Intelligent emulsification parameter adjusting method and system combined with multi-sensor fusion

Abstract

The invention discloses an intelligent emulsification parameter adjusting method and system combined with multi-sensor fusion, and relates to the technical field of emulsification control, and the method comprises the following steps: receiving an emulsification process constraint condition and product formula information of a to-be-emulsified product; calculating a water-oil ratio of the product formula information, and performing powder feeding collaborative analysis according to the water-oil ratio to obtain a powder ingredient feeding sequence; reversely deriving standard emulsification parameter adjustment parameters to obtain a standard emulsification parameter adjustment sequence; performing multi-dimensional emulsification performance dynamic prediction, and constructing a stage emulsification performance feature sequence; and dynamically adjusting and updating the standard emulsification parameter adjusting sequence in the main container tank according to the performance deviation. The technical problems that in the existing emulsification process, parameter adjustment is lack of accuracy, parameters are difficult to flexibly optimize according to product characteristics and real-time emulsification conditions, and then the quality and production efficiency of emulsified products are affected are solved, and the technical effects of achieving intelligent adjustment of the parameters in the emulsification process and improving the quality and production efficiency of the emulsified products are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of emulsification control, and particularly to an intelligent adjustment method and system for emulsification parameters combining multi-sensor fusion. Background Art

[0002] In the field of emulsification production, traditional methods for adjusting emulsification parameters face many difficulties. On the one hand, it is difficult to comprehensively consider the emulsification process constraint conditions and product formula information of the product to be emulsified. When determining the powder feeding sequence and emulsification parameters, there is often a lack of accurate basis, resulting in low efficiency in the emulsification process and uneven product quality. On the other hand, existing technologies cannot effectively utilize multi-sensor data, cannot monitor key indicators in the emulsification process in real time, cannot adjust deviations in the emulsification process in a timely manner, and it is difficult to achieve dynamic optimization of emulsification parameters.

[0003] There are technical problems in the existing emulsification process that the parameter adjustment lacks accuracy, it is difficult to flexibly optimize parameters according to product characteristics and real-time emulsification conditions, thereby affecting the quality of emulsified products and production efficiency. Summary of the Invention

[0004] This application provides an intelligent adjustment method and system for emulsification parameters combining multi-sensor fusion, which is used to solve the technical problems in the existing emulsification process that the parameter adjustment lacks accuracy, it is difficult to flexibly optimize parameters according to product characteristics and real-time emulsification conditions, thereby affecting the quality of emulsified products and production efficiency.

[0005] In view of the above problems, this application provides an intelligent adjustment method and system for emulsification parameters combining multi-sensor fusion.

[0006] In the first aspect of this application, an intelligent adjustment method for emulsification parameters combining multi-sensor fusion is provided. The method includes:

[0007] Receive the emulsification process constraint conditions and product formula information of the product to be emulsified, where the emulsification process constraint conditions include the critical emulsification time, target viscosity range, target homogenization threshold, and emulsification temperature range; calculate the water-oil ratio of the product formula information, and perform collaborative analysis of powder feeding based on the water-oil ratio to obtain the powder ingredient feeding sequence; reverse-deduce the standard emulsification parameter adjustment parameters according to the emulsification process constraint conditions and the powder ingredient feeding sequence to obtain the standard emulsification parameter adjustment sequence, where the standard emulsification parameter adjustment sequence is identified by a feeding time window sequence; perform multi-dimensional dynamic prediction of emulsification performance using the product formula information and the standard emulsification parameter adjustment sequence to construct a stage emulsification performance characteristic sequence; during the process of dynamically coupling and regulating the powder according to the standard emulsification parameter adjustment sequence with the feeding time window sequence as the parameter adjustment switching constraint, perform dynamic adjustment and update of the standard emulsification parameter adjustment sequence in the main container tank according to the performance deviation between the fusion emulsification characteristic information collected and transmitted back by the multi-sensor array and the stage emulsification performance characteristic sequence.

[0008] In the second aspect of the present application, an intelligent emulsification parameter adjustment system combined with multi-sensor fusion is provided. The system includes:

[0009] A product formula information receiving module, configured to receive the emulsification process constraint conditions and product formula information of the product to be emulsified, where the emulsification process constraint conditions include the critical emulsification time, target viscosity range, target homogenization threshold, and emulsification temperature range; a powder ingredient feeding sequence obtaining module, configured to calculate the water-oil ratio of the product formula information, and perform collaborative analysis of powder feeding based on the water-oil ratio to obtain the powder ingredient feeding sequence; a standard emulsification parameter adjustment sequence obtaining module, configured to reverse-deduce the standard emulsification parameter adjustment parameters according to the emulsification process constraint conditions and the powder ingredient feeding sequence to obtain the standard emulsification parameter adjustment sequence, where the standard emulsification parameter adjustment sequence is identified by a feeding time window sequence; a stage emulsification performance characteristic sequence constructing module, configured to perform multi-dimensional dynamic prediction of emulsification performance using the product formula information and the standard emulsification parameter adjustment sequence to construct a stage emulsification performance characteristic sequence; a standard emulsification parameter adjustment sequence adjusting module, configured to perform dynamic adjustment and update of the standard emulsification parameter adjustment sequence in the main container tank according to the performance deviation between the fusion emulsification characteristic information collected and transmitted back by the multi-sensor array and the stage emulsification performance characteristic sequence during the process of dynamically coupling and regulating the powder according to the standard emulsification parameter adjustment sequence with the feeding time window sequence as the parameter adjustment switching constraint.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] Receive the emulsification process constraints and product formula information of the product to be emulsified; calculate the water-oil ratio of the product formula information, and perform collaborative analysis of powder feeding according to the water-oil ratio to obtain the powder ingredient feeding sequence; reverse-derive the standard emulsification parameter adjustment parameters according to the emulsification process constraints and the powder ingredient feeding sequence to obtain the standard emulsification parameter adjustment sequence; use the product formula information and the standard emulsification parameter adjustment sequence to perform multi-dimensional dynamic prediction of emulsification performance and construct the stage emulsification performance characteristic sequence; with the feeding time window sequence as the parameter adjustment switching constraint, perform dynamic adjustment and update of the standard emulsification parameter adjustment sequence in the main container tank according to the performance deviation between the fusion emulsification characteristic information collected and transmitted by the multi-sensor array and the stage emulsification performance characteristic sequence. It achieves the technical effect of realizing the intelligent adjustment of emulsification process parameters and improving the quality and production efficiency of emulsified products. Brief Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0013] Figure 1 Schematic flow chart of the intelligent adjustment method for emulsification parameters combined with multi-sensor fusion provided by the embodiments of the present application;

[0014] Figure 2 Schematic structural diagram of the intelligent adjustment system for emulsification parameters combined with multi-sensor fusion provided by the embodiments of the present application.

[0015] Explanation of reference numerals: Product formula information receiving module 10, powder ingredient feeding sequence obtaining module 20, standard emulsification parameter adjustment sequence obtaining module 30, stage emulsification performance characteristic sequence constructing module 40, standard emulsification parameter adjustment sequence adjusting module 50. Detailed Description of the Embodiments

[0016] The present application provides an intelligent adjustment method and system for emulsification parameters combined with multi-sensor fusion, which is used to solve the technical problems that in the existing emulsification process, the parameter adjustment lacks accuracy, it is difficult to flexibly optimize parameters according to product characteristics and real-time emulsification conditions, and thus the quality and production efficiency of emulsified products are affected.

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0018] Example 1, as Figure 1 shown, the present application provides an intelligent adjustment method for emulsification parameters combined with multi-sensor fusion, and the method includes:

[0019] Step S100: Receive the emulsification process constraint conditions and product formula information of the product to be emulsified. Among them, the emulsification process constraint conditions include the critical emulsification time, the target viscosity range, the target homogenization threshold, and the emulsification temperature range.

[0020] Specifically, two types of key information are collected: the emulsification process constraint conditions and product formula information of the product to be emulsified. This information can be obtained from product design documents, production process specification documents, etc. Among them, the emulsification process constraint conditions play an important restrictive and guiding role in the emulsification process. The critical emulsification time stipulates the longest time required for the emulsification operation from start to completion, which affects the production rhythm; the target viscosity range determines the final viscosity that the product should reach, which is related to the use feeling and stability of the product; the target homogenization threshold measures the degree of uniform mixing of each component in the product, which is the key to ensuring the quality consistency of the product; the emulsification temperature range limits the temperature range suitable for the occurrence of the emulsification reaction, and too high or too low temperature may affect the emulsification effect. The product formula information details various raw materials participating in the emulsification and their dosages, which is an important basis for subsequent calculation of the water-oil ratio, analysis of the synergistic addition of powders, and derivation of standard emulsification parameter adjustment parameters.

