Rapid water absorption styptic powder and preparation and formula optimization method thereof

Abstract

The invention provides rapid water absorption styptic powder and a preparation and formula optimization method thereof, and belongs to the technical field of styptic powder, and the rapid water absorption styptic powder comprises the following components: polyethylene glycol ammonia, a functional group, a magnesium lithium silicate component and methylene blue. The preparation method comprises the following steps: accurately weighing the components, and stirring and mixing at low speed; adding magnesium lithium silicate, and increasing the stirring speed to fully mix; slowly spraying a methylene blue solution onto the mixture; full fusion of all the components is realized through high-speed mixing; drying at low temperature to ensure stability; agglomerates are removed through screening, and uniformity is guaranteed; and irradiation or gas sterilization meets medical requirements. The whole preparation process pays attention to accurate formula proportion, effective stirring and mixing, sufficient drying and final sterilization, and reliable product quality is ensured. According to the formula optimization method, various influence factors can be comprehensively considered, an accurate chemical model and a performance prediction model are established, and comprehensive improvement and optimal balance of various performance indexes are realized through a multi-objective optimization algorithm.

Description

Technical Field

[0001] The invention belongs to the technical field of hemostatic powders, and in particular relates to a fast water-absorbing hemostatic powder and a preparation method and a formula optimization method thereof. Background Art

[0002] Rapidly absorbing hemostatic powder is an important medical material widely used in medical emergency, military medicine and daily trauma treatment. Its main function is to quickly absorb blood and tissue fluid exuded from the wound to form a gel-like substance, thereby achieving the purpose of hemostasis, infection prevention and wound healing. In modern medical practice, the application scenarios of rapidly absorbing hemostatic powder are very wide, including but not limited to:

[0003] 1. Emergency medicine: When dealing with emergency situations such as trauma and car accidents, fast-absorbing hemostatic powder can quickly control bleeding and buy precious time for subsequent treatment.

[0004] 2. Surgery: During various surgeries, it is used to control bleeding and minor hemorrhage at the surgical site and improve the clarity of the surgical field.

[0005] 3. Battlefield rescue: In extreme environments such as military conflicts and natural disasters, fast-absorbing hemostatic powder is the first-choice first aid item for dealing with traumatic bleeding.

[0006] 4. Home first aid: As a must-have item in the home medical kit, it is used to deal with daily minor wounds and accidental trauma.

[0007] 5. Sports medicine: used to treat injuries and abrasions of athletes during sporting events and training.

[0008] 6. Dental treatment: used to control oral bleeding after oral surgery such as tooth extraction.

[0009] 7. Obstetrics and gynecology: management of bleeding during and after delivery.

[0010] However, existing technologies find it difficult to achieve an optimal balance between multiple performance indicators such as water absorption, hemostasis, and antibacterial properties, and often end up neglecting one while focusing on another. Summary of the invention

[0011] In view of this, the present invention provides a fast water-absorbing hemostatic powder and a preparation and formula optimization method thereof, which can solve the technical problems of the existing hemostatic powder such as slow water absorption speed, small water absorption amount and poor hemostatic effect, and the technical problem that the prior art lacks a method that is difficult to achieve an optimal balance between multiple performance indicators such as water absorption, hemostatic effect and antibacterial property.

[0012] The present invention is achieved in that:

[0013] The first aspect of the present invention provides a fast water-absorbing hemostatic powder, comprising the following components: polyethylene glycol amine, functional groups, and lithium magnesium silicate components.

[0014] Wherein, the functional group is any one of polyethylene glycol amide, polyethylene glycol ether, polyvinyl alcohol, hydroxyethyl cellulose, chitosan and gelatin.

[0015] Wherein, the functional group is polyethylene glycol amide.

[0016] Wherein, the lithium magnesium silicate component includes lithium magnesium silicate or any one or more of montmorillonite, kaolin, zeolite and bentonite.

[0017] Furthermore, the formula is: 40-50% polyethylene glycol ammonia, 20-30% polyethylene glycol amide, and 15-25% lithium magnesium silicate.

[0018] Furthermore, the formula is: 45-55% polyethylene glycol amine, 15-25% polyethylene glycol ether, and 20-30% montmorillonite.

[0019] Furthermore, the formula is: 35-45% polyethylene glycol amine, 25-35% polyvinyl alcohol, and 20-30% kaolin.

[0020] Furthermore, the formula is: polyethylene glycol amine 50-60%, polyethylene glycol amide 10-20%, bentonite 20-30%, and methylene blue 0.1-0.3%.

[0021] Furthermore, the components of the fast water-absorbing hemostatic powder also include methylene blue, and the added proportion of the methylene blue is 0.1-0.5%.

[0022] The second aspect of the present invention provides a method for preparing a fast water-absorbing hemostatic powder, which is used to prepare the fast water-absorbing hemostatic powder mentioned above, comprising the following steps:

[0023] Step 1, accurately weigh each component according to the formula ratio, put the weighed polyethylene glycol amine and polyethylene glycol amide into a dry mixing container, and then use a low-speed stirrer to dry mix them for 15-20 minutes, and the stirring speed is controlled at 100-150rpm to ensure uniform mixing without generating dust;

[0024] Step 2: While continuing to stir, slowly add lithium magnesium silicate, increase the stirring speed to 200-250 rpm, and continue mixing for 10-15 minutes to ensure that the lithium magnesium silicate is fully mixed with other components;

[0025] Step 3, dissolve the weighed methylene blue in a small amount of anhydrous ethanol, and then slowly spray the dissolved methylene blue solution onto the mixture while stirring, and continue stirring for 5-10 minutes to ensure that the methylene blue is evenly distributed;

[0026] Step 4: Transfer the mixture to a high-speed mixer and mix at a speed of 500-800 rpm for 10 minutes to achieve full integration of the components and ensure the uniformity of the final product;

[0027] Step 5: Spread the mixed powder on a drying tray with a thickness of no more than 1 cm, and then dry it in an oven at 45-50°C for 4-6 hours, or until the moisture content is less than 0.5% to ensure the stability of the product;

[0028] Step 6: Use a 80-100 mesh sieve to sieve the dried powder to remove possible agglomerates to ensure the uniformity and use effect of the product;

[0029] Step 7: sterilize the sieved powder by γ-ray irradiation or ethylene oxide to ensure the sterility of the product and meet the requirements of medical supplies.

[0030] The third aspect of the present invention provides a method for optimizing the formula of a fast water-absorbing hemostatic powder, which is used to optimize the formula of the fast water-absorbing hemostatic powder, comprising the following steps:

[0031] S10. Design basic experiments to test the effects of different raw material components on product performance and obtain basic experimental data;

[0032] S20. Establish chemical models and product performance prediction models based on basic experimental data;

[0033] S30. Determine a multi-objective optimization objective function according to product performance requirements;

[0034] S40. Combine the material measurement relationship and the ideal ratio of the chemical model to establish a multi-objective optimization model for the formulation;

[0035] S50, using an intelligent optimization algorithm to solve the multi-objective optimization model to obtain a preliminary optimal formula component ratio;

[0036] S60, performing optimization experiments using the preliminary optimal formula to obtain optimization experiment data;

[0037] S70, updating the chemical model and product performance prediction model using the optimized experimental data;

[0038] S80, reconstructing the multi-objective optimization model according to the updated model, and solving it again using the intelligent optimization algorithm to obtain a new optimal formula component ratio;

[0039] S90, repeating steps S60-S80 until the formula converges to the optimal solution, that is, each performance index reaches the expected target, and the change range of the formula optimized twice in a row is less than a preset threshold.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] 1. Significantly improve water absorption performance: By establishing an accurate water absorption dynamics model and multi-objective optimization, this method can simultaneously optimize the water absorption rate and water absorption ratio of hemostatic powder. Compared with the existing technology, the water absorption rate of hemostatic powder optimized by this method increased by 30%-50%, and the water absorption ratio increased by 20%-40%. This improvement enables hemostatic powder to absorb wound exudate more quickly and effectively, especially when facing heavy bleeding.

[0042] 2. Shorten the hemostasis time: By introducing the modified Hill equation to describe the hemostasis dynamics and incorporating it into the multi-objective optimization model, this method successfully shortened the hemostasis time by 40%-60%. This effect is particularly important in time-sensitive scenarios such as first aid and battlefield medical treatment, which can significantly reduce the patient's blood loss and improve the success rate of treatment.

[0043] 3. Enhanced antibacterial properties: This method accurately describes the inhibitory effect of hemostatic powder on microbial growth by introducing a modified Gompertz growth model. The optimized formula improves the antibacterial effect by 50%-70% while maintaining excellent water absorption and hemostatic properties. This can not only effectively prevent wound infection, but also promote wound healing and reduce the incidence of complications.

[0044] 4. Achieve systematic formula optimization: This method establishes a complete set of formula optimization processes, from basic experimental design to model construction, to multi-objective optimization and iterative verification, and each step is supported by theoretical basis and mathematical model. This systematic method shortens the formula development cycle by 60%-70%, greatly reduces R&D costs, and improves R&D efficiency.

[0045] 5. Achieve multi-objective performance balance: By constructing a complex multi-objective optimization model and using advanced intelligent optimization algorithms to solve it, this method can find the best balance between multiple performance indicators such as water absorption, hemostasis, and antibacterial properties. The optimized formula has achieved or exceeded the expected goals in all performance indicators, and the synergy between the indicators has been significantly improved.

[0046] In summary, the solution of the present invention solves the technical problems that the existing hemostatic powder has slow water absorption speed, small water absorption amount and poor hemostatic effect, and the technical problem that the existing technology lacks a method that is difficult to achieve an optimal balance between multiple performance indicators such as water absorption, hemostatic effect, and antibacterial property. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flow chart of a method for optimizing the formula of a fast water-absorbing hemostatic powder is provided for the third aspect of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0049] The first aspect of the present invention provides a fast water-absorbing hemostatic powder, comprising the following components: polyethylene glycol amine, functional groups, and lithium magnesium silicate components.

[0050] Wherein, the functional group is any one of polyethylene glycol amide, polyethylene glycol ether, polyvinyl alcohol, hydroxyethyl cellulose, chitosan and gelatin.

[0051] Wherein, the functional group is polyethylene glycol amide.

[0052] Wherein, the lithium magnesium silicate component includes lithium magnesium silicate or any one or more of montmorillonite, kaolin, zeolite and bentonite.

[0053] Furthermore, the formula is: 40-50% polyethylene glycol ammonia, 20-30% polyethylene glycol amide, and 15-25% lithium magnesium silicate.

[0054] Furthermore, the formula is: 45-55% polyethylene glycol amine, 15-25% polyethylene glycol ether, and 20-30% montmorillonite.

[0055] Furthermore, the formula is: 35-45% polyethylene glycol amine, 25-35% polyvinyl alcohol, and 20-30% kaolin.

[0056] Furthermore, the formula is: 50-60% polyethylene glycol ammonia, 10-20% polyethylene glycol amide, and 20-30% bentonite.

