Preparation method of plant-based bactericidal deodorant based on preparation parameter optimization

Abstract

The present application relates to the technical field of deodorant preparation, and specifically to a method for preparing a plant-based bactericidal deodorant based on optimization of preparation parameters, the method comprising: mixing plant extract raw materials, extracting and purifying to obtain a mixed plant extract; washing wintersweet petals, drying and grinding to obtain wintersweet petal powder, adding ethanol solution for heating and reflux, centrifuging, taking the supernatant and chitosan, glacial acetic acid, water, and silver nitrate solution for stirring, and adaptively adjusting the temperature of the solution heated and refluxed after stirring to obtain a bactericidal composite material; mixing clove essential oil, lavender essential oil, and green tea essential oil evenly to obtain a mixed deodorant essential oil; mixing the mixed plant extract, the mixed deodorant essential oil, cyclodextrin, and the bactericidal composite material, and stirring evenly to obtain a plant-based bactericidal deodorant. The present application aims to adaptively adjust the reflux temperature considering the historical heating and reflux conditions to improve the quality of the preparation of the plant-based bactericidal deodorant.

Description

Technical Field

[0001] The present application relates to the technical field of deodorant preparation, and in particular to a method for preparing a plant-based bactericidal deodorant based on optimization of preparation parameters. Background Art

[0002] With the continuous development of science and technology and industrial technology, people's awareness of environmental protection has gradually increased, and the importance of deodorization has also gradually increased. Nowadays, landfills, waste incineration plants, garbage transfer stations, feces treatment plants, public toilets and other facilities often produce odors during operation, which have a bad impact on the surrounding environment. Therefore, deodorants are widely used in the fields of environmental governance and animal husbandry, and with the improvement of environmental protection and sterilization requirements, deodorants have also derived plant-based bactericidal deodorants, which are not only environmentally friendly, but also can play a role in sterilization.

[0003] Plant-based bactericidal deodorants are mainly prepared from a variety of plant raw materials through multiple processes such as drying, grinding, purification, dissolution, and heating and reflux. In the preparation process, heating and reflux can improve the extraction efficiency, enhance the contact between the solvent and the solute, and thus improve the quality of the product. It is an important part of the preparation process, and one of the main factors affecting the effect of heating and reflux is the temperature of heating and reflux. If the temperature of heating and reflux is too high, it may cause the decomposition of the effective ingredients and unstable product quality, while if the temperature of heating and reflux is too low, it will lead to insufficient extraction and low uniformity of ingredients. However, in the production process of deodorants, the temperature of heating and reflux is usually based on preset parameters, and it is difficult to consider the current reflux state, which results in the inability to adaptively adjust the temperature of heating and reflux according to the actual situation, which ultimately leads to a decrease in the quality of the finished product. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a preparation method of a plant-based bactericidal deodorant based on preparation parameter optimization to solve the existing problems.

[0005] The preparation method of the plant-based bactericidal deodorant based on preparation parameter optimization of the present application adopts the following technical scheme:

[0006] One embodiment of the present application provides a method for preparing a plant-based bactericidal deodorant based on preparation parameter optimization, the method comprising the following steps:

[0007] Preparation of mixed plant extract: After mixing the plant extract raw materials, extract and purify them to obtain a mixed plant extract;

[0008] Preparation of wintersweet extract: Wash the wintersweet petals, dry and grind them to obtain wintersweet petal powder, add ethanol solution, heat and reflux, centrifuge, and take the supernatant to obtain the wintersweet extract;

[0009] Preparation of bactericidal composite material: Chitosan, glacial acetic acid, water, silver nitrate solution and wintersweet extract are stirred, and the stirred solution is heated to reflux under a suitable temperature range to obtain a bactericidal composite material; the adaptive adjustment process of the heating temperature during the reflux process is specifically as follows:

[0010] Collect grayscale images of the reflux section at different sampling times in the glass reflux reactor;

[0011] Based on the number and size differences between the condenser droplets in the grayscale image of the reflux section at adjacent sampling moments, the heating temperature high index at each sampling moment is determined;

[0012] Combined with the heating temperature high index and the steam rising state and droplet position distribution characteristics generated when the reflux temperature is overheated during the heating reflux process, the heating reflux temperature overheat index at each sampling moment is determined;

[0013] Based on the heating reflux temperature overheat index at all sampling moments, the heating reflux temperature overheat index at the next sampling moment is predicted, and the heating reflux temperature is adaptively controlled;

[0014] Preparation of mixed deodorant essential oil: Evenly mix clove essential oil, lavender essential oil and green tea essential oil to obtain mixed deodorant essential oil;

[0015] Preparation of plant-based bactericidal deodorant: Mix the mixed plant extract, mixed deodorizing essential oil, cyclodextrin and bactericidal composite material, and stir evenly to obtain the plant-based bactericidal deodorant.

[0016] Preferably, in the preparation process of the mixed plant extract, the raw materials of the plant extract are: mint, isatis indigo leaf, lemon, vanilla, sapodilla and aloe, and the weight ratio thereof is 1:1:1:1:1:2.

[0017] Preferably, the mass fraction of the ethanol solution used in the preparation of the chimonanthus praecox extract is 85%.