[0021] Step S200: Calculate the water-oil ratio of the product formula information, and perform a synergistic analysis of powder addition based on the water-oil ratio to obtain a powder ingredient addition sequence.

[0022] Specifically, accurately extract the oil phase volume and water phase volume data from the product formula information, and calculate the water-oil ratio through the ratio of the two. This water-oil ratio is the key basic data for subsequent analysis, which reflects the relative content relationship between oil and water in the product. Then, extract the physical property information of M types of powder ingredients from the product formula information, such as particle size, solubility, etc., and the addition amount of each powder. Combine this information with the water-oil ratio to perform a process compatibility analysis on the M types of powder ingredients, and judge whether adverse reactions, agglomeration, etc. will occur when different powders are mixed with each other under the condition of this water-oil ratio. On this basis, further optimize the addition priority synergistically. After comprehensively considering various factors, finally determine a reasonable powder ingredient addition sequence. This sequence clarifies the addition sequence of each powder ingredient in the emulsification process, thus ensuring the smooth progress of the emulsification process and product quality.

[0023] Step S300: Reverse-derive the standard emulsification parameter adjustment parameters according to the emulsification process constraint conditions and the powder ingredient feeding sequence to obtain a standard emulsification parameter adjustment sequence, where the standard emulsification parameter adjustment sequence is identified by a feeding time window sequence.

[0024] Specifically, based on the obtained emulsification process constraint conditions and the determined powder ingredient feeding sequence, carry out the reverse-derivation work of the standard emulsification parameter adjustment parameters. First, according to the water-oil ratio and the emulsification temperature range, query relevant emulsification process data through the network or call the built-in process model to locate the reference stirring speed. Then, based on the reference stirring speed, combined with the target viscosity range and the critical emulsification time, calculate and output the stirring speed interval. This stirring speed interval is one of the important constraint conditions for subsequent emulsification control. Then, using the stirring speed interval and the critical emulsification time as limiting conditions, comprehensively consider the physical property information of M types of powder ingredients and conduct a collaborative analysis of the feeding and stirring control. In this process, divide the critical emulsification time according to the feeding quality of the powder ingredients to obtain M stage feeding time windows to determine the optimal feeding time nodes for each type of powder. At the same time, match the rotation speed rules of the proportioning tank according to the characteristics of the powder to obtain M appropriate rotation speeds of the proportioning tank for controlling the feeding speed of the powder. In addition, through the stirring speed interval and the obtained rotation speed interval, optimize the stirring energy consumption of the main container, calculate M main container tank stirring intensity intervals, and ensure that the energy consumption is reduced as much as possible while meeting the emulsification requirements. Finally, according to the powder ingredient feeding sequence, orderly combine M stage feeding time windows, M rotation speeds of the proportioning tank, and M main container tank stirring intensity intervals to form a standard emulsification parameter adjustment sequence. This sequence is identified by the feeding time window sequence, clarifies the key parameters at different stages in the emulsification process, provides an accurate guiding basis for subsequent powder feeding and emulsification operations, ensures the scientific and efficient process of synchronizing emulsification and powder addition, and guarantees the product quality.

[0025] Step S400: Perform multi-dimensional dynamic prediction of the emulsification performance using the product formula information and the standard emulsification parameter adjustment sequence, and construct a stage emulsification performance characteristic sequence.

[0026] Specifically, the long short-term memory network (LSTM) algorithm is used for dynamic prediction of multi-dimensional emulsification performance to construct the stage emulsification performance feature sequence. LSTM is a special type of recurrent neural network (RNN) that can effectively process time series data and capture long-term dependencies in long sequences, making it very suitable for tasks with time series characteristics such as simulating the emulsification process. First, the product formula information is encoded, and information such as the types and contents of raw materials is converted into numerical vectors. For the standard emulsification parameter adjustment sequence, parameters such as stirring speed, dosing time window, and stirring intensity of the main container tank are also processed into corresponding vector forms. These vectors are sequentially input into the LSTM network in chronological order. During the training stage, a large amount of historical emulsification data is used, which includes the measured values of actual viscosity and homogenization degree at each stage under different product formulas and different emulsification parameters. The LSTM network continuously adjusts its own weights through the backpropagation algorithm to learn the complex relationship between the input parameters and the actual emulsification performance. During prediction, the LSTM network outputs the prediction results at each time step based on the input product formula information and the standard emulsification parameter adjustment sequence. For the expected viscosity gradient, the network predicts the viscosity values at the beginning and end of each stage, and then calculates the viscosity gradient; for the homogenization degree score, the network directly outputs the predicted values of the homogenization degree score corresponding to each stage. Finally, the predicted expected viscosity gradients and homogenization degree scores at each stage are sorted in order to construct the stage emulsification performance feature sequence.

[0027] Step S500: During the process of dynamically coupling and regulating the powder according to the standard emulsification parameter adjustment sequence with the dosing time window sequence as the parameter adjustment switching constraint, the standard emulsification parameter adjustment sequence in the main container tank is dynamically adjusted and updated according to the performance deviation between the fusion emulsification feature information collected and transmitted by the multi-sensor array and the stage emulsification performance feature sequence.

[0028] Specifically, the entire emulsification operation uses the time window sequence as a strict constraint for parameter switching, and performs dynamic coupling control of powders according to the determined standard emulsification parameter sequence. On the main container tank, a multi-sensor array is configured according to the physical characteristics of the emulsification process, such as a vibration viscosity sensor array and a conductivity sensor array, which will collect various characteristic information in the emulsification process in real time and transmit this information back. During the emulsification process, the fused emulsification characteristic information collected and transmitted back by the multi-sensor array is carefully compared with the previously constructed stage emulsification performance characteristic sequence. If there is a performance deviation between the two, the dynamic adjustment and update mechanism of the standard emulsification parameter sequence in the main container tank is activated. For example, if the actual collected viscosity value does not match the expected viscosity gradient in the stage emulsification performance characteristic sequence, or the real-time monitored homogeneity score does not meet the expected standard, according to the specific situation of the deviation, the parameters such as stirring intensity and powder delivery speed (related to the proportioning tank speed) in the standard emulsification parameter sequence are adjusted in a targeted manner to ensure that the subsequent emulsification process can move towards the expected performance target, continuously optimize the emulsification effect, and ensure the quality of the final product.

[0029] In a possible implementation, step S200 further includes:

[0030] Step S210: extracting the oil phase volume and the water phase volume from the product formula information, and calculating and outputting the water-to-oil ratio according to the oil phase volume and the water phase volume.

[0031] Step S220: extracting M physical property information of M kinds of powdered ingredients and M powder addition amounts from the product formula information.

[0032] Step S230: performing a process compatibility analysis on the M kinds of powdered ingredients according to the water-oil ratio and the M physical property information, and performing collaborative optimization of placement priorities according to the analysis results, and outputting the powdered ingredient placement sequence.

[0033] Specifically, we can accurately filter out the volume of the oil phase and the volume of the water phase from the product formula information we have obtained, and divide the volume of the oil phase by the volume of the water phase to get the water-to-oil ratio. The water-to-oil ratio is a key quantitative indicator that reflects the relative content of oil and water in the product and is an important basis for adjusting and optimizing a series of subsequent emulsification process parameters.

[0034] The product formula information is analyzed in depth. The product formula information records in detail the data of various raw materials involved in the emulsification process, including relevant information of M kinds of powdered ingredients, and extracts the physical property information of each of the M kinds of powdered ingredients. These property information covers the particle size, density, solubility, surface charge and other aspects of the powder, which have an important impact on the behavior of the powder in the emulsification process. At the same time, the corresponding addition amount data of each powdered ingredient is also extracted. This data clarifies the specific amount of each powder in the entire formula, which is crucial for the subsequent analysis of the concentration changes, interactions and effects on the quality of the final product during the emulsification process of the powder. Extracting this information provides key data support for the next step of conducting a coordinated analysis of powder placement based on the water-oil ratio and physical property information, and then determining a reasonable powder ingredient placement sequence.

