Multi-component collaborative optimization mixing machine stirring control method and system
By acquiring the liquid flow characteristics within the mixer and optimizing the mixing path, the problems of low efficiency and uneven mixing caused by unreasonable mixing path design in traditional mixers are solved, achieving efficient and uniform mixing results.
Patent Information
- Application Number
- CN202511670781.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-14
- Publication Date
- 2026-02-13
AI Technical Summary
The unreasonable design of the mixing path in traditional mixers leads to problems such as low mixing efficiency and uneven mixing.
By acquiring the liquid flow characteristics inside the mixer, the path of the first stirring component is initialized, and the path of the second stirring component is optimized for interaction effects using a piecewise optimization model. The optimized stirring path is output, and constraint optimization is performed by combining interaction constraints, ultimately controlling the stirring components to stir.
It improves mixing efficiency and effectiveness, achieving a highly efficient and uniform mixing process.
Smart Images
Figure CN121523027A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of intelligent control, in particular to a mixing machine stirring control method and system for multi-component collaborative optimization. BACKGROUND
[0002] In modern industrial production, the mixing process as the core link of the manufacturing process, its efficiency and uniformity have a crucial influence on product quality, performance and production cost. However, the traditional mixing machine often faces many challenges in the stirring process, the most significant of which is the mutual influence and limitation between the stirring components. When multiple stirring components in the mixing machine work simultaneously or alternately, their respective stirring paths and generated fluid dynamics effects will interweave, forming a complex mixing environment. In this environment, the motion trajectory and stirring effect of one stirring component may affect or limit the path of another stirring component, thereby affecting the overall mixing efficiency and uniformity. SUMMARY
[0003] The present application provides a mixing machine stirring control method and system for multi-component collaborative optimization, which solves the technical problem of low mixing efficiency and uneven mixing caused by unreasonable stirring path design of the mixing machine in the prior art.
[0004] In a first aspect, the present application provides a mixing machine stirring control method for multi-component collaborative optimization, which comprises: obtaining a first stirring component and a second stirring component of a mixing machine, the first stirring component being a component for stirring substances in the mixing machine, and the second stirring component being a component for assisting in stirring substances in the mixing machine; obtaining liquid flow characteristics in the mixing machine; initializing a first stirring path corresponding to the first stirring component, optimizing the first stirring path according to the liquid flow characteristics, obtaining a first optimized stirring path achieving a preset mixing effect; establishing a segmented optimization model, and according to the segmented optimization model, performing interactive effect optimization on a second stirring path corresponding to the second stirring component with the first optimized stirring path, and outputting a second optimized stirring path, wherein the segmented optimization model comprises an interactive constraint condition; performing constraint optimization on the first optimized stirring path and the second optimized stirring path according to the interactive constraint condition, and outputting a third optimized stirring path and a fourth optimized stirring path; and controlling the first stirring component and the second stirring component to stir according to the third optimized stirring path and the fourth optimized stirring path, respectively.
[0005] In a second aspect, the present application provides a mixing machine stirring control system for multi-component collaborative optimization, which comprises: The first acquisition module is used to acquire a first stirring component and a second stirring component of a mixing machine, the first stirring component is a component used for stirring substances in the mixing machine, and the second stirring component is a component used for assisting in stirring the substances in the mixing machine; the second acquisition module is used to acquire liquid flow characteristics in the mixing machine; the first optimization module is used to initialize a first stirring path corresponding to the first stirring component, to perform optimization on the first stirring path according to the liquid flow characteristics, to acquire a first optimized stirring path reaching a preset mixing effect; the second optimization module is used to establish a segmented optimization model, to perform interactive effect optimization on a second stirring path corresponding to the second stirring component according to the first optimized stirring path based on the segmented optimization model, and to output a second optimized stirring path, wherein the segmented optimization model comprises an interactive constraint condition; the optimization module is used to perform constraint optimization on the first optimized stirring path and the second optimized stirring path according to the interactive constraint condition, and to output a third optimized stirring path and a fourth optimized stirring path; and the control module is used to control the first stirring component and the second stirring component to perform stirring respectively according to the third optimized stirring path and the fourth optimized stirring path.
