Method and device for analyzing mixing capacity of high-speed mixing machine, computer equipment and storage medium

Abstract

The invention relates to a high-speed mixing machine mixing capability analysis method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: constructing a hybrid work simulation model for the high-speed mixer based on equipment information and process parameters of the high-speed mixer; simulating the mixing process of a plurality of materials in the tank body based on a mixing work simulation model, and determining the material quantity of each material in a plurality of material mixing areas of the tank body at different moments; for each moment, performing statistical analysis on each material quantity at the moment to obtain the mixing uniformity corresponding to the moment; and determining the mixing capability of the high-speed mixer based on the mixing uniformity corresponding to each moment. By adopting the method, quantitative evaluation of the mixing capacity of the high-speed mixer can be realized, and the accuracy of the evaluation result of the mixing capacity can be improved.

Description

Technical Field

[0001] The present application relates to the field of particle mixing technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for analyzing the mixing capacity of a high-speed mixer. Background Art

[0002] High-speed mixers, also known as high-speed mixers, are devices that mix materials through paddle stirring and tank rotation. They are widely used in the chemical, pharmaceutical and other industries. The mixing capacity of a high-speed mixer is affected by many factors, such as paddle design, material properties, and filling volume. How to evaluate the mixing capacity of a high-speed mixer is crucial.

[0003] In traditional technology, samples are taken from the mixed materials in real time to conduct elemental analysis, FT4 test, density test and other tests to evaluate the mixing uniformity of the materials. Due to the randomness of on-site sampling, the traditional method can usually only obtain qualitative evaluation results of the mixing ability, which has the problem of inaccurate evaluation results. Summary of the invention

[0004] Based on this, it is necessary to provide a high-mixer mixing capacity analysis method, device, computer equipment, computer-readable storage medium and computer program product that can improve the accuracy of evaluation results in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for analyzing the mixing capacity of a high-speed mixer. The method comprises:

[0006] Based on the equipment information and process parameters of the high-speed mixer, a mixing operation simulation model for the high-speed mixer is constructed; in the mixing operation simulation model, the tank of the high-speed mixer is divided into a plurality of mixing zones;

[0007] Based on the mixing work simulation model, the mixing process of multiple materials in the tank is simulated to determine the material quantity of each material in each mixing area at different times;

[0008] For each of the moments, statistical analysis is performed on the amounts of the materials at the moments to obtain the mixing uniformity corresponding to the moments;

[0009] Determining the mixing capacity of the high-speed mixer based on the mixing uniformity corresponding to each of the moments;

[0010] Preferably, for each of the moments, statistical analysis is performed on the amounts of the materials at the moment to obtain the mixing uniformity corresponding to the moment, including:

[0011] At each of the materials at each of the moments, determine the material uniformity corresponding to the material according to the respective material amounts of the material distributed in each of the mixing zones at the moments;

[0012] Statistical analysis is performed on the material uniformity of each of the materials to determine the mixing uniformity corresponding to the moment.

[0013] In one embodiment, the statistical analysis of the material amounts at the time to obtain the mixing uniformity corresponding to the time includes:

[0014] For each of the materials, determining reference distribution information of the material in an absolute mixed state;

[0015] Determine the material distribution information of the material in each of the mixing zones based on the material quantities of the material corresponding to the different mixing zones at the time;

[0016] Based on the differences between the material distribution information of each material in different mixing areas and the reference distribution information, the mixing uniformity of the tank at the moment is determined.

[0017] In one embodiment, the material quantity is mass; the material distribution information is actual mass fraction; the reference distribution information is reference mass fraction;

[0018] The determining the mixing uniformity of the tank at the moment based on the difference between the material distribution information of each material in different mixing zones and the reference distribution information includes:

[0019] For each of the materials, determining the relative difference between the actual mass fraction of the material in different mixing zones at the time and the reference mass fraction of the material;

[0020] The mean values ​​of the multiple relative differences corresponding to each of the materials are statistically analyzed to determine the mixing uniformity of the tank at the moment.

[0021] In one embodiment, the method further comprises:

[0022] In the case where the mixing capacity does not match the actual demand, selecting target particles from the material particles of each of the materials;

[0023] identifying a low activity area of ​​the tank based on the movement trajectory of the target particles during the simulated mixing process;

[0024] Determine the optimization parameters of the high-speed mixer according to the position of the low-activity area in the tank, and update the mixing operation simulation model accordingly; the optimization parameters include at least one of structural parameters or process parameters;

[0025] Return to the step of simulating the mixing process of multiple materials in the tank based on the mixing work simulation model, and determining the material quantity of each material in each mixing area at different times until the mixing capacity matches the actual demand.

[0026] In one embodiment, the method further comprises:

[0027] Obtaining the size of the high-speed mixer and the particle size of each of the materials;

[0028] According to the size and each of the particle sizes, determining the upper limit of the number of partitions of the tank of the high-speed mixer; the upper limit of the number of partitions is positively correlated with the size and negatively correlated with the maximum particle size among the particle sizes;

[0029] Based on the upper limit of the number of partitions, determining the number of divisions of the mixing zone in the tank;

[0030] Preferably, the upper limit a of the number of partitions can be expressed as:

[0031] a=w*l*h / (1000*Dmax^3)

[0032] Wherein, w is the equipment width of the high-speed mixer, l is the equipment length of the high-speed mixer, h is the equipment height of the high-speed mixer, and Dmax is the maximum particle size among the particle sizes;

[0033] Preferably, determining the number of divisions of the mixing zone in the tank based on the upper limit of the number of partitions includes:

[0034] On the basis of the upper limit of the number of partitions, the computing capacity attenuation coefficient is superimposed to determine the number of mixing zones in the tank; the computing capacity attenuation coefficient is determined according to the difference between the actual computing capacity of the equipment and the theoretical computing capacity of the equipment.

[0035] In one embodiment, determining the mixing capacity of the high-speed mixer based on the mixing uniformity corresponding to each of the moments includes:

[0036] For each of the moments, based on the hybrid operation simulation model, determining the torque and angular velocity of each blade of the high-speed hybrid at the moment;

[0037] According to each of the torques and each of the angular velocities, calculating the equipment power of the high-speed mixer at the moment;

[0038] Based on the respective equipment power and mixing uniformity at each of the moments, the correlation between the energy consumption of the high-speed mixer and the mixing uniformity during the simulated mixing of each of the materials is determined; the correlation is used to characterize the mixing capacity of the high-speed mixer.