[0018] Preferably, the order of adding the materials to be stirred during the preparation of the bactericidal composite material is: putting chitosan into glacial acetic acid and stirring to obtain a chitosan solution, then adding water and stirring, then adding silver nitrate solution and stirring, and then adding the wintersweet extract.

[0019] Preferably, during the preparation of the wintersweet extract and the preparation of the bactericidal composite material, the reflux time is 2 h and 1.5 h respectively.

[0020] Preferably, in the preparation process of the mixed deodorant essential oil, the weight ratio of clove essential oil, lavender essential oil and green tea essential oil is 1:1.2:1.

[0021] Preferably, the method for determining the high heating temperature index includes:

[0022] Obtain all closed contours in the grayscale image of the reflow section and record them as droplet contours;

[0023] Count the total number of pixels on each droplet outline as the length of each droplet outline; obtain the segmentation threshold of the length of all droplet outlines, and use the droplet outlines that are greater than or equal to the segmentation threshold as the tailing droplet outline; obtain the distance between the maximum ordinate and the minimum ordinate in each tailing droplet outline as the tailing length of the corresponding tailing droplet outline;

[0024] The difference between the number of droplet contours at each sampling moment and its previous sampling moment is calculated, and recorded as the difference of adjacent droplet contours; the sum of the absolute values ​​of the difference between the total number of pixel points of all droplet contours at each sampling moment and its previous sampling moment is calculated, and recorded as the difference in the number of adjacent droplet contour pixels; the sum of the difference in adjacent droplet contours and the difference in the number of adjacent droplet contour pixels is calculated, and recorded as the rapidity of droplet change at the corresponding sampling moment;

[0025] The difference between the number of tailing droplet profiles at each sampling moment and its previous sampling moment is calculated, which is recorded as the difference between adjacent tailing droplet profiles; the difference between the mean values ​​of the tailing lengths of all tailing droplet profiles at each sampling moment and its previous sampling moment is calculated, which is recorded as the difference between adjacent tailing droplet profile lengths; the sum of the difference between adjacent tailing droplet profiles and the difference between adjacent tailing droplet profile lengths is calculated, which is recorded as the tailing droplet reflux coefficient at each sampling moment;

[0026] The heating temperature high index at each sampling moment is positively correlated with the rapidity of droplet change and the reflux coefficient of the trailing droplet.

[0027] Preferably, the method for adaptively controlling the temperature of the heating reflux includes:

[0028] Based on the reflow area and condensation area in the grayscale image of the reflow section, the superheated steam rise index at each sampling moment is determined;

[0029] Based on the difference in droplet outline size and distance between pixel points at different positions from the bottom of the condenser tube in the grayscale image of the reflux section, the similarity index of high and low droplets at each sampling moment is determined;

[0030] Calculate the sum of the superheated steam rise index and the heating temperature high index at each sampling moment as the numerator;

[0031] The heating reflux temperature superheat index at each sampling moment is positively correlated with the numerator at the corresponding sampling moment, and negatively correlated with the high and low position droplet similarity index at the corresponding sampling moment;

[0032] The heating reflux temperature overheat indexes at all sampling moments constitute a heating reflux temperature overheat index sequence;

[0033] A prediction algorithm is used to predict the heating reflux temperature superheat index at the next sampling moment of the heating reflux temperature superheat index sequence;

[0034] The heating reflux temperature superheat index at the next sampling moment is used as the input of the PID control algorithm, and the heating reflux temperature control signal is output. The glass reflux reactor temperature control system converts the heating reflux temperature control signal into a current signal to control and adjust the heating temperature of the glass reflux reactor.

[0035] Preferably, the method for determining the superheated steam rise index includes:

[0036] The segmentation neural network is used to obtain the reflux area and condensation area in the grayscale image of the reflux section, and the image composed of the reflux area and the condensation area is used as the reflux section image; each row of pixel points in the reflux section image is respectively composed of a steam judgment sequence;

[0037] Calculate the absolute value of the difference between the grayscale mean of the reflow area in the reflow segment image at each sampling moment and its adjacent previous sampling moment, and record it as the reflow grayscale difference;

[0038] Calculate the variance, the total number of pixels in the reflux area, and the total number of pixels in the condensation area of ​​any steam judgment sequence at each sampling moment; calculate the ratio of the total number of pixels in the reflux area to the total number of pixels in the condensation area, and calculate the product of the ratio and the variance; record the average of the products of all steam judgment sequences at each sampling moment as the steam reflux chaos coefficient;

[0039] The superheated steam rise index at each sampling moment is positively correlated with the reflux grayscale difference at the corresponding sampling moment, and is positively correlated with the steam reflux chaos coefficient at the corresponding sampling moment.

[0040] Preferably, the method for determining the high-low droplet similarity index includes:

[0041] A reference point is preset, and the distance between each pixel in the reflow segment image and the reference point is calculated respectively, and all distances in the reflow segment image are clustered to obtain several clusters;

[0042] All droplet profiles in the cluster with the largest mean of all distances are recorded as high-position droplet profiles; all droplet profiles in the cluster with the smallest mean of all distances are recorded as low-position droplet profiles;

[0043] Calculate the difference between the mean number of pixels of all high-order droplet contours and all low-order droplet contours, and record it as the difference in the number of high- and low-order pixels;

[0044] Calculate the average distance between the center points of all high-position droplet contours and all low-position droplet contours, and record it as the difference in distance between the high-position and low-position center points;

[0045] The high- and low-order droplet similarity index at each sampling moment is positively correlated with the difference in the number of high- and low-order pixels at the corresponding sampling moment, and is negatively correlated with the difference in the distance between the high- and low-order center points at the corresponding sampling moment.

