Intelligent Design Method and System for the Outer Sole of a Fishing Shoe

Abstract

The present invention discloses an intelligent design method and system for the outer sole of a fishing shoe, which relates to the technical field of fishing shoe design. The method includes: determining the first design parameters of the sole body of the outer sole of the fishing shoe according to the three-dimensional shape and health parameters of the foot of the fishing shoe user; determining the second design parameters of the soft pins and the third design parameters of the hard shoe nails according to the gait characteristics, rhythm characteristics and pressure distribution data when the fishing shoe user moves; determining the fourth design parameters of the fine grooves on the surface of the soft pins according to the second design parameters of the soft pins, the usage frequency and wear mode of the reference fishing shoe of the fishing shoe user; generating the design drawings of the outer sole of the fishing shoe adapted to different fishing environments and user characteristics according to the optimized first design parameters, second design parameters, third design parameters and fourth design parameters.

Description

Technical Field

[0001] The present application relates to the technical field of fishing shoe design, and more particularly, to an intelligent design method and system for the outsole of a fishing shoe. Background Art

[0002] In recent years, the fishing sport has attracted more and more people to participate. Fishing is not only a leisure activity but also gradually becomes a competitive sport. With the increase in the number of participants, the demand for lower technical thresholds and enhanced fun in fishing techniques has also grown.

[0003] A fishing shoe is a type of footwear specifically designed for fishing activities and plays an important role among fishing enthusiasts. The main function of a fishing shoe is to provide good anti-slip effect to ensure that the fisherman remains stable and safe in various slippery water environments. Fishing shoes usually need to adapt to a variety of different environments, including muddy riverbanks, slippery rocks, and coasts that may be covered with seaweed, etc.

[0004] The existing design of the outsole of fishing shoes largely depends on manual experience and the intuition of designers. Manual experience and intuition may not be able to comprehensively consider all possible usage environments and conditions, resulting in limited adaptability of the design.

[0005] For the above problems, no effective solution has been proposed yet. Summary of the Invention

[0006] Embodiments of the present application provide an intelligent design method and system for the outsole of a fishing shoe to solve the above technical problems.

[0007] The present application provides an intelligent design method for the outsole of a fishing shoe, including:

[0008] Determining first design parameters of the sole body of the outsole of the fishing shoe according to the three-dimensional shape and health parameters of the foot of the fishing shoe user;

[0009] Determining second design parameters of the soft pins and third design parameters of the hard shoe nails according to the gait characteristics, rhythm characteristics, and pressure distribution data when the fishing shoe user moves; wherein, the soft pins and the hard shoe nails are both arranged on the sole body;

[0010] Determining fourth design parameters of the fine grooves on the surface of the soft pins according to the second design parameters of the soft pins, the usage frequency and wear pattern of the reference fishing shoe of the fishing shoe user;

[0011] Optimize the first design parameter, the second design parameter, the third design parameter, and the fourth design parameter according to the environmental characteristic data of the fishing ground where the fishing shoe user is located; wherein, the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature, and ground biological coverage.

[0012] Generate the design drawing of the outsole of the fishing shoe that adapts to different fishing environments and user characteristics according to the optimized first design parameter, second design parameter, third design parameter, and fourth design parameter.

[0013] This application provides an intelligent design system for the outsole of a fishing shoe, including:

[0014] A sole body design module, configured to determine the first design parameter of the sole body of the outsole of the fishing shoe according to the three-dimensional shape and health parameters of the foot of the fishing shoe user.

[0015] A rubber pin design module, configured to determine the second design parameter of the soft pin and the third design parameter of the hard shoe nail according to the gait characteristics, rhythm characteristics, and pressure distribution data when the fishing shoe user moves; wherein, the soft pin and the hard shoe nail are both arranged on the sole body.

[0016] A fine groove design module, configured to determine the fourth design parameter of the fine grooves on the surface of the soft pin according to the second design parameter of the soft pin, the usage frequency and wear mode of the reference fishing shoe of the fishing shoe user.

[0017] A design parameter optimization module, configured to optimize the first design parameter, the second design parameter, the third design parameter, and the fourth design parameter according to the environmental characteristic data of the fishing ground where the fishing shoe user is located; wherein, the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature, and ground biological coverage.

[0018] A comprehensive design drawing generation module, configured to generate the design drawing of the outsole of the fishing shoe that adapts to different fishing environments and user characteristics according to the optimized first design parameter, second design parameter, third design parameter, and fourth design parameter.

