Motion parameter determination method and device for retired photovoltaic panel particle screening device

Abstract

The invention discloses a motion parameter determination method and device for a decommissioned photovoltaic panel particle screening device, a storage medium and computer equipment. The method comprises the steps that a crushed mixture obtained after pyrolysis of a target decommissioned photovoltaic panel is obtained, the size and shape of each fragment are determined, and based on the size and shape, the decommissioned photovoltaic panel particle screening device is obtained; constructing a fragment model of the fragments through a hyper-quadratic surface equation; obtaining a modeling file of the particle screening device, determining a target motion parameter, and simulating the screening condition of each fragment in the particle screening device through a discrete element method to obtain a simulation result; calculating the wear value and screening efficiency of the particle screening device based on the simulation result, determining whether the target motion parameters meet preset requirements or not based on the wear value and the screening efficiency, if not, re-determining the target motion parameters, performing simulation through a discrete element method, and calculating the wear value and screening efficiency of the new target motion parameters. And taking the current target motion parameter as a final motion parameter until a preset requirement is met.

Description

Technical Field

[0001] The present application relates to the technical field of performance prediction of particle screening devices, and in particular to a method and device for determining motion parameters of a particle screening device for retired photovoltaic panels, a storage medium, and a computer device. Background Art

[0002] Photovoltaic energy has been widely used due to its inexhaustible supply, ubiquity, high capacity, and environmental friendliness. However, approximately 90% of retired photovoltaic panels are still directly disposed of in landfills, posing a potential threat to the environment. Retired photovoltaic panels contain aluminum frames, silicon, glass, and various metals attached to the solar cells (such as silver, copper, lead, and aluminum). These materials account for more than 80% of the total weight of retired photovoltaic panels. Through professional recycling, these materials can be effectively separated and reused, not only reducing the demand for new raw materials but also significantly saving production costs.

[0003] Currently, the industry's generally accepted recycling path for retired photovoltaic panels consists of two main steps: module stripping and cell leaching / etching. During the module stripping process, heat treatment is the most feasible and cost-effective method for separating the tempered glass and solar cells. After heat treatment, the photovoltaic panel is transformed into a mixture containing glass fragments of varying sizes, solar cell fragments, and various residual fragments. Before the leaching step, the solar cells need to be separated and enriched from this mixture, which significantly reduces the use of leaching chemicals and improves the subsequent leaching efficiency of valuable metals. Due to the differences in the physical properties of solar cells and glass after heat treatment, they have different particle size distributions, which can be effectively separated through mechanical methods such as screening. Therefore, using screening technology to efficiently separate glass, solar cells, and other substances from the mixture is a crucial step in the recycling of retired photovoltaic panels.

[0004] However, a review of existing experimental research reveals that experimental studies of the sieving behavior and mechanisms of particle sieving devices used to sieve mixtures derived from the pyrolysis of decommissioned photovoltaic panels are extremely limited due to the lag and inadequacy of current particle dynamic motion measurement and visualization technologies. Although existing numerical simulation methods have been widely used to simulate a variety of mechanical equipment and industrial processes, supplementing the microscopic behaviors and principles that are difficult to capture experimentally, the use of high-precision numerical simulation methods to study the sieving performance of particle sieving devices, especially for separating complex multi-component non-spherical mixtures, remains very limited, resulting in low accuracy in the simulation results. Summary of the Invention

[0005] In view of this, the present application provides a method and device for determining the motion parameters of a retired photovoltaic panel particle screening device, a storage medium, and a computer device. By using superquadratic surface equations, multi-component non-spherical particles such as glass fragments, solar cell fragments, and crushed residue powder of various shapes and sizes are accurately modeled, solving the problem of complex shape modeling in multi-phase particle systems; by combining the high-precision discrete element method (DEM) with the superquadratic surface equation, the morphology and motion behavior of complex non-spherical fragments can be accurately simulated, which is suitable for the screening of crushed mixtures of multi-component photovoltaic panels of various shapes and sizes, thereby improving the accuracy and realism of the simulation; by reasonably optimizing the motion parameters of the screen, while improving the screening efficiency, the wear and energy consumption of the particle screening device are reduced, the service life of the particle screening device is extended, and the operating cost is reduced. In addition, the embodiments of the present application are not only suitable for laboratory-scale screening simulations, but can also provide theoretical support and design basis for the industrial scale-up of the equipment, and have strong scalability and applicability.

[0006] According to one aspect of the present application, a method for determining motion parameters of a particle screening device for retired photovoltaic panels is provided, comprising:

[0007] Obtaining a crushed mixture of target retired photovoltaic panels after pyrolysis treatment, determining the size and shape of each fragment in the crushed mixture, and constructing a fragment model for each fragment based on the size and shape using a superquadratic surface equation, wherein the fragments in the crushed mixture include glass fragments, solar cell fragments, and residue fragments;

[0008] Obtaining a modeling file of a particle screening device, and determining target motion parameters from a plurality of sets of preset screen motion parameters; and performing a screening simulation of the screening of each fragment in the particle screening device using a discrete element method based on the modeling file, a fragment model corresponding to each fragment, and the target motion parameters to obtain a simulation result;

[0009] Based on the simulation results, the wear value and screening efficiency corresponding to the particle screening device are calculated, and based on the wear value and the screening efficiency, it is determined whether the target motion parameters meet the preset requirements. When the target motion parameters do not meet the preset requirements, the target motion parameters are re-determined from the multiple sets of preset screen motion parameters, and the screening simulation is performed again using the discrete element method. After the screening simulation, the wear value and screening efficiency corresponding to the new target motion parameters are calculated, and the process ends when the current target motion parameters meet the preset requirements. The current target motion parameters are used as the final motion parameters corresponding to the particle screening device.

[0010] According to another aspect of the present application, a motion parameter determination device for a decommissioned photovoltaic panel particle screening device is provided, comprising:

[0011] a fragment model construction module, configured to obtain a fragmented mixture of target decommissioned photovoltaic panels after pyrolysis treatment, determine the size and shape of each fragment in the fragmented mixture, and construct a fragment model for each fragment based on the size and shape using a superquadratic surface equation, wherein the fragments in the fragmented mixture include glass fragments, solar cell fragments, and residue fragments;

[0012] a simulation module, configured to obtain a modeling file of the particle screening device, determine target motion parameters from a plurality of sets of preset screen motion parameters, and perform a screening simulation of the screening of each fragment in the particle screening device using a discrete element method based on the modeling file, a fragment model corresponding to each fragment, and the target motion parameters, to obtain a simulation result;

[0013] A motion parameter determination module is used to calculate the wear value and screening efficiency corresponding to the particle screening device based on the simulation results, and determine whether the target motion parameters meet the preset requirements based on the wear value and the screening efficiency. When the target motion parameters do not meet the preset requirements, the target motion parameters are re-determined from the multiple sets of preset screen motion parameters, and the screening simulation is performed again through the discrete element method. After the screening simulation, the wear value and screening efficiency corresponding to the new target motion parameters are calculated until the current target motion parameters meet the preset requirements, and the current target motion parameters are used as the final motion parameters corresponding to the particle screening device.

