Method and device for optimizing a variable pitch screw chute model based on a response surface method

Abstract

The application provides a variable-pitch spiral chute model optimization method and device based on a response surface method, and relates to the field of bulk material conveying equipment optimization, and comprises the following steps: constructing a variable-pitch spiral chute three-dimensional model according to preset slope characteristic parameters; wherein the slope characteristic parameters comprise an initial slope, a terminal slope and a slope change law index; performing discrete element simulation on the variable-pitch spiral chute three-dimensional model to obtain bulk material sliding speeds of each speed measurement surface; constructing a response surface regression equation based on the variable-pitch spiral chute three-dimensional model and the corresponding bulk material sliding speeds; and performing genetic optimization on the response surface regression equation to obtain optimal slope characteristic parameters; wherein the optimal slope characteristic parameters are used for optimizing the variable-pitch spiral chute. The application can construct a response relationship between characteristic parameters and bulk material speed standard deviations in combination with the response surface method, balance the wear and tear of each part of the chute, and prolong the service life of the whole equipment.

Description

Technical Field

[0001] This application relates to the field of bulk material conveying equipment optimization, specifically a method and apparatus for optimizing a variable pitch spiral chute model based on response surface methodology. Background Technology

[0002] In traditional bulk material handling system designs, a series of pressing technical challenges arise when the material chute needs to traverse significant height differences. One major issue is the violent impact of the bulk material against the chute or other components during its high-speed descent, which can easily lead to material breakage. This not only reduces the yield and quality of the finished product but also generates severe dust pollution from the fine powder, contaminating the working environment and threatening the health of operators. Furthermore, the continuous friction and impact of the high-speed flowing material on the chute's inner wall causes rapid wear and tear on equipment components, significantly shortening the chute's lifespan, increasing maintenance and replacement costs, and potentially leading to production interruptions due to sudden equipment failures, severely impacting overall production efficiency.

[0003] To solve the problem of large-drop bulk material transfer, spiral chutes were developed. As a bulk material conveying device that does not require power drive, spiral chutes have significant advantages such as simple and compact structure, small footprint, stable operation, and convenient maintenance. In large-drop conveying scenarios, they can guide materials to fall smoothly along a spiral trajectory, effectively alleviating many problems of traditional straight chutes in large-drop transfer.

[0004] However, traditional spiral chutes generally employ a fixed pitch design, meaning the vertical distance between two adjacent spiral turns remains constant. This design has gradually revealed significant drawbacks in practical applications: when bulk material enters from the top inlet of the spiral chute, it slides down the spiral path along the inner wall of the chute under the continuous force of its own gravity. Due to the continuous work done by gravity, the slid speed of the bulk material increases with the height of the fall, exhibiting a trend of gradually increasing speed from the top to the bottom of the chute.

[0005] The increasing velocity of the slid material directly leads to a continuous increase in the impact and friction intensity on the inner wall of the chute. This phenomenon results in significant uneven wear on the spiral chute; the closer to the bottom of the chute, the more intense the impact and friction, and the faster the wear rate. In actual production, it often happens that the top and middle sections of the chute are still in good working condition, while the bottom section fails prematurely due to excessive wear and becomes unusable. In this case, the company has to replace the entire spiral chute, which not only significantly increases the maintenance and replacement costs of the equipment but also interrupts the production process due to equipment downtime, seriously affecting the continuity of production and overall efficiency.

[0006] Meanwhile, at the lowest outlet area of ​​the spiral chute, the downward velocity of the bulk material reaches its maximum, resulting in the most prominent problems such as material impact, dust generation, and material breakage. These problems gradually intensify from top to bottom within the spiral chute, posing significant challenges to the stable operation of the equipment, material quality assurance, and maintenance of the working environment.

[0007] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0008] To address the problems in the existing technology, this application provides a method and apparatus for optimizing the variable pitch spiral chute model based on response surface methodology. This method can combine response surface methodology to construct the response relationship between characteristic parameters and the standard deviation of material distribution velocity, balance the wear of various parts of the chute, and extend the overall service life of the equipment.

[0009] To solve the above-mentioned technical problems, this application provides the following technical solution: In a first aspect, this application provides an optimization method for a variable pitch spiral chute model based on the response surface methodology, including: A three-dimensional model of a variable pitch spiral chute is constructed based on preset slope characteristic parameters; wherein, the slope characteristic parameters include the initial slope, the end slope, and the slope change law index; Discrete element simulation was performed on the three-dimensional model of the variable pitch spiral chute to obtain the material sliding velocity of each velocity measuring surface; Based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity, a response surface regression equation is constructed. Genetic optimization is performed on the response surface regression equation to obtain the optimal slope characteristic parameter; wherein, the optimal slope characteristic parameter is used to optimize the variable pitch spiral chute.

[0010] Furthermore, the step of constructing a three-dimensional model of a variable-pitch spiral chute based on preset slope characteristic parameters includes: Candidate parameter combination schemes are generated based on the horizontal change gradients of the initial slope, the terminal slope, and the slope change law index. Generate the spiral development curve equation based on the candidate parameter combination scheme; The three-dimensional model of the variable pitch spiral chute is constructed using the spiral development curve equation; different combinations of candidate parameters correspond to different three-dimensional models of the variable pitch spiral chute.