[0019] Based on the embodiments provided in this application, the first design parameters of the sole body of the fishing shoe outsole are determined according to the three-dimensional shape and health parameters of the feet of the fishing shoe user; according to the gait characteristics, rhythm characteristics and pressure distribution data when the fishing shoe user moves, the second design parameters of the soft pins and the third design parameters of the hard shoe nails are determined; wherein, the soft pins and the hard shoe nails are both arranged on the sole body; according to the second design parameters of the soft pins, the usage frequency and wear mode of the reference fishing shoes of the fishing shoe user, the fourth design parameters of the fine grooves on the surface of the soft pins are determined; according to the environmental characteristic data of the fishing ground of the fishing shoe user, the first design parameters, the second design parameters, the third design parameters and the fourth design parameters are optimized; wherein, the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature and ground biological coverage; according to the optimized first design parameters, second design parameters, third design parameters and fourth design parameters, the design drawings of the fishing shoe outsole adapted to different fishing environments and user characteristics are generated. Thus, the diversity of different fishing environments and user characteristics is fully considered, and the adaptability of the design is improved.

[0020] Specifically, it has the following beneficial effects: By determining the first design parameters of the sole body according to the three-dimensional shape and health parameters of the user's feet, personalized sole design can be realized, improving the wearing comfort and adaptability; determining the second and third design parameters of the soft pins and the hard shoe nails according to the gait characteristics, rhythm characteristics and pressure distribution data can enhance the anti-slip and wear resistance of the sole, adapting to different fishing environments and user behavior habits; considering the usage frequency and wear mode when designing the soft pins and the hard shoe nails makes the sole more durable in high-wear areas and extends the service life of the product. Determining the fourth design parameters of the fine grooves according to the second design parameters of the soft pins can optimize the detailed design of the sole; optimizing all design parameters according to the environmental characteristic data of the fishing ground ensures that the sole design can adapt to different ground humidity, materials, stabilities, temperatures and biological coverage; the design drawings generated after comprehensively considering all design parameters can manufacture the fishing shoe outsole adapted to different fishing environments and user characteristics, thereby enhancing the user's wearing experience and satisfaction; through scientific design methods and precise parameter optimization, the market competitiveness of the product is improved, meeting the needs of consumers for high-performance fishing shoes; the optimized sole design can better adapt to the changing outdoor environment, providing users with safety guarantees and comfortable experiences in various terrains. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and the illustrative embodiments and descriptions thereof are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0022] Figure 1 Flow chart of an optional intelligent design method for the outsole of a fishing shoe according to an embodiment of the present application;

[0023] Figure 2 Structural diagram of an optional intelligent design system for the outsole of a fishing shoe according to an embodiment of the present application.

[0024] The realization, functional features and advantages of the object of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. Specific embodiments

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.

[0026] Optionally, as Figure 1 shown, the present application provides an intelligent design method for the outsole of a fishing shoe, including:

[0027] S101, determining the first design parameters of the sole body of the outsole of the fishing shoe according to the three-dimensional shape and health parameters of the foot of the fishing shoe user;

[0028] S102, determining the second design parameters of the soft pins and the third design parameters of the hard shoe nails according to the gait characteristics, rhythm characteristics and pressure distribution data when the fishing shoe user moves; wherein, the soft pins and the hard shoe nails are both arranged on the sole body;

[0029] S103, determining the fourth design parameters of the fine grooves on the surface of the soft pins according to the second design parameters of the soft pins, the usage frequency and wear mode of the reference fishing shoes of the fishing shoe user;

[0030] S104, optimizing the first design parameters, the second design parameters, the third design parameters and the fourth design parameters according to the environmental characteristic data of the fishing ground of the fishing shoe user; wherein, the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature and ground biological coverage;

[0031] S105, generating design drawings of the outsole of the fishing shoe adapted to different fishing environments and user characteristics according to the optimized first design parameters, second design parameters, third design parameters and fourth design parameters.

[0032] Based on the embodiments provided in the present application, according to the three-dimensional shape and health parameters of the feet of the fishing shoe user, determine the first design parameters of the sole body of the outer sole of the fishing shoe; according to the gait characteristics, rhythm characteristics and pressure distribution data when the fishing shoe user moves, determine the second design parameters of the soft pins and the third design parameters of the hard studs; wherein, the soft pins and the hard studs are both arranged on the sole body; according to the second design parameters of the soft pins, the usage frequency and wear pattern of the reference fishing shoes of the fishing shoe user, determine the fourth design parameters of the fine grooves on the surface of the soft pins; according to the environmental characteristic data of the fishing ground of the fishing shoe user, optimize the first design parameters, the second design parameters, the third design parameters and the fourth design parameters; wherein, the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature and ground biological coverage; according to the optimized first design parameters, second design parameters, third design parameters and fourth design parameters, generate the design drawings of the outer sole of the fishing shoe adapted to different fishing environments and user characteristics. Thus, the diversity of different fishing environments and user characteristics is fully considered, and the adaptability of the design is improved.