[0006] One or more technical solutions provided in the application have at least the following technical effects or advantages: Firstly, the first stirring component and the second stirring component of the mixing machine are acquired, the first stirring component is a component used for stirring substances in the mixing machine, and the second stirring component is a component used for assisting in stirring the substances in the mixing machine; meanwhile, the liquid flow characteristics in the mixing machine are acquired. By initializing the first stirring path corresponding to the first stirring component, the first stirring path is optimized according to the liquid flow characteristics, and the first optimized stirring path reaching the preset mixing effect is acquired. Then, the segmented optimization model is established, the second stirring path corresponding to the second stirring component is optimized according to the first optimized stirring path based on the segmented optimization model, and the second optimized stirring path is output, wherein the segmented optimization model comprises the interactive constraint condition. Then, the first optimized stirring path and the second optimized stirring path are optimized according to the interactive constraint condition, and the third optimized stirring path and the fourth optimized stirring path are output. Finally, the first stirring component and the second stirring component are controlled to perform stirring respectively according to the third optimized stirring path and the fourth optimized stirring path. The technical problem that the stirring path design of the mixing machine in the prior art is not reasonable enough, resulting in low mixing efficiency and uneven mixing is solved, and the technical effect of improving the mixing efficiency and effect is achieved by optimizing the stirring path. BRIEF DESCRIPTION OF DRAWINGS
[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort.
[0008] Figure 1 A flowchart of the mixing machine stirring control method of multi-component collaborative optimization provided by the embodiments of the present application is shown. Figure 2 A structural diagram of the mixing machine stirring control system of multi-component collaborative optimization provided by the embodiments of the present application is shown.
[0009] The reference signs are explained as follows: a first acquisition module 11, a second acquisition module 12, a first optimization module 13, a second optimization module 14, an optimization module 15, and a control module 16. DETAILED DESCRIPTION
[0010] The present application provides a mixing machine stirring control method and system of multi-component collaborative optimization, which solves the technical problem of low mixing efficiency and uneven mixing caused by unreasonable stirring path design of the mixing machine in the prior art.
[0011] The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort fall within the scope of protection of the present application.
[0012] It should be noted that the terms "comprising" and "having" are intended to cover the inclusions without being exclusive, for example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to the process, method, product or device.
[0013] Embodiment one, as shown in the present application provides a mixing machine stirring control method of multi-component collaborative optimization, wherein the method comprises: Figure 1 acquiring a first stirring component and a second stirring component of a mixing machine, the first stirring component is a component for stirring substances in the mixing machine, and the second stirring component is a component for assisting in stirring substances in the mixing machine.
[0014] The first stirring component is a component for stirring the substances in the mixing machine and is responsible for directly stirring the substances in the mixing machine, and the second stirring component is a component for assisting in stirring the substances in the mixing machine and is responsible for further enhancing the mixing effect.
[0015] The liquid flow characteristics in the mixing machine are obtained.
[0016] The liquid flow characteristics include the motion state, speed distribution, vortex formation and dissipation, shear force distribution, and the like of the liquid in the mixing machine. The liquid flow in the mixing machine is numerically simulated by using a computational fluid dynamics (CFD) software. By inputting the geometric structure of the mixing machine, the shape and motion parameters of the stirring components, the physical properties of the liquid, and the like, the software can simulate the flow of the liquid during the mixing process, including the velocity field, the pressure field, the vortex structure, and the like, and further obtain the liquid flow characteristics in the mixing machine.
[0017] The first stirring path corresponding to the first stirring component is initialized, and the first stirring path is optimized according to the liquid flow characteristics to obtain a first optimized stirring path achieving a preset mixing effect.
