Safety shoe wear-resistant material layout method based on ant colony optimization algorithm

Abstract

The invention discloses a safety shoe wear-resistant material layout method based on an ant colony optimization algorithm. The method comprises the following steps: S1, constructing a safety shoe wear data set; s2, dividing the surfaces of the safety shoes into a plurality of priority areas; s3, distributing pheromone initial concentration according to priority region distribution of the fractal feature value; s4, establishing an optimization objective function; s5, gradually selecting an optimal material layout path of a high-priority region; s6, calculating the fitness value of each path according to the path search result and the optimization objective function, and updating the path pheromone concentration by using the fitness value; s7, a final global optimal path is obtained through multiple rounds of iteration from S1 to S6; s8, outputting a final optimal layout scheme of the wear-resistant material of the safety shoes; and S9, verifying the final optimal layout scheme. According to the method, an efficient and scientific optimization scheme can be provided in the layout design of the wear-resistant material of the safety shoes, and remarkable technical values and economic benefits are brought to practical application.

Description

Technical Field

[0001] The invention relates to the technical field of special equipment, and in particular to a layout method of wear-resistant materials for labor protection shoes based on an ant colony optimization algorithm. Background Art

[0002] With the rapid development of materials science and optimization algorithms, as an important personal protective equipment, the wear resistance and economic benefits of labor protection shoes have received widespread attention. The wear resistance of labor protection shoes is directly related to the safety of users and the service life of shoes, while the reasonable distribution and optimized layout of wear-resistant materials are the key factors to improve their comprehensive performance. At present, the design method for the layout of wear-resistant materials of labor protection shoes still has many shortcomings, and it is difficult to meet the needs of multi-objective optimization while taking into account performance and cost.

[0003] At present, the layout design of wear-resistant materials for labor protection shoes mainly relies on experience and simple rule allocation. Designers select high-wear-resistant materials in high-wear areas according to the stress and wear characteristics of different parts of the labor protection shoes, and select low-cost ordinary materials in low-wear areas. Although this empirical design method can improve wear resistance to a certain extent, it has obvious limitations. In recent years, some studies have attempted to introduce optimization algorithms for material layout optimization. The optimization algorithm improves the efficiency of material utilization through certain mathematical models. However, due to the simple model construction and the failure to fully combine the specific usage scenarios and multi-objective requirements of labor protection shoes, its optimization effect is limited.

[0004] The existing technology has the following significant defects in the layout design of wear-resistant materials for labor protection shoes:

[0005] In summary, the existing technology lacks comprehensiveness, precision and adaptability in the layout design of wear-resistant materials, and cannot effectively balance multi-objective requirements. At the same time, the efficiency and quality of the optimization process are difficult to meet the requirements of practical applications. Therefore, a new technical solution is urgently needed to overcome the above-mentioned defects, so as to optimize the material utilization efficiency and comprehensively improve the comprehensive performance while improving the wear resistance. Summary of the invention

[0006] One purpose of the present invention is to propose a layout method of wear-resistant materials for labor protection shoes based on ant colony optimization algorithm. The present invention can provide an efficient and scientific optimization solution in the layout design of wear-resistant materials for labor protection shoes, bringing significant technical value and economic benefits to practical applications.

[0007] According to an embodiment of the present invention, a method for layout of wear-resistant materials of labor protection shoes based on ant colony optimization algorithm comprises the following steps:

[0008] S1. Conduct experimental tests on wear characteristics of different parts of safety shoes and collect feedback from users, and construct a wear data set of safety shoes including wear intensity, frequency distribution and stress distribution;

[0009] S2. Based on the wear data set of labor protection shoes, a fractal feature model of the labor protection shoe surface is constructed using a spatial fractal algorithm. The fractal feature values ​​of each part of the labor protection shoe surface are calculated. The fractal feature values ​​characterize the complexity and intensity distribution of regional wear. A fractal feature value distribution map is generated according to the size of the fractal feature value. The labor protection shoe surface is divided into multiple priority areas, where the higher the fractal feature value, the higher the priority of the area;

[0010] S3. Initialize the parameters of the ant colony optimization algorithm based on the fractal eigenvalue distribution map, including the number of ants, pheromone volatility coefficient, heuristic factor and number of iterations. Allocate the initial pheromone concentration according to the priority regional distribution of the fractal eigenvalue. The higher the fractal eigenvalue, the greater the pheromone concentration in the area, so as to guide the ant search path to preferentially cover the high wear area;

[0011] S4. Establish an optimization objective function. The optimization objective function comprehensively considers the wear performance, production cost, weight distribution and comfort of the wear-resistant materials of the labor protection shoes, and uses the priority area generated by the fractal algorithm as a constraint condition for the ant path search, that is, the high priority area needs to be covered first in the path search;

[0012] S5. The wear-resistant material layout problem of different parts of the safety shoes is transformed into an ant path search problem. The nodes in the ant path represent the type, thickness and arrangement of different materials. Each ant follows the principle of the joint action of pheromone concentration and heuristic factor in the path search, and gradually selects the optimal material layout path in the high-priority area.

[0013] S6. Calculate the fitness value of each path according to the path search results and the optimization objective function, and use the fitness value to update the path pheromone concentration. The higher the fitness, the greater the pheromone concentration of the path. When the fractal eigenvalue distribution changes during the optimization process, dynamically update the fractal priority area and its corresponding pheromone concentration distribution;

[0014] S7, through multiple rounds of iterations of S1-S6, gradually converge to the global optimal path, record the optimal material layout plan of the current path in each round of iteration, and obtain the final global optimal path;

[0015] S8. Based on the final global optimal path, output the optimal layout plan of the wear-resistant materials of the labor protection shoes, including the material type, thickness configuration and arrangement method of different areas, so that the wear-resistant materials of the labor protection shoes meet the wear-resistant performance requirements while taking into account the feasibility of the production process and cost control;

[0016] S9. Input the optimal material layout plan into the material performance test platform to verify the wear resistance, comfort and production feasibility. According to the verification results, adjust the material allocation of the area that does not meet the requirements in the plan, and re-iterate the optimization through fractal eigenvalue update.

