阅读说明：本技术 一种提高单甘酯乳化效果的方法及装置 (Method and device for improving monoglyceride emulsification effect ) 是由 孙敬章 于 2020-12-31 设计创作，主要内容包括：本发明提供了一种提高单甘酯乳化效果的方法及装置,包括：获得第一待乳化物集合信息；根据第一待乳化物集合信息,基于大数据获得第一预设乳化温度区间集合信息；获得第一单甘脂信息；分别将第一待乳化物集合信息作为第一输入信息、第一预设乳化温度区间集合信息作为第二输入信息、第一单甘脂信息作为第三输入信息；根据第一输入信息、第二输入信息和第三输入信息,通过第一神经网络模型,获得第一结果信息；获得第一操作指令；根据第一操作指令,将第一结果信息存储于第一乳化设备中；获得目标乳化物；获得目标乳化温度信息；获得第二操作指令；根据第二操作指令,将目标乳化物和第一单甘脂进行乳化,达到了提高乳化质量和产品质量的技术效果。(The invention provides a method and a device for improving the emulsification effect of monoglyceride, which comprises the following steps: acquiring first to-be-emulsified object set information; acquiring first preset emulsification temperature interval set information based on big data according to the first to-be-emulsified object set information; obtaining first monoglyceride information; respectively taking the first to-be-emulsified material set information as first input information, the first preset emulsification temperature interval set information as second input information, and the first monoglyceride information as third input information; obtaining first result information through a first neural network model according to the first input information, the second input information and the third input information; obtaining a first operation instruction; storing first result information in first emulsification equipment according to a first operation instruction; obtaining a target emulsion; obtaining target emulsification temperature information; obtaining a second operation instruction; and emulsifying the target emulsion and the first monoglyceride according to a second operation instruction, so that the technical effects of improving the emulsion quality and the product quality are achieved.)

1. A method of enhancing the emulsification effect of monoglyceride, wherein the method is used in an emulsification system and the emulsification system is communicatively coupled to a first emulsification device, wherein the method comprises:

acquiring first to-be-emulsified object set information, wherein the first to-be-emulsified object set information comprises multiple types of to-be-emulsified objects;

acquiring first preset emulsification temperature interval set information based on big data according to the first to-be-emulsified object set information, wherein the first preset emulsification temperature interval set information comprises preset emulsification temperature interval information of each to-be-emulsified object in multiple types of to-be-emulsified objects in the first to-be-emulsified object set information;

obtaining first monoglyceride information, wherein the first monoglyceride is an emulsifier;

respectively taking the first to-be-emulsified material set information as first input information, the first preset emulsification temperature interval set information as second input information, and the first monoglyceride information as third input information;

obtaining first result information through a first neural network model according to the first input information, the second input information and the third input information, wherein the first result information is first emulsification temperature information of each emulsion to be emulsified in the multiple types of emulsions to be emulsified in the first emulsion set information, and the first emulsification temperature information is an optimal emulsification temperature;

obtaining a first operation instruction;

sequentially storing the first result information in the first emulsification equipment according to the first operation instruction;

obtaining a target emulsion;

obtaining target emulsification temperature information of the target emulsion from the first emulsification device;

obtaining a second operation instruction;

and according to the second operation instruction, after the emulsification temperature of the first emulsification equipment is set according to the target emulsification temperature information, emulsifying the target emulsion and the first monoglyceride.

2. The method according to claim 1, wherein, after the setting of the emulsification temperature of the first emulsification apparatus according to the target emulsification temperature information according to the second operation instruction and before the emulsification of the target emulsion and the first monoglyceride, the method further comprises:

obtaining first vacuum degree numerical information of a first emulsification environment;

obtaining predetermined parameter information;

judging whether the first vacuum degree numerical value information meets the preset parameter information or not;

if not, obtaining a first vacuum processing instruction;

and adjusting the first vacuum degree numerical information according to the first vacuum processing instruction so that the first vacuum degree numerical information meets the preset parameter information.

3. The method of claim 1, wherein the method further comprises:

inputting the first input information, the second input information, and the third input information into the first neural network model, the model being trained using a plurality of sets of training data, each set of training data in the plurality of sets comprising: the first input information, the second input information, the third input information, and identification information identifying first result information;

obtaining first output information of the first neural network model, wherein the first output information includes first result information, and the first result information is first emulsification temperature information of each to-be-emulsified substance in the multiple types of to-be-emulsified substances in the first to-be-emulsified substance set information.

4. The method according to claim 1, wherein, after the setting of the emulsification temperature of the first emulsification apparatus according to the target emulsification temperature information according to the second operation instruction and before the emulsification of the target emulsion and the first monoglyceride, the method further comprises:

obtaining a first stirring speed interval range of the target emulsion;

inputting the first stirring speed interval range and the target emulsifying temperature information into a second neural network model, wherein the model is trained by using a plurality of groups of training data, and each group of training data in the plurality of groups comprises: a first stirring speed interval range, target emulsification temperature information and identification information identifying second result information;

obtaining second output information of the second neural network model, wherein the second output information comprises second result information, and the second result information is a first emulsification effect graph of the target emulsion;

determining the target stirring speed according to the second result information, wherein the target stirring speed is a stirring speed corresponding to the optimal emulsification effect;

obtaining a third operation instruction;

and setting the stirring speed of the first emulsifying equipment according to the target stirring speed according to the third operation instruction.

5. The method of claim 4, wherein the method further comprises:

obtaining target stirring time according to the target stirring speed;

obtaining a fourth operation instruction;

and setting the stirring time of the first emulsifying equipment according to the target stirring time according to the fourth operation instruction.