[0018] By initializing the first stirring path corresponding to the first stirring component in the mixing machine and optimizing the first stirring path according to the liquid flow characteristics, an appropriate optimization algorithm (such as a genetic algorithm, a particle swarm optimization algorithm, a gradient descent method, and the like) can be used to adjust the parameters (such as the speed, the direction, the trajectory shape, and the like) of the first stirring path in order to find the first optimized stirring path achieving the preset mixing effect.
[0019] Further, the method further comprises: According to the structural characteristics of the first stirring component, a first spatial degree of freedom is obtained; according to the structural characteristics of the second stirring component, a second spatial degree of freedom is obtained; the first spatial degree of freedom is taken as an optimization space solution set for optimizing the first stirring path; and the second spatial degree of freedom is taken as an optimization space solution set for optimizing the second stirring path.
[0020] Preferably, according to the structural features of the first stirring component such as shape, size, connection mode and driving mechanism, the type and range of movement that can be achieved in the mixer are determined, for example, if the first stirring component is a rotating paddle, it can have a rotational degree of freedom about its axis, and a possible moving degree of freedom along the axis direction, and the first spatial degree of freedom is obtained by quantifying these degrees of freedom; similarly, according to the structural features of the second stirring component, the type and range of movement are determined, and the second spatial degree of freedom is obtained; based on the first spatial degree of freedom, a set of all possible stirring paths of the first stirring component in the mixer is constructed, which is the optimization space solution set for the first stirring path optimization; based on the second spatial degree of freedom, and considering the stirring path of the first stirring component and the restriction of the internal structure of the mixer, a set of all possible stirring paths of the second stirring component in the mixer is constructed, which is the optimization space solution set for the second stirring path optimization.
[0021] Further, the method comprises: The liquid flow characteristics include stirring depth-flow influence coefficient and stirring width-flow influence coefficient, which are obtained by flow simulation using CFD software; the first stirring path is iterated multiple times according to the stirring depth-flow influence coefficient and the stirring width-flow influence coefficient, and the mixing effect of the stirring path after each iteration is evaluated, wherein the mixing effect evaluation includes shear force distribution, turbulence degree and dead zone elimination; until the stirring path reaching the preset mixing effect is obtained as the first optimized stirring path.
[0022] The liquid flow characteristics include stirring depth-flow influence coefficient and stirring width-flow influence coefficient, wherein the stirring depth-flow influence coefficient refers to the driving ability of depth to liquid flow, and the stirring width-flow influence coefficient refers to the driving ability of width to liquid flow. The stirring depth-flow influence coefficient and the stirring width-flow influence coefficient under the current stirring path can be obtained by simulating the liquid flow in the mixer using CFD software. According to the stirring depth-flow influence coefficient and the stirring width-flow influence coefficient, the first stirring path can be iterated multiple times using optimization algorithms such as genetic algorithm and particle swarm optimization algorithm, and the mixing effect of the stirring path after each iteration is evaluated, and the evaluation indexes include shear force distribution, turbulence degree and dead zone elimination. The shear force distribution is an index for evaluating stirring uniformity, the turbulence degree reflects the degree of chaos of liquid flow, and the dead zone refers to the area with weak liquid flow in the mixer. When the mixing effect of the stirring path reaches the preset standard or the optimization algorithm converges, the iteration process is stopped, and the stirring path at this time is output as the first optimized stirring path.
[0023] establish a segmented optimization model, and perform interactive effect optimization on the second stirring path corresponding to the second stirring component according to the first optimized stirring path based on the segmented optimization model, and output a second optimized stirring path, wherein the segmented optimization model comprises an interactive constraint condition.
[0024] By establishing a segmented optimization model, the segmented optimization model will perform optimization according to the mutual influence (i.e., interactive effect) between the first stirring path and the second stirring path under the interactive constraint condition, and output a second optimized stirring path.