[0017] Optionally, the S1 includes the following steps:

[0018] S11. Conduct experimental tests on the wear characteristics of different parts of the safety shoes, select test samples and divide the surface of the safety shoes into measurement areas according to actual usage scenarios. ;

[0019] S12. Use standard wear test equipment to measure each measurement area The wear depth and wear area are measured and the measurement area is calculated. Wear strength ;

[0020] in, For measurement area The wear depth, For measurement area The wear area;

[0021] S13. Count the wear frequency of each measurement area through long-term use feedback and record the wear frequency of each measurement area ;

[0022] S14. Measure the stress distribution of each measurement area based on wear experiments and material stress analysis, and collect the corresponding stress values ;

[0023] S15, the collected wear strength , Wear frequency and stress distribution The data were normalized respectively;

[0024] S16. Construct a labor protection shoe wear data set based on the normalized data:

[0025] ;

[0026] Where N is the total number of measurement areas divided, For measurement area The normalized wear intensity, For measurement area The normalized wear frequency, For measurement area The normalized stress value.

[0027] Optionally, S2 includes the following steps:

[0028] S21. Calculate each measurement area based on the labor protection shoe wear data set Comprehensive wear index :

[0029] ;

[0030] in, , , are weight coefficients, which are used to balance the effects of wear intensity, wear frequency and stress on the comprehensive wear index. The impact of

[0031] S22. Calculate each measurement area using multi-scale fractal analysis method The fractal eigenvalue of , the comprehensive wear index Considered as measurement area The spatial distribution function M(x, y) within the The position of the safety shoe surface in the coordinate system, set a series of scales s, and measure the area at each scale s. Divide into sub-areas of equal area, and calculate the cumulative value of the comprehensive wear index in each sub-area :

[0032] ;

[0033] in, is the area of ​​sub-region k;

[0034] Calculate the total cumulative comprehensive wear index S(s) at each scale s:

[0035] ;

[0036] Among them, q is the multifractal order parameter, which is used to emphasize the wear characteristics of different intensities;

[0037] Calculate the measurement area according to the total cumulative comprehensive wear index S(s) at different scales s The generalized fractal dimension :

[0038] ;

[0039] Through and The relationship is linearly fitted, and the slope equal , fractal eigenvalue It is expressed as:

[0040] ;

[0041] in, Characterizing the measurement area Wear complexity and intensity distribution;

[0042] S23, fractal eigenvalue Perform normalization to obtain the normalized fractal eigenvalue ;

[0043] S24, according to the normalized fractal eigenvalue The size of the shoe surface is divided into several priority areas:

[0044] ;

[0045] in, and is the priority threshold, satisfying , high-priority areas correspond to areas with high wear complexity and need to focus on strengthening the configuration of wear-resistant materials;

[0046] S25, generate a fractal eigenvalue distribution map, and measure the area The priority areas are marked on the surface map of the safety shoes, with different colors or symbols indicating different priority areas.

[0047] Optionally, S3 includes the following steps:

[0048] S31. Initialize the number of ants m and pheromone volatility coefficient of the ant colony optimization algorithm based on the fractal eigenvalue distribution map , Heuristic Factor And the number of iterations T:

[0049] S32. Allocate initial pheromone concentration according to priority area distribution of fractal eigenvalues , initial pheromone concentration It increases with the wear complexity of the region, so that ants prefer regions with high wear complexity for path search;

[0050] S33. For high priority areas, the initial pheromone concentration is further enhanced according to the power relationship based on the difference between its fractal characteristic value and the threshold value:

[0051] ;

[0052] in, It is a pheromone reinforcing factor. is the reinforcement index;

[0053] S34, generate the initial pheromone distribution map, and measure the area Initial pheromone concentration Mark on the surface of the safety shoes, using different color depths or symbol sizes to indicate the level of pheromone concentration.

[0054] Optionally, the number of ants m, the pheromone volatility coefficient , Heuristic Factor The settings of the number of iterations T specifically include:

[0055] Set the number of ants m, which is determined based on the number of priority areas and the overall wear complexity, so that the number of ants covers areas with high wear complexity:

[0056] ;

[0057] Where N is the total number of priority areas divided, is the ant population adjustment coefficient, For all normalized fractal eigenvalues The average value of Indicates rounding x upwards;

[0058] Calculation of pheromone volatility coefficient , pheromone volatility coefficient Reflects the decay rate of pheromones, adjusted based on the discrete degree of wear complexity, The larger the value, the more uneven the wear complexity distribution:

[0059] ;

[0060] in, is the pheromone volatilization adjustment coefficient, is the normalized fractal feature The standard deviation of

[0061] Heuristic Factor Measure the area from which ants Transfer to area The expected degree of wear of the target area and the distance between areas are taken into consideration;

[0062] The number of iterations T is determined by the logarithm of the number of ants and the number of priority regions.

[0063] Optionally, S4 includes the following steps:

[0064] S41. Comprehensive optimization objective function F(x) is established based on the wear performance, production cost, weight distribution and comfort of wear-resistant materials of labor protection shoes:

[0065] ;

[0066] in, , , , is the weight coefficient, P(x) is the wear performance objective function, C(x) is the production cost objective function, W(x) is the weight distribution objective function, and S(x) is the comfort objective function;

[0067] Define the wear performance objective function P(x):

[0068] ;

[0069] Where N is the total number of measurement areas, is the path coverage indicator function. When the path covers the measurement area hour, ,otherwise , is the wear performance adjustment factor;

[0070] Define the production cost objective function C(x):

[0071] ;

[0072] Where M is the total number of material types, is the unit area cost of the kth material, is the total area used by the kth material in the layout plan, is the material utilization coefficient, is the cost adjustment factor;

[0073] Define the weight distribution objective function W(x):

[0074] ;

[0075] in, For measurement area weight; is the average weight of all measurement areas, is the weight distribution adjustment factor;

[0076] Define the comfort objective function S(x):

[0077] ;

[0078] Among them, L is the total number of comfort indicators, is the score of the jth comfort index, is the weight coefficient of the jth comfort index, is the comfort enhancement factor, is the comfort adjustment factor;

[0079] S42, the priority area generated by the fractal algorithm is used as the constraint condition of the ant path search, and the high priority area needs to be covered first in the path search, and the path search constraint condition is defined as follows:

[0080] .