6. The method of claim 1, wherein the method further comprises:

obtaining first density information of the first monoglyceride;

obtaining second density information of the target emulsion;

obtaining a first density difference between the first density information and the second density information;

judging whether the first density difference value is within a preset difference value range or not;

if not, obtaining a first oscillation processing instruction;

according to the first oscillation processing instruction, after the emulsification of the target emulsion and the first monoglyceride is finished, oscillation processing is started on the target emulsion and the first monoglyceride.

7. The method of claim 6, wherein the method further comprises:

when the first density difference value is not in the preset difference value range, obtaining first molecular mass information of the first monoglyceride;

obtaining second molecular mass information of the target emulsion;

obtaining first mass information of the first monoglyceride in a first unit volume according to the first molecular mass information;

obtaining second mass information of the target emulsion in the first unit volume according to the second molecular mass information;

obtaining a first comparison result according to the first quality information and the second quality information;

obtaining a fifth operation instruction according to the first comparison result;

and according to the fifth operation instruction, refining the substances with low quality information in the first quality information and the second quality information.

8. An apparatus for enhancing the emulsification of monoglyceride, comprising:

the device comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining first to-be-emulsified object set information, and the first to-be-emulsified object set information comprises multiple types of to-be-emulsified objects;

a second obtaining unit, configured to obtain first preset emulsification temperature interval set information based on big data according to the first emulsion set information, where the first preset emulsification temperature interval set information includes preset emulsification temperature interval information of each of multiple types of emulsions to be emulsified in the first emulsion set information;

a third obtaining unit, configured to obtain first monoglyceride information, where the first monoglyceride is an emulsifier;

the first operation unit is used for respectively taking the first to-be-emulsified object set information as first input information, the first preset emulsification temperature interval set information as second input information and the first monoglyceride information as third input information;

a first input unit, configured to obtain first result information through a first neural network model according to the first input information, the second input information, and the third input information, where the first result information is first emulsification temperature information of each of multiple types of emulsions to be emulsified in the first emulsion set information, and the first emulsification temperature information is an optimal emulsification temperature;

a fourth obtaining unit configured to obtain the first operation instruction;

the first storage unit is used for sequentially storing the first result information in the first emulsification equipment according to the first operation instruction;

a fifth obtaining unit for obtaining a target emulsion;

a sixth obtaining unit, configured to obtain, from the first emulsification apparatus, target emulsification temperature information of the target emulsion;

a seventh obtaining unit configured to obtain a second operation instruction;

and the second operation unit is used for emulsifying the target emulsion and the first monoglyceride after the emulsifying temperature of the first emulsifying device is set according to the target emulsifying temperature information according to the second operation instruction.

9. An apparatus for enhancing the emulsification effect of monoglyceride, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the steps of the method according to any one of claims 1 to 7.

Technical Field

The invention relates to the technical field of monoglyceride emulsification, in particular to a method and a device for improving monoglyceride emulsification effect.

Background

Monoglycerides, i.e., fatty acid Monoglycerides (MAC), are an important class of nonionic surfactants. It contains a lipophilic long-chain alkyl and two hydrophilic hydroxyls, so that it has good surface activity, and can be used as emulsifier in the fields of food, cosmetics and medicine, etc. Recent research finds that certain monoglyceride shows certain antibacterial property, which makes monoglyceride research and development have important application value. Emulsification is the effect of one liquid being dispersed uniformly as very fine droplets in another liquid that is immiscible with each other. Emulsification is a liquid-liquid interface phenomenon, in which two immiscible liquids, such as oil and water, are separated into two layers in a container, with less dense oil on the upper layer and more dense water on the lower layer. If a suitable surfactant is added, the oil is dispersed in water under vigorous stirring to form an emulsion.

However, the applicant of the present invention finds that the prior art has at least the following technical problems:

in the prior art, the emulsification parameters cannot be set efficiently and reasonably, so that the conditions of insufficient emulsification and low emulsification quality are easily caused in the emulsification process, and the conditions of low quality and yield of final products are caused.

Disclosure of Invention

The embodiment of the invention provides a method and a device for improving the emulsification effect of monoglyceride, which solve the technical problems that the emulsification parameters cannot be reasonably set in the prior art, the conditions of insufficient emulsification and low emulsification quality are easily caused in the emulsification process, and the product quality and efficiency are affected, achieve the technical effects of accurately and reasonably setting the emulsification parameters, ensuring the sufficiency of emulsification and improving the emulsification quality and the product quality.

In view of the above problems, embodiments of the present application are proposed to provide a method and an apparatus for improving the emulsification effect of monoglyceride.

In a first aspect, the present invention provides a method for improving the emulsification effect of monoglyceride, wherein the method is used in an emulsification system, and the emulsification system is connected to a first emulsification device in communication, and the method comprises: acquiring first to-be-emulsified object set information, wherein the first to-be-emulsified object set information comprises multiple types of to-be-emulsified objects; acquiring first preset emulsification temperature interval set information based on big data according to the first to-be-emulsified object set information, wherein the first preset emulsification temperature interval set information comprises preset emulsification temperature interval information of each to-be-emulsified object in multiple types of to-be-emulsified objects in the first to-be-emulsified object set information; obtaining first monoglyceride information, wherein the first monoglyceride is an emulsifier; respectively taking the first to-be-emulsified material set information as first input information, the first preset emulsification temperature interval set information as second input information, and the first monoglyceride information as third input information; obtaining first result information through a first neural network model according to the first input information, the second input information and the third input information, wherein the first result information is first emulsification temperature information of each emulsion to be emulsified in the multiple types of emulsions to be emulsified in the first emulsion set information, and the first emulsification temperature information is an optimal emulsification temperature; obtaining a first operation instruction; sequentially storing the first result information in the first emulsification equipment according to the first operation instruction; obtaining a target emulsion; obtaining target emulsification temperature information of the target emulsion from the first emulsification device; obtaining a second operation instruction; and according to the second operation instruction, after the emulsification temperature of the first emulsification equipment is set according to the target emulsification temperature information, emulsifying the target emulsion and the first monoglyceride.