[0039] In a second aspect, the present application also provides a high-speed mixer mixing capacity analysis device. The device comprises:

[0040] A model building module is used to build a mixing work simulation model for the high-speed mixer based on the equipment information and process parameters of the high-speed mixer; in the mixing work simulation model, the tank of the high-speed mixer is divided into a plurality of mixing areas;

[0041] A mixing process simulation module, used to simulate the mixing process of multiple materials in the tank based on the mixing work simulation model, and determine the material quantity of each material in each mixing area at different times;

[0042] A mixing uniformity analysis module is used to perform statistical analysis on the amount of each material at each moment to obtain the mixing uniformity corresponding to the moment;

[0043] A mixing capacity determination module, used to determine the mixing capacity of the high speed mixer based on the mixing uniformity corresponding to each of the moments;

[0044] Preferably, the mixing uniformity analysis module is specifically used for:

[0045] At each of the materials at each of the moments, according to the amount of the materials distributed in each of the mixing zones at the moments, determine the material uniformity corresponding to the materials;

[0046] Statistical analysis is performed on the material uniformity of each of the materials to determine the mixing uniformity corresponding to the moment.

[0047] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0049] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0050] The above-mentioned high-mixer mixing capacity analysis method, device, computer equipment, computer-readable storage medium and computer program product construct a mixing work simulation model for the high-mixer based on the equipment information and process parameters of the high-mixer; based on the mixing work simulation model, simulate the mixing process of multiple materials to determine the material quantity of each material in each mixing zone in the tank at different times; for each moment, perform statistical analysis on the material quantity at that moment to obtain the mixing uniformity corresponding to that moment, and determine the mixing capacity of the high-mixer based on the mixing uniformity corresponding to each moment. In the above process, the material quantity of each material in different mixing zones at different times is accurately obtained by simulation, and the mixing uniformity is further obtained by statistically analyzing these material quantities, which can realize quantitative evaluation of the mixing capacity of the high-mixer, and is conducive to improving the accuracy of the mixing capacity evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] FIG1 is an application environment diagram of a method for analyzing mixing capacity of a high-speed mixer according to an embodiment;

[0052] FIG2 is a schematic flow diagram of a method for analyzing mixing capacity of a high-speed mixer in one embodiment;

[0053] FIG3 is a schematic flow chart of a method for analyzing mixing capacity of a high-speed mixer in another embodiment;

[0054] FIG4 is a structural block diagram of a device for analyzing mixing capacity of a high-speed mixer according to an embodiment;

[0055] FIG. 5 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] The high-mixer hybrid capability analysis method provided in the embodiment of the present application can be applied to the application environment shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through a network. The communication network can be a wired network or a wireless network. Therefore, the terminal 102 and the server 104 can be directly or indirectly connected through wired or wireless communication. For example, the terminal 102 can be indirectly connected to the server 104 through a wireless access point, or the terminal 102 is directly connected to the server 104 through the Internet, and the present application does not limit this.

[0058] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers.

[0059] It should be noted that the method for analyzing the mixing capacity of a high-speed mixer in the embodiment of the present application can be executed by the terminal 102 or the server 104 alone, or can be executed by the terminal 102 and the server 104 together. Taking the case where the terminal 102 executes alone as an example, in the process of analyzing the mixing capacity of the high-speed mixer, the terminal 102: constructs a mixing work simulation model for the high-speed mixer based on the equipment information and process parameters of the high-speed mixer; in the mixing work simulation model, the tank of the high-speed mixer is divided into multiple mixing areas; then, based on the mixing work simulation model, simulates the mixing process of multiple materials in the tank, and determines the material amount of each material in each mixing area at different times; for each moment, performs statistical analysis on the amount of each material at that moment, and obtains the mixing uniformity corresponding to that moment; based on the mixing uniformity corresponding to each moment, determines the mixing capacity of the high-speed mixer.

[0060] It can be understood that in some embodiments, if the computing power of the terminal 102 cannot meet the modeling requirements, the server 104 can build a mixed working simulation model for the high-speed mixer based on the equipment information and process parameters of the high-speed mixer, and the terminal 102 obtains the mixed working simulation model from the server 104, and simulates the mixing process of the high-speed mixer based on the mixed working simulation model to determine the mixing capacity of the high-speed mixer.

[0061] In one embodiment, as shown in FIG2 , a method for analyzing the mixing capability of a high-speed mixer is provided. The method may be executed by a computer device, and the computer device may be a terminal or a server as shown in FIG1 . In this embodiment, the method is applied to the terminal in FIG1 as an example for description, and the method includes the following steps:

[0062] Step S202: constructing a hybrid operation simulation model for the high-speed mixer based on the equipment information and process parameters of the high-speed mixer.

[0063] Among them, the equipment information of the high-speed mixer may include structural attribute information and material attribute information. The structural attribute information may specifically include tank structure information, blade structure information, etc., and the material attribute information may include tank material information and blade material information, etc. For example, the blade structure information may include information such as blade shape and size. The tank material information may include tank density, tank Poisson's ratio, tank shear modulus, etc. Further, the process parameters of the high-speed mixer may include rotation speed, filling amount, mixing time, etc.

[0064] Specifically, the terminal can obtain the equipment parameters and process parameters of the high-speed mixer, and model the high-speed mixer based on the equipment parameters and process parameters to obtain a mixing operation simulation model of the high-speed mixer. Furthermore, in the mixing operation simulation model, the tank of the high-speed mixer is divided into multiple mixing areas.

[0065] In an optional embodiment, the terminal can establish a three-dimensional model of the tank and blades of the high-speed mixer according to the equipment parameters of the high-speed mixer. On this basis, the motion parameters of the blades in the three-dimensional model are set according to the process parameters of the high-speed mixer, thereby obtaining a mixing work simulation model that can simulate the mixing work.

[0066] Step S204, based on the mixing work simulation model, simulate the mixing process of multiple materials in the tank, and determine the material quantity of each material in each mixing area at different times.

