Extraction method of antibacterial thuja sutchuenensis rhinitis drops

Abstract

The invention relates to the technical field of natural material extraction, in particular to an extraction method of antibacterial thuja sutchuenensis rhinitis drops, which is characterized by comprising the following steps: S1, raw material pretreatment: selecting fresh and dry thuja sutchuenensis branches and leaves, cleaning, drying, and crushing the dried thuja sutchuenensis branches and leaves into 80-120-mesh powder; s2, extraction treatment of thuja sutchuenensis: step 1, low-temperature ultrasonic-assisted extraction: using a mixed solution of ethanol with the volume fraction of 70% and water as an extraction solvent; ultrasonic conditions are as follows: mixing thuja sutchuenensis powder and a solvent in a ratio of 1: 8, setting ultrasonic power to be 300 specific W, controlling the temperature to be 30 DEG C, and controlling the extraction time to be 40 minutes; filtering: filtering the extracting solution, and removing residues to obtain a first extracting solution; step 2, enzymolysis extraction: selecting an enzyme preparation: selecting a compound enzyme of cellulase and pectinase; through the combination of low-temperature ultrasonic-assisted extraction, enzymolysis extraction, molecular sieving and other processes, the high purity of effective components is ensured, and the antibacterial effect is improved.

Description

Technical Field

[0001] The invention relates to the technical field of natural material extraction, in particular to a method for extracting antibacterial thuja nasal drops. Background Art

[0002] Thuja is a natural antibacterial plant, rich in active ingredients such as flavonoids, polyphenols, and volatile oils, which have good effects on relieving inflammation and inhibiting bacterial growth. Thuja extracts such as flavonoids, polyphenols, and volatile oils are widely used in the treatment of rhinitis due to their potential antiviral, anti-allergic, antibacterial, and antioxidant properties.

[0003] However, the thuja raw materials currently used in the production of nasal drops generally have the following disadvantages in the extraction process:

[0004] The extraction process is complicated and the purity is low. The existing technology mostly uses a single ethanol or water extraction method, which easily leads to low purity of ingredients and unsatisfactory antibacterial effect;

[0005] Loss of heat-sensitive components. Traditional extraction methods often require high-temperature heating, which can easily cause the decomposition and loss of heat-sensitive components such as volatile oils and polyphenols in Thuja, reducing the efficacy.

[0006] Therefore, a method for extracting antibacterial Thuja sutchuenensis rhinitis drops is proposed. Summary of the invention

[0007] The object of the present invention is to provide a method for extracting antibacterial Thuja sutchuenensis rhinitis drops to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for extracting antibacterial thuja rhinitis drops, comprising the following steps:

[0009] S1, raw material pretreatment: select fresh and dry thuja branches and leaves, wash and dry them, and crush the dry thuja branches and leaves into 80-120 mesh powder;

[0010] S2, Thuja sutchuenensis extraction treatment:

[0011] Step 1: Low temperature ultrasound assisted extraction:

[0012] Solvent: Using a mixture of 70% by volume of ethanol and water as the extraction solvent helps dissolve flavonoids and polyphenols;

[0013] Ultrasonic conditions: Thuja powder and solvent were mixed in a ratio of 1:8, the ultrasonic power was set to 300 W, the temperature was controlled at 30 °C, and the extraction time was 40 min;

[0014] Filtration: filtering the extract to remove residues and obtain a first extract;

[0015] Step 2: Enzymatic extraction:

[0016] Enzyme selection: Select a complex enzyme of cellulase and pectinase to further degrade the cell wall structure of Thuja sutchuenensis and release its internal components;

[0017] Enzymatic hydrolysis conditions: Add compound enzyme to the first extract, maintain the temperature at 45°C, pH value at 4.5-5, and perform enzymatic hydrolysis for 2 hours;

[0018] Filtration: After enzymatic hydrolysis, filter to obtain the second extract;

[0019] Step 3: Molecular sieving and purification:

[0020] Molecular sieve selection: Use macroporous resin for adsorption separation to remove unnecessary impurities and enrich the effective ingredients of flavonoids and polyphenols;

[0021] Elution: Use 30% ethanol solution to elute impurities, then use 70% ethanol to elute the active ingredients, and collect the 70% ethanol eluate;

[0022] Step 4: Nanoemulsification:

[0023] Emulsification process: The 70% ethanol eluate is processed by a high-speed emulsifier to form a uniform nanoemulsion, and the diameter of the nanoparticles is controlled at 100nm-200nm, which is convenient for absorption by the nasal mucosa;

[0024] Emulsification conditions: the speed of the emulsifier was controlled at 15000 rpm, the emulsification time was 10 minutes, and the process was carried out at room temperature;

[0025] Microencapsulation: Add microencapsulation materials (such as chitosan or gelatin) to the nanoemulsion to improve the stability and long-lasting effect of the active ingredients;

[0026] Step 5: Concentration and drying:

[0027] Concentration: The nanoemulsion is concentrated to 1 / 4 volume under vacuum to remove part of the solvent.

