Performance testing methods, equipment and media for electric mixers for oral liquids

Abstract

The present invention discloses a performance detection method, equipment and medium for an oral liquid electric mixer, and relates to a stirring-related field. The method comprises: performing modeling data acquisition according to a preset operating environment, stirring mechanism characteristics and cooling mechanism characteristics; establishing a dual-branch control mapping model; receiving information of oral liquid to be stirred for mechanical sensitivity analysis, stirring parameter and cooling parameter analysis; inputting a first stirring parameter and a first cooling parameter into a finite element model for operation simulation, obtaining stirring uniformity monitoring data and cooling monitoring data; performing stirring performance analysis; performing stirring cooling performance analysis, and optimizing cooling performance; and optimizing performance by making stirring performance optimization decisions and cooling performance optimization decisions. The method solves the technical problems that the stirring effect of the existing mixer is not ideal, and it is difficult to ensure the uniformity of the oral liquid and the stability of the effective ingredients, and achieves the technical effects of improving the stirring quality, improving the uniformity of the oral liquid and the stability of the effective ingredients.

Description

Technical Field

[0001] The present application relates to stirring-related fields, and in particular to a performance detection method, equipment and medium for an oral liquid electric stirrer. Background Art

[0002] In the pharmaceutical, health care and food industries, it is crucial to ensure the uniformity of oral liquids and the stability of active ingredients during the production and preparation of oral liquids. The uniformity of oral liquids directly affects the quality and efficacy of the product, while the temperature control during the stirring process is directly related to the activity of the active ingredients and the shelf life of the product. Therefore, it is crucial to optimize the performance of electric mixers for oral liquids. At present, the performance testing and optimization of electric mixers for oral liquids mainly rely on traditional experimental test methods, using a single stirring control loop, and failing to comprehensively consider the mutual influence of various factors during the stirring process, resulting in large fluctuations in the uniformity of the oral liquid, unsatisfactory stirring effect, and local overheating during the stirring process, which affects the activity of the active ingredients.

[0003] In the current related technologies, the performance test of electric mixers for oral liquids has the technical problem that the stirring effect is not ideal and it is difficult to ensure the uniformity of the oral liquid and the stability of the active ingredients. Summary of the invention

[0004] The present application provides a performance detection method, equipment and medium for an electric mixer for oral liquid, establishes a finite element model based on a preset operating environment, stirring mechanism characteristics and cooling mechanism characteristics of the electric mixer for oral liquid, establishes a dual-branch control mapping model combining a stirring control branch and a cooling control branch, coordinates cooling and stirring, performs mechanical sensitivity analysis according to the characteristics of the oral liquid to be stirred, generates a mechanical shear force constraint threshold, optimizes stirring and cooling parameters based on the dual-branch control mapping model, inputs the optimized parameters into the finite element model for operation simulation, monitors stirring uniformity and cooling effect in real time, analyzes stirring performance and cooling performance based on monitoring data, generates optimization decisions, and optimizes the performance of the electric mixer for oral liquid, thereby achieving coordinated optimization of stirring and cooling and achieving the technical effect of improving stirring quality, thereby improving the uniformity of the oral liquid and the stability of the effective ingredients.

[0005] The present application provides a performance testing method for an oral liquid electric mixer, including:

[0006] According to the preset operating environment, stirring mechanism characteristics and cooling mechanism characteristics of the oral liquid electric mixer, modeling data collection is performed to generate a finite element model; a dual-branch control mapping model of the oral liquid electric mixer is established, wherein the dual-branch control mapping model includes a stirring control branch and a cooling control branch; the oral liquid information to be stirred is received and mechanical sensitivity analysis is performed to generate a mechanical shear force constraint threshold, and the stirring parameter and cooling parameter analysis of the oral liquid information to be stirred is performed with the mechanical shear force constraint threshold as a constraint through the dual-branch control mapping model to generate a first stirring parameter and a first cooling parameter; the first stirring parameter and the first cooling parameter are input into the finite element model for operation simulation, and stirring uniformity monitoring data and cooling monitoring data are obtained; stirring performance analysis is performed based on the stirring uniformity monitoring data, and a stirring performance optimization decision is output; stirring cooling performance analysis is performed based on the cooling monitoring data, and cooling performance optimization is performed in combination with the cooling performance analysis result and the stirring performance optimization decision to generate a cooling performance optimization decision; the performance of the oral liquid electric mixer is optimized with the stirring performance optimization decision and the cooling performance optimization decision.

[0007] In a possible implementation, the information of the oral liquid to be stirred is received and mechanical sensitivity analysis is performed to generate a mechanical shear force constraint threshold, and the following processing is performed:

[0008] Based on the information of the oral liquid to be stirred, a plurality of oral liquid mixed components are extracted; a mechanically sensitive database is called to match the mechanical shear force of the plurality of oral liquid mixed components to generate the mechanical shear force constraint threshold; wherein the mechanically sensitive database includes a plurality of mechanically sensitive samples, and any mechanically sensitive sample includes an oral liquid component sample and a shear force threshold at which a property change will occur.

[0009] In a possible implementation, a dual-branch control mapping model of the oral liquid electric mixer is established, wherein the dual-branch control mapping model includes a stirring control branch and a cooling control branch, and the following processing is performed:

[0010] Taking the preset operating environment as a constraint, the stirring logs of the same family of mixers as the oral liquid electric mixer are collected to construct a stirring log record set, wherein any stirring log record in the stirring log record set includes oral liquid attribute information, stirring demand, and stirring parameters; the stirring control branch is constructed based on the training of the stirring log record set; the corresponding cooling log record set is collected based on the stirring log record set, wherein any cooling log record in the cooling log record set includes stirring parameters, cooling temperature and cooling parameters; the cooling control branch is constructed using the training of the cooling log record set; the cooling control branch is serially connected with the stirring control branch to generate the dual-branch control mapping model.