[0081] Optionally, S5 includes the following steps:

[0082] S51, initialize the ant colony according to the initial concentration of pheromone and the constraints of the priority region to generate an initial solution set;

[0083] S52, in each iteration, the ants follow the transition probability Select the measurement area to move next, building the complete material layout path:

[0084] ;

[0085] in, is the number of ants from the measurement area in the tth iteration Transfer to The probability of is the pheromone concentration on path (i, j), is the heuristic factor, For measurement area and The distance between and is the importance factor, For measurement area The set of accessible neighbors of ;

[0086] S53, after the path construction is completed, the comprehensive optimization target value F(x) of each ant is calculated and the solution set is evaluated according to the optimization target function;

[0087] S54, update pheromone concentration and path search constraints to enhance coverage of high priority areas:

[0088] ;

[0089] in, is the pheromone volatility coefficient, is the pheromone increment left by the k-th ant on the path (i, j) in the t-th iteration, defined as:

[0090] ;

[0091] Among them, Q is a constant, which represents the total amount of ant pheromone released. is the path length of the k-th ant in the t-th iteration;

[0092] The amount of pheromone reinforcement for high priority areas is defined as:

[0093] ;

[0094] in, is the pheromone reinforcement coefficient, which is used to strengthen the pheromone concentration of the paths between high priority areas.

[0095] Optionally, the S7 includes the following steps:

[0096] S71, based on path transfer probability In each iteration, the paths of all ants are constructed and the fitness value of each ant path is calculated. :

[0097] ;

[0098] in, , , , are the objective function values ​​corresponding to path k, , , , is the weight coefficient;

[0099] Select the optimal material layout path for the current iteration :

[0100] ;

[0101] S72, Optimal material layout path Each path (i, j) in the high-priority area is strengthened according to the pheromone reinforcement and the current fitness value. Update and strengthen pheromone increment:

[0102] ;

[0103] in, is the reinforcement coefficient, For the reinforcement index, is the current path length of the k-th ant in the t-th iteration;

[0104] and superimpose the updated reinforcement pheromone increment to the path pheromone concentration;

[0105] S73. Define a global convergence criterion, and terminate the iteration when one of the following conditions is met:

[0106] Reach the maximum number of iterations T;

[0107] continuous In the iterations, the change range of the optimal path target value satisfies:

[0108] ;

[0109] in, is the convergence threshold, Indicates that in the tth iteration, the optimal path The corresponding comprehensive optimization target value;

[0110] S74. Output the global optimal path solution :

[0111] .

[0112] Optionally, the S8 includes the following steps:

[0113] S81. According to the global optimal path plan, determine the measurement area and material layout requirements corresponding to each section of the path, and combine the fractal eigenvalues ​​of the path coverage area to clarify the wear complexity and performance requirements of each area.

[0114] S82. Based on the wear characteristics, fractal eigenvalues ​​and cost factors of the path coverage area, select the material type that best matches the regional needs, give priority to materials with both high wear resistance and economy, and comprehensively consider their ease of processing in actual production.

[0115] S83. Set the material thickness configuration according to the wear intensity, frequency distribution and stress characteristics of the area. Use thicker materials in high wear areas, while appropriately thinning the low wear areas to reduce material usage while maintaining a balance in overall performance.

[0116] S84. Combining the stress distribution characteristics of the region and the meshing results of the fractal model, the arrangement of materials is designed. Regular arrangement is used in high stress areas to enhance mechanical properties, while flexible distribution is used in low stress areas to improve comfort and adaptability.

[0117] S85. Comprehensively consider the material type, thickness configuration and arrangement of each area to form the optimal layout plan for wear-resistant materials of labor protection shoes. At the same time, evaluate the plan to ensure that it meets the requirements of wear resistance, production process feasibility and cost control.

[0118] The beneficial effects of the present invention are:

[0119] (1) The present invention constructs a fractal feature model of the surface of labor protection shoes by introducing a spatial fractal algorithm, accurately quantifies the complex wear characteristics into fractal feature values, and uses them to guide the initial pheromone allocation and path search strategy of the ant colony optimization algorithm. Compared with the traditional single optimization algorithm, the present invention allocates more initial pheromone concentrations in high wear complexity areas and significantly improves the coverage rate of high priority areas by strengthening the pheromone update mechanism, effectively avoiding the limitation of the ant colony algorithm falling into the local optimum due to the randomness of path search.

[0120] (2) Aiming at the actual application needs of labor protection shoes, the present invention establishes a multi-objective optimization model by comprehensively considering wear performance, material cost, weight distribution and comfort. By adjusting the weight of the path objective function and setting dynamic constraints, an effective balance between wear resistance and cost control is achieved. In the high wear area, the fractal eigenvalue and the fitness value are jointly optimized to ensure the reasonable distribution of high-quality materials, while reducing the material waste in the low wear area.

[0121] (3) The present invention introduces a dynamic reinforcement mechanism during the path pheromone update process to adaptively enhance pheromones in high-priority areas with higher fractal eigenvalues, thereby ensuring the global adaptability and flexibility of path search. By dynamically adjusting the comprehensive optimization target value and applying the global convergence criterion, it is possible to adapt to changes in wear characteristics in real time in different usage scenarios, and always maintain the global optimal performance of the optimized layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0123] Figure 1 This is a flow chart of a method for layout of wear-resistant materials for labor protection shoes based on ant colony optimization algorithm proposed by the present invention;

[0124] Figure 2 This is a convergence trend diagram of path fitness values ​​during multiple rounds of iterations in a labor protection shoe wear-resistant material layout method based on an ant colony optimization algorithm proposed by the present invention. DETAILED DESCRIPTION

[0125] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0126] refer to Figure 1-Figure 2 , a wear-resistant material layout method for labor protection shoes based on ant colony optimization algorithm, comprising the following steps:

[0127] S1. Conduct experimental tests on wear characteristics of different parts of safety shoes and collect feedback from users, and construct a wear data set of safety shoes including wear intensity, frequency distribution and stress distribution;

[0128] S2. Based on the wear data set of labor protection shoes, a fractal feature model of the labor protection shoe surface is constructed using a spatial fractal algorithm. The fractal feature values ​​of each part of the labor protection shoe surface are calculated. The fractal feature values ​​characterize the complexity and intensity distribution of regional wear. A fractal feature value distribution map is generated according to the size of the fractal feature value. The labor protection shoe surface is divided into multiple priority areas, where the higher the fractal feature value, the higher the priority of the area;

[0129] S3. Initialize the parameters of the ant colony optimization algorithm based on the fractal eigenvalue distribution map, including the number of ants, pheromone volatility coefficient, heuristic factor and number of iterations. Allocate the initial pheromone concentration according to the priority regional distribution of the fractal eigenvalue. The higher the fractal eigenvalue, the greater the pheromone concentration in the area, so as to guide the ant search path to preferentially cover the high wear area;

[0130] S4. Establish an optimization objective function. The optimization objective function comprehensively considers the wear performance, production cost, weight distribution and comfort of the wear-resistant materials of the labor protection shoes, and uses the priority area generated by the fractal algorithm as a constraint condition for the ant path search, that is, the high priority area needs to be covered first in the path search;

[0131] S5. The wear-resistant material layout problem of different parts of the safety shoes is transformed into an ant path search problem. The nodes in the ant path represent the type, thickness and arrangement of different materials. Each ant follows the principle of the joint action of pheromone concentration and heuristic factor in the path search, and gradually selects the optimal material layout path in the high-priority area.

[0132] S6. Calculate the fitness value of each path according to the path search results and the optimization objective function, and use the fitness value to update the path pheromone concentration. The higher the fitness, the greater the pheromone concentration of the path. When the fractal eigenvalue distribution changes during the optimization process, dynamically update the fractal priority area and its corresponding pheromone concentration distribution;

[0133] S7, through multiple rounds of iterations of S1-S6, gradually converge to the global optimal path, record the optimal material layout plan of the current path in each round of iteration, and obtain the final global optimal path;

[0134] S8. Based on the final global optimal path, output the optimal layout plan of the wear-resistant materials of the labor protection shoes, including the material type, thickness configuration and arrangement method of different areas, so that the wear-resistant materials of the labor protection shoes meet the wear-resistant performance requirements while taking into account the feasibility of the production process and cost control;

[0135] S9. Input the optimal material layout plan into the material performance test platform to verify the wear resistance, comfort and production feasibility. According to the verification results, adjust the material allocation of the area that does not meet the requirements in the plan, and re-iterate the optimization through fractal eigenvalue update.

[0136] In this implementation, S1 includes the following steps:

[0137] S11. Conduct experimental tests on the wear characteristics of different parts of the safety shoes, select test samples and divide the surface of the safety shoes into measurement areas according to actual usage scenarios. ;

[0138] S12. Use standard wear test equipment to measure each measurement area The wear depth and wear area are measured and the measurement area is calculated. Wear strength ;

[0139] in, For measurement area The wear depth, For measurement area Wear area;

[0140] S13. Count the wear frequency of each measurement area through long-term use feedback and record the wear frequency of each measurement area ;

[0141] S14. Measure the stress distribution of each measurement area based on wear experiments and material stress analysis, and collect the corresponding stress values ;

[0142] S15, the collected wear strength , Wear frequency and stress distribution The data were normalized respectively;

[0143] S16. Construct a labor protection shoe wear data set based on the normalized data:

[0144] ;

[0145] Where N is the total number of measurement areas divided, For measurement area The normalized wear intensity, For measurement area The normalized wear frequency, For measurement area The normalized stress value.

[0146] In this implementation, S2 includes the following steps:

[0147] S21. Calculate each measurement area based on the labor protection shoe wear data set Comprehensive wear index :

[0148] ;

[0149] in, , , are weight coefficients, which are used to balance the effects of wear intensity, wear frequency and stress on the comprehensive wear index. The impact of

[0150] S22. Calculate each measurement area using multi-scale fractal analysis method The fractal eigenvalue of , the comprehensive wear index Considered as measurement area The spatial distribution function M(x, y) within the The position of the safety shoe surface in the coordinate system, set a series of scales s, and measure the area at each scale s. Divide into sub-areas of equal area, and calculate the cumulative value of the comprehensive wear index in each sub-area :

[0151] ;

[0152] in, is the area of ​​sub-region k;

[0153] Calculate the total cumulative comprehensive wear index S(s) at each scale s:

[0154] ;

[0155] Among them, q is the multifractal order parameter, which is used to emphasize the wear characteristics of different intensities;

[0156] Calculate the measurement area according to the total cumulative comprehensive wear index S(s) at different scales s The generalized fractal dimension :

[0157] ;

[0158] Through and The relationship is linearly fitted, and the slope equal , fractal eigenvalue It is expressed as:

[0159] ;

[0160] in, Characterizing the measurement area Wear complexity and intensity distribution;

[0161] S23, fractal eigenvalue Perform normalization to obtain the normalized fractal eigenvalue ;

[0162] S24, according to the normalized fractal eigenvalue The size of the shoe surface is divided into several priority areas:

[0163] ;

[0164] in, and is the priority threshold, satisfying , high-priority areas correspond to areas with high wear complexity and need to focus on strengthening the configuration of wear-resistant materials;

[0165] S25, generate a fractal eigenvalue distribution map, and measure the area The priority areas are marked on the surface map of the safety shoes, with different colors or symbols indicating different priority areas.

[0166] This implementation method utilizes a spatial fractal algorithm to construct a fractal feature model of the surface of labor protection shoes and generates a priority area distribution map. The fractal algorithm is used to model the wear characteristics of labor protection shoes, and the complex wear characteristics are quantified into fractal feature values ​​and used as a basis for dividing regional priorities to guide subsequent optimization. This breaks through the limitations of traditional empirical regional division, making material allocation more scientific and accurate, and at the same time provides a reasonable basis for the pheromone initialization of the ant colony optimization algorithm.