[0033] Specifically, it has the following beneficial effects: By determining the first design parameters of the sole body according to the three-dimensional shape and health parameters of the user's feet, personalized sole design can be realized, improving the wearing comfort and adaptability; By determining the second and third design parameters of the soft pins and hard studs according to the gait characteristics, rhythm characteristics and pressure distribution data, the anti-slip and wear resistance of the sole can be enhanced, adapting to different fishing environments and user behavior habits; Considering the usage frequency and wear pattern when designing the soft pins and hard studs makes the sole more durable in high-wear areas and extends the service life of the product. Determining the fourth design parameters of the fine grooves according to the second design parameters of the soft pins can optimize the detailed design of the sole; Optimizing all design parameters according to the environmental characteristic data of the fishing ground ensures that the sole design can adapt to different ground humidity, materials, stabilities, temperatures and biological coverages; The design drawings generated after comprehensively considering all design parameters can manufacture the outer sole of the fishing shoe adapted to different fishing environments and user characteristics, thereby enhancing the user's wearing experience and satisfaction; Through scientific design methods and precise parameter optimization, the market competitiveness of the product is improved, meeting the needs of consumers for high-performance fishing shoes; The optimized sole design can better adapt to the changing outdoor environment, providing users with safety guarantees and comfortable experiences in various terrains.

[0034] Furthermore, the second design parameter includes the number, distribution, and depth protruding from the sole body surface of the soft pins; the third design parameter includes the number, distribution, and depth protruding from the sole body surface of the hard studs; according to the gait characteristics, rhythm characteristics, and pressure distribution data of the fishing shoe user during movement, the second design parameter of the soft pins and the third design parameter of the hard studs are determined and configured as follows:

[0035] Capture image frames of the walking posture of the fishing shoe user through a camera;

[0036] Use the inter-frame difference method to process the captured image frames of the walking posture to obtain the target gait area;

[0037] According to the pressure change information when the sole of the fishing shoe user contacts the ground monitored by the flexible thin-film pressure sensor array and the target gait area, use a convolutional neural network to extract gait characteristics and obtain gait characteristics; among them, the gait characteristics include muscle strength, tendons, bone length, bone density, and center of gravity during movement;

[0038] Use the music rhythm analysis algorithm combined with the gait characteristics to analyze the gait rhythm of the fishing shoe user and obtain rhythm characteristics; among them, the gait rhythm includes the extracted beat position, intensity, and duration;

[0039] Use an integrated sensing sheet design to measure the plantar pressure distribution of the fishing shoe user through the sensing sheet of the standardized DDR interface and obtain pressure distribution data;

[0040] According to the stress concentration areas and non-stress concentration areas characterized by the gait characteristics, rhythm characteristics, and pressure distribution data, determine the second design parameter of the soft pins and the third design parameter of the hard studs; among them, the pressure distribution data in the stress concentration areas is greater than the preset pressure distribution data threshold.

[0041] It should be noted that the stress concentration area represents the area with relatively large plantar pressure. These areas are usually caused by factors such as sudden changes in geometric shape, material discontinuity, or external load, resulting in a significant increase in stress. The area with relatively small plantar pressure, that is, the non-stress concentration area, is characterized by relatively uniform stress distribution and no obvious stress increase phenomenon, usually located in the middle area of the foot or the part with gentle geometric shape change. In the division of plantar pressure areas, in addition to the forefoot area, midfoot area, and hindfoot area, which are the main areas bearing pressure, according to the actual situation of plantar pressure distribution, areas with relatively small pressure can also be identified. These areas are of great significance for understanding the mechanical characteristics during human movement, preventing sports injuries, and designing suitable shoes. Soft pins are mainly distributed in the areas with relatively small plantar pressure to provide additional grip and cushioning, while hard spikes are arranged in the stress concentration areas to increase the horizontal thrust of the running shoes and increase the acceleration of the athlete.

[0042] Furthermore, the health parameters include the weight and activity intensity of the fishing shoe user; the first design parameters include the material, shape, and thickness of the sole body.

[0043] The soft pins are made of rubber or thermoplastic elastomer; the hard spikes are made of metal materials.

[0044] Optionally, as Figure 2 shown, the present application provides an intelligent design system for the outer sole of a fishing shoe, including:

[0045] The sole body design module 201 is used to determine the first design parameters of the sole body of the outer sole of the fishing shoe according to the three-dimensional shape of the foot of the fishing shoe user and the health parameters.

[0046] The rubber pin design module 202 is used to determine the second design parameters of the soft pins and the third design parameters of the hard spikes according to the gait characteristics, rhythm characteristics, and pressure distribution data when the fishing shoe user moves; wherein, the soft pins and the hard spikes are both arranged on the sole body.

[0047] The fine groove design module 203 is used to determine the fourth design parameters of the fine grooves on the surface of the soft pins according to the second design parameters of the soft pins, the usage frequency and wear mode of the reference fishing shoe of the fishing shoe user.

[0048] The design parameter optimization module 204 is used to optimize the first design parameters, the second design parameters, the third design parameters, and the fourth design parameters according to the environmental characteristic data of the fishing ground of the fishing shoe user; wherein, the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature, and ground biological coverage.