[0035] First, the process compatibility analysis is carried out based on the water-oil ratio and the physical properties of each of the M kinds of powdered ingredients. Combining the principles of chemistry and physics, the interaction of each powdered ingredient in the current water-oil system is simulated. For example, by comparing the solubility of the powders, if the solubility of the two powders in the same solvent (water phase or oil phase) is very different, some of the powders may not be fully dissolved and precipitate during the emulsification process, which is an incompatible situation; analyzing the surface charge of the powders, powders with opposite charges may agglomerate when they meet, which is also an incompatible feature. By analyzing all the powdered ingredients in pairs in this way, M groups of incompatible features are obtained. The initial powder is determined according to the water-oil ratio. If the proportion of the water phase in the system is high, the powder with good hydrophilicity and can be quickly dispersed in the water phase is preferred as the initial powder; if the proportion of the oil phase is high, the powder with good solubility in the oil phase or that can improve the performance of the oil phase system is selected. Then, according to the incompatible features of the M groups, the clustering algorithm is used to aggregate the powders with similar incompatibility into H groups. For example, some powders are easy to react with the electrolytes in the system, so they are grouped together. After that, a gradient ratio control rule is preset, and M delivery masses are extracted from M physical property information. With this rule as a constraint, H groups of powdered ingredients are sorted for intra-group connection delivery to obtain M powder delivery sequences. For example, to ensure uniform dispersion, powders are delivered in a way of more and less mass. Taking the initial delivered powder as the starting point, the M powder delivery sequences are enumerated for connection delivery combinations to generate multiple spare ingredient delivery sequences. Finally, M-1 groups of connection node powders are extracted from each spare ingredient delivery sequence, and their chemical stability is evaluated. If the connection node powder has no adverse conditions such as neutralization reaction, its powder mass ratio is calculated to obtain the average delivery ratio. All spare ingredient delivery sequences are evaluated in this way, and the multiple delivery ratio means obtained are serialized and sorted. The compatible ingredient delivery sequence corresponding to the maximum value is selected as the final powdered ingredient delivery sequence to achieve collaborative optimization of delivery priority.

[0036] In a possible implementation, step S230 further includes:

[0037] Step S231: Perform process incompatibility analysis based on the M physical property information to obtain M groups of incompatible features.

[0038] Step S232: Locate the initial feed powder according to the water-oil ratio.

[0039] Step S233: Polymerize the powder according to the M groups of incompatible features to obtain H groups of powdered ingredients.

[0040] Step S234: Perform in-group connection placement sorting on the H groups of powdered ingredients to obtain M powder feed sequences.

[0041] Step S235: Starting from the initial feed powder, perform connection placement combination enumeration on the M powder feed sequences to obtain multiple alternative ingredient feed sequences.

[0042] Step S236: Evaluate the process compatibility of the connection nodes of the multiple alternative ingredient feed sequences, and screen and locate the powdered ingredient feed sequence according to the evaluation results.

[0043] Specifically, first establish a database containing various physical and chemical rules to judge the incompatibility between powdered ingredients. For the M physical property information of M kinds of powdered ingredients, such as particle size distribution, density, surface tension, solubility parameter, etc., compare the physical property information of each kind of powdered ingredient pairwise. For example, judge the dissolution situation of two kinds of powders in the same solvent according to the solubility parameter. If the solubility of one kind of powder is extremely low in a specific water-oil ratio environment, while the solubility of the other kind of powder is high, and they may cause the precipitation of the powder with poor solubility due to solvent competition during the mixing process, mark this combination as a group of incompatible features; compare the surface tension data of the powders. When the surface tension difference between two kinds of powders is too large, it may lead to interface instability during the emulsification process, resulting in stratification or demulsification, which is also recorded as a group of incompatible features. Traverse all pairwise combinations of powdered ingredients in this way, and finally obtain M groups of features reflecting potential process incompatibility problems between different powdered ingredients, providing strong support for optimizing the powder feed order in the follow-up.

[0044] Locate the initial powder to be added according to the determined water-oil ratio. If the current water-oil ratio shows a high oil phase (high proportion of oil phase), the system has high viscosity and poor fluidity. Select powder materials such as xanthan gum, which belong to the thickener category. Since thickeners can build a basic structure in a high-viscosity system, preventing subsequent powder materials from locally accumulating due to excessive viscosity and affecting the uniformity and stability of the entire system, such thickener powder materials will be positioned as the initial powder to be added. Conversely, when the water-oil ratio shows a high water phase (high proportion of water phase), the system has strong fluidity but the powder materials are prone to sedimentation. Preferentially select hydrophobic powder materials such as titanium dioxide because hydrophobic powder materials can be quickly dispersed in a high-water-phase system through high-speed stirring (1200 - 1500 rpm), thus ensuring the dispersion effect of the system and preventing the powder materials from sedimenting. Therefore, hydrophobic powder materials will be determined as the initial powder to be added. Through such an analysis process that combines the water-oil ratio and the characteristics of the powder materials, the powder material that is most suitable for the first addition under the current water-oil ratio condition is finally accurately located, laying a foundation for the orderly addition of subsequent powder materials and the smooth progress of the entire process.

[0045] Based on the M groups of incompatible characteristics, use the clustering algorithm to perform a polymerization operation on M powdered ingredients. Traverse each group of incompatible characteristics and analyze the relationships among the powder materials involved. For example, if several powder materials have similar incompatible situations with other powder materials in terms of solubility, particle aggregation tendency, etc., the algorithm will group them together. Starting from the first powder material, check its incompatible relationships with other powder materials. If it is found that some powder materials all have similar reasons, such as being prone to agglomeration under a specific water-oil ratio, these powder materials will be aggregated together to form a preliminary group. As the algorithm gradually analyzes all the powder materials, continuously adjust and merge the groups. Finally, the M powdered ingredients are polymerized into H groups of powdered ingredients.

[0046] For each group of powdered ingredients, deeply analyze the physical properties of each powder material within it, such as solubility, dispersibility, reactivity, etc., and at the same time combine the previously obtained water-oil ratio and the addition amount of the powder materials. If there are two powder materials, a thickener and a stabilizer, within a group, since the thickener needs to form a certain network structure in the system first to better play the role of the stabilizer, the thickener should be added first. According to the preset gradient ratio control rule, combined with the addition quality of each powder material, determine the addition order of the powder materials within the group. For powder materials with a large addition quality, in order to avoid causing too much impact on the system during the addition process and affecting the dispersion and dissolution of other powder materials, they need to be added first or in batches. In this way, perform in-group sorting for each group of powdered ingredients to obtain H in-group powder addition subsequences. Considering the mutual influence and connection relationships of each group of powdered ingredients during the entire emulsification process, perform different combinations and adjustments on these H in-group powder addition subsequences, thereby generating M complete powder addition sequences, laying a foundation for subsequently screening out the optimal powder addition order.

[0047] Starting from the determined initial powder feed as the starting point, enumerate all possible combinations of the obtained M powder feed sequences. Arrange the powders in different sequences in a certain logical order to form multiple standby ingredient feed sequences, which cover all possible combinations of powder feed orders.

[0048] Evaluate the process compatibility of the connection nodes of each sequence in turn. Starting from the first standby ingredient feed sequence, extract M - 1 groups of connection node powders. These nodes are the key positions where powders with different feed orders come into contact and mix. Use methods such as chemical stability testing and simulated mixing experiments to comprehensively evaluate each group of connection node powders. For example, through chemical stability testing, detect whether these powders will undergo chemical reactions such as neutralization reactions and redox reactions that are unfavorable to the emulsification process during mixing; with the help of simulated mixing experiments, observe the dispersibility, solubility of the powders during the mixing process, and whether agglomeration phenomena occur, etc. If all the connection node powders of a standby ingredient feed sequence show good process compatibility, such as no chemical reactions and can be evenly mixed, calculate the mass ratio of these connection node powders to obtain the average feed ratio of this sequence. Evaluate all standby ingredient feed sequences in the same way to obtain multiple average feed ratios of compatible ingredient feed sequences. Then, serialize these average feed ratios and sort them in descending or ascending order. Usually, select the compatible ingredient feed sequence corresponding to the maximum value because this sequence may make the distribution of the powder in the system more reasonable and is more conducive to achieving a good emulsification effect under the premise of ensuring process compatibility, and finally determine it as the most suitable powder ingredient feed sequence.

[0049] In a possible implementation manner, step S300 further includes:

[0050] Step S310: Invoke the networked emulsification process according to the water - oil ratio and the emulsification temperature range to locate the reference stirring speed.

[0051] Step S320: Fit the stirring parameters according to the reference stirring speed, the target viscosity range, and the critical emulsification time, and output the stirring speed range.

[0052] Step S330: Using the stirring speed range as the emulsification control constraint and the critical emulsification time as the emulsification time constraint, perform a collaborative analysis of the feeding - stirring control according to the M physical property information, and output M standard emulsification parameters to form the standard emulsification parameter adjustment sequence, where the standard emulsification parameters include the rotation speed of the proportioning tank, the stirring intensity range of the main container tank, and the stage feeding time window.

