A Swing-AHP-based method for evaluating the number of icebreaking mechanism groups

Abstract

A method for evaluating the number of ice-breaking mechanism groups based on Swing-AHP weighting, belonging to the field of computer-aided design technology, aims to solve the problem of the lack of a quantitative evaluation system in the existing ice-breaking mechanism group design. The method includes the following steps: Step S10, collecting the height values ​​of sampling points on the road surface, calculating road feature parameters, importing the road feature parameters into 3D modeling software, constructing a 3D model of the road features, and constructing an ice layer model on the surface of the 3D model of the road features; Step S20, setting up a control group of 1 to 10 ice-breaking units for ice-breaking mechanisms with the same ice-breaking unit width. This invention achieves a comprehensive evaluation of the overall performance of ice-breaking mechanisms through a multi-dimensional index system and a scientific weighting algorithm; dual simulation verification ensures the accuracy and reliability of the results; applied to engineering design, it can effectively reduce R&D costs and trial-and-error risks, taking into account structural safety, operational efficiency, and economy, and has broad engineering applicability and scalability.

Description

Technical Field

[0001] This invention relates to the field of computer-aided design technology, and in particular to a method for evaluating the number of icebreaking mechanism groups based on Swing-AHP weighting. Background Technology

[0002] With the frequent occurrence of icy and snowy weather in northern my country during winter, road icing severely impacts traffic safety and efficiency, making ice-breaking and snow removal equipment a core component of winter road maintenance. Early ice-breaking mechanisms mostly adopted a single, integrated design. While simple in structure, this design had significant limitations when dealing with complex icy and snowy road surfaces in northern winters (such as uneven mountain roads and urban arterial roads with multiple layers of ice). To address these issues, the industry gradually shifted towards a modular ice-breaking mechanism design. By independently disassembling and combining ice-breaking units, it can flexibly adapt to different road surface shapes, distribute impact loads, and improve operational stability and maintenance convenience. However, the determination of the number of groups in the ice-breaking mechanism still largely relies on engineers' experience and trial-and-error experiments, lacking a systematic and quantitative comprehensive evaluation system, leading to numerous limitations in the design schemes.

[0003] The existing technologies suffer from the following core problems: First, the evaluation indicators are too simplistic. Traditional methods often focus on single indicators such as conformal error or energy consumption, neglecting key factors such as peak stress, load uniformity, reliability risk, and maintenance workload. This makes it difficult for design schemes to meet multiple objectives. Second, the weight allocation is highly subjective. In existing studies, indicator weights are often set directly based on experience, lacking scientific weighting methods and verification by domain experts. This results in biased evaluation results that fail to reflect actual engineering needs. Third, there is a lack of effective simulation verification. Most designs draw conclusions solely from theoretical calculations or single simulations, without cross-validation of the accuracy of key mechanical indicators. This can lead to problems such as insufficient structural strength and lower-than-expected operational efficiency when the schemes are implemented. Fourth, the impact of assembly consistency is not fully considered. Increasing the number of groups increases assembly difficulty and consistency control costs. However, existing methods do not incorporate assembly penalties into the evaluation system, resulting in some multi-group schemes having excellent theoretical performance but low feasibility in practical engineering applications.

[0004] Therefore, in order to address the shortcomings of existing technologies, developing a computer-aided design technology for the comprehensive evaluation of ice-breaking mechanisms by grouping, which can fully cover multiple dimensions such as safety, performance, and economy, adopts a scientific weighting method and combines dual simulation verification, and provides a precise and reliable theoretical basis for determining the optimal number of groups, has become an urgent technical problem to be solved in the field of optimization design of road ice-breaking and snow removal equipment. Summary of the Invention

[0005] To overcome the above-mentioned technical defects, the present invention provides a method for evaluating the number of icebreaking mechanism groups based on Swing-AHP weighting, so as to solve at least one technical problem existing in the background art.

[0006] This invention provides the following technical solution: a method for evaluating the number of icebreaking mechanism groups based on Swing-AHP weighting, comprising the following steps:

[0007] Step S10: Collect the height values ​​of sampling points on the road surface, calculate the road feature parameters, import the road feature parameters into the 3D modeling software, construct a 3D model of the road features, and construct an ice layer model on the surface of the 3D model of the road features.

