A method for preparing mountain green tea antihypertensive capsules based on parameter control

Abstract

The present application relates to the technical field of drug preparation, and specifically to a method for preparing mountain green tea antihypertensive capsules based on parameter control, the method comprising: crushing mountain green tea Chinese medicinal pieces into coarse powder, sieving to obtain fine powder, and sterilizing the fine powder by irradiation; adding the prepared ethanol to the percolation tank containing the remaining coarse powder, percolating after immersion, controlling the percolation speed to obtain the percolation liquid; concentrating the percolation liquid and collecting the paste to obtain the clear paste; adding the irradiated sterilized fine powder and corn starch, drying, mixing, crushing, granulating, drying, and filling into capsules to obtain mountain green tea antihypertensive capsules. The present application aims to control the percolation speed based on the concentration change characteristics of the percolation liquid to improve the product quality of mountain green tea capsules.

Description

Technical Field

[0001] The present application relates to the technical field of drug preparation, and in particular to a method for preparing mountain green tea blood pressure lowering capsules based on parameter control. Background Art

[0002] As people's living standards continue to improve, hypertension has gradually become one of the diseases with a higher incidence rate, and people's demand for hypertension drugs has become increasingly greater. Therefore, the preparation method of mountain green tea antihypertensive capsules for hypertension is also constantly being improved and improved. In the preparation method of mountain green tea antihypertensive capsules, it is necessary to extract the active ingredients in the medicinal materials through multiple processes, among which percolation is one of the important processes for extracting medicinal ingredients. Usually, the medicinal materials are first impregnated with 70% ethanol, and then the active ingredients in the medicinal materials are extracted by percolation.

[0003] In order to ensure the continuity of production, the percolation replenishment speed is usually pre-set manually. However, due to the different effects of medicinal material soaking, there may be a certain deviation between the liquid output speed and the pre-set replenishment speed, which may lead to the percolation speed being too fast or too slow, affecting the quality of the percolation liquid, and thus affecting the product quality of Shanlucha Antihypertensive Capsules. Summary of the invention

[0004] In view of the above, it is necessary to provide a method for preparing mountain green tea antihypertensive capsules based on parameter control, control the percolation rate based on the concentration change characteristics of the percolation liquid, and improve the product quality of mountain green tea capsules.

[0005] One embodiment of the present application provides a method for preparing mountain green tea blood pressure lowering capsules based on parameter control, the method comprising:

[0006] The Chinese medicinal slices of mountain green tea are crushed into coarse powder, sieved to obtain fine powder, and the fine powder is sterilized by irradiation; the prepared ethanol is added to the percolation tank containing the remaining coarse powder, and percolation is performed after immersion, and the percolation speed is controlled to obtain the percolation liquid:

[0007] The process of controlling the percolation rate is as follows:

[0008] (1) collecting grayscale images of the percolation liquid at each moment; dividing the pixel points in the grayscale image of the percolation liquid to obtain droplet pixel points and percolation liquid pixel points;

[0009] (2) Obtain the degree of percolation anomaly at each moment based on the grayscale approximate characteristics of the droplet pixel at each moment and its neighboring moments;

[0010] (3) According to the height of the percolation liquid level at each moment, combined with the grayscale change characteristics of the percolation liquid pixel points and the droplet pixel points, the normal degree of the percolation difference at each moment is obtained;

[0011] (4) Controlling the percolation rate based on the degree of percolation abnormality and the degree of normal percolation difference at each moment;

[0012] The percolated liquid is concentrated and collected into a paste to obtain a clear paste; the irradiated sterilized fine powder and corn starch are added, dried, mixed, crushed, granulated, dried again, and filled into capsules to obtain mountain green tea blood pressure lowering capsules.

[0013] The specific operation process of crushing the Chinese medicinal pieces of mountain green tea into coarse powder and sieving to obtain fine powder is as follows:

[0014] The Chinese medicinal slices of mountain green tea are crushed; the crushed materials are passed through a 24-mesh sieve until all of them pass through the 24-mesh sieve to obtain a coarse powder of the Chinese medicinal slices of mountain green tea; the coarse powder of the Chinese medicinal slices of mountain green tea is then passed through a 100-mesh sieve until the mass ratio of the sieved fine powder of the Chinese medicinal slices of mountain green tea to the Chinese medicinal slices of mountain green tea is 1:20, and then the screening is stopped.

[0015] Wherein, the concentration of the prepared ethanol is 70%.

[0016] Wherein, the immersion time is 18 hours.

[0017] The specific process of obtaining the droplet pixel points and the percolation liquid pixel points is as follows:

[0018] Perform edge detection on the grayscale image of the percolation liquid, take the extension line of the upper edge of the receiving container as the area boundary line, and obtain the falling area and the receiving area; obtain the edge of the percolation liquid droplet in the falling area to obtain the droplet pixel point; perform threshold segmentation on the grayscale values ​​of all pixel points in the receiving area to obtain the grayscale segmentation threshold, and take the pixel points with grayscale values ​​less than or equal to the grayscale segmentation threshold as the percolation liquid pixel points.