[0011] Furthermore, the discrete element simulation of the three-dimensional model of the variable pitch spiral chute to obtain the material sliding velocity at each velocity measuring surface includes: Discrete element simulation was performed on the three-dimensional model of the variable pitch spiral chute to simulate the sliding process of bulk material from top to bottom along the corresponding chute and obtain the corresponding bulk material motion trajectory. Multiple velocity measuring surfaces are set for the chute corresponding to the three-dimensional model of the variable pitch spiral chute, and the material sliding velocity of each velocity measuring surface is extracted based on the material movement trajectory.

[0012] Furthermore, the construction of the response surface regression equation based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity includes: Extract the corresponding slope feature parameters and bulk material sliding velocity from the three-dimensional model of the variable pitch spiral chute; Calculate the standard deviation of the bulk material sliding velocity based on the aforementioned bulk material sliding velocity; The response surface regression equation is constructed using the slope characteristic parameter as the independent variable and the standard deviation of the bulk material sliding velocity as the dependent variable; wherein, the response surface regression equation is constructed using a multivariate quadratic polynomial regression method. The significance test and residual analysis were performed on the response surface regression equation to verify its prediction accuracy.

[0013] Furthermore, the genetic optimization of the response surface regression equation to obtain the optimal slope feature parameters includes: Initialize the genetic algorithm parameters, set the population size, number of iterations, crossover probability and mutation probability, and encode the slope characteristic parameters of the variable pitch spiral chute into chromosome individuals; A fitness function is established based on the response surface regression equation, with the standard deviation of the bulk material sliding velocity as the target value. The slope characteristic parameters are gradually optimized through genetic operations of selection, crossover, and mutation. The iterative optimization process of the genetic algorithm is executed, and the changing trend of the optimal solution in each generation is recorded until the genetic algorithm parameters converge, so as to obtain the optimal slope characteristic parameter that minimizes the standard deviation of the material sliding velocity.

[0014] Furthermore, the aforementioned optimization method for the variable pitch spiral chute model based on response surface methodology also includes: A three-dimensional verification model is constructed based on the optimal slope feature parameters; Discrete element simulation is performed on the three-dimensional verification model according to the preset boundary conditions, and the velocity field of the loose material sliding in each region of the chute surface, the contact force of the inner wall of the chute, or the cumulative wear energy is obtained; wherein, the boundary conditions include particle size distribution, material density, chute surface roughness, and gravity field; The structural durability and long-term operational stability of the three-dimensional verification model are evaluated based on the velocity field of the material sliding in each region of the chute surface, the contact force on the inner wall of the chute, or the cumulative wear energy.

[0015] Secondly, this application provides a variable pitch spiral chute model optimization device based on the response surface methodology, comprising: A three-dimensional model construction unit is used to construct a three-dimensional model of a variable pitch spiral chute based on preset slope characteristic parameters; wherein, the slope characteristic parameters include the initial slope, the end slope, and the slope change law index; The slip velocity determination unit is used to perform discrete element simulation on the three-dimensional model of the variable pitch spiral chute to obtain the slip velocity of the bulk material on each velocity measuring surface. The regression equation generation unit is used to construct a response surface regression equation based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity. The optimal parameter determination unit is used to perform genetic optimization on the response surface regression equation to obtain the optimal slope characteristic parameters; wherein, the optimal slope characteristic parameters are used to optimize the variable pitch spiral chute.

[0016] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the variable pitch spiral chute model optimization method based on response surface methodology.

[0017] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the optimization method for the variable pitch spiral chute model based on the response surface methodology.

[0018] Fifthly, this application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the variable pitch spiral chute model optimization method based on response surface methodology.

[0019] To address the problems in existing technologies, this application provides a method and apparatus for optimizing variable pitch spiral chute models based on response surface methodology. This method optimizes slope characteristic parameters (initial slope, terminal slope, and slope change index) using response surface methodology, generating a chute simulation model corresponding to the optimal slope characteristic parameters. This enables precise control of the spiral curve's slope, continuously reducing the spiral helix angle, ensuring a consistent material flow velocity along the path, and preventing material accumulation. The chute structure manufactured based on this simulation model is simple and effectively solves the problems of gradually increasing wear from top to bottom and uneven material flow velocity in traditional constant pitch chutes. This extends equipment lifespan, reduces maintenance costs, and improves the stability of bulk material conveying. It can be widely applied in bulk material conveying scenarios in mining, metallurgy, and coal industries. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a plan and elevation view of the variable pitch spiral chute in an embodiment of this application; Figure 2 This is a flowchart of the variable pitch spiral chute model optimization method based on response surface methodology in the embodiments of this application; Figure 3 This is a flowchart illustrating the construction of a three-dimensional model of a variable pitch spiral chute in an embodiment of this application. Figure 4 This is a flowchart illustrating the process of obtaining the material sliding velocity at each velocity measuring surface in the embodiments of this application; Figure 5 This is a flowchart illustrating the construction of the response surface regression equation in an embodiment of this application; Figure 6 This is a flowchart illustrating the process of obtaining the optimal slope characteristic parameters in the embodiments of this application; Figure 7 This is a flowchart illustrating the evaluation of the structural durability and long-term operational stability of the three-dimensional verification model in this application embodiment; Figure 8 This is a structural diagram of the variable pitch spiral chute model optimization device based on response surface methodology in the embodiments of this application; Figure 9 This is a schematic diagram of the structure of the electronic device in the embodiments of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0023] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0024] Provide users with corresponding operation entry points, allowing them to choose to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0025] In one embodiment, see Figure 1 and Figure 2 In order to construct the response relationship between characteristic parameters and the standard deviation of material velocity using response surface methodology, balance the wear of different parts of the chute, and extend the overall service life of the equipment, this application provides a variable pitch spiral chute model optimization method based on response surface methodology, including: S101: Construct a three-dimensional model of a variable pitch spiral chute based on preset slope characteristic parameters; wherein, the slope characteristic parameters include the initial slope, the end slope, and the slope change law index; S102: Perform discrete element simulation on the three-dimensional model of the variable pitch spiral chute to obtain the material sliding velocity of each velocity measuring surface; S103: Construct a response surface regression equation based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity; S104: Genetic optimization is performed on the response surface regression equation to obtain the optimal slope characteristic parameter; wherein, the optimal slope characteristic parameter is used to optimize the variable pitch spiral chute.

