first-stage drying control scheme, which is used to guide the parameter adjustment during the optimization process. The first-stage drying control adjustment scheme is a new control scheme generated by randomly adjusting the original control scheme according to the first-stage optimal adjustment constraint space. The drying control evaluation coefficient of the first adjustment scheme is the evaluation coefficient obtained after the drying control prediction evaluation of the first-stage drying control adjustment scheme, which is used to quantify the effect of the new scheme and evaluate whether it meets the preset evaluation criteria.

[0088] First, the global mining of drying control in the pre-freezing stage is carried out according to the drugs to be dried, and the drying control parameters and conditions are comprehensively analyzed to determine all possible adjustment ranges and establish the first-stage optimization adjustment constraint space. According to the first-stage optimization adjustment constraint space, the first-stage freeze-drying control scheme is randomly adjusted, and optimization algorithms and simulation tools, such as genetic algorithms or particle swarm optimization, are used to explore different parameter combinations, and parameter combinations are randomly selected to generate the first-stage drying control adjustment scheme.

[0089] Using the same method as in the above steps S41 to S43, simulate drying the first-stage drying control adjustment scheme to generate the first-stage simulated drug moisture change adjustment curve, and use the similarity tool to evaluate the similarity between the first-stage simulated drug moisture change adjustment curve and the first-stage expected drug moisture change curve to generate the first adjustment scheme drying control evaluation coefficient. This coefficient reflects the predicted effect of the adjustment scheme.

[0090] Determine whether the drying control evaluation coefficient of the first adjustment scheme satisfies the first-stage drying control prediction evaluation constraint. If so, add the first-stage drying control adjustment scheme to the first-stage freeze-drying control optimization scheme. If not, repeat steps S622 to S626, and continue to adjust the first-stage freeze-drying control scheme according to the first-stage optimization adjustment constraint space until a first-stage freeze-drying control optimization scheme that satisfies the constraint condition is generated.

[0091] Through the above process, the drying control scheme in the pre-freezing stage can be systematically optimized to ensure that it can meet the preset standards and improve the drying effect and quality of the medicine. Similarly, those skilled in the art can use the same method to obtain the second stage freeze-drying control optimization scheme and the third stage freeze-drying control optimization scheme, and the specification will not be repeated here.

[0092] Optionally, step S621 in the embodiment of the present application further includes:

[0093] Based on the drug drying requirements and the drug basic data, the to-be-dried drugs are retrieved for associated samples to determine a set of associated sample drugs that meet similar association constraints; pre-freezing stage drying control is retrieved based on the associated sample drug set to obtain a set of associated sample pre-freezing stage drying control schemes; drying control feature intervals are identified based on the associated sample pre-freezing stage drying control scheme set to obtain an associated pre-freezing stage drying control domain; drying control constraint information of the vacuum freeze-drying equipment is collected to generate a drying control constraint domain; the intersection of the associated pre-freezing stage drying control domain and the drying control constraint domain is obtained to generate the first stage optimal adjustment constraint space.

[0094] Specifically, the associated sample drug set refers to a set of drugs that are similar to the drugs to be dried in terms of drying requirements and basic data. The associated sample pre-freezing stage drying control plan set is a set of drying control plans formulated for the associated sample drug set in the pre-freezing stage. The associated pre-freezing stage drying control domain is the drying control feature interval identified based on the associated sample pre-freezing stage drying control plan set. The drying control constraint domain refers to the control parameter domain generated by the actual operating limitations of the vacuum freeze-drying equipment.

[0095] First, by searching the drug drying requirements and basic data, database retrieval tools and data mining tools are used to retrieve a set of drugs that are similar to the drugs to be dried in terms of characteristics and requirements from the database to form a set of associated sample drugs. Then, the drying control schemes in the pre-freezing stage are extracted from these associated sample drug sets to form a set of associated sample pre-freezing stage drying control schemes. Then, data analysis tools such as Python and Excel are used to analyze the drying control schemes of these associated samples, identify the intervals of key parameters, and determine the associated pre-freezing stage drying control domain. At the same time, according to the operating manual and experimental data of the vacuum freeze-drying equipment, the temperature and time limits of the equipment are determined to form a drying control constraint domain. Finally, the intersection of the associated pre-freezing stage drying control domain and the drying control constraint domain is calculated to generate the first stage optimization adjustment constraint space. Through these steps, the parameter adjustment range of the pre-freezing stage can be scientifically and reasonably determined, providing a basis for optimizing the drying control scheme. Similarly, in the process of obtaining the second and third stage freeze-drying control optimization schemes, the same method can be used to determine the second stage optimization adjustment constraint space and the third stage optimization adjustment constraint space.