In a second aspect, the present invention provides an apparatus for increasing the emulsifying effect of monoglyceride, the apparatus comprising:

the device comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining first to-be-emulsified object set information, and the first to-be-emulsified object set information comprises multiple types of to-be-emulsified objects;

a second obtaining unit, configured to obtain first preset emulsification temperature interval set information based on big data according to the first emulsion set information, where the first preset emulsification temperature interval set information includes preset emulsification temperature interval information of each of multiple types of emulsions to be emulsified in the first emulsion set information;

a third obtaining unit, configured to obtain first monoglyceride information, where the first monoglyceride is an emulsifier;

the first operation unit is used for respectively taking the first to-be-emulsified object set information as first input information, the first preset emulsification temperature interval set information as second input information and the first monoglyceride information as third input information;

a first input unit, configured to obtain first result information through a first neural network model according to the first input information, the second input information, and the third input information, where the first result information is first emulsification temperature information of each of multiple types of emulsions to be emulsified in the first emulsion set information, and the first emulsification temperature information is an optimal emulsification temperature;

a fourth obtaining unit configured to obtain the first operation instruction;

the first storage unit is used for sequentially storing the first result information in the first emulsification equipment according to the first operation instruction;

a fifth obtaining unit for obtaining a target emulsion;

a sixth obtaining unit, configured to obtain, from the first emulsification apparatus, target emulsification temperature information of the target emulsion;

a seventh obtaining unit configured to obtain a second operation instruction;

and the second operation unit is used for emulsifying the target emulsion and the first monoglyceride after the emulsifying temperature of the first emulsifying device is set according to the target emulsifying temperature information according to the second operation instruction.

In a third aspect, the present invention provides an apparatus for improving the emulsification effect of monoglyceride, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method of the first aspect.

One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:

the embodiment of the invention provides a method and a device for improving monoglyceride emulsification effect, wherein the method is used in an emulsification system, and the emulsification system is in communication connection with first emulsification equipment, wherein the method comprises the following steps: acquiring first to-be-emulsified object set information, wherein the first to-be-emulsified object set information comprises multiple types of to-be-emulsified objects; acquiring first preset emulsification temperature interval set information based on big data according to the first to-be-emulsified object set information, wherein the first preset emulsification temperature interval set information comprises preset emulsification temperature interval information of each to-be-emulsified object in multiple types of to-be-emulsified objects in the first to-be-emulsified object set information; obtaining first monoglyceride information, wherein the first monoglyceride is an emulsifier; respectively taking the first to-be-emulsified material set information as first input information, the first preset emulsification temperature interval set information as second input information, and the first monoglyceride information as third input information; obtaining first result information through a first neural network model according to the first input information, the second input information and the third input information, wherein the first result information is first emulsification temperature information of each emulsion to be emulsified in the multiple types of emulsions to be emulsified in the first emulsion set information, and the first emulsification temperature information is an optimal emulsification temperature; obtaining a first operation instruction; sequentially storing the first result information in the first emulsification equipment according to the first operation instruction; obtaining a target emulsion; obtaining target emulsification temperature information of the target emulsion from the first emulsification device; obtaining a second operation instruction; according to the second operating instruction, according to target emulsification temperature information sets for behind the emulsification temperature of first emulsification equipment, will the target emulsion with first simple glyceride emulsify to solved unable reasonable emulsification parameter of setting for among the prior art, leaded to the emulsification in-process to appear the circumstances that the emulsification is insufficient, emulsification quality is low easily, makeed product quality and efficiency receive the technical problem who influences, reached accurate reasonable emulsification parameter of setting for, guarantee the emulsification sufficiency, improve the technological effect of emulsification quality and product quality.

The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.

Drawings

FIG. 1 is a schematic flow chart of a method for improving the emulsification effect of monoglyceride according to an embodiment of the present invention;

FIG. 2 is a schematic structural diagram of an apparatus for improving emulsification of monoglyceride according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of another exemplary electronic device in an embodiment of the present invention.

Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a first operating unit 14, a first input unit 15, a fourth obtaining unit 16, a first storage unit 17, a fifth obtaining unit 18, a sixth obtaining unit 19, a seventh obtaining unit 20, a second operating unit 21, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.

Detailed Description

The embodiment of the invention provides a method and a device for improving the emulsification effect of monoglyceride, which are used for solving the technical problems that the emulsification parameters cannot be reasonably set in the prior art, so that the conditions of insufficient emulsification and low emulsification quality are easily caused in the emulsification process, and the product quality and efficiency are affected, so that the technical effects of accurately and reasonably setting the emulsification parameters, ensuring the sufficiency of emulsification and improving the emulsification quality and the product quality are achieved.

Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

Summary of the application

Monoglycerides, i.e., fatty acid Monoglycerides (MAC), are an important class of nonionic surfactants. It contains a lipophilic long-chain alkyl and two hydrophilic hydroxyls, so that it has good surface activity, and can be used as emulsifier in the fields of food, cosmetics and medicine, etc. Recent research finds that certain monoglyceride shows certain antibacterial property, which makes monoglyceride research and development have important application value. Emulsification is the effect of one liquid being dispersed uniformly as very fine droplets in another liquid that is immiscible with each other. Emulsification is a liquid-liquid interface phenomenon, in which two immiscible liquids, such as oil and water, are separated into two layers in a container, with less dense oil on the upper layer and more dense water on the lower layer. If a suitable surfactant is added, the oil is dispersed in water under vigorous stirring to form an emulsion.