[0067] It can be understood that due to the differences in material and shape of different materials, the specific mixing process is also different. Based on this, in order to simulate the mixing process of materials in the tank body, it is also necessary to set the material property parameters and interaction parameters of the mixing work simulation model. The material property parameters, for example, may include loose density, tap density, dynamic angle of repose, Poisson's ratio, shear modulus, material moisture content, particle size distribution, morphology, etc. The interaction parameters, for example, may include: inter-particle restitution coefficient, inter-particle static friction coefficient, inter-particle rolling friction coefficient, particle and tank inner wall restitution coefficient, particle and tank inner wall static friction coefficient, particle and tank inner wall rolling friction coefficient, etc. Wherein, the materials to be mixed are all in a granular state.

[0068] Specifically, the terminal can obtain the material attribute parameters of the material, and build a three-dimensional discrete element model of the sample material based on the material attribute parameters. Then, on the basis of the three-dimensional discrete element model and the mixing work simulation model, the interaction parameters are set, the mixing process simulation is performed, and the matching of the simulation results with the actual mixing results of the material is determined. In the case where the simulation results do not match the actual mixing results, the interaction parameters are adjusted, and the mixing process simulation is performed again until the simulation results match the actual mixing results, and the model parameter calibration for the material is completed. Repeat the above process until all the materials to be mixed have completed the parameter calibration, and a mixing work simulation model that can simulate the mixing process of these materials in the tank can be obtained.

[0069] The terminal can simulate the mixing process of each material in the tank body by using the mixed work simulation model with completed parameter calibration, and then determine the material quantity of each material in each mixing zone at different times through the model. The material quantity can refer to the material mass or the material volume. In a specific implementation, the terminal can distinguish and mark different types of materials in the process of simulating material mixing. During the material mixing simulation process, the computer can intercept the three-dimensional working image of the tank body at a certain moment, and the three-dimensional working image of the tank body includes the tank body image and the material images of each material in the tank body. Then, the terminal can separate the three-dimensional material images of each material from the three-dimensional working image of the tank body through the material mark, and then the material quantity statistics can be performed for different mixing zones and different materials. Exemplarily, the volume of material j falling into the mixing zone i can be counted through the three-dimensional image of the material at time t, and then the mass Mt_ij of material j in the mixing zone i at time t can be calculated according to the density of the material j.

[0070] In a specific embodiment, the construction process of the mixing work simulation model includes: obtaining sample data of sample materials mixed in a high-speed mixer; constructing a high-speed mixer three-dimensional model including a high-speed mixer tank and blades based on the structural attribute information and material attribute information of the high-speed mixer; constructing an initial working model based on the process attribute information and the high-speed mixer three-dimensional model of the high-speed mixer; simulating the mixing process of the sample materials under the model parameters corresponding to the actual process parameters according to the material information of the sample materials and the initial working model, and determining the simulated mixing uniformity; in the case where the simulated mixing uniformity does not match the actual mixing uniformity, adjusting the model parameters of the initial working model according to the difference between the two, and returning to the step of simulating the mixing process of the sample materials under the model parameters corresponding to the actual process parameters to determine the simulated mixing uniformity; in the case where the simulated mixing uniformity matches the actual mixing uniformity, obtaining a mixing work simulation model for simulating the mixing process of materials in the high-speed mixer tank. Wherein, the sample data includes the actual process parameters and the actual mixing uniformity; the model parameters include the interaction parameters, modulus, etc. between the high-speed mixer and the material. The simulated mixing uniformity does not match the actual mixing uniformity, which may mean that the difference between the two is greater than or equal to the set threshold.

[0071] Step S206: for each moment, statistical analysis is performed on the amount of each material at that moment to obtain the mixing uniformity corresponding to that moment.

[0072] Among them, the mixing uniformity corresponding to a certain moment can represent the uniform distribution degree of various materials in the high-speed mixer tank at that moment.

[0073] In an optional embodiment, for each moment, a statistical analysis is performed on the amount of each material at that moment to obtain the mixing uniformity corresponding to that moment, including: for each material at each moment, according to the material amount of the material distributed in each mixing area at that moment, determining the material uniformity corresponding to the material; performing a statistical analysis on the material uniformity of each material to determine the mixing uniformity corresponding to that moment.

[0074] Specifically, the terminal can determine the mixing uniformity by comparing the material amounts of different mixing areas. It can be understood that for the same material, the closer the material amounts of different mixing areas are, the better the mixing uniformity. Based on this, the terminal can determine the material uniformity corresponding to the material for each material at a specific moment by comparing the material amount difference between the material amounts of each material in each mixing area. Exemplarily, the terminal can determine the material uniformity based on the maximum material amount difference, and the material uniformity is inversely correlated with the maximum difference. The terminal can also determine the material uniformity based on the median of the material amount difference and the span of the distribution interval, and the material uniformity is inversely correlated with the median of the difference and the span of the distribution interval. After obtaining the material uniformity of each material, the terminal can determine the mixing uniformity corresponding to the moment by performing statistical calculations on the uniformity of each material.

[0075] In an optional embodiment, the terminal can determine the reference distribution information of each material in an absolute mixing state at a specific moment; then, based on the material amount of each material corresponding to each mixing zone at that moment, determine the material distribution information of each material in each mixing zone; then, based on the difference between the material distribution information of each material in each mixing zone and the reference distribution information, determine the mixing uniformity of the tank at that moment. The smaller the difference between the material distribution information and the reference distribution information, the better the mixing uniformity of the tank.

[0076] Step S208: determining the mixing capacity of the high-speed mixer based on the mixing uniformity corresponding to each moment.

[0077] The mixing capacity of the high-speed mixer can be characterized by the maximum value of the mixing uniformity of the high-speed mixer, by the relationship between the mixing uniformity and the working time, or by the correlation between the energy consumption and the mixing uniformity.

[0078] Optionally, the terminal can determine the mixing uniformity curve of the high-speed mixer tank as a function of the working time based on the mixing uniformity corresponding to each moment, and then determine the maximum mixing uniformity of the high-speed mixer. The maximum mixing uniformity can characterize the mixing ability of the high-speed mixer for these materials.

[0079] Optionally, the terminal can determine the torque and angular velocity of each blade of the high-speed mixer at each moment based on the mixing work simulation model; calculate the equipment power of the high-speed mixer at that moment according to each torque and each angular velocity; determine the correlation between the energy consumption and mixing uniformity of the high-speed mixer during the simulated mixing process of each material based on the respective equipment power and mixing uniformity at each moment, and then obtain the correlation to characterize the mixing capacity of the high-speed mixer.