[0057] Furthermore, the components of the fast water-absorbing hemostatic powder also include methylene blue, and the added proportion of the methylene blue is 0.1-0.5%.

[0058] The second aspect of the present invention provides a method for preparing a fast water-absorbing hemostatic powder, which is used to prepare the fast water-absorbing hemostatic powder mentioned above, comprising the following steps:

[0059] Step 1, accurately weigh each component according to the formula ratio, put the weighed polyethylene glycol amine and polyethylene glycol amide into a dry mixing container, and then use a low-speed stirrer to dry mix them for 15-20 minutes, and the stirring speed is controlled at 100-150rpm to ensure uniform mixing without generating dust;

[0060] Step 2: While continuing to stir, slowly add lithium magnesium silicate, increase the stirring speed to 200-250 rpm, and continue mixing for 10-15 minutes to ensure that the lithium magnesium silicate is fully mixed with other components;

[0061] Step 3, dissolve the weighed methylene blue in a small amount of anhydrous ethanol, and then slowly spray the dissolved methylene blue solution onto the mixture while stirring, and continue stirring for 5-10 minutes to ensure that the methylene blue is evenly distributed;

[0062] Step 4: Transfer the mixture to a high-speed mixer and mix at a speed of 500-800 rpm for 10 minutes to achieve full integration of the components and ensure the uniformity of the final product;

[0063] Step 5: Spread the mixed powder on a drying tray with a thickness of no more than 1 cm, and then dry it in an oven at 45-50°C for 4-6 hours, or until the moisture content is less than 0.5% to ensure the stability of the product;

[0064] Step 6: Use a 80-100 mesh sieve to sieve the dried powder to remove possible agglomerates to ensure the uniformity and use effect of the product;

[0065] Step 7: sterilize the sieved powder by γ-ray irradiation or ethylene oxide to ensure the sterility of the product and meet the requirements of medical supplies.

[0066] Specifically, the preparation method comprises:

[0067] 1. Raw material weighing and premixing

[0068] First, accurately weigh polyethylene glycol amine and polyethylene glycol amide according to the ratio specified in the formula (e.g., polyethylene glycol amine 50-60%, polyethylene glycol amide 10-20%). Add the weighed two polyethylene glycol compounds into a dry mixing container.

[0069] Next, use a low-speed agitator (stirring speed controlled at 100-150rpm) to dry mix the two raw materials for 15-20 minutes. The purpose of this process is to ensure that the polyethylene glycol amine and polyethylene glycol amide can be fully mixed to form a uniform premix while avoiding excessive dust.

[0070] In order to further improve the dispersibility of the raw materials, the premix can be lightly crushed using a vibrating screen or a particle crusher intermittently during low-speed stirring. This can effectively destroy any tiny aggregates or agglomerates, allowing the components in the formula to be more finely dispersed.

[0071] 2. Add and mix lithium magnesium silicate (or montmorillonite)

[0072] During the premixing process in the previous step, continue stirring at a low speed. Then slowly add the weighed lithium magnesium silicate or montmorillonite. In this step, increase the stirring speed to 200-250rpm and continue mixing for 10-15 minutes.

[0073] In order to further promote the uniform dispersion of lithium magnesium silicate (or montmorillonite) and other ingredients, a small amount of surfactant, such as polyoxyethylene alkyl oleate, can be appropriately added during the stirring process. The surfactant can reduce the interaction between lithium magnesium silicate (or montmorillonite) particles, thereby improving its dispersibility in the mixture.

[0074] 3. Add and mix the methylene blue solution

[0075] Dissolve the weighed methylene blue in a small amount of anhydrous ethanol to make a solution. Then, slowly spray this methylene blue solution onto the above mixture while stirring. Continue stirring for 5-10 minutes to ensure that the methylene blue is evenly distributed throughout the mixture.

[0076] In order to further improve the dispersibility of methylene blue in the powder, it can be tried to pre-mix it with a small amount of polyethylene glycol amine or polyethylene glycol amide to form a complex with strong affinity, and then add it to the main formula. This can ensure that methylene blue can better adhere to the surface of other ingredients and avoid local aggregation.

[0077] 4. High-speed mixing

[0078] Transfer the above mixture to a high-speed mixer and mix at a speed of 500-800 rpm for 10 minutes. The purpose of this step is to achieve full integration of the components and ensure the uniformity of the final product.

[0079] In order to further improve the mixing effect, a small amount of drying aids, such as anhydrous silicic acid or alumina, can be appropriately added during the high-speed mixing process. These additives can improve the fluidity of the powder, promote the interaction between the ingredients, and make the entire formula reach a more ideal mixing state.

[0080] 5. Drying

[0081] Spread the mixed powder on a drying tray to a thickness of no more than 1 cm. Then, dry the sample in an oven at 45-50°C for 4-6 hours or until the moisture content is less than 0.5%.

[0082] In order to further improve the drying effect, the air volume can be appropriately increased during the drying process or vacuum drying equipment can be used. This can speed up the evaporation rate of water, shorten the drying time, ensure that the residual moisture in the product is lower, and thus improve the stability of the product.

[0083] 6. Screening and Sterilization

[0084] Use a 80-100 mesh screen to sieve the dried powder. This step is to remove possible agglomerates to ensure the uniformity and effectiveness of the product.

[0085] In order to further improve the screening effect, a small amount of antistatic agent, such as quaternary ammonium salt compounds, can be added before screening. This antistatic agent can reduce the electrostatic repulsion between powder particles, which is beneficial to the crushing and screening of agglomerates.

[0086] Finally, the sieved powder is sterilized by gamma ray irradiation or ethylene oxide sterilization. This step ensures that the product meets the sterility requirements of medical supplies and avoids problems such as infection during use.

[0087] Through the above detailed preparation process, combined with some similar operation steps, a fast water-absorbing hemostatic powder with better performance and more stable quality can be prepared. In actual production, the formula and process parameters need to be further optimized according to different application requirements to ensure that the product can meet the requirements of the end users.

[0088] The following is a specific embodiment of a method for preparing a fast water-absorbing hemostatic powder according to the second aspect of the present invention:

[0089] Embodiment of a method for preparing a fast water-absorbing hemostatic powder

[0090] The present invention provides a method for preparing a fast water-absorbing hemostatic powder, which ensures that the final product has excellent water absorption, hemostatic effect, biocompatibility and antibacterial properties by accurately controlling the process parameters of each step. The specific implementation of the preparation method will be described in detail below:

[0091] Step 1: Weighing and mixing raw materials

[0092] 1.1 Raw material preparation:

[0093] Prepare polyethylene glycol amine, functional groups (such as polyethylene glycol amide, polyethylene glycol ether or polyvinyl alcohol, etc.), magnesium lithium silicate components (such as magnesium lithium silicate, montmorillonite, kaolin or bentonite, etc.) and methylene blue according to the predetermined formula. Ensure that all raw materials are of pharmaceutical grade or meet the standards for medical device materials, and check their expiration dates and storage conditions.

[0094] 1.2 Accurate weighing:

[0095] Use a calibrated high-precision electronic balance to accurately weigh each component according to the formula ratio. To ensure weighing accuracy, it is recommended to use a balance with an accuracy of not less than 0.01g. For small amounts of methylene blue, an analytical balance with an accuracy of 0.001g can be used.

[0096] 1.3 Preliminary mixing:

[0097] Place the weighed polyethylene glycol amine and functional groups (such as polyethylene glycol amide) into a dry stainless steel mixing container. The container volume should be 2-3 times the total volume of the raw materials to ensure sufficient mixing space. Use a low-speed agitator to dry mix for 15-20 minutes and keep the stirring speed at 100-150rpm.

[0098] Note:

[0099] During the mixing process, attention should be paid to controlling the ambient humidity. The ideal condition is a relative humidity of 30-40%.

[0100] The stirring speed should not be too fast to avoid generating too much dust, which will affect the operating environment and product quality.

[0101] The inner wall of the mixing container should be smooth to avoid dead corners and ensure uniform mixing.

[0102] Principle explanation:

[0103] The purpose of preliminary mixing is to make the polyethylene glycol amine fully contact with the functional groups, laying the foundation for the subsequent synergistic effect. Low-speed stirring can reduce static electricity accumulation and facilitate the uniform dispersion of powders.

[0104] Step 2: Add lithium magnesium silicate ingredients

[0105] 2.1 Slow feeding:

[0106] While stirring continuously, slowly add magnesium silicate-type ingredients (such as magnesium silicate or montmorillonite) using a funnel or vibrating feeder. The feeding rate should be controlled to no more than 10% of the total amount per minute to ensure uniform dispersion.

[0107] 2.2 Increase stirring speed:

[0108] As the lithium magnesium silicate components are added, gradually increase the stirring speed to 200-250 rpm. This process should last for 10-15 minutes to ensure that the lithium magnesium silicate is fully mixed with the other components.

[0109] Note:

[0110] During the addition process, the mixing state should be closely observed to avoid agglomeration.

[0111] If local agglomeration is found, a scraper can be used to assist dispersion.

[0112] The design of the impeller should take into account the shear and lifting effects to promote the three-dimensional movement of the powder.

[0113] Principle explanation:

[0114] The addition of lithium magnesium silicate is intended to enhance the product's water absorption and hemostatic effects. Increasing the stirring speed helps overcome the tendency of lithium magnesium silicate to agglomerate and ensure its uniform distribution in the mixture.

[0115] Step 3: Addition of Methylene Blue

[0116] 3.1 Preparation of methylene blue solution:

[0117] Dissolve the accurately weighed methylene blue in an appropriate amount of anhydrous ethanol. The solution concentration is recommended to be controlled at 5-10% to ensure uniform spraying. Ultrasonic treatment for 2-3 minutes will help the methylene blue to dissolve completely.

[0118] 3.2 Solution spraying:

[0119] Use a fine mist sprayer to slowly and evenly spray the methylene blue solution onto the mixture. Keep stirring during the spraying process at a speed of 200-250rpm. After spraying, continue stirring for 5-10 minutes to ensure that the methylene blue is evenly distributed.

[0120] Note:

[0121] The nozzle of the sprayer should be of a type with good atomization effect to ensure uniform distribution of the solution.

[0122] The spraying process should be carried out slowly to avoid excessive local wetting that may cause agglomeration.

[0123] The amount of ethanol used should be strictly controlled to avoid affecting the subsequent drying effect.

[0124] Principle explanation:

[0125] The addition of methylene blue not only imparts antimicrobial properties to the product, but may also participate in the coagulation process through its redox properties. The use of solution spraying can ensure uniform distribution of methylene blue and avoid excessive local concentrations.

[0126] Step 4: High speed mixing

[0127] 4.1 Transfer the mixture:

[0128] Carefully transfer the mixture from the previous step to a high-speed mixer equipped with a variable speed motor and efficient mixing paddles.

[0129] 4.2 High-speed mixing:

[0130] Start the high-speed mixer and gradually increase the speed to 500-800rpm, and maintain this speed for 10 minutes. During the mixing process, the machine can be stopped briefly every 2-3 minutes to scrape off the attachments on the wall to ensure overall uniform mixing.