[0167] In this implementation, S3 includes the following steps:

[0168] S31. Initialize the number of ants m and pheromone volatility coefficient of the ant colony optimization algorithm based on the fractal eigenvalue distribution map , Heuristic Factor And the number of iterations T:

[0169] S32. Allocate initial pheromone concentration according to priority area distribution of fractal eigenvalues , initial pheromone concentration It increases with the wear complexity of the region, so that ants prefer regions with high wear complexity for path search;

[0170] S33. For high priority areas, the initial pheromone concentration is further enhanced according to the power relationship based on the difference between its fractal characteristic value and the threshold value:

[0171] ;

[0172] in, It is a pheromone reinforcing factor. is the reinforcement index;

[0173] S34, generate the initial pheromone distribution map, and measure the area Initial pheromone concentration Mark on the surface of the safety shoes, using different color depths or symbol sizes to indicate the level of pheromone concentration.

[0174] In this embodiment, the number of ants m, the pheromone volatility coefficient , Heuristic Factor The settings of the number of iterations T specifically include:

[0175] Set the number of ants m, which is determined based on the number of priority areas and the overall wear complexity, so that the number of ants covers areas with high wear complexity:

[0176] ;

[0177] Where N is the total number of priority areas divided, is the ant population adjustment coefficient, For all normalized fractal eigenvalues The average value of Indicates rounding x upwards;

[0178] Calculation of pheromone volatility coefficient , pheromone volatility coefficient Reflects the decay rate of pheromones, adjusted based on the discrete degree of wear complexity, The larger the value, the more uneven the wear complexity distribution:

[0179] ;

[0180] in, is the pheromone volatilization adjustment coefficient, is the normalized fractal feature The standard deviation of

[0181] Heuristic Factor Measure the area from which ants Transfer to area The expected degree of wear of the target area and the distance between areas are taken into consideration;

[0182] The number of iterations T is determined by the logarithm of the number of ants and the number of priority regions.

[0183] This implementation method initializes the parameters of the ant colony optimization algorithm based on the fractal eigenvalue distribution map. The creativity is reflected in dynamically adjusting the initial pheromone concentration according to the fractal eigenvalue priority distribution, so that high-priority areas have higher pheromone attraction and guide ants to preferentially cover key areas.

[0184] In this implementation, S4 includes the following steps:

[0185] S41. Comprehensive optimization objective function F(x) is established based on the wear performance, production cost, weight distribution and comfort of wear-resistant materials of labor protection shoes:

[0186] ;

[0187] in, , , , is the weight coefficient, P(x) is the wear performance objective function, C(x) is the production cost objective function, W(x) is the weight distribution objective function, and S(x) is the comfort objective function;

[0188] Define the wear performance objective function P(x):

[0189] ;

[0190] Where N is the total number of measurement areas, is the path coverage indicator function. When the path covers the measurement area hour, ,otherwise , is the wear performance adjustment factor;

[0191] Define the production cost objective function C(x):

[0192] ;

[0193] Where M is the total number of material types, is the unit area cost of the kth material, is the total area used by the kth material in the layout plan, is the material utilization coefficient, is the cost adjustment factor;

[0194] Define the weight distribution objective function W(x):

[0195] ;

[0196] in, For measurement area weight; is the average weight of all measurement areas, is the weight distribution adjustment factor;

[0197] Define the comfort objective function S(x):

[0198] ;

[0199] Among them, L is the total number of comfort indicators, is the score of the jth comfort index, is the weight coefficient of the jth comfort index, is the comfort enhancement factor, is the comfort adjustment factor;

[0200] S42, the priority area generated by the fractal algorithm is used as the constraint condition of the ant path search, and the high priority area needs to be covered first in the path search, and the path search constraint condition is defined as follows:

[0201] .

[0202] In this implementation, S5 includes the following steps:

[0203] S51, initialize the ant colony according to the initial concentration of pheromone and the constraints of the priority region to generate an initial solution set;

[0204] S52, in each iteration, the ants follow the transition probability Select the measurement area to move next, building the complete material layout path:

[0205] ;

[0206] in, is the number of ants from the measurement area in the tth iteration Transfer to The probability of is the pheromone concentration on path (i, j), is the heuristic factor, For measurement area and The distance between and is the importance factor, For measurement area The set of accessible neighbors of ;

[0207] S53, after the path construction is completed, the comprehensive optimization target value F(x) of each ant is calculated and the solution set is evaluated according to the optimization target function;

[0208] S54, update pheromone concentration and path search constraints to enhance coverage of high priority areas:

[0209] ;

[0210] in, is the pheromone volatility coefficient, is the pheromone increment left by the k-th ant on the path (i, j) in the t-th iteration, defined as:

[0211] ;

[0212] Among them, Q is a constant, which represents the total amount of ant pheromone released. is the path length of the k-th ant in the t-th iteration;

[0213] The amount of pheromone reinforcement for high priority areas is defined as:

[0214] ;

[0215] in, is the pheromone reinforcement coefficient, which is used to strengthen the pheromone concentration of the paths between high priority areas.