[0046] This application has at least the following beneficial effects:

[0047] The present application determines the heating temperature overload index at each sampling moment based on the number and size differences between the droplets in the condenser in the grayscale image of the reflux section at adjacent sampling moments, and characterizes the appearance characteristics, growth characteristics, and tailing characteristics of the droplets during the heating reflux process by combining the rapidity of the droplet change and the tailing droplet reflux coefficient. The beneficial effect is that it takes into account the different characteristics of the droplets appearing in the condenser when the heating reflux temperature is high, thereby improving the accuracy of the judgment that the heating reflux temperature is high. The present application combines the heating temperature overload index and the steam rising state and droplet position distribution characteristics generated when the reflux temperature is overheated during the heating reflux process to determine the heating reflux temperature overheat index at each sampling moment. The beneficial effect is that it takes into account the steam rising characteristics and the distribution characteristics of high and low position droplets under the heating reflux temperature overheat state. The present application predicts the heating reflux temperature overheat index at the next sampling moment based on the heating reflux temperature overheat index at all sampling moments, and adaptively controls the heating reflux temperature. The beneficial effect is that it solves the problem that it is difficult to consider the historical reflux state in the heating reflux process of the bactericidal composite material of the current plant-based bactericidal deodorant, and realizes the adaptive adjustment of the future temperature based on the reflux section conditions of the historical heating reflux, thereby improving the quality of the preparation of the plant-based bactericidal deodorant. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flow chart of the preparation method of the plant-based bactericidal deodorant based on preparation parameter optimization provided in this application;

[0050] Figure 2 Schematic diagram of edge detection;

[0051] Figure 3 This is a schematic diagram of regional division;

[0052] Figure 4Schematic diagram of clustering effect;

[0053] Figure 5 Constructing a flow chart for the indicators in the adaptive regulation process of the heating temperature during the reflow process;

[0054] Figure 6 Flow chart of the steps for adaptively controlling the temperature of the heating reflux. DETAILED DESCRIPTION

[0055] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the preparation method of the plant-based bactericidal deodorant based on the optimization of preparation parameters proposed in the present application, its specific implementation method, structure, characteristics and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0056] 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.

[0057] The specific scheme of the preparation method of the plant-based bactericidal deodorant based on the optimization of preparation parameters provided in the present application is described in detail below with reference to the accompanying drawings.

[0058] An embodiment of the present application provides a method for preparing a plant-based bactericidal deodorant based on optimization of preparation parameters.

[0059] Specifically, the following preparation method of the plant-based bactericidal deodorant based on the optimization of preparation parameters is provided, please refer to Figure 1 , the method comprises the following steps:

[0060] Preparation of mixed plant extract: The raw materials of the mixed plant extract are mint, isatis indigo leaf, lemon, vanilla, sapodilla, and aloe vera in a weight ratio of 1:1:1:1:1:2. The plant extract raw materials are mixed and then extracted and purified to obtain a mixed plant extract.

[0061] Preparation of wintersweet extract: Wash and dry 10 kg of wintersweet petals, and grind them into wintersweet petal powder with a particle size of 35 μm using a grinder. Add 40 L of 85% ethanol solution to 2 kg of wintersweet petal powder, heat to 80°C and reflux for 2 hours, and finally centrifuge for 10 minutes to obtain the supernatant to obtain the wintersweet extract.

[0062] Preparation of bactericidal composite material: put 1 kg of chitosan into 100 L of 1% glacial acetic acid, stir for 15 min to obtain a chitosan solution, take 2 L of chitosan solution and put it into 24 L of water, stir for 15 min, and add 1 L of 0.1 mol / L silver nitrate solution, stir for 30 min, then add 0.8 L of the wintersweet extract prepared above, heat to 70-90 ° C and reflux for 1.5 h to obtain a bactericidal composite material.

[0063] Preferably, in order to make the heating temperature during the reflux process of preparing the sterilization composite material conform to the actual situation, in one embodiment of the present application, the adaptive adjustment process of the heating temperature during the reflux process is specifically as follows:

[0064] The first step is to collect grayscale images of the reflux section of the glass reflux reactor at different sampling times.

[0065] An industrial high-definition CMOS camera is used to collect image data of the reflux section of the glass reflux reactor at every interval T to obtain an RGB image of the reflux section. The grayscale value averaging method is used to convert the RGB image of the reflux section into a grayscale image. The grayscale image is preprocessed using the Laplace operator and wavelet denoising algorithm to obtain a preprocessed grayscale image. The grayscale value averaging method, the Laplace operator, and the wavelet denoising algorithm are all well-known technologies, and the present application will not repeat the process. The interval time T is set to 0.5s in this embodiment.