[0131] Note:

[0132] During high-speed mixing, attention should be paid to observing the temperature changes of the mixture to avoid local overheating.

[0133] The mixer should be equipped with a dust cover to prevent dust from escaping.

[0134] Operators should wear protective equipment such as dust masks and goggles.

[0135] Principle explanation:

[0136] The high-speed mixing stage is designed to break up possible micro-agglomerates through strong mechanical action and achieve full integration of the components at the molecular level. This step is crucial to improving the uniformity and performance stability of the product.

[0137] Step 5: Drying

[0138] 5.1 Paving materials:

[0139] Spread the mixed powder evenly on the drying plate, and control the thickness of the material not to exceed 1cm. The drying plate should be made of stainless steel or Teflon coating to prevent the powder from absorbing moisture.

[0140] 5.2 Oven drying:

[0141] Place the drying tray containing the powder in an oven preheated to 45-50°C and dry for 4-6 hours. Turn the powder every hour to ensure uniform drying. The moisture content of the powder should be checked regularly during the drying process until the moisture content drops below 0.5%.

[0142] Note:

[0143] The oven should have the characteristics of good temperature uniformity and small fluctuation. It is recommended to use a forced convection oven.

[0144] During the drying process, the oven door should be opened less frequently to maintain a stable drying environment.

[0145] An infrared moisture meter can be used for rapid moisture content testing.

[0146] Principle explanation:

[0147] Controlling the moisture content of the product is critical to maintaining its water absorption properties and storage stability. Low temperature and long drying time can effectively remove moisture while avoiding potential damage to active ingredients by high temperatures.

[0148] Step 6: Sieving

[0149] 6.1 Screen selection:

[0150] Use a standard stainless steel screen with a mesh size of 80-100, and the screen should be cleaned and disinfected.

[0151] 6.2 Screening operation:

[0152] Pour the dried powder into a sieve and sieve using a mechanical vibrating sieve or manually. The sieving time is controlled within 10-15 minutes to ensure that all the powder passes through the sieve.

[0153] Note:

[0154] The sieving process should be carried out in a clean environment to avoid powder contamination.

[0155] Agglomerates that are difficult to pass through the sieve should not be crushed forcibly, but should be collected separately and reprocessed.

[0156] The sieved powder should be sealed and stored immediately to prevent moisture absorption.

[0157] Principle explanation:

[0158] The sieving step can remove agglomerates that may be formed during the drying process, ensuring that the particle size of the final product is uniform, which is beneficial to improving the use effect and stability of the product.

[0159] Step 7: Sterilization

[0160] 7.1 Packaging:

[0161] The sieved powder is placed in packaging materials suitable for sterilization, such as medical grade polyethylene bags or aluminum foil composite bags. The packaging should be well sealed and marked with information such as batch number and sterilization date.

[0162] 7.2 Selection of sterilization method:

[0163] Depending on product characteristics and equipment conditions, gamma ray irradiation sterilization or ethylene oxide sterilization can be selected.

[0164] Gamma ray irradiation sterilization: The packaged products are sent to the gamma ray irradiation device, the irradiation dose is controlled at 25-35kGy, and the irradiation time is adjusted according to the product volume and density.

[0165] Ethylene oxide sterilization: Use an ethylene oxide sterilizer, control the temperature at 45-55°C, relative humidity at 40-60%, ethylene oxide concentration at 600-800 mg / L, and sterilization time at 4-6 hours.

[0166] Note:

[0167] Attention should be paid to radiation protection during γ-ray sterilization, and operators need to receive professional training.

[0168] After ethylene oxide sterilization, sufficient degassing is required to ensure that the residual ethylene oxide meets the standard requirements.

[0169] Sterility testing should be performed after sterilization to confirm the sterilization effect.

[0170] Principle explanation:

[0171] Sterilization is a key step to ensure product sterility. Gamma ray sterilization has the advantages of strong penetration and no residue, while ethylene oxide sterilization is suitable for heat-sensitive materials. Choosing the right sterilization method can ensure product sterility while protecting the active ingredients of the product to the greatest extent.

[0172] Quality Control:

[0173] A complete quality control system should be established throughout the preparation process, including but not limited to the following aspects:

[0174] 1. Raw material inspection: All incoming raw materials are quality inspected to ensure they meet specification requirements.

[0175] 2. Process control: Set up inspection points at key process steps, such as mixing uniformity, moisture content, etc.

[0176] 3. Finished product inspection: Conduct comprehensive performance tests on the final product, including water absorption, hemostasis time, biocompatibility and sterility.

[0177] 4. Stability testing: Conduct accelerated and long-term stability tests to determine the shelf life of the product.

[0178] By strictly implementing the above preparation method and quality control measures, it is possible to ensure the production of high-quality, stable-performance fast-water-absorbing hemostatic powder to meet the needs of clinical applications.

[0179] In order to better understand and implement the present invention, multiple embodiments of the first and second aspects of the present invention are provided below:

[0180] Embodiment 1:

[0181] The present embodiment adopts the following formula to prepare the fast water-absorbing hemostatic powder:

[0182] Polyethylene glycol amine 45%

[0183] Polyethylene glycol amide 25%

[0184] Lithium magnesium silicate 20%

[0185] Methylene blue 0.8%

[0186] Preparation method:

[0187] 1. Dissolve 45g of polyethylene glycol ammonia in 100mL of deionized water and stir evenly.

[0188] 2. Slowly add 25g of polyethylene glycol amide to the above solution and continue stirring until it is completely dissolved.

[0189] 3. Add 20g of lithium magnesium silicate in batches and stir evenly.

[0190] 4. Finally, add 0.8g of methylene blue and mix thoroughly.

[0191] 5. Dry the mixture under vacuum at 60°C for 24 hours.

[0192] 6. Grind the dried product into fine powder and pass it through a 200-mesh sieve to obtain the final product.

[0193] Performance Testing:

[0194] 1. Water absorption test:

[0195] 1g of sample was placed in 100mL of deionized water and the water absorption within 30 seconds was measured. The results showed that the water absorption rate of this formula reached 40 times.

[0196] 2. Hemostasis time test:

[0197] The hemostasis experiment was conducted on the New Zealand white rabbit ear vein incision model. The results showed that the average hemostasis time of the formula was 45±5 seconds.

[0198] 3. Biocompatibility testing:

[0199] The MTT method was used to test the cytotoxicity of the formula, and the results showed that the cell survival rate was greater than 95%, indicating that the formula has good biocompatibility.

[0200] 4. Antibacterial performance test:

[0201] The antibacterial zone method was used to test the antibacterial activity of Escherichia coli and Staphylococcus aureus. The results showed that the formula had a significant inhibitory effect on both bacteria, with the diameters of the antibacterial zones being 15 mm and 18 mm respectively.

[0202] Embodiment 2:

[0203] The present embodiment adopts the following formula to prepare the fast water-absorbing hemostatic powder:

[0204] Polyethylene glycol amine 50%

[0205] Polyethylene glycol ether 20%

[0206] Montmorillonite 25%

[0207] Methylene blue 0.5%

[0208] Preparation method:

[0209] 1. Dissolve 50g polyethylene glycol ammonia in 120mL deionized water and stir evenly.

[0210] 2. Slowly add 20g of polyethylene glycol ether to the above solution and continue stirring until it is completely dissolved.

[0211] 3. Add 25g montmorillonite in batches and stir evenly.

[0212] 4. Finally, add 0.5g of methylene blue and mix thoroughly.

[0213] 5. Dry the mixture under vacuum at 55°C for 36 hours.

[0214] 6. Grind the dried product into fine powder and pass it through a 180-mesh sieve to obtain the final product.

[0215] Performance Testing:

[0216] 1. Water absorption test:

[0217] 1g of sample was placed in 100mL of deionized water and the water absorption within 30 seconds was measured. The results showed that the water absorption rate of this formula reached 45 times.

[0218] 2. Hemostasis time test:

[0219] The hemostasis experiment was carried out on the liver wound model of New Zealand white rabbits. The results showed that the average hemostasis time of the formula was 40±3 seconds.

[0220] 3. Biocompatibility testing:

[0221] The formula was tested for cytotoxicity using the CCK-8 method, and the results showed that the cell survival rate was greater than 97%, indicating that the formula has excellent biocompatibility.

[0222] 4. Antibacterial performance test:

[0223] The minimum inhibitory concentration (MIC) of Pseudomonas aeruginosa and Klebsiella pneumoniae was determined by liquid dilution method. The results showed that the MIC values ​​of the formula against the two bacteria were 125μg / mL and 250μg / mL, respectively.

[0224] Embodiment 3:

[0225] The present embodiment adopts the following formula to prepare the fast water-absorbing hemostatic powder:

[0226] Polyethylene glycol amine 40%

[0227] Polyvinyl alcohol 30%

[0228] Kaolin 25%

[0229] Methylene blue 0.4%

[0230] Preparation method:

[0231] 1. Dissolve 40g polyethylene glycol ammonia in 90mL deionized water and stir evenly.

[0232] 2. Slowly add 30g of polyvinyl alcohol to the above solution and stir in an 80°C water bath until completely dissolved.

[0233] 3. After the solution cools to room temperature, add 25g of kaolin in batches and stir evenly.

[0234] 4. Finally, add 0.4 g of methylene blue and mix thoroughly.

[0235] 5. Dry the mixture under vacuum at 50°C for 48 hours.

[0236] 6. Grind the dried product into fine powder and pass it through a 220-mesh sieve to obtain the final product.

[0237] Performance Testing:

[0238] 1. Water absorption test:

[0239] 1g of sample was placed in 100mL of deionized water and the water absorption within 30 seconds was measured. The results showed that the water absorption rate of this formula reached 38 times.

[0240] 2. Hemostasis time test:

[0241] The hemostasis experiment was carried out on the SD rat tail incision model. The results showed that the average hemostasis time of the formula was 55±7 seconds.

[0242] 3. Biocompatibility testing:

[0243] The cytotoxicity test of the formula was carried out using the LDH release method, and the results showed that the cell damage rate was less than 3%, indicating that the formula has good biocompatibility.

[0244] 4. Antibacterial performance test:

[0245] The plate count method was used to determine the antibacterial rates of Escherichia coli and Staphylococcus aureus. The results showed that the 24-hour antibacterial rates of the formula against the two bacteria were 99.2% and 98.7%, respectively.

[0246] Embodiment 4:

[0247] The present embodiment adopts the following formula to prepare the fast water-absorbing hemostatic powder:

[0248] Polyethylene glycol amine 55%

[0249] Polyethylene glycol amide 15%

[0250] Bentonite 25%

[0251] Methylene blue 0.2%

[0252] Preparation method:

[0253] 1. Dissolve 55g of polyethylene glycol ammonia in 130mL of deionized water and stir evenly.

[0254] 2. Slowly add 15g of polyethylene glycol amide to the above solution and continue stirring until it is completely dissolved.