[0026] It is understood that the purpose of this invention is to overcome the problems of uneven material descent velocity and uneven chute wear in existing medium-pitch spiral chutes. It provides an optimization design method for variable-pitch spiral chutes based on response surface methodology. By focusing on the slope variation characteristics of the spiral's unfolded plane, it selects characteristic parameters directly related to the slope variation, and combines the response surface methodology to construct the response relationship between the characteristic parameters and the standard deviation of the material velocity. This optimizes the slope variation law, achieving a more consistent material descent velocity from top to bottom along the chute, balancing wear across different parts of the chute, extending the overall service life of the equipment, and avoiding the problems of material splashing, impact crushing, and severe dust generation caused by gravity, which are exacerbated from top to bottom, thus improving the stability and reliability of material conveying.

[0027] Figure 1 The diagram shows plan and elevation views of a variable-pitch spiral chute constructed based on a chute model corresponding to the optimal slope characteristic parameters generated by the method provided in this application. It includes a chute body 1 with a total height of H and a base circle diameter of D. The chute body 1 extends in a spiral shape, with the pitch gradually decreasing from top to bottom to ensure that the sliding speed of the bulk material as it slides down the chute body 1 is relatively uniform.

[0028] The initial slope of the top of the chute's unfolded line is k1, and the slope of the bottom end is k2. Based on engineering design experience, the helix angle at the top of the chute is set to α1 = 65°~80°, corresponding to a k1 value range of 2.14~5.67; the helix angle at the bottom is set to α1 = 35°~50°, corresponding to a k1 value range of 0.70~1.19.

[0029] Define the equation of the spiral development as z(x) = A x m+1 +B, where A and B are constants determined by k1, k2, and the total unfolded length x of the chute. The value of m ranges from 0.8 to 1.2. When m=1, the slope decreases linearly; when m>1, the slope decreases more slowly in the early stage and more quickly in the later stage; when m<1, the slope decreases more quickly in the early stage and more slowly in the later stage.

[0030] The response surface methodology was used to optimize the chute model. The specific steps are as follows: First, based on the range of values ​​for the initial slope k1, the terminal slope k2, and the slope change law exponent m, the Box-Behnken response surface methodology is used to determine the horizontal gradient of each parameter and construct the experimental scheme. Second, based on the experimental plan, the equation of the spiral development curve was derived, and a three-dimensional model of the variable pitch spiral chute was constructed. Third, the falling velocity of the bulk material on each velocity measuring surface was collected by discrete element simulation, and the standard deviation of the velocity was calculated. Fourth, using the characteristic parameters as independent variables and the speed standard deviation as the response value, a response surface regression equation is constructed, and the effectiveness of the model is verified by analysis of variance. Fifth, based on the response surface regression equation, the optimal combination of parameters that minimizes the velocity standard deviation is obtained and verified through simulation.

[0031] Among them, the real-time pitch P(x) of the chute body and the slope k(x) of the unfolding curve satisfy P(x)=πD·k(x), where D is the base circle diameter of the chute, which is a constant value; the pitch changes smoothly and gradually along the axial height without abrupt changes.

[0032] It should also be noted that the embodiments of this application are based on specific base circle radii and specific material parameters. When the base circle radius and material parameters are different, the range of values ​​for the top and bottom helix angles (corresponding slope values) needs to be adjusted appropriately. Technicians need to obtain the optimal design parameters based on experience or experiments.

[0033] Specifically, the initial slope, the final slope, and the slope change index are used as core slope characteristic parameters. Multiple candidate parameter schemes are generated by scientifically combining reasonable horizontal gradients for each parameter. Then, based on each candidate parameter scheme, the corresponding spiral curve equation is derived. These equations are imported into 3D modeling software and, combined with basic structural parameters such as the chute width and surface inclination angle, a series of variable-pitch spiral chute 3D models with differentiated pitch variation characteristics are constructed. Different parameter combinations correspond to different trends in the chute pitch from the initial to the final segment; some exhibit a gradual change from fast to slow, while others show a linear and uniform change, thus forming a model library covering various structural possibilities. Next, these 3D models were imported one by one into the discrete element simulation platform. Parameters such as the bulk particle properties and the contact properties between the particles and the chute, which had been calibrated through previous physical experiments, were linked. Simulation boundary conditions, such as material feed rate and initial velocity, were set to match actual working conditions. The simulation was run, and multiple velocity measuring surfaces were set at different axial heights of the chute. The sliding velocity data of bulk particles at each measuring surface were accurately collected, including key indicators such as average velocity and velocity distribution standard deviation, to quantify the control effect of different chute structures on the bulk material movement state. Subsequently, using the initial slope, terminal slope, and slope change law index as input variables, and the bulk material sliding velocity at each measuring surface as the output response, the response surface methodology was used to fit and analyze the input and output data. A response surface regression equation that accurately maps the nonlinear relationship between parameters and performance was constructed. The fitting accuracy and significance of the equation were verified using methods such as analysis of variance to ensure that the equation reliably reflects the influence of chute structural parameters on the bulk material sliding velocity. Finally, with the optimization objectives of uniformity of the sliding velocity of bulk material in the chute and the compliance of the final velocity, the response surface regression equation is used as the fitness function, and a genetic algorithm is called for global optimization. Through evolutionary operations such as selection, crossover, and mutation, the parameter combination is iteratively updated, and the initial slope, final slope, and slope change law index that enable the bulk material to achieve the optimal motion state in the chute are finally selected. These are used as the optimal slope feature parameters to guide the structural optimization of the variable pitch spiral chute, thereby improving the core performance of the chute, such as sorting efficiency and processing capacity.