[0019] The specific operation of measuring the abnormality of percolation at each moment is as follows:

[0020] For the grayscale image of the percolate at each moment, the average grayscale value of all droplet pixels is recorded as the first grayscale average at each moment; the average grayscale value of all percolate pixels is recorded as the second grayscale average at each moment; based on the difference between the first grayscale average and the second grayscale average, the regional grayscale difference at each moment is obtained;

[0021] Merge the difference between each moment and the first grayscale mean value of a preset number of moments before to obtain the droplet time grayscale difference at each moment;

[0022] Based on the regional grayscale difference and the droplet time grayscale difference at each moment, the similarity of the percolation liquid at each moment is obtained; wherein the regional grayscale difference and the droplet time grayscale difference are both negatively correlated with the similarity of the percolation liquid;

[0023] According to the difference in the number of percolation liquid pixels at each moment and the adjacent moments, and the inverse proportional mapping result of the corresponding sampling time interval, the growth rate of the container liquid level at each moment is obtained;

[0024] Obtain the average distance between all adjacent droplet edges at each moment;

[0025] Based on the difference between the mean value of the distance at each moment and the previously preset number of moments, combined with the discrete degree of the growth rate of the liquid level in the container during the corresponding time period, the degree of change of the percolation rate at each moment is obtained;

[0026] The degree of percolation abnormality at each moment is obtained by inversely mapping the degree of change of the percolation velocity at each moment and the degree of similarity of the percolation liquid.

[0027] The specific steps of determining the normal degree of the percolation difference at each moment include:

[0028] For the grayscale image of the percolation liquid at each moment, the height of the percolation liquid level is fused with the total number of the corresponding moments, and then the inverse is calculated to obtain the initial percolation characteristics at each moment.

[0029] According to the second gray value mean, the same calculation method as the drop time gray difference is used to obtain the gray difference of the percolation liquid time at each moment; the inverse proportional mapping result of the drop time gray difference at each moment and the gray difference of the percolation liquid time is used as the normality of the percolation liquid concentration at each moment;

[0030] The initial percolation characteristics at each time and the normal level of percolation liquid concentration were combined to obtain the normal level of percolation difference at each time.

[0031] Wherein, the step of controlling the percolation rate comprises:

[0032] The inverse proportional mapping result of the percolation abnormality at each moment and the normal percolation difference is used as the percolation adjustment factor at each moment;

[0033] The percolation adjustment factors at the current moment and all previous moments are threshold segmented to obtain the current segmentation threshold; based on the percolation adjustment factor at the current moment and the segmentation threshold, the liquid replenishment valve and the liquid outlet valve are controlled using PID control.

[0034] The specific operation of concentrating the percolate and collecting the paste to obtain the clear paste is as follows:

[0035] During the first concentration, the vacuum degree is controlled at 0.05-0.09MPa, the temperature is 70±5℃, and the steam pressure is 0.1-0.3MPa. When the relative density of the concentrated liquid is 1.05-1.10, the concentration is stopped.

[0036] During the second concentration, the vacuum degree is controlled at 0.05-0.09MPa, the temperature is 70±5℃, the steam pressure is 0.1-0.3MPa, and the concentration is stopped when the relative density of the concentrate is 1.15-1.18.

[0037] The specific operation of adding the fine powder and corn starch and drying is to control the drying temperature to 55-65° C. and the vacuum degree to ≥0.07 MPa.

[0038] This application has at least the following beneficial effects:

[0039] The present application performs visual-assisted monitoring of the percolation process in the preparation process of mountain green tea antihypertensive capsules. First, the grayscale image of the percolation liquid is collected, and the pixel points are divided to obtain droplet pixel points and percolation liquid pixel points; the percolation liquid droplets are distinguished from the collected percolation liquid to facilitate the subsequent concentration difference characteristic analysis between the two; the degree of percolation abnormality and the normal degree of percolation difference are constructed to characterize the concentration of the percolation process and the abnormal concentration change. The beneficial effect is that the concentration characteristics of the percolation liquid at different times when it falls are compared with the concentration characteristics of the percolation liquid at all previous times to determine whether the concentration of the percolation liquid is normal, and then the percolation speed is controlled. This solves the problem that the percolation process in the current preparation process of mountain green tea antihypertensive capsules pre-sets the percolation speed for continuous production, and cannot be adjusted according to the actual percolation situation, resulting in poor percolation effect and low percolation efficiency. The efficiency of the percolation process is improved, and the product quality and production efficiency in the subsequent production process are guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flow chart of a method for preparing mountain green tea antihypertensive capsules based on parameter control provided in this application;

[0041] Figure 2 Schematic diagram of image data acquisition provided for this application;

[0042] Figure 3 A schematic diagram of the area division provided for this application;

[0043] Figure 4 A flow chart for controlling the percolation rate provided for this application. DETAILED DESCRIPTION

[0044] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0046] It should also be noted that the terms "first" and "second" in this application and its drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flow chart includes one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0047] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0048] This application first proposes a method for preparing mountain green tea antihypertensive capsules based on parameter control, which is applied to the technical field of drug preparation. Figure 1 , the method comprising:

[0049] (A) Mountain Green Tea Extraction Process

[0050] A1: Material preparation

[0051] Collect Chinese medicinal materials, ethanol, and corn starch. Weigh 1440 kg of purified mountain green tea Chinese medicinal slices and set aside.