[0014] According to another aspect of the present application, a storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the motion parameter determination method of the retired photovoltaic panel particle screening device is implemented.

[0015] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the program, the motion parameter determination method of the above-mentioned retired photovoltaic panel particle screening device is implemented.

[0016] By means of the above technical solution, the present application provides a method and device for determining the motion parameters of a particle screening device for retired photovoltaic panels, a storage medium, and a computer device. First, a crushed mixture after pyrolysis treatment can be collected from retired photovoltaic panels. Next, the size and shape of these fragments are measured and recorded. Based on the measured size and shape data, a mathematical model can be constructed for each fragment using a superquadratic surface equation. The modeling file of the particle screening device is obtained, and a set of parameters is selected from the preset multiple sets of screen motion parameters as the initial target motion parameters. The discrete element method (DEM) is used to combine the modeling file of the particle screening device, the fragment model of each fragment, and the selected target motion parameters to simulate the screening process of these fragments in the screening device. Furthermore, the wear value and screening efficiency of the particle screening device can be calculated based on the simulation results. Subsequently, the calculated wear value and screening efficiency are compared with the preset requirements. If the wear value and screening efficiency corresponding to the particle screening device do not meet the preset requirements under the target motion parameters, a new set of parameters can be selected from the preset multiple sets of screen motion parameters and the above screening simulation process is repeated. This process is iterated until a set of screen motion parameters that meet the preset requirements is found. At this point, the iterative process ends, and this set of screen motion parameters is the final motion parameters corresponding to the particle screening device. The embodiment of the present application uses superquadratic surface equations to accurately model multi-component non-spherical particles such as glass fragments, solar cell fragments, and crushed residue powder of various shapes and sizes, thereby solving the problem of complex shape modeling in multi-phase particle systems; by combining the high-precision discrete element method (DEM) with the superquadratic surface equation, the morphology and motion behavior of complex non-spherical fragments can be accurately simulated, which is suitable for screening of crushed mixtures of photovoltaic panels of various shapes and sizes of multiple components, thereby improving the accuracy and reality of the simulation; by rationally optimizing the screen motion parameters, while improving the screening efficiency, the wear and energy consumption of the particle screening device are reduced, the service life of the particle screening device is extended, and the operating cost is reduced. In addition, the embodiment of the present application is not only applicable to laboratory-scale screening simulations, but also can provide theoretical support and design basis for the industrial amplification of equipment, with strong scalability and applicability.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 A schematic flow chart of a method for determining motion parameters of a particle screening device for retired photovoltaic panels provided in an embodiment of the present application is shown;

[0020] Figure 2 A schematic diagram of the mesh movement of a banana screen provided in an embodiment of the present application is shown;

[0021] Figure 3 A schematic structural diagram of a banana screen provided in an embodiment of the present application is shown;

[0022] Figure 4 A schematic diagram showing a geometric model of a banana sieve provided in an embodiment of the present application is shown;

[0023] Figure 5 A schematic diagram showing the screening results of each stage of a banana sieve provided in an embodiment of the present application is shown;

[0024] Figure 6 A schematic diagram showing the screening efficiency of a banana screen provided in an embodiment of the present application is shown;

[0025] Figure 7 A schematic structural diagram of a motion parameter determination device for a retired photovoltaic panel particle screening device provided in an embodiment of the present application is shown;

[0026] Figure 8 A schematic diagram of the device structure of a computer device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0028] In this embodiment, a method for determining motion parameters of a particle screening device for retired photovoltaic panels is provided. Figure 1 As shown, the method includes:

[0029] Step 101: Obtain a crushed mixture of target retired photovoltaic panels after pyrolysis treatment, determine the size and shape of each fragment in the crushed mixture, and construct a fragment model of each fragment based on the size and shape using a superquadratic surface equation, wherein the fragments in the crushed mixture include glass fragments, solar cell fragments, and residue fragments.

[0030] The embodiment of the present application provides a method for determining the motion parameters of a particle screening device for retired photovoltaic panels, which can find the most suitable sieve motion parameters for screening the crushed mixture after pyrolysis of retired photovoltaic panels in the process of simulating and optimizing the particle screening process using the discrete element method. Among them, the particle screening device refers to a device that screens the crushed mixture after pyrolysis of retired photovoltaic panels to separate glass fragments, solar cell fragments and residue fragments. Specifically, a sieve can be set in the particle screening device, and different parts of the sieve are set with different apertures. In this way, after screening, fragments smaller than the aperture can be obtained under different apertures, thereby achieving fragment separation.

[0031] First, a mixture of pyrolyzed fragments can be collected from decommissioned photovoltaic panels. These mixtures contain glass fragments, solar cell fragments, and other debris fragments. Next, the size and shape of these fragments are measured and recorded. In order to accurately describe the non-spherical characteristics of glass fragments, solar cell fragments, and crushed residues in the crushed mixture, superquadratic surface equations can be used to model each type of fragment. That is, based on the measured size and shape data, a mathematical model can be constructed for each fragment using superquadratic surface equations. The superquadratic surface equation is a mathematical tool that can describe various complex shapes and is suitable for the diversity of fragment morphologies described here. The superquadratic surface equation is as follows:

[0032]

[0033] Where a, b, and c represent the semi-axis lengths of the fragments in the X, Y, and Z directions, respectively; n1 and n2 represent shape indices, which control the sharpness of the fragment edges. A larger shape index indicates a sharper edge. Based on the morphology of the main components in the crushed mixture, glass is represented by layers, solar cells by flakes, and crushed residues by smaller, non-spherical fragments. This results in a ternary mixed fragment consisting of three components.

[0034] Specifically, according to the shapes and sizes of different fragments, superquadratic surface equations corresponding to different fragments can be obtained.

[0035] Step 102: Obtain a modeling file of the particle screening device, and determine target motion parameters from multiple sets of preset screen motion parameters. Based on the modeling file, the fragment models corresponding to each of the fragments, and the target motion parameters, perform a screening simulation on the screening conditions of each of the fragments in the particle screening device through the discrete element method to obtain a simulation result.

