Optimization method, electronic device, storage medium, and program product for a beater fan classifier

Abstract

This invention provides an optimization method, electronic device, storage medium, and program product for a leaf-beating air separator, relating to the field of cigarette production technology. The method includes: constructing a simulation model of the leaf-beating air separator; obtaining parameter sets corresponding to each parameter to be optimized on the air guide plate, and determining multiple candidate value groups based on the parameter sets, wherein each parameter set includes multiple candidate values ​​corresponding to the parameter to be optimized; constructing an air guide plate model for each candidate value group; after associating the air guide plate model with the simulation model, performing simulation on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value group; and determining the target values ​​for each parameter to be optimized on the air guide plate based on the relative standard deviation of wind speed corresponding to all candidate value groups. This invention can improve the airflow uniformity of the leaf-beating air separator and enhance the sorting effect.

Description

Technical Field

[0001] This invention relates to the field of cigarette production technology, and in particular to an optimization method for a leaf-beating air separator, an electronic device, a storage medium, and a program product. Background Technology

[0002] Leaf threshing and air separation is a key process in tobacco processing lines. The uniformity and stability of the flow field inside the leaf threshing and air separation device directly affect the separation effect, purity, and breakage rate of tobacco leaves and stems.

[0003] During the actual operation of the blade-breaking air separator, some material tends to concentrate in the upper area of ​​the bin after being thrown out, causing it to be sucked away by the top outlet within a short air classification path, affecting the sufficiency and uniformity of classification. Therefore, there is an urgent need for a method to improve the uniformity of the air field and enhance the classification effect of the blade-breaking air separator. Summary of the Invention

[0004] This invention provides an optimization method, electronic device, storage medium, and program product for a leaf-beating air separator, which can improve the uniformity of the airflow field and enhance the sorting effect.

[0005] In a first aspect, the optimization method for a leaf-trimming air distributor provided in this embodiment of the invention includes: constructing a simulation model of the leaf-trimming air distributor; obtaining a parameter set corresponding to each parameter to be optimized of the air guide plate, and determining multiple candidate value groups based on the parameter set, wherein each parameter set includes multiple candidate values ​​corresponding to the parameter to be optimized; constructing an air guide plate model for each candidate value group; after associating the air guide plate model with the simulation model, performing simulation on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value group; and determining the target value of each parameter to be optimized of the air guide plate based on the relative standard deviation of wind speed corresponding to all candidate value groups.

[0006] Secondly, the optimization device for a leaf-striking air distributor provided in this embodiment of the invention includes: a first model construction module for constructing a simulation model of the leaf-striking air distributor; a value group determination module for obtaining a parameter set corresponding to each parameter to be optimized of the air guide plate, and determining multiple candidate value groups based on the parameter set, wherein each parameter set includes multiple candidate values ​​corresponding to the parameter to be optimized; a second model construction module for constructing an air guide plate model for each candidate value group; a simulation module for performing simulation on the simulation model after associating the air guide plate model with the simulation model, and obtaining the relative standard deviation of wind speed corresponding to the candidate value group; and a value determination module for determining the target value of each parameter to be optimized of the air guide plate based on the relative standard deviation of wind speed corresponding to all candidate value groups.

[0007] Thirdly, the electronic device provided in the embodiments of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the optimization method of the leaf-beating air separator as in any embodiment of the present invention.

[0008] Fourthly, the computer-readable storage medium provided in the embodiments of the present invention stores a computer program thereon, which, when executed by a processor, implements the optimization method of the leaf-beating air divider as in any embodiment of the present invention.

[0009] Fifthly, the computer program product provided in the embodiments of the present invention includes a computer program that, when executed by a processor, implements the optimization method for the leaf-beating air distributor as described in any embodiment of the present invention.

[0010] In this embodiment of the invention, by constructing a simulation model of the blade-splitting air separator, the airflow motion state can be analyzed in a virtual environment, reducing the actual test cost and improving the analysis efficiency. The parameter sets corresponding to each parameter to be optimized for the air guide plate are obtained, and multiple candidate value groups are determined based on the parameter sets. Each parameter set includes multiple candidate values ​​for the corresponding parameter to be optimized, thereby achieving a systematic combination of the air guide plate structural parameters and providing a foundation for subsequent optimization. For each candidate value group, an air guide plate model is constructed based on the candidate value group, thereby forming air guide plate schemes under various structural parameters and improving the comprehensiveness of the optimization process. After associating the air guide plate model with the simulation model, simulation is performed on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value group, thereby quantitatively characterizing the influence of different parameter combinations on the wind field uniformity. Based on the relative standard deviation of wind speed corresponding to all candidate value groups, the target values ​​of each parameter to be optimized for the air guide plate are determined, thereby obtaining the optimal parameter combination that makes the wind speed distribution more uniform and improving the sorting effect of the blade-splitting air separator. Therefore, this embodiment of the invention effectively reduces experimental costs and improves optimization efficiency by constructing a simulation model of the leaf-splitting air separator and conducting systematic simulation analysis using multiple sets of candidate values. Based on this, by quantitatively analyzing the relative standard deviation of wind speed corresponding to each set of candidate values, the target values ​​for each parameter to be optimized are determined, realizing the shift from empirical adjustment to quantitative optimization. Furthermore, through synergistic optimization among the parameters to be optimized, the uniformity of wind speed distribution is significantly improved, enhancing the sorting effect of the leaf-splitting air separator. Attached Figure Description