[0052] A2: Crushing and sieving

[0053] Add the Chinese medicinal slices of mountain green tea into a coarse crusher for crushing. Pass the crushed material through a 24-mesh sieve until all of it passes through the 24-mesh sieve to obtain the coarse powder of the Chinese medicinal slices of mountain green tea. Replace the sieve of the vortex oscillating sieve with a 100-mesh sieve, and then sieve the coarse powder of the Chinese medicinal slices of mountain green tea. Stop sieving after sieving 72 kg of fine powder of the Chinese medicinal slices of mountain green tea.

[0054] A3: Irradiation sterilization

[0055] The fine powder of Shanlu Tea Chinese herbal medicine slices is sterilized by 60Co irradiation, and the sterilized fine powder is transported to the temporary storage room of raw and auxiliary materials in the clean area.

[0056] A4: Prepared with 70% ethanol

[0057] Use fresh ethanol or recycled ethanol for preparation, and the preparation ratio shall be operated according to the Standard Operating Procedure for Preparation of Cleaners and Disinfectants. Use an alcohol meter to confirm that the concentration of the prepared ethanol solution is 70%. Pump the prepared 70% ethanol solution into the ethanol high-level metering tank, and prepare a total of 12,000L of 70% ethanol.

[0058] A5: Percolation

[0059] Divide the mountain green tea coarse powder into two parts and put them into the No. 1 and No. 2 percolation tanks respectively. Then add the 70% ethanol solution in the high-level metering tank into the percolation tank. The liquid level of the solution must be higher than the coarse powder surface. Accurately measure the amount of 70% ethanol and soak for 18 hours. Open the percolation valve to start percolation. As the percolation liquid flows out, add 70% ethanol and percolate at a speed of 1520ml~1900ml per minute.

[0060] (1) Collecting the grayscale image of the percolation liquid at each moment; dividing the pixel points in the grayscale image of the percolation liquid to obtain droplet pixel points and percolation liquid pixel points.

[0061] A CMOS high-definition industrial camera is set on the side of the liquid outlet of the percolation tank. In order to more clearly distinguish the percolation droplets, a white baffle is set on the opposite side of the CMOS high-definition industrial camera. Image data is collected at every interval T between the liquid outlet and the percolation liquid receiving container to obtain an RGB image of the percolation liquid, and the image is pre-processed. The percolation liquid receiving container is a transparent glass container.

[0062] It should be noted that the image preprocessing can use relevant image processing algorithms to grayscale the image, enhance contrast and brightness, denoise, standardize, etc. The implementer can preprocess the image according to the actual situation, and this application does not impose any restrictions on this.

[0063] In this embodiment, the time interval T is set to 0.5s; the RGB image is converted into a grayscale image using the grayscale value averaging method, and finally the grayscale image is denoised using mean filtering to obtain a percolation liquid grayscale image.

[0064] Among them, the image data acquisition schematic diagram is as follows: Figure 2 shown.

[0065] Since the shooting position is fixed, the positions of the falling area and the area of ​​the percolation liquid receiving container in the image are also fixed. Based on this, the area in the grayscale image of the percolation liquid is divided as follows:

[0066] The embodiment of the present application uses the grayscale image of the percolation liquid as input, uses the Canny edge detection algorithm and the corrosion and expansion algorithm, and outputs all the edges in the grayscale image of the percolation liquid, uses the extension line of the upper edge of the container in the image as the region boundary, records the area at and above the region boundary as the falling area, and records the area below the region boundary as the receiving area. Among them, the Canny edge detection algorithm and the corrosion and expansion algorithm are both well-known technologies, and this application will not repeat them.

[0067] The regional division diagram is as follows: Figure 3 shown.

[0068] During the percolation process, the percolation liquid will drip out along the liquid outlet and eventually fall into the percolation liquid receiving container. Under normal percolation conditions, the concentration of the percolation liquid in the percolation liquid receiving container is similar to that of the dripping percolation liquid, which is mainly manifested in the grayscale image as the grayscale values ​​of the two being closer, and the similarity is high at multiple moments. When the percolation speed is abnormal, the extraction effect may be reduced, the effective ingredients in the percolation liquid droplets are less, and the color of the droplets is lighter. In the percolation liquid image, each drop of the percolation liquid usually forms an irregular closed edge, which is mainly manifested as a closed edge in the falling area.

[0069] Using the edge detection algorithm, the edge of the percolation liquid droplet in the falling area is obtained to obtain the droplet pixel point.

[0070] As an embodiment, the edge in the falling area is taken as input, and the contour tracking algorithm is used to output the closed edges in the falling area, and these closed edges are the edges of the percolation liquid droplets. The contour tracking algorithm is a well-known technology and will not be described in detail in this application. One percolation liquid droplet edge corresponds to one droplet, and the pixel points in one droplet are recorded as droplet pixels.

[0071] The gray values ​​of all pixels in the receiving area are classified to obtain the permeate pixels.