[0034] As described above, the variable pitch spiral chute model optimization method based on response surface methodology provided in this application can optimize slope characteristic parameters (initial slope, terminal slope, and slope change law index) through response surface methodology, generate a chute simulation model corresponding to the optimal slope characteristic parameters, achieve precise control of the spiral curve slope, continuously reduce the spiral helix angle, make the material flow velocity along the path more consistent, and eliminate material accumulation. The chute structure manufactured according to this simulation model is simple and can effectively solve the problems of gradual wear from top to bottom and uneven material velocity in traditional constant pitch chutes, extend equipment service life, reduce maintenance costs, and improve the stability of bulk material conveying. It can be widely used in bulk material conveying scenarios in mining, metallurgy, coal and other industries.

[0035] In one embodiment, see Figure 3 The step of constructing a three-dimensional model of a variable-pitch spiral chute based on preset slope characteristic parameters includes: S201: Generate candidate parameter combination schemes based on the horizontal change gradients of the initial slope, the terminal slope, and the slope change law index; S202: Generate the spiral development curve equation based on the candidate parameter combination scheme; S203: Construct the three-dimensional model of the variable pitch spiral chute using the spiral development curve equation; wherein, different combinations of candidate parameters correspond to different three-dimensional models of the variable pitch spiral chute.

[0036] Understandably, the parametric modeling of variable pitch spiral chutes initially revolves around three core control parameters: initial slope, end slope, and slope variation index. Multiple sets of level values ​​with reasonable gradients are set for each parameter. Through scientific combination methods, candidate parameter combinations covering different pitch variation trends are generated, ensuring that the schemes include both conventional linear gradient patterns and various special nonlinear variation forms, comprehensively covering potential structural optimization directions.

[0037] For each set of candidate parameter combinations, the equation of the spiral development curve is derived by combining the mathematical principles of spiral development curve. The axial height of the chute is used as the independent variable, the initial slope is used as the initial rate of change of the curve, and the final slope is used as the final rate of change of the curve. A smooth transition function between the two is constructed through the slope change law exponent, which accurately describes the characteristics of the continuous change of pitch with axial height. This allows the equation corresponding to each set of parameter combinations to intuitively reflect the dynamic change logic of pitch, providing a precise mathematical basis for subsequent 3D modeling.

[0038] The derived spiral curve equation is then imported into 3D modeling software. Parametric modeling techniques are used to spirally scan the 2D curve along the central axis to generate a 3D surface. This surface is then solidified by combining the basic structural parameters of the chute, ultimately constructing a complete 3D model of the variable-pitch spiral chute. Different combinations of candidate parameters will give the chute different pitch variation trends and overall structural forms, resulting in a series of differentiated 3D models.

[0039] As can be seen from the above description, the variable pitch spiral chute model optimization method based on response surface methodology provided in this application can construct a three-dimensional model of the variable pitch spiral chute according to preset slope characteristic parameters.

[0040] In one embodiment, see Figure 4 The discrete element simulation of the three-dimensional model of the variable pitch spiral chute is performed to obtain the material sliding velocity at each velocity measuring surface, including: S301: Perform discrete element simulation on the three-dimensional model of the variable pitch spiral chute to simulate the sliding process of bulk material from top to bottom along the corresponding chute and obtain the corresponding bulk material motion trajectory. S302: Set multiple velocity measuring surfaces for the chute corresponding to the three-dimensional model of the variable pitch spiral chute, and extract the material sliding velocity of each velocity measuring surface based on the material movement trajectory.

[0041] Understandably, the completed 3D model of the variable-pitch spiral chute is imported into the discrete element simulation platform. First, the model undergoes geometric cleaning and mesh simplification to adapt to the simulation requirements. Then, it is associated with material properties such as particle density, particle size distribution, and static friction coefficient, which were previously calibrated through physical experiments, as well as contact parameters such as rolling friction coefficient and coefficient of recovery between the particles and the chute body. Next, boundary conditions such as the initial material feeding position, feeding speed, and feeding quantity are set to match actual working conditions. Then, the simulation is started to simulate the process of the bulk material sliding down the chute from top to bottom, and the trajectory tracking function of the simulation platform records the data in real time. The spatial position change of each bulk material particle in the chute is recorded to generate a complete bulk material motion trajectory dataset. Then, multiple velocity measuring surfaces are evenly set according to the axial height of the chute. Each velocity measuring surface is perpendicular to the central axis of the chute and covers the entire cross section of the chute. Based on the acquired bulk material motion trajectory data, the instantaneous velocity of all bulk material particles at each velocity measuring surface when passing through the cross section is extracted. Through statistical analysis, the bulk material sliding velocity of each velocity measuring surface (including but not limited to key indicators such as the average sliding velocity of bulk material, the standard deviation of velocity distribution, and the maximum and minimum velocity difference) is obtained, thereby quantifying the motion state of bulk material at different axial positions.