[0255] 3. Add 25g bentonite in batches and stir evenly.

[0256] 4. Finally, add 0.2g of methylene blue and mix thoroughly.

[0257] 5. The mixture was dried under vacuum at 65°C for 30 hours.

[0258] 6. Grind the dried product into fine powder and pass it through a 200-mesh sieve to obtain the final product.

[0259] Performance Testing:

[0260] 1. Water absorption test:

[0261] 1g of sample was placed in 100mL of deionized water and the water absorption within 30 seconds was measured. The results showed that the water absorption rate of this formula reached 50 times.

[0262] 2. Hemostasis time test:

[0263] The hemostasis experiment was carried out on the femoral artery puncture model of New Zealand white rabbits. The results showed that the average hemostasis time of the formula was 35±4 seconds.

[0264] 3. Biocompatibility testing:

[0265] The cytotoxicity test of the formula was carried out using the cell direct contact method. The results showed that the cells had normal morphology, good adherent growth, and a cell survival rate of more than 98%, indicating that the formula has excellent biocompatibility.

[0266] 4. Antibacterial performance test:

[0267] The time-kill curve method was used to determine the antibacterial kinetics of Pseudomonas aeruginosa and Staphylococcus epidermidis. The results showed that the formula had a good bactericidal effect on both bacteria, and the colony count could be reduced by more than 3 orders of magnitude within 6 hours.

[0268] Comparative Example 1:

[0269] This example uses the following formula to prepare the control sample:

[0270] Polyethylene glycol amine 70%

[0271] Lithium magnesium silicate 30%

[0272] Preparation method:

[0273] 1. Dissolve 70g polyethylene glycol ammonia in 150mL deionized water and stir evenly.

[0274] 2. Add 30g of lithium magnesium silicate in batches and stir evenly.

[0275] 3. Dry the mixture under vacuum at 60°C for 24 hours.

[0276] 4. Grind the dried product into fine powder and pass it through a 200-mesh sieve to obtain the final product.

[0277] Performance Testing:

[0278] 1. Water absorption test:

[0279] 1g of sample was placed in 100mL of deionized water and the water absorption within 30 seconds was measured. The results showed that the water absorption rate of this formula was only 25 times.

[0280] 2. Hemostasis time test:

[0281] The hemostasis experiment was conducted on the New Zealand white rabbit ear vein incision model. The results showed that the average hemostasis time of the formula was 90±10 seconds.

[0282] 3. Biocompatibility testing:

[0283] The MTT method was used to test the cytotoxicity of the formula, and the results showed that the cell survival rate was 85%, which was slightly lower than that of Examples 1-4.

[0284] 4. Antibacterial performance test:

[0285] The antibacterial zone method was used to test the antibacterial activity of Escherichia coli and Staphylococcus aureus. The results showed that the formula had no significant inhibitory effect on both bacteria, and the diameter of the antibacterial zone was less than 8 mm.

[0286] Comparative Example 2:

[0287] This example uses the following formula to prepare the control sample:

[0288] Polyethylene glycol amine 60%

[0289] Polyethylene glycol amide 40%

[0290] Preparation method:

[0291] 1. Dissolve 60g polyethylene glycol ammonia in 120mL deionized water and stir evenly.

[0292] 2. Slowly add 40g of polyethylene glycol amide to the above solution and continue stirring until it is completely dissolved.

[0293] 3. The mixture was dried under vacuum at 55°C for 36 hours.

[0294] 4. Grind the dried product into fine powder and pass it through a 180-mesh sieve to obtain the final product.

[0295] Performance Testing:

[0296] 1. Water absorption test:

[0297] 1g of sample was placed in 100mL of deionized water and the water absorption within 30 seconds was measured. The results showed that the water absorption rate of the formula was 30 times.

[0298] 2. Hemostasis time test:

[0299] The hemostasis experiment was carried out on the liver wound model of New Zealand white rabbits. The results showed that the average hemostasis time of the formula was 75±8 seconds.

[0300] 3. Biocompatibility testing:

[0301] The CCK-8 method was used to perform a cytotoxicity test on the formulation, and the results showed that the cell survival rate was 92%, which was slightly lower than that of Examples 1-4.

[0302] 4. Antibacterial performance test:

[0303] The minimum inhibitory concentration (MIC) of Pseudomonas aeruginosa and Klebsiella pneumoniae was determined by liquid dilution method. The results showed that the MIC values ​​of the formula against both bacteria were greater than 1000 μg / mL, indicating that the antibacterial effect was poor.

[0304] Comprehensive performance comparison:

[0305] In order to more intuitively compare the performance of each embodiment, the main performance indicators are summarized as shown in Table 1:

[0306] Table 1 Effect data table of multiple embodiments

[0307]

[0308] It can be seen from the above table that the various performance indicators of Examples 1-4 are better than those of Comparative Example 1 and Comparative Example 2. The specific analysis is as follows:

[0309] 1. Water absorption performance:

[0310] The water absorption rates of Examples 1-4 are all between 38-50 times, while those of Comparative Examples 1 and 2 are only 25-30 times. This indicates that the addition of functional groups (such as polyethylene glycol amide, polyethylene glycol ether, etc.) and lithium magnesium silicate components (such as lithium magnesium silicate, montmorillonite, etc.) can significantly improve the water absorption performance of the product.

[0311] 2.Hemostatic effect:

[0312] The hemostasis time of Examples 1-4 is generally between 35-55 seconds, while the hemostasis time of Comparative Examples 1 and 2 is between 75-90 seconds. This shows that the formula combination of the present invention can significantly shorten the hemostasis time and improve the hemostasis efficiency.

[0313] 3. Biocompatibility:

[0314] The cell survival rates of Examples 1-4 were all above 95%, showing excellent biocompatibility. In contrast, the cell survival rates of Control Examples 1 and 2 were slightly lower, at 85% and 92%, respectively. This shows that the formula combination of the present invention not only has a good hemostatic effect, but also can ensure high biosafety.

[0315] 4. Antibacterial properties:

[0316] Examples 1-4 all showed good antibacterial effects, whether using the inhibition zone method, minimum inhibitory concentration determination or inhibition rate determination, they all showed effective inhibition of common pathogens. In contrast, the antibacterial effects of control examples 1 and 2 were poor, which may be due to the lack of methylene blue, an antibacterial component.

[0317] Further analysis:

[0318] 5. Synergistic effect of formula:

[0319] By comparing Examples 1-4 and Comparative Examples 1-2, it can be observed that there is an obvious synergistic effect between the components:

[0320] a) Synergy between polyethylene glycol amine and functional groups:

[0321] In Examples 1-4, the combination of polyethylene glycol amine and different functional groups (such as polyethylene glycol amide, polyethylene glycol ether, and polyvinyl alcohol) significantly improved the water absorption performance and hemostatic effect of the product. This may be because different types of hydrophilic groups increase the interaction between the material and water molecules, and also improve the contact area between the material and blood components.

[0322] b) The role of lithium magnesium silicate ingredients:

[0323] The ingredients such as lithium magnesium silicate, montmorillonite, kaolin or bentonite added in Examples 1-4 not only enhance the water absorption capacity of the material, but also promote platelet aggregation and coagulation factor activation through their unique layered structure and ion exchange capacity, thereby accelerating the hemostasis process.

[0324] c) Multiple effects of methylene blue:

[0325] Methylene blue not only gives the product antibacterial properties, but also may participate in the coagulation process through its redox properties, indirectly promoting the hemostatic effect. In addition, the presence of methylene blue makes the product have a certain visibility, which is conducive to accurate positioning during clinical use.

[0326] 6.Performance stability analysis:

[0327] In order to evaluate the performance stability of the product of the present invention, an accelerated aging test was performed on Example 2:

[0328] The samples were stored in an environment of 40°C and 75% relative humidity for 3 months, and their main performance indicators were tested once a month. The results are shown in Table 2:

[0329] Table 2 Storage time performance table

[0330]

[0331] As can be seen from the above table, after 3 months of accelerated aging, the various performance indicators of the product still remain at a high level, indicating that the formula of the present invention has good stability and a long shelf life.

[0332] Conclusion: The formula of the rapid water-absorbing hemostatic powder proposed in the present invention achieves excellent water absorption, rapid hemostatic effect, good biocompatibility and antibacterial properties through the synergistic effect of polyethylene glycol amine, functional groups, lithium magnesium silicate components and methylene blue. Compared with the control group, the formula of the present invention shows significant advantages in all performance indicators.

[0333] like Figure 1 As shown, the third aspect of the present invention provides a method for optimizing the formula of a fast water-absorbing hemostatic powder, which is used to optimize the formula of the fast water-absorbing hemostatic powder, comprising the following steps:

[0334] S10. Design basic experiments to test the effects of different raw material components on product performance and obtain basic experimental data;

[0335] S20. Establish chemical models and product performance prediction models based on basic experimental data;

[0336] S30. Determine a multi-objective optimization objective function according to product performance requirements;

[0337] S40. Combine the material measurement relationship and the ideal ratio of the chemical model to establish a multi-objective optimization model for the formulation;

[0338] S50, using an intelligent optimization algorithm to solve the multi-objective optimization model to obtain a preliminary optimal formula component ratio;

[0339] S60, performing optimization experiments using the preliminary optimal formula to obtain optimization experiment data;

[0340] S70, updating the chemical model and product performance prediction model using the optimized experimental data;

[0341] S80, reconstructing the multi-objective optimization model according to the updated model, and solving it again using the intelligent optimization algorithm to obtain a new optimal formula component ratio;

[0342] S90, repeating steps S60-S80 until the formula converges to the optimal solution, that is, each performance index reaches the expected target, and the change range of the formula optimized twice in a row is less than a preset threshold.

[0343] Specifically, the formula of this fast-absorbing hemostatic powder cleverly combines multiple key ingredients to solve the technical problems existing in traditional hemostatic materials.

[0344] First, as the main component, polyethylene glycol amine has the characteristic of rapid water absorption, which can quickly absorb blood and tissue fluid exuded from the wound surface, greatly increasing the water absorption speed and amount. This helps to stop bleeding quickly and shorten the time of hemostasis.

[0345] Secondly, the formula contains functional groups such as polyethylene glycol amide and polyethylene glycol ether, which can further enhance the water absorption capacity and hemostatic effect. They can not only quickly absorb blood, but also form a dense gel network on the wound surface, which improves the adhesion and sealing of the material and greatly reduces the risk of rebleeding.

[0346] In addition, lithium magnesium silicate ingredients such as montmorillonite and kaolin are added to the formula. These ingredients can improve the hemostatic effect and further enhance the closure of the wound. They also have a certain antibacterial effect, which is beneficial to the healing of the wound.

[0347] Finally, a small amount of methylene blue as an indicator can not only visually display the hemostatic effect, but also has antibacterial and wound healing effects, creating a more favorable healing environment for the wound.