[0028] Freeze drying: The concentrated solution is freeze dried to obtain a raw material powder extract for the preparation of Thuja sutchuenensis rhinitis drops.

[0029] Preferably, the step 4: nanoemulsification includes emulsification parameter regulation during the emulsification process, controlling the particle size and distribution uniformity, and dynamically adjusting the speed of the high-speed emulsifier to adapt to the viscosity differences of different batches of raw materials. The dynamic adjustment range of the emulsifier speed is dynamically adjusted between 12,000-18,000 rpm, the emulsification time is maintained at 8 to 12 minutes, and the emulsified particle size is between 100-200 nm. The particle size distribution of the sample is detected by a laser particle size analyzer, and the data of each batch is recorded for easy traceability.

[0030] Preferably, the step four: nanoemulsification includes dynamic optimization of the emulsifier concentration to adapt to the polarity and viscosity of different raw materials. According to the solubility of the Thuja sutchuenensis extract, the emulsifier concentration can be dynamically adjusted between 1% and 3%. The stratification of the emulsion is detected by accelerated centrifugation test at a speed of 4,000 rpm for 10 minutes.

[0031] Preferably, the step 4: nanoemulsification includes temperature control, the emulsification temperature control range is 25-30° C., and the emulsion is quickly cooled to room temperature immediately after the emulsification is completed.

[0032] Preferably, the step 4: nanoemulsification step includes an automated monitoring system, which monitors and provides feedback on various parameters during the emulsification process in real time, and sets real-time monitoring sensors at key points in the entire emulsification process to automatically monitor key parameters such as temperature, rotation speed, particle size, and pH value.

[0033] Preferably, speed control and real-time monitoring are carried out by installing a real-time speed sensor of the high-speed emulsifier, and the speed is appropriately adjusted according to the viscosity of each batch of raw materials. The automatic speed regulation function is configured, and the system automatically adjusts to the set range when encountering an abnormality, thereby improving the adaptability of the system;

[0034] Temperature monitoring and regulation: a high-precision temperature sensor is configured in the emulsification tank, and the temperature range is controlled at ±0.5°C. When the temperature deviates from the set value, the system will automatically issue an adjustment instruction to start the heating or cooling module.

[0035] Preferably, the particle size monitoring and automatic adjustment use an online laser particle size analyzer to monitor the particle size distribution of the emulsion in real time, and feed the data back to the automatic control system, collect particle size data every 1 to 2 minutes, obtain real-time particle size distribution information of the emulsion through the laser particle size analyzer, and automatically adjust the rotation speed and emulsifier concentration according to the particle size change to ensure that the emulsion particle size is stable within 100-200 nm, and use the feedback formula to achieve adaptive adjustment of the rotation speed and emulsifier concentration of the emulsifier:

[0036] V new =V current +k·(D target -D current )

[0037] Where: V new is the adjusted speed, V current is the current speed, D target Specifically, the target particle size, D current is the current particle size, and k is the adjustment coefficient.

[0038] Preferably, pH value and stability monitoring. During the emulsification process, slight changes in pH value may affect the stability of the emulsion. Therefore, real-time monitoring and adjustment of pH value is key. Install a high-precision pH sensor to monitor the pH value of the emulsion in real time. The pH value range is controlled between 5.0-6.5 to avoid emulsion instability caused by acid-base changes. If the pH value deviates from the set range, the system will automatically add acid or alkali regulator to adjust it to a stable value. The automated system will make adjustments based on the following formula:

[0039] pH new =pH current +k·(pH target -pH current ) specifically specifically

[0040] Where: pH new is the adjusted pH value, pH current is the current pH value, pH target is the target pH value, and k is the adjustment factor.