[0072] As an embodiment, for a receiving area, the grayscale values ​​of all pixels in the area are used as input, and the grayscale value segmentation threshold is outputted using the Otsu threshold method, and all pixels with grayscale values ​​less than or equal to the segmentation threshold are used as percolate pixels. The Otsu threshold method is a well-known technology, and the present application will not repeat its process.

[0073] It should be noted that if the edge is in the falling area and the receiving area at the same time, the edge is determined to be an edge in the falling area.

[0074] (2) According to the grayscale approximate characteristics of the droplet pixel at each time and the adjacent time, the degree of percolation anomaly at each time is obtained:

[0075] (2a) Determine the similarity of the percolation liquid at each moment based on the distribution difference of the grayscale value of the droplet pixel at each moment and the previous moment.

[0076] For the grayscale image of the percolation liquid at each moment, the mean grayscale value of all droplet pixel points is recorded as the first grayscale mean at each moment; the mean grayscale value of all droplet pixel points is recorded as the second grayscale mean at each moment; based on the difference between the first grayscale mean and the second grayscale mean, the regional grayscale difference at each moment is obtained; the difference between each moment and the first grayscale mean of a preset number of moments before is merged to obtain the droplet time grayscale difference at each moment; based on the regional grayscale difference and the droplet time grayscale difference at each moment, the percolation liquid similarity at each moment is obtained; wherein, the regional grayscale difference and the droplet time grayscale difference are both negatively correlated with the similarity of the percolation liquid.

[0077] It should be noted that the difference represents the numerical difference between variables, specifically: difference, absolute value of difference, ratio, etc.; a positive correlation represents a trend that as one variable increases, another variable also increases; a negative correlation represents a trend that as one variable increases, another variable also decreases; implementers can choose appropriate methods for calculation, and this application does not impose any restrictions on this.

[0078] As an example, the percolate similarity degree A at each moment can be calculated as follows:

[0079]

[0080] B is the regional grayscale difference at each moment, specifically the difference between the first grayscale mean and the second grayscale mean at each moment;

[0081] C is the grayscale difference of the droplet at each moment, specifically: the difference between each moment and the first grayscale mean value of the previous preset number of moments is accumulated and summed up; wherein the preset number is 5, and the implementer can adjust it according to the actual situation.

[0082] As another embodiment of the present application, the percolation liquid similarity degree A at each moment can be obtained by A=exp(-BC).

[0083] The greater the similarity of the percolation liquid at each moment, the closer the concentration of the current percolation liquid droplets at the outlet is to the concentration of the percolation liquid in the receiving container, and the concentration change of the percolation liquid droplets is small, indicating that the current percolation effect is more balanced and the corresponding percolation abnormality is smaller.

[0084] (2b) Based on the difference in the number of percolation liquid pixels at each moment and the adjacent moments and the distance distribution between the droplet edges, the degree of change in the percolation velocity at each moment is obtained.

[0085] The difference in the number of percolation liquid pixels at each moment and the adjacent moments is mapped in inverse proportion to the corresponding sampling time interval and is used as the growth rate of the container liquid level at each moment.

[0086] It should be noted that there are many methods for classifying pixels according to their grayscale values, such as clustering, threshold segmentation, etc. The inverse proportional mapping results in some embodiments of the present application are the results of subtracting, dividing, or finding the inverse between variables; implementers can choose the appropriate method according to actual conditions, and the present application does not impose any restrictions on this.

[0087] As an embodiment, the ratio of the difference between the number of percolation liquid pixels in the percolation liquid receiving container at each moment and the previous moment to the sampling time interval is calculated as the container liquid level growth rate.

[0088] The mean distance between all adjacent droplet edges at each moment is obtained; based on the difference between each moment and the mean distance at a preset number of moments before, combined with the discrete degree of the container liquid level growth rate in the corresponding time period, the degree of change of the percolation rate at each moment is obtained.

[0089] It should be noted that the distance between the edges of adjacent droplets can be measured using Euclidean distance, Manhattan distance, etc.; the degree of discreteness describes the dispersion of the data, that is, the distribution of data points relative to the center position of the data set. Commonly used discreteness measures include variance, standard deviation, range value, etc.; this application does not impose any restrictions on this.

[0090] As an embodiment, for any two adjacent droplet edges, the pixel point with the smallest ordinate in the droplet edge is taken as the representative point, and the Euclidean distance between the two representative points is calculated as the Euclidean distance between the two adjacent droplets; the mean Euclidean distance between all adjacent droplet edges at each moment is calculated, and the difference between each moment and the mean Euclidean distance of the previous 5 moments is summed, and the inverse of the product of the summation result and the variance of the growth rate sequence at each moment is taken as the degree of change of the percolation rate at each moment.

[0091] It should be understood that the greater the degree of change in the percolation rate at each moment, the greater the fluctuation in the percolation rate, the more likely it is that there are abnormalities in the liquid discharge rate and the liquid replenishment rate, and the greater the corresponding degree of percolation abnormality.

[0092] It should be noted that in the above processing method, if sufficient time data cannot be obtained before each time, data filling is performed by using mean filling, wherein mean filling is a well-known technology and will not be described in detail in this application.

[0093] (2c) The degree of percolation abnormality at each moment is obtained by inversely mapping the degree of change of the percolation velocity at each moment and the degree of similarity of the percolation liquid.