[0025] Further, the method for performing interactive effect optimization on the second stirring path corresponding to the second stirring component according to the first optimized stirring path based on the segmented optimization model comprises: Collecting flow field variation characteristics corresponding to the first optimized stirring path; performing flow field interactive effect influence analysis on the stirring path obtained by each iteration of the second stirring component according to the flow field variation characteristics corresponding to the first optimized stirring path, and obtaining a flow field influence index; and outputting a second optimized stirring path until the flow field influence index is less than a preset threshold.
[0026] Preferably, the internal flow field of the mixing machine under the first optimized stirring path is simulated using CFD software, and flow field variation characteristics corresponding to the first optimized stirring path, such as flow velocity distribution, vortex structure, turbulence intensity, and shear force distribution, are collected; for the stirring path obtained by each iteration of the second stirring component, the flow field is also simulated using CFD software, and the flow field interactive effect between the second stirring path and the first optimized stirring path is analyzed, including how the flow fields generated by the two stirring paths influence each other and how such mutual influence changes the overall flow field structure in the mixing machine, so as to obtain a flow field influence index. The flow field influence index can quantitatively represent the strength and influence range of the flow field interactive effect, for example, flow velocity difference, vortex intensity change, and shear force distribution unevenness can be used as the flow field influence index. According to the flow field influence index, the stirring path of the second stirring component is iteratively adjusted to reduce the negative influence of the flow field interactive effect while maintaining or enhancing the mixing effect of the stirring path. After each iteration, flow field simulation and interactive effect analysis are performed again to evaluate the flow field influence index of the current stirring path. According to the specific process requirements of the mixing machine, the material characteristics, and the design of the stirring component, a preset threshold is set, which represents an acceptable level of flow field interactive effect. When the flow field influence index is less than the preset threshold, it is considered that the stirring path of the second stirring component has been sufficiently optimized, and the second optimized stirring path can be output.
[0027] Further, the segmented optimization model comprises an activation function, and the activation function comprises: Collecting the real-time mixing degree in the mixing machine; setting a ladder mixing degree, when the real-time mixing degree reaches the mixing degree of the corresponding ladder in the ladder mixing degree, activating the segmented optimization instruction; the activation function optimizes the first optimization stirring path corresponding to the first stirring component according to the segmented optimization instruction, and outputs the optimized first optimization stirring path; according to the optimized first optimization stirring path, the second optimization stirring path is outputted.
[0028] The segmented optimization model is composed of an activation function, which triggers the segmented optimization instruction by monitoring the real-time mixing degree in the mixing machine, and then dynamically adjusts the stirring path according to the actual mixing progress during the mixing process, so as to achieve more efficient mixing effect. Specifically, the mixing degree information in the mixing machine is collected in real time by sensors or numerical simulation, and the mixing degree information reflects the current mixing uniformity or progress; according to the mixing process requirements, a series of ladder mixing degrees are preset, which represent the key nodes or stages in the mixing process; when the real-time mixing degree reaches a certain ladder mixing degree, the activation function will trigger the corresponding segmented optimization instruction, which is used to instruct the system to optimize the stirring path of the current stage; according to the segmented optimization instruction, the first optimization stirring path corresponding to the first stirring component is optimized, and the optimized first optimization stirring path is outputted; according to the optimized first optimization stirring path, the second stirring path of the second stirring component is optimized, and the optimized second optimization stirring path is outputted.
[0029] Further, before analyzing the flow field interaction effect influence of the stirring path obtained by each iteration of the second stirring component, the method further comprises: According to the flow field change characteristics, the efficiency of the second stirring component is analyzed, and the efficiency influence is outputted; if the efficiency influence is greater than the preset efficiency influence, the first optimization stirring path is optimized with the preset efficiency influence, and the optimized first optimization stirring path is outputted.