[0008] Step S20: For ice-breaking mechanisms with the same ice-breaking wheel width, set up ice-breaking unit group control groups of 1 to 10 ice-breaking unit combinations, unify the ice-breaking unit parameters of each group, and only change the number of independent ice-breaking units within the ice-breaking unit group.

[0009] Step S30: Set evaluation indicators that include four dimensions: safety indicators, performance indicators, economic indicators, and additional indicators.

[0010] Step S40: Use the Swing method to determine the initial weights of the evaluation indicators in step S30, combine them with expert scoring for verification, calculate the weights of the judgment matrix using the AHP method, and then fuse them to obtain the final weights.

[0011] Step S50: Use MATLAB software to construct a dynamic model of the ice-breaking mechanism, set the simulation parameters of the road surface and perform simulation. Use the extreme value method to convert the simulation values ​​of the evaluation index into normalized scores and calculate the comprehensive scores of each group.

[0012] Step S60: Import the three-dimensional model of the road surface features and the three-dimensional model assembly of the ice-breaking mechanism into ANSYS software for simulation. If the error between the mechanical index values ​​obtained by ANSYS software and MATLAB software simulation is greater than the preset threshold, return to step S50 to adjust the MATLAB dynamic model parameters and recalculate. If the error between the mechanical index values ​​obtained by ANSYS software and MATLAB software simulation is less than or equal to the preset threshold, proceed to step S70.

[0013] Step S70: Select the group of icebreaking mechanisms that meets the mechanical constraints and has the highest comprehensive score from the ANSYS software simulation verification results as the optimal icebreaking mechanism group.

[0014] Furthermore, the performance indicators in step S30 include the single-time road surface exposure rate, which is equal to the ratio of the exposed road surface area after a single ice-breaking operation to the total road surface area within the operating width of the ice-breaking mechanism, and is used to evaluate the ice-breaking coverage rate of a single operation.

[0015] Furthermore, the road surface characteristic parameters in step S10 include the root mean square height of the surface, the average wavelength, and the peak-to-valley difference.

[0016] Furthermore, the icebreaking unit parameters in step S20 include the icebreaking wheel width, the yield strength of the spring steel, the elastic modulus of the spring steel, the Poisson's ratio of the spring steel, the outer diameter of the icebreaking wheel, the equivalent mass of each icebreaking unit, and the stiffness and damping of the external straight spring.

[0017] Furthermore, the safety indicators in step S30 include peak stress, load standard deviation, and impact peak-to-peak value; performance indicators include single-time road surface exposure rate, ice-breaking efficiency, and energy consumption per unit time; economic indicators include manufacturing cost, system complexity, reliability risk, and maintenance workload; and additional indicators include assembly penalties.

[0018] Furthermore, the expert scoring verification in step S40 involves evaluating the rationality of the initial weight values ​​of the evaluation indicators in step S30, providing expert-recommended values ​​for the weights of the evaluation indicators, and then merging the initial values ​​of the evaluation indicators with the expert-recommended values ​​to obtain the corrected initial weights using the Swing method.

[0019] Furthermore, the threshold in step S60 is 5%.

[0020] Furthermore, the ice-breaking mechanism grouping in step S70 satisfies the following mechanical constraints: peak stress is less than or equal to 80% of the allowable stress of the ice-breaking tool material, load standard deviation is less than or equal to 20% of peak stress, and impact peak-to-peak value is less than or equal to 30% of peak stress.

[0021] Furthermore, step S70 also includes selecting the ice-breaking mechanism group with the second highest comprehensive score if none of the ice-breaking mechanism groups meet the mechanical constraints, and then repeating the mechanical index verification.