[0049] The comprehensive design drawing generation module 205 is used to generate design drawings of the outsole of fishing shoes that adapt to different fishing environments and user characteristics according to the optimized first design parameter, second design parameter, third design parameter, and fourth design parameter.

[0050] Furthermore, among them, the second design parameter includes the number, distribution, and depth protruding from the surface of the sole body of the soft pins; the third design parameter includes the number, distribution, and depth protruding from the surface of the sole body of the hard shoe nails; the rubber pin design module determines the second design parameter of the soft pins and the third design parameter of the hard shoe nails according to the gait characteristics, rhythm characteristics, and pressure distribution data when the fishing shoe user moves, and is configured to:

[0051] Capture image frames of the walking posture of the fishing shoe user through a camera;

[0052] Process the captured image frames of the walking posture using the inter-frame difference method to obtain the target gait area;

[0053] According to the pressure change information when the sole of the fishing shoe user contacts the ground monitored by the flexible film pressure sensor array and the target gait area, use a convolutional neural network to extract gait characteristics to obtain gait characteristics; among them, the gait characteristics include muscle strength, tendons, bone length, bone density, and center of gravity during movement;

[0054] Use the music rhythm analysis algorithm combined with gait characteristics to analyze the gait rhythm of the fishing shoe user to obtain rhythm characteristics; among them, the gait rhythm includes extracting the beat position, intensity, and duration;

[0055] Use an integrated sensing sheet design to measure the plantar pressure distribution of the fishing shoe user through the sensing sheet with a standardized DDR interface to obtain pressure distribution data;

[0056] According to the stress concentration area and non-stress concentration area characterized by gait characteristics, rhythm characteristics, and pressure distribution data, determine the second design parameter of the soft pins and the third design parameter of the hard shoe nails; among them, the pressure distribution data in the stress concentration area is greater than the preset pressure distribution data threshold.

[0057] Furthermore, the fourth design parameter includes the geometric shape, number, distribution, and depth of the fine grooves; the fine groove design module determines the fourth design parameter of the fine grooves on the surface of the soft pins according to the second design parameter of the soft pins, the usage frequency and wear mode of the reference fishing shoes of the fishing shoe user, and is configured to:

[0058] Determine the area of the fine groove design according to the second design parameter of the soft pins;

[0059] Based on the wear patterns and usage frequency of reference fishing shoes of fishing shoe users, high wear areas and low wear areas are identified from the areas of the thin groove design;

[0060] Among them, data can be obtained through image recognition technology, and the wear image of the sole can be analyzed to determine the degree and distribution of wear. For example, in high-wear areas, V-shaped or W-shaped grooves are designed to increase wear resistance and grip; in low-wear areas, U-shaped or straight grooves are designed to reduce weight and maintain a certain grip.

[0061] Computational fluid dynamics algorithms are used to simulate the effects of different groove distributions on the grip of the sole, and the grip simulation results are obtained;

[0062] According to the simulation results of grip impact, a cross-scale topology optimization algorithm based on coupled deep learning is used to generate the optimal microscopic fine groove structure and the optimal macroscopic fine groove structure under various boundary conditions;

[0063] This method can simultaneously consider the microscopic topological configuration and macroscopic material distribution of the structure, effectively releasing design potential and further improving the lightweight effect and performance of the sole.

[0064] Among them, the optimal microscopic groove structure means that at the microscopic scale, the geometry, texture and material properties of the grooves are optimized to provide the best grip and wear resistance; the optimal macroscopic groove structure means that at the macroscopic scale, the depth, distribution and number of the grooves are optimized to adapt to the mechanical requirements of the sole and the gait characteristics of the user; the boundary conditions include physical boundary conditions, mechanical boundary conditions, material boundary conditions and environmental boundary conditions;

[0065] Physical boundary conditions: These are the physical limitations of the slot structure when it contacts other parts of the sole or the ground, such as friction coefficient, contact area, etc. Mechanical boundary conditions: These involve the mechanical response of the slot structure when subjected to force, such as stress distribution and deformation in different areas. Material boundary conditions: The material properties of the slot structure, such as elastic modulus, hardness, etc., which may vary in different areas. Environmental boundary conditions: The performance of the slot structure under different environmental conditions, such as grip on slippery surfaces.

[0066] Among them, the optimization objective functions of both the macroscopic fine groove structure and the microscopic fine groove structure are It is expressed as:

[0067]

[0068] in, represents a set of design parameters; Indicates the weight of the design; represents the drag of the design, and the optimization goal is to minimize the combination of weight and drag;

[0069] Determine the fourth design parameter of the micro-grooves on the surface of the soft pin according to the optimal micro-groove structure and the optimal macro-groove structure.