[0096] Exemplarily, for an antibiotic preparation, SQL is used to retrieve drugs similar to the antibiotic preparation in drying requirements and basic data from the database to form an associated sample drug set. The drying control scheme for the pre-freezing stage is extracted from the associated sample drug set to form an associated sample pre-freezing stage drying control scheme set. Python is used to analyze the drying control scheme for the associated sample pre-freezing stage, and the range of key parameters is identified as: temperature range is -40℃ to -35℃, and time range is 1.5 hours to 2.5 hours. According to the operating manual and experimental data of the vacuum freeze-drying equipment, the temperature and time limits of the equipment are determined as follows: the operating temperature range of the equipment is -50℃ to -30℃, and the time range is 1 hour to 3 hours. The temperature intersection of the two is calculated to be -40℃ to -35℃, and the time intersection is 1.5 hours to 2.5 hours, generating the first stage optimization adjustment constraint space.

[0097] In summary, the drug quality preservation drying method based on vacuum freeze drying technology provided in the embodiments of the present application has the following technical effects:

[0098] Obtain the drug drying requirements of the drug to be dried, clarify the final drying target of the drug, and provide a target basis for subsequent drying control decisions. Collect the basic information of the drug to be dried, obtain the basic data of the drug, provide necessary data support for the drying control decision, and ensure that the drying plan can be customized according to the characteristics of the drug, so as to protect the structure and activity of the drug. Activate the multi-stage control decision channel for drug freeze drying, combine the drug drying requirements and the basic data of the drug to make a drying control decision for the vacuum freeze drying equipment, and generate a vacuum freeze drying control plan, wherein the vacuum freeze drying control plan includes a first-stage freeze drying control plan, a second-stage freeze drying control plan, and a third-stage freeze drying control plan, so as to achieve refined management of the drying process, provide a preliminary, data-based control plan for the drying process, and ensure that each stage of the drying process can achieve the expected effect. Perform drying control prediction and evaluation on the first-stage freeze drying control plan, the second-stage freeze drying control plan, and the third-stage freeze drying control plan, respectively, obtain the drying control prediction and evaluation results of each stage, pre-discover and solve possible problems, and improve the success rate and efficiency of the drying process. Determine whether the prediction and evaluation results of the drying control at each stage meet the prediction and evaluation constraints of the drying control at each stage. If the prediction and evaluation results of the drying control at each stage do not meet the prediction and evaluation constraints of the drying control at each stage, optimize the vacuum freeze-drying control scheme according to the prediction and evaluation constraints of the drying control at each stage, generate a freeze-drying control optimization scheme, and generate a more effective control scheme through continuous optimization to improve the efficiency of the drying process and the quality of the medicine, and ensure that the final drying effect meets the requirements. Based on the vacuum freeze-drying equipment, the medicine to be dried is dried according to the freeze-drying control optimization scheme to achieve an efficient and high-quality drying process, maintain the active ingredients and stability of the medicine, and extend the shelf life of the medicine.

[0099] In general, the embodiment of the present application uses a multi-stage control decision channel to formulate specific control schemes for the three drying stages according to the specific drying requirements of different medicines, and predicts, evaluates and dynamically optimizes the control scheme for each stage before actual drying, pre-identifies and solves potential problems, and achieves refined control of the vacuum freeze-drying process of medicines, improves the stability and efficiency of the drying process, minimizes damage to medicines during the drying process, maintains the molecular structure and performance of medicines, and thereby improves the stability and shelf life of medicines.

[0100] Embodiment 2, as Figure 4 As shown, the embodiment of the present application provides a drug quality preservation drying device based on vacuum freeze drying technology, and the device includes:

[0101] The demand acquisition module 10 is used to obtain the drug drying requirements of the drug to be dried.

[0102] The basic information collection module 20 is used to collect the basic information of the medicine to be dried and obtain basic data of the medicine.

[0103] A control scheme generating module 30, wherein the control scheme generating module is used to activate a multi-stage control decision channel for freeze-drying of drugs, and makes a drying control decision on the vacuum freeze-drying equipment in combination with the drug drying requirements and the drug basic data, and generates a vacuum freeze-drying control scheme, wherein the vacuum freeze-drying control scheme includes a first-stage freeze-drying control scheme, a second-stage freeze-drying control scheme and a third-stage freeze-drying control scheme.