[0036] In this embodiment, a modeling file of a particle screening device is obtained, and the modeling file contains information such as the geometry and material properties of the particle screening device. In addition, a group of preset screen motion parameters can be selected as the initial target motion parameters. The screen motion parameters can include a parameter combination of parameters such as the vibration frequency, amplitude, and elliptical motion size of a set of screens. The parameter combinations contained in different screen motion parameters are different. After determining the target motion parameters, the discrete element method (DEM) can be further used to simulate the screening process of these fragments in the screening device in combination with the modeling file of the particle screening device, the fragment model of each fragment and the selected target motion parameters. DEM is a numerical method for simulating the behavior of granular materials, and is particularly suitable for dealing with such problems involving a large number of irregularly shaped fragments. After the simulation is completed, the simulation results are collected and analyzed, and these results provide key information such as the motion trajectory of the fragments on the screen.

[0037] Step 103: Based on the simulation results, calculate the wear value and screening efficiency corresponding to the particle screening device, and based on the wear value and the screening efficiency, determine whether the target motion parameters meet the preset requirements. When the target motion parameters do not meet the preset requirements, redetermine the target motion parameters from the multiple sets of preset screen motion parameters, and perform screening simulation again through the discrete element method. After the screening simulation, calculate the wear value and screening efficiency corresponding to the new target motion parameters, and end until the current target motion parameters meet the preset requirements, and use the current target motion parameters as the final motion parameters corresponding to the particle screening device.

[0038] In this embodiment, the wear value and screening efficiency of the particle screening device can be further calculated based on the simulation results. During the screening process, friction will be generated between the fragments and the screen, as well as between the fragments and the side walls of the particle screening device. This friction will eventually cause wear of the particle screening device. Screening efficiency refers to the proportion of fragments that the particle screening device successfully separates from the crushed mixture. Subsequently, the calculated wear value and screening efficiency are compared with the preset requirements. Here, the preset requirements can be requirements for wear value and screening efficiency separately, for example, the preset requirement is that the wear value is less than A and the screening efficiency is greater than B; or it can be a combined requirement for wear value and screening efficiency, for example, the calculated wear value is converted into a score C, and the screening efficiency is converted into a score D, and finally the weighted addition is used to obtain a total score. The preset requirement is a preset total score. If the total score obtained by the weighted addition is greater than the preset total score, it means that the preset requirement is met. In addition, the preset requirements can also be requirements of other methods, which are not limited here. If the wear and screening efficiency of the particle screening device under the target motion parameters do not meet the preset requirements, the particle screening device's mesh motion parameters need to be adjusted. Specifically, a new set of mesh motion parameters can be selected from the multiple preset sets of mesh motion parameters as the target parameters, and the above screening simulation process can be repeated. This process is iterative until a set of mesh motion parameters that meets the preset requirements is found. At this point, the iterative process ends, and this set of mesh motion parameters becomes the final motion parameters for the particle screening device.

[0039] By applying the technical solution of this embodiment, first, the crushed mixture after pyrolysis treatment can be collected from retired photovoltaic panels. Next, the size and shape of these fragments are measured and recorded. Based on the measured size and shape data, a mathematical model can be constructed for each fragment using the superquadratic surface equation. The modeling file of the particle screening device is obtained, and a group is selected as the initial target motion parameters from the preset multiple sets of screen motion parameters. The discrete element method (DEM) is used to simulate the screening process of these fragments in the screening device in combination with the modeling file of the particle screening device, the fragment model of each fragment and the selected target motion parameters. Furthermore, the wear value and screening efficiency of the particle screening device can be calculated based on the simulation results. Subsequently, the calculated wear value and screening efficiency are compared with the preset requirements. If the wear value and screening efficiency corresponding to the particle screening device do not meet the preset requirements under the target motion parameters, a new group can be selected from the aforementioned preset multiple sets of screen motion parameters as the new target motion parameters, and the above-mentioned screening simulation process is repeated. This process is iterated until a set of screen motion parameters that meet the preset requirements is found. At this point, the iterative process ends, and this set of screen motion parameters is the final motion parameters corresponding to the particle screening device. The embodiment of the present application uses superquadratic surface equations to accurately model multi-component non-spherical particles such as glass fragments, solar cell fragments, and crushed residue powder of various shapes and sizes, thereby solving the problem of complex shape modeling in multi-phase particle systems; by combining the high-precision discrete element method (DEM) with the superquadratic surface equation, the morphology and motion behavior of complex non-spherical fragments can be accurately simulated, which is suitable for screening of crushed mixtures of photovoltaic panels of various shapes and sizes of multiple components, thereby improving the accuracy and reality of the simulation; by rationally optimizing the screen motion parameters, while improving the screening efficiency, the wear and energy consumption of the particle screening device are reduced, the service life of the particle screening device is extended, and the operating cost is reduced. In addition, the embodiment of the present application is not only applicable to laboratory-scale screening simulations, but also can provide theoretical support and design basis for the industrial amplification of equipment, with strong scalability and applicability.

[0040] In an embodiment of the present application, optionally, the particle screening device is a banana screen; before the step 102 of "based on the modeling file, the fragment model corresponding to each of the fragments and the target motion parameters, the screening situation of each of the fragments in the particle screening device is simulated by the discrete element method", the method also includes: setting the screen movement during the vibration process of the banana screen based on the first motion and the second motion, so that when the screening simulation is performed by the discrete element method, the first motion and the second motion are superimposed, and the superimposed motion is used as the actual screen movement during the vibration process of the banana screen, wherein the first motion is the elliptical motion of the screen at the vibration frequency along the center of mass on the vertical plane of the screen, the center of mass of the screen is determined based on the center point of the screen when the screen is stationary, the second motion is the motion on the cross-section of the screen, and the screen motion parameters include the major axis size and minor axis size of the ellipse corresponding to the elliptical motion.

[0041] In this embodiment, the particle screening device used when screening the crushed mixture after pyrolysis of retired photovoltaic panels can be specifically a banana screen. The banana screen, also known as the linear constant thickness vibrating screen, is a large and medium-sized screening machine with a large screening capacity. The banana screen is mainly composed of a screen box, a screen mesh, an exciter, a vibration damping spring and a lower seat frame. Among them, the screen box is approximately in the shape of a banana, and the screen surface is composed of several sections with different angles. The exciter serves as a vibration source, driven by an electric motor, and transmits power through a pulley, a V-belt and a universal joint shaft. The screen beam adopts a box beam structure, which enhances the overall rigidity and stability of the screening machine.