[0348] In summary, this fast-absorbing hemostatic powder effectively solves the problems of slow water absorption, small water absorption, poor hemostatic effect, poor adhesion, and high risk of rebleeding in traditional hemostatic materials by cleverly combining multiple ingredients, providing an excellent solution for rapid hemostasis and healing of wounds.

[0349] The following is a description of a specific implementation of the recipe optimization method of the third aspect of the present invention:

[0350] The specific implementation method of step S10 is to design and perform basic experiments to test the effects of different raw material components on product performance and obtain basic experimental data. The purpose of this step is to establish a preliminary data foundation to provide a basis for subsequent modeling and optimization. The specific implementation process is as follows: First, determine the experimental design scheme. The present invention adopts an orthogonal experimental design method, which can examine the effects of multiple factors within a small number of experiments and has high efficiency. Orthogonal experimental design is based on an orthogonal table, and a suitable orthogonal table is selected according to the number of factors and the number of levels. The factors considered in the present invention include the molecular weight of polyethylene glycol amine, the type of functional group, the type and amount of lithium magnesium silicate components, the amount of methylene blue, etc. Secondly, determine the level of each factor. The molecular weight of polyethylene glycol amine is selected at three levels, such as 2000, 4000, and 6000 Daltons; the types of functional groups are selected at six levels, namely polyethylene glycol amide, polyethylene glycol ether, polyvinyl alcohol, hydroxyethyl cellulose, chitosan, and gelatin; the types of magnesium silicate lithium components are selected at five levels, namely magnesium silicate lithium, montmorillonite, kaolin, zeolite, and bentonite, and the dosage is selected at three levels, such as 5%, 10%, and 15%; the dosage of methylene blue is selected at three levels, such as 0.1%, 0.5%, and 1%. Then, the experimental combination is arranged according to the selected orthogonal table. Assuming that the L18 (37) orthogonal table is selected, 18 groups of experiments can be arranged, and the factor level combination of each group of experiments is determined according to the orthogonal table. Next, prepare the experimental materials and instruments. According to the experimental design, prepare various raw materials, including polyethylene glycol amine with different molecular weights, various functional groups, magnesium silicate lithium components, and methylene blue. Prepare experimental instruments, such as precision balance, stirrer, oven, water absorption performance tester, hemostasis time tester, antibacterial performance test equipment, etc. After that, prepare samples according to the experimental design. For each group of experiments, weigh the raw materials according to the set formula ratio, mix them thoroughly and evenly, and prepare hemostatic powder samples. The ambient temperature and humidity need to be controlled during sample preparation to ensure the consistency of experimental conditions. After preparation, perform performance tests on each group of samples. The test items include water absorption rate, water absorption ratio, hemostasis time and antibacterial performance. The water absorption rate test can use the filter paper method to record the change in water absorption per unit time; the water absorption ratio test can use the centrifugation method to determine the weight increase of the sample under saturated water absorption state; the hemostasis time test can use the in vitro simulated bleeding experiment to record the time required for complete hemostasis; the antibacterial performance test can use the inhibition zone method or colony counting method to evaluate the inhibitory effect of the sample on common pathogens. Each test is repeated at least 3 times, and the average value is taken as the final result. Finally, organize the experimental data and establish an experimental database. The factor levels and corresponding performance test results of each group of experiments were entered into the database to prepare for subsequent analysis. The degree and significance of the influence of each factor on the performance index were preliminarily analyzed through statistical methods such as variance analysis. The implementation of this step laid a solid data foundation for subsequent modeling and optimization, and also preliminarily revealed the influence of each raw material component on product performance.

[0351] The specific implementation method of step S20 is to establish a chemical model and a product performance prediction model based on basic experimental data. The purpose of this step is to convert experimental data into a theoretical model that can be used for optimization through mathematical modeling. The specific implementation process is as follows: First, establish a chemical model. The chemical model is based on the chemical properties and interactions of each component to establish a theoretical ratio model similar to a chemical equation. Considering the water absorption capacity of polyethylene glycol ammonia, the synergistic effect of functional groups, the adsorption performance of magnesium lithium silicate components, and the indication and antibacterial effect of methylene blue, the ideal ratio relationship of each component is obtained. The process of establishing the chemical model is as follows: Analyze the structural characteristics of polyethylene glycol ammonia, consider the relationship between the number of its hydrophilic groups and molecular weight, and establish a functional relationship between the water absorption capacity of polyethylene glycol ammonia and molecular weight. Analyze the chemical structure of each functional group, consider its interaction mode with water molecules, such as hydrogen bond formation, electrostatic effect, etc., and establish a synergistic function of functional groups on water absorption performance. Analyze the crystal structure and surface characteristics of magnesium lithium silicate components, consider its adsorption mechanism for water molecules, and establish a relationship function between the adsorption performance and dosage of magnesium lithium silicate components. The molecular structure and antibacterial mechanism of methylene blue were analyzed, and the relationship function between the dosage of methylene blue and antibacterial performance was established. Taking the above factors into consideration, a chemical equilibrium equation containing the interaction of multiple components was established, which reflects the ratio relationship of each component under ideal conditions. Secondly, a product performance prediction model was established. The product performance prediction model is based on a machine learning algorithm to perform nonlinear mapping modeling on experimental data. The specific steps are as follows: Data preprocessing. Standardize the experimental data to eliminate the influence of different dimensions. Check whether there are outliers or missing values ​​in the data, and process them if necessary. Feature engineering. According to the theoretical analysis of the chemical model, construct new feature variables, such as component interaction terms, nonlinear transformations, etc., to enhance the expression ability of the model. Model selection. Considering that there may be a complex nonlinear relationship between the performance of hemostatic powder and the raw material components, select machine learning algorithms suitable for dealing with nonlinear problems, such as support vector regression (SVR), random forest, gradient boosting tree, etc. Model training. The processed data set is divided into a training set and a validation set, and the ratio can be set to 7:3. Use the training set to train the selected machine learning model and adjust the model hyperparameters through cross-validation to obtain the best performance. Model evaluation. Use the validation set to evaluate the prediction performance of the model and calculate indicators such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination R2. If the performance does not meet the requirements, return to the model selection step and try other algorithms or adjust features. Model interpretation. Use techniques such as feature importance analysis and partial dependence plots to explain the model's prediction results and verify whether the model is consistent with chemical theory. Finally, integrate the chemical model and performance prediction model.The ideal ratio relationship in the chemical model is introduced as prior knowledge into the machine learning model, which can be achieved by using the Bayesian method or constrained optimization. For example, the prediction results of the chemical model can be used as an additional feature of the machine learning model, or the constraints of the chemical model can be added to the loss function of the machine learning model. This integration can improve the interpretability and generalization ability of the model. Through the above steps, a comprehensive model based on chemical theory and data-driven is established. This model can accurately predict the product performance under different formula combinations, providing a theoretical basis for the subsequent optimization process.

[0352] The specific implementation method of step S30 is to determine the multi-objective optimization objective function according to the product performance requirements. The purpose of this step is to convert the product performance requirements into quantifiable mathematical expressions to provide clear goals for the subsequent optimization process. The specific implementation process is as follows: First, clarify the product performance requirements. According to the application scenarios and market demand of hemostatic powder, determine the key performance indicators and their target values. The present invention mainly considers four performance indicators: water absorption rate, water absorption ratio, hemostatic time and antibacterial performance. Secondly, define the mathematical expression of each performance indicator. The water absorption rate can be expressed by the amount of water absorbed per unit time, recorded as , the unit is g / s; the water absorption rate can be expressed by the weight increase times under saturated water absorption state, recorded as , dimensionless; the hemostasis time is directly expressed as the time required for complete hemostasis, recorded as , the unit is s; the antibacterial performance can be expressed by the diameter of the inhibition zone or the colony reduction rate, recorded as , in units of mm or %. Then, determine the target direction of each performance indicator. The water absorption rate and water absorption ratio need to be maximized, the hemostasis time needs to be minimized, and the antibacterial performance needs to be maximized. Next, construct a normalization function. Since the dimensions and numerical ranges of each performance indicator are different, normalization processing is required to unify its value range to the [0,1] interval. The following normalization function can be used:

[0353]

[0354]

[0355]

[0356]

[0357] in, , , , are the minimum acceptable values ​​of each indicator, , , , are the ideal maximum or minimum values ​​of each indicator. These boundary values ​​can be determined based on market research, competitive product analysis or expert experience. Then, the weight coefficient is introduced. Considering that different performance indicators may have different degrees of influence on product quality, the weight coefficient is introduced to reflect the relative importance of each indicator. The weight coefficient can be determined by the analytic hierarchy process (AHP) or the expert scoring method. The weight coefficients of the four performance indicators are , , , , and satisfy Finally, construct the multi-objective optimization objective function. Use the weighted sum method to combine multiple objectives into a comprehensive objective function:

[0358]

[0359] The optimization goal is to maximize the function F. In addition, the constraints of each performance indicator need to be considered, such as:

[0360]

[0361]

[0362]

[0363]

[0364] These constraints ensure that the optimization results meet the minimum performance requirements. In order to enhance the robustness of the model, a penalty term can be introduced to impose a penalty on the objective function when a certain performance indicator approaches the constraint boundary. The modified objective function can be expressed as:

[0365]

[0366] in, is the penalty coefficient, is the degree of violation of the constraint. By adjusting the penalty coefficient, the optimization goal and constraint satisfaction can be balanced. The implementation of this step transforms the product performance requirements into a clear mathematical optimization problem, laying the foundation for subsequent optimization solutions. At the same time, by introducing weights and constraints, it is ensured that the optimization results can meet the actual application needs.

[0367] The specific implementation method of step S40 is to combine the material measurement relationship and the ideal ratio of the chemical model to establish a multi-objective optimization model for the formula. The purpose of this step is to construct a comprehensive mathematical model to link the formula components, material relationships and performance indicators, and provide a solution basis for the subsequent optimization algorithm. The specific implementation process is as follows: First, determine the model variables. Based on the experimental factors determined in step S10, the molecular weight of polyethylene glycol ammonia, the type and amount of functional groups, the type and amount of lithium magnesium silicate components, and the amount of methylene blue are used as independent variables. The molecular weight of polyethylene glycol ammonia is , the functional group concentration vector is , the concentration vector of magnesium-lithium silicate components is , the concentration of methylene blue is . The water absorption rate , water absorption rate , Hemostasis time , antibacterial properties As the dependent variable. Secondly, establish the material measurement relationship equation. Considering the mass balance relationship between the components, the following constraint equation can be established:

[0368]

[0369] in, is the concentration of polyethylene glycol ammonia. This equation ensures that the sum of the mass fractions of all components is 1. Then, the ideal ratio of the chemical model is combined. The chemical model established in step S20 provides the ideal ratio relationship between the components. Introducing these relationships into the optimization model can improve the rationality and interpretability of the model. For example, the following relationship equation can be established:

[0370]

[0371]

[0372]

[0373] in, , , , , , are undetermined parameters, which can be obtained by fitting the experimental data. These equations reflect the interaction and dependence between different components. Next, a performance prediction equation is established. Based on the product performance prediction model established in step S20, a nonlinear mapping relationship between independent variables and dependent variables is constructed. Considering the complexity of the performance of hemostatic powder, a more complex mathematical model is used here to describe each performance index:

[0374] 1. Water absorption rate equation (based on modified Langmuir adsorption kinetics):

[0375]

[0376] in, is the water absorption, is the maximum water absorption capacity, For time, , , , are the concentrations of each component, and is the rate constant, , , , , is a non-integer power exponent.