In order to solve the technical problems, the technical scheme provided by the invention has the following general idea:

the embodiment of the application provides a method for improving the emulsification effect of monoglyceride, the method is used in an emulsification system, and the emulsification system is in communication connection with a first emulsification device, wherein the method comprises the following steps: acquiring first to-be-emulsified object set information, wherein the first to-be-emulsified object set information comprises multiple types of to-be-emulsified objects; acquiring first preset emulsification temperature interval set information based on big data according to the first to-be-emulsified object set information, wherein the first preset emulsification temperature interval set information comprises preset emulsification temperature interval information of each to-be-emulsified object in multiple types of to-be-emulsified objects in the first to-be-emulsified object set information; obtaining first monoglyceride information, wherein the first monoglyceride is an emulsifier; respectively taking the first to-be-emulsified material set information as first input information, the first preset emulsification temperature interval set information as second input information, and the first monoglyceride information as third input information; obtaining first result information through a first neural network model according to the first input information, the second input information and the third input information, wherein the first result information is first emulsification temperature information of each emulsion to be emulsified in the multiple types of emulsions to be emulsified in the first emulsion set information, and the first emulsification temperature information is an optimal emulsification temperature; obtaining a first operation instruction; sequentially storing the first result information in the first emulsification equipment according to the first operation instruction; obtaining a target emulsion; obtaining target emulsification temperature information of the target emulsion from the first emulsification device; obtaining a second operation instruction; and according to the second operation instruction, after the emulsification temperature of the first emulsification equipment is set according to the target emulsification temperature information, emulsifying the target emulsion and the first monoglyceride.

After the fundamental principle of the present application is introduced, the technical solutions of the present invention are described in detail with reference to the accompanying drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.

Example one

FIG. 1 is a schematic flow chart of a method for improving the emulsification effect of monoglyceride according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a method for improving emulsification effect of monoglyceride, the method being used in an emulsification system, and the emulsification system being communicatively connected to a first emulsification apparatus, the method comprising:

step 100: acquiring first to-be-emulsified object set information, wherein the first to-be-emulsified object set information comprises multiple types of to-be-emulsified objects;

specifically, the emulsification system is a system for controlling and monitoring the emulsification process in this embodiment, and the emulsification system is in communication with the first emulsification apparatus, so that the emulsification apparatus can be controlled by the emulsification system during actual use. Further, first emulsion set information is obtained, wherein the first emulsion set information includes multiple types of emulsions to be emulsified, that is, the first emulsion set information includes different types of materials to be emulsified.

Step 200: acquiring first preset emulsification temperature interval set information based on big data according to the first to-be-emulsified object set information, wherein the first preset emulsification temperature interval set information comprises preset emulsification temperature interval information of each to-be-emulsified object in multiple types of to-be-emulsified objects in the first to-be-emulsified object set information;

specifically, after the first set information of the emulsion to be detected is obtained, the emulsification system can obtain first preset emulsification temperature interval set information based on big data acquisition, the first preset emulsification temperature interval set information includes preset emulsification temperature interval information of each kind of emulsion to be detected in the first set information of the emulsion to be detected, that is, the preset emulsification temperature interval information is emulsification temperature interval range information of each kind of emulsion to be detected when the emulsion is emulsified, that is, an emulsification temperature range in which each kind of emulsion to be detected is to be located, if the emulsification temperature exceeds the preset emulsification range, the performance of the emulsion to be detected may be affected, and further the quality of the product is affected.

Step 300: obtaining first monoglyceride information, wherein the first monoglyceride is an emulsifier;

specifically, the first monoglyceride is an emulsifier to be used during emulsification, the first monoglyceride information includes but is not limited to the component composition, application scene, preparation time, delivery information and the like of the first monoglyceride, when the first monoglyceride is used as the emulsifier, the first monoglyceride can be applied to food additives, emulsifiers applied to cosmetics and medical ointments such as skin care grease, cold cream, emulsion, hair cream and the like, fiber finishing agents, antistatic agents of polyethylene, polypropylene, foamed polystyrene and the like, preservatives used in meat products, dairy products and beer after being compounded with glycerol monolaurate, fruit preservatives used in fruit fresh-keeping agents and the like, and glycerol monooleate used in precision antirust lubricants of machine parts, food additives and the like. The high-purity monoglyceride can be used as an internal lubricant in PVC plastic processing.

Step 400: respectively taking the first to-be-emulsified material set information as first input information, the first preset emulsification temperature interval set information as second input information, and the first monoglyceride information as third input information;

step 500: obtaining first result information through a first neural network model according to the first input information, the second input information and the third input information, wherein the first result information is first emulsification temperature information of each emulsion to be emulsified in the multiple types of emulsions to be emulsified in the first emulsion set information, and the first emulsification temperature information is an optimal emulsification temperature;

further, in order to accurately obtain the optimal emulsification temperature, step 500 of the embodiment of the present application further includes:

step 510: inputting the first input information, the second input information, and the third input information into the first neural network model, the model being trained using a plurality of sets of training data, each set of training data in the plurality of sets comprising: the first input information, the second input information, the third input information, and identification information identifying first result information;

step 520: obtaining first output information of the first neural network model, wherein the first output information includes first result information, and the first result information is first emulsification temperature information of each to-be-emulsified substance in the multiple types of to-be-emulsified substances in the first to-be-emulsified substance set information.