[0011] In a possible implementation, the cooling control branch and the stirring control branch are connected in series to generate the dual-branch control mapping model, and the following processing is performed:

[0012] A mapping relationship between the oral liquid attribute information and the cooling temperature is established, and a mapping database about the oral liquid attribute and the cooling temperature is generated; the mapping database is connected to the cooling control branch, and the input oral liquid attribute information and the output stirring parameter of the stirring control branch are received through the cooling control branch, and the mapping database is called to match the input oral liquid attribute information with the cooling target, and the cooling parameter analysis is performed in combination with the cooling target and the stirring parameter.

[0013] In a possible implementation, stirring performance analysis is performed based on the stirring uniformity monitoring data, a stirring performance optimization decision is output, and the following processing is performed:

[0014] The stirring uniformity monitoring data includes oral liquid component distribution data at multiple points in the stirring container; uniformity identification is performed based on the oral liquid component distribution data at the multiple points to generate a uniformity index; it is determined whether the uniformity index is greater than or equal to a preset uniformity, and if not, a stirring optimization instruction is generated; under the constraint of the mechanical shear force constraint threshold, stirring parameters are iteratively adjusted based on the stirring optimization instruction, and uniformity iterative analysis is performed through the finite element model until the iterative uniformity is greater than or equal to the preset uniformity, and the stirring performance optimization decision is generated.

[0015] In a possible implementation, stirring cooling performance analysis is performed based on the cooling monitoring data, and cooling performance optimization is performed in combination with the cooling performance analysis result and the stirring performance optimization decision, a cooling performance optimization decision is generated, and the following processing is performed:

[0016] Based on the cooling control branch, a preset cooling temperature is read, and the preset cooling temperature is a matching temperature obtained by the cooling control branch calling a mapping database to match the cooling target of the oral liquid information to be stirred; the cooling monitoring data includes temperature data of multiple points in the stirring container; it is determined whether the temperature data of the multiple points all meet the preset cooling temperature; if so, the cooling control branch is continued to be called, and cooling parameter analysis is performed according to the stirring performance optimization decision to generate the cooling performance optimization decision.

[0017] In a possible implementation, it is determined whether the temperature data of the plurality of points all meet the preset cooling temperature, and the following processing is performed:

[0018] If not, perform iterative adjustment of cooling parameters, and perform iterative temperature monitoring and analysis through the finite element model until the temperature data of multiple points after iterative adjustment meet the preset cooling temperature, and generate iterative optimization data and initial cooling performance optimization decision; construct a mapping relationship between cooling parameters and cooling temperature based on the iterative optimization data; based on the mapping relationship, optimize the initial cooling performance optimization decision based on the stirring performance optimization decision, and generate the cooling performance optimization decision.

[0019] The present application also provides an electronic device, including:

[0020] A memory for storing executable instructions;

[0021] The processor is used to implement the performance detection method of the oral liquid electric mixer when executing the executable instructions stored in the memory.

[0022] The present application also provides a computer-readable storage medium, comprising:

[0023] A computer program is stored thereon, and when the program is executed by a processor, a performance detection method of an oral liquid electric mixer is implemented.

[0024] The performance detection method, equipment and medium of the oral liquid electric mixer proposed in this application are firstly to collect modeling data according to the preset operating environment, stirring mechanism characteristics and cooling mechanism characteristics of the oral liquid electric mixer, generate a finite element model, and then establish a dual-branch control mapping model of the oral liquid electric mixer, wherein the dual-branch control mapping model includes a stirring control branch and a cooling control branch, and then receive the information of the oral liquid to be stirred and perform mechanical sensitivity analysis to generate a mechanical shear force constraint threshold, and use the dual-branch control mapping model to analyze the stirring parameters and cooling parameters of the oral liquid to be stirred with the mechanical shear force constraint threshold as a constraint to generate a first stirring parameter and a first cooling parameter, and then input the first stirring parameter and the first cooling parameter into the finite element model for operation simulation, and obtain stirring uniformity monitoring data and cooling monitoring data, and then perform stirring performance analysis based on the stirring uniformity monitoring data, output a stirring performance optimization decision, perform stirring cooling performance analysis based on the cooling monitoring data, and optimize the cooling performance in combination with the cooling performance analysis results and the stirring performance optimization decision, generate a cooling performance optimization decision, and finally optimize the performance of the oral liquid electric mixer with the stirring performance optimization decision and the cooling performance optimization decision, thereby achieving the technical effect of improving the stirring quality and thereby improving the uniformity of the oral liquid and the stability of the effective ingredients. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the method according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0026] Figure 1 A schematic flow chart of a method for detecting the performance of an electric mixer for oral liquid provided in an embodiment of the present application.