[0022] The technical effects and advantages of this invention are as follows:

[0023] This invention simulates ice-covered conditions by selecting typical icy and snowy road surfaces, collecting surface unevenness data, calculating key parameters such as root mean square height and average wavelength, and establishing a road surface feature model. It sets up control groups of 1 to 10 ice-breaking mechanisms, unifying the cutter material, spring stiffness, and basic structural parameters, only changing the number of groups. It defines multi-dimensional evaluation factors including safety, performance, economy, and additional categories, clarifies the quantification methods for each indicator, uses the Swing method to determine the initial weights of the indicators and verifies them with experts, combines them with the AHP method to calculate the weights, and integrates them to obtain the final weighted weights. It constructs a comprehensive score calculation formula, integrates the previously obtained weighted weights, and calculates the comprehensive score of each group through MATLAB simulation. Finally, it uses ANSYS software mechanical simulation to verify the accuracy of key mechanical indicators, controlling the error within ±5%. Based on the maximum comprehensive score and the compliance of mechanical indicators, it determines the optimal number of groups.

[0024] This invention achieves a comprehensive evaluation of the overall performance of icebreaking mechanisms through a multi-dimensional index system and a scientific weighting algorithm; dual simulation verification ensures the accuracy and reliability of the results; the process is clear and the parameters are well-defined, which can be directly applied to engineering design, effectively reducing R&D costs and trial-and-error risks, while taking into account structural safety, operational efficiency and economy, and has broad engineering applicability and scalability. Attached Figure Description

[0025] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Reference Figure 1 As shown, this invention provides a method for evaluating the number of icebreaking mechanism groups based on Swing-AHP weighting, including the following steps:

[0028] Step S10: Collect road surface feature data and establish a road surface feature model.

[0029] This step aims to obtain the physical characteristics of real ice and snow road surfaces, providing a basic environment that fits the actual working conditions for subsequent simulations. The specific steps include step S11 and step S12.

[0030] Step S11, Road surface data collection:

[0031] Typical snow-covered roads were selected as sampling targets, including highways, urban arterial roads, and mountain roads. A laser rangefinder-type pavement profilometer was used for data acquisition. During data collection, a sampling path was set along the longitudinal direction of the road, with a total sampling length L of 10m and a sampling frequency of 500Hz. The height values ​​z of several pavement surface sampling points were then obtained. i ;

[0032] Step S12, Calculation of key road feature parameters:

[0033] Calculate the root mean square height of the surface The three road surface characteristic parameters are: average wavelength λ and peak-to-valley difference Δh.

[0034]

[0035]

[0036]

[0037] In the formula, N is the total number of road surface sampling points. Let be the height value of the i-th road surface sampling point. The height of all road surface sampling points is the arithmetic mean, where L is the total length of the road surface sampling. This represents the total number of road surface peaks within the sampling range. This represents the maximum height value among the sampling points on the road surface. This represents the minimum height value among the sampling points on the road surface.

[0038] Step S13, Road surface and ice layer model construction:

[0039] Using the 3D modeling tools of Solidworks software, three core pavement feature parameters—root mean square height, average wavelength, and peak-to-valley difference—were imported to construct a 3D pavement feature model. Subsequently, a uniformly thick ice layer model was covered on the surface of the 3D pavement feature model. The ice layer thickness was set to 5cm, which is the thickness of refreezing ice commonly found in northern winters. The ice layer model adopted the properties of an elastomer material, and its fracture energy was set to 2.5J / cm².

[0040] Step S20: Fix the parameters of a single ice-breaking unit, construct a three-dimensional model assembly of the ice-breaking mechanism, and set up 1-10 groups of control test groups:

[0041] This step involves fixing the parameters of a single ice-breaking unit and using the controlled variable method to eliminate interference from non-target factors, ensuring that the number of groups is a unique variable.

[0042] Parameters of the 3D model assembly of the icebreaking mechanism: An icebreaking mechanism with an icebreaking wheel width of 0.425m was selected as the icebreaking unit. Group control groups of 1 to 10 icebreaking units were set up, i.e., n=1,2,...,10, corresponding to a total icebreaking mechanism width of 0.425m to 4.25m, covering common operating widths of icebreaking equipment. To ensure fairness in the evaluation, the icebreaking unit parameters of each group of icebreaking mechanisms were kept consistent and uniform. The icebreaking unit parameters included the icebreaking wheel width, yield strength of the spring steel, elastic modulus of the spring steel, Poisson's ratio of the spring steel, outer diameter of the icebreaking wheel, equivalent mass of each group of icebreaking units, and stiffness and damping of the external straight spring. The ice-breaking blades use cross-shaped pin teeth. The materials for the ice-breaking blades and ice-breaking wheels are spring steel with a yield strength of 785 MPa, an elastic modulus of 206 GPa, and a Poisson's ratio of 0.3. The outer diameter of the ice-breaking wheel is 620 mm. The equivalent mass of each ice-breaking unit is 120 kg. Each ice-breaking unit includes an external straight spring with a stiffness of k = 15130.43 N / m and a damping of c = 269.6 N·s / m.