Specifically, after the first to-be-emulsified object set information, the first preset emulsification temperature interval set information and the first monoglyceride information are sequentially obtained, the first to-be-emulsified object set information can be respectively used as first input information, the first preset emulsification temperature interval set information can be used as second input information, and the first monoglyceride information can be used as third input information, then the first input information, the second input information and the third input information are input into the first neural network model, and first result information output by the first neural network model is obtained, the first result information at this time is first emulsification temperature information of each to-be-emulsified object in a plurality of types of to-be-emulsified objects in the first to-be-emulsified object set information, and the first emulsification temperature information is an optimal emulsification temperature, that is, the optimal emulsification temperature of each type of emulsified object when being emulsified with the first monoglyceride can be obtained through the first neural network model, and then the parameters of the emulsifying equipment can be set through the optimal emulsifying temperature.

Furthermore, the training model is a neural network model in the machine learning model, and the machine learning model can continuously learn through a large amount of data, further continuously correct the model, and finally obtain satisfactory experience to process other data. The machine model is obtained by training a plurality of groups of training data, and the process of training the neural network model by the training data is essentially the process of supervised learning. The training model in the embodiment of the application is obtained by training a plurality of sets of training data through machine learning, and each set of training data in the plurality of sets includes first input information, second input information, third input information and identification information for identifying the first result information.

And taking the identification information of the first result as supervision data. And in the input of corresponding training data, respectively performing supervised learning on the first input information, the second input information and the third input information, and determining that the output information of the training model reaches a convergence state. Comparing the first result information with the output result of the training model, and when the first result information is consistent with the output result of the training model, finishing the supervised learning of the group of data and carrying out the supervised learning of the next group of data; when the output result is inconsistent with the first result information of the identification, the training model carries out self-correction until the output result is consistent with the first result information of the identification, the group of supervised learning is finished, and the next group of data supervised learning is carried out; and (4) through supervised learning of a large amount of data, enabling the output result of the machine learning model to reach a convergence state, and finishing the supervised learning. Through the process of supervising and learning the training model, the first result information output by the training model is more accurate, and the first emulsification temperature information of each to-be-emulsified object in the various to-be-emulsified objects in the first to-be-emulsified object set information can be accurately obtained, so that emulsification setting can be conducted according to the optimal emulsification temperature in a targeted mode, and the emulsification quality is improved.

Step 600: obtaining a first operation instruction;

step 700: sequentially storing the first result information in the first emulsification equipment according to the first operation instruction;

specifically, after obtaining the optimal emulsification temperature of the different types of emulsions to be treated, a first operation instruction can be generated, and then under the instruction of the first operation instruction, first result information is sequentially stored in first emulsification equipment, that is, the optimal emulsification temperature of the different types of emulsions to be treated when emulsifying in first monoglyceride is input into the first emulsification equipment, so that data can be correspondingly called and processed when actual emulsification is needed.

Step 800: obtaining a target emulsion;

step 900: obtaining target emulsification temperature information of the target emulsion from the first emulsification device;

specifically, the target emulsion is a material that needs to be emulsified currently, that is, a material that needs to be emulsified with the first monoglyceride currently, for example, a component material such as a cosmetic or a pharmaceutical ointment, and then from the optimal emulsification temperatures of different types of emulsions to be emulsified stored in the first emulsification device, the optimal emulsification temperature of the target emulsion, that is, the optimal emulsification temperature of the target emulsion can be determined.

Step 1000: obtaining a second operation instruction;

step 1100: and according to the second operation instruction, after the emulsification temperature of the first emulsification equipment is set according to the target emulsification temperature information, emulsifying the target emulsion and the first monoglyceride.

Particularly, after obtaining the target emulsification temperature of target emulsion, can generate second operating instruction afterwards, then under second operating instruction's instruction, set for the emulsification temperature parameter in first emulsification equipment according to target emulsification temperature information, and then can emulsify target emulsion and first simple glyceride after emulsification temperature has set for, thereby solved unable reasonable emulsification parameter of setting for among the prior art, lead to the emulsification abundant appearing in the emulsification process easily, the condition that emulsification quality is low, make product quality and efficiency receive the technical problem who influences, accurate reasonable emulsification parameter of setting for has been reached, guarantee the emulsification sufficiency, improve the technical effect of emulsification quality and product quality.

Further, in order to accurately determine and adaptively adjust the emulsification environment, step 1100 in this embodiment of the present application further includes:

step 1110: obtaining first vacuum degree numerical information of a first emulsification environment;

step 1120: obtaining predetermined parameter information;

step 1130: judging whether the first vacuum degree numerical value information meets the preset parameter information or not;

step 1140: if not, obtaining a first vacuum processing instruction;

step 1150: and adjusting the first vacuum degree numerical information according to the first vacuum processing instruction so that the first vacuum degree numerical information meets the preset parameter information.

Specifically, before stirring and emulsifying, the emulsifying environment needs to be analyzed and judged, first vacuum degree numerical value information of a first emulsifying environment needs to be obtained, and then predetermined parameter information is obtained, the predetermined parameter information is vacuum degree information in preset environmental parameters, whether the first vacuum degree numerical value information meets the predetermined parameter information is judged, if not, a first vacuum processing instruction is correspondingly generated, then, under the instruction of the first vacuum processing instruction, the first vacuum degree numerical value information is adjusted, so that the first vacuum degree numerical value information can meet the predetermined parameter information in the emulsifying environment, the vacuum degree meeting requirements of the emulsifying environment are guaranteed, and the phenomenon that bubbles enter an emulsifying mixture due to the fact that the stirring speed is too fast, and the emulsifying effect is affected is avoided.