[0041] Preferably, the automated monitoring system also includes a real-time data acquisition and automatic early warning system. By integrating and analyzing the real-time monitoring data of key parameters (such as rotation speed, temperature, particle size, pH value), an early warning system is established. When any parameter deviates from the set range, the system will automatically send out an alarm signal and make corresponding adjustments. During the entire emulsification process, the system will record various parameters at fixed intervals, and automatically perform data analysis to detect abnormal values. Combined with historical data, dynamic threshold adjustments are performed to improve the ability to identify abnormal situations;

[0042] The automatic early warning mechanism sets three levels of warnings: slight deviation (±5%), moderate deviation (±10%) and severe deviation (more than ±15%). When moderate or severe deviation occurs, the system will suspend the emulsification process and prompt the operator to check to reduce the occurrence rate of defective products.

[0043] Preferably, the automated monitoring system also includes data accumulation and model self-learning. The system will record the production data and adjustment strategy of each batch, and gradually improve the model parameters. The automated system will archive the emulsification parameters and product quality results of each batch for incremental learning of the model, thereby improving the system's ability to adapt to emulsification conditions. Through the big data analysis model, parameters such as temperature, rotation speed, and particle size are dynamically adjusted to optimize the production conditions of future batches.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. Specific multi-step extraction with high purity: Through the combination of low-temperature ultrasound-assisted extraction, enzymatic extraction and molecular sieving, the high purity of the active ingredients is ensured and the antibacterial effect is improved.

[0046] 2. Specific protection of heat-sensitive ingredients: The use of low-temperature extraction technology reduces the damage to heat-sensitive ingredients and ensures the stability of active ingredients such as volatile oils and polyphenols.

[0047] 3. Specifically, stability is improved: microencapsulation technology is used to encapsulate volatile ingredients to prevent oxidation of ingredients and improve the storage stability of the preparation.

[0048] 4. Specific high bioavailability: Through nanoemulsification technology, the extract is made into nanoemulsion, which improves the nasal absorption rate and bioavailability of the ingredients and makes the antibacterial effect more significant

[0049] 5. Specifically, the uniformity of emulsification is improved: precise speed and time control ensures consistent particle size and avoids fluctuations in absorption rate caused by uneven emulsification.

[0050] 6. Specific real-time data monitoring and feedback: Through online monitoring and automatic adjustment of temperature, rotation speed, particle size, and pH value, the system can respond to changes in production conditions in a timely manner to ensure a stable production process.

[0051] 7. Specific multi-level protection of early warning mechanism: automatic early warning and shutdown functions greatly reduce the defective rate and ensure production quality.

[0052] 8. Specifically, data accumulation and model optimization: Data accumulation and model self-learning improve the system's adaptability to different batches of raw materials, making production more intelligent and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 The present invention is a flow chart of the extraction method of the antibacterial thuja rhinitis drops;

[0055] Figure 2 It is a flow chart of the Thuja sutchuenensis extraction processing steps of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] See also Figure 1 to Figure 2 , the present invention provides a technical solution:

[0058] A method for extracting antibacterial thuja rhinitis drops comprises the following steps:

[0059] S1, raw material pretreatment: select fresh and dry thuja branches and leaves, wash and dry them, and crush the dry thuja branches and leaves into 80-120 mesh powder;

[0060] S2, Thuja sutchuenensis extraction treatment:

[0061] Step 1: Low temperature ultrasound assisted extraction:

[0062] Solvent: Using a mixture of 70% by volume of ethanol and water as the extraction solvent helps dissolve flavonoids and polyphenols;

[0063] Ultrasonic conditions: Thuja powder and solvent were mixed in a ratio of 1:8, the ultrasonic power was set to 300 W, the temperature was controlled at 30 °C, and the extraction time was 40 min;

[0064] Filtration: filtering the extract to remove residues and obtain a first extract;

[0065] Step 2: Enzymatic extraction:

[0066] Enzyme selection: Select a complex enzyme of cellulase and pectinase to further degrade the cell wall structure of Thuja sutchuenensis and release its internal components;

[0067] Enzymatic hydrolysis conditions: Add compound enzyme to the first extract, maintain the temperature at 45°C, pH value at 4.5-5, and perform enzymatic hydrolysis for 2 hours;

[0068] Filtration: After enzymatic hydrolysis, filter to obtain the second extract;

[0069] Step 3: Molecular sieving and purification:

[0070] Molecular sieve selection: Use macroporous resin for adsorption separation to remove unnecessary impurities and enrich the effective ingredients of flavonoids and polyphenols;