[0104] The scheme prediction and evaluation module 40 is used to perform drying control prediction and evaluation on the first stage freeze-drying control scheme, the second stage freeze-drying control scheme and the third stage freeze-drying control scheme respectively, and obtain drying control prediction and evaluation results for each stage.

[0105] The judgment module 50 is used to judge whether the prediction and evaluation results of the drying control at each stage meet the prediction and evaluation constraints of the drying control at each stage.

[0106] The scheme optimization module 60 is used to optimize and adjust the vacuum freeze-drying control scheme according to the prediction and evaluation constraints of the drying control at each stage if the prediction and evaluation results of the drying control at each stage do not meet the prediction and evaluation constraints of the drying control at each stage, so as to generate a freeze-drying control optimization scheme.

[0107] A solution execution module 70 is used to dry the medicine to be dried based on the vacuum freeze-drying equipment according to the freeze-drying control optimization solution.

[0108] Furthermore, the control scheme generation module 30 of the embodiment of the present application is also used to perform the following steps:

[0109] The multi-stage control decision channel for drug freeze-drying includes a pre-freezing stage drying control decision channel, a sublimation drying stage control decision channel and an analytical drying stage control decision channel; based on the drug drying requirements and the drug basic data, the first-stage freeze-drying control plan is generated according to the pre-freezing stage drying control decision channel; based on the drug drying requirements and the drug basic data, the second-stage freeze-drying control plan is generated according to the sublimation drying stage control decision channel; based on the drug drying requirements and the drug basic data, the third-stage freeze-drying control plan is generated according to the analytical drying stage control decision channel; the vacuum freeze-drying control plan is generated according to the first-stage freeze-drying control plan, the second-stage freeze-drying control plan and the third-stage freeze-drying control plan.

[0110] Furthermore, the control scheme generation module 30 of the embodiment of the present application is also used to perform the following steps:

[0111] According to the vacuum freeze-drying equipment, drug drying control record retrieval is performed to obtain a drug drying demand record set, a drug basic data record set and a drug drying control scheme record set; according to the drug drying control scheme record set, feature combing is performed to obtain a pre-freezing stage drying control scheme record set, a sublimation drying stage control scheme record set and a parsing drying stage control scheme record set; according to the drug drying demand record set, the drug basic data record set and the pre-freezing stage drying control scheme record set, the pre-freezing stage drying control decision channel is built; according to the drug drying demand record set, the drug basic data record set and the sublimation drying stage control scheme record set, the sublimation drying stage control decision channel is built; according to the drug drying demand record set, the drug basic data record set and the parsing drying stage control scheme record set, the parsing drying stage control decision channel is built; the pre-freezing stage drying control decision channel, the sublimation drying stage control decision channel and the parsing drying stage control decision channel are connected to generate the drug freeze-drying multi-stage control decision channel.

[0112] Furthermore, the prediction and evaluation module 40 of the embodiment of the present application is also used to perform the following steps:

[0113] Establish the first-stage expected drug moisture change curve, the second-stage expected drug moisture change curve and the third-stage expected drug moisture change curve of the drug to be dried; simulate the drying of the drug to be dried according to the first-stage freeze-drying control scheme, the second-stage freeze-drying control scheme and the third-stage freeze-drying control scheme to generate the first-stage simulated drug moisture change curve, the second-stage simulated drug moisture change curve and the third-stage simulated drug moisture change curve; perform a similarity evaluation based on the first-stage expected drug moisture change curve and the first-stage simulated drug moisture change curve to generate a first-stage drying control prediction evaluation coefficient; perform a similarity evaluation based on the second-stage expected drug moisture change curve and the second-stage simulated drug moisture change curve to generate a second-stage drying control prediction evaluation coefficient; perform a similarity evaluation based on the third-stage expected drug moisture change curve and the third-stage simulated drug moisture change curve to generate a third-stage drying control prediction evaluation coefficient; add the first-stage drying control prediction evaluation coefficient, the second-stage drying control prediction evaluation coefficient and the third-stage drying control prediction evaluation coefficient to the drying control prediction evaluation results of each stage.