[0027] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0028] Description of the reference numerals: input device 201 , processor 202 , memory 203 , output device 204 . DETAILED DESCRIPTION

[0029] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0030] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0031] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0032] The present application embodiment provides a performance detection method for an oral liquid electric mixer, such as Figure 1 As shown, the method includes:

[0033] Step S100, according to the preset operating environment, stirring mechanism characteristics and cooling mechanism characteristics of the oral liquid electric mixer, modeling data collection is performed to generate a finite element model. Specifically, various environmental parameters of the oral liquid electric mixer are expected to work, such as temperature range, humidity level, air circulation, vibration level and electromagnetic interference, etc., to ensure that the finite element model can accurately reflect the physical phenomena in the actual working environment. The various physical parameters of the stirring mechanism are measured in detail, including but not limited to the shape (such as blades, propellers, etc.), size (length, width, angle, etc.), material (stainless steel, plastic, etc.) of the stirring blade, and the speed range and torque characteristics of the stirring shaft. These data are used to simulate the fluid dynamics behavior during the stirring process. Analyze and record the structural design of the cooling mechanism, such as the layout of the cooling water channel, the specifications of the cooling fan, the type of cooling medium (water, air, etc.) and its flow rate, temperature, etc., evaluate the efficiency of the cooling system, and analyze how it affects the heat transfer and temperature distribution during the stirring process. Use various sensors and measuring equipment (such as thermometers, hygrometers, vibration sensors, flow meters, etc.) to collect all the above characteristic data for establishing an accurate finite element model.

[0034] Use CAD software or related tools to create a three-dimensional geometric model of the oral liquid electric mixer, import the geometric model into the finite element analysis (FEA) software, and set the physical properties of the material (such as density, elastic modulus, thermal conductivity, etc.), boundary conditions (such as fixed constraints, load conditions, etc.) and initial conditions (such as initial temperature, pressure, etc.) according to the collected data, divide the grid to discretize the continuum into a finite number of units, each unit has specific physical properties, set the solver parameters, and run the FEA software to generate a finite element model that can simulate the physical phenomena during the mixing and cooling process.

[0035] Step S200, establish a dual-branch control mapping model of the oral liquid electric mixer, wherein the dual-branch control mapping model includes a stirring control branch and a cooling control branch. Specifically, determine the control targets of stirring and cooling respectively, for example, the target of stirring control is to achieve a certain stirring uniformity, and the target of cooling control is to maintain the temperature of the oral liquid within a specific range. Identify the key control variables that affect the stirring and cooling effects, wherein the stirring control variables include stirring speed, stirring time, shape and position of the stirring blades, etc.; the cooling control variables include the temperature of the cooling medium, flow rate, layout and efficiency of the cooling system, etc.

[0036] Based on classical control theory (such as PID control), modern control theory (such as fuzzy control, neural network control) or hybrid control methods, control strategies are designed for stirring and cooling respectively. According to the mutual influence between stirring and cooling, such as the influence of heat generated by stirring on the cooling effect, a mapping relationship is established between the stirring and cooling control strategies to ensure that the two can work in coordination and jointly achieve the overall control goal. The mapping relationship refers to the interaction and dependency between the stirring and cooling control branches, which describes how to adjust the control response of another branch according to the control input of one branch. A dual-branch control mapping model is constructed using mathematical models, simulation tools or programming languages ​​(such as MATLAB / Simulink, Python, etc.), which receives control inputs of stirring and cooling and outputs corresponding control responses. Among them, the dual-branch control mapping model is a control system model that includes two relatively independent control branches (stirring and cooling), and coordinates with each other through a mapping relationship to achieve the overall control goal (the state or performance index expected to be achieved in the control system, including stirring uniformity and temperature range). The dual-branch control mapping model is verified through experimental data, simulation results or historical experience, and necessary adjustments and optimizations are made to the model based on the verification results, including improving the control strategy, adjusting the control parameters or introducing new control variables, so that the model can accurately reflect the actual behavior of the mixing and cooling process and maintain stability and reliability under different working conditions.

[0037] In a possible implementation, a dual-branch control mapping model of the oral liquid electric mixer is established, wherein the dual-branch control mapping model includes a stirring control branch and a cooling control branch, and step S200 further includes step S210, wherein the stirring logs of the same family of mixers as the oral liquid electric mixer are collected with the preset operating environment as a constraint, and a stirring log record set is constructed, wherein any stirring log record in the stirring log record set includes oral liquid attribute information, stirring demand, and stirring parameters. Specifically, with the preset operating environment as a constraint, the same family of mixers are retrieved, and the same family of mixers refers to mixers with similar or identical design, function, and configuration as the oral liquid electric mixer, and the products produced are the same or similar to the oral liquid, and the production environment is the same or similar to the preset operating environment, so that the operating parameters and effects of the same family of mixers are comparable to those of the oral liquid electric mixer. For mixers of the same type or similar configuration, the mixing logs during the actual production process are collected. These logs record the detailed parameters and results of the mixer when performing the mixing task under specific conditions, including but not limited to the composition information of the oral liquid (such as the viscosity and density of different ingredients), the mixing requirements (such as the mixing time and the mixing speed requirements), and the actual mixing parameters (such as the mixing speed, the shape of the mixing paddle, the height of the mixing liquid level, etc.). The collected log data is organized into a structured set of mixing log records, each of which corresponds to a mixing process and contains all the above relevant information.