[0080] The above-mentioned high-mixer mixing capacity analysis method builds a mixing work simulation model for the high-mixer based on the equipment information and process parameters of the high-mixer; based on the mixing work simulation model, simulates the mixing process of multiple materials to determine the material quantity of each material in each mixing zone in the tank at different times; for each moment, statistical analysis is performed on the material quantity at that moment to obtain the mixing uniformity corresponding to that moment, and the mixing capacity of the high-mixer is determined based on the mixing uniformity corresponding to each moment. In the above process, the material quantity of each material in different mixing zones at different times is accurately obtained by simulation, and the mixing uniformity is further obtained by statistically analyzing these material quantities, which can realize quantitative evaluation of the mixing capacity of the high-mixer and is conducive to improving the accuracy of the mixing capacity evaluation results.

[0081] In one embodiment, step S206 includes: for each material, determining the reference distribution information of the material in an absolute mixing state; based on the material quantity of the material corresponding to each mixing zone at that moment, determining the material distribution information of the material in each mixing zone; based on the difference between the material distribution information of each material in different mixing zones and the reference distribution information, determining the mixing uniformity of the tank at that moment.

[0082] Among them, the reference distribution information and the material distribution information are the same type of information. For example, the reference distribution information and the material distribution information can be mass fractions or volume fractions. The mass fraction of a certain material in a certain mixing zone represents the proportion of the mass of the material in the mixing zone relative to the total mass of the materials in the mixing zone. Exemplarily, the mass fraction of the jth material in the mixing zone i at time t can be expressed as S(i,j,t)=Mt_ij / (Mt_i1+…+Mt_ij+…+Mt_im). Mt_ij represents the mass of material j in the mixing zone i at time t, and m is the number of material types.

[0083] The absolute mixing state refers to the ideal state in which the material tends to be homogenized at a macroscopic level. Mechanical stirring can effectively mix different types of material particles. For example, in a continuously rotating mixing device, powder particles are constantly dispersed and spread out at the edge of their surface, and the particles penetrate and blend with each other. This mixing process can achieve a completely uniform mixing degree at a macroscopic level. Specifically, the terminal can determine the reference material amount in the absolute mixing state for each material based on the total amount of the material and the number of mixing zones. The reference material amount is the ratio of the total amount of the material to the number of mixing zones. Then, based on the reference material amount of each material, the terminal can determine the reference distribution information corresponding to each material. Exemplarily, the reference distribution information Sj=Hj / (H1+…+Hj+…+Hm). Hj represents the reference material amount of material j.

[0084] Furthermore, for each material at each moment, the terminal can determine the material distribution information of each material in each mixing zone based on the material quantity of each material in each mixing zone at that moment, and then determine the mixing uniformity of the tank at that moment based on the difference between the material distribution information of each material in different mixing zones and the reference distribution information. The difference between the material distribution information and the reference distribution information can be represented by a difference or a ratio, which is not limited here.

[0085] It can be understood that the smaller the difference between the material distribution information and the reference distribution information, the closer the actual mixing state of the material is to the absolute mixing state; for the same mixing zone, the more types of materials close to the absolute mixing state, the better the mixing uniformity in the mixing zone; for the same material, the more mixing zones close to the absolute mixing state, the better the mixing uniformity of the material. Optionally, the terminal can determine the mixing uniformity of the tank according to the number of materials close to the absolute mixing state in each material, and / or the number of mixing zones close to the absolute mixing state for the same material.

[0086] In the above embodiment, the reference distribution information of the material in an absolute mixed state is used as a reference, and the mixing uniformity of the tank at that moment is determined based on the differences between the material distribution information of each material in different mixing areas and the reference distribution information. This can further improve the accuracy of the mixing uniformity, thereby improving the accuracy of the mixing capacity of the mixer determined based on the mixing uniformity.

[0087] In one embodiment, the material quantity is mass; the material distribution information is the actual mass fraction; and the reference distribution information is the reference mass fraction. In the case of this embodiment, based on the differences between the material distribution information of each material in different mixing zones and the reference distribution information, the mixing uniformity of the tank at that moment is determined, including: for each material, determining the relative difference between the actual mass fraction of the material in different mixing zones at that moment and the reference mass fraction of the material; and performing mean statistics on multiple relative differences corresponding to each material to determine the mixing uniformity of the tank at that moment.

[0088] Among them, the specific definition of mass fraction is as mentioned above and will not be repeated here. Specifically, the terminal can determine the relative difference between the actual mass fraction of each material in different mixing areas at that moment and the reference mass fraction of the material by taking the difference or ratio for each material. Then, the mean statistics of multiple relative differences corresponding to each material are performed to determine the mixing uniformity of the tank at that moment. Optionally, the terminal can determine the geometric mean or arithmetic mean of each relative difference as the mixing uniformity of the tank.

[0089] In a specific implementation, the mixing uniformity St of the high-speed mixer tank at time t is expressed as:

[0090]

[0091] Where n is the number of partitions, and m is the number of material types. Sj represents the reference mass fraction of material j in the absolute mixing state. Furthermore, if there is no material in the mixing zone i at time t, that is, Mt_i1+…+Mt_ij+…+Mt_im=0, then the actual mass fraction S(i,j,t) assigned to the partition is the set value, which can be Sj and is not limited here. It can be understood that in this case, the closer the mixing uniformity St is to zero, the stronger the mixing ability of the high-speed mixer is.

[0092] In the above embodiment, the relative differences between the actual mass fractions of different materials in different mixing zones and the reference mass fractions of the materials are averaged and the mixing uniformity of the tank is determined, which can further improve the accuracy of the mixing uniformity.

[0093] In one embodiment, the method for analyzing the mixing capacity of a high-speed mixer also includes: when the mixing capacity does not match the actual demand, selecting target particles from the material particles of each material; identifying the low-activity area of ​​the tank based on the movement trajectory of the target particles in the simulated mixing process; determining the optimization parameters of the high-speed mixer according to the position of the low-activity area in the tank, and updating the mixing work simulation model accordingly; returning to the step of simulating the mixing process of multiple materials in the tank based on the mixing work simulation model, and determining the material quantity of each material in each mixing area at different times, until the mixing capacity matches the actual demand.

[0094] The optimization parameters include at least one of structural parameters or process parameters. The structural parameters may include blade structure, blade position, etc. The blade structure may include blade length, blade angle, blade shape, blade quantity, etc. The blade position refers to the position of each set of blades when multiple sets of blades are arranged in a high-speed mixer. The process parameters may include rotation speed, filling amount, mixing time, etc.