[0070] Furthermore, use the computational fluid dynamics algorithm to simulate the influence of different groove distributions on the sole grip, which is configured as:

[0071] Determine the mechanical model according to the physical phenomena of the sole in contact with the ground; wherein, the physical phenomena include the shape of the sole, material properties, and ground conditions;

[0072] Establish a geometric model of the sole and the ground according to the mechanical model; wherein, the geometric model includes the distribution and geometric shape of the micro-grooves;

[0073] Perform mesh division on the geometric model based on the structured grid type to discretize the continuous flow region;

[0074] Define the initial conditions, wall conditions, inlet boundary conditions, and outlet boundary conditions of the simulation; wherein, the inlet boundary conditions include velocity and pressure; the outlet boundary conditions include pressure and flow rate; the initial conditions include the state of the fluid at the start of the simulation; the wall condition is the no-slip condition or friction condition set for the contact surface between the sole and the ground;

[0075] Select a turbulence model for numerical simulation to obtain the fluid velocity, pressure, and turbulence characteristics at each grid point; wherein, the turbulence model includes at least one of the following: Spalart-Allmaras model, k-ε model, k-ω model;

[0076] After the simulation is completed, use a post-processor to analyze the velocity field, pressure field, and turbulence characteristics, and visually display the analysis results;

[0077] Compare the simulation results of different groove distributions, analyze the influence of groove design on fluid velocity, pressure, and turbulence characteristics; identify the way in which groove design affects fluid flow;

[0078] Conduct a sensitivity analysis, change the distribution and shape of the micro-grooves, and analyze the influence of the changes in the micro-grooves on the velocity field, pressure field, and turbulence characteristics.

[0079] Furthermore, use a post-processor to analyze the velocity field, pressure field, and turbulence characteristics, and visually display the analysis results, which is configured as:

[0080] Use a post-processor to analyze the velocity field, calculate the average velocity, maximum velocity, and non-uniformity of the velocity distribution in the contact area between the sole and the ground;

[0081] Analyze the direction and velocity change of fluid flow based on the following formula; ;

[0082] Among them, is the speed change caused by the fine grooves; is the drag coefficient; is the fluid density, is the fluid velocity; is the area of the fine grooves; Among them, the drag coefficient refers to the ratio of the resistance suffered by the fluid during movement to the fluid density, velocity, and resistance area. This is a dimensionless coefficient used to describe the degree of obstruction when the fluid passes through pipes, holes, or other objects. In the design of fishing shoes, the drag coefficient can help understand and predict the flow resistance of the fluid (such as water, mud, etc.) when the sole contacts the ground, thereby optimizing the sole design to improve the grip and reduce slippage.

[0083] Analyze the pressure field to determine the maximum pressure and pressure distribution in the contact area between the sole and the ground; identify the pressure concentration areas;

[0084] Analyze the turbulence characteristics; among them, the turbulence characteristics include turbulent kinetic energy, turbulent stress, and turbulent frequency; evaluate the impact of turbulence on the sole grip;

[0085] Use the visualization tools of computational fluid dynamics software to display the velocity field, pressure field, and turbulence characteristics in the form of graphics and animations;

[0086] Create streamline diagrams, isosurface diagrams, and vector diagrams to show the fluid flow and the interaction between the sole and the ground;

[0087] Through color coding, show the velocity magnitude, pressure level, and turbulence intensity in different regions.

[0088] Furthermore, ground organisms include mosses, ferns, and algae; the design parameter optimization module optimizes the first design parameter, the second design parameter, the third design parameter, and the fourth design parameter according to the environmental characteristic data of the fishing ground where the fishing shoe user is located, and is configured to:

[0089] Let be the set of design variables; among them, is the first design parameter; is the second design parameter; is the third design parameter; is the fourth design parameter;

[0090] Define the set of objective functions for the multi-objective optimization problem and map the design variables to the objective function space; among them, the set of objective functions ; represents the performance indicators related to the design parameters; the performance indicators include grip, wear resistance, weight, and cost;

[0091] The NSGA-II algorithm is used to optimize the set of design variables ; among them, the NSGA-II algorithm maintains the diversity of solutions through non-dominated sorting and crowding degree calculation; the goal of the NSGA-II algorithm is to find the Pareto optimal solution set;

[0092] Perform fast non-dominated sorting on the population and divide the population into multiple non-dominated levels;

[0093] Calculate the crowding degree of individuals within each non-dominated level to maintain the diversity of the population; the formula for calculating the crowding degree is expressed as: ; where, is the crowding degree of individual ; is the number of objective functions in the set of objective functions; j is the cumulative index; and are the function values of two adjacent individuals of individual on the th objective function; is the maximum value of the th objective function; is the minimum value of the th objective function;

[0094] Select individuals according to the non-dominated level and crowding degree to form a new parental population;

[0095] Perform crossover and mutation operations on the parental population to generate a new offspring population;

[0096] Repeat the operations of non-dominated sorting, crowding degree calculation, crossover and mutation until the number of iterations is satisfied;

[0097] Analyze the obtained Pareto optimal solution set to optimize the first design parameter, the second design parameter, the third design parameter and the fourth design parameter.