[0053] Specifically, according to the calculated water-oil ratio and the given emulsification temperature range, rich emulsification process data stored in the database are called through networking. These data cover the process parameters of various successful emulsification cases under different water-oil ratios and temperature conditions. Screen and match these data to find the process record that best fits the current water-oil ratio and emulsification temperature range, and extract the corresponding reference stirring speed from it, which serves as the basis for subsequent adjustment of the stirring parameters.

[0054] Starting from the located reference stirring speed, combined with the target viscosity range and the critical emulsification time, a mathematical model including the relationship between stirring speed, time, and viscosity is constructed. This model is based on physical principles such as the influence of stirring on the mixing degree of materials and the intermolecular forces during the emulsification process, and can simulate the change of emulsion viscosity at different time points under different stirring speeds. Substitute the reference stirring speed into the model, and at the same time set the upper and lower limits of the target viscosity range and the critical emulsification time as boundary conditions. The model will take time as a variable, from the start of emulsification to the critical emulsification time, continuously adjust the simulated value of the stirring speed, and observe whether the change curve of the emulsion viscosity with time can fall within the target viscosity range at different stirring speeds. For example, first try to gradually increase the stirring speed based on the reference stirring speed and simulate the viscosity change process of the emulsion under this speed change until the emulsion reaches the upper limit of the target viscosity range within the critical emulsification time; then start from the reference stirring speed and gradually decrease the stirring speed to conduct the same simulation until the emulsion reaches the lower limit of the target viscosity range within the critical emulsification time. Through multiple such simulations and calculations, finally determine a stirring speed change range that can make the emulsion reach the target viscosity range within the critical emulsification time. This range is the output stirring speed interval. It provides an important speed control basis for subsequent emulsification operations, ensuring that the emulsification process can meet the time requirements and reach the expected viscosity standard.

[0055] Using the two key limiting conditions of the obtained stirring speed range and the critical emulsification time, combined with the M physical property information of each of the M powdery ingredients, a comprehensive and detailed feeding-stirring control collaborative analysis is carried out to determine M standard emulsification parameters, and finally a standard emulsification parameter adjustment sequence is formed. Regarding the M feeding masses in the M physical property information as the feeding time weights, the critical emulsification time is reasonably divided according to these weights, so as to obtain M stage feeding time windows. This means that for the powdery material with a large feeding mass, the corresponding feeding time window will be relatively long to ensure sufficient dispersion in the system. According to the M physical property information, matching is carried out according to the pre-set ratio tank rotation speed rule to obtain M ratio tank rotation speeds corresponding to each powdery material. For example, if the powdery material has large particles and poor fluidity, a higher ratio tank rotation speed is required to ensure its smooth spreading into the main container tank. Based on the stirring speed range, combined with the M obtained rotation speed ranges, the main container stirring energy consumption is optimized through an energy consumption calculation model to obtain M main container tank stirring intensity ranges. In this process, it is necessary to ensure that the stirring intensity can meet the requirements of powder dispersion and emulsification, and at the same time, reduce the energy consumption as much as possible. Finally, according to the determined powdery ingredient feeding sequence, the above-obtained M stage feeding time windows, M ratio tank rotation speeds, and M main container tank stirring intensity ranges are assembled in the corresponding order to form a complete and orderly standard emulsification parameter adjustment sequence, providing a clear and reliable guiding basis for subsequent precise emulsification operations.

[0056] In a possible implementation manner, step S330 further includes:

[0057] Step S331: Using the M feeding masses in the M physical property information as the feeding time weights, divide the critical emulsification time to obtain M stage feeding time windows.

[0058] Step S332: Matching the ratio tank rotation speed rule according to the M physical property information to obtain M ratio tank rotation speeds.

[0059] Step S333: Optimize the main container stirring energy consumption according to the stirring speed range and the M rotation speed ranges to obtain M main container tank stirring intensity ranges.

[0060] Step S334: Assemble the M stage feeding time windows, M ratio tank rotation speeds, and M main container tank stirring intensity ranges according to the powdery ingredient feeding sequence to obtain the standard emulsification parameter adjustment sequence.

[0061] Specifically, M feeding masses are extracted from the M physical property information of the M powdery ingredients. These feeding masses will be used as the feeding time weights for dividing the critical emulsification time. First, special treatment is carried out on the critical emulsification time to calculate its Value. K is a preset parameter, a constant preset according to factors such as the overall requirements of the emulsification process and the characteristics of the powder materials, which determines the total time benchmark for segmentation. Based on this times the critical emulsification time as the total amount, according to the proportion of the feeding mass of each powder material in the total feeding mass of all powder materials, this period of time is subdivided. For example, if there are three powder materials, with feeding masses of 10 grams, 20 grams, and 30 grams respectively, and the total feeding mass is 60 grams, the proportion of the feeding mass of the first powder material is Then within times the critical emulsification time, the feeding time window allocated to the first powder material is And so on, performing such calculations and allocations for all M powder materials, finally obtaining M stage feeding time windows, determining the precise feeding time range for each powder material during the emulsification process, which helps to ensure the orderliness and efficiency of the emulsification process.

[0062] Process the physical property information of M powdered ingredients according to the given mixing tank rotation speed rules, and then obtain M mixing tank rotation speeds. Water-absorbing powder materials need to use a high rotation speed of 500 rpm to enhance the centrifugal force to prevent caking; hydrophobic powder materials should be fed in batches at a low rotation speed of 300 rpm to avoid local accumulation; temperature-sensitive powder materials use a low rotation speed of 200 rpm to reduce heat generation due to friction. Analyze the physical property information of M powdered ingredients one by one. For the powder material determined to be water-absorbing, according to the rule, the corresponding mixing tank rotation speed is set to 500 rpm to ensure that during the stirring process, the powder material can be fully dispersed due to the centrifugal force generated by the high rotation speed and prevent caking. If a certain powder material is identified as hydrophobic, according to the rule, its low rotation speed of 300 rpm is determined and it is fed in batches, so that the hydrophobic powder material can be more evenly mixed with the liquid and avoid local accumulation. When a certain powder material is detected to be temperature-sensitive, its corresponding mixing tank rotation speed is set to 200 rpm, and the rotation speed is reduced to reduce heat generation due to friction and prevent the temperature-sensitive powder material from changing its properties due to temperature changes. By matching with the rules one by one in this way, the corresponding mixing tank rotation speeds of M powdered ingredients are finally determined.

[0063] Taking the stirring speed range and M rotation speed ranges as key input conditions, the optimization work of the stirring energy consumption of the main container is carried out, aiming to obtain the stirring intensity ranges of M main containers. First, an energy consumption calculation model is established, which comprehensively considers many factors in the stirring process, such as the shape and size of the stirring impeller, the characteristics of the materials (including density, viscosity, etc.), and the influence laws of the stirring speed and rotation speed on the energy consumption. Different combinations of stirring speed and rotation speed are selected from the stirring speed range and M rotation speed ranges, and these combinations are successively input into the energy consumption calculation model for simulation calculation. Each time a set of parameters is input, the energy consumed during the stirring of the main container tank under this parameter combination will be calculated. During the simulation calculation process, the energy consumption data is continuously monitored and analyzed, and at the same time, combined with the actual emulsification effect requirements, such as the dispersion degree of the powder materials and the uniformity of the emulsion. If a certain parameter combination has a low energy consumption but cannot disperse the powder materials sufficiently, resulting in a poor emulsification effect, then this combination will not be selected. The combinations of the stirring speed and rotation speed are continuously adjusted, and through multiple iterative calculations, the stirring intensity combination with the lowest energy consumption is found under the premise of meeting the emulsification quality requirements. After a series of optimization calculations, for M different working conditions (corresponding to the feeding and emulsification processes of M kinds of powdered ingredients), the optimal stirring intensity ranges are respectively determined, that is, the stirring intensity ranges of M main container tanks are obtained.

[0064] According to the determined feeding sequence of the powdered ingredients, the obtained M stage feeding time windows, M rotation speeds of the proportioning tanks, and M stirring intensity ranges of the main container tanks are integrated in an orderly manner. According to the sequence of powder feeding, the corresponding time windows, rotation speeds, and stirring intensity ranges are arranged in one-to-one correspondence, so as to construct a complete standard emulsification parameter adjustment sequence. This sequence provides an accurate operation guide for parameter adjustment in the emulsification process, ensuring that the emulsification process can proceed smoothly according to the expected quality and efficiency requirements, and guaranteeing the precise control and stable operation of the entire emulsification process.

[0065] In a possible implementation manner, step S500 further includes:

[0066] Step S510: Configure a multi-sensor array in the main container tank according to the physical characteristics of the emulsification process, wherein the multi-sensor array includes a vibrating viscometer sensor array and a conductivity sensor array.