[0011] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating an optimization method for a leaf-beating air distributor provided in an embodiment of the present invention; Figure 2 This is another flowchart illustrating the optimized method for the leaf-beating air distributor provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the external structure of the leaf-beating air distributor provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the internal structure of a leaf-beating air distributor provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of an arc-shaped air guide plate installed inside the blade-beating air distributor provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an optimized device for a leaf-beating air distributor provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Figure 1This is a flowchart illustrating an optimization method for a leaf-trimming air distributor provided in an embodiment of the present invention. This optimization method is applicable to scenarios where air guide plates are installed on the leaf-trimming air distributor to optimize its parameters. The optimization method can be executed by an optimization device for the leaf-trimming air distributor provided in this embodiment, which can be implemented using software and / or hardware. In a specific embodiment, this device can be integrated into an electronic device, such as a computer or server. The following embodiment illustrates this by integrating the optimization device for the leaf-trimming air distributor into an electronic device. (See also...) Figure 1 The optimization method for the leaf-beating air distributor in this embodiment may include the following steps: Step 101: Construct a simulation model of the leaf-splitting air distributor.

[0016] A leaf-beating air separator is a device that separates tobacco leaves from impurities through airflow during tobacco processing. A simulation model is a basic simulation environment built upon the overall structure of the leaf-beating air separator to simulate airflow. This simulation model includes the air separator's outer shell, inlet and outlet structures, and the fluid domain corresponding to the internal air duct space.

[0017] Specifically, based on the three-dimensional structural model of the blade-beating air separator, a simulation model is constructed to simulate the airflow inside the blade-beating air separator.

[0018] Step 102: Obtain the parameter set corresponding to each parameter to be optimized of the air guide plate, and determine multiple candidate value groups based on the parameter set. Each parameter set includes multiple candidate values ​​corresponding to the parameter to be optimized.

[0019] The parameters to be optimized refer to the structural parameters in the guide vane structure that affect the wind field distribution. These include at least geometric dimensions such as the diameter of the semicircle, the radius of the transition arc, and the length of the vane. Further parameters, such as the thickness and material of the guide vane, may be included depending on actual needs. A parameter set is a collection of multiple candidate values ​​pre-defined for each parameter to be optimized. Each parameter corresponds to multiple candidate values. A candidate value is a specific numerical value in the parameter set that characterizes the possible values ​​of a given parameter. A candidate value group is a combination of parameters formed by selecting one value from each candidate value in the parameter sets.

[0020] Specifically, firstly, several parameters in the wind deflector that affect the wind field distribution are identified, and several possible values ​​are set for each parameter to form a corresponding parameter set. Then, a candidate value is selected from each parameter set and combined to obtain multiple candidate value groups. Each candidate value group corresponds to a wind deflector structural parameter scheme, which is used to build the wind deflector model and perform simulation calculations, thereby enabling comparative analysis of the wind field distribution under different parameter combinations.

[0021] For example, assuming the parameters to be optimized are A, B, and C, the parameter set corresponding to A can be {150, 200, 250}, the parameter set corresponding to B can be {50, 60, 70}, and the parameter set corresponding to C can be {400, 450, 500}. By selecting a candidate value from each parameter set and combining them, 27 candidate value groups can be formed. Each candidate value group is used to characterize a set of wind deflector structural parameter combinations. In an optional implementation, to reduce the number of candidate value groups and improve experimental efficiency, the candidate value groups can be screened based on an orthogonal design method.

[0022] Step 103: For each candidate value group, construct the wind guide plate model based on the candidate value group.

[0023] A wind deflector model is a three-dimensional model of a wind deflector constructed based on parameters from a specific set of candidate values.

[0024] Specifically, for each candidate value group obtained from the parameter set combination, it is used as the design parameter of the wind deflector, and a corresponding three-dimensional geometric model of the wind deflector is constructed. This model is then integrated into the simulation model of the blade splitter to simulate the flow distribution of airflow under the action of the wind deflector, thereby evaluating the impact of the parameter combination on the wind field uniformity and the relative standard deviation of wind speed.

[0025] Continuing from the previous example, for each of the 27 candidate value groups, a corresponding wind deflector model is constructed, resulting in 27 wind deflector models.

[0026] Step 104: After associating the wind deflector model with the simulation model, perform simulation on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value group.

[0027] Correlation refers to placing the wind deflector model in the corresponding position of the simulation model, making it part of the simulation calculation. Relative standard deviation of wind speed refers to the degree of fluctuation of wind speed at each measuring point relative to the average wind speed within a selected area (e.g., a cross-section or the entire flow field). It is an indicator used to evaluate the uniformity of the wind field, reflecting the degree of fluctuation of wind speed at each measuring point within the selected area. The relative standard deviation (RSD) is the ratio of the standard deviation of each measuring point's data from the average value to the average value, usually expressed as a percentage.

[0028] Specifically, for each candidate value group, its corresponding air guide plate model is placed into the simulation model to form a computable and associated simulation model. Computational Fluid Dynamics (CFD) is then used to perform numerical simulations on the associated model, thereby obtaining the wind speed, pressure, flow direction, and vortex distribution inside the air distributor under the action of the air guide plate corresponding to the candidate value group. Subsequently, wind speed data at each measuring point is extracted from a selected area (e.g., a measurement section) of the simulation model, and the average wind speed of the selected area is calculated based on this wind speed data. Its calculation formula is , where v i Let be the wind speed at the i-th measuring point, and n be the total number of measuring points. Next, calculate the standard deviation of the wind speed. Its calculation formula is Finally, the relative standard deviation (RSD) of the wind speed is calculated based on the average wind speed and the standard deviation. The formula is as follows: .