[0098] Furthermore, the health parameters include the weight and activity intensity of the fishing shoe user; the first design parameter includes the material, shape and thickness of the sole body; the sole body is made of felt; the rubber pins are made of butyl rubber;

[0099] The soft pins are made of rubber or thermoplastic elastomer; the hard shoe nails are made of metal materials.

[0100] It should be noted that in this application, the embodiments implemented on the intelligent design system side of the fishing shoe outsole can be mutually referred to the embodiments implemented on the intelligent design method side of the fishing shoe outsole, and this application will not elaborate one by one.

[0101] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. An intelligent design method for the outsole of a fishing shoe, characterized in that, Including: Determine the first design parameters of the sole body of the outer sole of the fishing shoes according to the three-dimensional shape and health parameters of the feet of the fishing shoes user; Determine the second design parameters of the soft pins and the third design parameters of the hard shoe nails according to the gait characteristics, rhythm characteristics and pressure distribution data when the fishing shoes user moves; wherein, the soft pins and the hard shoe nails are both arranged on the sole body; Determine the fourth design parameters of the fine grooves on the surface of the soft pins according to the second design parameters of the soft pins, the usage frequency and wear pattern of the reference fishing shoes of the fishing shoes user; Optimize the first design parameters, the second design parameters, the third design parameters and the fourth design parameters according to the environmental characteristic data of the fishing ground of the fishing shoes user; wherein, the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature and ground biological coverage; Generate the design drawings of the outer sole of the fishing shoes adapted to different fishing environments and user characteristics according to the optimized first design parameters, second design parameters, third design parameters and fourth design parameters; The fourth design parameters include the geometric shape, quantity, distribution and depth of the fine grooves; The determining the fourth design parameters of the fine grooves on the surface of the soft pins according to the second design parameters of the soft pins, the usage frequency and wear pattern of the reference fishing shoes of the fishing shoes user is configured as: Determine the area of the fine groove design according to the second design parameters of the soft pins; Identify the high-wear area and the low-wear area from the area of the fine groove design according to the wear pattern and usage frequency of the reference fishing shoes of the fishing shoes user; Use the computational fluid dynamics algorithm to simulate the influence of different fine groove distributions on the sole grip, and obtain the simulation result of the grip influence; According to the simulation result of the grip influence, adopt the cross-scale topology optimization algorithm based on coupled deep learning to generate the optimal microscopic fine groove structure and the optimal macroscopic fine groove structure under various boundary conditions; Wherein, the optimal microscopic fine groove structure means that at the microscopic scale, the geometric shape, texture and material properties of the fine grooves are optimized to provide the optimal grip and wear resistance; the optimal macroscopic fine groove structure means that at the macroscopic scale, the depth, distribution and quantity of the fine grooves are optimized to adapt to the mechanical requirements of the sole and the gait characteristics of the user; the boundary conditions include physical boundary conditions, mechanical boundary conditions, material boundary conditions and environmental boundary conditions; Wherein, the optimization objective function O of both the macroscopic fine groove structure and the microscopic fine groove structure is expressed as: ； Among them, P represents the set of design parameters; represents the weight of the design; represents the drag of the design, and the optimization goal is to minimize the combination of weight and drag; Determine the fourth design parameters of the fine grooves on the surface of the soft pins according to the optimal microscopic fine groove structure and the optimal macroscopic fine groove structure.

2. The intelligent design method of the fishing shoe outsole according to claim 1, characterized in that, The second design parameter includes the number, distribution, and depth protruding from the surface of the sole body of the soft pins; the third design parameter includes the number, distribution, and depth protruding from the surface of the sole body of the hard studs; determining the second design parameter of the soft pins and the third design parameter of the hard studs according to the gait characteristics, rhythm characteristics, and pressure distribution data when the fishing shoe user moves is configured as follows: Capturing an image frame of the walking posture of the fishing shoe user through a camera; Processing the captured image frame of the walking posture using the inter-frame difference method to obtain a target gait area; According to the pressure change information when the sole of the fishing shoe user contacts the ground monitored by the flexible thin-film pressure sensor array and the target gait area, using a convolutional neural network to extract gait characteristics to obtain the gait characteristics; wherein, the gait characteristics include muscle strength, tendons, bone length, bone density, and center of gravity during movement; Using a music rhythm analysis algorithm in combination with the gait characteristics to analyze the gait rhythm of the fishing shoe user to obtain the rhythm characteristics; wherein, the gait rhythm includes extracting the beat position, intensity, and duration; Using an integrated sensing sheet design, measuring the plantar pressure distribution of the fishing shoe user through the sensing sheet of the standardized DDR interface to obtain the pressure distribution data; Determining the second design parameter of the soft pins and the third design parameter of the hard studs according to the stress concentration areas and non-stress concentration areas characterized by the gait characteristics, the rhythm characteristics, and the pressure distribution data; wherein, the pressure distribution data in the stress concentration areas is greater than a preset pressure distribution data threshold.