[0030] Preferably, the efficiency of the second stirring component under the given flow field variation characteristics is evaluated using numerical simulation, the efficiency can be defined as the mixing work done per unit time, or the energy consumption required to achieve a certain mixing uniformity, etc., and the efficiency is quantified as an efficiency impact, which represents the degree of positive or negative impact of the flow field variation on the efficiency of the second stirring component; a preset efficiency impact is set according to the process requirements, production targets or equipment performance, which represents the upper limit of acceptable efficiency loss, and the efficiency impact obtained by analysis is compared with the preset efficiency impact, if the efficiency impact is greater than the preset efficiency impact, it means that the current first optimization stirring path has too much negative impact on the efficiency of the second stirring component, and feedback optimization is required for the first optimization stirring path to reduce the negative impact on the efficiency of the second stirring component; the feedback optimization can be based on the results of numerical simulation, by repeatedly testing different first stirring paths, the optimal solution that can maintain or improve the efficiency of the first stirring component and control the efficiency loss of the second stirring component within the preset range is found; the optimized first optimization stirring path is output, which can maximize the reduction of negative impact on the efficiency of the second stirring component while ensuring the mixing effect.
[0031] The first optimization stirring path and the second optimization stirring path are constrained and optimized according to the interaction constraint condition, and a third optimization stirring path and a fourth optimization stirring path are output.
[0032] The first optimization stirring path and the second optimization stirring path are further constrained and optimized according to the interaction constraint condition, and a third optimization stirring path and a fourth optimization stirring path are output.
[0033] Further, the first optimization stirring path and the second optimization stirring path are constrained and optimized according to the interaction constraint condition, and a third optimization stirring path and a fourth optimization stirring path are output. The interaction constraint condition includes spatial conflict constraint, mixing efficiency constraint and mixing energy consumption constraint; the first optimization stirring path and the second optimization stirring path are analyzed respectively, and spatial conflict index, mixing efficiency index and mixing energy consumption index are output; the spatial conflict index, the mixing efficiency index and the mixing energy consumption index are used to establish a target function, the sum of the spatial conflict index, the mixing efficiency index and the mixing energy consumption index is minimized, and a third optimization stirring path and a fourth optimization stirring path are output.
[0034] The interaction constraints include spatial conflict constraints, mixing efficiency constraints, and mixing energy consumption constraints. The spatial conflict index, the mixing efficiency index, and the mixing energy consumption index are output by analyzing the first and second optimized stirring paths respectively. Specifically, the spatial conflict index is an index for measuring whether the two stirring paths conflict or interfere with each other in the physical space. The CFD software is used to simulate the movement trajectories of the two stirring paths in the mixer, and whether there is collision or interference is checked. If there is, the degree or frequency of the conflict is calculated as the spatial conflict index. The mixing efficiency index is an index for evaluating the influence of the stirring paths on the mixing effect. It is usually related to the mixing uniformity, mixing time, etc. The mixing efficiency under the action of the two stirring paths is calculated through CFD simulation, and the synergistic effect or interference effect between them is considered to obtain the mixing efficiency index. The mixing energy consumption index is an index for measuring the energy consumption of the stirring paths during operation. According to the power, speed, running time, etc. of the stirring equipment, and the specific shape and speed of the stirring path, the energy consumption of the two stirring paths is calculated, and the mutual influence between them is considered to obtain the mixing energy consumption index. Based on the spatial conflict index, the mixing efficiency index, and the mixing energy consumption index, the objective function is established as where C is the spatial conflict index, E is the mixing efficiency index, P is the mixing energy consumption index, 、 、 is the weight coefficient. The sum of the spatial conflict index, the mixing efficiency index, and the mixing energy consumption index is minimized as the objective to optimize the stirring path. The objective function is solved by using an optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm, etc.) to find the third and fourth optimized stirring paths that satisfy all the interaction constraints and minimize the objective function. During the optimization process, the algorithm will continuously generate new stirring path candidate solutions, and their spatial conflict index, mixing efficiency index, and mixing energy consumption index are evaluated through simulation and calculation. Then, according to the value of the objective function, the candidate solutions are selected to be retained or eliminated, and new candidate solutions are generated through genetic, mutation, etc. operations until the stopping criteria (such as reaching the preset number of iterations, the change of the objective function value being less than a certain threshold, etc.) are met.