[0066] The preprocessed grayscale image is then divided into regions of interest using a segmentation neural network. The segmentation neural network used in this embodiment is a Mask R-CNN neural network, the cross entropy loss function used is a loss function, and the data set used is a grayscale image data set of the reflux section of a glass reflux reactor. Mask R-CNN is a well-known technology, and the specific method is not introduced here.

[0067] The pixels that need to be segmented are divided into two categories, that is, the labeling process of the training set is: single-channel semantic labeling, the corresponding position pixel belongs to the non-glass reactor reflux section area is labeled as 0, and the glass reactor reflux section area is labeled as 1. The non-glass reactor reflux section area and the glass reactor reflux section area in the preprocessed grayscale image are obtained by segmentation neural network. Then the glass reactor reflux section area is taken as the area of ​​interest, and only the pixels marked as 1 in the grayscale image are analyzed, and the pixels marked as 1 in the grayscale image are composed of the reflux section grayscale image. This process is a well-known technology, and the specific method is not introduced in this application.

[0068] At this point, grayscale images of the reflux section of the glass reflux reactor at different sampling times can be acquired.

[0069] The second step is to determine the heating temperature high index at each sampling moment based on the number and size differences between the condenser droplets in the grayscale image of the reflux section at adjacent sampling moments.

[0070] During the heating and reflux process, the solution evaporates due to heating, and the steam will form droplets when it encounters cooling in the condenser. The droplets will continue to grow due to the continuous cooling and liquefaction of the steam and the fusion of small droplets. When the droplets grow to a certain extent, they will fall due to gravity, resulting in a tailing phenomenon. The faster the droplet growth rate in the continuously collected grayscale image of the reflux section, the higher the reflux temperature at this time. Based on this, in view of the changes in the droplets at adjacent sampling moments, the steps of determining the high heating temperature index at each sampling moment in this embodiment are as follows:

[0071] The grayscale image of the reflow segment is used as input, and the Canny edge detection algorithm and the edge expansion and corrosion algorithm are used to output the edges in the grayscale image of the reflow segment. Then, the edges in the grayscale image of the reflow segment are used as input, and the contour tracking algorithm is used to output all closed contours in the grayscale image of the reflow segment, and each closed contour is recorded as a droplet contour. The Canny edge detection algorithm, the edge expansion and corrosion algorithm, and the contour tracking algorithm are all well-known technologies, and the process will not be described in detail in this application. The schematic diagram of edge detection is shown in FIG. Figure 2 shown.

[0072] Typically, each closed contour in the reflux section is the contour of a droplet formed by the liquefaction of the solution vapor in the condenser, and before the droplets reflux and fall, the position of the droplets is usually fixed, that is, as the heating reflux proceeds, the number of droplets will increase and their size will gradually increase until they reflux and fall.

[0073] Based on this, this embodiment counts the total number of pixels on each droplet outline as the length of each droplet outline, and uses the length of all droplet outlines as input, and uses the Otsu threshold method to output the segmentation threshold of the droplet outline length. The droplet outlines with all droplet outlines longer than or equal to the segmentation threshold are taken as the tailing droplet outline, and the remaining droplet outlines are not processed. The two pixel points with the smallest and largest ordinates in each tailing droplet outline are obtained, and the distance between the two pixel points in the longitudinal direction is calculated, and used as the tailing length of the tailing droplet outline. The Otsu threshold method is a well-known technology and will not be described in detail. The distance is the Euclidean distance, and other distance calculation methods may also be used in other embodiments of the present application.

[0074] Here, based on the difference between the number of droplet contours at adjacent sampling moments and the total number of pixel points of the droplet contours, the rapidity of droplet change at each sampling moment is determined. Taking the rapidity of droplet change at the i-th sampling moment as an example, it is specifically:

[0075] First, the difference between the number of droplet contours at the i-th sampling moment and its adjacent previous sampling moment is calculated, which is recorded as the adjacent droplet contour difference; the sum of the absolute values ​​of the difference between the total number of pixel points of all droplet contours at the i-th sampling moment and its adjacent previous sampling moment is calculated, which is recorded as the adjacent droplet contour pixel number difference;

[0076] Then, the sum of the difference in the contours of adjacent droplets and the difference in the number of pixels of the contours of adjacent droplets is calculated and recorded as the rapidity of the droplet change at the i-th sampling moment.

[0077] Here, based on the difference between the number of trailing droplet profiles at adjacent sampling moments and the tailing lengths of the trailing droplet profiles, the trailing droplet reflow coefficient at each sampling moment is determined. Taking the trailing droplet reflow coefficient at the i-th sampling moment as an example, it is specifically:

[0078] First, the difference between the number of tailing droplet profiles at the i-th sampling moment and its previous adjacent sampling moment is calculated, which is recorded as the difference between adjacent tailing droplet profiles; the difference between the mean tailing lengths of all tailing droplet profiles at the i-th sampling moment and its previous adjacent sampling moment is calculated, which is recorded as the difference between adjacent tailing droplet profile lengths;

[0079] Then, the sum of the difference between the contours of adjacent trailing droplets and the difference between the contour lengths of adjacent trailing droplets is calculated and recorded as the trailing droplet reflow coefficient at the i-th sampling moment.