[0029] Continuing the previous example, 27 wind deflector models are associated with the simulation model, and the associated simulation model is simulated to obtain the relative standard deviation of wind speed for each group of candidate values. Specifically, for any group of candidate values ​​corresponding to the simulation model, after the simulation is completed, a reference section is determined at the midpoint of the air distributor's width direction. This section is perpendicular to the ground and the width direction of the air distributor. On this reference section, a square measurement area (e.g., 1m long × 1m wide) is determined as the measurement surface, with the origin 0.15m in front of the air knife outlet (in this embodiment, other locations can also be selected as the origin, without limitation). An XY coordinate system is established on this measurement surface to determine the position of each measuring point. Measuring points are evenly distributed on this measurement surface using a grid method, with a spacing of 0.5m between adjacent measuring points. The measurement surface can then be divided into 3 equal parts in both the length and width directions, forming a total of 3 × 3 = 9 measuring points. Assuming that the wind speed data (unit m / s) of each measuring point obtained by the simulation model corresponding to one group of candidate values ​​is shown in Table 1, it can be obtained using the formula... The calculated average wind speed at the wind speed measurement section is 22.26 m / s, the standard deviation of the wind speed is 4.83 m / s, and the relative standard deviation of the wind speed is 21.73%. Step 105: Determine the target values ​​of each parameter to be optimized for the wind guide plate based on the relative standard deviation of wind speed corresponding to all candidate value groups.

[0030] The target value refers to the final value selected for each parameter to be optimized in order to achieve optimal wind speed uniformity, that is, the value of each parameter to be optimized that minimizes the relative standard deviation of wind speed.

[0031] Specifically, for each candidate value group, the relative standard deviation of wind speed corresponding to that candidate value group can be obtained through simulation. Then, the relative standard deviations of wind speed corresponding to all candidate value groups are compared, and the candidate value group with the smallest relative standard deviation of wind speed is determined. The values ​​of each parameter in this candidate value group are then used as the target values ​​of each parameter to be optimized for the wind guide plate.

[0032] Optionally, based on the relative standard deviation of wind speed corresponding to all candidate value groups, the target values ​​of each parameter to be optimized of the wind guide plate are determined, including: taking each candidate value in the candidate value group with the smallest relative standard deviation of wind speed as the target value of each parameter to be optimized of the wind guide plate.

[0033] Continuing with the previous example, after obtaining the relative standard deviations of wind speed for each of the 27 candidate value groups, the relative standard deviations of wind speed for each candidate value group are compared, and the candidate value group with the smallest relative standard deviation of wind speed is determined. If the candidate values ​​in the candidate value group with the smallest relative standard deviation of wind speed are 250, 70 and 450 respectively, then the target value of the parameter A to be optimized for the wind guide plate is 250, the target value of the parameter B to be optimized is 70 and the target value of the parameter C to be optimized is 450.

[0034] In this embodiment, by constructing a simulation model of the blade-splitting air separator, the airflow motion state can be analyzed in a virtual environment, reducing the actual test cost and improving the analysis efficiency. The parameter sets corresponding to each parameter to be optimized for the air guide plate are obtained, and multiple candidate value groups are determined based on the parameter sets. Each parameter set includes multiple candidate values ​​for the corresponding parameter to be optimized, thereby achieving a systematic combination of the air guide plate structural parameters and providing a foundation for subsequent optimization. For each candidate value group, an air guide plate model is constructed based on the candidate value group, thus forming air guide plate schemes under various structural parameters and improving the comprehensiveness of the optimization process. After associating the air guide plate model with the simulation model, simulation is performed on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value group, thereby quantitatively characterizing the impact of different parameter combinations on the wind field uniformity. Based on the relative standard deviation of wind speed corresponding to all candidate value groups, the target values ​​of each parameter to be optimized for the air guide plate are determined, thereby obtaining the optimal parameter combination that makes the wind speed distribution more uniform and improving the sorting effect of the blade-splitting air separator. Therefore, this embodiment of the invention effectively reduces experimental costs and improves optimization efficiency by constructing a simulation model of the leaf-splitting air separator and conducting systematic simulation analysis using multiple sets of candidate values. Based on this, by quantitatively analyzing the relative standard deviation of wind speed corresponding to each set of candidate values, the target values ​​for each parameter to be optimized are determined, realizing the shift from empirical adjustment to quantitative optimization. Furthermore, through synergistic optimization among the parameters to be optimized, the uniformity of wind speed distribution is significantly improved, enhancing the sorting effect of the leaf-splitting air separator.

[0035] Figure 2 This is another flowchart illustrating the optimized method for the leaf-beating air distributor provided in this embodiment of the invention, as shown below. Figure 2 As shown, the optimization method for the leaf-beating air distributor in this embodiment may include: Step 201: Construct a simulation model of the leaf-splitting air distributor.

[0036] Optionally, a simulation model of the leaf-beating air distributor is constructed, including: obtaining a three-dimensional model of the leaf-beating air distributor and determining the fluid domain based on the three-dimensional model; setting the boundary conditions of the fluid domain and meshing the fluid domain to obtain the simulation model.

[0037] A 3D model refers to a three-dimensional geometric model of the blade-beating air distributor constructed using modeling software. It describes the spatial shape, dimensions, and structural relationships between the components of the equipment. A fluid domain refers to the spatial region in the 3D model used for gas flow; that is, the internal space where air can flow. It is usually obtained by extracting or enclosing the solid model. Boundary conditions are physical constraints applied to the boundaries of the fluid domain during simulation calculations. They describe the state of airflow entering, exiting, and interacting with the walls, such as inlet flow rate, outlet pressure, and wall characteristics. Meshing refers to the process of discretizing the fluid domain into multiple small computational units (mesh cells) to enable numerical calculations of fluid flow. A simulation model is a model created after defining the fluid domain, setting boundary conditions, and meshing, which can be used for numerical calculations to simulate the airflow within the blade-beating air distributor.