[0095] It can be understood that the mixing capacity determined by the simulation in this application can be used to guide actual production applications. Specifically, when the mixing capacity of the high-speed mixer meets the actual application requirements, the staff can use the analysis results as a reference and control the high-speed mixer to perform actual mixing operations on these materials according to the process parameters used in the simulation process.

[0096] When the mixing capacity of the high-speed mixer does not match the actual demand, it means that the structure or process parameters of the high-speed mixer need to be optimized before it can be used for actual mixing operations. In this case, the terminal can select target particles from the material particles of each material. The material types of these target particles can be the same or different. Then, the terminal can identify the low-activity area of ​​the tank based on the running trajectory of the target particles in the simulated mixing process. The low-activity area refers to an area where the activity intensity or activity frequency of the particles is relatively low. Among them, the activity intensity can be characterized by the trajectory length of the particles in the area, and the activity frequency can be characterized by the number of times the particles appear in the area or the number of times the activity direction changes.

[0097] In an optional embodiment, the terminal can select the longest time Δt required for each blade to make one revolution, and output no less than 2 frames of velocity vector results from time t to time t+Δt. After outputting the two velocity vectors with the highest speed for each mixing zone, the terminal can integrate the velocity vectors of each frame to determine the low-speed area. The terminal can also slice through the axis of the main propeller, randomly select x particles on the slice surface, generate the trajectory of these particles based on the simulated mixing process, and determine the activity of the particles in the tank based on the trajectory. Then, the terminal can combine the above results to determine the movement pattern of the particles and identify low-activity areas for optimization.

[0098] Furthermore, after identifying the low-activity area, the terminal can determine the optimization parameters of the high-speed mixer according to the location of the low-activity area in the tank, and update the mixing operation simulation model accordingly. For example, the blade length or blade angle can be increased to enhance the movement of particles in the low-activity area; for another example, the blade position can be adjusted to make it closer to the low-activity area to achieve the purpose of enhancing the activity; for another example, the blade speed or mixing time can be increased to enhance the movement of particles in the low-activity area.

[0099] After obtaining the updated mixing work simulation model, the terminal can return to the mixing work simulation model to simulate the mixing process of multiple materials in the tank, and determine the steps of the material quantity of each material in each mixing area at different times. In this cycle, the mixing capacity of the high-speed mixer can be improved through multiple rounds of simulation and optimization until the mixing capacity matches the actual demand.

[0100] In the above embodiments, the mixing capacity of the high-speed mixer is optimized by adjusting the structural parameters or process parameters, which is not limited by the influence of the hardware and software environment such as experimental equipment and experimental conditions on the mixing capacity of the high-speed mixer, and has positive significance for the selection and design of the high-speed mixer.

[0101] In a specific embodiment, the method for analyzing the mixing capacity of a high-speed mixer further includes: obtaining the size of the high-speed mixer and the particle size of each material; determining the upper limit of the number of partitions of the tank of the high-speed mixer according to the size and the particle size; and determining the number of mixing zones in the tank based on the upper limit of the number of partitions.

[0102] The upper limit of the number of partitions is positively correlated with the size of the high-speed mixer and negatively correlated with the maximum particle size among all particle sizes. In actual applications, the terminal can divide the tank of the high-speed mixer into multiple mixing zones based on the size of the high-speed mixer and the particle size of the material to be mixed, so as to ensure that the analysis process of the mixing capacity matches the actual application scenario, thereby improving the accuracy of the analysis results.

[0103] Specifically, the terminal can obtain the size of the high-speed mixer and the particle size of each material. The size may include at least one of the equipment length, width or height, and may also include the tank diameter, etc. The particle size of a certain material may refer to the median particle size, mean particle size, maximum or minimum particle size, etc. of the material. According to the size of the high-speed mixer and the particle size of each material, the terminal can determine the upper limit of the number of partitions of the tank of the high-speed mixer. Exemplarily, the upper limit of the number of partitions a can be expressed as: a=w*l*h / (1000*Dmax^3), where w is the equipment width of the high-speed mixer, l is the equipment length of the high-speed mixer, h is the equipment height of the high-speed mixer, and Dmax is the maximum particle size of each particle size to ensure that a certain amount of material particles can be placed in each direction.

[0104] After determining the upper limit of the number of partitions, the terminal can directly determine the number of divisions of the mixing area in the tank according to the upper limit of the number of partitions. For example, based on the upper limit of the number of partitions, subtract the preset number to obtain the number of divisions of the mixing area in the tank. In a specific implementation, the number of divisions n of the mixing area satisfies 27≤n≤a, which can avoid the problem of low accuracy of the analysis result caused by a small number of partitions.

[0105] It can be understood that the more mixing zones are divided, the greater the amount of calculation in the process of mixing capacity analysis, the higher the computing power requirements for the computer, but the more accurate the determined mixing capacity analysis results are. Based on this, the terminal can also determine the number of mixing zones in the tank based on the upper limit of the number of partitions, combined with the computing power of its own equipment and actual application needs. For example, when the computing power of its own equipment is strong, the largest possible number of partitions is selected on the basis of meeting the upper limit of the number of partitions. In a specific implementation, the determined number of mixing zone divisions n can be expressed as: n=k*a. Among them, k<1 is the computing power attenuation coefficient, which can be determined by the difference between the actual computing power of the equipment and the theoretical computing power of the equipment.

[0106] Among them, the computing power of a computer device, usually referred to as "computing power", refers to the ability of a computer or computing system to process data and execute instructions per unit time. Computing power is an important indicator to measure the speed and ability of a computer or computing device to perform computing tasks. It is usually measured in floating-point operations per second (FLOPS) to measure the processing speed and computing power of a computing device or system. The computing power of a computer is affected by many factors, including hardware configurations such as processor speed, memory capacity, and storage bandwidth. In this embodiment, the computing power can be read through a tool or determined based on the hardware configuration of the terminal device. For example, the processor speed of the terminal device can be divided by the theoretical processor speed matched by the upper limit of the number of partitions to obtain the computing power attenuation coefficient, and then the number of divisions of the mixing zone can be determined.

[0107] In the above embodiment, the number of partitions of the mixing zone of the high-speed mixer is determined in combination with the size of the high-speed mixer and the particle size of each material, which can make the number of partitions more consistent with the actual application scenario, and is conducive to further improving the accuracy of the mixing capacity analysis results.