[0216] In this implementation, S7 includes the following steps:

[0217] S71, based on path transfer probability In each iteration, the paths of all ants are constructed and the fitness value of each ant path is calculated. :

[0218] ;

[0219] in, , , , are the objective function values ​​corresponding to path k, , , , is the weight coefficient;

[0220] Select the optimal material layout path for the current iteration :

[0221] ;

[0222] S72, Optimal material layout path Each path (i, j) in the high-priority area is strengthened according to the pheromone reinforcement and the current fitness value. Update and strengthen pheromone increment:

[0223] ;

[0224] in, is the reinforcement coefficient, For the reinforcement index, is the current path length of the k-th ant in the t-th iteration;

[0225] and superimpose the updated reinforcement pheromone increment to the path pheromone concentration;

[0226] S73. Define a global convergence criterion, and terminate the iteration when one of the following conditions is met:

[0227] Reach the maximum number of iterations T;

[0228] continuous In the iterations, the change range of the optimal path target value satisfies:

[0229] ;

[0230] in, is the convergence threshold, Indicates that in the tth iteration, the optimal path The corresponding comprehensive optimization target value is calculated by the comprehensive optimization objective function, which is the result of multi-objective weights such as wear resistance, material cost, weight distribution and comfort;

[0231] The judgment formula is used to determine whether the ant colony optimization algorithm has reached a convergence state. When the relative change rate of the optimal path target value is less than the preset threshold When , it means that the optimization effect of multiple rounds of iterations tends to be stable, and the improvement brought by subsequent iterations is no longer significant. The optimization process can be terminated in advance, thereby improving the calculation efficiency. In the optimization of the layout of wear-resistant materials of labor protection shoes, it can avoid the algorithm from wasting computing resources in unnecessary iterations, and at the same time ensure that the final output optimization solution has sufficient stability and reliability.

[0232] S74. Output the global optimal path solution :

[0233] .

[0234] This implementation method improves the global optimization capability of the algorithm by recording the optimal path and strengthening the pheromone concentration strategy in multiple rounds of iterations, dynamically adjusting the path search results and strengthening the pheromone distribution of the optimal path, and sets multi-objective fitness convergence criteria to ensure the stability and efficiency of the algorithm under complex objective constraints, significantly improving the optimization efficiency and scientificity of the wear-resistant material layout, and reflecting the practical applicability and intelligent characteristics of the algorithm.

[0235] In this implementation, S8 includes the following steps:

[0236] S81. According to the global optimal path plan, determine the measurement area and material layout requirements corresponding to each section of the path, and combine the fractal eigenvalues ​​of the path coverage area to clarify the wear complexity and performance requirements of each area.

[0237] S82. Based on the wear characteristics, fractal eigenvalues ​​and cost factors of the path coverage area, select the material type that best matches the regional needs, give priority to materials with both high wear resistance and economy, and comprehensively consider their ease of processing in actual production.

[0238] S83. Set the material thickness configuration according to the wear intensity, frequency distribution and stress characteristics of the area. Use thicker materials in high wear areas, while appropriately thinning the low wear areas to reduce material usage while maintaining a balance in overall performance.

[0239] S84. Combining the stress distribution characteristics of the region and the meshing results of the fractal model, the arrangement of materials is designed. Regular arrangement is used in high stress areas to enhance mechanical properties, while flexible distribution is used in low stress areas to improve comfort and adaptability.

[0240] S85. Comprehensively consider the material type, thickness configuration and arrangement of each area to form the optimal layout plan for wear-resistant materials of labor protection shoes. At the same time, evaluate the plan to ensure that it meets the requirements of wear resistance, production process feasibility and cost control.

[0241] Example:

[0242] Embodiment In a mining safety shoe manufacturing project, the implementer decided to use the method of the present invention to optimize the layout of wear-resistant materials of the safety shoes in order to solve the problem of severe sole wear of workers in complex scenes such as gravel roads, high humidity environments, and gravel slopes for a long time.

[0243] In June 2024, a labor protection shoe manufacturing project was launched in a mine quarry. In the first phase of the project, the labor protection shoes currently used by workers were tested. It was found that the wear of different parts of the soles varied significantly. Taking the shoes of worker number A101 as an example, after 45 days of continuous use, the wear depth of the outer edge of the forefoot of the sole reached 5.6 mm, while that of the heel area was only 1.2 mm. This wear difference led to early failure of the soles, with an average service life of only 90 days and serious waste of materials.

[0244] To solve this problem, the implementers collected the wear data of labor protection shoes of 20 consecutive workers. The test method includes installing smart sensors on the soles to record the force and wear of each step, and finally generating a detailed data set including wear depth, wear area and stress value. In the embodiment, the average wear depth of the forefoot of the sole numbered A101 is 5.4 mm, the heel is 1.0 mm, and the wear in the middle part is relatively uniform, with an average of 3.2 mm. Combined with the fractal algorithm, a distribution diagram of fractal eigenvalues ​​of different parts of the sole is generated. The figure shows that the fractal eigenvalues ​​of the outer edge (numbered R4) and the inner edge (numbered R3) of the forefoot of the sole are 1.52 and 1.48, which are high priority areas, while the fractal eigenvalues ​​of the middle and heel areas of the sole are lower, with an average of 1.20.

[0245] Entering the optimization stage, the implementer inputs the data into the ant colony optimization algorithm model, initializes the ant colony parameters (the number of ants is 100, the maximum number of iterations is 50) and allocates the initial concentration of pheromones according to the priority of the fractal eigenvalues. Each ant generates a material layout path in the simulation environment according to the path transfer probability. In the embodiment, the path selected by the ant numbered Ant-07 in the third round of iteration covers the key areas of the forefoot and heel of the sole, but fails to take into account the middle area. Its fitness value is 0.65, while the ant numbered Ant-18 covers all high-priority areas in the path, and its fitness value reaches 0.88.

[0246] By the 28th iteration, the model converged and generated the optimal path, outputting the distribution plan of wear-resistant materials. Specifically, the outer edge (R4) and inner edge (R3) of the forefoot of the sole use high-wear-resistant materials with a thickness of 5 mm; the middle area (R2, R5) uses medium-wear-resistant materials with a thickness of 3 mm; the heel area (R6) uses ordinary materials with a thickness of 2 mm. The entire optimization process took 3 hours, which is about 60% shorter than the traditional empirical design method.

[0247] The implementers then manufactured 10 pairs of safety shoes based on the optimization plan and conducted a 30-day field test in a mine. After the test, the average wear depth of the forefoot of the shoe No. A102 was reduced to 1.8 mm, while the wear depths of the heel and middle were 0.5 mm and 1.2 mm respectively. The sole maintained good functionality. Compared with the safety shoes designed by traditional methods, the method of the present invention significantly improves the service life of the sole and reduces the material cost.