[0071] Elution: Use 30% ethanol solution to elute impurities, then use 70% ethanol to elute the active ingredients, and collect the 70% ethanol eluate;

[0072] Step 4: Nanoemulsification:

[0073] Emulsification process: The 70% ethanol eluate is processed by a high-speed emulsifier to form a uniform nanoemulsion, and the diameter of the nanoparticles is controlled at 100nm-200nm, which is convenient for absorption by the nasal mucosa;

[0074] Emulsification conditions: the speed of the emulsifier was controlled at 15000 rpm, the emulsification time was 10 minutes, and the process was carried out at room temperature;

[0075] Microencapsulation: Add microencapsulation materials (such as chitosan or gelatin) to the nanoemulsion to improve the stability and long-lasting effect of the active ingredients;

[0076] Step 5: Concentration and drying:

[0077] Concentration: The nanoemulsion is concentrated to 1 / 4 volume under vacuum to remove part of the solvent.

[0078] Freeze drying: The concentrated solution is freeze dried to obtain a raw material powder extract for the preparation of Thuja sutchuenensis rhinitis drops.

[0079] Specifically, the step 4: nano-emulsification includes emulsification parameter regulation during the emulsification process, controlling the particle size and distribution uniformity, and dynamically adjusting the speed of the high-speed emulsifier to adapt to the viscosity differences of different batches of raw materials. The dynamic adjustment range of the emulsifier speed is dynamically adjusted between 12,000-18,000 rpm, the emulsification time is maintained at 8 to 12 minutes, and the emulsified particle size is between 100-200 nm. The particle size distribution of the sample is detected by a laser particle size analyzer, and the data of each batch is recorded for easy traceability.

[0080] Specifically, the step four: nanoemulsification includes dynamic optimization of the emulsifier concentration to adapt to the polarity and viscosity of different raw materials. According to the solubility of the thuja extract, the emulsifier concentration can be dynamically adjusted between 1% and 3%. The stratification of the emulsion is detected by accelerated centrifugation test at a speed of 4,000 rpm for 10 minutes.

[0081] Specifically, the step 4: nanoemulsification includes temperature control, the emulsification temperature control range is 25-30° C., and the emulsion is quickly cooled to room temperature immediately after the emulsification is completed.

[0082] Specifically, the step 4: nanoemulsification step includes an automated monitoring system, which monitors and provides feedback on various parameters during the emulsification process in real time. Real-time monitoring sensors are set at key points in the entire emulsification process to automatically monitor key parameters such as temperature, rotation speed, particle size, and pH value.

[0083] Specifically, speed control and real-time monitoring, install a real-time speed sensor for the high-speed emulsifier, adjust the speed appropriately according to the viscosity of each batch of raw materials, configure the automatic speed regulation function, and when encountering an abnormality, the system automatically adjusts to the set range to improve the adaptability of the system;

[0084] Temperature monitoring and regulation: a high-precision temperature sensor is configured in the emulsification tank, and the temperature range is controlled at ±0.5°C. When the temperature deviates from the set value, the system will automatically issue an adjustment instruction to start the heating or cooling module.

[0085] Specifically, particle size monitoring and automatic adjustment use an online laser particle size analyzer to monitor the particle size distribution of the emulsion in real time, and feed the data back to the automatic control system. The particle size data is collected every 1 to 2 minutes, and the real-time particle size distribution information of the emulsion is obtained through the laser particle size analyzer. According to the change of particle size, the rotation speed and emulsifier concentration are automatically adjusted to ensure that the emulsion particle size is stable within 100-200 nm. The feedback formula is used to achieve adaptive adjustment of the rotation speed and emulsifier concentration of the emulsifier:

[0086] V new =V current +k·(D target -D current )

[0087] Where: V new is the adjusted speed, V current is the current speed, D target Specifically, the target particle size, D current is the current particle size, and k is the adjustment coefficient.

[0088] Specifically, pH value and stability monitoring. During the emulsification process, a slight change in pH value may affect the stability of the emulsion. Therefore, real-time monitoring and adjustment of pH value is key. Install a high-precision pH sensor to monitor the pH value of the emulsion in real time. The pH value range is controlled between 5.0-6.5 to avoid emulsion instability caused by acid-base changes. If the pH value deviates from the set range, the system will automatically add acid or alkali regulator to adjust it to a stable value. The automation system will make adjustments based on the following formula:

[0089] pH new =pH current +k·(pH target -pH current ) specifically specifically

[0090] Where: pH new is the adjusted pH value, pH current is the current pH value, pH target is the target pH value, and k is the adjustment factor.