[0038] Step S220, constructing the stirring control branch based on the training of the stirring log record set. Specifically, using machine learning or statistical methods, based on the data in the stirring log record set, train a model that can predict or optimize the stirring parameters. This model can automatically adjust the stirring parameters according to the attribute information and stirring requirements of the oral liquid to achieve the best stirring effect. This model is the stirring control branch. Step S230, based on the stirring log record set, collect the corresponding cooling log record set, wherein any cooling log record in the cooling log record set includes stirring parameters, cooling temperature and cooling parameters; Step S240, constructing the cooling control branch using the training of the cooling log record set. Specifically, retrieve the corresponding cooling log according to the stirring log record set, the cooling log includes parameters such as stirring parameters, cooling temperature, cooling time, flow rate of cooling medium during or after stirring, and how these parameters affect the final quality of the oral liquid. The cooling log data is organized into a structured cooling log record set. Similar to the stirring control branch, the data in the cooling log record set is used to train a model that can predict or optimize the cooling parameters. This model can dynamically adjust the cooling strategy according to the actual situation during the stirring process to ensure that the oral liquid can quickly and evenly reach the required temperature during or after stirring. This model is the cooling control branch. Step S250, the cooling control branch is connected in series with the stirring control branch to generate the dual-branch control mapping model. Specifically, the trained stirring control branch and the cooling control branch are connected in series to form a complete dual-branch control mapping model. This model can calculate the appropriate stirring parameters through the stirring control branch after receiving the oral liquid attribute information and stirring requirements, and adjust the cooling parameters through the cooling control branch during or after stirring to achieve the best stirring and cooling effect. This implementation method uses historical data and machine learning technology to construct a dual-branch control mapping model, making full use of the experience and laws in the historical data, and achieving the technical effect of realizing precise control of the stirring and cooling process.

[0039] In a possible implementation, the cooling control branch is connected in series with the stirring control branch to generate the dual-branch control mapping model, and step S250 further includes step S251, establishing a mapping relationship between the oral liquid attribute information and the cooling temperature, and generating a mapping database about the oral liquid attribute and the cooling temperature. Specifically, a large amount of data about different oral liquid attributes (such as ingredients, concentrations, volumes, initial temperatures, pH values, etc.) and the optimal cooling temperatures required for these oral liquids in a stable state are obtained through experiments, historical records or expert advice. The relationship between the oral liquid attributes and the required cooling temperature is analyzed using statistical analysis and machine learning techniques (such as regression analysis, decision trees, neural networks, etc.). The purpose of this step is to find out the potential patterns and rules between the attributes and the temperature. Based on the analysis results, a mapping relationship between the oral liquid attributes and the cooling temperature is constructed. This mapping relationship can be a mathematical model or a lookup table that can predict the required cooling temperature based on the input oral liquid attributes. The mapping relationship is stored in a database to form a mapping database. Step S252, connect the mapping database with the cooling control branch, receive the input oral liquid attribute information and output stirring parameters of the stirring control branch through the cooling control branch, call the mapping database to match the input oral liquid attribute information with the cooling target, and analyze the cooling parameters in combination with the cooling target and stirring parameters. Specifically, the cooling control branch receives the attribute information and stirring parameters of the oral liquid from the stirring control branch, and these information are the basis for determining the cooling strategy. Using the mapping database, query and determine the required cooling target temperature according to the received oral liquid attribute information, which is the key to ensure that the oral liquid can maintain stability after stirring. Combined with the cooling target and stirring parameters (such as stirring speed, stirring time, etc.), analyze and determine the optimal cooling parameters, including the temperature, flow rate, wind force, etc. of the cooling medium, in order to achieve the cooling target in the most efficient way. The analyzed cooling parameters are output to the cooling actuator, such as a cooling fan, a cooling water circulation system, etc., to implement the cooling operation. This implementation method connects the stirring control branch and the cooling control branch in series and establishes a mapping relationship between the oral liquid properties and the cooling temperature, thereby ensuring that the property changes of the oral liquid can be responded to in a timely manner during the stirring process, and maintaining the stability of the oral liquid by adjusting the cooling parameters, thereby achieving the technical effect of achieving precise control of the oral liquid preparation process.

[0040] Step S300, receiving the information of the oral liquid to be stirred and performing mechanical sensitivity analysis, generating a mechanical shear force constraint threshold, using the dual-branch control mapping model to use the mechanical shear force constraint threshold as a constraint, performing stirring parameter and cooling parameter analysis on the oral liquid to be stirred, and generating a first stirring parameter and a first cooling parameter. Specifically, receiving the physical and chemical property information of the oral liquid to be stirred, such as composition, viscosity, density, thermosensitivity, etc., from user input, database or sensor and other devices. Performing mechanical sensitivity analysis on the oral liquid to be stirred, the mechanical sensitivity analysis is to evaluate the changes in performance indicators such as stability, solubility, and active ingredient retention rate of the oral liquid to be stirred when it is subjected to mechanical forces (such as shear force, pressure, etc.). Using experimental data or empirical formulas, determine the mechanical shear force sensitivity threshold of the oral liquid, that is, the maximum mechanical shear force allowed without damaging the quality of the oral liquid. Exceeding this value will lead to a decrease in the quality of the oral liquid or a loss of active ingredients. The result of the mechanical sensitivity analysis is converted into a mechanical shear force constraint threshold as a constraint condition for the optimization of stirring parameters. Load and activate the established dual-branch control mapping model, which contains the mapping relationship and control strategy of the stirring control branch and the cooling control branch. In the stirring control branch of the dual-branch control mapping model, the mechanical shear force constraint threshold is used as the constraint condition to iteratively analyze the stirring parameters (such as stirring speed, stirring time, stirring blade shape and position, etc.) to find a set of stirring parameters that can meet the stirring uniformity requirements and do not exceed the mechanical shear force constraint threshold, that is, the first stirring parameters. In the cooling control branch of the dual-branch control mapping model, combined with the influence of the heat generated during the stirring process on the cooling effect, the cooling parameters (such as cooling medium temperature, flow rate, cooling system layout, etc.) are analyzed to ensure that the temperature of the oral liquid can be kept within a safe and effective range during the stirring process, and at the same time coordinate with the stirring parameters to form a complementary optimization effect. After the above analysis, the first stirring parameter and the first cooling parameter are output. These parameters are optimized control parameters that can simultaneously meet the stirring and cooling requirements.