[0248] By comparing with the traditional method, the specific data are as follows Table 1:

[0249] Table 1 Comparison of key performances between the present invention and the traditional method in the optimization process of wear-resistant material layout of labor protection shoes

[0250] index Method of the present invention Traditional methods Average service life (days) 150 90 Material cost saving rate (%) 20 0 High priority area coverage 98% 70% Optimization time (hours) 3 7

[0251] In the entire embodiment, the implementer not only solves the problems of short life and high cost caused by severe sole wear through the method of the present invention, but also improves the optimization efficiency and realizes the scientific design and efficient production of the wear-resistant material layout of labor protection shoes.

[0252] The present invention constructs a fractal feature model of the surface of labor protection shoes by introducing a spatial fractal algorithm, and accurately quantifies the complex wear characteristics into fractal feature values, which are used to guide the initial pheromone allocation and path search strategy of the ant colony optimization algorithm. Compared with the traditional single optimization algorithm, the present invention allocates more initial pheromone concentrations in high wear complexity areas and significantly improves the coverage of high priority areas by strengthening the pheromone update mechanism, effectively avoiding the limitation of the ant colony algorithm falling into local optimality due to the randomness of path search.

[0253] Aiming at the actual application needs of labor protection shoes, the present invention establishes a multi-objective optimization model by comprehensively considering wear performance, material cost, weight distribution and comfort. By adjusting the weight of the path objective function and setting dynamic constraint conditions, an effective balance between wear resistance and cost control is achieved. In the high wear area, the joint optimization of fractal eigenvalues ​​and fitness values ​​is used to ensure the reasonable distribution of high-quality materials, while reducing material waste in the low wear area.

[0254] The present invention introduces a dynamic reinforcement mechanism in the process of updating path pheromones to adaptively enhance pheromones in high-priority areas with higher fractal eigenvalues, thereby ensuring the global adaptability and flexibility of path search. Through the dynamic adjustment of comprehensive optimization target values ​​and the application of global convergence criteria, it can adapt to changes in wear characteristics in real time in different usage scenarios and always maintain the global optimal performance of the optimized layout.

[0255] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A wear-resistant material layout method for labor protection shoes based on ant colony optimization algorithm, characterized in that: The steps include: S1. Conduct experimental tests on wear characteristics of different parts of safety shoes and collect usage feedback to build a wear data set for safety shoes. S2. Based on the wear data set of labor protection shoes, a fractal feature model of the labor protection shoe surface is constructed using a spatial fractal algorithm, the fractal feature values ​​of each part of the labor protection shoe surface are calculated, and a fractal feature value distribution map is generated according to the size of the fractal feature value, and the labor protection shoe surface is divided into multiple priority areas; S3, initializing the parameters of the ant colony optimization algorithm based on the fractal eigenvalue distribution map, and allocating the initial concentration of pheromone according to the priority area distribution of the fractal eigenvalue; S4, establishing an optimization objective function, and using the priority area generated by the fractal algorithm as a constraint condition for ant path search; S5. Convert the wear-resistant material layout problem of different parts of the safety shoes into an ant path search problem, and gradually select the optimal material layout path for high-priority areas; S6. Calculate the fitness value of each path according to the path search results and the optimization objective function, and use the fitness value to update the path pheromone concentration. The higher the fitness, the greater the pheromone concentration of the path. When the fractal eigenvalue distribution changes during the optimization process, dynamically update the fractal priority area and its corresponding pheromone concentration distribution; S7, through multiple rounds of iterations of S1-S6, gradually converge to the global optimal path, record the optimal material layout plan of the current path in each round of iteration, and obtain the final global optimal path; S8. Output the final optimal layout plan of the wear-resistant materials of the labor protection shoes according to the final global optimal path; S9. Input the final optimal layout plan into the material performance test platform to verify the wear resistance, comfort and production feasibility.

2. The method for layout of wear-resistant materials for labor protection shoes based on ant colony optimization algorithm according to claim 1, characterized in that: The S1 comprises the following steps: S11. Conduct experimental tests on the wear characteristics of different parts of the safety shoes, select test samples and divide the surface of the safety shoes into measurement areas according to actual usage scenarios. ; S12. Use standard wear test equipment to measure each measurement area. The wear depth and wear area are measured and the measurement area is calculated. Wear strength ; in, For measurement area The wear depth, For measurement area Wear area; S13. Count the wear frequency of each measurement area through long-term use feedback and record the wear frequency of each measurement area ; S14. Measure the stress distribution of each measurement area based on wear experiments and material stress analysis, and collect the corresponding stress values ; S15, the collected wear strength , Wear frequency and stress distribution The data were normalized respectively; S16. Construct a labor protection shoe wear data set based on the normalized data: ； Where N is the total number of measurement areas divided, For measurement area The normalized wear intensity, For measurement area The normalized wear frequency, For measurement area The normalized stress value.

3. The method for layout of wear-resistant materials for labor protection shoes based on ant colony optimization algorithm according to claim 1, characterized in that: The S2 comprises the following steps: S21. Calculate each measurement area based on the labor protection shoe wear data set Comprehensive wear index : ； in, , , are weight coefficients, which are used to balance the effects of wear intensity, wear frequency and stress on the comprehensive wear index. The impact of S22. Calculate each measurement area using multi-scale fractal analysis method The fractal eigenvalue of , the comprehensive wear index Considered as measurement area The spatial distribution function M(x, y) within the The position of the safety shoe surface in the coordinate system, set a series of scales s, and measure the area at each scale s. Divide into sub-areas of equal area, and calculate the cumulative value of the comprehensive wear index in each sub-area : ； in, is the area of ​​sub-region k; Calculate the total cumulative comprehensive wear index S(s) at each scale s: ； Among them, q is the multifractal order parameter, which is used to emphasize the wear characteristics of different intensities; Calculate the measurement area according to the total cumulative comprehensive wear index S(s) at different scales s The generalized fractal dimension : ； Through and The relationship is linearly fitted, and the slope equal , fractal eigenvalue It is expressed as: ； in, Characterizing the measurement area Wear complexity and intensity distribution; S23, fractal eigenvalue Perform normalization to obtain the normalized fractal eigenvalue ; S24, according to the normalized fractal eigenvalue The size of the shoe surface is divided into several priority areas: ； in, and is the priority threshold, satisfying , high-priority areas correspond to areas with high wear complexity and need to focus on strengthening the configuration of wear-resistant materials; S25, generate a fractal eigenvalue distribution map, and measure the area The priority areas are marked on the surface map of the safety shoes, with different colors or symbols indicating different priority areas.