[0067] Step S520: Extract the first standard emulsification parameters from the standard emulsification parameter adjustment sequence, wherein the first standard emulsification parameters include the first rotation speed of the proportioning tank, the first stirring intensity range of the main container tank, and the first stage feeding time window.

[0068] Step S530: Locate the first proportioning tank for the first feeding ingredient in the proportioning tank array according to the powdered ingredient feeding sequence.

[0069] Step S540: Extract the median value from the stirring intensity range of the first main container tank to obtain the first emulsification control parameter.

[0070] Step S550: During the collaborative control process of powder feeding - emulsification parameter adjustment for the first proportioning tank and the main container tank using the rotation speed of the first proportioning tank and the first emulsification control parameter, dynamically adjust and optimize the first emulsification control parameter according to the real - time emulsification performance characteristics transmitted back by the multi - sensor array.

[0071] Step S560: After the collaborative control duration reaches the first - stage feeding time window, perform the collaborative control update of proportioning tank switching and powder feeding - emulsification parameter adjustment according to the standard emulsification parameter adjustment sequence.

[0072] Step S570: And so on until the execution of the standard emulsification parameter adjustment sequence ends.

[0073] Specifically, since the emulsification process involves changes in multiple physical properties, in order to accurately monitor and control the emulsification process, a multi - sensor array needs to be configured in the main container tank. According to the important influence of key physical properties such as viscosity and homogenization degree on the emulsification effect during the emulsification process, the configured multi - sensor array includes a vibrating viscosity sensor array and a conductivity sensor array (for monitoring the homogenization degree). The vibrating viscosity sensor array interacts with the emulsion through its own vibration characteristics, and accurately measures the viscosity of the emulsion according to the changes in parameters such as vibration frequency and amplitude. When the viscosity of the emulsion changes, such as due to the addition of powder, an increase in stirring time, etc., the vibrating viscosity sensor can promptly capture these changes and convert them into electrical signals for transmission. The conductivity sensor array is used to monitor the homogenization degree of the emulsion. Because during the emulsification process, as the powder is evenly dispersed in the emulsion, the conductivity of the emulsion will change accordingly. By real - time monitoring and analysis of the conductivity data, the homogenization degree of the emulsion can be inferred, providing important data support for subsequent emulsification operations and ensuring that the entire emulsification process is in the best state.

[0074] From the constructed standard emulsification parameter adjustment sequence, extract the parameters required for initiating the first powder feeding and emulsification operation, namely the first standard emulsification parameters. This standard emulsification parameter adjustment sequence was previously derived through comprehensive deduction based on various factors such as emulsification process constraints, product formula information, and powder feeding sequences, covering the detailed parameter settings for each stage of the entire emulsification process. The first mixing tank rotation speed extracted from this sequence determines the speed at which the mixing tank loaded with the first powder feeds the powder into the main container tank in the initial stage, ensuring that the powder can enter the main container tank evenly at the expected rate. The first main container tank stirring intensity range defines the stirring force range of the main container tank when the first powder is added, creating a suitable stirring environment for the preliminary dispersion and emulsification of the powder. The first stage feeding time window clarifies the duration of the first powder feeding and preliminary emulsification under specific stirring conditions, precisely controlling the initial stage of the entire emulsification process from a time dimension. By extracting this set of first standard emulsification parameters, it provides a key initial setting basis for the first powder feeding-emulsification operation in the emulsification process.

[0075] According to the determined powder ingredient feeding sequence, accurately locate the first mixing tank for feeding the first ingredient in the mixing tank array to ensure that the powder can enter the main container tank accurately as planned to participate in emulsification.

[0076] Extract the median value of the first main container tank stirring intensity range, and use the obtained value as the first emulsification control parameter, which is used to initially control the stirring intensity of the main container tank and create suitable conditions for the dispersion and emulsification of the powder.

[0077] Start the coordinated operation of the first mixing tank and the main container tank. The first mixing tank evenly feeds the powder into the main container tank according to the first mixing tank rotation speed, and the main container tank performs stirring operations according to the first emulsification control parameter, starting the coordinated control process of powder feeding-emulsification parameter adjustment. During this process, the multi-sensor array (including the vibrating viscosity sensor array and the conductivity sensor array) continuously collects the emulsification performance characteristic data of the emulsion in the main container tank, such as the real-time viscosity and conductivity of the emulsion, and transmits these data back. Compare and analyze the transmitted real-time emulsification performance characteristics with the expected ideal emulsification performance indicators. Once a deviation is found, the first emulsification control parameter will be dynamically adjusted according to the specific situation of the deviation. For example, if the real-time viscosity is lower than expected, appropriately increase the stirring intensity in the first emulsification control parameter; if the conductivity indicates uneven dispersion of the powder, that is, the homogenization degree does not meet the expectation, adjust the stirring speed or stirring time accordingly to achieve dynamic parameter adjustment and optimization of the first emulsification control parameter, ensuring that the emulsification process progresses towards the expected quality standard.

[0078] When the first proportioning tank and the main container tank work together according to the established rotation speed of the first proportioning tank and the first emulsification control parameters, and the collaborative control duration reaches the preset first-stage feeding time window, control the proportioning tank to switch, that is, switch from the current first proportioning tank that is feeding powder to the next proportioning tank for feeding powder, ensuring that the powder enters the main container tank in sequence according to the predetermined order. After completing the proportioning tank switch, update the collaborative control of powder feeding and emulsification parameter adjustment according to the parameter settings of the next stage in the standard emulsification parameter adjustment sequence. This includes adjusting the rotation speed of the new proportioning tank so that the new powder enters the main container tank at an appropriate speed, and at the same time, determining the new emulsification control parameters according to the corresponding stirring intensity range of the second main container tank in the standard emulsification parameter adjustment sequence, so as to readjust the stirring intensity of the main container tank to meet the emulsification requirements of the newly added powder, ensuring that the entire emulsification process can proceed continuously, stably and efficiently, creating good conditions for the emulsification of the powder in the next stage.

[0079] After that, continuously repeat the above operations, continuously extract parameters, operate equipment, monitor data and optimize parameters according to the standard emulsification parameter adjustment sequence until the entire standard emulsification parameter adjustment sequence is completely executed, so as to achieve precise control and efficient operation of the entire emulsification process and ensure the quality of the final emulsified product.

[0080] In a possible implementation manner, step S550 further includes:

[0081] Step S551: When the collaborative control duration of the powder feeding-emulsification parameter adjustment of the first proportioning tank and the main container tank by the rotation speed of the first proportioning tank and the first emulsification control parameters reaches the first-stage feeding time window, activate the multi-sensor array to collect the emulsification performance of the main container tank to obtain the real-time emulsification performance characteristics.

[0082] Step S552: If the deviation between the real-time emulsification performance characteristics and the first-stage emulsification performance characteristics meets the preset performance deviation scale, then after the collaborative control duration reaches the first-stage feeding time window, perform proportioning tank switching and update of the collaborative control of powder feeding-emulsification parameter adjustment according to the standard emulsification parameter adjustment sequence.

[0083] Step S553: If the deviation between the real-time emulsification performance characteristics and the first-stage emulsification performance characteristics does not meet the preset performance deviation scale, then according to the deviation direction of the real-time emulsification performance characteristics, adjust the parameter of the first emulsification control parameter in the first main container tank stirring intensity range, and output the first optimized control parameter.

[0084] Step S554: Replace the first emulsification control parameter with the first optimized control parameter and perform the collaborative control of powder feeding-emulsification parameter adjustment in the first-stage feeding time window.

[0085] Specifically, when the first proportioning tank discharges powder into the main container tank at the rotation speed of the first proportioning tank, and the main container tank stirs according to the first emulsification control parameters, the two cooperate to carry out the powder discharging-emulsification parameter adjustment work. As time goes by, when the cooperative control duration reaches times the first-stage discharging time window, the multi-sensor array installed on the main container tank is activated. This array includes a vibrating viscosity sensor array and a conductivity sensor array. The vibrating viscosity sensor array starts to detect the viscosity change of the emulsion in the main container tank, and the conductivity sensor array monitors the homogenization degree of the emulsion. Through the cooperative work of these sensors, the current emulsification performance data of the emulsion in the main container tank is comprehensively collected, and then the real-time emulsification performance characteristics reflecting the real-time state of the emulsion are obtained, providing a key basis for judging whether the emulsification process is normal and whether parameters need to be adjusted in the follow-up.