[0041] In a possible implementation, the information of the oral liquid to be stirred is received and a mechanical sensitivity analysis is performed to generate a mechanical shear force constraint threshold. Step S300 further includes step S310, extracting a plurality of oral liquid mixed components based on the oral liquid information to be stirred. Specifically, detailed information about the oral liquid to be stirred is received, including the formula of the oral liquid (which components are included and their proportions), the purpose of the oral liquid (such as medicine, health care product, food, etc.), and any special processing requirements or precautions. From the formula or description of the oral liquid, the names and proportions of all mixed components are extracted using natural language processing (NLP) or preset parsing rules, and the extracted components are classified based on a knowledge base or expert system to identify which components are sensitive to mechanical shear force. Step S320, calling a mechanical sensitive database to match the mechanical shear force of the plurality of oral liquid mixed components, and generating the mechanical shear force constraint threshold, wherein the mechanical sensitive database includes a plurality of mechanical sensitive samples, and any mechanical sensitive sample includes an oral liquid component sample and a shear force threshold at which a property change will occur. Specifically, according to the component data sorted out in step S310, a query request is initiated to the mechanical sensitive database to find the mechanical shear force information related to these components, wherein the mechanical sensitive database stores data on the sensitivity of different materials (such as oral liquid components) to mechanical shear force, including information such as the shear force threshold of each material. The mechanical sensitive database uses a matching algorithm, such as similarity calculation, machine learning model, etc., to evaluate the mechanical shear force sensitivity of each component under specific conditions and find the corresponding shear force threshold. For multiple mixed components, the shear force threshold of each component is combined, and a mechanical shear force constraint threshold applicable to the entire oral liquid is finally determined by weighted average, minimum value selection or other logical rules. Different oral liquid components have different mechanical sensitivities. This implementation method extracts mixed components, calls the mechanical sensitive database for matching analysis, and generates the corresponding mechanical shear force constraint threshold according to the matching results, thereby realizing the accurate analysis and determination of the mechanical shear force constraint threshold, and achieving the technical effect of ensuring that the stirring process is carried out within a safe range.

[0042] Step S400, input the first stirring parameter and the first cooling parameter into the finite element model for operation simulation, and obtain stirring uniformity monitoring data and cooling monitoring data. Specifically, the generated first stirring parameter (such as stirring speed, stirring time, stirring blade position, etc.) and the first cooling parameter (such as cooling medium flow, cooling temperature, cooling time, etc.) are used as input conditions and imported into the established finite element model. In the finite element model, according to the input stirring parameter and cooling parameter, the initial state of the stirring mechanism, cooling mechanism and oral liquid is initialized, including setting the motion trajectory and speed of the stirring blade, the temperature distribution of the cooling system, the fluid flow state, and the initial temperature, viscosity and other physical properties of the oral liquid. Start the operation simulation function of the finite element model to simulate the dynamic changes of the oral liquid during the stirring and cooling process. The finite element model calculates the flow, mixing, heat conduction and other processes of the oral liquid according to the input parameters and the preset physical laws, and updates the state data in the model in real time. During the simulation process, the stirring uniformity and cooling effect are monitored in real time. The stirring uniformity refers to the degree to which the components in the oral liquid are evenly distributed during the stirring process. It is used to measure the stirring effect and affects the quality and stability of the oral liquid. The stirring uniformity monitoring data includes the concentration distribution of the components in the oral liquid, the mixing uniformity index, etc. The cooling monitoring data is the data obtained by monitoring the performance of the cooling system, which reflects the control effect of the cooling system on the temperature of the oral liquid, including the temperature change of the oral liquid, the temperature distribution of the cooling medium, the heat exchange efficiency, etc. The monitored stirring uniformity data and cooling monitoring data are recorded. When the simulation reaches the preset time point or meets other termination conditions, the simulation process is stopped and the monitoring data is output.

[0043] Step S500, based on the stirring uniformity monitoring data, stirring performance analysis is performed, and a stirring performance optimization decision is output. Specifically, key parameters are extracted from the obtained stirring uniformity monitoring data, such as the concentration distribution of each component in the oral liquid, mixing uniformity indicators (such as variance, standard deviation), and the existence of local low mixing areas. The sorted stirring uniformity monitoring data is analyzed using statistical methods and engineering analysis software, including calculating the quantitative indicators of mixing uniformity, comparing with preset standards or historical data, and evaluating whether there are problems such as uneven mixing, local over-stirring or under-stirring during the stirring process, which will affect the quality and stability of the oral liquid. According to the results of the stirring performance evaluation, the problems existing in the stirring process are identified. For example, the mixing uniformity is not up to standard, the stirring efficiency is too low, etc. The identified problems are analyzed to find out their root causes, such as design defects of the stirring mechanism, improper setting of stirring parameters, or the physical properties of the oral liquid itself. Based on the results of problem identification and cause analysis, a strategy for optimizing stirring performance is formulated, including adjusting stirring parameters (such as stirring speed, stirring time, stirring blade shape and position, etc.), improving the design of the stirring mechanism, or changing the pretreatment method of the oral liquid. Organize the formulated mixing performance optimization strategy into specific optimization decisions, including specific operation steps, adjustment parameter values, and expected optimization effects.