Further, in order to accurately obtain the optimal stirring speed, step 1100 in this embodiment of the present application further includes:

step 1160: obtaining a first stirring speed interval range of the target emulsion;

step 1170: inputting the first stirring speed interval range and the target emulsifying temperature information into a second neural network model, wherein the model is trained by using a plurality of groups of training data, and each group of training data in the plurality of groups comprises: a first stirring speed interval range, target emulsification temperature information and identification information identifying second result information;

step 1180: obtaining second output information of the second neural network model, wherein the second output information comprises second result information, and the second result information is a first emulsification effect graph of the target emulsion;

step 1190: determining the target stirring speed according to the second result information, wherein the target stirring speed is a stirring speed corresponding to the optimal emulsification effect;

step 11100: obtaining a third operation instruction;

step 11110: and setting the stirring speed of the first emulsifying equipment according to the target stirring speed according to the third operation instruction.

Specifically, a first stirring speed interval range of the target emulsion is obtained, wherein the first stirring speed interval range is a preset stirring speed interval of the target emulsion, and then the first stirring speed interval range and the target emulsification temperature information are input into a second neural network model to obtain second result information of the second neural network model, the second result information at this time is a first emulsification effect graph of the target emulsion, that is, a graph between the emulsification speed and the emulsification effect is obtained on the premise that the optimal emulsification temperature is fixed, and then the target stirring speed can be determined from the first emulsification effect graph, that is, the stirring speed corresponding to the optimal emulsification effect can be used as the target stirring speed, that is, the optimal stirring speed, and further after a third operation instruction is generated, the stirring speed of the first emulsification device can be set according to the target stirring speed according to the third operation instruction, thereby achieving the purpose of further improving the emulsification quality and the emulsification effect by setting the optimal stirring speed.

Further, in order to accurately set the stirring time of the emulsifying apparatus, step 1190 in this embodiment of the present application further includes:

step 1191: obtaining target stirring time according to the target stirring speed;

step 1192: obtaining a fourth operation instruction;

step 1193: and setting the stirring time of the first emulsifying equipment according to the target stirring time according to the fourth operation instruction.

Specifically, after the target stirring speed is obtained, the target stirring time can be obtained through the time length consumed by the target stirring speed to achieve the optimal emulsification effect, a fourth operation instruction is further generated, and then the stirring time parameter of the first emulsification equipment is set according to the fourth operation instruction and the target stirring time, so that the purpose of further improving the emulsification quality and the emulsification effect is achieved by setting the optimal stirring time.

Further, in order to further achieve the purpose of improving the emulsification effect, step 300 in this embodiment of the present application further includes:

step 310: obtaining first density information of the first monoglyceride;

step 320: obtaining second density information of the target emulsion;

step 330: obtaining a first density difference between the first density information and the second density information;

step 340: judging whether the first density difference value is within a preset difference value range or not;

step 350: if not, obtaining a first oscillation processing instruction;

step 360: according to the first oscillation processing instruction, after the emulsification of the target emulsion and the first monoglyceride is finished, oscillation processing is started on the target emulsion and the first monoglyceride.

Specifically, first density information of first monoglyceride and second density information of a target emulsion are respectively obtained, a first density difference between the first monoglyceride and the target emulsion can be obtained through the first density information and the second density information, whether the first density difference is within a preset difference range or not is judged, the preset difference is a preset density difference condition, if the first density difference is not within the preset difference range, a certain gravity difference exists between the first monoglyceride and the target emulsion, if centrifugal force generated by stirring is large, a material with a large intermediate density between the first monoglyceride and the target emulsion deviates towards a certain direction, stirring and emulsifying effects can be affected, a first oscillation processing instruction needs to be generated, and after emulsification and stirring between the target emulsion and the first monoglyceride are finished according to the first oscillation processing instruction, oscillation processing is started on the target emulsion and the first monoglyceride, and can be random or regular oscillation, and further, can also vibrate the processing at the in-process of emulsification stirring to further reach the purpose that improves the emulsification effect.

Further, in order to further achieve the purpose of improving the emulsification effect, step 350 in this embodiment of the present application further includes:

step 351: when the first density difference value is not in the preset difference value range, obtaining first molecular mass information of the first monoglyceride;

step 352: obtaining second molecular mass information of the target emulsion;

step 353: obtaining first mass information of the first monoglyceride in a first unit volume according to the first molecular mass information;

step 354: obtaining second mass information of the target emulsion in the first unit volume according to the second molecular mass information;

step 355: obtaining a first comparison result according to the first quality information and the second quality information;

step 356: obtaining a fifth operation instruction according to the first comparison result;

step 357: and according to the fifth operation instruction, refining the substances with low quality information in the first quality information and the second quality information.

Specifically, when the first density difference is not within the preset difference range, the first molecular mass information of the first monoglyceride and the second molecular mass information of the target emulsion can be further obtained, further, the first mass information of the first monoglyceride in the first unit volume can be obtained through the first molecular mass information, the second mass information of the target emulsion in the first unit volume can be obtained through the second molecular mass information, then, after the first mass information and the second mass information are compared, the first comparison result is obtained, a fifth operation instruction is generated according to the first comparison result, that is, the mass difference between the first mass information and the second mass information can be obtained through comparison, then, corresponding refining processing can be performed under the mass of the fifth operation instruction, that is, a substance with low mass information in the first mass information and the second mass information is refined, for example, when the first mass of the first monoglyceride is judged to be large, the first monoglyceride is refined, otherwise, when the second mass of the target emulsion is judged to be large, the target emulsion is refined, so that the masses in the unit volume between the first monoglyceride and the target emulsion can be close to the same as possible, and the stirring uniformity and the emulsifying quality are further improved.