[0114] Furthermore, the solution optimization module 60 of the embodiment of the present application is also used to perform the following steps:

[0115] The drying control prediction and evaluation constraints of each stage include the first-stage drying control prediction and evaluation constraints, the second-stage drying control prediction and evaluation constraints and the third-stage drying control prediction and evaluation constraints; if the first-stage drying control prediction and evaluation coefficient does not meet the first-stage drying control prediction and evaluation constraints, the first-stage freeze-drying control scheme is optimized and adjusted according to the first-stage drying control prediction and evaluation constraints to obtain the first-stage freeze-drying control optimization scheme; if the second-stage drying control prediction and evaluation coefficient does not meet the second-stage drying control prediction and evaluation constraints, the second-stage freeze-drying control scheme is optimized and adjusted according to the second-stage drying control prediction and evaluation constraints to obtain the second-stage freeze-drying control optimization scheme; if the third-stage drying control prediction and evaluation coefficient does not meet the third-stage drying control prediction and evaluation constraints, the third-stage freeze-drying control scheme is optimized and adjusted according to the third-stage drying control prediction and evaluation constraints to obtain the third-stage freeze-drying control optimization scheme; the freeze-drying control optimization scheme is generated according to the first-stage freeze-drying control optimization scheme, the second-stage freeze-drying control optimization scheme and the third-stage freeze-drying control optimization scheme.

[0116] Preferably, the solution optimization module 60 of the embodiment of the present application is also used to perform the following steps:

[0117] A global mining of drying control in the pre-freezing stage is performed according to the to-be-dried medicine, and a first-stage optimization adjustment constraint space is established; the first-stage freeze-drying control scheme is randomly adjusted according to the first-stage optimization adjustment constraint space to generate a first-stage drying control adjustment scheme; a drying control prediction and evaluation is performed according to the first-stage drying control adjustment scheme to obtain a drying control evaluation coefficient of the first adjustment scheme; it is determined whether the drying control evaluation coefficient of the first adjustment scheme satisfies the first-stage drying control prediction and evaluation constraints; if the drying control evaluation coefficient of the first adjustment scheme satisfies the first-stage drying control prediction and evaluation constraints, the first-stage drying control adjustment scheme is added to the first-stage freeze-drying control optimization scheme; if the drying control evaluation coefficient of the first adjustment scheme does not satisfy the first-stage drying control prediction and evaluation constraints, the first-stage freeze-drying control scheme is continuously adjusted according to the first-stage optimization adjustment constraint space until the first-stage freeze-drying control optimization scheme is generated.

[0118] Optionally, the solution optimization module 60 of the embodiment of the present application is further configured to perform the following steps:

[0119] Based on the drug drying requirements and the drug basic data, the to-be-dried drugs are retrieved for associated samples to determine a set of associated sample drugs that meet similar association constraints; pre-freezing stage drying control is retrieved based on the associated sample drug set to obtain a set of associated sample pre-freezing stage drying control schemes; drying control feature intervals are identified based on the associated sample pre-freezing stage drying control scheme set to obtain an associated pre-freezing stage drying control domain; drying control constraint information of the vacuum freeze-drying equipment is collected to generate a drying control constraint domain; the intersection of the associated pre-freezing stage drying control domain and the drying control constraint domain is obtained to generate the first stage optimal adjustment constraint space.

[0120] Through the above detailed description of the drug quality preservation and drying method based on vacuum freeze-drying technology in this specification, those skilled in the art can clearly understand the drug quality preservation and drying device based on vacuum freeze-drying technology in this embodiment. For the device disclosed in Example 2, since it corresponds to the method disclosed in Example 1, it has corresponding functional modules and beneficial effects. For relevant matters, please refer to the method part description.