[0044] In a possible implementation, stirring performance analysis is performed based on the stirring uniformity monitoring data, and a stirring performance optimization decision is output. Step S500 further includes step S510, wherein the stirring uniformity monitoring data includes oral liquid component distribution data at multiple points in the stirring container, and uniformity identification is performed based on the oral liquid component distribution data at the multiple points to generate a uniformity index. Specifically, the stirring uniformity monitoring data includes specific values ​​or relative proportions of the concentration, density, viscosity, etc. of each component of the oral liquid at multiple preset points in the stirring container (such as the bottom, middle, top, and different radial positions of the container). Statistical analysis is performed on the oral liquid component distribution data collected at multiple points, and statistical quantities such as the mean value, standard deviation, and variance of the components at each point are calculated. These statistics are used in combination with a preset uniformity evaluation algorithm (such as a fit evaluation based on the least squares method, a uniformity measurement based on entropy, etc.) to generate an index (value or parameter) reflecting the overall uniformity of the oral liquid.

[0045] Step S520, determine whether the uniformity index is greater than or equal to the preset uniformity, if not, generate a stirring optimization instruction. Specifically, compare the calculated uniformity index with the preset uniformity threshold (set according to the oral liquid quality requirements, production process standards, etc.). If the uniformity index is less than the preset uniformity, it means that the stirring effect does not meet expectations and a stirring optimization instruction needs to be generated. Step S530, under the constraint of the mechanical shear force constraint threshold, iteratively adjust the stirring parameters based on the stirring optimization instruction, and perform iterative uniformity analysis through the finite element model until the iterative uniformity is greater than or equal to the preset uniformity, and generate the stirring performance optimization decision. Specifically, under the constraint of the mechanical shear force constraint threshold (i.e., ensuring that the shear force generated during the stirring process will not have an adverse effect on the oral liquid ingredients), the stirring parameters (such as stirring speed, stirring time, stirring blade position, etc.) are iteratively adjusted according to the stirring optimization instructions. After each parameter adjustment, the finite element model is used for simulation operation to obtain new stirring uniformity monitoring data, and the process of steps S510 to S520 is repeated to perform uniformity iterative analysis until the uniformity index obtained by the iteration is greater than or equal to the preset uniformity. At this time, it is considered that the stirring parameters have reached the optimal or near-optimal state, and the final stirring performance optimization decision is generated. This implementation method ensures that the oral liquid meets the expected uniformity requirements during the stirring process through data collection, analysis and iterative optimization processes. This optimization method based on data-driven and model simulation achieves the technical effect of improving the accuracy and reliability of stirring performance analysis.

[0046] Step S600, based on the cooling monitoring data, the stirring cooling performance is analyzed, and the cooling performance is optimized in combination with the cooling performance analysis results and the stirring performance optimization decision, and a cooling performance optimization decision is generated. Specifically, from the obtained cooling monitoring data, key parameters are extracted, such as the temperature change of the oral liquid during stirring, the temperature distribution of the cooling medium, the heat exchange efficiency, etc., these data are statistically analyzed, the hot spots, cold spots or temperature uneven areas in the cooling process are identified, and the cooling efficiency is evaluated whether it meets the preset requirements. Based on the collected cooling monitoring data, the cooling performance is evaluated using engineering analysis software or algorithms, and the evaluation content includes cooling speed, temperature stability, heat exchange efficiency, etc. The evaluation results are compared with the preset cooling performance standards or historical data to determine whether the current cooling performance meets the standards. If the cooling performance does not meet the standards, the problems in the cooling process are identified, such as insufficient cooling medium flow, cooling system blockage, reduced cooler efficiency, etc., and the identified problems are analyzed to find out their root causes, such as design defects, equipment aging, etc. Combined with the cooling performance evaluation results, problem identification and cause analysis, formulate cooling performance optimization strategies, including adjusting cooling parameters (such as cooling medium flow, temperature, pressure, etc.), improving cooling system design, replacing high-efficiency cooling components, etc. When formulating optimization strategies, it is also necessary to combine the impact of stirring performance optimization decisions to ensure coordination and balance between the two. Organize the formulated cooling performance optimization strategies into specific optimization decisions, including specific operating steps, adjustment parameter values, and expected optimization effects.

[0047] In a possible implementation, the stirring cooling performance analysis is performed based on the cooling monitoring data, and the cooling performance optimization is performed in combination with the cooling performance analysis result and the stirring performance optimization decision to generate the cooling performance optimization decision. Step S600 further includes step S610, based on the cooling control branch, the preset cooling temperature is the matching temperature obtained by the cooling control branch calling the mapping database to match the cooling target of the oral liquid to be stirred. Specifically, the preset cooling temperature is the cooling temperature expected to be achieved by matching the mapping database according to the characteristics and production requirements of the oral liquid to be stirred, and is the benchmark for cooling performance analysis and optimization. Step S620, the cooling monitoring data includes the temperature data of multiple points in the stirring container; step S630, judging whether the temperature data of the multiple points all meet the preset cooling temperature. Specifically, the temperature data of each point is compared with the preset cooling temperature. If the temperature data of all points are within the allowable range of the preset cooling temperature (considering a certain temperature fluctuation or error), it is considered that the preset cooling temperature is met. If the temperature data of any point is out of range, it is considered that the preset cooling temperature is not met. Step S640, if yes, continue to call the cooling control branch, perform cooling parameter analysis according to the stirring performance optimization decision, and generate the cooling performance optimization decision. Specifically, if it is judged that the temperature data of all points meet the preset cooling temperature, it means that the current cooling effect is good, which further indicates that the cooling parameter analysis according to the dual-branch control mapping model has a good effect. Since the stirring parameters have been optimized at this time, the new stirring parameters, that is, the stirring performance optimization decision, are input into the dual-branch control mapping model, and the cooling parameter analysis is performed again through the cooling control branch in the model to obtain a cooling performance optimization decision corresponding to the stirring performance optimization decision. This implementation method continues to call the cooling control branch for optimization analysis on the basis of confirming that the cooling effect is good, thereby achieving the matching of cooling performance and stirring performance, and achieving the technical effect of ensuring that the overall performance of the oral liquid electric mixer reaches the best.