[0035] The first and second stirring components are controlled according to the third and fourth optimized stirring paths respectively to perform stirring.
[0036] According to the third and fourth optimized stirring paths that have been determined, the first and second stirring components are controlled respectively to perform stirring operations, so that a high-efficiency and uniform mixing effect is achieved.
[0037] In summary, the embodiments of the present application have at least the following technical effects: Firstly, the first stirring part and the second stirring part of the mixing machine are obtained, the first stirring part is a part for stirring substances in the mixing machine, and the second stirring part is a part for assisting in stirring substances in the mixing machine; meanwhile, liquid flow characteristics in the mixing machine are obtained. By initializing a first stirring path corresponding to the first stirring part, the first stirring path is optimized according to the liquid flow characteristics, and a first optimized stirring path reaching a preset mixing effect is obtained. Then, a segmented optimization model is established, and the second stirring path corresponding to the second stirring part is interactively optimized according to the first optimized stirring path based on the segmented optimization model, and a second optimized stirring path is output, wherein the segmented optimization model comprises an interactive constraint condition. Then, the first optimized stirring path and the second optimized stirring path are constrained and optimized according to the interactive constraint condition, and a third optimized stirring path and a fourth optimized stirring path are output. Finally, the first stirring part and the second stirring part are controlled to stir respectively according to the third optimized stirring path and the fourth optimized stirring path. The technical problem that the stirring path design of the mixing machine in the prior art is not reasonable enough, resulting in low mixing efficiency and uneven mixing, is solved, and the technical effect of improving the mixing efficiency and effect is achieved by optimizing the stirring path.
[0038] In the embodiment two, based on the same inventive concept as the mixing machine stirring control method of multi-component cooperative optimization in the foregoing embodiments, as shown in the embodiment two, Figure 2 As shown in the embodiment two, the application provides a mixing machine stirring control system of multi-component cooperative optimization, wherein the system comprises: A first obtaining module 11 is configured to obtain a first stirring part and a second stirring part of a mixing machine, the first stirring part is a part for stirring substances in the mixing machine, and the second stirring part is a part for assisting in stirring substances in the mixing machine; a second obtaining module 12 is configured to obtain liquid flow characteristics in the mixing machine; a first optimization module 13 is configured to initialize a first stirring path corresponding to the first stirring part, and to optimize the first stirring path according to the liquid flow characteristics, so as to obtain a first optimized stirring path reaching a preset mixing effect; a second optimization module 14 is configured to establish a segmented optimization model, and to interactively optimize a second stirring path corresponding to the second stirring part according to the first optimized stirring path based on the segmented optimization model, so as to output a second optimized stirring path, wherein the segmented optimization model comprises an interactive constraint condition; an optimization module 15 is configured to constrain and optimize the first optimized stirring path and the second optimized stirring path according to the interactive constraint condition, so as to output a third optimized stirring path and a fourth optimized stirring path; and a control module 16 is configured to control the first stirring part and the second stirring part to stir respectively according to the third optimized stirring path and the fourth optimized stirring path.
[0039] Further, the first optimization module 13 is configured to perform the following method: According to the structural characteristics of the first stirring component, a first spatial degree of freedom is obtained; according to the structural characteristics of the second stirring component, a second spatial degree of freedom is obtained; the first spatial degree of freedom is used as the searching space solution set of the first stirring path optimization; the second spatial degree of freedom is used as the searching space solution set of the second stirring path optimization.