[0042] As can be seen from the above description, the variable pitch spiral chute model optimization method based on response surface methodology provided in this application can perform discrete element simulation on the three-dimensional model of the variable pitch spiral chute to obtain the material sliding velocity of each velocity measuring surface.

[0043] In one embodiment, see Figure 5 The construction of the response surface regression equation based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity includes: S401: Extract the corresponding slope feature parameters and bulk material sliding velocity from the three-dimensional model of the variable pitch spiral chute; S402: Calculate the standard deviation of the bulk material sliding velocity based on the bulk material sliding velocity; S403: The response surface regression equation is constructed using the slope characteristic parameter as the independent variable and the standard deviation of the bulk material sliding velocity as the dependent variable; wherein, the response surface regression equation is constructed using a multivariate quadratic polynomial regression method. S404: Perform significance testing and residual analysis on the response surface regression equation to verify the prediction accuracy of the response surface regression equation.

[0044] Understandably, the first step is to extract three core slope characteristic parameters—initial slope, terminal slope, and slope variation index—from the constructed 3D model of the variable-pitch spiral chute. Simultaneously, material sliding velocity data collected from each velocity measuring surface during discrete element simulation are retrieved. Then, for each velocity measuring surface's material sliding velocity dataset, the standard deviation is calculated using statistical methods to quantify the dispersion of the material velocity at that location, reflecting the effect of the variable-pitch structure on the uniformity of material movement. Next, the initial slope, terminal slope, and slope variation index are used as independent variables in the response surface regression equation, and the standard deviation of the material sliding velocity at each velocity measuring surface is used as the dependent variable. A multivariate quadratic polynomial regression method is employed to construct a response surface regression equation that maps the nonlinear relationship between structural parameters and velocity uniformity. The equation includes not only the first and second-order terms of each independent variable but also interaction terms between different independent variables, ensuring a comprehensive capture of the coupling effects between parameters. Finally, the significance of the constructed response surface regression equation was tested. The significance level of the equation as a whole and each regression term was determined by analysis of variance. At the same time, residual analysis was carried out to observe the distribution pattern, normality and randomness of the residuals, thereby verifying the fitting accuracy and predictive reliability of the equation, and providing a solid model foundation for subsequent response surface-based structural optimization.

[0045] As can be seen from the above description, the variable pitch spiral chute model optimization method based on response surface methodology provided in this application can construct a response surface regression equation based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity.

[0046] In one embodiment, see Figure 6 The genetic optimization of the response surface regression equation to obtain the optimal slope feature parameters includes: S501: Initialize the genetic algorithm parameters, set the population size, number of iterations, crossover probability and mutation probability, and encode the slope characteristic parameters of the variable pitch spiral chute into chromosome individuals; S502: Based on the response surface regression equation, establish a fitness function, take the standard deviation of the bulk material sliding velocity as the target value, and gradually optimize the slope characteristic parameters through genetic operations of selection, crossover and mutation; S503: Execute the iterative optimization process of the genetic algorithm, record the changing trend of the optimal solution in each generation, until the genetic algorithm parameters converge, and obtain the optimal slope characteristic parameter that minimizes the standard deviation of the material sliding velocity.

[0047] Understandably, the optimization requirements and computational resources of the variable-pitch spiral chute are considered first. Core parameters such as population size, number of iterations, crossover probability, and mutation probability of the genetic algorithm are then rationally set. Next, the three sets of slope characteristic parameters—initial slope, terminal slope, and slope change index—are encoded as chromosomes recognizable by the algorithm, constructing the initial population. Then, a fitness function is established based on the validated response surface regression equation. Using the standard deviation of the bulk material sliding velocity as the optimization objective, the slope characteristic parameters are iteratively optimized through genetic operations such as selection, crossover, and mutation. The trend of the optimal solution is recorded in each iteration until the algorithm converges, ultimately yielding the optimal slope characteristic parameters that minimize the standard deviation of the bulk material sliding velocity.

[0048] As can be seen from the above description, the variable pitch spiral chute model optimization method based on response surface methodology provided in this application can perform genetic optimization on the response surface regression equation to obtain the optimal slope characteristic parameters.

[0049] In one embodiment, see Figure 7 The optimization method for the variable pitch spiral chute model based on response surface methodology further includes: S601: Construct a three-dimensional verification model based on the optimal slope feature parameters; S602: Perform discrete element simulation on the three-dimensional verification model according to the preset boundary conditions, and obtain the velocity field of the loose material sliding in each region of the chute surface, the contact force of the inner wall of the chute, or the cumulative wear energy; wherein, the boundary conditions include particle size distribution, material density, chute surface roughness, and gravity field; S603: Evaluate the structural durability and long-term operational stability of the three-dimensional verification model based on the velocity field of the material sliding in each region of the chute surface, the contact force on the inner wall of the chute, or the cumulative wear energy.