[0048] In a possible implementation, it is determined whether the temperature data of the multiple points all meet the preset cooling temperature, and step S600 further includes step S650. If not, it is performed to iteratively adjust the cooling parameters, and the temperature is iteratively monitored and analyzed through the finite element model until the temperature data of the multiple points after the iterative adjustment all meet the preset cooling temperature, and iterative optimization data and initial cooling performance optimization decisions are generated. Specifically, it is determined whether the temperature data of any point does not meet the preset cooling temperature according to the result of step S630. If not, the temperature of which points is too high or too low is analyzed, and the cooling parameters (such as the flow rate, temperature, flow rate, etc. of the cooling medium) and the adjustment direction (increase or decrease) that need to be adjusted are preliminarily determined. Then, according to the preliminarily determined adjustment direction and amplitude, the cooling parameters are adjusted for the first time, and then, the finite element model is simulated again to obtain new temperature distribution data, and this process is repeated until the temperature data of all points meet the preset cooling temperature. In the iterative process, the parameter value and the corresponding temperature data of each adjustment are recorded to form iterative optimization data. When the temperature data of all points meet the preset cooling temperature, the initial cooling performance optimization decision is generated according to the parameter value of the last iteration adjustment. Step S660, based on the iterative optimization data, a mapping relationship of cooling parameter-cooling temperature is constructed. Specifically, the iterative optimization data is analyzed to find the correlation between the cooling parameter and the cooling temperature. Based on the analysis results, a mapping relationship model between the cooling parameter and the cooling temperature is constructed. This model can reflect the changing law of temperature distribution in the stirring container under different cooling parameter settings. Step S670, based on the mapping relationship, the initial cooling performance optimization decision is optimized based on the stirring performance optimization decision, and the cooling performance optimization decision is generated. Specifically, the mapping relationship between the stirring performance optimization decision and the cooling parameter-cooling temperature is comprehensively evaluated to evaluate the mutual influence between the two. On the basis of the evaluation, the initial cooling performance optimization decision is further optimized so that the cooling performance and the stirring performance reach the best balance and meet the production requirements at the same time. After optimization, the final cooling performance optimization decision is generated, which is the basis for subsequent adjustment and optimization of the cooling system of the oral liquid electric mixer. This implementation method is constructed by iterative adjustment and mapping relationship, and the technical effect of ensuring the accuracy and effectiveness of cooling performance optimization is achieved.

[0049] Step S700, the performance of the oral liquid electric mixer is optimized with the stirring performance optimization decision and the cooling performance optimization decision. Specifically, the generated stirring performance optimization decision and the cooling performance optimization decision are integrated, and the two decisions include adjustment suggestions for multiple parameters such as stirring speed, stirring time, stirring mode, cooling medium flow, cooling medium temperature, cooling time, etc. According to the integrated optimization decision, the control system of the oral liquid electric mixer is adjusted, including adjusting the parameters such as stirring speed and stirring time in the stirring control branch, and adjusting the parameters such as cooling medium flow and temperature in the cooling control branch. After the parameter adjustment, the oral liquid electric mixer is system-checked to ensure that all adjustments have been correctly implemented and the system can operate stably. Use the actual oral liquid to be stirred or simulated materials, run the oral liquid electric mixer under the adjusted parameter settings, and verify the effectiveness of the optimization decision by monitoring the stirring uniformity, cooling effect and other performance indicators of the mixer (such as energy consumption, noise, etc.). If it is found during the performance verification process that some parameter settings still need further adjustment, the verification results are fed back to the optimization decision and necessary fine-tuning is performed. Feedback adjustment is an iterative process used to optimize the performance of the oral liquid electric mixer. The embodiment of the present application adopts a finite element model based on the preset operating environment, stirring mechanism characteristics and cooling mechanism characteristics of the oral liquid electric mixer, establishes a dual-branch control mapping model combining a stirring control branch and a cooling control branch, performs coordinated control of cooling and stirring, performs mechanical sensitivity analysis according to the characteristics of the oral liquid to be stirred, generates a mechanical shear force constraint threshold, and optimizes the stirring and cooling parameters based on the dual-branch control mapping model, inputs the optimized parameters into the finite element model for operation simulation, monitors the stirring uniformity and cooling effect in real time, analyzes the stirring performance and cooling performance based on the monitoring data, generates optimization decisions, and optimizes the performance of the oral liquid electric mixer. Technical means such as performance optimization realize the coordinated optimization of stirring and cooling, achieves the technical effect of improving the stirring quality, and then improving the uniformity of the oral liquid and the stability of the effective ingredients.

[0050] Based on the foregoing embodiments, the embodiments of the present application further provide an electronic device and a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of the electronic device, it can implement the method described in any of the foregoing embodiments.

[0051] Figure 2 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing an implementation of the present invention. Figure 2The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. The electronic device is in the form of a general computing device, and its components may include but are not limited to an input device 201, a processor 202, a memory 203, and an output device 204. Among them, the processor 202 may be one or more; the memory 203 may include a computer-readable medium and at least one program product, and the program product has a set (at least one) of program modules, which are configured to perform the functions of each embodiment of the present application.