[0040] Further, the optimization module 15 is configured to perform the following method: The interaction constraint conditions include spatial conflict constraints, mixing efficiency constraints and mixing energy consumption constraints; the first and second optimized stirring paths are analyzed respectively, and spatial conflict indicators, mixing efficiency indicators and mixing energy consumption indicators are output; the spatial conflict indicators, the mixing efficiency indicators and the mixing energy consumption indicators are used to establish a target function, and the sum of the spatial conflict indicators, the mixing efficiency indicators and the mixing energy consumption indicators is minimized as the target, and the third and fourth optimized stirring paths are output.
[0041] Further, the first optimization module 13 is configured to perform the following method: The liquid flow characteristics include stirring depth-flow influence coefficient and stirring width-flow influence coefficient, and the liquid flow characteristics are obtained by using CFD software for flow simulation; the first stirring path is iterated multiple times according to the stirring depth-flow influence coefficient and the stirring width-flow influence coefficient, and the mixing effect of each iteration of the stirring path is evaluated, wherein the mixing effect evaluation includes shear force distribution, turbulence degree and dead zone elimination; until the stirring path reaching the preset mixing effect is obtained as the first optimized stirring path.
[0042] Further, the second optimization module 14 is configured to perform the following method: The flow field variation characteristics corresponding to the first optimized stirring path are collected; the flow field interaction effect influence analysis is performed on the stirring path obtained by each iteration of the second stirring component according to the flow field variation characteristics corresponding to the first optimized stirring path, and the flow field influence indicator is obtained; until the flow field influence indicator is less than a preset threshold, the second optimized stirring path is output.
[0043] Further, the second optimization module 14 is configured to perform the following method: According to the flow field variation characteristics, the efficiency of the second stirring component is analyzed, and the efficiency influence is output; if the efficiency influence is greater than a preset efficiency influence, the first optimized stirring path is optimized by feedback according to the preset efficiency influence, and the optimized first optimized stirring path is output.
[0044] Furthermore, the second optimization module 14 is used to perform the following method: The real-time mixing degree in the mixer is collected; a stepped mixing degree is set, and when the real-time mixing degree reaches the mixing degree of the corresponding step in the stepped mixing degree, a segmented optimization instruction is activated; the activation function optimizes the first optimal mixing path corresponding to the first stirring component according to the segmented optimization instruction, and outputs the optimized first optimal mixing path; according to the optimized first optimal mixing path, the optimized second optimal mixing path is output.
[0045] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0046] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
[0047] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.
Claims
1. A mixer stirring control method with multi-component collaborative optimization, characterized in that, The method includes: Obtain a first stirring component and a second stirring component of a mixer, wherein the first stirring component is a component for stirring the substances inside the mixer, and the second stirring component is a component for assisting in stirring the substances inside the mixer; Obtain the liquid flow characteristics within the mixer; Initialize the first stirring path corresponding to the first stirring component, and optimize the first stirring path according to the liquid flow characteristics to obtain a first optimized stirring path that achieves the preset mixing effect. A segmented optimization model is established. Based on the segmented optimization model, the second stirring path corresponding to the second stirring component is optimized by interaction effect using the first optimized stirring path, and the second optimized stirring path is output. The segmented optimization model includes interaction constraints. Based on the interactive constraints, the first and second optimal stirring paths are constrained and optimized, and the third and fourth optimal stirring paths are output. The first stirring component and the second stirring component are controlled to stir according to the third optimal stirring path and the fourth optimal stirring path, respectively.
2. The multi-component collaborative optimization mixer stirring control method as described in claim 1, characterized in that, The method further includes: Based on the structural features of the first stirring component, the first spatial degree of freedom is obtained; Based on the structural features of the second stirring component, the second spatial degree of freedom is obtained; The first spatial degree of freedom is used as the optimization space solution set for optimizing the first stirring path; The second spatial degree of freedom is used as the optimization space solution set for the second stirring path.