[0080] Finally, based on the rapidity of droplet change at the i-th sampling moment and the tailing droplet reflux coefficient of the rapidity of droplet change at the i-th sampling moment, the heating temperature high index at the i-th sampling moment is determined; among them, the heating temperature high index at the i-th sampling moment is positively correlated with the rapidity of droplet change at the i-th sampling moment, and is positively correlated with the tailing droplet reflux coefficient at the i-th sampling moment.

[0081] It can be understood that a positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. The specific relationship can be a multiplication relationship, an addition relationship, or the idempotence of an exponential function, which is determined by actual application and is not specifically limited in this application.

[0082] In one embodiment of the present application, the sum of the rapidity of the droplet change at the i-th sampling moment and the tailing droplet reflow coefficient at the i-th sampling moment is used as the heating temperature high index at the i-th sampling moment.

[0083] In another embodiment of the present application, the product of the rapidity of the droplet change at the i-th sampling moment and the tailing droplet reflow coefficient at the i-th sampling moment is used as the heating temperature high index at the i-th sampling moment.

[0084] In other embodiments of the present application, other positive correlations may be used to determine the heating temperature high index at the i-th sampling moment.

[0085] It should be noted that when the total number of droplet contours at the i-th sampling moment is greater than that at the previous moment, and the number of pixels contained in each droplet contour increases compared to the previous moment, it means that the increase in the number of droplets in the reflux section condenser at the i-th sampling moment is greater, and the increase in the size of the droplets is more obvious, and the rapidity of the change is greater. When the total number of trailing droplets at the i-th sampling moment is greater than that at the previous moment, and the average length of the trailing droplets is longer than that at the previous moment, it means that the degree of reflux and fall of the droplets in the condenser is greater at this time. When the rapidity of droplet change and the reflux coefficient of the trailing droplets at the i-th sampling moment are greater, it means that the temperature of the heating reflux process is relatively high and the reflux speed is relatively rapid.

[0086] At this point, the heating temperature high index at each sampling moment can be obtained through the above method.

[0087] The third step is to determine the heating reflux temperature overheating index at each sampling moment by combining the heating temperature high index and the steam rising state and droplet position distribution characteristics generated when the reflux temperature is overheated during the heating reflux process.

[0088] When the heating temperature during the heating and reflux process is too high, due to the limited cooling efficiency of the condenser, the solution vapor will gather in the condenser and appear in a spray state, which will cause the condenser to be blocked by the solution vapor, which is more blurred than the condensed water flow area. When the temperature during the heating and reflux process is more moderate, the rising solution vapor is less, and most of it is liquefied to form droplets at the bottom of the condenser, while fewer droplets are formed at the top of the condenser. Accordingly, with respect to the distribution of steam droplets in the condenser, the steps for determining the heating and reflux temperature superheat index at each sampling moment in this embodiment are as follows:

[0089] Accordingly, this embodiment uses the grayscale image of the reflux section as the input of the segmentation neural network, uses the cross entropy loss function as the loss function, uses the same method as in the first step, that is, uses the Mask R-CNN neural network, and uses the data set of the grayscale image data set of the reflux section condenser of the glass reflux reactor. The pixels belonging to the reflux area are marked as 0, and the pixels belonging to the condensation area are marked as 1, and the reflux section image is output. The reflux area and the condensation area in the grayscale image of the reflux section are obtained by the segmentation neural network. This process is a well-known technology, and the specific method is not introduced in this application.

[0090] In one embodiment of the present application, the regional division diagram is as follows: Figure 3 shown.

[0091] Preferably, the pixels with the same ordinate in the reflux section image are arranged in ascending order of the abscissa of the pixels to construct a steam judgment sequence, and the total number of pixels in the condensation area and the reflux area is counted.

[0092] In one embodiment of the present application, the pixel point in the lower left corner of the reflux section image is used as the reference point, the Euclidean distance between the center point of all droplet contours and the reference point is calculated, and these Euclidean distances are used as the input of the clustering algorithm. In one embodiment of the present application, the number of clusters is set to 2, and the output is two clusters. Since the droplets at the pixel points at the bottom of the condenser relative to other positions such as the top and middle of the condenser may be different, the reference point is set accordingly to measure the positional relationship between other pixel points relative to the reference point. In other embodiments of the present application, the pixel points in the lower right corner or other positions can also be used as reference points, and the specific situation is set by the implementer.

[0093] In one embodiment of the present application, a K-med iods clustering algorithm is used for clustering analysis. In other embodiments of the present application, other algorithms such as a K-means clustering algorithm may also be used for clustering. The K-med iods clustering algorithm is a well-known technology and will not be described in detail. Figure 4 shown.

[0094] Calculate the mean of the Euclidean distance in the two clusters, record all the droplet profiles in the cluster with the largest mean of the Euclidean distance as the high-position droplet profile, and record all the droplet profiles in the cluster with the smallest mean of the Euclidean distance as the low-position droplet profile. And calculate the mean of the Euclidean distance between the center points of all the droplet profiles in the two clusters.