For information storage based on the block chain, embodiment 500 of the present application further includes:

step 530: obtaining a first set emulsification temperature and a second set emulsification temperature according to the first result information, and repeating the steps until obtaining an Nth set emulsification temperature;

step 540: generating a first verification code according to the first set emulsification temperature, wherein the first verification code corresponds to the first set emulsification temperature one to one;

step 550: generating a second verification code according to the second set emulsification temperature and the first verification code; by analogy, generating an Nth verification code according to the Nth set emulsification temperature and the Nth-1 verification code, wherein N is a natural number greater than 1;

step 560: and respectively copying and storing all the set emulsification temperatures and the verification codes on M devices, wherein M is a natural number greater than 1.

Specifically, in order to ensure the storage safety of the set emulsification temperature, namely the data safety stored on the emulsification equipment, a first verification code is generated according to the first set emulsification temperature, wherein the first verification code corresponds to the first set emulsification temperature one by one; and generating a second verification code … according to the second set emulsification temperature and the first verification code in the same way, taking the first set emulsification temperature and the first verification code as a first storage unit, taking the second set emulsification temperature and the second verification code as a second storage unit … in the same way, and obtaining N storage units in total. The authentication code information serves as subject identification information, and the identification information of the subject is used to distinguish from other subjects. When the set emulsification temperature needs to be called, after each next node receives the data stored by the previous node, the data is verified through a 'common recognition mechanism' and then stored, and each storage unit is connected in series through a Hash technology, so that the data information of the set emulsification temperature is not easy to lose and damage.

In order to perform information processing based on the block chain, step 500 of the embodiment of the present application further includes:

step 570: taking the Nth set emulsification temperature and the Nth-1 verification code as an Nth block;

step 580: obtaining the recording time of the Nth block, wherein the recording time of the Nth block represents the time required to be recorded by the Nth block;

step 590: obtaining the first equipment with the fastest transport capacity in the M pieces of equipment according to the recording time of the Nth block;

step 5100: and sending the recording right of the Nth block to the first equipment.

Specifically, the Nth set emulsification temperature and the Nth-1 verification code are blocked to generate a plurality of blocks, and the Nth equipment node is added into a block chain after the blocks are identified. And the Nth block recording time is the time used for the equipment node to verify through a 'consensus mechanism' based on the obtained Nth verification code information and the Nth set emulsification temperature, and the equipment node is stored and added into the original block after the verification is passed. The maximum transportation capacity is expressed by calculating a random number meeting a rule through AND or calculation according to the calculation capacity of the M devices, namely, the probability of obtaining the recording authority of the current time is higher for the device with the maximum transportation capacity. The equipment with the fastest transport capacity is selected as the block recording equipment, so that the real-time performance of data interaction under the chain in the block chain is improved, the safe, effective and stable operation of a decentralized block chain system is guaranteed, the efficiency of block chain message processing is improved, and the technical effects of improving the accuracy and the efficiency of set emulsification temperature information storage and information processing are achieved.

Example two

Based on the same inventive concept as the method for improving the monoglyceride emulsification effect in the previous embodiment, the present invention also provides an apparatus for improving the monoglyceride emulsification effect, as shown in fig. 2, the apparatus comprising:

a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first to-be-emulsified-aggregate information, where the first to-be-emulsified-aggregate information includes multiple types of to-be-emulsified aggregates;

a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain first preset emulsification temperature interval set information based on big data according to the first to-be-emulsified-material set information, where the first preset emulsification temperature interval set information includes preset emulsification temperature interval information of each to-be-emulsified material of multiple types of to-be-emulsified materials in the first to-be-emulsified-material set information;

a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain first monoglyceride information, where the first monoglyceride is an emulsifier;

a first operation unit 14, where the first operation unit 14 is configured to use the first to-be-emulsified object set information as first input information, the first preset emulsification temperature interval set information as second input information, and the first monoglyceride information as third input information, respectively;

a first input unit 15, where the first input unit 15 is configured to obtain first result information through a first neural network model according to the first input information, the second input information, and the third input information, where the first result information is first emulsification temperature information of each of multiple types of emulsions to be emulsified in the first emulsion set information, and the first emulsification temperature information is an optimal emulsification temperature;

a fourth obtaining unit 16, wherein the fourth obtaining unit 16 is used for obtaining the first operation instruction;

the first storage unit 17, the first storage unit 17 is configured to sequentially store the first result information in the first emulsification device according to the first operation instruction;

a fifth obtaining unit 18, wherein the fifth obtaining unit 18 is used for obtaining the target emulsion;

a sixth obtaining unit 19, where the sixth obtaining unit 19 is configured to obtain target emulsification temperature information of the target emulsion from the first emulsification apparatus;

a seventh obtaining unit 20, wherein the seventh obtaining unit 20 is configured to obtain a second operation instruction;

a second operation unit 21, where the second operation unit 21 is configured to set an emulsification temperature of the first emulsification apparatus according to the target emulsification temperature information according to the second operation instruction, and then emulsify the target emulsion and the first monoglyceride.

Further, the apparatus further comprises:

an eighth obtaining unit, configured to obtain first vacuum degree numerical information of the first emulsification environment;

a ninth obtaining unit configured to obtain predetermined parameter information;

the first judgment unit is used for judging whether the first vacuum degree numerical value information meets the preset parameter information or not;

a tenth obtaining unit for obtaining the first vacuum processing instruction if not satisfied;

and the third operation unit is used for adjusting the first vacuum degree numerical information according to the first vacuum processing instruction so as to enable the first vacuum degree numerical information to meet the preset parameter information.

Further, the apparatus further comprises:

a second input unit, configured to input the first input information, the second input information, and the third input information into the first neural network model, where the model is trained using multiple sets of training data, and each set of training data in the multiple sets includes: the first input information, the second input information, the third input information, and identification information identifying first result information;

an eleventh obtaining unit, configured to obtain first output information of the first neural network model, where the first output information includes first result information, and the first result information is first emulsification temperature information of each of the multiple types of emulsions to be emulsified in the first emulsion set information.