4. The method for layout of wear-resistant materials for labor protection shoes based on ant colony optimization algorithm according to claim 1, characterized in that: The S3 comprises the following steps: S31. Initialize the number of ants m and pheromone volatility coefficient of the ant colony optimization algorithm based on the fractal eigenvalue distribution map , Heuristic Factor And the number of iterations T: S32. Allocate initial pheromone concentration according to priority area distribution of fractal eigenvalues , initial pheromone concentration It increases with the wear complexity of the region, so that ants prefer regions with high wear complexity for path search; S33. For high priority areas, the initial pheromone concentration is further enhanced according to the power relationship based on the difference between its fractal characteristic value and the threshold value: ； in, It is a pheromone reinforcing factor. is the reinforcement index; S34, generate the initial pheromone distribution map, and measure the area Initial pheromone concentration Mark on the surface of the safety shoes, using different color depths or symbol sizes to indicate the level of pheromone concentration.

5. The method for layout of wear-resistant materials for labor protection shoes based on ant colony optimization algorithm according to claim 4 is characterized in that: The number of ants m, the pheromone volatility coefficient , Heuristic Factor The settings of the number of iterations T specifically include: Set the number of ants m, which is determined based on the number of priority areas and the overall wear complexity, so that the number of ants covers areas with high wear complexity: ； Where N is the total number of priority areas divided, is the ant population adjustment coefficient, For all normalized fractal eigenvalues The average value of Indicates rounding x upwards; Calculation of pheromone volatility coefficient , pheromone volatility coefficient Reflects the decay rate of pheromones, adjusted based on the discrete degree of wear complexity, The larger the value, the more uneven the wear complexity distribution: ； in, is the pheromone volatilization adjustment coefficient, is the normalized fractal feature The standard deviation of Heuristic Factor Measure ants from area Transfer to area The expected degree of wear of the target area and the distance between areas are taken into consideration; The number of iterations T is determined by the logarithm of the number of ants and the number of priority regions.

6. The method for layout of wear-resistant materials for labor protection shoes based on ant colony optimization algorithm according to claim 1, characterized in that: The S4 comprises the following steps: S41. Comprehensive optimization objective function F(x) is established based on the wear performance, production cost, weight distribution and comfort of wear-resistant materials of labor protection shoes: ； in, , , , is the weight coefficient, P(x) is the wear performance objective function, C(x) is the production cost objective function, W(x) is the weight distribution objective function, and S(x) is the comfort objective function; Define the wear performance objective function P(x): ； Where N is the total number of measurement areas, is the path coverage indicator function. When the path covers the measurement area hour, ,otherwise , is the wear performance adjustment factor; Define the production cost objective function C(x): ； Where M is the total number of material types, is the unit area cost of the kth material, is the total area used by the kth material in the layout plan, is the material utilization coefficient, is the cost adjustment factor; Define the weight distribution objective function W(x): ； in, For measurement area weight; is the average weight of all measurement areas, is the weight distribution adjustment factor; Define the comfort objective function S(x): ； Among them, L is the total number of comfort indicators, is the score of the jth comfort index, is the weight coefficient of the jth comfort index, is the comfort enhancement factor, is the comfort adjustment factor; S42, the priority area generated by the fractal algorithm is used as the constraint condition of the ant path search, and the high priority area needs to be covered first in the path search, and the path search constraint condition is defined as follows: 。 7. The method for layout of wear-resistant materials for labor protection shoes based on ant colony optimization algorithm according to claim 1, characterized in that: The S5 comprises the following steps: S51, initialize the ant colony according to the initial concentration of pheromone and the constraints of the priority region to generate an initial solution set; S52, in each iteration, the ants follow the transition probability Select the measurement area to move next, building the complete material layout path: ； in, is the number of ants from the measurement area in the tth iteration Transfer to The probability of is the pheromone concentration on path (i, j), is the heuristic factor, For measurement area and The distance between and is the importance factor, For measurement area The set of accessible neighbors of ; S53, after the path construction is completed, the comprehensive optimization target value F(x) of each ant is calculated and the solution set is evaluated according to the optimization target function; S54, update pheromone concentration and path search constraints to enhance coverage of high priority areas: ； in, is the pheromone volatility coefficient, is the pheromone increment left by the k-th ant on the path (i, j) in the t-th iteration, defined as: ； Among them, Q is a constant, which represents the total amount of ant pheromone released. is the path length of the k-th ant in the t-th iteration; The amount of pheromone reinforcement for high priority areas is defined as: ； in, is the pheromone reinforcement coefficient, which is used to strengthen the pheromone concentration of the paths between high priority areas.

8. The method for layout of wear-resistant materials for labor protection shoes based on ant colony optimization algorithm according to claim 1, characterized in that: The S7 comprises the following steps: S71, based on path transfer probability In each iteration, the paths of all ants are constructed and the fitness value of each ant path is calculated. : ； in, , , , are the objective function values ​​corresponding to path k, , , , is the weight coefficient; Select the optimal material layout path for the current iteration : ； S72, Optimal material layout path Each path (i, j) in the high-priority area is strengthened according to the pheromone reinforcement and the current fitness value. Update and strengthen pheromone increment: ； in, is the reinforcement coefficient, For the reinforcement index, is the current path length of the k-th ant in the t-th iteration; and superimpose the updated reinforcement pheromone increment to the path pheromone concentration; S73. Define a global convergence criterion, and terminate the iteration when one of the following conditions is met: Reach the maximum number of iterations T; continuous In the iterations, the change range of the optimal path target value satisfies: ； in, is the convergence threshold, Indicates that in the tth iteration, the optimal path The corresponding comprehensive optimization target value; S74. Output the global optimal path solution : 。

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