[0043] The 3D model assembly structure of the ice-breaking mechanism: Each group of ice-breaking units is suspended from the upper rigid main beam by vertical external straight springs. The upper end of the external straight spring is hinged to the main beam mounting base, and the lower end is hinged to the ice-breaking unit bracket. The ice-breaking unit includes an ice-breaking wheel and an external straight spring. The bottom of the ice-breaking unit bracket is connected to the ice-breaking wheel group through a bearing seat, and the ice-breaking wheel group can rotate freely around the axis of the bearing. The ice-breaking units of each ice-breaking wheel group are closely arranged and evenly spaced along the main beam. They are elastically floated by the external straight springs to adapt to the undulations of the road surface. The overall structure is compact and the force is uniform, enabling efficient and stable ice-breaking operations. The 3D model assembly parameters and structure of the ice-breaking mechanism are input into Solidworks software to construct the 3D model assembly of the ice-breaking mechanism. Only the number of ice-breaking unit groups is changed, that is, by increasing or decreasing the number of independent ice-breaking units, different ice-breaking unit grouping schemes are formed. The control groups of 1 to 10 ice-breaking units are numbered G1, G2, ..., G9 and G10 respectively.

[0044] Step S30: Define a multi-dimensional evaluation index system

[0045] This step selects 11 evaluation indicators from four dimensions: safety, performance, economy, and added value.

[0046] Safety indicators:

[0047] Safety indicators include peak stress, load standard deviation, and impact peak-to-peak value, which are used to ensure the structural safety and operational stability of the icebreaking mechanism.

[0048] Peak stress This indicates the maximum stress value that the ice-breaking mechanism bears during the ice-breaking process, which directly determines whether the ice-breaking mechanism will undergo plastic deformation or breakage.

[0049] Load standard deviation This represents the standard deviation of the stress value of the ice-breaking blade during the ice-breaking process; the smaller the fluctuation, the more stable the operation.

[0050] Peak-to-peak value It represents the difference between the maximum and minimum values ​​in the stress time series, characterizing the instantaneous impact intensity.

[0051] Performance metrics

[0052] Performance metrics include single-time road surface exposure rate, ice-breaking efficiency, and energy consumption per unit time, which are used to measure the effectiveness and efficiency of ice-breaking operations.

[0053] The single-cycle road surface exposure rate η represents the proportion of road surface exposed after one clearing operation within the working width of the ice-breaking mechanism under re-icing conditions. It is used to evaluate the ice-breaking coverage rate of a single operation, and the quantitative formula is:

[0054]

[0055] In the formula, This refers to the area of ​​the road surface exposed after one ice-breaking operation. The total road surface area within the working width of the ice-breaking mechanism is represented by the MATLAB simulation, which divides the ice surface into tiny units of 1mm×1mm. When the cumulative impact energy of the ice-breaking unit is greater than the fracture energy of the ice, it is determined to be broken. The fracture energy of the refreezing ice is taken as 2.5J / cm². The cumulative broken unit area is the area of ​​the road surface exposed after one ice-breaking operation. It is directly measured in ANSYS software through the broken area of ​​the ice layer.

[0056] Icebreaking efficiency P represents the volume of refreezed ice removed per unit time, reflecting operational efficiency. Icebreaking efficiency P is equal to the number of breaking units multiplied by the unit volume, then divided by the operation time.

[0057] Energy consumption per unit time (E) represents the energy consumed by the icebreaking mechanism per unit of time, and is an important performance indicator for evaluating the economic efficiency of the operation. Energy consumption per unit time (E) equals the total energy consumed by the icebreaking mechanism during the operation time divided by the operation time.