[0052] The memory 203 shown in the embodiment of the present invention may adopt any combination of one or more computer-readable media; the computer-readable storage medium may be, but is not limited to, an infrared, semiconductor system, device or component, or any combination of the above, for storing software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the performance detection method for the electric mixer for oral liquid in the embodiment of the present invention. The processor 202 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 203, thereby realizing the performance detection method for the electric mixer for oral liquid mentioned above.

[0053] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A performance testing method for an oral liquid electric stirrer, characterized in that: include: According to the preset operating environment, stirring mechanism characteristics and cooling mechanism characteristics of the oral liquid electric mixer, the modeling data is collected to generate a finite element model; Establishing a dual-branch control mapping model of the oral liquid electric mixer, wherein the dual-branch control mapping model includes a stirring control branch and a cooling control branch; Receiving information of the oral liquid to be stirred and performing mechanical sensitivity analysis to generate a mechanical shear force constraint threshold, and using the dual-branch control mapping model to perform stirring parameter and cooling parameter analysis on the information of the oral liquid to be stirred with the mechanical shear force constraint threshold as a constraint to generate a first stirring parameter and a first cooling parameter; Inputting the first stirring parameter and the first cooling parameter into the finite element model to perform operation simulation, and obtaining stirring uniformity monitoring data and cooling monitoring data; Perform stirring performance analysis based on the stirring uniformity monitoring data and output a stirring performance optimization decision; Perform stirring cooling performance analysis based on the cooling monitoring data, and optimize the cooling performance in combination with the cooling performance analysis result and the stirring performance optimization decision to generate a cooling performance optimization decision; Optimizing the performance of the oral liquid electric mixer based on the stirring performance optimization decision and the cooling performance optimization decision; Receive information of the oral liquid to be stirred and perform mechanical sensitivity analysis to generate a mechanical shear force constraint threshold, including: Extracting a plurality of oral liquid mixed components based on the oral liquid to be stirred information; Calling a mechanical sensitive database to match the mechanical shear force of the plurality of oral liquid mixed components to generate the mechanical shear force constraint threshold; Wherein, the mechanical sensitive database includes a plurality of mechanical sensitive samples, and any mechanical sensitive sample includes an oral liquid component sample and a shear force threshold at which a property change will occur; A dual-branch control mapping model of the oral liquid electric mixer is established, wherein the dual-branch control mapping model includes a stirring control branch and a cooling control branch, including: Taking the preset operating environment as a constraint, collecting the stirring logs of the same family of stirring machines of the oral liquid electric stirring machine, and constructing a stirring log record set, wherein any stirring log record in the stirring log record set includes oral liquid attribute information, stirring demand, and stirring parameters; Building the agitation control branch based on the agitation log record set training; Collecting a corresponding cooling log record set based on the stirring log record set, wherein any cooling log record in the cooling log record set includes a stirring parameter, a cooling temperature, and a cooling parameter; The cooling control branch is constructed by training the cooling log record set; Connecting the cooling control branch and the stirring control branch in series to generate the dual-branch control mapping model; Establishing a mapping relationship between the oral liquid property information and the cooling temperature, and generating a mapping database about the oral liquid property and the cooling temperature; Connecting the mapping database with the cooling control branch, receiving the input oral liquid attribute information and the output stirring parameter of the stirring control branch through the cooling control branch, calling the mapping database to match the input oral liquid attribute information with a cooling target, and performing cooling parameter analysis in combination with the cooling target and the stirring parameter; Based on the stirring uniformity monitoring data, stirring performance analysis is performed to output a stirring performance optimization decision, including: The stirring uniformity monitoring data includes oral liquid component distribution data at multiple points in the stirring container; Performing uniformity identification based on the oral liquid component distribution data at the plurality of points to generate a uniformity index; Determine whether the uniformity index is greater than or equal to a preset uniformity, and if not, generate a stirring optimization instruction; Under the constraint of the mechanical shear force constraint threshold, iteratively adjust the stirring parameters based on the stirring optimization instruction, and perform iterative uniformity analysis through the finite element model until the iterative uniformity is greater than or equal to the preset uniformity, thereby generating the stirring performance optimization decision; Based on the cooling monitoring data, stirring cooling performance analysis is performed, and cooling performance optimization is performed in combination with the cooling performance analysis result and the stirring performance optimization decision, and a cooling performance optimization decision is generated, including: Reading a preset cooling temperature based on the cooling control branch, the preset cooling temperature is a matching temperature obtained by the cooling control branch calling a mapping database to match the oral liquid to be stirred information with a cooling target; The cooling monitoring data includes temperature data of multiple points in the stirring container; Determining whether the temperature data of the plurality of points all meet the preset cooling temperature; If so, continue to call the cooling control branch, perform cooling parameter analysis according to the stirring performance optimization decision, and generate the cooling performance optimization decision; If not, iteratively adjust the cooling parameters, and perform iterative temperature monitoring and analysis through the finite element model until the temperature data of multiple points after iterative adjustment meet the preset cooling temperature, and generate iterative optimization data and initial cooling performance optimization decision; Constructing a mapping relationship between cooling parameters and cooling temperature based on the iterative optimization data; Based on the mapping relationship, the initial cooling performance optimization decision is optimized based on the stirring performance optimization decision to generate the cooling performance optimization decision.

2. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable instructions; The processor is used to implement the performance detection method of the oral liquid electric mixer according to claim 1 when executing the executable instructions stored in the memory.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the performance detection method of the oral liquid electric mixer as claimed in claim 1 is implemented.

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