[0038] Specifically, a three-dimensional geometric model of the blade-splitter air separator is obtained, and the region where air can flow, i.e., the fluid domain, is extracted or constructed based on this model. Then, corresponding boundary conditions are set at the inlet, outlet, and wall of the fluid domain to reflect the flow state of the airflow under actual working conditions. Next, the fluid domain is meshed and discretized into multiple computational units to form a simulation model that can be used for numerical calculations, which is then used to simulate the flow of air inside the air separator.

[0039] For example, a three-dimensional assembly model including the air distributor shell, inlet, and internal structure is constructed, and the connections between each structure are sealed to prevent fluid leakage due to gaps, thus completing the basic structural modeling. Next, the openings in the model are closed and the airflow region is extracted to determine the fluid domain. Subsequently, the analysis type is set to internal flow analysis, and small cavities that do not have flow conditions are ignored. Then, boundary conditions are set at the boundary of the fluid domain and the fluid domain is meshed. Further, the wall roughness parameters are set according to the actual material of the air duct, and corresponding wall boundary conditions are set on the surface of the air guide plate. Finally, numerical solutions are performed and the convergence of the calculation is monitored. When the residuals decrease to the preset range and the engineering objectives tend to stabilize, the construction of the simulation model is completed.

[0040] Optionally, after constructing the simulation model of the leaf-splitter, the process further includes: performing a simulation on the simulation model to obtain the simulated wind speed at each measuring point on the measuring surface; obtaining the actual wind speed at each measuring point on the measuring surface of the leaf-splitter; calculating the relative error of each measuring point based on the simulated wind speed and the actual wind speed; determining the simulation model to be valid when the relative error of each measuring point is less than or equal to a preset threshold; and returning to the step of constructing the simulation model of the leaf-splitter when the relative error of any measuring point is greater than the preset threshold.

[0041] The measurement surface refers to the selected spatial cross-section inside the air separator used to extract wind speed data; it serves as a reference plane for quantitative analysis of airflow distribution. Measurement points are discrete locations on the measurement surface arranged according to preset rules (such as a grid method), used to collect or extract wind speed values ​​at those locations. Simulated wind speed refers to the airflow velocity value calculated at the measurement point location using computational fluid dynamics simulation methods. Actual wind speed refers to the airflow velocity value actually measured at the corresponding measurement point location using an anemometer during the actual operation of the blade-splitting air separator. Relative error is an index characterizing the degree of deviation between the simulation result and the actual measurement result, usually expressed as the ratio of the difference between the two to the actual wind speed or reference value. Preset threshold refers to a pre-defined allowable error range used as a criterion for judging the consistency between the simulation result and the actual situation.

[0042] Specifically, numerical simulations are performed in the simulation model to obtain the simulated wind speed at each measuring point on the measurement surface; simultaneously, the actual wind speed data at each corresponding measuring point is acquired from the actual operating blade-striking air distributor. Then, the relative error between the simulated and actual wind speeds at each measuring point is calculated to measure the degree of agreement between the simulation results and the actual flow field. When the relative errors at all measuring points are less than or equal to a pre-set error threshold, it indicates that the simulation model can accurately reflect the actual airflow distribution, thus determining the simulation model to be effective. Conversely, when the relative error at any measuring point exceeds the pre-set threshold, it indicates a significant deviation between the simulation model and the actual flow field, requiring a re-execution of the simulation model construction steps to correct the model and improve the accuracy of the simulation results.

[0043] For example, assuming the simulated wind speed data (unit: m / s) at each measuring point is shown in Table 2, and the actual wind speed data (unit: m / s) at the corresponding measuring point is shown in Table 3, with a preset threshold of 10%, then based on the simulated wind speed and the actual wind speed at each measuring point, the relative error range of each measuring point is calculated to be approximately 3.3% to 5.9%. At this point, the relative error of all measuring points is less than the preset threshold of 10%, indicating that there is a high consistency between the simulation results and the actual measurement results. Therefore, it is determined that the constructed simulation model is effective and can be used for subsequent wind speed distribution analysis and wind guide plate parameter optimization. Step 202: Obtain the parameter set corresponding to each parameter to be optimized of the air guide plate. Each parameter set includes multiple candidate values ​​for the corresponding parameter to be optimized, and the number of candidate values ​​included in each parameter set is the same.

[0044] Optionally, the parameters to be optimized include at least the semicircle diameter, the transition arc radius, and the plate length.

[0045] The semicircle diameter refers to the diameter of the semicircular geometric part corresponding to the arc structure in the wind deflector. The transition arc radius refers to the radius of curvature of the transition arc used to connect different geometric segments (such as straight segments and arc segments) in the wind deflector structure. The deflector length refers to the overall length of the wind deflector along the airflow direction or the direction of structural extension.

[0046] Specifically, for each parameter to be optimized in the air guide plate, a corresponding parameter set is set. Each parameter set consists of multiple candidate values, used to describe the different discrete levels of the parameter in the design space. To meet the basic requirements of orthogonal experimental design, the number of candidate values ​​in the parameter sets corresponding to each parameter to be optimized is kept consistent, ensuring that each parameter has the same number of levels. This allows for the construction of a standard orthogonal array (such as L9(3)). k(e.g., to achieve a balanced distribution of the levels of each factor. By using the same number of candidate values, it is possible to ensure that the combinations of each factor in the orthogonal experiment have statistical balance and representativeness, avoiding incomplete experimental space or combination bias due to inconsistent levels, thereby improving the comparability of subsequent simulation analysis results and the reliability of parameter influence analysis.)