[0377] 2. Water absorption rate equation (based on the hybrid model of Freundlich isotherm and BET theory):

[0378]

[0379] in, is the water absorption rate, To balance the water absorption, is the initial weight, and is the Freundlich constant, is the BET constant, is the saturation concentration, and is the fitting parameter.

[0380] 3. Hemostasis time equation (based on modified Hill equation):

[0381]

[0382] in, To stop bleeding time, and are the maximum and minimum hemostasis time, is the half-maximal effect concentration, is the Hill coefficient, , , , is a non-integer power exponent, and is the time constant.

[0383] 4. Antimicrobial performance equation (based on the modified Gompertz growth model):

[0384]

[0385]

[0386] in, is colony forming unit, is the logarithmic value of the asymptotic colony count, is the maximum specific growth rate, is the lag period, is the base of natural logarithms, , , , is the fitting parameter, , , , , is a non-integer power exponent, is the initial colony number.

[0387] Then, component selection constraints were added. Considering the selectivity of functional groups and lithium magnesium silicate components, binary variables were introduced:

[0388] , in ,

[0389] , in ,

[0390] in, and is the actual concentration of the corresponding component, and is a binary selection variable. Finally, integrate the objective function and constraints. Combine the multi-objective optimization objective function defined in step S30 with the above equations and constraints to form a complete optimization model:

[0391] maximize:

[0392] Constraints:

[0393] 1. Material balance equation

[0394] 2. Ideal ratio equation of chemical model

[0395] 3. Performance prediction equation (water absorption rate, water absorption ratio, hemostasis time, antibacterial performance)

[0396] 4. Component Selection Constraints

[0397] 5. Variable scope constraints (such as , wait)

[0398] This optimization model is a mixed integer nonlinear programming (MINLP) problem, which contains continuous variables and discrete variables and is highly nonlinear and complex. To solve such a problem, advanced optimization algorithms are required, such as genetic algorithms, particle swarm optimization, simulated annealing and other intelligent optimization methods. Through this step, a comprehensive mathematical model is established, which closely links the formula components, material relationships and performance indicators, laying the foundation for the next step of optimization solution. This model not only takes into account the complex interactions between the components, but also incorporates the support of chemical theory and experimental data, and has a strong theoretical basis and practical application value.

[0399] The specific implementation method of step S50 is to solve the multi-objective optimization model using an intelligent optimization algorithm to obtain a preliminary optimal formula component ratio. The purpose of this step is to search for the optimal solution in a complex solution space through a computer algorithm to obtain a formula combination that meets the multi-objective requirements. Considering the complexity of the problem, the present invention selects a hybrid algorithm combining a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO) to solve it. The specific implementation process is as follows: First, initialize the algorithm parameters. For the genetic algorithm, it is necessary to set parameters such as population size, crossover probability, mutation probability, and maximum number of iterations. The population size can be set to 100-200, the crossover probability can be set to 0.7-0.9, the mutation probability can be set to 0.01-0.05, and the maximum number of iterations can be set to 1000-5000. For the particle swarm optimization algorithm, it is necessary to set parameters such as the number of particles, inertia weight, individual learning factor, and group learning factor. The number of particles can be set to 50-100, the inertia weight can be linearly decreased from 0.9 to 0.4, and the individual learning factor and the group learning factor can be set to 2. Secondly, encode the decision variables. Encode the decision variables in the optimization model (molecular weight of polyethylene glycol amine, concentration of each functional group, concentration of each lithium magnesium silicate component, and concentration of methylene blue) as chromosomes or particle position vectors. For continuous variables, real number encoding can be used directly; for discrete variables (such as the selection of functional groups and lithium magnesium silicate components), binary encoding can be used. For example, a chromosome can be represented as:

[0400]

[0401] in, and is a binary variable, and the others are real variables. Then, the fitness function is defined. The multi-objective optimization objective function established in step S40 is directly used as the fitness function. For solutions that do not meet the constraints, the penalty function method is used, that is:

[0402]

[0403] in, is the original objective function, For the The degree of violation of a constraint condition, is the penalty coefficient, is the number of constraints. The penalty coefficient can be adjusted adaptively and gradually increased with the iteration process. Next, the hybrid solution process of genetic algorithm and particle swarm optimization algorithm is implemented. The basic idea of ​​the hybrid algorithm is: use genetic algorithm for global search, inject elite individuals in the current population into the particle swarm every certain number of generations, and use the local search ability of particle swarm algorithm for refined search. The specific steps are as follows: Initialize GA population and PSO particle swarm, and randomly generate initial solutions that meet the constraints. Evaluate the GA population and calculate the fitness value of each individual. Perform GA selection, crossover and mutation operations to generate a new generation of population. Every N generations (such as N=10), inject the first M elite individuals (such as M=10) in the GA population into the PSO particle swarm to replace the particles with lower fitness in the PSO. Update the speed and position of the PSO particle swarm. Evaluate the updated PSO particle swarm, update the individual optimal position and the global optimal position. Feed the global optimal solution of PSO back to the GA population to replace the worst individual in GA. Repeat steps 3-7 until the maximum number of iterations is reached or the convergence condition is met. During the iteration process, in order to improve the efficiency of the algorithm and the quality of the solution, the following strategies can be adopted: Adaptive crossover and mutation: Dynamically adjust the crossover probability and mutation probability according to the diversity of the population to balance exploration and development. Elite retention strategy: Retain a certain proportion of elite individuals in GA and directly enter the next generation to prevent good solutions from being destroyed. Taboo search: Introduce a short-term memory mechanism in PSO to avoid particles repeatedly searching for explored areas. Local search: Perform local search on the excellent solutions obtained by GA and PSO, such as fine-tuning with simulated annealing algorithm. Multi-population parallel: Use multiple sub-populations to evolve in parallel and perform migration operations regularly to increase population diversity. Finally, post-process and analyze the optimization results. Extract the values ​​of each decision variable from the optimal solution output by the algorithm to obtain the preliminary preferred formula component ratio. Perform sensitivity analysis on the optimization results to evaluate the degree of influence of each component on the objective function. Plot the change curve of the objective function with the number of iterations to analyze the convergence performance of the algorithm. Compare the optimization results obtained with different numbers of runs to evaluate the stability and reliability of the solution. Through this step, the intelligent optimization algorithm was used to search for a preliminary optimal formula that meets multiple objective requirements in the huge solution space. This formula comprehensively considers multiple objectives such as water absorption, hemostasis and antibacterial properties, and meets various physical and chemical constraints. However, due to the complexity and uncertainty of the model, this preliminary optimal formula still needs to be verified and further optimized through actual experiments.

[0404] The specific implementation method of step S60 is to use the preliminary preferred formula to perform optimization experiments and obtain optimization experimental data. The purpose of this step is to verify the actual effect of the theoretical optimization results and obtain more accurate experimental data to provide a basis for subsequent model updates and further optimization. The specific implementation process is as follows: First, prepare experimental materials. According to the preliminary preferred formula obtained in step S50, prepare corresponding raw materials, including polyethylene glycol amine with a specific molecular weight, selected functional groups, lithium magnesium silicate components and methylene blue. The purity and quality of the raw materials have an important influence on the experimental results, so it is necessary to use analytical grade reagents and perform necessary purity tests. Secondly, design an experimental plan. In order to comprehensively evaluate the performance of the preferred formula and take into account the experimental error and repeatability, the following experimental plan is designed: Central Composite Design (CCD): With the preferred formula as the center point, the content of each component fluctuates by ±5%~10%, and a set of CCD experiments is designed, usually including 2n vertex points, 2n axis points and 3~5 center point repetitions, where n is the number of factors. For example, for the four main factors (polyethylene glycol amine, functional groups, magnesium lithium silicate components, and methylene blue), a total of 16 vertex points, 8 axis points, and 5 center point repetitions are required, totaling 29 groups of experiments. Single factor experiment: Based on the optimal formula, the content of each component is changed separately to study the effect of a single factor on performance. Each factor is set at 5 to 7 levels, and each level is repeated 3 times. Stability experiment: The optimal formula samples are stored under different temperature and humidity conditions, and their performance changes are tested regularly to evaluate the stability of the formula. Conditions such as 25°C / 60%RH and 40°C / 75%RH can be selected, and the test cycle is 0, 1, 2, 4, 8, and 12 weeks. Then, prepare the experimental samples. According to the experimental design plan, accurately weigh each component and use an appropriate mixing method to prepare the hemostatic powder sample. The ambient temperature and humidity need to be controlled during the mixing process to prevent the raw materials from absorbing moisture. A V-type mixer or a three-dimensional motion mixer can be used to ensure the uniformity of the sample. The prepared samples need to be numbered and sealed to prevent moisture or contamination. Next, perform performance testing. The prepared samples were subjected to comprehensive performance tests, including but not limited to the following items: Water absorption performance test: The water absorption rate was determined by the filter paper method, and the change in water absorption per unit time was recorded; the water absorption ratio was determined by the centrifugation method, and the weight increase multiple under saturated water absorption was calculated. Hemostatic performance test: In vitro simulated bleeding experiments, such as rabbit ear artery bleeding model or rat tail artery transection model, were used to determine the time required for complete hemostasis. Antibacterial performance test: The inhibition zone method or colony counting method was used to evaluate the inhibitory effect of the samples on common pathogens (such as Staphylococcus aureus and Escherichia coli). Microstructure analysis: Scanning electron microscopy (SEM) was used to observe the microscopic morphology of the samples and analyze the pore structure and surface characteristics. Thermogravimetric analysis (TGA): The thermal stability and component decomposition characteristics of the samples were determined.Particle size analysis: The particle size distribution of the sample was determined by a laser particle size analyzer. pH determination: The pH value of the sample water dispersion was measured to evaluate its effect on the wound environment. Biocompatibility test: The cytotoxicity of the sample was evaluated by a cytotoxicity test (such as the MTT method). Each test was repeated at least 3 times, and the average value was taken as the final result. During the test, the experimental conditions, including temperature, humidity, and calibration of the test instrument, need to be strictly controlled to ensure the accuracy and repeatability of the data. Then, data processing and statistical analysis. The obtained experimental data were systematically organized and analyzed, including: Descriptive statistics: Calculate the mean, standard deviation, coefficient of variation and other statistical quantities of each performance index to evaluate the discreteness and stability of the data. Analysis of variance (ANOVA): Analyze the significance of the influence of each factor on the performance index and identify the key influencing factors. Response surface analysis: Based on the CCD experimental data, a quadratic polynomial regression model was established to analyze the interaction between factors and find the optimal formulation area. Correlation analysis: Study the correlation between different performance indicators, such as the relationship between water absorption performance and hemostasis time. Principal component analysis (PCA): Dimensionality reduction is performed on multiple performance indicators, main features are extracted, and the performance of the formula is comprehensively evaluated. Finally, result evaluation and report writing are performed. The experimental results are compared with theoretical predictions to analyze the reasons for the differences. Evaluate whether the optimal formula has achieved the expected performance goals and identify areas that need further optimization. Write a detailed experimental report, including experimental methods, data analysis results, conclusions, and suggestions. Through this step, the actual performance data of the preliminary optimal formula are obtained. These data not only verify the effectiveness of theoretical optimization, but also provide a reliable experimental basis for subsequent model updates and further optimization. At the same time, through systematic experimental design and data analysis, an in-depth understanding of the influence of each component on performance is obtained, which points out the direction for further optimization of the formula.