[0050] Understandably, the optimal slope characteristic parameters obtained through a genetic algorithm are first used to reconstruct a three-dimensional verification model of the variable-pitch spiral chute, ensuring that the model can reproduce the pitch variation pattern corresponding to the optimal parameters. Then, according to preset boundary conditions, parameters such as particle size distribution, material density, chute surface roughness, and gravity field are imported into a discrete element simulation platform to perform simulation calculations on the three-dimensional verification model. Simultaneously, core data such as the velocity field of the bulk material sliding in various regions of the chute surface, the contact force on the chute inner wall, and the cumulative wear energy are collected. Finally, by analyzing these data, the force distribution, wear risk, and bulk material movement stability in different regions of the chute are evaluated. The specific evaluation process can refer to existing technologies to verify the structural durability and long-term operational reliability of the chute under optimal parameters.

[0051] As can be seen from the above description, the variable pitch spiral chute model optimization method based on response surface methodology provided in this application can evaluate the structural durability and long-term operational stability of the three-dimensional verification model.

[0052] This embodiment focuses on the coal bulk material conveying operation and provides a detailed explanation of the variable pitch spiral chute optimization design method based on response surface methodology. The specific basic parameters are as follows: The base circle diameter of the chute is D = 600 mm, and the base circle circumference is πD = 1884.96 mm. The total number of spiral turns N = 4 turns, and the total unfolded length X = N × πD = 4 × 1884.96 = 7539.84 mm; The total axial height of the chute is H = 1800 mm; Bulk material parameters: density 1300kg / m³, particle size 10~20mm, friction coefficient between bulk material and tank surface 0.3; Optimization parameter range: initial slope k1 = 2.14~5.67 (corresponding to α1 = 65°~80°), terminal slope k2 = 0.70~1.19 (corresponding to α2 = 35°~50°), slope change law index m = 0.9~1.1; Velocity measuring surface settings: Divide the spiral path into 8 equal velocity measuring surfaces (2 at the midpoint of each revolution), collect the average slip velocity of the loose material on each velocity measuring surface, and calculate the velocity standard deviation y.

[0053] The optimization steps of response surface methodology are as follows: 1. Parameter level design: The Box-Behnken design was adopted, and each of the three sets of optimization parameters was divided into three levels, resulting in a total of 17 experimental schemes. The specific levels are shown in Table 1 below.

[0054] Table 1 Characteristic Parameter Level Table

[0055] 2. Experimental Model Construction: Based on k1, k2, and m for each experimental scheme, the equation of the spiral development curve z(x) is derived. Taking the medium-level parameters (k1=3.91, k2=0.95, m=1.0) as an example, when the development curve adopts a parabolic form with a linearly changing slope (m=1), and satisfies the conditions that the starting height is 0 and the ending height is the total height of 1800mm, substituting z(x)=Ax² into the equation yields the development curve equation z(x)=0.000033x. 2 The corresponding real-time pitch P(x) = πD·k(x) = 1884.96 × 0.000066x achieves a linear decrease in pitch from top to bottom. A 3D model of the chute corresponding to this parameter combination is constructed using 3D modeling software for discrete element simulation.

[0056] 3. Experimental Data Acquisition: Using discrete element simulation software, simulations were performed on 17 experimental scheme models. The bulk material parameters and boundary conditions were set to be consistent with the actual working conditions. The simulation time was 30 seconds. After the bulk material motion stabilized, the average sliding velocity of the bulk material on 8 velocity measuring surfaces was collected, and the velocity standard deviation y of each scheme was calculated. The experimental data are shown in Table 2 below.

[0057] Table 2 Simulation Results

[0058] 4. Response surface model construction: The 17 sets of experimental data were imported into Design-Expert software for multiple quadratic regression analysis to obtain the response surface regression equation: y=10.6+1.9k1+1.7k2+1.4m-0.6k1k2-0.5k1m-0.4k2m-1.2k1²-1.1k2²-1.0m² Analysis of variance showed that the model had a determination coefficient of R² = 0.985 and a significance level of P < 0.001, indicating a good model fit with no loss of fit, and it could be used for subsequent optimization.

[0059] 5. Optimization and Verification: Based on the above regression equation, a genetic algorithm was used for numerical optimization. The optimal parameter combination that minimizes the velocity standard deviation y was found to be: k1=3.88, k2=0.93, m=1.01. The corresponding predicted velocity standard deviation y=10.5mm / s.

[0060] 6. Verification: Based on the optimal parameter combination, the 3D model and discrete element simulation model of the chute were reconstructed and verified by simulation. The actual speed standard deviation y = 10.8 mm / s was obtained, which is 2.9% different from the model prediction value. This is less than 5%, indicating that the optimal parameter combination is effective and meets the design requirements.

[0061] In summary, the method provided in this application has at least the following characteristics / advantages: 1. The pitch of the spiral chute gradually decreases from top to bottom to make the sliding speed of the bulk material from top to bottom more uniform; 2. Taking the spiral chute development line z(x) as the optimization object, the initial slope k1, the end slope k2, and the slope change law exponent m as the characteristic parameters, and the standard deviation of the bulk material sliding velocity extracted by discrete element simulation as the target parameter, a response surface optimization model is established. 3. The standard deviation of the bulk material sliding velocity extracted by discrete element simulation post-processing is used as the characterization value of the corresponding chute model.