[0058] Economic indicators

[0059] Economic indicators include manufacturing costs, system complexity, reliability risks, and maintenance workload, which are used to measure the total life cycle cost.

[0060] Manufacturing cost is calculated by grouping manufacturing costs per unit. Manufacturing cost includes the manufacturing costs of cutting tools, connectors, and drive components. The quantification formula is as follows:

[0061]

[0062] In the formula, cost is the manufacturing cost. The base cost for a single ice-breaking unit is set at 2000 yuan per unit. The cost of connecting and assembling between groups is set at 300 yuan per unit, where n is the number of icebreaking mechanism groups.

[0063] System complexity This indicates the difficulty of assembling components, which is a static structural attribute. The quantification formula is:

[0064]

[0065] In the formula, Here, C0 is the basic complexity of a single icebreaking unit, and C0 takes a value of 1. c k is the grouping incremental complexity coefficient. c The value is 1, and n is the number of icebreaking mechanism groups;

[0066] Reliability risks The formula for representing component failure rate and downtime losses, based on the statistical relationship between the number of groups and the failure rate, is as follows:

[0067]

[0068] In the formula, r0 is the basic failure rate of a single ice-breaking unit, and r0 takes a value of 0.05, k r For the grouped incremental risk coefficient, k r The value is 0.05, and n is the number of ice-breaking mechanism groups;

[0069] Maintenance workload Based on the number of maintenance cycles per year, the more groups there are, the more maintenance points there are. The quantification formula is as follows:

[0070]

[0071] In the formula, t0 is the maintenance time of a single icebreaking unit, which is 4 hours / time; n is the number of icebreaking mechanism groups; and m is the average number of maintenance times per year for a single icebreaking unit, which is 2 times / year.

[0072] 4) Additional Indicators

[0073] Additional indicators include assembly penalties, which serve as a supplement to the feasibility of engineering applications.

[0074] Assembly penalty P assem This indicates the increased difficulty in controlling assembly consistency due to an increase in the number of groups, and is used to assess the increased difficulty in controlling assembly consistency caused by an increase in the number of groups. The quantification formula is:

[0075]

[0076] In the formula, k n k is the assembly penalty coefficient. n The value is 0.6, where n is the number of groups in the ice-breaking mechanism. The larger the value, the higher the assembly difficulty.

[0077] Step S40: Perform fusion weighting using Swing combined with AHP method:

[0078] Step S40 specifically includes steps S41, S42, and S43.

[0079] Step S41: Use the Swing method to determine the initial weights of the indicators.

[0080] Based on engineering experience, the importance of 11 indicators was ranked and initial weights were assigned. Example initial weights are as follows: single-time road surface exposure rate: 0.40; peak stress: 0.12; load standard deviation: 0.10; peak-to-peak impact: 0.04; ice-breaking efficiency: 0.10; energy consumption per unit time: 0.06; manufacturing cost: 0.05; system complexity: 0.03; reliability risk: 0.05; maintenance workload: 0.025; and assembly penalty: 0.025.

[0081] After obtaining the initial subjective weight values, five experts with experience in the research and development or application of ice and snow equipment were invited to evaluate the rationality of the initial weight values ​​for the 11 indicators. The experts provided a rationality score, reasons for correction, and expert recommendations on the weights.

[0082] Reasonableness rating criteria: 5 points for extremely reasonable, fully meeting project requirements; 4 points for reasonably reasonable, no adjustment needed; 3 points for basically reasonable, requiring minor adjustments; 2 points for unreasonable, requiring significant adjustments; 1 point for extremely unreasonable, requiring resetting.

[0083] If the standard deviation of the reasonableness score for the same indicator is greater than 1.2, it indicates a significant divergence of opinions among experts. An expert discussion should be organized to focus on the points of disagreement, and a consensus should be reached by combining engineering case studies. A new reasonableness score for the indicator should then be given. The arithmetic mean of the expert recommendations from the five experts is used to obtain the expert recommendation value for that weight.