[0405] The specific implementation method of step S70 is to use the optimized experimental data to update the chemical model and the product performance prediction model, adjust the ideal ratio relationship and performance prediction parameters, and improve the accuracy and prediction ability of the model. The purpose of this step is to integrate the newly obtained experimental data into the existing model so that the model can more accurately reflect the actual situation, thereby providing a more reliable theoretical basis for the next round of optimization. The specific implementation process is as follows: First, data preprocessing. The optimized experimental data obtained in step S60 are cleaned and standardized. Check whether there are outliers in the data. If so, analyze the reasons and decide whether to eliminate them. Standardize the data to the [0,1] interval to eliminate the influence of different dimensions. Secondly, update the chemical model. Based on the new experimental data, re-evaluate and adjust the parameters in the chemical model. The specific steps are as follows: Re-fit the ideal ratio relationship between the components. For example, for the ratio relationship between polyethylene glycol amine and methylene blue:

[0406]

[0407] Re-estimate the parameters using nonlinear least squares and . Adjust the interaction coefficients between components. For example, introduce interaction terms into the material balance equation:

[0408]

[0409] in, and is the interaction coefficient, which needs to be re-estimated based on the new data. Update the chemical reaction kinetic parameters. For example, for the water absorption rate equation:

[0410]

[0411] Re-estimate rate constants , and index , , , , .

[0412] Then, update the product performance prediction model. Use machine learning algorithms to retrain the performance prediction model by combining the original data and the newly obtained optimized experimental data. The specific steps are as follows: Feature engineering. Based on the updated chemical model, construct new feature variables, such as component interaction terms, nonlinear transformations, etc. Model selection. Considering the increase in data volume and possible changes in nonlinear relationships, re-evaluate the model selection. You can try a variety of algorithms, such as support vector regression (SVR), random forest, gradient boosting tree, deep neural network (DNN), etc., and select the best model through cross-validation. Model training. Divide the data set into training set, validation set, and test set, and the ratio can be set to 6:2:2. Use the training set to train the selected model, use the validation set to adjust the hyperparameters, and finally use the test set to evaluate the model performance. Model integration. Consider using model integration methods, such as bagging, boosting, or stacking, to integrate the prediction results of multiple base models to improve the stability and accuracy of the prediction. Model interpretation. Use methods such as SHAP (SHapley Additive exPlanations) value or LIME (Local Interpretable Model-agnostic Explanations) to interpret the prediction results of the model and verify whether they are consistent with chemical theory. Next, adjust the ideal ratio relationship. Based on the updated chemical model and performance prediction model, re-evaluate the ideal ratio relationship of each component. The specific steps are as follows: Sensitivity analysis. Perform sensitivity analysis on the updated model to evaluate the impact of each component on the performance index. Based on this, adjust the importance weight of the component. Pareto optimal analysis. Under the framework of multi-objective optimization, find the Pareto optimal solution set between each performance index and analyze the performance trade-off relationship under different ratios. Determine the ideal ratio interval. Combine the results of sensitivity analysis and Pareto optimal analysis to determine a new ideal ratio interval for each component. These intervals will be used as constraints for the next round of optimization. Then, evaluate the model performance. Use multiple indicators to evaluate the performance of the updated model, including: root mean square error (RMSE), mean absolute error (MAE), determination coefficient R2, etc., to compare the prediction accuracy of the model before and after the update. Residual analysis, check the distribution characteristics of the model prediction error and evaluate the unbiasedness of the model. Cross-validation, use k-fold cross-validation (such as k=10) to evaluate the generalization ability of the model. Finally, model validation and documentation. Use additional experimental data (if any) to independently validate the updated model and evaluate its predictive performance on new data. Write a detailed model update report, including data processing methods, model structure changes, parameter adjustment process, performance evaluation results, etc. Version control and archive the updated model parameters, code, and documents to track the evolution of the model.Through this step, the new experimental data are integrated into the existing theoretical model, which improves the accuracy and predictive ability of the model. The updated model can not only better explain the experimental observations, but also provide more reliable theoretical guidance for the next round of formula optimization. This iterative model update process reflects the adaptability and continuous improvement ability of the method of the present invention, which helps to continuously improve the performance of the hemostatic powder formula.

[0413] The specific implementation method of step S80 is to reconstruct the multi-objective optimization model based on the updated model, and use the intelligent optimization algorithm to solve again to obtain a new optimal formula component ratio. The purpose of this step is to search for a better formula combination in a larger solution space based on a more accurate model. The specific implementation process is as follows: First, reconstruct the multi-objective optimization model. Based on the chemical model and product performance prediction model updated in step S70, reconstruct the mathematical expression of the optimization problem. The objective function can be expressed as:

[0414]

[0415] in, , , , are the normalized functions of water absorption rate, water absorption ratio, hemostasis time, and antibacterial performance, respectively. , , , is the corresponding weight coefficient. The constraints include:

[0416] 1. Material balance constraints:

[0417]

[0418] 2. Constraints on component content:

[0419] ,

[0420] ,

[0421]

[0422]

[0423] 3. Performance indicator constraints:

[0424]

[0425]

[0426]

[0427]

[0428] 4. Component selection constraints:

[0429] ,

[0430] ,

[0431] in, and Represent the selection variables of functional groups and magnesium lithium silicate components respectively. Secondly, select and configure the optimization algorithm. Considering the complexity and multi-objective characteristics of the problem, this step uses the Multi-Objective Evolutionary Algorithm (MOEA) to solve the optimization problem. Specifically, the NSGA-III (Non-dominated Sorting Genetic Algorithm III) algorithm is selected, which has good performance and convergence characteristics when dealing with multi-objective optimization problems. The main steps of the NSGA-III algorithm are as follows:

[0432] 1. Initialize the population: randomly generate an initial set of solutions that satisfy the constraints.

[0433] 2. Non-dominated sorting: Perform non-dominated sorting on the individuals in the population and divide the population into different non-dominated frontiers.

[0434] 3. Reference point generation: Generate uniformly distributed reference points in the target space.

[0435] 4. Selection operation: Based on non-dominated sorting and reference points, select outstanding individuals to enter the next generation.

[0436] 5. Genetic operations: perform crossover and mutation on selected individuals to generate offspring populations.

[0437] 6. Environmental selection: Merge the parent and offspring populations, perform non-dominated sorting and reference point association again, and select the individuals that will eventually enter the next generation.

[0438] 7. Repeat steps 2-6 until the termination condition is reached.

[0439] The algorithm parameters are set as follows: the population size is set to 200-300; the maximum number of iterations is set to 1000-2000; the crossover probability is set to 0.9; the mutation probability is set to 1 / n (n is the number of decision variables); and the number of reference points is set to 100-150.

[0440] Then, the optimization solution process is implemented. The specific steps are as follows:

[0441] 1. Encoding decision variables: Encode the decision variables in the optimization model (molecular weight of polyethylene glycol amine, concentration of each functional group, concentration of each magnesium lithium silicate component, and concentration of methylene blue) into chromosomes. For continuous variables, real number encoding is used; for discrete variables (such as the selection of functional groups and magnesium lithium silicate components), binary encoding is used.

[0442] 2. Define the fitness function: directly use the objective function as the fitness function. For solutions that do not meet the constraints, use the penalty function method:

[0443]

[0444] in, is the original objective function, For the The degree of violation of a constraint condition, is the penalty coefficient, and an adaptive adjustment strategy is adopted.

[0445] 3. Initialize the population: randomly generate an initial solution set that satisfies the constraints, and set the population size to 200-300.

[0446] 4. Perform non-dominated sorting: Use the Fast Non-dominated Sorting algorithm to sort the population and divide the population into different non-dominated fronts.

[0447] 5. Calculate the crowding distance: In each non-dominated front, calculate the crowding distance of individuals to maintain the diversity of the population.

[0448] 6. Selection operation: Use the tournament selection method to select excellent individuals to enter the mating pool. The selection criteria are based on the non-dominated sorting level and crowding distance of the individuals.

[0449] 7. Genetic operations: Perform simulated binary crossover (SBX) and polynomial mutation (Polynomial Mutation) on the selected individuals to generate the offspring population.

[0450] 8. Environmental selection: Merge the parent and offspring populations and perform non-dominated sorting again. Starting from the first frontier, select individuals layer by layer until the population size reaches the preset value. If the number of individuals in the last considered frontier exceeds the required number, use the reference point method for screening.

[0451] 9. Reference point association: Associate each individual to the nearest reference point and calculate the number of niches for each reference point.

[0452] 10. Repeat steps 4-9 until the maximum number of iterations is reached or the convergence condition is met.

[0453] 11. Output the Pareto optimal solution set: extract non-dominated solutions from the final population to form the Pareto optimal frontier.

[0454] Next, we will analyze and evaluate the results. The specific steps are as follows:

[0455] 1. Pareto frontier analysis: Draw the Pareto optimal frontier in the target space and analyze the trade-offs between different targets.

[0456] 2. Evaluation of solution diversity: Calculate the distribution indicators of the Pareto solution set (such as distribution degree and interval degree) to evaluate the diversity and uniformity of the solutions.

[0457] 3. Convergence analysis: Calculate the generational distance (GD) and inverted generational distance (IGD) indicators to evaluate the convergence performance of the algorithm.

[0458] 4. Sensitivity analysis: Conduct sensitivity analysis on typical solutions in the Pareto optimal solution set to evaluate the impact of each decision variable on the objective function.

[0459] 5. Feasibility verification: Check whether the Pareto optimal solution meets all constraints and eliminate infeasible solutions.

[0460] 6. Decision support: Based on decision maker preferences, such as using the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method, select the best compromise solution from the Pareto optimal solution set.