[0108] In one embodiment, the mixing capacity of the high-speed mixer is determined based on the mixing uniformity corresponding to each moment, including: for each moment, based on the mixing work simulation model, determining the torque and angular velocity of each blade of the high-speed mixer at that moment; according to each torque and each angular velocity, calculating the equipment power of the high-speed mixer at that moment; based on the respective equipment power and mixing uniformity at each moment, determining the correlation between the energy consumption of the high-speed mixer and the mixing uniformity during the simulated mixing process of each material.

[0109] The correlation between the energy consumption of the high-speed mixer and the mixing uniformity is used to characterize the mixing capacity of the high-speed mixer. Specifically, the terminal can determine the torque T of each blade of the high-speed mixer at each moment t based on the mixing work simulation model. k and angular velocity w k , and then the equipment power Pt of the high-speed mixer at that moment is obtained statistically. For example, Pt can be expressed as ∑T k *w k . Where k is greater than 1 and less than p, and p is the number of blades. The energy consumption Wt of the high-speed mixer is the integral of power over time, which can be expressed as ∫Ptdt. Based on this, the terminal can obtain the energy consumption at each moment by integrating the power of each device at each moment, and then determine the correlation between the energy consumption of the high-speed mixer and the mixing uniformity during the simulated mixing process of each material based on the power of each device at each moment and the mixing uniformity.

[0110] In a specific implementation, the terminal can draw an energy consumption-mixing uniformity chart with the equipment energy consumption Wt as the horizontal coordinate and the mixing uniformity St as the vertical coordinate, and determine the mathematical model describing the correlation between energy consumption and mixing uniformity by fitting each group of data in the above chart. It can be understood that according to the mathematical model, the limit mixing uniformity of the continuous increase of energy consumption Wt can be determined, and the turning point (Wt, St) at which the numerical decrease rate of the mixing uniformity St (the closer St is to 0, the higher the degree of material mixing) slows down as the energy consumption Wt increases can also be determined as the maximum efficiency mixing uniformity. The maximum efficiency mixing uniformity can be used as the mixing termination condition of the actual mixing process when the mixing requirements are not high but the energy consumption requirements are high. The corresponding time t can be used to determine the mixing time; the limit mixing uniformity can be used as the mixing termination condition of the actual mixing process when the mixing requirements are high.

[0111] In the above embodiment, the equipment power of the high-speed mixer in the simulated mixing process is counted, and then the correlation between the energy consumption of the high-speed mixer and the mixing uniformity in the simulated mixing process of each material is determined, which can provide a reference for determining the subsequent process parameters, and thus can take into account both the mixing effect between the materials and the equipment energy consumption.

[0112] In one embodiment, another high-mixer mixing capability analysis method is provided, which can be applied to a computer device, such as the terminal in FIG1 . In this embodiment, as shown in FIG3 , the high-mixer mixing capability analysis method includes:

[0113] Step S301, obtaining equipment information and process parameters of a high-speed mixer, and material attribute information of each material;

[0114] Among them, equipment information includes size; material attribute information includes particle size;

[0115] Step S302, determining the upper limit of the number of partitions of the tank of the high-speed mixer according to the size of the high-speed mixer and the particle size of each material;

[0116] Among them, the upper limit of the number of partitions is positively correlated with the size, and negatively correlated with the maximum particle size among all particle sizes;

[0117] Step S303, determining the number n of mixing zones in the tank of the high-speed mixer based on the upper limit of the number of zones;

[0118] Step S304, constructing a mixing operation simulation model for the high-speed mixer according to the equipment information and process parameters of the high-speed mixer;

[0119] Among them, in the mixing work simulation model, the tank of the high-speed mixer is divided into n mixing areas;

[0120] Step S305, determining the model parameters of the mixing work simulation model based on the property information of each material, simulating the mixing process of each material in the tank, and determining the material mass of each material in each mixing area at different times;

[0121] Step S306, for each material, determining a reference mass fraction of the material in an absolute mixed state;

[0122] Step S307, for each moment, based on the material masses of the material corresponding to the different mixing zones at that moment, determine the material distribution information of the material in each mixing zone;

[0123] Step S308, for each material, determining the relative difference between the actual mass fraction of the material in different mixing zones at that moment and the reference mass fraction of the material;

[0124] Step S309, performing mean statistics on multiple relative differences corresponding to each material to determine the mixing uniformity in the high-speed mixer tank at that moment;

[0125] Step S310, determining the mixing capacity of the high-speed mixer based on the mixing uniformity corresponding to each moment;

[0126] Step S311, when the mixing capacity does not match the actual demand, select target particles from the material particles of each material;

[0127] Step S312, identifying the low activity area of ​​the tank based on the movement trajectory of the target particles during the simulated mixing process;

[0128] Step S313, determining the optimization parameters of the high-pressure mixer according to the location of the low-activity area in the tank, and updating the mixing operation simulation model accordingly; returning to step S305;

[0129] Wherein, the optimization parameter includes at least one of a structural parameter or a process parameter;

[0130] Step S314, when the hybrid capacity matches the actual demand, output the hybrid capacity analysis result;

[0131] Among them, the mixing capacity analysis results include the structural parameters and process parameters of the high-speed mixer.

[0132] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0133] Based on the same inventive concept, the embodiment of the present application also provides a high-mixer mixing capacity analysis device for implementing the high-mixer mixing capacity analysis method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more high-mixer mixing capacity analysis device embodiments provided below can refer to the limitations of the high-mixer mixing capacity analysis method above, and will not be repeated here.