Further, the apparatus further comprises:

a twelfth obtaining unit, configured to obtain a first stirring speed interval range of the target emulsion;

a third input unit, configured to input the first stirring speed interval range and the target emulsification temperature information into a second neural network model, where the model is trained using multiple sets of training data, and each set of training data in the multiple sets includes: a first stirring speed interval range, target emulsification temperature information and identification information identifying second result information;

a thirteenth obtaining unit, configured to obtain second output information of the second neural network model, where the second output information includes second result information, and the second result information is a first emulsification effect map of the target emulsion;

a first determining unit, configured to determine the target stirring speed according to the second result information, where the target stirring speed is a stirring speed corresponding to an optimal emulsification effect;

a fourteenth obtaining unit configured to obtain a third operation instruction;

and the fourth operation unit is used for setting the stirring speed of the first emulsification equipment according to the target stirring speed according to the third operation instruction.

Further, the apparatus further comprises:

a fifteenth obtaining unit, configured to obtain a target stirring time according to the target stirring speed;

a sixteenth obtaining unit, configured to obtain a fourth operation instruction;

a fifth operation unit, configured to set, according to the fourth operation instruction, the stirring time of the first emulsification device according to the target stirring time.

Further, the apparatus further comprises:

a seventeenth obtaining unit configured to obtain first density information of the first monoglyceride;

an eighteenth obtaining unit, configured to obtain second density information of the target emulsion;

a nineteenth obtaining unit that obtains a first density difference between the first density information and the second density information;

a second judging unit, configured to judge whether the first density difference is within a preset difference range;

a twentieth obtaining unit, configured to obtain the first oscillation processing instruction if the first oscillation processing instruction is not in the first oscillation processing state;

a sixth operation unit, configured to, according to the first oscillation processing instruction, start oscillation processing on the target emulsion and the first monoglyceride after emulsification of the target emulsion and the first monoglyceride is completed.

Further, the apparatus further comprises:

a twenty-first obtaining unit, configured to obtain first molecular mass information of the first monoglyceride when the first density difference is not within the preset difference range;

a twenty-second obtaining unit for obtaining second molecular mass information of the target emulsion;

a twenty-third obtaining unit configured to obtain first mass information of the first monoglyceride in a first unit volume based on the first molecular mass information;

a twenty-fourth obtaining unit, configured to obtain second mass information of the target emulsion in the first unit volume according to the second molecular mass information;

a twenty-fifth obtaining unit, configured to obtain a first comparison result according to the first quality information and the second quality information;

a twenty-sixth obtaining unit, configured to obtain a fifth operation instruction according to the first comparison result;

a seventh operation unit configured to perform thinning processing on a substance with low quality information of the first quality information and the second quality information according to the fifth operation instruction.

Various modifications and embodiments of the method for improving the monoglyceride emulsification effect in the first embodiment of fig. 1 are also applicable to the apparatus for improving the monoglyceride emulsification effect in this embodiment, and the method for implementing the apparatus for improving the monoglyceride emulsification effect in this embodiment will be apparent to those skilled in the art from the foregoing detailed description of the method for improving the monoglyceride emulsification effect, and therefore, for the sake of brevity of description, detailed description thereof will not be provided herein.

EXAMPLE III

Based on the same inventive concept as the method for improving the emulsification effect of monoglyceride in the previous embodiment, the present invention further provides an exemplary electronic device, as shown in fig. 3, including a memory 304, a processor 302, and a computer program stored on the memory 304 and executable on the processor 302, wherein the processor 302 executes the computer program to implement the steps of any one of the methods for improving the emulsification effect of monoglyceride described above.

Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.

One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:

the embodiment of the invention provides a method and a device for improving monoglyceride emulsification effect, wherein the method is used in an emulsification system, and the emulsification system is in communication connection with first emulsification equipment, wherein the method comprises the following steps: acquiring first to-be-emulsified object set information, wherein the first to-be-emulsified object set information comprises multiple types of to-be-emulsified objects; acquiring first preset emulsification temperature interval set information based on big data according to the first to-be-emulsified object set information, wherein the first preset emulsification temperature interval set information comprises preset emulsification temperature interval information of each to-be-emulsified object in multiple types of to-be-emulsified objects in the first to-be-emulsified object set information; obtaining first monoglyceride information, wherein the first monoglyceride is an emulsifier; respectively taking the first to-be-emulsified material set information as first input information, the first preset emulsification temperature interval set information as second input information, and the first monoglyceride information as third input information; obtaining first result information through a first neural network model according to the first input information, the second input information and the third input information, wherein the first result information is first emulsification temperature information of each emulsion to be emulsified in the multiple types of emulsions to be emulsified in the first emulsion set information, and the first emulsification temperature information is an optimal emulsification temperature; obtaining a first operation instruction; sequentially storing the first result information in the first emulsification equipment according to the first operation instruction; obtaining a target emulsion; obtaining target emulsification temperature information of the target emulsion from the first emulsification device; obtaining a second operation instruction; according to the second operating instruction, according to target emulsification temperature information sets for behind the emulsification temperature of first emulsification equipment, will the target emulsion with first simple glyceride emulsify to solved unable reasonable emulsification parameter of setting for among the prior art, leaded to the emulsification in-process to appear the circumstances that the emulsification is insufficient, emulsification quality is low easily, makeed product quality and efficiency receive the technical problem who influences, reached accurate reasonable emulsification parameter of setting for, guarantee the emulsification sufficiency, improve the technological effect of emulsification quality and product quality.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.