[0121] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A drug quality-preserving drying method based on vacuum freeze-drying technology, characterized in that: The method comprises: Obtain drug drying requirements of the drug to be dried; Collecting basic information of the drug to be dried to obtain basic drug data; Activate the drug freeze-drying multi-stage control decision channel, make a drying control decision on the vacuum freeze-drying equipment in combination with the drug drying requirements and the drug basic data, and generate a vacuum freeze-drying control plan, wherein the vacuum freeze-drying control plan includes a first-stage freeze-drying control plan, a second-stage freeze-drying control plan, and a third-stage freeze-drying control plan; Respectively performing drying control prediction and evaluation on the first-stage freeze-drying control scheme, the second-stage freeze-drying control scheme, and the third-stage freeze-drying control scheme to obtain drying control prediction and evaluation results for each stage; Determining whether the prediction and evaluation results of the drying control at each stage meet the prediction and evaluation constraints of the drying control at each stage; If the prediction and evaluation results of the drying control at each stage do not satisfy the prediction and evaluation constraints of the drying control at each stage, optimizing and adjusting the vacuum freeze-drying control scheme according to the prediction and evaluation constraints of the drying control at each stage to generate a freeze-drying control optimization scheme; Based on the vacuum freeze-drying equipment, the medicine to be dried is dried according to the freeze-drying control optimization scheme; Among them, activating the drug freeze-drying multi-stage control decision channel, combining the drug drying requirements and the drug basic data to make a drying control decision for the vacuum freeze-drying equipment, and generating a vacuum freeze-drying control plan, including: The multi-stage control decision channel for drug freeze drying includes a pre-freezing stage drying control decision channel, a sublimation drying stage control decision channel and an analytical drying stage control decision channel; Based on the drug drying requirements and the drug basic data, and according to the pre-freezing stage drying control decision channel, generating the first stage freeze-drying control plan; Based on the drug drying requirements and the drug basic data, and according to the sublimation drying stage control decision channel, a second-stage freeze-drying control plan is generated; Based on the drug drying requirements and the drug basic data, and according to the analytical drying stage control decision channel, the third stage freeze-drying control plan is generated; Generate the vacuum freeze-drying control plan according to the first-stage freeze-drying control plan, the second-stage freeze-drying control plan and the third-stage freeze-drying control plan; The steps of constructing the multi-stage control decision channel for drug freeze drying include: Retrieve drug drying control records according to the vacuum freeze drying equipment to obtain a drug drying requirement record set, a drug basic data record set, and a drug drying control plan record set; According to the drug drying control scheme record set, feature combing is performed to obtain a pre-freezing stage drying control scheme record set, a sublimation drying stage control scheme record set, and a parsing drying stage control scheme record set; According to the drug drying demand record set, the drug basic data record set and the pre-freezing stage drying control plan record set, the pre-freezing stage drying control decision channel is established; According to the drug drying requirement record set, the drug basic data record set and the sublimation drying stage control plan record set, the sublimation drying stage control decision channel is established; According to the drug drying requirement record set, the drug basic data record set and the analytical drying stage control plan record set, the analytical drying stage control decision channel is established; Connecting the pre-freezing stage drying control decision channel, the sublimation drying stage control decision channel and the analytical drying stage control decision channel to generate the drug freeze-drying multi-stage control decision channel; The first stage freeze-drying control scheme, the second stage freeze-drying control scheme and the third stage freeze-drying control scheme are respectively subjected to drying control prediction evaluation to obtain drying control prediction evaluation results of each stage, including: Establishing a first-stage expected drug moisture change curve, a second-stage expected drug moisture change curve, and a third-stage expected drug moisture change curve for the drug to be dried; Perform simulated drying of the drug to be dried according to the first-stage freeze-drying control scheme, the second-stage freeze-drying control scheme, and the third-stage freeze-drying control scheme, and generate a first-stage simulated drug moisture change curve, a second-stage simulated drug moisture change curve, and a third-stage simulated drug moisture change curve; Based on the expected drug moisture change curve in the first stage and the simulated drug moisture change curve in the first stage, a similarity evaluation is performed to generate a first stage drying control prediction evaluation coefficient; Based on the expected drug moisture change curve in the second stage and the simulated drug moisture change curve in the second stage, a similarity evaluation is performed to generate a second stage drying control prediction evaluation coefficient; Based on the expected drug moisture change curve in the third stage and the simulated drug moisture change curve in the third stage, a similarity evaluation is performed to generate a third stage drying control prediction evaluation coefficient; adding the first-stage drying control prediction evaluation coefficient, the second-stage drying control prediction evaluation coefficient and the third-stage drying control prediction evaluation coefficient to the drying control prediction evaluation results of each stage; If the prediction and evaluation results of the drying control at each stage do not satisfy the prediction and evaluation constraints of the drying control at each stage, the vacuum freeze-drying control scheme is optimized and adjusted according to the prediction and evaluation constraints of the drying control at each stage to generate a freeze-drying control optimization scheme, including: The drying control prediction and evaluation constraints at each stage include the first stage drying control prediction and evaluation constraints, the second stage drying control prediction and evaluation constraints and the third stage drying control prediction and evaluation constraints; If the first-stage drying control prediction evaluation coefficient does not satisfy the first-stage drying control prediction evaluation constraint, optimizing and adjusting the first-stage freeze-drying control scheme according to the first-stage drying control prediction evaluation constraint to obtain the first-stage freeze-drying control optimization scheme; If the second-stage drying control prediction evaluation coefficient does not satisfy the second-stage drying control prediction evaluation constraint, optimizing and adjusting the second-stage freeze-drying control scheme according to the second-stage drying control prediction evaluation constraint to obtain the second-stage freeze-drying control optimization scheme; If the third-stage drying control prediction evaluation coefficient does not satisfy the third-stage drying control prediction evaluation constraint, optimizing and adjusting the third-stage freeze-drying control scheme according to the third-stage drying control prediction evaluation constraint to obtain the third-stage freeze-drying control optimization scheme; Generate the freeze-drying control optimization scheme according to the first-stage freeze-drying control optimization scheme, the second-stage freeze-drying control optimization scheme and the third-stage freeze-drying control optimization scheme; If the first-stage drying control prediction evaluation coefficient does not satisfy the first-stage drying control prediction evaluation constraint, optimizing and adjusting the first-stage freeze-drying control scheme according to the first-stage drying control prediction evaluation constraint to obtain the first-stage freeze-drying control optimization scheme, including: Perform global mining of drying control in the pre-freezing stage according to the medicine to be dried, and establish a first-stage optimization adjustment constraint space; Randomly adjusting the first-stage freeze-drying control plan according to the first-stage optimal adjustment constraint space to generate a first-stage drying control adjustment plan; Perform a drying control prediction evaluation according to the first-stage drying control adjustment plan to obtain a drying control evaluation coefficient for the first adjustment plan; Determining whether the drying control evaluation coefficient of the first adjustment scheme satisfies the first-stage drying control prediction evaluation constraint; If the drying control evaluation coefficient of the first adjustment scheme satisfies the first-stage drying control prediction evaluation constraint, adding the first-stage drying control adjustment scheme to the first-stage freeze-drying control optimization scheme; If the drying control evaluation coefficient of the first adjustment scheme does not satisfy the first-stage drying control prediction evaluation constraint, continue to adjust the first-stage freeze-drying control scheme according to the first-stage optimal adjustment constraint space until the first-stage freeze-drying control optimal scheme is generated; According to the drug to be dried, global mining of drying control in the pre-freezing stage is performed to establish the first stage optimization adjustment constraint space, including: Based on the drug drying requirements and the drug basic data, performing associated sample retrieval on the drug to be dried to determine an associated sample drug set that satisfies similar association constraints; Perform a pre-freezing stage drying control search based on the associated sample medicine set to obtain an associated sample pre-freezing stage drying control solution set; Perform drying control feature interval identification according to the associated sample pre-freezing stage drying control scheme set to obtain an associated pre-freezing stage drying control domain; Collecting drying control constraint information of the vacuum freeze-drying equipment to generate a drying control constraint domain; The intersection of the associated pre-freezing stage drying control domain and the drying control constraint domain is obtained to generate the first stage optimization adjustment constraint space.