[0084] The revised initial weights w of the Swing method iThe calculation method is as follows: After discussion, the rationality score of the consensus opinion of five experts on a certain indicator is obtained. If the experts give 5 points, it means that the initial weight value is completely in line with the actual project. At this time, no modification is needed and the original initial weight value is directly retained. The expert's suggested value is only used for verification. In this case, the integrated weight is the initial weight value accounting for 100%. If the experts give 4 points, it means that the initial weight value is basically fine. At this time, the initial weight value is the main value and the expert's suggested value is only used for reference. In this case, the integrated weight is the initial weight value accounting for 90% and the expert's suggested value accounting for 10%. If the experts give 3 points, it means that the initial weight value is feasible, but needs to be slightly adjusted. At this time, the initial weight value and the expert's suggested value are integrated with equal weight. In this case, the integrated weight is the initial weight value and the expert's suggested value each accounting for 50%. If the experts give 2 points, it means that the initial weight value has a large deviation and needs to be significantly adjusted. At this time, the expert's suggested value is the main value and the initial weight value is used as an auxiliary value. In this case, the integrated weight is the expert's suggested value accounting for 90% and the initial weight value accounting for 10%. If the experts give 1 point, it means that the initial weight value does not meet the requirements at all. At this time, the expert's suggested value is directly adopted and the initial weight value is discarded. The integrated weight is the expert's suggested value accounting for 100%. The fusion weights are the corrected initial weights w of the Swing method. i '.

[0085] Step S42: Calculate the weights of the judgment matrix using the AHP method.

[0086] First, 11 influencing factors are given: C1 is peak stress, C2 is single-time road surface exposure rate, C3 is ice-breaking efficiency, C4 is load standard deviation, C5 is energy consumption per unit time, C6 is peak-to-peak impact, C7 is manufacturing cost, C8 is maintenance workload, C9 is system complexity, C10 is assembly penalty, and C... 11 To assess reliability risk, a 1-9 scale is used to define pairwise importance; then an 11×11 judgment matrix A is constructed, and finally, the AHP (Analytic Hierarchy Process) weights are obtained. w i ’’ The AHP method includes calculating the weight vector using the sum-product method, a consistency check, and AHP weights. wi'' Sure.

[0087] Step S43, Weight fusion

[0088] Using an equal-weight fusion strategy, the final weight W is calculated. i To ensure the scientific validity of the weighting by combining subjective experience with objective calculation, the formula is as follows:

[0089]

[0090] In the formula, α is the fusion coefficient of the modified subjective initial weights, β is the fusion coefficient of the AHP method weights, α=β=0.5, and the final weight W i The sum is 1. w i’ For the revised initial weights of the Swing method, w i ’’ The weights are determined by the AHP method.

[0091] Step S50: Calculate the overall score for each group using MATLAB programming simulation.

[0092] The specific steps of step S50 include steps S51, S52, S53 and S54;

[0093] Step S51, Dynamic Model Construction:

[0094] The ice-breaking mechanism is simplified as a single-degree-of-freedom vibration system, and the dynamic equation is:

[0095]

[0096] In the formula, m is the equivalent mass of a single icebreaker wheel, taken as 120 kg; k is the spring stiffness, taken as 15130.43 N / m; c is the damping, taken as 269.6 N·s / m; and z(t) is the displacement of the road surface on the icebreaker wheel. The velocity signal is given by x(t), and the displacement of the icebreaker wheel is given by x(t). The icebreaker wheel mechanism is simplified into an independent single-degree-of-freedom element. The road surface topography is incorporated as the base motion into the dynamic response of the icebreaker wheel to obtain its displacement response x(t) and velocity. and acceleration Then, the instantaneous contact force is calculated.

[0097] Step S52, Simulation parameter settings

[0098] The simulation parameters were set, including the root mean square height of the surface, the average wavelength, the peak-to-valley difference, the road length, the operating speed, the time step, and the sampling frequency. The road length was 10m, the operating speed was 2~10m / s, the time step was 0.002s, and the sampling frequency was 500Hz.

[0099] Step S53, Normalization Process

[0100] The extreme value method was used to convert the simulated values ​​of each weight index of the 11 evaluation indicators into normalized scores in the 0-1 interval:

[0101] For positive indicators, a larger value is better:

[0102]

[0103] For negative indicators, smaller values ​​are better:

[0104]

[0105] In the formula, xi Let x be the simulated value of the i-th index. max x represents the maximum value of this indicator in control groups 1-10. min This is the minimum value of this indicator in the control groups of 1 to 10.