[0094] The percolation abnormality is positively correlated with the percolation velocity variation, and negatively correlated with the percolation liquid similarity.

[0095] As an example, the degree of percolation abnormality at each moment can be calculated by the ratio of the degree of change in the percolation velocity at each moment to the degree of similarity of the percolation liquid. The greater the degree of percolation abnormality at each moment, the greater the difference between the percolation liquid droplets at the current moment and the percolation liquid in the percolation liquid receiving container, and the greater the degree of change in the percolation velocity, which indicates that the content of the active ingredients of the medicinal materials in the solvent during the percolation process at the current moment is less, and the percolation extraction effect is worse.

[0096] Before the percolation process, the medicinal materials will be soaked for a long time. At this time, some of the active ingredients in the medicinal materials will first dissolve in the solvent. Therefore, in the early stage of percolation, the concentration of the percolation liquid droplets will be relatively large, and the concentration will gradually decrease to the normal level after a period of time. When the percolation liquid receiving container initially receives the percolation liquid, due to the small volume of the percolation liquid and the large volume of the receiving container, the droplets are more dispersed after dripping into the receiving container, so the light transmittance is stronger, resulting in a lighter color of the percolation liquid in the receiving container. After percolation for a period of time, the concentration color will gradually turn into a normal color. When the percolation is normal, as the percolation liquid level in the receiving container rises, its color depth will gradually be consistent with the percolation liquid droplets, and the concentration is relatively large; when the percolation liquid replenishment speed is too fast or too slow, the percolation liquid droplets will cause the percolation liquid concentration to be low due to the abnormal solvent replenishment speed, and then the percolation liquid in the receiving container will still have a low concentration and a lighter color even if the liquid level rises.

[0097] (3) According to the height of the percolation liquid level at each moment, combined with the grayscale change characteristics of the percolation liquid pixel points and the droplet pixel points, the normal degree of the percolation difference at each moment is obtained.

[0098] (3a) For the grayscale image of the percolation liquid at each moment, the height of the percolation liquid level is fused with the corresponding total number of moments, and then the inverse is calculated to obtain the initial percolation characteristics at each moment.

[0099] As an embodiment, the difference between the height of the grayscale image of the percolation liquid and the minimum value of the ordinate of the pixel point of the percolation liquid in the receiving area at each moment is calculated and recorded as the height of the percolation liquid level at each moment; the inverse of the product of the height of the grayscale image of the percolation liquid and the total number of percolation moments is taken as the initial percolation feature at each moment. The larger the initial percolation feature at each moment, the more it indicates that the current percolation process is in the initial stage, and the concentration difference between the percolation liquid in the percolation liquid receiving container and the percolation liquid droplets is normal, and the larger the normality of the corresponding percolation difference.

[0100] (3b) According to the grayscale changes of the percolation liquid pixel points and the droplet pixel points at each moment and the previous moment, the normal degree of the percolation liquid concentration at each moment is obtained.

[0101] According to the second grayscale value mean, the same calculation method as the drop time grayscale difference is adopted to obtain the percolation liquid time grayscale difference at each moment; the inverse proportional mapping result of the drop time grayscale difference at each moment and the percolation liquid time grayscale difference is used as the normal level of the percolation liquid concentration at each moment.

[0102] As an example, the normal degree D of the percolate concentration at each moment can be calculated as follows:

[0103]

[0104] C is the grayscale difference of the droplet at each moment, specifically: obtain the difference between each moment and the second grayscale mean value of the previous five moments, and then sum them up.

[0105] E is the grayscale difference of the percolation liquid at each moment.

[0106] α is an adjustment parameter preset to be greater than zero, α = 0.001;

[0107] It should be understood that the greater the normality of the percolation liquid concentration at each moment, the closer the percolation liquid concentration in the receiving container at the current moment and the concentration change of the percolation liquid droplets are to the normal concentration change characteristics in the initial stage of normal percolation; the greater the normality of the corresponding percolation difference, the more normal the concentration difference between the percolation liquid and the percolation liquid droplets in the receiving container at the current moment is.

[0108] (3c) The initial percolation characteristics at each time and the normal level of percolation liquid concentration are combined to obtain the normal level of percolation difference at each time.

[0109] As an example, the normal degree of percolation difference at each moment can be obtained by calculating the product of the initial percolation characteristics and the normal degree of percolation liquid concentration. The greater the normal degree of percolation difference at each moment, the greater the concentration difference between the percolation liquid and the percolation liquid droplets in the receiving container at the current moment belongs to the normal difference at the initial stage of percolation, and the percolation speed does not need to be adjusted.

[0110] (4) The percolation rate is controlled based on the degree of percolation abnormality and the degree of normal percolation difference at each moment.

[0111] As an example, the percolation adjustment factor at a single moment can be calculated by the ratio of the percolation abnormality to the normal percolation difference at a single moment. The larger the percolation adjustment factor at a single moment is, the more likely it is that the percolation process at the current moment is abnormal, the more likely the percolation replenishment rate and the percolation discharge rate are too fast or too slow, and the more likely it is that the percolation rate needs to be adjusted.