2. A drug quality-preserving drying device based on vacuum freeze-drying technology, characterized in that: The device is used to perform the drug quality preservation drying method based on vacuum freeze drying technology according to claim 1, and the device comprises: A demand acquisition module, the demand acquisition module is used to obtain the drug drying requirements of the drug to be dried; A basic information collection module, which is used to collect basic information of the drug to be dried and obtain basic drug data; A control scheme generating module, wherein the control scheme generating module is used to activate a multi-stage control decision channel for drug freeze drying, and to make a drying control decision on the vacuum freeze drying equipment in combination with the drug drying requirements and the drug basic data, and to generate a vacuum freeze drying control scheme, wherein the vacuum freeze drying control scheme includes a first-stage freeze drying control scheme, a second-stage freeze drying control scheme, and a third-stage freeze drying control scheme; A scheme prediction and evaluation module, wherein the scheme prediction and evaluation module is used to respectively perform drying control prediction and evaluation on the first stage freeze-drying control scheme, the second stage freeze-drying control scheme and the third stage freeze-drying control scheme to obtain drying control prediction and evaluation results for each stage; A judgment module, the judgment module is used to judge whether the prediction and evaluation results of the drying control at each stage meet the prediction and evaluation constraints of the drying control at each stage; A scheme optimization module, wherein if the prediction and evaluation results of the drying control at each stage do not satisfy the prediction and evaluation constraints of the drying control at each stage, the vacuum freeze-drying control scheme is optimized and adjusted according to the prediction and evaluation constraints of the drying control at each stage to generate a freeze-drying control optimization scheme; A scheme execution module is used to dry the medicine to be dried based on the vacuum freeze-drying equipment and according to the freeze-drying control optimization scheme.

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