[0042] Because the banana vibrating screen's mesh motion is a complex, composite motion, before using the discrete element method to simulate the operation of the banana vibrating screen, you can set up a primary motion and a secondary motion separately, using the simple primary and secondary motions to represent the complex mesh motion. The primary motion is an elliptical motion. Specifically, the primary motion refers to the elliptical motion of the mesh around its center of mass on the vertical plane of the mesh at a certain vibration frequency. This motion helps loosen, stratify, and filter the material on the screen surface. The center of mass of the mesh is determined based on its center point when the mesh is stationary. In one embodiment, to ensure that the mesh motion in the simulation model matches the actual motion of a banana vibrating screen, the center of mass position o when the mesh is stationary is set at the mesh center point. Elliptical motion is described by two parameters: the major axis dimension and the minor axis dimension. The major axis dimension determines the maximum vertical amplitude of the mesh, while the minor axis dimension determines the horizontal amplitude of the mesh. These two parameters together determine the shape and size of the elliptical motion. Therefore, the mesh motion parameters can include the major and minor axis dimensions of the elliptical motion. By adjusting the major and minor axis dimensions, the mesh motion pattern can be adjusted.

[0043] The second motion is the movement of the screen mesh on its cross-section. Specifically, the second motion refers to the movement of the screen mesh on its cross-section (i.e., a plane parallel to the screen surface). This second motion helps further improve the screening effect by changing the material's trajectory on the screen surface, promoting relative movement and separation between material particles.

[0044] When simulating screening using the discrete element method, the first motion (elliptical motion) and the second motion (motion on the screen section) can be superimposed to obtain the actual motion state of the screen during vibration. According to the principle of linear superposition, the motion of the screen is superimposed as an elliptical motion around the perpendicular line at point o and through the center of the screen, such as Figure 2 As shown. Among them, Figure 2 The figure represents four different b / a scenarios, where a represents the major axis of the ellipse and b represents the minor axis. This superposition accounts for the combined vertical and horizontal motion of the screen, more accurately reflecting the actual screen motion during screening. The resulting superposition is a complex spatial motion that combines the amplitude and frequency of the elliptical motion with the motion pattern of the screen cross-section. This motion is crucial for simulating the material's trajectory on the screen surface, screening efficiency, and wear.

[0045] The embodiment of the present application considers the elliptical motion of the banana sieve mesh and the motion on the cross-section of the mesh, and superimposes these two motions to more accurately simulate the motion state of the mesh during the actual screening process. This setting method helps to improve the accuracy and reliability of the screening simulation.

[0046] In an embodiment of the present application, optionally, before step 102, the method further includes: constructing a wall model and a screen model of the particle screening device according to the particle screening device to obtain an overall three-dimensional model; meshing the wall model through a triangular mesh, and meshing the screen through a locally encrypted mesh to obtain an overall three-dimensional model after division; performing a mesh inspection on the overall three-dimensional model after division through a mesh quality inspection tool, and when the inspection passes, exporting the overall three-dimensional model after division to obtain a modeling file of the particle screening device.

[0047] In this embodiment, first, according to the actual structure and size of the particle screening device, a wall model and a screen model are constructed using a three-dimensional modeling software (such as Gambit, etc.). The wall model represents the fixed parts of the screening device, such as the side walls, top cover and base, etc.; the screen model represents the working part of the screening device, which is used to separate fragments of different sizes. The constructed wall model and screen model are merged into an overall three-dimensional model. In order to convert the three-dimensional model into a numerical model that can be used for discrete element method simulation, the wall model and the screen model can be meshed. Specifically, the wall model can be triangular meshed using meshing software (such as Gambit, etc.). Triangular mesh is a commonly used mesh type because it can flexibly adapt to various complex geometric shapes. The screen is a key component in the particle screening device, and its meshing requires higher precision to capture the interaction between the fragments and the screen. Therefore, in the screen area, a local encrypted mesh can be used for division, which can be achieved by setting local encryption parameters in the meshing software.

[0048] After meshing is complete, you can use a mesh quality checker (such as Gambit) to check the mesh quality of the entire 3D model. This is a critical step in ensuring simulation accuracy and stability. Specifically, you can use meshing tools to check mesh metrics such as AspectRatio and Skewness. These metrics reflect the geometric quality and numerical stability of the mesh. If the mesh quality is found to not meet the requirements, you can re-mesh it or adjust the mesh parameters to create a new mesh.

[0049] Once the mesh quality check passes, the entire 3D model is exported as a modeling file for DEM simulation. This exported modeling file serves as input data for DEM simulation, simulating the movement trajectory of debris in the particle screening device, screening efficiency, and wear.

[0050] The embodiment of the present application constructs a three-dimensional model of the particle screening device, performs mesh division and mesh quality inspection, and finally obtains a modeling file that can be used for DEM simulation, which can greatly improve the accuracy and stability of subsequent simulations.

[0051] In the embodiment of the present application, optionally, when the particle screening device includes only one layer of screen, the wear value corresponding to the particle screening device is calculated based on the following formula:

[0052]

[0053] Among them, EM SS is the wear value, k is the wear coefficient, t p is the total screening time of the particle screening device, is the Heaviside function, is the velocity vector of the target fragments that come into contact with the particle screening device at the time of contact, is the position vector of the target fragment in contact with the particle screening device at the time of contact, f(γ) is the wear influence factor related to γ, γ is the impact angle of the target fragment in contact with the particle screening device at the time of contact, and F is the mass of the target fragment;

[0054] Accordingly, the screening efficiency of the particle screening device is calculated based on the following formula:

[0055]

[0056] Among them, η s is the screening efficiency, m s1 is the mass of the fragments passing through the sieve, m s2 It is the total mass of the fragments in the crushed mixture whose size is smaller than the maximum mesh size of the sieve.

[0057] In this embodiment, since the screen and the wall are meshed, different units are obtained. Therefore, the degree of wear of each unit can be evaluated by the sum of the wear caused to the unit by all fragments in each time step. By integrating over the entire screening time, the total wear caused by the collision of fragments with the wall and the screen can be calculated. In the above formula for calculating the wear value, k is the wear coefficient, which represents the degree of wear. It is a parameter that describes the wear rate of a material under specific conditions. It is related to factors such as the hardness, toughness, and surface roughness of the material and can be set according to the specific screen material. f represents the wear influencing factor related to the impact angle γ. This function f(γ) describes how the impact angle affects the degree of wear. This is a function related to the velocity and position vectors of the target fragment. This function takes into account the impact of the target fragment's position and direction on the screen on wear. F represents the mass of the target fragment. The product of mass and velocity can be regarded as the momentum of the target fragment. The greater the momentum, the greater the wear caused by the impact.