[0134] In one embodiment, as shown in FIG4 , a high-speed mixer mixing capacity analysis device is provided, comprising:

[0135] Step S301, obtaining equipment information and process parameters of a high-speed mixer, and material attribute information of each material;

[0136] Among them, equipment information includes size; material attribute information includes particle size;

[0137] Step S302, determining the upper limit of the number of partitions of the tank of the high-speed mixer according to the size of the high-speed mixer and the particle size of each material;

[0138] Among them, the upper limit of the number of partitions is positively correlated with the size, and negatively correlated with the maximum particle size among all particle sizes;

[0139] Step S303, determining the number n of mixing zones in the tank of the high-speed mixer based on the upper limit of the number of zones;

[0140] Step S304, constructing a mixing operation simulation model for the high-speed mixer according to the equipment information and process parameters of the high-speed mixer;

[0141] Among them, in the mixing work simulation model, the tank of the high-speed mixer is divided into n mixing areas;

[0142] Step S305, determining the model parameters of the mixing work simulation model based on the property information of each material, simulating the mixing process of each material in the tank, and determining the material mass of each material in each mixing area at different times;

[0143] Step S306, for each material, determining a reference mass fraction of the material in an absolute mixed state;

[0144] Step S307, for each moment, based on the material masses of the material corresponding to the different mixing zones at that moment, determine the material distribution information of the material in each mixing zone;

[0145] Step S308, for each material, determining the relative difference between the actual mass fraction of the material in different mixing zones at that moment and the reference mass fraction of the material;

[0146] Step S309, performing mean statistics on multiple relative differences corresponding to each material to determine the mixing uniformity in the high-speed mixer tank at that moment;

[0147] Step S310, determining the mixing capacity of the high-speed mixer based on the mixing uniformity corresponding to each moment;

[0148] Step S311, when the mixing capacity does not match the actual demand, select target particles from the material particles of each material;

[0149] Step S312, identifying the low activity area of ​​the tank based on the movement trajectory of the target particles during the simulated mixing process;

[0150] Step S313, determining the optimization parameters of the high-pressure mixer according to the location of the low-activity area in the tank, and updating the mixing operation simulation model accordingly; returning to step S305;

[0151] Wherein, the optimization parameter includes at least one of a structural parameter or a process parameter;

[0152] Step S314, when the hybrid capacity matches the actual demand, output the hybrid capacity analysis result;

[0153] Among them, the mixing capacity analysis results include the structural parameters and process parameters of the high-speed mixer.

[0154] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0155] Based on the same inventive concept, the embodiment of the present application also provides a high-mixer mixing capacity analysis device for implementing the high-mixer mixing capacity analysis method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more high-mixer mixing capacity analysis device embodiments provided below can refer to the limitations of the high-mixer mixing capacity analysis method above, and will not be repeated here.

[0156] In an exemplary embodiment, as shown in FIG4 , a high-speed mixer mixing capacity analysis device is provided, comprising:

[0157] The model building module 401 is used to build a mixing work simulation model for the high-speed mixer based on the equipment information and process parameters of the high-speed mixer; in the mixing work simulation model, the tank of the high-speed mixer is divided into multiple mixing areas;

[0158] A mixing process simulation module 402 is used to simulate the mixing process of multiple materials in the tank based on the mixing work simulation model, and determine the material quantity of each material in each mixing area at different times;

[0159] The mixing uniformity analysis module 403 is used to perform statistical analysis on the amount of each material at each moment to obtain the mixing uniformity corresponding to the moment;

[0160] A mixing capacity determination module 404, for determining the mixing capacity of the high speed mixer based on the mixing uniformity corresponding to each moment;

[0161] Preferably, the mixing uniformity analysis module 403 is specifically used to: at each moment, for each material, determine the material uniformity corresponding to the material according to the material quantity distributed in each mixing area at that moment; perform statistical analysis on the material uniformity of each material to determine the mixing uniformity corresponding to the moment.

[0162] In one embodiment, the mixing uniformity analysis module 403 includes: a reference information determination unit, which is used to determine, for each material, the reference distribution information of the material in an absolute mixing state; a distribution information determination unit, which is used to determine the material distribution information of the material in each mixing zone based on the material quantity corresponding to each mixing zone at a certain moment; and a mixing uniformity determination unit, which is used to determine the mixing uniformity of the tank at a certain moment based on the difference between the material distribution information of each material in different mixing zones and the reference distribution information.

[0163] In one embodiment, the material quantity is mass; the material distribution information is the actual mass fraction; and the reference distribution information is the reference mass fraction. In the case of this embodiment, the mixing uniformity determination unit is specifically used to: for each material, determine the relative difference between the actual mass fraction of the material in different mixing zones at a time and the reference mass fraction of the material; perform mean statistics on multiple relative differences corresponding to each material, and determine the mixing uniformity of the tank at a time.

[0164] In one embodiment, the high-speed mixer mixing capacity analysis device also includes an optimization module, which is used to: select target particles from the material particles of each material when the mixing capacity does not match the actual demand; identify the low-activity area of ​​the tank based on the movement trajectory of the target particles in the simulated mixing process; determine the optimization parameters of the high-speed mixer according to the position of the low-activity area in the tank, and update the mixing work simulation model accordingly; the optimization parameters include at least one of the structural parameters or the process parameters; return to the step of simulating the mixing process of multiple materials in the tank based on the mixing work simulation model, and determine the material quantity of each material in each mixing area at different times, until the mixing capacity matches the actual demand.

[0165] In one embodiment, the high-speed mixer mixing capacity analysis device further includes a partition number determination module, which is used to: obtain the size of the high-speed mixer and the particle size of each material; determine the upper limit of the number of partitions of the tank of the high-speed mixer according to the size and each particle size; the upper limit of the number of partitions is positively correlated with the size and negatively correlated with the maximum particle size among the particle sizes; based on the upper limit of the number of partitions, determine the number of mixing zones in the tank; preferably, the upper limit of the number of partitions a can be expressed as:

[0166] a=w*l*h / (1000*Dmax^3)

[0167] Wherein, w is the equipment width of the high-speed mixer, l is the equipment length of the high-speed mixer, h is the equipment height of the high-speed mixer, and Dmax is the maximum particle size among the particle sizes;

[0168] Preferably, determining the number of divisions of the mixing zone in the tank based on the upper limit of the number of partitions includes:

[0169] On the basis of the upper limit of the number of partitions, the computing capacity attenuation coefficient is superimposed to determine the number of mixing zones in the tank; the computing capacity attenuation coefficient is determined according to the difference between the actual computing capacity of the equipment and the theoretical computing capacity of the equipment.

[0170] In one of the embodiments, the mixing capacity determination module 404 is specifically used to: for each moment, based on the mixing work simulation model, determine the torque and angular velocity of each blade of the high-speed mixer at the moment; according to each torque and each angular velocity, count the equipment power of the high-speed mixer at the moment; based on the respective equipment power and mixing uniformity at each moment, determine the correlation between the energy consumption of the high-speed mixer and the mixing uniformity during the simulated mixing process of each material; the correlation is used to characterize the mixing capacity of the high-speed mixer.