[0062] Based on the same inventive concept, this application also provides a variable pitch spiral chute model optimization device based on response surface methodology, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the problem-solving principle of the variable pitch spiral chute model optimization device based on response surface methodology is similar to that of the variable pitch spiral chute model optimization method based on response surface methodology, the implementation of the variable pitch spiral chute model optimization device based on response surface methodology can refer to the implementation of the software performance benchmark determination method, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0063] In one embodiment, see Figure 8 In order to construct the response relationship between characteristic parameters and the standard deviation of material velocity using response surface methodology, balance the wear of different parts of the chute, and extend the overall service life of the equipment, this application provides a variable pitch spiral chute model optimization device based on response surface methodology, comprising: The three-dimensional model construction unit 701 is used to construct a three-dimensional model of a variable pitch spiral chute based on preset slope characteristic parameters; wherein, the slope characteristic parameters include the initial slope, the end slope, and the slope change law index; The slippage velocity determination unit 702 is used to perform discrete element simulation on the three-dimensional model of the variable pitch spiral chute to obtain the slippage velocity of the bulk material on each velocity measuring surface. The regression equation generation unit 703 is used to construct a response surface regression equation based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity. The optimal parameter determination unit 704 is used to perform genetic optimization on the response surface regression equation to obtain the optimal slope characteristic parameter; wherein, the optimal slope characteristic parameter is used to optimize the variable pitch spiral chute.

[0064] From a hardware perspective, in order to combine the response surface methodology to construct the response relationship between characteristic parameters and the standard deviation of material velocity, balance the wear of various parts of the chute, and extend the overall service life of the equipment, this application provides an embodiment of an electronic device for implementing all or part of the aforementioned response surface methodology-based variable pitch spiral chute model optimization method. The electronic device specifically includes the following components: The system comprises a processor, a memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the response surface methodology-based variable pitch spiral chute model optimization device and core business systems, user terminals, and related databases and other related devices; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the response surface methodology-based variable pitch spiral chute model optimization method and the embodiment of the response surface methodology-based variable pitch spiral chute model optimization device, the contents of which are incorporated herein by reference, and repeated details will not be described again.

[0065] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0066] In practical applications, the optimization method for the variable pitch spiral chute model based on response surface methodology can be partially executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0067] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0068] Figure 9This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 9 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 9 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0069] In one embodiment, the optimization method for the variable pitch spiral chute model based on response surface methodology can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control: S101: Construct a three-dimensional model of a variable pitch spiral chute based on preset slope characteristic parameters; wherein, the slope characteristic parameters include the initial slope, the end slope, and the slope change law index; S102: Perform discrete element simulation on the three-dimensional model of the variable pitch spiral chute to obtain the material sliding velocity of each velocity measuring surface; S103: Construct a response surface regression equation based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity; S104: Genetic optimization is performed on the response surface regression equation to obtain the optimal slope characteristic parameter; wherein, the optimal slope characteristic parameter is used to optimize the variable pitch spiral chute.

[0070] As described above, the variable pitch spiral chute model optimization method based on response surface methodology provided in this application can optimize slope characteristic parameters (initial slope, terminal slope, and slope change law index) through response surface methodology, generate a chute simulation model corresponding to the optimal slope characteristic parameters, achieve precise control of the spiral curve slope, continuously reduce the spiral helix angle, make the material flow velocity along the path more consistent, and eliminate material accumulation. The chute structure manufactured according to this simulation model is simple and can effectively solve the problems of gradual wear from top to bottom and uneven material velocity in traditional constant pitch chutes, extend equipment service life, reduce maintenance costs, and improve the stability of bulk material conveying. It can be widely used in bulk material conveying scenarios in mining, metallurgy, coal and other industries.

[0071] In another embodiment, the variable pitch spiral chute model optimization device based on response surface methodology can be configured separately from the central processing unit 9100. For example, the data composite transmission device based on response surface methodology can be configured as a chip connected to the central processing unit 9100, and the function of the variable pitch spiral chute model optimization method based on response surface methodology can be realized through the control of the central processing unit.

[0072] like Figure 9As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 9 All components shown; in addition, the electronic device 9600 may also include Figure 9 For components not shown, please refer to existing technologies.

[0073] like Figure 9 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0074] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0075] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0076] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0077] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0078] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0079] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is also coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131.

[0080] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the response surface methodology-based variable pitch spiral chute model optimization method described in the above embodiments, where the execution subject is a server or client. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the response surface methodology-based variable pitch spiral chute model optimization method described in the above embodiments, where the execution subject is a server or client. For example, when the processor executes the computer program, it implements the following steps: S101: Construct a three-dimensional model of a variable pitch spiral chute based on preset slope characteristic parameters; wherein, the slope characteristic parameters include the initial slope, the end slope, and the slope change law index; S102: Perform discrete element simulation on the three-dimensional model of the variable pitch spiral chute to obtain the material sliding velocity of each velocity measuring surface; S103: Construct a response surface regression equation based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity; S104: Genetic optimization is performed on the response surface regression equation to obtain the optimal slope characteristic parameter; wherein, the optimal slope characteristic parameter is used to optimize the variable pitch spiral chute.

[0081] As described above, the variable pitch spiral chute model optimization method based on response surface methodology provided in this application can optimize slope characteristic parameters (initial slope, terminal slope, and slope change law index) through response surface methodology, generate a chute simulation model corresponding to the optimal slope characteristic parameters, achieve precise control of the spiral curve slope, continuously reduce the spiral helix angle, make the material flow velocity along the path more consistent, and eliminate material accumulation. The chute structure manufactured according to this simulation model is simple and can effectively solve the problems of gradual wear from top to bottom and uneven material velocity in traditional constant pitch chutes, extend equipment service life, reduce maintenance costs, and improve the stability of bulk material conveying. It can be widely used in bulk material conveying scenarios in mining, metallurgy, coal and other industries.