3. The intelligent design method for the outer sole of a fishing shoe according to claim 1, characterized in that The health parameters include the weight and activity intensity of the fishing shoe user; the first design parameter includes the material, shape, and thickness of the sole body; The soft pins are made of rubber or thermoplastic elastomer; the hard studs are made of metal material.

4. An intelligent design system for the outsole of a fishing shoe, the system implementing the method according to claim 1, characterized in that, Including: A sole body design module for determining the first design parameter of the sole body of the outer sole of the fishing shoe according to the three-dimensional shape of the fishing shoe user's foot and the health parameters; A rubber pin design module for determining the second design parameter of the soft pins and the third design parameter of the hard studs according to the gait characteristics, rhythm characteristics, and pressure distribution data when the fishing shoe user moves; wherein, the soft pins and the hard studs are both arranged on the sole body; A fine groove design module for determining the fourth design parameter of the fine grooves on the surface of the soft pins according to the second design parameter of the soft pins, the usage frequency and wear pattern of the reference fishing shoe of the fishing shoe user; A design parameter optimization module for optimizing the first design parameter, the second design parameter, the third design parameter, and the fourth design parameter according to the environmental characteristic data of the fishing ground where the fishing shoes user is located; wherein, the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature, and ground biological coverage; A comprehensive design drawing generation module for generating design drawings of the outsole of the fishing shoes adapted to different fishing environments and user characteristics according to the first design parameter, the second design parameter, the third design parameter, and the fourth design parameter that have been optimized; The fourth design parameter includes the geometric shape, quantity, distribution, and depth of the fine grooves; the fine groove design module determines the fourth design parameter of the fine grooves on the surface of the soft pin according to the second design parameter of the soft pin, the usage frequency and wear pattern of the reference fishing shoes of the fishing shoes user, and is configured as: Determine the area of fine groove design according to the second design parameter of the soft pin; Identify the high-wear area and low-wear area from the area of fine groove design according to the wear pattern and usage frequency of the reference fishing shoes of the fishing shoes user; Use the computational fluid dynamics algorithm to simulate the influence of different fine groove distributions on the sole grip, and obtain the simulation result of the grip influence; According to the simulation result of the grip influence, adopt the cross-scale topology optimization algorithm based on coupled deep learning to generate the optimal microscopic fine groove structure and the optimal macroscopic fine groove structure under various boundary conditions; Wherein, the optimal microscopic fine groove structure means that at the microscopic scale, the geometric shape, texture, and material properties of the fine grooves are optimized to provide optimal grip and wear resistance; the optimal macroscopic fine groove structure means that at the macroscopic scale, the depth, distribution, and quantity of the fine grooves are optimized to adapt to the mechanical requirements of the sole and the gait characteristics of the user; the boundary conditions include physical boundary conditions, mechanical boundary conditions, material boundary conditions, and environmental boundary conditions; Wherein, the optimization objective function O of both the macroscopic fine groove structure and the microscopic fine groove structure is expressed as: ； Among them, P represents the set of design parameters; represents the weight of the design; represents the drag of the design, and the optimization goal is to minimize the combination of weight and drag; Determine the fourth design parameter of the fine grooves on the surface of the soft pin according to the optimal microscopic fine groove structure and the optimal macroscopic fine groove structure.

5. The intelligent design system for the outer sole of a fishing shoe according to claim 4, characterized in that, Wherein, The second design parameter includes the quantity, distribution, and depth protruding from the surface of the sole main body of the soft pins; the third design parameter includes the quantity, distribution, and depth protruding from the surface of the sole main body of the hard shoe nails; the rubber pin design module determines the second design parameter of the soft pins and the third design parameter of the hard shoe nails according to the gait characteristics, rhythm characteristics, and pressure distribution data when the fishing shoes user moves, and is configured as: Capture the image frames of the walking posture of the fishing shoes user through a camera; Process the captured image frames of the walking posture using the inter-frame difference method to obtain the target gait area; Based on the pressure change information and the target gait area when the sole of the fishing shoe user contacts the ground monitored by the flexible thin-film pressure sensor array, a convolutional neural network is used to extract gait features to obtain the gait features; wherein, the gait features include muscle strength, tendons, bone length, bone density, and center of gravity during movement. Using a music rhythm analysis algorithm in combination with the gait features, analyze the gait rhythm of the fishing shoe user to obtain the rhythm features; wherein, the gait rhythm includes extracting the beat position, intensity, and duration. Using an integrated sensing chip design, measure the plantar pressure distribution of the fishing shoe user through the sensing chip with a standardized DDR interface to obtain the pressure distribution data. Based on the stress concentration areas and non-stress concentration areas characterized by the gait features, the rhythm features, and the pressure distribution data, determine the second design parameter of the soft pin and the third design parameter of the hard shoe nail; wherein, the pressure distribution data in the stress concentration area is greater than the preset pressure distribution data threshold.