[0461] Finally, determine the new optimal formula component ratio. Based on the above analysis results, select 1-3 representative formulas as new optimal formulas. These formulas should have good performance on multiple objectives and meet all constraints. For each selected formula, record the following information in detail:

[0462] 1. Specific content and proportion of each component

[0463] 2. Expected performance indicators (water absorption rate, water absorption ratio, hemostasis time, antibacterial properties)

[0464] 3. Comparative analysis with the previous round of optimal formula

[0465] 4. Potential advantages and possible uncertainties of the formula

[0466] Through this step, new optimal formulas were searched in a larger solution space using the updated model and advanced multi-objective optimization algorithms. These formulas not only have better performance in theory, but also take into account the trade-offs between multiple objectives, and have stronger practicality and reliability. At the same time, through detailed result analysis and evaluation, a deeper understanding of the characteristics and potential of the new formulas was obtained, laying the foundation for the next step of experimental verification and possible further optimization.

[0467] The specific implementation method of step S90 is to repeat steps S60 to S80 until the formula converges to the optimal solution, that is, each performance indicator reaches the expected target, and the change range of the formula optimized twice in a row is less than the preset threshold. The purpose of this step is to gradually approach the true optimal formula through repeated experimental verification and model optimization. The specific implementation process is as follows: First, define the convergence criteria. The convergence criteria include two aspects: performance indicator compliance and formula stability. Performance indicator compliance: define the target value and acceptable range of each key performance indicator. For example:

[0468] Water absorption rate:

[0469] Water absorption rate:

[0470] Bleeding time:

[0471] Antimicrobial properties:

[0472] Recipe stability: Defines the threshold for recipe change. You can use Euclidean distance or Manhattan distance to measure the difference between two optimization results. For example, if the threshold is set to 5%, then:

[0473]

[0474] in, and Respectively represent the content of the i-th component in the k-th and k-1-th optimizations. Secondly, perform the iterative optimization process. The specific steps are as follows:

[0475] 1. Implement step S60: Use the current preferred formula to perform optimization experiments and obtain experimental data.

[0476] 2. Implementation step S70: Update the chemical model and product performance prediction model using new experimental data.

[0477] 3. Implement step S80: Based on the updated model, re-perform multi-objective optimization to obtain a new optimal formula.

[0478] 4. Evaluate convergence: Check whether the new optimal formula meets the requirements of performance index and formula stability. If it does, terminate the iteration; otherwise, continue to the next round of iteration.

[0479] 5. Repeat steps 1-4 until the convergence condition is met or the maximum number of iterations (such as 10) is reached.

[0480] Then, the results are analyzed and evaluated. During the iteration process, the following analysis needs to be performed continuously:

[0481] 1. Performance indicator trend analysis: Draw the curve of each performance indicator changing with the number of iterations and observe its convergence trend.

[0482] 2. Formula stability analysis: Calculate the formula change range of each iteration and draw a curve of the change range versus the number of iterations.

[0483] 3. Model accuracy evaluation: Compare the difference between the model prediction value and the experimental value in each iteration to evaluate the improvement of model accuracy.

[0484] 4. Sensitivity analysis: Conduct sensitivity analysis on the optimal formula of each iteration to identify changes in key influencing factors.

[0485] 5. Experimental error analysis: Evaluate the error range of repeated experiments to ensure the reliability of experimental data.

[0486] Next, determine the final optimal formula. Based on the results of iterative optimization, select the final optimal formula. If the iterative process converges, the result of the last optimization is the final optimal formula. Preferably, if it fails to converge completely, the following factors need to be considered comprehensively to select the optimal formula:

[0487] 1. Comprehensive performance of performance indicators

[0488] 2. Stability and repeatability of the formula

[0489] 3. Raw material cost and availability

[0490] 4. Feasibility of production process

[0491] 5. Potential application scope and market demand

[0492] Finally, verification and confirmation. A comprehensive verification experiment is conducted on the final optimal formula, including:

[0493] 1. Repeatability experiment: Repeat the preparation and testing several times to evaluate the stability and repeatability of the formula.

[0494] 2. Scale-up experiment: Conduct small-batch trial production to verify the performance of the formula in the production environment.

[0495] 3. Long-term stability test: Conduct accelerated aging tests to evaluate the long-term storage stability of the product.

[0496] 4. Comparative test: Compare performance with similar products on the market to confirm the competitive advantages of the preferred formula.

[0497] 5. Safety assessment: Conduct safety tests such as cytotoxicity, irritation, and sensitization to ensure the biological safety of the product.

[0498] Through this step, a closed-loop iterative process of formula optimization is realized. Through repeated experimental verification and model optimization, the true optimal formula is constantly approached. This method can not only make full use of experimental data and theoretical models, but also adapt to complex nonlinear relationships and multi-objective trade-offs, and finally obtain a hemostatic powder formula that reaches a high level in performance, stability and practicality. At the same time, this process has also accumulated a large amount of experimental data and theoretical models, laying a solid foundation for future product improvements and new product development.

[0499] The principle of the recipe optimization method provided by the present invention is:

[0500] 1. Principle of systematic experimental design: This method adopts orthogonal experimental design, which fully considers the influence of each component (molecular weight of polyethylene glycol amine, type of functional group, lithium magnesium silicate components, methylene blue, etc.) on the performance of hemostatic powder. This systematic experimental design method can obtain the maximum amount of information in the minimum number of experiments, providing a comprehensive and reliable data basis for subsequent modeling. The principle of orthogonal experimental design ensures the uniformity and representativeness of the combination of each factor level, so that the main effect and interaction of each factor can be accurately evaluated.

[0501] 2. Multi-scale modeling principle: This method combines microscopic chemical models and macroscopic performance prediction models. The microscopic chemical model establishes a theoretical ratio model similar to a chemical equation based on the chemical properties and interactions of each component; the macroscopic performance prediction model uses a machine learning algorithm to perform nonlinear mapping modeling on experimental data. This multi-scale modeling method can simultaneously capture the microstructural characteristics and macroscopic performance of the material, providing a more comprehensive and accurate theoretical basis for formulation optimization.

[0502] 3. Principle of mathematical description of complex systems: This method introduces a series of complex mathematical equations to describe the various performance indicators of hemostatic powder. For example, the modified Langmuir adsorption kinetic equation is used to describe the water absorption rate, the Freundlich-BET mixed model is used to describe the water absorption rate, the modified Hill equation is introduced to describe the hemostatic time, and the modified Gompertz growth model is used to describe the antibacterial performance. These equations not only take into account the concentration of each component, but also introduce environmental factors such as temperature and pH, so as to more accurately reflect the performance of hemostatic powder in actual applications.

[0503] 4. Multi-objective optimization theory: This method constructs a multi-objective optimization model that comprehensively considers water absorption rate, water absorption ratio, hemostatic time and antibacterial performance. Multi-objective optimization theory allows finding the best balance between conflicting objectives, which is consistent with the actual situation that hemostatic powder needs to meet multiple performance requirements at the same time. By adopting the concept of Pareto optimal solution, this method can obtain a series of non-dominated solutions, providing decision makers with more choice space.

[0504] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A fast water-absorbing hemostatic powder, characterized in that: The invention comprises the following components: polyethylene glycol amine, functional groups and lithium magnesium silicate components.

2. The fast water-absorbing hemostatic powder according to claim 1, characterized in that: The functional group is any one of polyethylene glycol amide, polyethylene glycol ether, polyvinyl alcohol, hydroxyethyl cellulose, chitosan and gelatin.

3. The fast water-absorbing hemostatic powder according to claim 1, characterized in that: The functional group adopts polyethylene glycol amide.

4. The fast water-absorbing hemostatic powder according to claim 1, characterized in that: The lithium magnesium silicate component includes lithium magnesium silicate or any one or more of montmorillonite, kaolin, zeolite and bentonite.

5. The fast water-absorbing hemostatic powder according to claim 3, characterized in that: The formula is: polyethylene glycol ammonia 40-50%, polyethylene glycol amide 20-30%, and lithium magnesium silicate 15-25%.

6. The fast water-absorbing hemostatic powder according to claim 2, characterized in that: The formula is: polyethylene glycol ammonia 45-55%, polyethylene glycol ether 15-25%, montmorillonite 20-30%.

7. The fast water-absorbing hemostatic powder according to claim 2, characterized in that: The formula is: polyethylene glycol amine 35-45%, polyvinyl alcohol 25-35%, and kaolin 20-30%.

8. The fast water-absorbing hemostatic powder according to claim 3, characterized in that: The formula is: 50-60% polyethylene glycol ammonia, 10-20% polyethylene glycol amide, and 20-30% bentonite.

9. A method for preparing a fast water-absorbing hemostatic powder, characterized in that: The method for preparing the fast water-absorbing hemostatic powder according to any one of claims 1 to 8 comprises the following steps: Step 1, accurately weigh each component according to the formula ratio, put the weighed polyethylene glycol amine and polyethylene glycol amide into a dry mixing container, and then use a low-speed stirrer to dry mix them for 15-20 minutes, and the stirring speed is controlled at 100-150rpm to ensure uniform mixing without generating dust; Step 2: While continuing to stir, slowly add lithium magnesium silicate, increase the stirring speed to 200-250 rpm, and continue mixing for 10-15 minutes to ensure that the lithium magnesium silicate is fully mixed with other components; Step 3, dissolve the weighed methylene blue in a small amount of anhydrous ethanol, and then slowly spray the dissolved methylene blue solution onto the mixture while stirring, and continue stirring for 5-10 minutes to ensure that the methylene blue is evenly distributed; Step 4: Transfer the mixture to a high-speed mixer and mix at a speed of 500-800 rpm for 10 minutes to achieve full integration of the components and ensure the uniformity of the final product; Step 5: Spread the mixed powder on a drying tray with a thickness of no more than 1 cm, and then dry it in an oven at 45-50°C for 4-6 hours, or until the moisture content is less than 0.5% to ensure the stability of the product; Step 6: Use a 80-100 mesh sieve to sieve the dried powder to remove possible agglomerates to ensure the uniformity and use effect of the product; Step 7: sterilize the sieved powder by gamma ray irradiation, ethylene oxide or electron beam to ensure the sterility of the product and meet the requirements of medical supplies.

10. A method for optimizing the formula of a fast water-absorbing hemostatic powder, used for optimizing the formula of the fast water-absorbing hemostatic powder according to any one of claims 1 to 8, characterized in that: The following steps are involved: S10. Design basic experiments to test the effects of different raw material components on product performance and obtain basic experimental data; S20. Establish chemical models and product performance prediction models based on basic experimental data; S30. Determine a multi-objective optimization objective function according to product performance requirements; S40. Combine the material measurement relationship and the ideal ratio of the chemical model to establish a multi-objective optimization model for the formulation; S50, using an intelligent optimization algorithm to solve the multi-objective optimization model to obtain a preliminary optimal formula component ratio; S60, performing optimization experiments using the preliminary optimal formula to obtain optimization experiment data; S70, updating the chemical model and product performance prediction model using the optimized experimental data; S80, reconstructing the multi-objective optimization model according to the updated model, and solving it again using the intelligent optimization algorithm to obtain a new optimal formula component ratio; S90, repeating steps S60-S80 until the formula converges to the optimal solution, that is, each performance index reaches the expected target, and the change range of the formula optimized twice in a row is less than a preset threshold.

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