[0095] Here, based on the reflow area and condensation area in the grayscale image of the reflow section, the superheated steam rise index at each sampling moment is determined. Taking the superheated steam rise index at the i-th sampling moment as an example, it is specifically:

[0096] First, the absolute value of the difference between the grayscale mean of the reflow area in the reflow segment image at each sampling moment and the previous sampling moment is calculated, which is recorded as the reflow grayscale difference; the variance, the total number of pixels in the reflow area, and the total number of pixels in the condensation area of ​​any steam judgment sequence at each sampling moment are calculated; the ratio of the total number of pixels in the reflow area to the total number of pixels in the condensation area is calculated, and the product of the ratio and the variance is calculated; the mean of the product of all steam judgment sequences at each sampling moment is recorded as the steam reflow chaos coefficient;

[0097] Then, the superheated steam rise index at each sampling moment is positively correlated with the reflux grayscale difference at the corresponding sampling moment, and is positively correlated with the steam reflux chaos coefficient at the corresponding sampling moment.

[0098] In one embodiment of the present application, the product of the reflux grayscale difference at the i-th sampling moment and the steam reflux chaos coefficient at the i-th sampling moment is used as the superheated steam rise index at the i-th sampling moment.

[0099] In another embodiment of the present application, the sum of the reflux grayscale difference at the i-th sampling moment and the steam reflux chaos coefficient at the i-th sampling moment is used as the superheated steam rise index at the i-th sampling moment.

[0100] In other embodiments of the present application, other positive correlations may be used to determine the superheated steam rise index at the i-th sampling moment.

[0101] Here, based on the difference in droplet outline size and distance of the pixel points at different positions from the bottom of the condenser in the grayscale image of the reflux section, the high and low droplet similarity index at each sampling moment is determined. Taking the high and low droplet similarity index at the i-th sampling moment as an example, it is specifically:

[0102] First, the difference between the mean number of pixels of all high-order droplet contours and all low-order droplet contours is calculated, which is recorded as the difference in the number of high- and low-order pixels; the mean distance between the center points of all high-order droplet contours and all low-order droplet contours is calculated, which is recorded as the difference in the distance between the high- and low-order center points;

[0103] Then, the high-low droplet similarity index at each sampling moment is positively correlated with the difference in the number of high-low pixel at the corresponding sampling moment, and negatively correlated with the difference in the distance between the high-low center point at the corresponding sampling moment.

[0104] In one embodiment of the present application, the ratio of the difference in the number of high and low bit pixels at the i-th sampling moment to the difference in the distance between the high and low bit center points at the i-th sampling moment is used as the high and low bit droplet similarity index at the i-th sampling moment.

[0105] In other embodiments of the present application, the difference between the number of high-order and low-order pixels at the i-th sampling moment and the difference between the distances between the high-order and low-order center points at the i-th sampling moment can also be used as the high-order and low-order droplet similarity index at the i-th sampling moment.

[0106] Finally, the sum of the superheated steam rise index and the heating temperature high index at each sampling moment is calculated as the numerator; the heating reflux temperature superheat index at each sampling moment is positively correlated with the numerator at the corresponding sampling moment, and negatively correlated with the high and low position droplet similarity index at the corresponding sampling moment.

[0107] In one embodiment of the present application, the ratio of the numerator at the i-th sampling moment to the high and low droplet similarity indexes at the i-th sampling moment is used as the heating reflux temperature superheat index at the i-th sampling moment.

[0108] In other embodiments of the present application, the difference between the numerator at the i-th sampling moment and the high and low droplet similarity indexes at the i-th sampling moment can also be used as the heating reflux temperature superheat index at the i-th sampling moment.

[0109] It can be understood that a positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. The specific relationship can be a multiplication relationship, an addition relationship, or the power of an exponential function, which is determined by actual applications and this application does not impose any special restrictions. A negative correlation means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. It can be a subtraction relationship, a division relationship, etc., which is determined by actual applications and this application does not impose any special restrictions.

[0110] It should be noted that when the grayscale mean of the reflux area at the i-th sampling moment is more different from the grayscale mean of the previous moment, and the proportion of pixels in the reflux area in the steam judgment sequence is larger and the variance is larger, it means that there are more steam in the reflux area during the reflux process and the steam rise is higher. When the high-order droplet contour contains more pixels than the low-order droplet contour at the i-th sampling moment, and the density difference between the high-order and low-order droplet contours is larger, it means that the heating reflux temperature is more moderate and the difference between the droplets is small. At the i-th sampling moment, the higher the degree of superheated steam rise, the greater the difference between the high-order and low-order droplets, and the larger the heating temperature high index, it means that the possibility of overheating of the heating reflux temperature is higher, and the heating reflux temperature needs to be adjusted.

[0111] At this point, the heating reflux temperature superheat index at each sampling moment can be obtained through the above method.

[0112] The fourth step is to predict the heating reflux temperature overheat index at the next sampling moment based on the heating reflux temperature overheat index at all sampling moments, and adaptively control the heating reflux temperature.

[0113] Arrange all heating reflux temperature superheat indexes in ascending order according to the sampling time, construct a heating reflux temperature superheat index sequence, and use the heating reflux temperature superheat index sequence as the input of the ARIMA prediction algorithm. In one embodiment of the present application, the necessary parameters p, d, and q of the ARIMA prediction algorithm are set to 2, 1, and 1 respectively, and the output is the heating reflux temperature superheat index at the next sampling time. The ARMIA prediction algorithm is a well-known technology, and the present application will not repeat its process.