[0047] For example, suppose the parameters to be optimized are A (semicircle diameter), B (transition arc radius) and C (plate length). The parameter set corresponding to A can be {150, 200, 250}, the parameter set corresponding to B can be {50, 60, 70}, and the parameter set corresponding to C can be {400, 450, 500}.

[0048] Step 203: Determine the orthogonal array L based on the parameter set. n (m k ), where n is the number of candidate value groups, m is the number of candidate values ​​included in each parameter set, and k is the number of parameters to be optimized.

[0049] Orthogonal array L n (m k Orthogonal experimental design refers to a standardized experimental arrangement table used to ensure that different levels of factors are evenly distributed within a limited number of experiments, thereby achieving efficient analysis of the influence of multiple factors.

[0050] Specifically, when optimizing the air guide plate using multiple parameters, the first step is to construct the parameter level system required for the orthogonal experimental design based on the parameter set (i.e., the set of candidate values) corresponding to each parameter to be optimized. Then, based on the structural characteristics of the parameter set, the orthogonal array L is determined. n (m k The orthogonal array is defined as follows: L represents the orthogonal array, n represents the number of trials (i.e., the number of candidate value groups), m represents the number of levels for each factor (i.e., the number of candidate values ​​contained in each parameter set), and k represents the number of factors (i.e., the number of parameters to be optimized). By using this orthogonal array, a multi-parameter combination space can be covered with fewer simulations while ensuring a balanced distribution of parameter level combinations. This allows for efficient screening and optimization analysis of subsequent wind deflector structural parameters.

[0051] Continuing with the previous example, as shown in Table 4, the orthogonal array L is determined based on the parameter set corresponding to each parameter to be optimized. n (m k ). Step 204, based on the orthogonal array L n (m k ), to determine multiple candidate value groups.

[0052] Continuing with the previous example, as shown in Table 4, each row in the orthogonal array corresponds to a candidate value group, which represents the combination of wind deflector parameters used in a simulation calculation. For example, experiment number 1 corresponds to candidate value groups A1, B1, and C1, i.e., A is 150mm, B is 50mm, and C is 400mm; experiment number 2 corresponds to candidate value groups A1, B2, and C2, and so on, thus forming 9 candidate value groups for subsequent simulation analysis.

[0053] Step 205: For each candidate value group, construct the wind guide plate model based on the candidate value group.

[0054] Continuing from the previous example, for each of the nine candidate value groups, a corresponding wind deflector model is constructed, resulting in nine wind deflector models.

[0055] Step 206: After associating the wind deflector model with the simulation model, perform simulation on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value group.

[0056] Continuing with the previous example, by performing simulations on the simulation models corresponding to the 9 candidate value groups, we can obtain the relative standard deviation of wind speed for each candidate value group, as shown in Table 5. The relative standard deviations of wind speed for the 9 candidate value groups are 25.12%, 24.04%, 23.88%, 22.78%, 21.73%, 20.82%, 19.89%, 18.81%, and 17.54%, respectively. Step 207: For each parameter to be optimized, determine the mean of the relative standard deviation of wind speed when the parameter to be optimized takes the same candidate value.

[0057] Specifically, the average relative standard deviation of wind speed in all experiments is calculated when each parameter to be optimized is fixed at a certain value. This average is used to determine the overall contribution of the parameter to wind speed uniformity under different values.

[0058] Continuing with the previous example, for the parameter to be optimized, A (the diameter of the semicircle), we calculate the mean relative standard deviation of wind speed corresponding to A1 (150mm), including the relative standard deviations of wind speed for experiments 1, 2, and 3, which are 25.12%, 24.04%, and 23.88%, respectively. The mean relative standard deviation of wind speed for A1 (150mm) is 24.35%. Similarly, we calculate the mean relative standard deviation of wind speed for A2 (200mm) corresponding to experiments 4, 5, and 6, which is 21.78%. And so on. Based on the experimental results in Table 5, we calculate the mean relative standard deviation K of wind speed for each parameter to be optimized under different candidate values. Specifically, for parameter A (semicircle diameter), the mean relative standard deviation of wind speed K for candidate values ​​A1, A2, and A3 are 24.347, 21.777, and 18.747, respectively; for parameter B (transition arc radius), the mean relative standard deviation of wind speed K for candidate values ​​B1, B2, and B3 are 22.597, 21.527, and 20.747, respectively; and for parameter C (plate length), the mean relative standard deviation of wind speed K for candidate values ​​C1, C2, and C3 are 21.583, 21.453, and 21.833, respectively.

[0059] Step 208: Select the candidate value with the smallest mean as the target value of the parameter to be optimized.

[0060] Continuing the previous example, for the parameter to be optimized, A (semicircle diameter), the K values ​​for candidate values ​​A1, A2, and A3 are 24.347, 21.777, and 18.747, respectively. Therefore, A3 (250mm), with the smallest K value, is selected as the target value for A. Similarly, for the parameter to be optimized, B (transition arc radius), the K values ​​for B1, B2, and B3 are 22.597, 21.527, and 20.747, respectively. B3 (70mm) is selected as the target value for B. For the parameter to be optimized, C (plate length), the K values ​​for C1, C2, and C3 are 21.583, 21.453, and 21.833, respectively. C2 (450mm) is selected as the target value for C. Therefore, the optimal combination of the parameters to be optimized is determined to be A3 (250mm), B3 (70mm), and C2 (450mm). In other words, when the semicircle diameter is 250mm, the transition arc radius is 70mm, and the plate length is 450mm, the combination of structural parameters of the wind guide plate can make the relative standard deviation of the cross-sectional wind speed reach a small value, thereby effectively improving the wind field distribution and increasing the uniformity of airflow.