6. The intelligent design system of the fishing shoe outsole according to claim 4, characterized in that The use of the computational fluid dynamics algorithm to simulate the influence of different groove distributions on the sole grip is configured as follows: Based on the physical phenomena of the sole contacting the ground, determine the mechanical model; wherein, the physical phenomena include the shape of the sole, material properties, and ground conditions. Based on the mechanical model, establish a geometric model of the sole and the ground; wherein, the geometric model includes the distribution and geometric shape of the grooves. Based on the structured grid type, perform grid division on the geometric model to discretize the continuous flow area. Define the initial conditions, wall conditions, inlet boundary conditions, and outlet boundary conditions for the simulation; wherein, the inlet boundary conditions include velocity and pressure; the outlet boundary conditions include pressure and flow rate; the initial conditions include the state of the fluid at the start of the simulation; the wall condition is a no-slip condition or a friction condition set for the contact surface between the sole and the ground. Select a turbulence model for numerical simulation to obtain the fluid velocity, pressure, and turbulence characteristics at each grid point; wherein, the turbulence model includes at least one of the following: Spalart-Allmaras model, k-ε model, k-ω model. After the simulation is completed, use a post-processor to analyze the velocity field, pressure field, and turbulence characteristics, and visually display the analysis results. Compare the simulation results of different groove distributions, analyze the influence of groove design on fluid velocity, pressure, and turbulence characteristics; identify the ways in which groove design affects fluid flow. Perform a sensitivity analysis, change the distribution and shape of the grooves, and analyze the influence of the changes in the grooves on the velocity field, pressure field, and turbulence characteristics.

7. The intelligent design system of the fishing shoe outsole according to claim 6, characterized in that, The use of the post-processor to analyze the velocity field, pressure field, and turbulence characteristics, and visually display the analysis results is configured as follows: Use the post-processor to analyze the velocity field, calculate the average velocity, maximum velocity, and non-uniformity of the velocity distribution in the contact area between the sole and the ground. Analyze the direction and velocity change of fluid flow based on the following formula; ; where, is the velocity change caused by the fine groove; is the drag coefficient; P is the fluid density, and V is the fluid velocity; is the area of the fine groove; Analyze the pressure field, determine the maximum pressure and pressure distribution in the contact area between the sole and the ground; identify the pressure concentration areas. Analyze the turbulence characteristics; wherein, the turbulence characteristics include turbulent kinetic energy, turbulent stress, and turbulent frequency; evaluate the impact of turbulence on the sole grip; Use the visualization tool of computational fluid dynamics software to display the velocity field, pressure field, and turbulence characteristics in the form of graphs and animations; Produce streamline diagrams, isosurface diagrams, and vector diagrams to display the fluid flow and the interaction between the sole and the ground; Through color coding, display the velocity magnitude, pressure level, and turbulence intensity in different regions.

8. The intelligent design system of the fishing shoe outsole according to claim 7, characterized in that, The ground organisms include mosses, ferns, and algae; the design parameter optimization module optimizes the first design parameter, the second design parameter, the third design parameter, and the fourth design parameter according to the environmental characteristic data of the fishing ground where the fishing shoe user is located, and is configured to: Let be the set of design variables; where, x1 is the first design parameter; x2 is the second design parameter; x3 is the third design parameter; x4 is the fourth design parameter; Define the set of objective functions for the multi-objective optimization problem and map the design variables to the objective function space; wherein, the set of objective functions ; represents the performance metrics related to the design parameters; the performance metrics include grip, wear resistance, weight, and cost; Use the NSGA-II algorithm to optimize the set of design variables X; wherein, the NSGA-II algorithm maintains the diversity of solutions through non-dominated sorting and crowding degree calculation; the goal of the NSGA-II algorithm is to find the Pareto optimal solution set; Perform fast non-dominated sorting on the population and divide the population into multiple non-dominated levels; Calculate the crowding degree of individuals within each non-dominated layer to maintain the diversity of the population; the crowding degree calculation formula is expressed as: ; where is the crowding degree of individual s; is the number of objective functions in the set of objective functions; j is the cumulative index; and are the function values of two adjacent individuals of individual s on the i-th objective function; is the maximum value of the i-th objective function; is the minimum value of the i-th objective function; Select individuals according to the non-dominated level and crowding degree to form a new parental population; Perform crossover and mutation operations on the parental population to generate a new offspring population; Repeat the non-dominated sorting, crowding degree calculation, crossover, and mutation operations until the iteration number is satisfied; Analyze the obtained Pareto optimal solution set to optimize the first design parameter, the second design parameter, the third design parameter, and the fourth design parameter.

9. The intelligent design system for the outer sole of a fishing shoe according to claim 8, wherein, The health parameters include the weight and activity intensity of the fishing shoe user; the first design parameter includes the material, shape, and thickness of the sole body; The soft pins are made of rubber or thermoplastic elastomer; the hard studs are made of metal materials.

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