[0082] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for optimizing a variable-pitch spiral chute model based on response surface methodology, characterized in that, include: A three-dimensional model of a variable pitch spiral chute is constructed based on preset slope characteristic parameters; wherein, the slope characteristic parameters include the initial slope, the end slope, and the slope change law index; Discrete element simulation was performed on the three-dimensional model of the variable pitch spiral chute to obtain the material sliding velocity of each velocity measuring surface; Based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity, a response surface regression equation is constructed. Genetic optimization is performed on the response surface regression equation to obtain the optimal slope characteristic parameter; wherein, the optimal slope characteristic parameter is used to optimize the variable pitch spiral chute.

2. The optimization method for the variable pitch spiral chute model based on response surface methodology according to claim 1, characterized in that, The construction of a three-dimensional model of a variable-pitch spiral chute based on preset slope characteristic parameters includes: Candidate parameter combination schemes are generated based on the horizontal change gradients of the initial slope, the terminal slope, and the slope change law index. Generate the spiral development curve equation based on the candidate parameter combination scheme; The three-dimensional model of the variable pitch spiral chute is constructed using the spiral development curve equation; different combinations of candidate parameters correspond to different three-dimensional models of the variable pitch spiral chute.

3. The optimization method for the variable pitch spiral chute model based on response surface methodology according to claim 1, characterized in that, The discrete element simulation of the three-dimensional model of the variable pitch spiral chute is performed to obtain the material sliding velocity at each velocity measuring surface, including: Discrete element simulation was performed on the three-dimensional model of the variable pitch spiral chute to simulate the sliding process of bulk material from top to bottom along the corresponding chute and obtain the corresponding bulk material motion trajectory. Multiple velocity measuring surfaces are set for the chute corresponding to the three-dimensional model of the variable pitch spiral chute, and the material sliding velocity of each velocity measuring surface is extracted based on the material movement trajectory.

4. The optimization method for the variable pitch spiral chute model based on response surface methodology according to claim 1, characterized in that, The construction of the response surface regression equation based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity includes: Extract the corresponding slope feature parameters and bulk material sliding velocity from the three-dimensional model of the variable pitch spiral chute; Calculate the standard deviation of the bulk material sliding velocity based on the aforementioned bulk material sliding velocity; The response surface regression equation is constructed using the slope characteristic parameter as the independent variable and the standard deviation of the bulk material sliding velocity as the dependent variable; wherein, the response surface regression equation is constructed using a multivariate quadratic polynomial regression method. The significance test and residual analysis were performed on the response surface regression equation to verify its prediction accuracy.

5. The optimization method for a variable pitch spiral chute model based on response surface methodology according to claim 1, characterized in that, The genetic optimization of the response surface regression equation to obtain the optimal slope feature parameters includes: Initialize the genetic algorithm parameters, set the population size, number of iterations, crossover probability and mutation probability, and encode the slope characteristic parameters of the variable pitch spiral chute into chromosome individuals; A fitness function is established based on the response surface regression equation, with the standard deviation of the bulk material sliding velocity as the target value. The slope characteristic parameters are gradually optimized through genetic operations of selection, crossover, and mutation. The iterative optimization process of the genetic algorithm is executed, and the changing trend of the optimal solution in each generation is recorded until the genetic algorithm parameters converge, so as to obtain the optimal slope characteristic parameters that minimize the standard deviation of the material sliding velocity.

6. The optimization method for a variable pitch spiral chute model based on response surface methodology according to claim 1, characterized in that, Also includes: A three-dimensional verification model is constructed based on the optimal slope feature parameters; Discrete element simulation is performed on the three-dimensional verification model according to the preset boundary conditions, and the velocity field of the loose material sliding in each region of the chute surface, the contact force of the inner wall of the chute, or the cumulative wear energy is obtained; wherein, the boundary conditions include particle size distribution, material density, chute surface roughness, and gravity field; The structural durability and long-term operational stability of the three-dimensional verification model are evaluated based on the velocity field of the material sliding in each region of the chute surface, the contact force on the inner wall of the chute, or the cumulative wear energy.

7. A model optimization device for a variable pitch spiral chute based on response surface methodology, characterized in that, include: A three-dimensional model construction unit is used to construct a three-dimensional model of a variable pitch spiral chute based on preset slope characteristic parameters; wherein, the slope characteristic parameters include the initial slope, the end slope, and the slope change law index; The slip velocity determination unit is used to perform discrete element simulation on the three-dimensional model of the variable pitch spiral chute to obtain the slip velocity of the bulk material on each velocity measuring surface. The regression equation generation unit is used to construct a response surface regression equation based on the three-dimensional model of the variable pitch spiral chute and the corresponding bulk material sliding velocity. The optimal parameter determination unit is used to perform genetic optimization on the response surface regression equation to obtain the optimal slope characteristic parameters; wherein, the optimal slope characteristic parameters are used to optimize the variable pitch spiral chute.

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 steps of the variable pitch spiral chute model optimization method based on response surface methodology as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the variable pitch spiral chute model optimization method based on the response surface methodology as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the variable pitch spiral chute model optimization method based on the response surface methodology as described in any one of claims 1 to 6.

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