3. The multi-component collaborative optimization mixer stirring control method as described in claim 1, characterized in that, The first and second optimal stirring paths are constrained and optimized based on the aforementioned interactive constraints. include: The interaction constraints include spatial conflict constraints, hybrid efficiency constraints, and hybrid energy consumption constraints. The first and second optimal mixing paths are analyzed respectively, and the spatial conflict index, mixing efficiency index and mixing energy consumption index are output. Using the spatial conflict index, the mixing efficiency index, and the mixing energy consumption index, an objective function is established to minimize the sum of the spatial conflict index, the mixing efficiency index, and the mixing energy consumption index, and the third and fourth optimal mixing paths are output.
4. The multi-component collaborative optimization mixer stirring control method as described in claim 1, characterized in that, The method involves optimizing the first stirring path based on the described liquid flow characteristics. include: The liquid flow characteristics include the stirring depth-flow influence coefficient and the stirring width-flow influence coefficient, which are obtained by flow simulation using CFD software. The first stirring path is iterated multiple times according to the stirring depth-flow influence coefficient and the stirring width-flow influence coefficient, and the mixing effect of the stirring path in each iteration is evaluated. The mixing effect evaluation includes shear force distribution, turbulence degree and dead zone elimination. The process continues until a mixing path that achieves the preset mixing effect is obtained, which is then used as the first optimal mixing path.
5. The multi-component collaborative optimization mixer stirring control method as described in claim 1, characterized in that, According to the segmented optimization model, the second stirring path corresponding to the second stirring component is optimized based on the first optimized stirring path through interactive effects. The method includes: Collect the flow field change characteristics corresponding to the first optimized stirring path; Based on the flow field change characteristics corresponding to the first optimal stirring path, the flow field interaction effect influence analysis is performed on the stirring path obtained by the second stirring component in each iteration to obtain the flow field influence index. The second optimal stirring path is output when the flow field influence index is less than the preset threshold.
6. The multi-component collaborative optimization mixer stirring control method as described in claim 5, characterized in that, Before performing flow field interaction effect analysis on the stirring path obtained in each iteration of the second stirring component, the method further includes: The efficiency of the second stirring component is analyzed based on the flow field change characteristics, and the efficiency impact is output. If the efficiency impact is greater than the preset efficiency impact, the first optimal stirring path is optimized by feedback based on the preset efficiency impact, and the optimized first optimal stirring path is output.
7. The multi-component collaborative optimization mixer stirring control method as described in claim 1, characterized in that, The piecewise optimization model includes an activation function, which includes: The real-time mixing degree in the mixer is collected; Set a step mixing degree. When the real-time mixing degree reaches the mixing degree of the corresponding step in the step mixing degree, activate the segmented optimization instruction. The activation function optimizes the first optimal stirring path corresponding to the first stirring component according to the segmented optimization instruction, and outputs the optimized first optimal stirring path. Based on the optimized first optimal mixing path, output the optimized second optimal mixing path.
8. A mixer stirring control system with multi-component collaborative optimization, characterized in that, For implementing the multi-component collaborative optimization mixer stirring control method according to any one of claims 1-7, the system comprises: The first acquisition module is used to acquire a first stirring component and a second stirring component of a mixer. The first stirring component is a component used to stir the substances inside the mixer, and the second stirring component is a component used to assist in stirring the substances inside the mixer. The second acquisition module is used to acquire the liquid flow characteristics inside the mixer; The first optimization module is used to initialize the first stirring path corresponding to the first stirring component, optimize the first stirring path according to the liquid flow characteristics, and obtain the first optimized stirring path that achieves the preset mixing effect. The second optimization module is used to establish a segmented optimization model, and according to the segmented optimization model, to perform interactive effect optimization on the second stirring path corresponding to the second stirring component with the first optimized stirring path, and output the second optimized stirring path. The segmented optimization model includes interactive constraints. The optimization module is used to perform constraint optimization on the first optimal stirring path and the second optimal stirring path according to the interactive constraint conditions, and output the third optimal stirring path and the fourth optimal stirring path. The control module is used to control the first stirring component and the second stirring component to stir according to the third optimal stirring path and the fourth optimal stirring path, respectively.