[0061] Furthermore, based on the K values ​​corresponding to each parameter to be optimized under different candidate values, the range R value of each parameter to be optimized can be calculated. The range R value is the difference between the maximum and minimum K values ​​corresponding to the same parameter to be optimized. The calculated range R values ​​for parameters A, B, and C are 5.600, 1.850, and 0.380, respectively. By comparing the range R values ​​of each parameter to be optimized, it can be seen that parameter A has the greatest impact on the relative standard deviation of wind speed, followed by parameter B, while parameter C has the least impact.

[0062] For example, Figure 3 This is a schematic diagram of the external structure of a leaf-beating air distributor provided in an embodiment of the present invention. For example... Figure 3 As shown: 1. Tobacco leaf conveying channel; 2. Circulating air duct; 3. Heavy material discharge port; 4. Light material discharge port; 6. Throwing fan; 7. Throwing air duct; 11. Circulating fan.

[0063] Figure 4 This is a schematic diagram of the internal structure of a leaf-beating air distributor provided in an embodiment of the present invention; as shown. Figure 4 As shown: 1. Tobacco leaf conveying channel; 2. Circulating air duct; 3. Heavy material outlet; 4. Light material outlet; 5. Added arc-shaped air guide plate; 6. Throwing fan; 7. Throwing air duct; 8. Belt conveyor; 9. Throwing air knife; 10. Front and rear air chamber partitions of the air separator; 11. Circulating fan; 12. Air separator chamber. In the air separator chamber (12), the air-separated airflow generated by the circulating fan (11) enters the air separator chamber (12) through the circulating air duct (2). This airflow is divided into two groups of vertically rising airflows acting on the front and rear air chambers by the front and rear air chamber partitions (10). After passing through the air separator chamber, the airflow completes circulation through the air duct. After the leaf threshing process, the mixture of tobacco leaves and tobacco stems after separation is thrown into the air separator chamber (12) by the tobacco belt conveyor (8) through the tobacco leaf conveying channel (1). The throwing airflow generated by the throwing fan (6) disperses the tobacco leaves entering the air separation chamber through the throwing air duct (7), and air separation is carried out by the circulating airflow. Because the tobacco leaves are thin and have a large area, they are subjected to a large lifting force from the airflow and have a low suspension velocity. When the speed of the rising airflow is between the suspension velocities of the tobacco leaves and the tobacco stems, the tobacco leaves will be lifted by the airflow and drift with the wind to the light material outlet (4) at the top of the air separation chamber. The tobacco stems (especially the leaves containing stems) have a high suspension velocity due to their regular shape, high density, and small wind-receiving area, and the airflow cannot blow them. Under the action of gravity, they will sink and be discharged from the heavy material outlet (3) at the bottom of the air separation chamber to enter the next stage of processing. The added arc-shaped air guide plate structure (5) can increase the air separation path, improve the uniformity of the air field, and thus improve the air separation efficiency.

[0064] Figure 5This is a schematic diagram of an arc-shaped air guide plate installed inside the blade-beating air distributor provided in an embodiment of the present invention; as shown. Figure 5 As shown: 500, flow guide; 510, first arc portion; 520, connecting portion; 530, second arc portion; 540, bending portion; 550, fastener; 560, end plate; 570, arc-shaped flow guide plate.

[0065] In this embodiment, by constructing a simulation model of the blade-striking air distributor, the airflow motion state can be analyzed in a virtual environment, reducing the actual experimental cost and improving the analysis efficiency; the parameter set corresponding to each parameter to be optimized of the air guide plate is obtained, and the orthogonal array L is determined based on the parameter set. n (m k This allows for the orderly combination design of multiple parameters, reducing the number of experiments and improving analytical efficiency; based on the orthogonal array L... n (m k Multiple candidate value groups are identified to reduce the number of parameter combinations while ensuring the representativeness of the experiment, thus improving the systematic nature of the optimization process. For each candidate value group, a wind guide plate model is constructed based on the candidate value group, thereby forming wind guide plate schemes under various structural parameters for easy comparison and analysis. After associating the wind guide plate model with the simulation model, simulation is performed on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value group, thereby quantitatively characterizing the impact of different parameter combinations on wind field uniformity. For each parameter to be optimized, the mean of the relative standard deviation of wind speed for the parameter to be optimized under the same candidate value is determined, thereby eliminating the randomness of a single experiment and improving the reliability of the evaluation results. The candidate value with the smallest mean is taken as the target value of the parameter to be optimized, thereby obtaining the optimal parameter combination that makes the wind speed distribution more uniform and improving the sorting effect of the blade separator. Therefore, this embodiment of the invention constructs a simulation model of the blade-striking air separator and combines orthogonal experiments to perform candidate value combinations and simulation analysis on multiple parameters to be optimized on the air guide plate. Based on the relative standard deviation of wind speed, the mean value of each candidate value is analyzed, and the target value of each parameter to be optimized is determined based on the principle of minimizing the mean value. This reduces the experimental cost, improves the parameter optimization efficiency, and improves the uniformity of wind speed distribution and the sorting effect of the blade-striking air separator.