[0106] Step S54: Calculate the overall score for each group.

[0107] The overall score AI of the nth control group n The calculation formula is:

[0108]

[0109] In the formula, W i For the final weighting of the i-th indicator obtained in step S43, S i ' is the normalized score of the i-th indicator, AI n A higher value indicates better overall performance.

[0110] Step S60: Perform mechanical simulation verification using ANSYS software.

[0111] This step involves cross-validation of safety-related metrics to ensure the accuracy of the MATLAB simulation results.

[0112] Step S61, Simulation Model Construction

[0113] The 3D model of the road surface features from step S13 and the 3D model assembly of the ice-breaking mechanism from step S20 were imported into ANSYS software, and simulation was performed using the explicit dynamics module LS-DYNA. The material properties of the ice-breaking mechanism were set to spring steel, with an elastic modulus of 206 GPa, a Poisson's ratio of 0.3, and a yield strength of 785 MPa. The ice layer material was set as refreezing ice, with an elastic modulus of 3.5 GPa and a Poisson's ratio of 0.25.

[0114] Step S62, Mesh Generation and Boundary Conditions

[0115] In ANSYS software, a 3D model of the ice-breaking blade, ice layer, and road surface features was meshed. Boundary conditions: the road surface was fixed, the ice-breaking mechanism moved longitudinally at a set operating speed, and a downward pressure of 500 kg was applied.

[0116] Step S63, Mechanical index extraction and error verification

[0117] Run ANSYS software simulation to extract the peak stress of each control group. Load standard deviation Impact peak-to-peak value The numerical values ​​are compared with the corresponding index values ​​in the MATLAB simulation to calculate the error. The error formula is as follows:

[0118]

[0119] In the formula σ ANSYS These are the mechanical property values ​​obtained from ANSYS software simulation. The mechanical properties include safety-related properties, such as σ. MATLAB The values ​​are the corresponding mechanical parameters obtained from the MATLAB simulation, including safety parameters. If the error is greater than 5%, return to step S50 to adjust the MATLAB dynamic model parameters and recalculate; if the error is less than or equal to 5%, proceed to step S70.

[0120] Step S70: Determine the optimal number of groups.

[0121] First, from the 10 control groups, the three candidate icebreaking mechanism groups with the highest comprehensive scores were selected. Then, the ANSYS software verification results of the candidate groups were checked to see if they met the mechanical constraints. Finally, all schemes that met the mechanical constraints were selected from the candidate icebreaking mechanism groups: if multiple groups met the constraints, the group with the highest score was selected as the optimal group; if only one group met the constraints, the icebreaking mechanism group was the optimal icebreaking mechanism group; if none of the candidate groups met the constraints, the icebreaking mechanism group with the second highest comprehensive score was selected in turn, and the mechanical index verification was repeated until the icebreaking mechanism group that met the constraints and had the highest score was determined.

[0122] The formula for calculating mechanical constraints is:

[0123] σ max ≤[σ]×80%

[0124] S σ ≤σ max ×20%

[0125] Δσ pp ≤σ max ×30%

[0126] In the formula, [σ] represents the allowable stress of the ice-breaking tool material, and σ max For peak stress, S σ Let Δσ be the standard deviation of the load. pp For peak-to-peak impact.