[0058] When calculating the screening efficiency of the particle screening device, since different parts of the screen have different apertures, m s1 In fact, it is the total mass of all fragments that pass through the screen in the crushed mixture (whether they are residue fragments, solar cell fragments, or glass fragments), m s2In fact, it is the total mass of the fragments in the crushed mixture that are smaller than the maximum mesh size of the screen (whether they are residue fragments, solar cell fragments, or glass fragments). For example, the mesh size of the screen can be set to three sizes, namely a, b, and c. The size of the residue fragments in the mixture fragments after pyrolysis is less than the size of the solar cell fragments and less than the size of the glass fragments. Therefore, the size of the residue fragments can be set to be less than a and less than the size of the solar cell fragments, the size of the solar cell fragments is less than b and less than the size of the glass fragments, and the size of the glass fragments is less than c. In this way, different fragments can be separated during the screening process. Finally, the total mass of the fragments under the screen is recorded as m s1 The total mass of the fragments in the crushed mixture that are smaller than the maximum mesh size c of the sieve is recorded as m s2 .

[0059] In an embodiment of the present application, optionally, the step 103 of "determining whether the target motion parameters meet the preset requirements based on the wear value and the screening efficiency" includes: calculating the number of screenings of the particle screening device under the target motion parameters based on the wear value and the maximum wear value of the particle screening device at the end of the wear period, and determining the single screening cost of the particle screening device based on the number of screenings; calculating the single recovery waste cost of the particle screening device under the target motion parameters based on the screening efficiency; calculating the single total cost based on the single screening cost and the single recovery waste cost, and judging whether the target motion parameters meet the preset requirements based on the single total cost.

[0060] In this embodiment, it is possible to determine whether the target motion parameters meet the preset requirements based on the following method. First, the number of screenings and the cost of a single screening can be calculated. Specifically, the theoretical maximum wear of the particle screening device at the end of the wear stage can be obtained first, which is the maximum degree of wear that the particle screening device can withstand. Afterwards, based on the current wear value (i.e., the amount of wear of the particle screening device after one screening) and the maximum wear value, it is possible to calculate how many times the particle screening device can perform the same screening operation under the target motion parameters until the wear limit is reached. Based on the known number of screenings, the total cost (which may include the cost of the particle screening device, etc.) can be apportioned to each screening to obtain the cost of a single screening. This cost reflects the cost loss of each screening operation under the target motion parameters. At the same time, the cost of a single recovery waste can also be calculated based on the screening efficiency. Screening efficiency refers to the ability of the particle screening device to separate fragments from the crushed mixture. If the screening efficiency is not high, more raw materials will not be properly separated, resulting in a waste of resources. According to the screening efficiency, it is possible to calculate how many fragments are wasted (i.e., the parts that are not properly separated) in each screening operation under the target motion parameters. The waste cost may depend on the value of these wasted fragments, etc. Secondly, the single total cost is calculated and it is determined whether the target motion parameters meet the requirements. Here, the single total cost is the sum (or weighted sum) of the single screening cost and the single recovery waste cost. It reflects the total cost loss of each screening operation under the target motion parameters. Finally, the single total cost can be compared with the preset cost threshold. If the single total cost is lower than the preset cost threshold, the target motion parameters are considered to be economically feasible and meet the preset requirements; otherwise, adjustments need to be made to reduce the single total cost. The embodiment of the present application optimizes the motion parameters of the particle screening device by comprehensively considering the impact of wear and screening efficiency on cost to maximize economic benefits.

[0061] In an embodiment of the present application, optionally, the step 102 of "based on the modeling file, the fragment model corresponding to each of the fragments and the target motion parameters, the screening situation of each of the fragments in the particle screening device is simulated by the discrete element method to obtain a simulation result" includes: importing the modeling file and the fragment model corresponding to each of the fragments into a simulation environment using the discrete element method, so as to restore the particle screening device and each fragment in the crushed mixture in the simulation environment; in the simulation environment, based on the target motion parameters, controlling the movement of the screen model, and determining the surrounding area corresponding to each fragment model in each time step. Box; for each target fragment model, determine whether there is an intersecting target bounding box in the bounding box corresponding to the target fragment model and the bounding boxes corresponding to the remaining object models, and if so, determine the midpoint of the bounding box of the target fragment model, and determine whether there is contact between the target fragment model and the object model corresponding to the target bounding box according to the midpoint, wherein the object model includes a fragment model, a wall model and a screen model; based on the contact judgment result, determine the new coordinate position corresponding to each of the fragment models in the next time step, and perform contact judgment again according to the new coordinate position of each of the fragment models, until the preset conditions are met to terminate and obtain the simulation result.

[0062] In this embodiment, first, the modeling file of the particle screening device and the fragment model corresponding to each fragment are imported into a simulation environment using the discrete element method. In this way, the actual particle screening device and each fragment in the crushed mixture can be accurately restored in the simulation environment, ensuring the accuracy and authenticity of the simulation. Then, in the simulation environment, the motion of the screen model is controlled based on the target motion parameters (such as the vibration frequency and amplitude of the screen). At the same time, within each time step, a bounding box (Bounding Box) is determined for each fragment model. This is a simple geometric shape (such as a cube) for quickly detecting whether the fragment model may intersect with other object models. Specifically, each fragment model can be used as a target fragment model, and for each target fragment model, its bounding box is checked to see if it intersects with the bounding boxes of the remaining object models (including other fragment models, wall models, and screen models). If there is an intersecting target bounding box, it is further determined whether the target fragment model actually contacts the object model corresponding to the target bounding box. Specifically, the midpoint of the bounding box of the target fragment model can be calculated, and whether contact occurs can be determined based on the midpoint position. Here, contact judgment is the core of DEM simulation, which determines the interaction forces between fragments and how the fragments respond to these forces (such as bouncing, rolling or sliding). Correct contact judgment is crucial to the accuracy of the simulation results. Subsequently, based on the results of the contact judgment, the new coordinate position of each fragment model in the next time step can be calculated according to the laws of physics (such as Newton's second law). Then, contact judgment is performed again based on these new coordinate positions, and the cycle repeats until the preset conditions are met (such as the simulation reaches a set time length, the number of cycles reaches a preset number, etc.). When the preset conditions are met, the simulation ends and the simulation results are output. The simulation results can include the screening efficiency of the fragments, the distribution of the fragments after screening, the accumulation of fragments on the screen, etc. In fact, during the screening simulation process, it would be very time-consuming to directly detect the contact between each fragment and all other fragments, walls and screens. Before contact detection, the embodiment of the present application first determines whether there is a bounding box that intersects with the bounding box of each target fragment model. If there is no bounding box that intersects with it, it means that there is currently no object model in contact with the target fragment model. Otherwise, it means that there may be an object model in contact with the target fragment model. Then further contact detection is performed. This can greatly improve the efficiency of contact detection and reduce computer resource consumption.