[0086] Compare the collected real-time emulsification performance characteristics with the preset first-stage emulsification performance characteristics to judge whether the deviation between the two meets the preset performance deviation scale. If this scale is met, it means that the current emulsification process is within the expected range. Then, when the cooperative control duration reaches the first-stage discharging time window, the normal operation process is updated according to the standard emulsification parameter adjustment sequence, that is, switch to the next proportioning tank and update the cooperative control parameters of powder discharging-emulsification parameter adjustment to ensure the continuous and stable progress of the emulsification process.

[0087] However, if it is found that the deviation between the real-time emulsification performance characteristics and the first-stage emulsification performance characteristics does not meet the preset performance deviation scale, targeted measures will be taken according to the deviation direction of the real-time emulsification performance characteristics. For example, if the actual viscosity of the emulsion is lower than the expected value, or the homogenization degree does not reach the standard, within the stirring intensity range of the first main container tank, the first emulsification control parameters are adjusted and updated, and by adjusting parameters such as the stirring intensity, the first optimized control parameters more in line with the current emulsification requirements are output.

[0088] When, through the comparative analysis of the real-time emulsification performance characteristics and the first-stage emulsification performance characteristics, it is found that the deviation does not meet the preset performance deviation scale, and then the first optimized control parameters are obtained. At this time, the newly obtained first optimized control parameters are used to replace the original first emulsification control parameters, and the cooperative control conditions of powder discharging-emulsification parameter adjustment are re-established. According to the adjusted parameters, continue to carry out the cooperative control of powder discharging-emulsification parameter adjustment, but this time the control duration is times the first-stage discharging time window. In this During this period, the first proportioning tank feeds powder materials into the main container tank at the rotation speed of the first proportioning tank. The main container tank stirs according to the first optimized control parameters, so that the emulsion is further emulsified under the action of the new parameters, to improve the situation that did not conform to the expected emulsification performance before, and develop in the direction more in line with the emulsification performance characteristics of the first stage, ensuring the quality and stability of the emulsification process.

[0089] In a possible implementation manner, step S236 further includes:

[0090] Step S2361: Extract M - 1 groups of connecting node powder materials from the first standby ingredient feeding sequence.

[0091] Step S2362: Conduct a chemical stability evaluation on the M - 1 groups of connecting node powder materials. If there is no neutralization reaction in the M - 1 groups of connecting node powder materials, calculate the powder mass ratio of the M - 1 groups of connecting node powder materials to obtain the first feeding ratio average value.

[0092] Step S2363: By analogy, conduct a process compatibility evaluation of the connecting nodes for the multiple standby ingredient feeding sequences to screen and obtain the multiple feeding ratio average values of the multiple compatible ingredient feeding sequences.

[0093] Step S2364: Serialize the multiple feeding ratio average values, and extract the compatible ingredient feeding sequence corresponding to the maximum value according to the sorting result as the powder ingredient feeding sequence.

[0094] Specifically, extract M - 1 groups of connecting node powder materials from the first standby ingredient feeding sequence. These connecting node powder materials refer to the powder combinations involved in the transition stage of different powder feeding orders, and they are used to judge the process compatibility of the entire feeding sequence.

[0095] Conduct a chemical stability evaluation on the extracted M - 1 groups of connecting node powder materials. The chemical stability evaluation mainly detects whether neutralization reactions will occur between these powder materials, because neutralization reactions will affect the performance of the powder materials during the emulsification process and the quality of the final product. If after detection, there is no neutralization reaction in the M - 1 groups of connecting node powder materials, then further calculate the powder mass ratio of these powder materials and average the calculation results to obtain the first feeding ratio average value. This average value is an important indicator for measuring the process compatibility of this standby ingredient feeding sequence.

[0096] In the above - mentioned manner, successively conduct the same process compatibility evaluation operations of the connecting nodes for the multiple standby ingredient feeding sequences. For each standby ingredient feeding sequence, it is necessary to extract its M - 1 groups of connecting node powder materials, conduct a chemical stability evaluation and a powder mass ratio calculation, and then screen and obtain the feeding ratio average values corresponding to the multiple compatible ingredient feeding sequences respectively.

[0097] After completing the process compatibility evaluation of the connection nodes of multiple alternative ingredient feeding sequences and obtaining the average feeding ratios of each compatible ingredient feeding sequence, arrange these average feeding ratios in ascending or descending order to form an ordered sequence of numbers, which is the serialization process. Through this serialization process, the magnitude relationship between each average feeding ratio is clear at a glance. Find the maximum value in this ordered sequence. After finding the maximum value, extract the corresponding compatible ingredient feeding sequence based on the correspondence between the maximum value and each compatible ingredient feeding sequence. This extracted sequence comprehensively considers the chemical stability and mass ratio relationship between the powder materials and performs optimally in terms of process compatibility. Therefore, it will be determined as the powder ingredient feeding sequence during the actual emulsification operation to ensure the smooth progress of the emulsification process to the greatest extent during the powder feeding link and improve the quality of the final emulsified product.

[0098] In a possible implementation manner, step S234 further includes:

[0099] Step S2341: Preset a gradient ratio regulation rule.

[0100] Step S2342: Extract M feeding masses from the M pieces of physical property information.

[0101] Step S2343: Using the gradient ratio regulation rule as the feeding mass constraint, perform in-group connection feeding sorting of the H groups of powder ingredients according to the M feeding masses to obtain the M powder feeding sequences.

[0102] Specifically, preset a gradient ratio regulation rule. This rule clearly stipulates that when feeding powder materials, the operation should be carried out in a pattern of more mass first and then less mass. This means that during the actual powder feeding process, first feed the powder material with a larger mass, and then feed the powder material with a smaller mass, alternating in this way. Such a preset rule helps to optimize the mixing effect of the powder materials during the emulsification process. For example, after the powder material with a larger mass is put into the main container tank, it can first occupy a certain space and be preliminarily dispersed. Subsequently, the powder material with a smaller mass put in can better fill the gaps between the powder materials with a larger mass, thereby improving the overall dispersion uniformity of the powder materials and enabling the powder materials to be more fully integrated with other raw materials in the subsequent stirring and emulsification link, improving the quality and stability of the emulsified product.

[0103] Extract the corresponding M feeding masses from the physical property information of each of the M powder ingredients. These feeding mass data reflect the dosage of each powder material during the emulsification process and are one of the key bases for performing powder feeding sorting.

[0104] Using the preset gradient ratio control rule as the constraint condition for the feeding quality, combined with the M feeding qualities just extracted, the in-group connection feeding sorting of H groups of powdery ingredients is carried out. During the sorting process, according to the gradient ratio control rule, the powdery materials with large quality and the powdery materials with small quality are alternately arranged in the feeding order, so that the powdery materials show regular gradient changes during the feeding process. Through such operations, finally M powdery material feeding sequences that meet the requirements are obtained. These sequences not only consider the physical properties of the powdery materials themselves (reflected by the feeding quality), but also follow the preset gradient ratio control rule, laying a solid foundation for determining the optimal powdery material feeding order subsequently, and helping to improve the efficiency and quality of the entire emulsification process.

[0105] Embodiment 2, based on the same inventive concept as the intelligent adjustment method for emulsification parameters combined with multi-sensor fusion in the foregoing embodiment, as Figure 2 shown, the present application provides an intelligent adjustment system for emulsification parameters combined with multi-sensor fusion. The system in the embodiments of the present application and the method embodiments are based on the same inventive concept. Among them, the system includes:

[0106] A product formula information receiving module 10, configured to receive the emulsification process constraint conditions and product formula information of the product to be emulsified, wherein the emulsification process constraint conditions include the critical emulsification time, the target viscosity range, the target homogenization threshold, and the emulsification temperature range.

[0107] A powdery ingredient feeding sequence obtaining module 20, configured to calculate the water-oil ratio of the product formula information, and perform cooperative analysis of powdery ingredient feeding according to the water-oil ratio to obtain a powdery ingredient feeding sequence.

[0108] A standard emulsification parameter adjustment sequence obtaining module 30, configured to reversely deduce standard emulsification parameter adjustment parameters according to the emulsification process constraint conditions and the powdery ingredient feeding sequence to obtain a standard emulsification parameter adjustment sequence, wherein the standard emulsification parameter adjustment sequence is identified by a feeding time window sequence.

[0109] A stage emulsification performance characteristic sequence construction module 40, configured to perform multi-dimensional dynamic prediction of emulsification performance by using the product formula information and the standard emulsification parameter adjustment sequence, and construct a stage emulsification performance characteristic sequence.