[0112] The percolation adjustment factors at all times are cross-validated to obtain the division threshold of the percolation adjustment factor, and the percolation adjustment factor at the current time and the corresponding division threshold are used as the input of the PID control algorithm. The PID control algorithm is used to adjust the liquid replenishment rate and the liquid discharge rate, and the obtained percolation liquid is combined and collected in a storage tank for standby use. The PID control algorithm is a well-known technology, and this application will not repeat it in detail.

[0113] Among them, the flow chart for controlling the percolation rate is as follows: Figure 4 shown.

[0114] A6: Condensed

[0115] The percolate is pumped into a single-effect concentrator in batches through a storage tank to recover ethanol and concentrate to obtain a concentrated solution.

[0116] As an example, during concentration, the vacuum degree is controlled to be 0.05 MPa, the temperature is 70° C., and the steam pressure is 0.1 MPa. When the relative density of the concentrated liquid is 1.05 (70° C.), the concentration is stopped.

[0117] As another example, during concentration, the vacuum degree is controlled to be 0.06 MPa, the temperature is 65° C., and the steam pressure is 0.3 MPa. When the relative density of the concentrated liquid is 1.07 (70° C.), the concentration is stopped.

[0118] As another embodiment, the vacuum degree is controlled at 0.09 MPa, the temperature is controlled at 75° C., and the steam pressure is controlled at 0.3 MPa during concentration. When the relative density of the concentrated liquid is 1.10 (70° C.), the concentration is stopped.

[0119] A7: Collect the cream

[0120] Continue to concentrate under reduced pressure to obtain a clear paste.

[0121] As an example, the temperature in the spherical tank is controlled at 65°C, the vacuum is maintained at 0.05 MPa, and the steam pressure is adjusted to 0.1 MPa. When the concentrated paste is concentrated to a relative density of 1.15 (70°C), the concentration is stopped.

[0122] As another embodiment, the temperature in the spherical tank is controlled at 70°C, the vacuum is maintained at 0.07 MPa, and the steam pressure is adjusted to 0.1 MPa. When the concentrated paste is concentrated to a relative density of 1.15 (70°C), the concentration is stopped.

[0123] As another embodiment, the temperature in the spherical tank is controlled at 75°C, the vacuum is maintained at 0.09 MPa, and the steam pressure is adjusted to 0.3 MPa. When the concentrated paste is concentrated to a relative density of 1.18 (70°C), the concentration is stopped.

[0124] A8: Extract drying

[0125] Take 1 / 5 of the weight of the mountain green tea paste and mountain green tea Chinese medicine powder each time, pour them into the trough mixer, and start mixing for 8 to 10 minutes. Evenly load the mixed materials into a baking tray covered with a thin layer of corn starch, with a thickness of 2 to 3 cm. Dry according to the "Standard Operation and Maintenance Procedures for FZG-20 Square Vacuum Dryer". Each box is dried for 7 to 8 hours, and 5 boxes are required for each batch. After drying, remove the baking tray and pour the dry paste into the stainless steel receiving trough to dry to room temperature.

[0126] As an embodiment, the drying temperature is controlled to be 55° C. and the vacuum degree is 0.07 MPa.

[0127] As another embodiment, the drying temperature is controlled to be 58° C. and the vacuum degree is 0.09 MPa.

[0128] As another embodiment, the drying temperature is controlled at 65° C. and the vacuum degree is controlled at 0.14 MPa.

[0129] A9: Determination of moisture content in dry paste

[0130] On-site QA inspectors measure the moisture content of each box of finished materials according to the "FD-K1 Standard Operation and Maintenance Procedures for Chinese and Western Medicine Moisture Meters". The moisture content of the dry paste is ≤7%.

[0131] A10: Dry paste crushing

[0132] Install a 100-mesh screen and prepare two clean stainless steel barrels lined with double-layer medicinal low-density polyethylene bags to exchange for the paste powder. Add materials at a uniform speed until the dry paste is crushed.

[0133] A11: Hybrid

[0134] Start the vacuum feeder, suck the Shanlucha Jiangyan Capsule Extract Powder into the mixer, and start mixing for 60 minutes. After mixing, unload the materials and unload the mixed powder into double-layer medicinal low-density polyethylene bags.

[0135] At this point, the mountain green tea blood pressure lowering capsule mixed powder is obtained.

[0136] (B) Process flow of mountain green tea antihypertensive capsule preparation

[0137] B1: Material Preparation

[0138] Obtain mountain green tea antihypertensive capsule mixed powder, corn starch, and ethanol from the raw material warehouse, and divide the mountain green tea antihypertensive capsule mixed powder, corn starch, and ethanol into four equal portions.

[0139] B2: Granulation

[0140] Start the equipment according to the "GHL-250 Wet Granulator Standard Operation and Maintenance Procedures", add one portion of Shanlucha Antihypertensive Capsule Mixed Powder and corn starch to the wet granulator, start stirring at low speed and cutting at low speed for 1 minute, add one portion of ethanol, and then start stirring at high speed and cutting at high speed for 1 minute to obtain soft material (the remaining 3 portions are obtained by the same method to obtain wet soft material). Add the wet soft material to the swing granulator and pass it through a 16-mesh screen to obtain wet granules.