[0063] In an embodiment of the present application, optionally, the “determining whether contact occurs between the target fragment model and the object model corresponding to the target bounding box based on the midpoint” includes: when the object model corresponding to the target bounding box is a fragment model, based on the coordinates corresponding to the midpoint, respectively calculating the first value of the superquadratic surface equation corresponding to the target fragment model and the second value of the superquadratic surface equation of the object model corresponding to the target bounding box; if the first value and the second value are both less than zero, determining that contact occurs between the target fragment model and the object model corresponding to the target bounding box; when the object model corresponding to the target bounding box is a wall model or a screen model, based on the coordinates corresponding to the midpoint, respectively calculating the first value of the superquadratic surface equation corresponding to the target fragment model and the second value of the plane equation of the object model corresponding to the target bounding box; if the first value is less than zero and the second value is less than or equal to zero, determining that contact occurs between the target fragment model and the object model corresponding to the target bounding box.

[0064] In this embodiment, when the object model corresponding to the target bounding box is a fragment model, it indicates that there may be contact between the target fragment model and another fragment model. At this time, the midpoint coordinates of the target fragment model bounding box can be substituted into the superquadratic surface equation of the target fragment model to obtain a first value. If the first value is less than zero, it means that the midpoint is located inside the target fragment model. Then, the midpoint coordinates are substituted into the superquadratic surface equation of the object model corresponding to the target bounding box to obtain a second value. Similarly, if the second value is less than zero, it means that the midpoint is also located inside the fragment model. If both the first value and the second value are less than zero, it is determined that contact occurs between the target fragment model and the fragment model corresponding to the target bounding box.

[0065] For example, if the midpoint X0 satisfies F i (X0)<0 and F j (X0)<0, then the fragment model i and the fragment model j are in contact state, where F i The superquadratic surface equation of fragment model i, F j Represents the superquadratic surface equation of fragment model j.

[0066] When the object model corresponding to the target bounding box is a wall model or a screen model, it indicates that there may be contact between the target fragment model and the wall model or screen model. In this case, the midpoint coordinates of the target fragment model bounding box can be substituted into the superquadratic surface equation of the target fragment model to obtain a first value. If the first value is less than zero, it means that the midpoint is located inside the target fragment model. Then, the midpoint coordinates are substituted into the plane equation of the wall model or screen model corresponding to the target bounding box to obtain a second value. If the first value is less than zero and the second value is less than or equal to zero, it is determined that there is contact between the target fragment model and the wall model or screen model corresponding to the target bounding box. Among them, the wall model and the screen model can usually be simplified to a plane or a group of planes. Therefore, plane equations are used here to represent these models. It should be noted that because the wall may not be deformed when the fragment contacts the wall, the restriction here is that as long as the second value is less than or equal to zero and the first value is less than zero, it means that there is contact between the two.

[0067] The embodiment of the present application combines the superquadratic surface equation and the plane equation to determine the contact situation between the fragment model and other object models, which can greatly improve the contact calculation efficiency.

[0068] Furthermore, another method for determining motion parameters of a particle screening device for retired photovoltaic panels is provided, the method comprising:

[0069] In this example, a simplified laboratory-scale banana sieve 3D geometric model was constructed using the aforementioned modeling method for the particle screening device. Parts that have little impact on the movement of the fragments were omitted to simulate the screening process of the laboratory-scale banana sieve. A virtual fragment feed end was defined at the inlet of the banana sieve, such as Figure 3 As shown, the crushed mixture is fed into the banana screen before the motor is turned on. Figure 3 The following parts are shown in the figure: Feed End, Screen Surface, Vibration Motor and Support Spring. The geometric model of the banana screen mainly consists of a screen box and a screen with a mesh size suitable for the material, such as Figure 4 As shown, the setting angles of the screens at different stages are determined after multiple tests.

[0070] By performing simulation analysis in the discrete element method simulation environment, the typical screening behavior of the laboratory banana-shaped vibrating screen can be obtained: Figure 5As can be seen, at the beginning of the simulation, a crushed mixture is first generated and released from the feeder. The fragments in the crushed mixture fall along the inclined screens, where they come into contact. Smaller fragments (such as residue fragments) pass through the meshes, while larger fragments remain on the screens. The first two screens have a larger inclination angle, resulting in a looser distribution of fragments. On the last three screens, due to the smaller inclination angles, fragments begin to accumulate. Screening efficiency varies depending on the screen; a larger inclination angle does not necessarily mean higher screening efficiency, as screening efficiency is also related to the residence time of the fragments on the screen. Figure 6 The overall screening efficiency and the local screening efficiency of each part of the screen are shown. The overall screening efficiency rises sharply in the initial stage, and then gradually stabilizes and no longer increases further. Under this operating condition, the fastest increase in screening efficiency is from the second screen surface to the third screen surface, and the slowest increase is from the fourth screen surface to the fifth screen surface. The screening efficiency varies between different screen surfaces, and the greater the inclination angle, the higher the screening efficiency. Therefore, the modeling method proposed in the embodiment of the present application can reasonably predict the screening behavior of retired photovoltaic panels. By simulating different screen motion parameters and calculating the wear value and screening efficiency of the particle screening device after each simulation, the final motion parameters of the particle screening device can eventually be found from multiple screen motion parameters.

[0071] Further, as Figure 1 The specific implementation of the method, the embodiment of the present application provides a motion parameter determination device for a retired photovoltaic panel particle screening device, such as Figure 7 As shown, the device includes:

[0072] a fragment model construction module, configured to obtain a fragmented mixture of target decommissioned photovoltaic panels after pyrolysis treatment, determine the size and shape of each fragment in the fragmented mixture, and construct a fragment model for each fragment based on the size and shape using a superquadratic surface equation, wherein the fragments in the fragmented mixture include glass fragments, solar cell fragments, and residue fragments;

[0073] a simulation module, configured to obtain a modeling file of the particle screening device, determine target motion parameters from a plurality of sets of preset screen motion parameters, and perform a screening simulation of the screening of each fragment in the particle screening device using a discrete element method based on the modeling file, a fragment model corresponding to each fragment, and the target motion parameters, to obtain a simulation result;

[0074] A motion parameter determination module is used to calculate the wear value and screening efficiency corresponding to the particle screening device based on the simulation results, and determine whether the target motion parameters meet the preset requirements based on the wear value and the screening efficiency. When the target motion parameters do not meet the preset requirements, the target motion parameters are re-determined from the multiple sets of preset screen motion parameters, and the screening simulation is performed again through the discrete element method. After the screening simulation, the wear value and screening efficiency corresponding to the new target motion parameters are calculated until the current target motion parameters meet the preset requirements, and the current target motion parameters are used as the final motion parameters corresponding to the particle screening device.