[0127] This invention, through clear process design, explicit parameter settings, scientific quantitative methods, and rigorous verification procedures, ensures the accuracy and engineering practicality of the evaluation results, and can be directly applied to the structural optimization design of ice-breaking and snow-removing equipment. Finally: The above description is merely a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for evaluating the number of groups of icebreaking mechanisms based on Swing-AHP weighting, characterized in that, Includes the following steps: Step S10: Collect the height values ​​of sampling points on the road surface, calculate the road feature parameters, import the road feature parameters into the 3D modeling software, construct a 3D model of the road features, and construct an ice layer model on the surface of the 3D model of the road features. Step S20: For ice-breaking mechanisms with the same ice-breaking wheel width, set up ice-breaking unit group control groups of 1 to 10 ice-breaking unit combinations, unify the ice-breaking unit parameters of each group, and only change the number of independent ice-breaking units within the ice-breaking unit group. Step S30: Set evaluation indicators that include four dimensions: safety indicators, performance indicators, economic indicators, and additional indicators. Step S40: Use the Swing method to determine the initial weights of the evaluation indicators in step S30, combine them with expert scoring for verification, calculate the weights of the judgment matrix using the AHP method, and then fuse them to obtain the final weights. Step S50: Use MATLAB software to construct a dynamic model of the ice-breaking mechanism, set the simulation parameters of the road surface and perform simulation. Use the extreme value method to convert the simulation values ​​of the evaluation index into normalized scores and calculate the comprehensive scores of each group. Step S60: Import the three-dimensional model of the road surface features and the three-dimensional model assembly of the ice-breaking mechanism into ANSYS software for simulation. If the error of the mechanical index value obtained after simulation using ANSYS software and MATLAB software is greater than the preset threshold, return to step S50 to adjust the MATLAB dynamic model parameters and re-simulate and calculate. If the error of the mechanical index value obtained after simulation using ANSYS software and MATLAB software is less than or equal to the preset threshold, proceed to step S70. Step S70: Select the group of icebreaking mechanisms that meets the mechanical constraints and has the highest comprehensive score from the ANSYS software simulation verification results as the optimal icebreaking mechanism group.

2. The method according to claim 1, wherein the method is a method for evaluating the number of groups of icebreaking mechanisms based on Swing-AHP weighting. The performance indicators in step S30 include the single-time road surface exposure rate, which is equal to the ratio of the exposed road surface area after a single ice-breaking operation to the total road surface area within the operating width of the ice-breaking mechanism, and is used to evaluate the ice-breaking coverage rate of a single operation.

3. The method according to claim 1, wherein the method is characterized in that: The road surface characteristic parameters in step S10 include the root mean square height of the surface, the average wavelength, and the peak-to-valley difference.

4. The method according to claim 1, wherein the method is a method for evaluating the number of groups of icebreaking mechanisms based on Swing-AHP weighting. The icebreaking unit parameters in step S20 include the icebreaking wheel width, the yield strength of the spring steel, the elastic modulus of the spring steel, the Poisson's ratio of the spring steel, the outer diameter of the icebreaking wheel, the equivalent mass of each icebreaking unit, and the stiffness and damping of the external straight spring.

5. The method for evaluating the number of icebreaking mechanisms based on Swing-AHP weighting according to claim 2, characterized in that: The safety indicators in step S30 include peak stress, load standard deviation, and impact peak-to-peak value; the performance indicators include single-time road surface exposure rate, ice-breaking efficiency, and energy consumption per unit time; the economic indicators include manufacturing cost, system complexity, reliability risk, and maintenance workload; and the additional indicators include assembly penalties.

6. The method for evaluating the number of icebreaking mechanisms based on Swing-AHP weighting according to claim 1, characterized in that: The expert scoring verification in step S40 involves evaluating the rationality of the initial weight values ​​of the evaluation indicators in step S30, providing expert-recommended values ​​for the weights of the evaluation indicators, and then merging the initial values ​​of the evaluation indicators with the expert-recommended values ​​to obtain the corrected initial weights using the Swing method.

7. The method for evaluating the number of icebreaking mechanisms based on Swing-AHP weighting according to claim 1, characterized in that: The threshold in step S60 is 5%.

8. The method for evaluating the number of icebreaking mechanisms based on Swing-AHP weighting according to claim 1, characterized in that: The ice-breaking mechanism grouping in step S70 satisfies the mechanical constraints that the peak stress is less than or equal to 80% of the allowable stress of the ice-breaking blade material, the load standard deviation is less than or equal to 20% of the peak stress, and the impact peak-to-peak value is less than or equal to 30% of the peak stress.

9. The method for evaluating the number of icebreaking mechanisms based on Swing-AHP weighting according to claim 8, characterized in that: Step S70 also includes selecting the ice-breaking mechanism group with the second highest comprehensive score if none of the ice-breaking mechanism groups meet the mechanical constraints, and then repeating the mechanical index verification.

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