[0114] The heating reflux temperature overheat index at the next sampling moment is used as the input of the PID algorithm, and the PID algorithm outputs a heating reflux temperature control signal. The glass reflux reactor temperature control system converts the heating reflux temperature control signal into a current signal to control and adjust the heating temperature of the glass reflux reactor, thereby ensuring the adaptability of the heating reflux temperature and improving the preparation quality of the sterilization composite material.

[0115] Among them, the indicator construction flow chart of the adaptive adjustment process of the heating temperature during the reflow process is as follows: Figure 5 shown.

[0116] Thus, the temperature of heating and reflux can be adaptively controlled by the above method to obtain a high-quality sterilization composite material. The flow chart of the steps of adaptively controlling the temperature of heating and reflux is as follows: Figure 6 shown.

[0117] Preparation of mixed deodorant essential oil: Clove essential oil, lavender essential oil and green tea essential oil are evenly mixed in a weight ratio of 1:1.2:1 to obtain a mixed deodorant essential oil.

[0118] Preparation of plant-based bactericidal deodorant: Mix the mixed plant extract, mixed deodorizing essential oil, cyclodextrin and bactericidal composite material, and stir evenly to obtain the plant-based bactericidal deodorant.

[0119] Ammonia and hydrogen sulfide were introduced into the poisoning test device respectively, and the device was closed after reaching a certain concentration. 50 mL of bactericidal deodorant was sprayed into the poisoning test device, and the concentrations of ammonia and hydrogen sulfide within 6 hours were observed. The test results are shown in Table 1. The antibacterial properties of plant-based bactericidal deodorants were tested in accordance with QB / T2591-2003 "Antibacterial Plastics - Antibacterial Performance Test Method and Antibacterial Effect", and the test results are shown in Table 2.

[0120] Table 1

[0121]

[0122] Table 2

[0123]

[0124] So far, the plant-based bactericidal deodorant can be prepared according to the above-mentioned process preparation flow. The plant-based bactericidal deodorant prepared by the preparation method of this embodiment has good quality.

[0125] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0126] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "including one..." does not exclude the existence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0127] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention invented herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art not invented by the present application.

[0128] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A method for preparing a plant-based bactericidal deodorant based on optimization of preparation parameters, characterized in that: The method comprises the following steps: Preparation of mixed plant extract: After mixing the plant extract raw materials, extract and purify them to obtain a mixed plant extract; Preparation of wintersweet extract: Wash the wintersweet petals, dry and grind them to obtain wintersweet petal powder, add ethanol solution, heat and reflux, centrifuge, and take the supernatant to obtain the wintersweet extract; Preparation of bactericidal composite material: Chitosan, glacial acetic acid, water, silver nitrate solution and wintersweet extract are stirred, and the stirred solution is heated to reflux under a suitable temperature range to obtain a bactericidal composite material; the adaptive adjustment process of the heating temperature during the reflux process is specifically as follows: Collect grayscale images of the reflux section at different sampling times in the glass reflux reactor; Based on the number and size differences between the condenser droplets in the grayscale image of the reflux section at adjacent sampling moments, the heating temperature high index at each sampling moment is determined; Combined with the heating temperature high index and the steam rising state and droplet position distribution characteristics generated when the reflux temperature is overheated during the heating reflux process, the heating reflux temperature overheat index at each sampling moment is determined; Based on the heating reflux temperature overheat index at all sampling moments, the heating reflux temperature overheat index at the next sampling moment is predicted, and the heating reflux temperature is adaptively controlled; Preparation of mixed deodorant essential oil: Evenly mix clove essential oil, lavender essential oil and green tea essential oil to obtain mixed deodorant essential oil; Preparation of plant-based bactericidal deodorant: Mixing a mixed plant extract, a mixed deodorizing essential oil, cyclodextrin and a bactericidal composite material, and stirring evenly to obtain a plant-based bactericidal deodorant; The method for determining the high heating temperature index includes: Obtain all closed contours in the grayscale image of the reflow section and record them as droplet contours; Count the total number of pixels on each droplet outline as the length of each droplet outline; obtain the segmentation threshold of the length of all droplet outlines, and use the droplet outlines that are greater than or equal to the segmentation threshold as the tailing droplet outline; obtain the distance between the maximum ordinate and the minimum ordinate in each tailing droplet outline as the tailing length of the corresponding tailing droplet outline; The difference between the number of droplet contours at each sampling moment and its previous sampling moment is calculated, and recorded as the difference of adjacent droplet contours; the sum of the absolute values ​​of the difference between the total number of pixel points of all droplet contours at each sampling moment and its previous sampling moment is calculated, and recorded as the difference in the number of adjacent droplet contour pixels; the sum of the difference in adjacent droplet contours and the difference in the number of adjacent droplet contour pixels is calculated, and recorded as the rapidity of droplet change at the corresponding sampling moment; The difference between the number of tailing droplet profiles at each sampling moment and its previous sampling moment is calculated, which is recorded as the difference between adjacent tailing droplet profiles; the difference between the mean values ​​of the tailing lengths of all tailing droplet profiles at each sampling moment and its previous sampling moment is calculated, which is recorded as the difference between adjacent tailing droplet profile lengths; the sum of the difference between adjacent tailing droplet profiles and the difference between adjacent tailing droplet profile lengths is calculated, which is recorded as the tailing droplet reflux coefficient at each sampling moment; The heating temperature high index at each sampling moment is positively correlated with the rapidity of droplet change and the reflux coefficient of the trailing droplet; The method for adaptively controlling the temperature of the heating reflux comprises: Based on the reflux area and condensation area in the grayscale image of the reflux section, the superheated steam rise index at each sampling moment is determined; Based on the difference in droplet outline size and distance between pixel points at different positions from the bottom of the condenser tube in the grayscale image of the reflux section, the similarity index of high and low droplets at each sampling moment is determined; Calculate the sum of the superheated steam rise index and the heating temperature high index at each sampling moment as the numerator; The heating reflux temperature superheat index at each sampling moment is positively correlated with the numerator at the corresponding sampling moment, and negatively correlated with the high and low position droplet similarity index at the corresponding sampling moment; The heating reflux temperature overheat indexes at all sampling moments constitute a heating reflux temperature overheat index sequence; A prediction algorithm is used to predict the heating reflux temperature superheat index at the next sampling moment of the heating reflux temperature superheat index sequence; The heating reflux temperature superheat index at the next sampling moment is used as the input of the PID control algorithm, and the heating reflux temperature control signal is output. The glass reflux reactor temperature control system converts the heating reflux temperature control signal into a current signal to control and adjust the heating temperature of the glass reflux reactor.