[0091] Specifically, the automated monitoring system also includes a real-time data acquisition and automatic early warning system. By integrating and analyzing the real-time monitoring data of key parameters (such as rotation speed, temperature, particle size, and pH value), an early warning system is established. When any parameter deviates from the set range, the system will automatically send out an alarm signal and make corresponding adjustments. During the entire emulsification process, the system will record various parameters at fixed intervals, and automatically perform data analysis to detect abnormal values. Combined with historical data, dynamic threshold adjustments are performed to improve the ability to identify abnormal situations.

[0092] The automatic early warning mechanism sets three levels of warnings: slight deviation (±5%), moderate deviation (±10%) and severe deviation (more than ±15%). When moderate or severe deviation occurs, the system will suspend the emulsification process and prompt the operator to check to reduce the occurrence rate of defective products.

[0093] Specifically, the automated monitoring system also includes data accumulation and model self-learning. The system will record the production data and adjustment strategies for each batch, and gradually improve the model parameters. The automated system will archive the emulsification parameters and product quality results of each batch for incremental learning of the model, thereby improving the system's ability to adapt to emulsification conditions. Through the big data analysis model, the temperature, rotation speed, particle size and other parameters can be dynamically adjusted to optimize the production conditions of future batches.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for extracting antibacterial thuja rhinitis drops, characterized in that: The steps include: S1, raw material pretreatment: select fresh and dry thuja branches and leaves, wash and dry them, and crush the dry thuja branches and leaves into 80-120 mesh powder; S2, Thuja sutchuenensis extraction treatment: Step 1: Low temperature ultrasound assisted extraction: Solvent: A mixture of ethanol and water with a volume fraction of 70% was used as the extraction solvent; Ultrasonic conditions: Thuja powder and solvent were mixed in a ratio of 1:8, the ultrasonic power was set to 300 W, the temperature was controlled at 30 °C, and the extraction time was 40 min; Filtration: filtering the extract to remove residues and obtain a first extract; Step 2: Enzymatic extraction: Enzyme preparation selection: choose a complex enzyme of cellulase and pectinase; Enzymatic hydrolysis conditions: Add compound enzyme to the first extract, maintain the temperature at 45°C, pH value at 4.5-5, and perform enzymatic hydrolysis for 2 hours; Filtration: After enzymatic hydrolysis, filter to obtain the second extract; Step 3: Molecular sieving and purification: Molecular sieve selection: Use macroporous resin for adsorption separation to remove unnecessary impurities and enrich the effective ingredients of flavonoids and polyphenols; Elution: Use 30% ethanol solution to elute impurities, then use 70% ethanol to elute the active ingredients, and collect the 70% ethanol eluate; Step 4: Nanoemulsification: Emulsification process: 70% ethanol eluate is processed by a high-speed emulsifier to prepare a uniform nanoemulsion, and the diameter of the nanoparticles is controlled within 100nm-200nm; Emulsification conditions: the speed of the emulsifier was controlled at 15000 rpm, the emulsification time was 10 minutes, and the process was carried out at room temperature; Microcapsule encapsulation: Add microcapsule materials to the nanoemulsion to improve the stability and long-lasting effect of the active ingredients; Step 5: Concentration and drying: Concentration: The nanoemulsion is concentrated to 1 / 4 volume under vacuum to remove part of the solvent. Freeze drying: The concentrated solution is freeze dried to obtain a raw material powder extract for the preparation of Thuja sutchuenensis rhinitis drops.

2. The method for extracting the antibacterial Thuja nasal drops according to claim 1, wherein: The step 4: nano-emulsification includes emulsification parameter regulation. During the emulsification process, the particle size and distribution uniformity are controlled. The speed of the high-speed emulsifier is dynamically adjusted to adapt to the viscosity difference of different batches of raw materials. The speed of the emulsifier is dynamically adjusted between 12,000-18,000 rpm. The emulsification time is maintained at 8 to 12 minutes. The emulsified particle size is between 100-200 nm. The particle size distribution of the sample is detected by a laser particle size analyzer, and the data of each batch is recorded.

3. The method for extracting the antibacterial Thuja nasal drops according to claim 1, characterized in that: The step four: nanoemulsification includes dynamic optimization of the emulsifier concentration to adapt to the polarity and viscosity of different raw materials. According to the solubility of the thuja extract, the emulsifier concentration can be dynamically adjusted between 1% and 3%. The stratification of the emulsion is detected by accelerated centrifugation test at a speed of 4,000 rpm for 10 minutes.