[0171] Each module in the above-mentioned high-mixer mixing capacity analysis device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0172] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG5. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a high-mixer hybrid capability analysis method is implemented. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse, etc.

[0173] Those skilled in the art will understand that the structure shown in FIG. 5 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0174] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of any of the above method embodiments when executing the computer program.

[0175] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above method embodiments are implemented.

[0176] In one embodiment, a computer program product is provided, including a computer program, which implements the steps of any of the above method embodiments when executed by a processor.

[0177] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards in the relevant regions and areas. In addition, the subject can choose not to authorize the subject information and related data, or refuse or conveniently refuse to push information, etc.

[0178] In this application, when collecting and processing relevant data during the instance application, the requirements of the laws and regulations of the relevant regions should be strictly followed to obtain the informed consent or separate consent of the personal information subject, and subsequent data use and processing should be carried out within the scope of authorization of the laws and regulations and the personal information subject.

[0179] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0180] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for analyzing the mixing capacity of a high-speed mixer, characterized in that: The method comprises: Based on the equipment information and process parameters of the high-speed mixer, a mixing operation simulation model for the high-speed mixer is constructed; in the mixing operation simulation model, the tank of the high-speed mixer is divided into a plurality of mixing zones; Based on the mixing work simulation model, the mixing process of multiple materials in the tank is simulated to determine the material quantity of each material in each mixing area at different times; For each of the moments, statistical analysis is performed on the amounts of the materials at the moments to obtain the mixing uniformity corresponding to the moments; Determining the mixing capacity of the high-speed mixer based on the mixing uniformity corresponding to each of the moments; Preferably, for each of the moments, statistical analysis is performed on the amounts of the materials at the moment to obtain the mixing uniformity corresponding to the moment, including: At each of the materials at each of the moments, according to the amount of the materials distributed in each of the mixing zones at the moments, determine the material uniformity corresponding to the materials; Statistical analysis is performed on the material uniformity of each of the materials to determine the mixing uniformity corresponding to the moment.

2. The method according to claim 1, characterized in that The statistical analysis of the amount of each material at the time to obtain the mixing uniformity corresponding to the time includes: For each of the materials, determining reference distribution information of the material in an absolute mixed state; Determine the material distribution information of the material in each of the mixing zones based on the material quantities of the material corresponding to the different mixing zones at the time; Based on the differences between the material distribution information of each material in different mixing areas and the reference distribution information, the mixing uniformity of the tank at the moment is determined.

3. The method according to claim 2, characterized in that The material quantity is mass; the material distribution information is actual mass fraction; the reference distribution information is reference mass fraction; The determining the mixing uniformity of the tank at the moment based on the difference between the material distribution information of each material in different mixing zones and the reference distribution information includes: For each of the materials, determining the relative difference between the actual mass fraction of the material in different mixing zones at the time and the reference mass fraction of the material; The mean values ​​of the multiple relative differences corresponding to each of the materials are statistically analyzed to determine the mixing uniformity of the tank at the moment.

4. The method according to claim 1, characterized in that: The method further comprises: In the case where the mixing capacity does not match the actual demand, selecting target particles from the material particles of each of the materials; identifying a low activity area of ​​the tank based on the movement trajectory of the target particles during the simulated mixing process; Determine the optimization parameters of the high-speed mixer according to the position of the low-activity area in the tank, and update the mixing operation simulation model accordingly; the optimization parameters include at least one of structural parameters or process parameters; Return to the step of simulating the mixing process of multiple materials in the tank based on the mixing work simulation model, and determining the material quantity of each material in each mixing area at different times until the mixing capacity matches the actual demand.

5. The method according to claim 1, characterized in that The method further comprises: Obtaining the size of the high-speed mixer and the particle size of each of the materials; According to the size and each of the particle sizes, determining the upper limit of the number of partitions of the tank of the high-speed mixer; the upper limit of the number of partitions is positively correlated with the size and negatively correlated with the maximum particle size among the particle sizes; Based on the upper limit of the number of partitions, determining the number of divisions of the mixing zone in the tank; Preferably, the upper limit a of the number of partitions can be expressed as: a=w*l*h / (1000*Dmax^3) Wherein, w is the equipment width of the high-speed mixer, l is the equipment length of the high-speed mixer, h is the equipment height of the high-speed mixer, and Dmax is the maximum particle size among the particle sizes; Preferably, determining the number of divisions of the mixing zone in the tank based on the upper limit of the number of partitions includes: On the basis of the upper limit of the number of partitions, the computing capacity attenuation coefficient is superimposed to determine the number of mixing zones in the tank; the computing capacity attenuation coefficient is determined according to the difference between the actual computing capacity of the equipment and the theoretical computing capacity of the equipment.

6. The method according to any one of claims 1 to 5, characterized in that The determining the mixing capacity of the high speed mixer based on the mixing uniformity corresponding to each of the moments includes: For each of the moments, based on the hybrid operation simulation model, determining the torque and angular velocity of each blade of the high-speed hybrid at the moment; According to each of the torques and each of the angular velocities, calculating the equipment power of the high-speed mixer at the moment; Based on the respective equipment power and mixing uniformity at each of the moments, the correlation between the energy consumption of the high-speed mixer and the mixing uniformity during the simulated mixing of each of the materials is determined; the correlation is used to characterize the mixing capacity of the high-speed mixer.

7. A high-speed mixer mixing capacity analysis device, characterized in that: The device comprises: A model building module is used to build a mixing work simulation model for the high-speed mixer based on the equipment information and process parameters of the high-speed mixer; in the mixing work simulation model, the tank of the high-speed mixer is divided into a plurality of mixing areas; A mixing process simulation module, used to simulate the mixing process of multiple materials in the tank based on the mixing work simulation model, and determine the material quantity of each material in each mixing area at different times; A mixing uniformity analysis module is used to perform statistical analysis on the amount of each material at each moment to obtain the mixing uniformity corresponding to the moment; A mixing capacity determination module, used to determine the mixing capacity of the high speed mixer based on the mixing uniformity corresponding to each of the moments; Preferably, the mixing uniformity analysis module is specifically used for: At each of the materials at each of the moments, according to the amount of the materials distributed in each of the mixing zones at the moments, determine the material uniformity corresponding to the materials; Statistical analysis is performed on the material uniformity of each of the materials to determine the mixing uniformity corresponding to the moment.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images