2. The method for preparing a plant-based bactericidal deodorant based on preparation parameter optimization according to claim 1, characterized in that: In the preparation process of the mixed plant extract, the raw materials of the plant extract are: mint, isatis indigo leaf, lemon, vanilla, sapodilla and aloe, and the weight ratio thereof is 1:1:1:1:1:

2.

3. The method for preparing a plant-based bactericidal deodorant based on preparation parameter optimization according to claim 1, characterized in that: The mass fraction of the ethanol solution used in the preparation process of the wintersweet extract is 85%.

4. The method for preparing a plant-based bactericidal deodorant based on preparation parameter optimization according to claim 1, characterized in that: The order of adding the materials to be stirred during the preparation of the bactericidal composite material is as follows: chitosan is added into glacial acetic acid and stirred to obtain a chitosan solution, then water is added and stirred, then silver nitrate solution is added and stirred, and then the wintersweet extract is added.

5. The method for preparing a plant-based bactericidal deodorant based on preparation parameter optimization according to claim 1, characterized in that: During the preparation of the wintersweet extract and the preparation of the bactericidal composite material, the reflux time was 2 hours and 1.5 hours respectively.

6. The method for preparing a plant-based bactericidal deodorant based on preparation parameter optimization according to claim 1, characterized in that: In the preparation process of the mixed deodorant essential oil, the weight ratio of clove essential oil, lavender essential oil and green tea essential oil is 1:1.2:

1.

7. The method for preparing a plant-based bactericidal deodorant based on preparation parameter optimization according to claim 1, characterized in that: The method for determining the superheated steam rise index comprises: The segmentation neural network is used to obtain the reflux area and condensation area in the grayscale image of the reflux section, and the image composed of the reflux area and the condensation area is used as the reflux section image; each row of pixel points in the reflux section image is respectively composed of a steam judgment sequence; Calculate the absolute value of the difference between the grayscale mean of the reflow area in the reflow segment image at each sampling moment and its adjacent previous sampling moment, and record it as the reflow grayscale difference; Calculate the variance, the total number of pixels in the reflux area, and the total number of pixels in the condensation area of ​​any steam judgment sequence at each sampling moment; calculate the ratio of the total number of pixels in the reflux area to the total number of pixels in the condensation area, and calculate the product of the ratio and the variance; record the average of the products of all steam judgment sequences at each sampling moment as the steam reflux chaos coefficient; The superheated steam rise index at each sampling moment is positively correlated with the reflux grayscale difference at the corresponding sampling moment, and is positively correlated with the steam reflux chaos coefficient at the corresponding sampling moment.

8. The method for preparing a plant-based bactericidal deodorant based on preparation parameter optimization according to claim 1, characterized in that: The method for determining the high and low position droplet similarity index comprises: A reference point is preset, and the distance between each pixel in the reflow segment image and the reference point is calculated respectively, and all distances in the reflow segment image are clustered to obtain several clusters; All droplet profiles in the cluster with the largest mean of all distances are recorded as high-position droplet profiles; all droplet profiles in the cluster with the smallest mean of all distances are recorded as low-position droplet profiles; Calculate the difference between the mean number of pixels of all high-order droplet contours and all low-order droplet contours, and record it as the difference in the number of high- and low-order pixels; Calculate the average distance between the center points of all high-position droplet contours and all low-position droplet contours, and record it as the difference in the distance between the high-position and low-position center points; The high- and low-order droplet similarity index at each sampling moment is positively correlated with the difference in the number of high- and low-order pixels at the corresponding sampling moment, and is negatively correlated with the difference in the distance between the high- and low-order center points at the corresponding sampling moment.

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