4. The method for extracting the antibacterial Thuja nasal drops according to claim 1, characterized in that: The step 4 includes temperature control in the nano-emulsification process, and the emulsification temperature control range is 25-30° C. After the emulsification is completed, the emulsion is quickly cooled to room temperature.

5. A method for extracting an antibacterial thuja rhinitis drops according to any one of claims 1 to 4, characterized in that: The step 4: nano-emulsification step includes an automated monitoring system, which monitors and provides feedback on various parameters during the emulsification process in real time. Real-time monitoring sensors are set at key points in the entire emulsification process to automatically monitor key parameters such as temperature, rotation speed, particle size, and pH value.

6. The method for extracting the antibacterial Thuja nasal drops according to claim 5, characterized in that: Speed ​​control and real-time monitoring: install a real-time speed sensor for the high-speed emulsifier, adjust the speed appropriately according to the viscosity of each batch of raw materials, and configure the automatic speed regulation function. When encountering an abnormality, the system automatically adjusts to the set range to improve the adaptability of the system; Temperature monitoring and regulation: a high-precision temperature sensor is configured in the emulsification tank, and the temperature range is controlled at ±0.5°C. When the temperature deviates from the set value, the system will automatically issue an adjustment instruction to start the heating or cooling module.

7. The method for extracting the antibacterial Thuja nasal drops according to claim 6, characterized in that: Particle size monitoring and automatic adjustment: Use an online laser particle size analyzer to monitor the particle size distribution of the emulsion in real time, and feed the data back to the automatic control system. Collect particle size data every 1 to 2 minutes, and obtain real-time particle size distribution information of the emulsion through the laser particle size analyzer. According to the change of particle size, automatically adjust the speed and emulsifier concentration to ensure that the emulsion particle size is stable within 100-200 nm. Use the feedback formula to achieve adaptive adjustment of the speed and emulsifier concentration of the emulsifier: V new =V current +k·(D target -D current ) Where: V new is the adjusted speed, V current is the current speed, D target Specifically, the target particle size, D current is the current particle size, and k is the adjustment coefficient.

8. The method for extracting the antibacterial Thuja nasal drops according to claim 7, characterized in that: pH value and stability monitoring. During the emulsification process, a slight change in pH value may affect the stability of the emulsion. Therefore, real-time monitoring and adjustment of pH value is the key. Install a high-precision pH sensor to monitor the pH value of the emulsion in real time. The pH value range is controlled between 5.0-6.5 to avoid emulsion instability caused by acid-base changes. If the pH value deviates from the set range, the system will automatically add acid or alkali regulator to adjust to a stable value. The automation system will make adjustments based on the following formula: pH new =pH current +k·(pH target -pH current ) specifically specifically Where: pH new is the adjusted pH value, pH current is the current pH value, pH target is the target pH value, and k is the adjustment factor.

9. The method for extracting the antibacterial Thuja nasal drops according to claim 5, characterized in that: The automated monitoring system also includes a real-time data acquisition and automatic early warning system. By integrating and analyzing the real-time monitoring data of key parameters such as rotation speed, temperature, particle size, and pH value, an early warning system is established. When any parameter deviates from the set range, the system will automatically send out an alarm signal and make corresponding adjustments. During the entire emulsification process, the system will record various parameters at fixed intervals, and automatically perform data analysis to detect abnormal values. Combined with historical data, dynamic threshold adjustments are made to improve the ability to identify abnormal situations; The automatic early warning mechanism sets three levels of warnings: slight deviation (±5%), moderate deviation (±10%) and severe deviation (more than ±15%). When moderate or severe deviation occurs, the system will suspend the emulsification process and prompt the operator to check to reduce the occurrence rate of defective products.

10. The method for extracting the antibacterial Thuja chinensis rhinitis drops according to claim 5, characterized in that: The automated monitoring system also includes data accumulation and model self-learning. The system will record the production data and adjustment strategies for each batch, and gradually improve the model parameters. The automated system will archive the emulsification parameters and product quality results of each batch for incremental learning of the model, thereby improving the system's ability to adapt to emulsification conditions. Through the big data analysis model, the temperature, rotation speed, and particle size parameters can be dynamically adjusted to optimize the production conditions of future batches.

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