[0141] B3: Dry

[0142] Start the equipment according to the "CT-C-Ⅱ Purification Explosion-proof Hot Air Circulation Oven Standard Operation and Maintenance Procedures", set the temperature to 80℃, and spread each 2 portions of prepared wet granules relatively evenly on the drying plate, the drying temperature is 75-85℃, the drying time is about 2 hours, ensure that the granules are dried evenly, and turn the plate every 60 minutes. On-site QA uses a Chinese and Western medicine moisture meter to measure the moisture content of each pot of dry granules, and the moisture content must not exceed 7.0%. Put the dry granules into a stainless steel barrel, weigh them, and move them to the dry granule storage area.

[0143] B4: Whole grain

[0144] Start the equipment according to the "LYK-160D Swinging Granulator Standard Operation and Maintenance Procedures", add the dry granules into the swinging granulator and sieve through an 18-mesh screen for granulation, and then put the granules after granulation into a stainless steel barrel lined with a medicinal low-density polyethylene bag.

[0145] B5: Granular Mixing

[0146] Start the equipment according to the "EYH-4000 Two-Dimensional Motion Mixer Standard Operation and Maintenance Procedures", add the whole granules of Shanlucha Antihypertensive Capsules into the two-dimensional motion mixer through the vacuum feeder, and mix for 30 minutes. Use a stainless steel barrel lined with a double-layer medicinal low-density polyethylene bag to collect the mixed material.

[0147] B6: Capsule filling operation process

[0148] Use the capsule mold to obtain the mixed granules of mountain green tea antihypertensive capsules and gelatin hollow capsules in batches at the intermediate station 1 and the capsule temporary storage room, and operate according to the "NJP-1200 fully automatic hard capsule filling machine standard operation and maintenance procedures", "PG-7000A pharmaceutical polishing machine standard operation and maintenance procedures", and "Pharmaceutical vacuum cleaner standard operation and maintenance procedures". Add the mixed granules and gelatin hollow capsules to the capsule filling machine, perform capsule filling production operations, and obtain mountain green tea antihypertensive capsules. Each mountain green tea antihypertensive capsule is filled with 0.35g, the filling amount difference limit is ±9.0%, and the control filling amount range is ±8.5% (0.321g-0.379g).

[0149] At this point, a method for preparing mountain green tea antihypertensive capsules based on parameter control has been completed.

[0150] The embodiment of the present application provides a method for preparing mountain green tea antihypertensive capsules based on parameter control, comprising: first collecting a grayscale image of the percolation liquid and dividing the pixel points to obtain droplet pixel points and percolation liquid pixel points by visually assisting monitoring of the percolation process in the preparation process of the mountain green tea antihypertensive capsules; distinguishing the percolation liquid droplets from the collected percolation liquid to facilitate the subsequent analysis of the concentration difference characteristics between the two; constructing the degree of percolation abnormality and the degree of normality of percolation difference to characterize the concentration of the percolation process and the abnormal concentration change. The beneficial effect is that the concentration characteristics of the percolation liquid at different times when it falls are compared with the concentration characteristics of the percolation liquid at all previous times to determine whether the concentration of the percolation liquid is normal, and then control the percolation speed, which solves the problem that the percolation speed is pre-set for continuous production in the current preparation process of mountain green tea antihypertensive capsules, and then cannot be adjusted according to the actual percolation situation, resulting in poor percolation effect and low percolation efficiency, improves the efficiency of the percolation process, and also ensures the product quality and production efficiency in the subsequent production process.

[0151] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0152] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application 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 make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for preparing mountain green tea antihypertensive capsules based on parameter control, characterized in that: The method includes: The Chinese medicinal pieces of mountain green tea are crushed into coarse powder, sieved to obtain fine powder, and the fine powder is sterilized by irradiation; the prepared ethanol is added to the percolation tank containing the remaining coarse powder, and percolation is performed after immersion, and the percolation speed is controlled to obtain a percolation liquid; The process of controlling the percolation rate is as follows: (1) collecting grayscale images of the percolation liquid at each moment; dividing the pixel points in the grayscale image of the percolation liquid to obtain droplet pixel points and percolation liquid pixel points; (2) Obtain the degree of percolation anomaly at each moment based on the grayscale approximate characteristics of the droplet pixel at each moment and its neighboring moments; (3) According to the height of the percolation liquid level at each moment, combined with the grayscale change characteristics of the percolation liquid pixel points and the droplet pixel points, the normal degree of the percolation difference at each moment is obtained; (4) Controlling the percolation rate based on the degree of percolation abnormality and the degree of normal percolation difference at each moment; The percolated liquid is concentrated and collected into a paste to obtain a clear paste; the irradiated sterilized fine powder and corn starch are added, dried, mixed, crushed, granulated, dried again, and filled into capsules to obtain mountain green tea blood pressure lowering capsules.