[0066] Figure 6 This is a schematic diagram of the structure of an optimized device for a leaf-beating air distributor provided in an embodiment of the present invention, as shown below. Figure 6 As shown, the device includes: The first model construction module 601 is used to construct a simulation model of the leaf-beating air distributor; The value group determination module 602 is used to obtain the parameter set corresponding to each parameter to be optimized of the air guide plate, and determine multiple candidate value groups according to the parameter set, wherein each parameter set includes multiple candidate values ​​corresponding to the parameter to be optimized; The second model construction module 603 is used to construct a wind guide plate model for each candidate value group. The simulation module 604 is used to perform simulation on the simulation model after associating the wind guide plate model with the simulation model, and to obtain the relative standard deviation of wind speed corresponding to the candidate value group. The value determination module 605 is used to determine the target values ​​of each parameter to be optimized for the wind guide plate based on the relative standard deviation of wind speed corresponding to all candidate value groups.

[0067] In one embodiment, the first model construction module 601 is specifically used for: Obtain a 3D model of the leaf-splitting air distributor and determine the fluid domain based on the 3D model; Set the boundary conditions of the fluid domain and mesh the fluid domain to obtain the simulation model.

[0068] In one embodiment, after constructing a simulation model of the leaf-splitting air distributor, the first model construction module 601 is further configured to: Simulations were performed on the simulation model to obtain the simulated wind speed at each measuring point on the measurement surface. Obtain the actual wind speed at each measuring point on the measuring surface of the blade-beating air distributor; Calculate the relative error of each measuring point based on the simulated wind speed and the actual wind speed; The simulation model is considered valid when the relative error of each measuring point is less than or equal to the preset threshold. When the relative error of any measuring point exceeds the preset threshold, return to the step of constructing the simulation model of the leaf-splitter.

[0069] In one embodiment, each parameter set includes the same number of candidate values; the value group determination module 602 determines multiple candidate value groups based on the parameter set, including: Determine the orthogonal array L based on the parameter set. n (m k ), where n is the number of candidate value groups, m is the number of candidate values ​​included in each parameter set, and k is the number of parameters to be optimized; According to the orthogonal array L n (m k ), to determine multiple candidate value groups.

[0070] In one embodiment, the value determination module 605 is specifically used for: For each parameter to be optimized, determine the mean of the relative standard deviation of wind speed when the parameter to be optimized takes the same candidate value; The candidate value with the smallest mean is taken as the target value of the parameter to be optimized.

[0071] In one embodiment, the value determination module 605 is specifically used for: Each candidate value in the candidate value group with the smallest relative standard deviation of wind speed is taken as the target value of each parameter to be optimized for the wind guide plate.

[0072] In one embodiment, the parameters to be optimized include at least the semicircle diameter, the transition arc radius, and the plate length.

[0073] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0074] The device of this invention constructs a simulation model of a blade-splitting air separator, enabling analysis of airflow motion in a virtual environment, reducing actual experimental costs and improving analysis efficiency. It acquires parameter sets corresponding to each parameter to be optimized for the air guide plate, and determines multiple candidate value groups based on these parameter sets. Each parameter set includes multiple candidate values ​​for the corresponding parameter to be optimized, thus achieving a systematic combination of air guide plate structural parameters and providing a foundation for subsequent optimization. For each candidate value group, an air guide plate model is constructed, enabling the formation of air guide plate schemes under various structural parameters, improving the comprehensiveness of the optimization process. After associating the air guide plate model with the simulation model, simulation is performed on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value groups, thereby quantitatively characterizing the impact of different parameter combinations on wind field uniformity. Based on the relative standard deviation of wind speed corresponding to all candidate value groups, the target values ​​for each parameter to be optimized for the air guide plate are determined, thereby obtaining the optimal parameter combination that makes the wind speed distribution more uniform and improving the sorting effect of the blade-splitting air separator. Therefore, this embodiment of the invention effectively reduces experimental costs and improves optimization efficiency by constructing a simulation model of the leaf-splitting air separator and conducting systematic simulation analysis using multiple sets of candidate values. Based on this, by quantitatively analyzing the relative standard deviation of wind speed corresponding to each set of candidate values, the target values ​​for each parameter to be optimized are determined, realizing the shift from empirical adjustment to quantitative optimization. Furthermore, through synergistic optimization among the parameters to be optimized, the uniformity of wind speed distribution is significantly improved, enhancing the sorting effect of the leaf-splitting air separator.

[0075] The following is for reference. Figure 7 It shows a schematic diagram of the structure of a computer system 700 suitable for implementing an electronic device according to embodiments of the present invention. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0076] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 702 or programs loaded from storage section 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the computer system 700. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0077] The following components are connected to I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube, liquid crystal display, etc., and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card, such as a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 710 as needed so that computer programs read from it can be installed into storage section 708 as needed.

[0078] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs the functions defined above in the system of this invention.

[0079] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, etc., or any suitable combination thereof.

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0081] The modules and / or units described in the embodiments of the present invention can be implemented in software or hardware. The described modules and / or units can also be housed in a processor; for example, a processor can be described as including a first model building module, a value group determination module, a second model building module, a simulation module, and a value determination module. The names of these modules do not necessarily limit the module itself under certain circumstances.

[0082] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include: A simulation model of the leaf-striking air distributor is constructed; the parameter set corresponding to each parameter to be optimized of the air guide plate is obtained, and multiple candidate value groups are determined based on the parameter set, wherein each parameter set includes multiple candidate values ​​for the corresponding parameter to be optimized; for each candidate value group, an air guide plate model is constructed based on the candidate value group; after associating the air guide plate model with the simulation model, simulation is performed on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value group; based on the relative standard deviation of wind speed corresponding to all candidate value groups, the target values ​​of each parameter to be optimized of the air guide plate are determined.