[0110] A standard emulsification parameter adjustment sequence adjustment module 50, configured to perform dynamic adjustment and update of the standard emulsification parameter adjustment sequence in the main container tank according to the performance deviation between the fusion emulsification characteristic information collected and transmitted back by the multi-sensor array and the stage emulsification performance characteristic sequence during the process of dynamically coupling and controlling the powdery materials based on the standard emulsification parameter adjustment sequence with the feeding time window sequence as the parameter switching constraint.

[0111] Furthermore, the system is also used to implement the following functions:

[0112] Extract the oil phase volume and the water phase volume from the product formula information, and calculate and output the water-oil ratio according to the oil phase volume and the water phase volume; extract M physical property information and M powder addition amounts of M powdery ingredients from the product formula information; perform process compatibility analysis on the M powdery ingredients according to the water-oil ratio and the M physical property information, and perform collaborative optimization of the feeding priority according to the analysis results, and output the feeding sequence of the powdery ingredients.

[0113] Furthermore, the system is also used to implement the following functions:

[0114] Perform process incompatibility analysis based on the M physical property information to obtain M groups of incompatible characteristics; locate the initially fed powder according to the water-oil ratio; perform powder aggregation according to the M groups of incompatible characteristics to obtain H groups of powdery ingredients; perform intra-group connection feeding sorting on the H groups of powdery ingredients to obtain M powder feeding sequences; starting from the initially fed powder, perform connection feeding combination enumeration on the M powder feeding sequences to obtain multiple alternative ingredient feeding sequences; perform process compatibility evaluation on the connection nodes of the multiple alternative ingredient feeding sequences, and screen and locate the feeding sequence of the powdery ingredients according to the evaluation results.

[0115] Furthermore, the system is also used to implement the following functions:

[0116] Call the network emulsification process according to the water-oil ratio and the emulsification temperature range to locate the reference stirring speed; perform stirring parameter fitting according to the reference stirring speed, the target viscosity range and the critical emulsification time, and output the stirring speed range; use the stirring speed range as the emulsification control constraint and the critical emulsification time as the emulsification time constraint, and perform collaborative analysis of feeding-stirring control according to the M physical property information, and output M standard emulsification parameters to form the standard emulsification parameter adjustment sequence, where the standard emulsification parameters include the rotation speed of the proportioning tank, the stirring intensity range of the main container tank and the stage feeding time window.

[0117] Furthermore, the system is also used to implement the following functions:

[0118] Take the M feeding masses in the M physical property information as the feeding time weights, and divide the critical emulsification time to obtain M stage feeding time windows; perform proportioning tank rotation speed rule matching according to the M physical property information to obtain M rotation speeds of the proportioning tank; perform optimization of the main container stirring energy consumption according to the stirring speed range and the M rotation speed ranges to obtain M stirring intensity ranges of the main container tank; assemble the M stage feeding time windows, the M rotation speeds of the proportioning tank and the M stirring intensity ranges of the main container tank according to the feeding sequence of the powdery ingredients to obtain the standard emulsification parameter adjustment sequence.

[0119] Furthermore, the system is also used to implement the following functions:

[0120] According to the physical characteristics of the emulsification process, a multi-sensor array is configured in the main container tank. Among them, the multi-sensor array includes a vibrating viscosity sensor array and a conductivity sensor array; extract the first standard emulsification parameter from the standard emulsification parameter adjustment sequence. Among them, the first standard emulsification parameter includes the rotation speed of the first proportioning tank, the stirring intensity range of the first main container tank, and the first-stage feeding time window; according to the powder ingredient feeding sequence, locate the first proportioning tank for the first feeding ingredient in the proportioning tank array; extract the median value of the stirring intensity range of the first main container tank to obtain the first emulsification control parameter; in the process of collaborative control of powder feeding-emulsification parameter adjustment for the first proportioning tank and the main container tank using the rotation speed of the first proportioning tank and the first emulsification control parameter, dynamically adjust and optimize the first emulsification control parameter according to the real-time emulsification performance characteristics transmitted back by the multi-sensor array; after the collaborative control duration reaches the first-stage feeding time window, perform proportioning tank switching and collaborative control update of powder feeding-emulsification parameter adjustment according to the standard emulsification parameter adjustment sequence; and so on until the execution of the standard emulsification parameter adjustment sequence ends.

[0121] Furthermore, the system is also used to implement the following functions:

[0122] When the collaborative control duration of powder feeding-emulsification parameter adjustment for the first proportioning tank and the main container tank using the rotation speed of the first proportioning tank and the first emulsification control parameter reaches After the first-stage feeding time window, activate the multi-sensor array to collect the emulsification performance of the main container tank to obtain the real-time emulsification performance characteristics; if the deviation between the real-time emulsification performance characteristics and the first-stage emulsification performance characteristics meets the preset performance deviation scale, then after the collaborative control duration reaches the first-stage feeding time window, perform proportioning tank switching and collaborative control update of powder feeding-emulsification parameter adjustment according to the standard emulsification parameter adjustment sequence; if the deviation between the real-time emulsification performance characteristics and the first-stage emulsification performance characteristics does not meet the preset performance deviation scale, then according to the deviation direction of the real-time emulsification performance characteristics, adjust and update the first emulsification control parameter in the stirring intensity range of the first main container tank, and output the first optimized control parameter; use the first optimized control parameter to replace the first emulsification control parameter to perform The collaborative control of powder feeding-emulsification parameter adjustment for the first-stage feeding time window.

[0123] Furthermore, the system is also used to implement the following functions:

[0124] Extract M-1 groups of connecting node powders from the first standby ingredient feeding sequence; perform a chemical stability evaluation on the M-1 groups of connecting node powders. If there is no neutralization reaction in the M-1 groups of connecting node powders, calculate the powder mass ratio of the M-1 groups of connecting node powders to obtain the first feeding ratio average value; and so on, perform a process compatibility evaluation of the connecting nodes for the multiple standby ingredient feeding sequences to screen out multiple feeding ratio average values of multiple compatible ingredient feeding sequences; serialize the multiple feeding ratio average values, and extract the compatible ingredient feeding sequence corresponding to the maximum value according to the sorting result as the powder ingredient feeding sequence.

[0125] Furthermore, the system is also used to implement the following functions:

[0126] Preset a gradient ratio regulation rule; extract M feeding masses from the M physical property information; use the gradient ratio regulation rule as the feeding mass constraint, and perform an in-group connecting feeding sorting of the H groups of powder ingredients according to the M feeding masses to obtain the M powder feeding sequences.

[0127] It should be noted that the above-mentioned sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification is provided. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0128] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0129] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. An intelligent adjustment method for emulsification parameters combining multi-sensor fusion, characterized in that, The method includes: Receiving the emulsification process constraints and product formula information of the product to be emulsified, wherein the emulsification process constraints include the critical emulsification time, the target viscosity range, the target homogenization threshold, and the emulsification temperature range; Calculating the water-oil ratio of the product formula information, and performing collaborative analysis of powder feeding according to the water-oil ratio to obtain the powder ingredient feeding sequence; Reverse-deriving the standard emulsification parameter adjustment parameters according to the emulsification process constraints and the powder ingredient feeding sequence to obtain the standard emulsification parameter adjustment sequence, wherein the standard emulsification parameter adjustment sequence is identified by the feeding time window sequence; Performing multi-dimensional dynamic prediction of the emulsification performance using the product formula information and the standard emulsification parameter adjustment sequence to construct the stage emulsification performance characteristic sequence; During the process of dynamically coupling and regulating the powder according to the standard emulsification parameter adjustment sequence with the feeding time window sequence as the parameter adjustment switching constraint, based on the performance deviation between the fusion emulsification characteristic information collected and transmitted back by the multi-sensor array and the stage emulsification performance characteristic sequence, perform dynamic adjustment and update of the standard emulsification parameter adjustment sequence in the main container tank.

2. The intelligent adjustment method for emulsification parameters combining multi-sensor fusion according to claim 1, characterized in that Calculating the water-oil ratio of the product formula information, and performing collaborative analysis of powder feeding according to the water-oil ratio to obtain the powder ingredient feeding sequence, the method includes: Extracting the oil phase volume and water phase volume from the product formula information, and calculating and outputting the water-oil ratio according to the oil phase volume and water phase volume; Extracting the M physical property information and M powder addition amounts of M powder ingredients from the product formula information; Performing process compatibility analysis on the M powder ingredients according to the water-oil ratio and the M physical property information, and performing collaborative optimization of the feeding priorities according to the analysis results to output the powder ingredient feeding sequence.