2. The method for preparing mountain green tea antihypertensive capsules based on parameter control according to claim 1, characterized in that: The specific operation process of crushing the Chinese medicinal pieces of mountain green tea into coarse powder and sieving to obtain fine powder is as follows: The Chinese medicinal slices of mountain green tea are crushed; the crushed materials are passed through a 24-mesh sieve until all of them pass through the 24-mesh sieve to obtain a coarse powder of the Chinese medicinal slices of mountain green tea; the coarse powder of the Chinese medicinal slices of mountain green tea is then passed through a 100-mesh sieve until the mass ratio of the sieved fine powder of the Chinese medicinal slices of mountain green tea to the Chinese medicinal slices of mountain green tea is 1:20, and then the screening is stopped.

3. The method for preparing mountain green tea antihypertensive capsules based on parameter control according to claim 1, characterized in that: The prepared ethanol concentration is 70%.

4. The method for preparing mountain green tea antihypertensive capsules based on parameter control according to claim 1, characterized in that: The duration of the immersion is 18 hours.

5. The method for preparing mountain green tea antihypertensive capsules based on parameter control according to claim 1, characterized in that: The specific process of obtaining the droplet pixel points and the percolation liquid pixel points is as follows: Perform edge detection on the grayscale image of the percolation liquid, take the extension line of the upper edge of the receiving container as the area boundary line, and obtain the falling area and the receiving area; obtain the edge of the percolation liquid droplet in the falling area to obtain the droplet pixel point; perform threshold segmentation on the grayscale values ​​of all pixel points in the receiving area to obtain the grayscale segmentation threshold, and take the pixel points with grayscale values ​​less than or equal to the grayscale segmentation threshold as the percolation liquid pixel points.

6. The method for preparing mountain green tea antihypertensive capsules based on parameter control according to claim 1, characterized in that: The specific operation of obtaining the percolation abnormality degree at each moment is: For the grayscale image of the percolate at each moment, the average grayscale value of all droplet pixels is recorded as the first grayscale average at each moment; the average grayscale value of all percolate pixels is recorded as the second grayscale average at each moment; based on the difference between the first grayscale average and the second grayscale average, the regional grayscale difference at each moment is obtained; Merge the difference between each moment and the first grayscale mean value of a preset number of moments before to obtain the droplet time grayscale difference at each moment; Based on the regional grayscale difference and the droplet time grayscale difference at each moment, the similarity of the percolation liquid at each moment is obtained; wherein the regional grayscale difference and the droplet time grayscale difference are both negatively correlated with the similarity of the percolation liquid; According to the difference in the number of percolation liquid pixels at each moment and the adjacent moments, and the inverse proportional mapping result of the corresponding sampling time interval, the growth rate of the container liquid level at each moment is obtained; Obtain the average distance between all adjacent droplet edges at each moment; Based on the difference between the mean value of the distance at each moment and the previously preset number of moments, combined with the discrete degree of the growth rate of the liquid level in the container during the corresponding time period, the degree of change of the percolation rate at each moment is obtained; The degree of percolation abnormality at each moment is obtained by inversely mapping the degree of change of the percolation velocity at each moment and the degree of similarity of the percolation liquid.

7. The method for preparing mountain green tea blood pressure lowering capsules based on parameter control according to claim 6, characterized in that: The specific steps of obtaining the normal degree of percolation difference at each moment include: For the grayscale image of the percolation liquid at each moment, the height of the percolation liquid level is fused with the total number of the corresponding moments, and then the inverse is calculated to obtain the initial percolation characteristics at each moment. According to the second gray value mean, the same calculation method as the drop time gray difference is used to obtain the gray difference of the percolation liquid time at each moment; the inverse proportional mapping result of the drop time gray difference at each moment and the gray difference of the percolation liquid time is used as the normality of the percolation liquid concentration at each moment; The initial percolation characteristics at each time and the normal level of percolation liquid concentration were combined to obtain the normal level of percolation difference at each time.

8. The method for preparing mountain green tea blood pressure lowering capsules based on parameter control according to claim 1, characterized in that: The step of controlling the percolation rate based on the percolation abnormality and the percolation difference normality at each moment comprises: The inverse proportional mapping result of the percolation abnormality at each moment and the normal percolation difference is used as the percolation adjustment factor at each moment; The percolation adjustment factors at the current moment and all previous moments are threshold segmented to obtain the current segmentation threshold; based on the percolation adjustment factor at the current moment and the segmentation threshold, the PID control algorithm is used to control the liquid replenishment valve and the liquid outlet valve.

9. The method for preparing mountain green tea blood pressure lowering capsules based on parameter control according to claim 1, characterized in that: The specific operation of concentrating the percolate and collecting the paste to obtain the clear paste is as follows: During the first concentration, the vacuum degree is controlled at 0.05-0.09MPa, the temperature is 70±5℃, and the steam pressure is 0.1-0.3MPa. When the relative density of the concentrated liquid is 1.05-1.10, the concentration is stopped. During the second concentration, the vacuum degree is controlled at 0.05-0.09MPa, the temperature is 70±5℃, the steam pressure is 0.1-0.3MPa, and the concentration is stopped when the relative density of the concentrate is 1.15-1.

18.

10. The method for preparing mountain green tea blood pressure lowering capsules based on parameter control according to claim 1, characterized in that: The specific operation of adding the fine powder and corn starch and drying is to control the drying temperature to 55-65° C. and the vacuum degree to ≥0.07 MPa.

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