[0075] Optionally, the particle screening device is a banana screen; the device further comprises:

[0076] A motion setting module is used to set the screen movement during the vibration of the banana screen based on the first motion and the second motion before performing screening simulation on the screening conditions of each fragment in the particle screening device based on the modeling file, the fragment model corresponding to each fragment and the target motion parameters by the discrete element method, so that when the screening simulation is performed by the discrete element method, the first motion and the second motion are superimposed, and the superimposed motion is used as the actual screen movement during the vibration of the banana screen, wherein the first motion is the elliptical motion of the screen in the vertical plane of the screen with the center of mass at the vibration frequency, the center of mass of the screen is determined based on the center point of the screen when the screen is stationary, the second motion is the motion on the cross-section of the screen, and the screen motion parameters include the major axis size and minor axis size of the ellipse corresponding to the elliptical motion.

[0077] Optionally, the device further comprises a modeling module; the modeling module is configured to:

[0078] Before obtaining the modeling file of the particle screening device, a wall model and a screen model of the particle screening device are constructed according to the particle screening device to obtain an overall three-dimensional model;

[0079] Meshing the wall model using triangular meshes and meshing the screen using local encrypted meshes to obtain a divided overall three-dimensional model;

[0080] The mesh quality inspection tool is used to perform mesh inspection on the divided overall three-dimensional model. When the inspection passes, the divided overall three-dimensional model is exported to obtain a modeling file of the particle screening device.

[0081] Optionally, when the particle screening device includes only one layer of screen, the wear value corresponding to the particle screening device is calculated based on the following formula:

[0082]

[0083] Among them, EM SS is the wear value, k is the wear coefficient, t p is the total screening time of the particle screening device, is the Heaviside function, is the velocity vector of the target fragments that come into contact with the particle screening device at the time of contact, is the position vector of the target fragment in contact with the particle screening device at the time of contact, f(γ) is the wear influence factor related to γ, γ is the impact angle of the target fragment in contact with the particle screening device at the time of contact, and F is the mass of the target fragment;

[0084] Accordingly, the screening efficiency of the particle screening device is calculated based on the following formula:

[0085]

[0086] Among them, η s is the screening efficiency, m s1 is the mass of the fragments passing through the sieve, m s2 It is the total mass of the fragments in the crushed mixture whose size is smaller than the maximum mesh size of the sieve.

[0087] Optionally, the motion parameter determination module is configured to:

[0088] Based on the wear value and the maximum wear value of the particle screening device at the end of the wear period, calculating the number of screenings of the particle screening device under the target motion parameters, and determining a single screening cost of the particle screening device based on the number of screenings;

[0089] Based on the screening efficiency, calculating the single recovery waste cost of the particle screening device under the target motion parameters;

[0090] According to the single screening cost and the single recovery waste cost, a single total cost is calculated, and based on the single total cost, it is determined whether the target motion parameter meets the preset requirements.

[0091] Optionally, the simulation module is used to:

[0092] Importing the modeling file and the fragment models corresponding to the fragments into a simulation environment using a discrete element method, so as to restore the particle screening device and each fragment in the crushed mixture in the simulation environment;

[0093] In the simulation environment, based on the target motion parameters, the movement of the mesh model is controlled, and at the same time, a bounding box corresponding to each fragment model is determined within each time step;

[0094] For each target fragment model, determining whether a bounding box corresponding to the target fragment model intersects with the bounding boxes corresponding to the remaining object models, and if so, determining the midpoint of the bounding box of the target fragment model, and determining whether the target fragment model contacts the object model corresponding to the target bounding box based on the midpoint, wherein the object model includes a fragment model, a wall model, and a screen model;

[0095] Based on the contact judgment result, the new coordinate position corresponding to each fragment model in the next time step is determined, and contact judgment is performed again according to the new coordinate position of each fragment model until the preset conditions are met to obtain the simulation result.

[0096] Optionally, the simulation module is further configured to:

[0097] When the object model corresponding to the target bounding box is a fragment model, calculating, based on the coordinates corresponding to the midpoint, a first value of a superquadratic surface equation corresponding to the target fragment model and a second value of the superquadratic surface equation of the object model corresponding to the target bounding box, respectively, and if both the first value and the second value are less than zero, determining that contact occurs between the target fragment model and the object model corresponding to the target bounding box;

[0098] When the object model corresponding to the target bounding box is a wall model or a screen model, based on the coordinates corresponding to the midpoint, the first value of the superquadratic surface equation corresponding to the target fragment model and the second value of the plane equation of the object model corresponding to the target bounding box are calculated respectively. If the first value is less than zero and the second value is less than or equal to zero, it is determined that contact occurs between the target fragment model and the object model corresponding to the target bounding box.

[0099] It should be noted that for other corresponding descriptions of the various functional units involved in the motion parameter determination device of a retired photovoltaic panel particle screening device provided in the embodiment of the present application, reference can be made to Figures 1 to 6 The corresponding description in the method will not be repeated here.

[0100] The present application also provides a computer device, which can be a personal computer, a server, a network device, etc. Figure 8As shown, the computer device includes a bus, a processor, a memory, and a communication interface, and may also include an input / output interface and a display device. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store location information. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps of each method embodiment are implemented.

[0101] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0102] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium may be non-volatile or volatile, and stores a computer program thereon. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0103] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0104] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0105] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0106] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for determining motion parameters of a particle screening device for retired photovoltaic panels, characterized in that: include: Obtaining a crushed mixture of target retired photovoltaic panels after pyrolysis treatment, determining the size and shape of each fragment in the crushed mixture, and constructing a fragment model for each fragment based on the size and shape using a superquadratic surface equation, wherein the fragments in the crushed mixture include glass fragments, solar cell fragments, and residue fragments; Obtaining a modeling file of a particle screening device, and determining target motion parameters from a plurality of sets of preset screen motion parameters; and performing a screening simulation of the screening of each fragment in the particle screening device using a discrete element method based on the modeling file, a fragment model corresponding to each fragment, and the target motion parameters to obtain a simulation result; Based on the simulation results, the wear value and screening efficiency corresponding to the particle screening device are calculated, and based on the wear value and the screening efficiency, it is determined whether the target motion parameters meet the preset requirements. When the target motion parameters do not meet the preset requirements, the target motion parameters are re-determined from the multiple sets of preset screen motion parameters, and the screening simulation is performed again using the discrete element method. After the screening simulation, the wear value and screening efficiency corresponding to the new target motion parameters are calculated, and the process ends when the current target motion parameters meet the preset requirements. The current target motion parameters are used as the final motion parameters corresponding to the particle screening device.