[0083] The technical solution of this invention constructs a simulation model of a blade-splitting air separator, enabling analysis of airflow motion in a virtual environment, reducing actual experimental costs and improving analysis efficiency. It obtains parameter sets corresponding to each parameter to be optimized for the air guide plate, and determines multiple candidate value groups based on these parameter sets. Each parameter set includes multiple candidate values ​​for the corresponding parameter to be optimized, thus achieving a systematic combination of air guide plate structural parameters and providing a foundation for subsequent optimization. For each candidate value group, an air guide plate model is constructed, enabling the formation of air guide plate schemes under various structural parameters, improving the comprehensiveness of the optimization process. After associating the air guide plate model with the simulation model, simulation is performed on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value groups, thereby quantitatively characterizing the impact of different parameter combinations on wind field uniformity. Based on the relative standard deviation of wind speed corresponding to all candidate value groups, the target values ​​for each parameter to be optimized for the air guide plate are determined, thereby obtaining the optimal parameter combination that makes the wind speed distribution more uniform and improving the sorting effect of the blade-splitting air separator. Therefore, this embodiment of the invention effectively reduces experimental costs and improves optimization efficiency by constructing a simulation model of the leaf-splitting air separator and conducting systematic simulation analysis using multiple sets of candidate values. Based on this, by quantitatively analyzing the relative standard deviation of wind speed corresponding to each set of candidate values, the target values ​​for each parameter to be optimized are determined, realizing the shift from empirical adjustment to quantitative optimization. Furthermore, through synergistic optimization among the parameters to be optimized, the uniformity of wind speed distribution is significantly improved, enhancing the sorting effect of the leaf-splitting air separator.

[0084] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the optimization method for the leaf-beating air distributor as provided in any embodiment of this invention.

[0085] In the implementation of a computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​as well as conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0086] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0087] It should be noted that the collection, use, storage, sharing, and transfer of user personal information involved in the technical solution of this invention all comply with the provisions of relevant laws and regulations, and require notification to the user and obtaining the user's consent or authorization. Where applicable, user personal information has undergone de-identification and / or anonymization and / or encryption technical processing. In addition, a corresponding operation entry is provided for the user to choose to agree to or reject the automated decision result; if the user chooses to reject, the process proceeds to the expert decision-making process.

[0088] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An optimization method for a leaf-splitting air distributor, characterized in that, The method includes: Construct a simulation model of a leaf-beating air distributor; Obtain the parameter set corresponding to each parameter to be optimized of the air guide plate, and determine multiple candidate value groups based on the parameter set, wherein each parameter set includes multiple candidate values ​​corresponding to the parameter to be optimized; For each candidate value group, a wind guide plate model is constructed based on the candidate value group; After associating the wind guide plate model with the simulation model, simulation is performed on the simulation model to obtain the relative standard deviation of wind speed corresponding to the candidate value group. Based on the relative standard deviation of wind speed corresponding to all candidate value groups, the target values ​​of each parameter to be optimized for the wind guide plate are determined.

2. The method according to claim 1, characterized in that, The simulation model for constructing the leaf-beating air distributor includes: Obtain a three-dimensional model of the leaf-splitting air distributor, and determine the fluid domain based on the three-dimensional model; Set the boundary conditions of the fluid domain and mesh the fluid domain to obtain the simulation model.

3. The method according to claim 1 or 2, characterized in that, After constructing a simulation model of the leaf-beating air distributor, the method further includes: Simulations were performed on the simulation model to obtain the simulated wind speeds at each measuring point on the measurement surface. The actual wind speed at each measuring point on the measuring surface of the blade-beating air distributor is obtained; Calculate the relative error of each measuring point based on the simulated wind speed and the actual wind speed; When the relative error of each measuring point is less than or equal to the preset threshold, the simulation model is deemed valid. When the relative error of any measuring point exceeds the preset threshold, return to the step of constructing the simulation model of the leaf-splitter.

4. The method according to claim 1, characterized in that, Each parameter set includes the same number of candidate values; the step of determining multiple candidate value groups based on the parameter set includes: determining a orthogonal table L according to the parameter set n (m k ), wherein n is the number of candidate value groups, m is the number of candidate values included in each parameter set, and k is the number of parameters to be optimized; According to the orthogonal array L n (m k ), to determine multiple candidate value groups.

5. The method according to claim 4, characterized in that, The step of determining the target values ​​of each parameter to be optimized for the wind guide plate based on the relative standard deviation of wind speed corresponding to all candidate value groups includes: For each parameter to be optimized, determine the mean of the relative standard deviation of wind speed when the parameter to be optimized takes the same candidate value; The candidate value with the smallest mean is taken as the target value of the parameter to be optimized.

6. The method according to claim 1, characterized in that, The step of determining the target values ​​of each parameter to be optimized for the wind guide plate based on the relative standard deviation of wind speed corresponding to all candidate value groups includes: Each candidate value in the candidate value group with the smallest relative standard deviation of wind speed is taken as the target value of each parameter to be optimized of the wind guide plate.

7. The method according to claim 1, characterized in that, The parameters to be optimized include at least the semicircle diameter, the transition arc radius, and the plate length.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the optimization method for the leaf-beating air distributor as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the optimization method for the leaf-beating air divider as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the optimization method for the leaf-beating air distributor as described in any one of claims 1 to 7.

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