3. The intelligent adjustment method of emulsification parameters combining multi-sensor fusion according to claim 2, characterized in that Performing process compatibility analysis on the M powder ingredients according to the water-oil ratio and the M physical property information, and performing collaborative optimization of the feeding priorities according to the analysis results to output the powder ingredient feeding sequence, the method includes: Performing process incompatibility analysis based on the M physical property information to obtain M groups of incompatibility characteristics; Locating the initial feeding powder according to the water-oil ratio; Aggregating the powders according to the M groups of incompatibility characteristics to obtain H groups of powder ingredients; Performing intra-group sequential feeding sorting on the H groups of powder ingredients to obtain M powder feeding sequences; Starting from the initial feeding powder, performing enumeration of the connection feeding combinations of the M powder feeding sequences to obtain multiple alternative ingredient feeding sequences; Performing process compatibility evaluation on the connection nodes of the multiple alternative ingredient feeding sequences, and screening and locating the powder ingredient feeding sequence according to the evaluation results.

4. The intelligent adjustment method for emulsification parameters combining multi-sensor fusion according to claim 3, characterized in that Reverse-deriving the standard emulsification parameter adjustment parameters according to the emulsification process constraints and the powder ingredient feeding sequence to obtain the standard emulsification parameter adjustment sequence, the method includes: Performing networked emulsification process call according to the water-oil ratio and the emulsification temperature range to locate the reference stirring speed; Performing stirring parameter fitting according to the reference stirring speed, the target viscosity range, and the critical emulsification time, and outputting the stirring speed range; Taking the stirring speed range as the emulsification control constraint and the critical emulsification time as the emulsification time constraint, perform a collaborative analysis of the feeding-stirring control based on the M physical property information, and output M standard emulsification parameters to form the standard emulsification parameter adjustment sequence, where the standard emulsification parameters include the rotation speed of the proportioning tank, the stirring intensity range of the main container tank, and the stage feeding time window.

5. The intelligent adjustment method for emulsification parameters combining multi-sensor fusion according to claim 4, characterized in that Reverse-derive the standard emulsification parameter adjustment parameters according to the emulsification process constraint conditions and the powder ingredient feeding sequence to obtain the standard emulsification parameter adjustment sequence. The method includes: Using the M dosing masses among the M physical property information as dosing time weights, divide the critical emulsification time to obtain M staged dosing time windows; Perform rotation speed rule matching for the proportioning tank according to the M physical property information to obtain M rotation speeds of the proportioning tank; Optimize the stirring energy consumption of the main container according to the stirring speed range and M rotation speed ranges to obtain M stirring intensity ranges of the main container tank; Assemble the M stage feeding time windows, M rotation speeds of the proportioning tank, and M stirring intensity ranges of the main container tank according to the powder ingredient feeding sequence to obtain the standard emulsification parameter adjustment sequence.

6. The intelligent adjustment method for emulsification parameters combining multi-sensor fusion according to claim 5, characterized in that During the dynamic coupling regulation process of the powder according to the standard emulsification parameter adjustment sequence, according to the performance deviation between the fusion emulsification characteristic information collected and transmitted back by the multi-sensor array and the stage emulsification performance characteristic sequence, perform dynamic adjustment and update of the standard emulsification parameter adjustment sequence in the main container tank. The method includes: Configure a multi-sensor array in the main container tank according to the physical properties of the emulsification process, where the multi-sensor array includes a vibrating viscometer sensor array and a conductivity sensor array; Extract the first standard emulsification parameter from the standard emulsification parameter adjustment sequence, where the first standard emulsification parameter includes the first rotation speed of the proportioning tank, the first stirring intensity range of the main container tank, and the first stage feeding time window; Locate the first proportioning tank for the first feeding ingredient in the proportioning tank array according to the powder ingredient feeding sequence; Extract the intermediate value of the first stirring intensity range of the main container tank to obtain the first emulsification control parameter; During the collaborative control process of the powder feeding-emulsification parameter adjustment of the first proportioning tank and the main container tank using the first rotation speed of the proportioning tank and the first emulsification control parameter, perform dynamic parameter adjustment and optimization of the first emulsification control parameter according to the real-time emulsification performance characteristics transmitted back by the multi-sensor array; After the collaborative control duration reaches the first stage feeding time window, perform collaborative control update of the proportioning tank switching and powder feeding-emulsification parameter adjustment according to the standard emulsification parameter adjustment sequence; And so on until the execution of the standard emulsification parameter adjustment sequence ends.

7. The intelligent adjustment method for emulsification parameters combining multi-sensor fusion according to claim 6, wherein, Perform dynamic parameter adjustment and optimization of the first emulsification control parameter according to the real-time emulsification performance characteristics transmitted back by the multi-sensor array. The method includes: When the collaborative control duration of the powder feeding - emulsification parameter adjustment of the first proportioning tank and the main container tank with respect to the rotation speed of the first proportioning tank and the first emulsification control parameter reaches after the first - stage feeding time window, activate the multi - sensor array to collect the emulsification performance of the main container tank, and obtain the real - time emulsification performance characteristics; If the deviation between the real-time emulsification performance characteristics and the first stage emulsification performance characteristics meets the preset performance deviation scale, after the collaborative control duration reaches the first stage feeding time window, perform collaborative control update of the proportioning tank switching and powder feeding-emulsification parameter adjustment according to the standard emulsification parameter adjustment sequence; If the deviation between the real-time emulsification performance characteristics and the emulsification performance characteristics in the first stage does not meet the preset performance deviation scale, then according to the deviation direction of the real-time emulsification performance characteristics, parameter adjustment and update of the first emulsification control parameters are carried out within the stirring intensity range of the first main container tank, and the first optimized control parameters are output; Replace the first emulsification control parameter with the first optimized control parameter and perform The powder feeding-emulsification parameter adjustment collaborative control of the powder feeding time window in the first stage.

8. The intelligent adjustment method of emulsification parameters combining multi-sensor fusion according to claim 3, characterized in that Perform a process compatibility evaluation on the connection nodes of the multiple spare ingredient feeding sequences, and screen and locate the powder ingredient feeding sequence according to the evaluation results. The method includes: Extract M - 1 groups of connecting node powders from the first spare ingredient feeding sequence; Conduct a chemical stability evaluation on the M - 1 groups of connecting node powders. If there is no neutralization reaction in the M - 1 groups of connecting node powders, calculate the powder mass ratio of the M - 1 groups of connecting node powders to obtain the first average feeding ratio; And so on, perform a process compatibility evaluation on the connection nodes of the multiple spare ingredient feeding sequences to screen and obtain multiple average feeding ratios of multiple compatible ingredient feeding sequences; Serialize the multiple average feeding ratios, and extract the compatible ingredient feeding sequence corresponding to the maximum value according to the sorting result as the powder ingredient feeding sequence.

9. The intelligent adjustment method for emulsification parameters combining multi-sensor fusion according to claim 3, characterized in that Perform an in-group connection feeding sorting on the H groups of powder ingredients to obtain M powder feeding sequences. The method includes: Preset a gradient ratio control rule; Extract M feeding masses from the M physical property information; Using the gradient ratio control rule as the feeding mass constraint, perform an in-group connection feeding sorting on the H groups of powder ingredients according to the M feeding masses to obtain the M powder feeding sequences.

10. An intelligent emulsification parameter adjustment system integrating multi-sensor fusion, characterized in that, The system is used to implement the intelligent adjustment method of emulsification parameters combining multi-sensor fusion according to any one of claims 1 - 9. The system includes: A product formula information receiving module, configured to receive the emulsification process constraint conditions and product formula information of the product to be emulsified, where the emulsification process constraint conditions include the critical emulsification time, target viscosity range, target homogenization threshold, and emulsification temperature range; A powder ingredient feeding sequence obtaining module, configured to calculate the water-oil ratio of the product formula information, and perform a collaborative analysis of powder ingredient feeding according to the water-oil ratio to obtain a powder ingredient feeding sequence; A standard emulsification parameter adjustment sequence obtaining module, configured to reversely deduce standard emulsification parameter adjustment parameters according to the emulsification process constraint conditions and the powder ingredient feeding sequence to obtain a standard emulsification parameter adjustment sequence, where the standard emulsification parameter adjustment sequence is identified by a feeding time window sequence; A stage emulsification performance characteristic sequence construction module, configured to perform multi-dimensional dynamic prediction of emulsification performance using the product formula information and the standard emulsification parameter adjustment sequence, and construct a stage emulsification performance characteristic sequence; A standard emulsification parameter adjustment sequence adjustment module, configured to perform dynamic adjustment and update of the standard emulsification parameter adjustment sequence in the main container tank according to the performance deviation between the fusion emulsification characteristic information collected and transmitted back by the multi-sensor array and the stage emulsification performance characteristic sequence during the process of dynamically coupling and regulating the powder according to the feeding time window sequence as the parameter adjustment switching constraint.

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