2. The method according to claim 1, characterized in that The particle screening device is a banana screen; before performing screening simulation on the screening conditions of each fragment in the particle screening device using a discrete element method based on the modeling file, the fragment model corresponding to each fragment, and the target motion parameter, the method further includes: The screen movement during the vibration process of the banana screen is set based on the first movement and the second movement, so that when the screening simulation is performed through the discrete element method, the first movement and the second movement are superimposed, and the superimposed movement is used as the actual screen movement during the vibration process of the banana screen, wherein the first movement is the elliptical movement of the screen on the vertical plane of the screen with the center of mass at the vibration frequency, and the center of mass of the screen is determined based on the center point of the screen when the screen is stationary, and the second movement is the movement on the cross-section of the screen, and the screen movement parameters include the major axis size and minor axis size of the ellipse corresponding to the elliptical movement.

3. The method according to claim 2, characterized in that Before obtaining the modeling file of the particle screening device, the method further includes: According to the particle screening device, a wall model and a screen model of the particle screening device are constructed to obtain an overall three-dimensional model; Meshing the wall model using triangular meshes and meshing the screen using local encrypted meshes to obtain a divided overall three-dimensional model; The mesh quality inspection tool is used to perform mesh inspection on the divided overall three-dimensional model. When the inspection passes, the divided overall three-dimensional model is exported to obtain a modeling file of the particle screening device.

4. The method according to claim 1, wherein When the particle screening device includes only one layer of screen, the wear value corresponding to the particle screening device is calculated based on the following formula: Among them, EM SS is the wear value, k is the wear coefficient, t p is the total screening time of the particle screening device, is the Heaviside function, is the velocity vector of the target fragments that come into contact with the particle screening device at the time of contact, is the position vector of the target fragment in contact with the particle screening device at the time of contact, f(γ) is the wear influence factor related to γ, γ is the impact angle of the target fragment in contact with the particle screening device at the time of contact, and F is the mass of the target fragment; Accordingly, the screening efficiency of the particle screening device is calculated based on the following formula: Among them, η s is the screening efficiency, m s1 is the mass of the fragments passing through the sieve, m s2 It is the total mass of the fragments in the crushed mixture whose size is smaller than the maximum mesh size of the sieve.

5. The method according to claim 4, characterized in that The determining, based on the wear value and the screening efficiency, whether the target motion parameter meets a preset requirement includes: Based on the wear value and the maximum wear value of the particle screening device at the end of the wear period, calculating the number of screenings of the particle screening device under the target motion parameters, and determining a single screening cost of the particle screening device based on the number of screenings; Based on the screening efficiency, calculating the single recovery waste cost of the particle screening device under the target motion parameters; According to the single screening cost and the single recovery waste cost, a single total cost is calculated, and based on the single total cost, it is determined whether the target motion parameter meets the preset requirements.

6. The method according to claim 1, characterized in that The method of performing a screening simulation on the screening conditions of each fragment in the particle screening device based on the modeling file, the fragment model corresponding to each fragment, and the target motion parameter by using a discrete element method to obtain a simulation result includes: Importing the modeling file and the fragment models corresponding to the fragments into a simulation environment using a discrete element method, so as to restore the particle screening device and each fragment in the crushed mixture in the simulation environment; In the simulation environment, based on the target motion parameters, the movement of the mesh model is controlled, and at the same time, a bounding box corresponding to each fragment model is determined within each time step; For each target fragment model, determining whether a bounding box corresponding to the target fragment model intersects with the bounding boxes corresponding to the remaining object models, and if so, determining the midpoint of the bounding box of the target fragment model, and determining whether the target fragment model contacts the object model corresponding to the target bounding box based on the midpoint, wherein the object model includes a fragment model, a wall model, and a screen model; Based on the contact judgment result, the new coordinate position corresponding to each fragment model in the next time step is determined, and contact judgment is performed again according to the new coordinate position of each fragment model until the preset conditions are met to obtain the simulation result.

7. The method according to claim 6, characterized in that The determining, based on the midpoint, whether contact occurs between the target fragment model and the object model corresponding to the target bounding box includes: When the object model corresponding to the target bounding box is a fragment model, calculating, based on the coordinates corresponding to the midpoint, a first value of a superquadratic surface equation corresponding to the target fragment model and a second value of the superquadratic surface equation of the object model corresponding to the target bounding box, respectively, and if both the first value and the second value are less than zero, determining that contact occurs between the target fragment model and the object model corresponding to the target bounding box; When the object model corresponding to the target bounding box is a wall model or a screen model, based on the coordinates corresponding to the midpoint, the first value of the superquadratic surface equation corresponding to the target fragment model and the second value of the plane equation of the object model corresponding to the target bounding box are calculated respectively. If the first value is less than zero and the second value is less than or equal to zero, it is determined that contact occurs between the target fragment model and the object model corresponding to the target bounding box.

8. A motion parameter determination device for a particle screening device for retired photovoltaic panels, characterized in that: include: a fragment model construction module, configured to obtain a fragmented mixture of target decommissioned photovoltaic panels after pyrolysis treatment, determine the size and shape of each fragment in the fragmented mixture, and construct a fragment model for each fragment based on the size and shape using a superquadratic surface equation, wherein the fragments in the fragmented mixture include glass fragments, solar cell fragments, and residue fragments; a simulation module, configured to obtain a modeling file of the particle screening device, determine target motion parameters from a plurality of sets of preset screen motion parameters, and perform a screening simulation of the screening of each fragment in the particle screening device using a discrete element method based on the modeling file, a fragment model corresponding to each fragment, and the target motion parameters, to obtain a simulation result; A motion parameter determination module is used to calculate the wear value and screening efficiency corresponding to the particle screening device based on the simulation results, and determine whether the target motion parameters meet the preset requirements based on the wear value and the screening efficiency. When the target motion parameters do not meet the preset requirements, the target motion parameters are re-determined from the multiple sets of preset screen motion parameters, and the screening simulation is performed again through the discrete element method. After the screening simulation, the wear value and screening efficiency corresponding to the new target motion parameters are calculated until the current target motion parameters meet the preset requirements, and the current target motion parameters are used as the final motion parameters corresponding to the particle screening device.

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

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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