Intelligent design method and system for fishing shoe outsole

Abstract

The invention discloses an intelligent design method and system for a fishing shoe outsole, and relates to the technical field of fishing shoe design, and the method comprises the steps: determining a first design parameter of a sole main body of the fishing shoe outsole according to the three-dimensional shape and health parameters of the foot of a fishing shoe user; determining a second design parameter of the soft pin and a third design parameter of the hard spike according to the gait characteristic, the rhythm characteristic and the pressure distribution data when the fishing shoe user moves; according to the second design parameter of the soft pin and the use frequency and wear mode of the reference fishing shoe of the fishing shoe user, determining a fourth design parameter of a thin groove in the surface of the soft pin; and according to the first design parameter, the second design parameter, the third design parameter and the fourth design parameter which are optimized, generating a design drawing of the fishing shoe outsole adapted to different fishing environments and user characteristics.

Description

Technical Field

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

[0002] In recent years, fishing has attracted more and more people to participate. Fishing is not only a leisure activity, but also gradually becomes a competitive sport. As the number of participants increases, the demand for lower thresholds for fishing techniques and increased interest has also increased.

[0003] Fishing shoes are a type of footwear designed specifically for fishing activities, and they play an important role among fishing enthusiasts. The main function of fishing shoes is to provide good anti-slip effects to ensure that anglers remain stable and safe in various slippery water environments. Fishing shoes usually need to adapt to a variety of different environments, including muddy river banks, slippery rocks, and coasts that may be covered with seaweed.

[0004] The existing outsole design of fishing shoes relies heavily on manual experience and the designer's intuition, which may not fully take into account all possible usage environments and conditions, resulting in limited design adaptability.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

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

[0007] The present application provides an intelligent design method for the outsole of a fishing shoe, comprising: Determining first design parameters of a sole body of an outsole of the fishing shoe according to a three-dimensional shape of a foot of a user of the fishing shoe and health parameters; Determine the second design parameter of the soft pin and the third design parameter of the hard spike according to the gait characteristics, rhythm characteristics and pressure distribution data of the user of the fishing shoes when moving; wherein the soft pin and the hard spike are both arranged on the sole body; Determining fourth design parameters of the fine grooves on the surface of the soft pin according to the second design parameters of the soft pin, the usage frequency and wear pattern of the reference fishing shoes of the fishing shoe user; The first design parameter, the second design parameter, the third design parameter and the fourth design parameter are optimized according to the environmental characteristic data of the fishing ground of the user of the fishing shoes; wherein the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature and ground biological coverage; According to the optimized first design parameter, second design parameter, third design parameter and fourth design parameter, a design drawing of the fishing shoe outer sole adapted to different fishing environments and user characteristics is generated.

[0008] The present application provides an intelligent design system for the outsole of a fishing shoe, comprising: A sole body design module, used to determine a first design parameter of a sole body of the outsole of the fishing shoe according to the three-dimensional shape of the foot and health parameters of the user of the fishing shoe; A rubber pin design module, used to determine a second design parameter of a soft pin and a third design parameter of a hard spike according to gait characteristics, rhythm characteristics and pressure distribution data of a user of the fishing shoes when moving; wherein the soft pin and the hard spike are both arranged on the sole body; a fine groove design module, for determining fourth design parameters of the fine groove on the surface of the soft pin according to the second design parameters of the soft pin, the use frequency and wear pattern of the reference fishing shoes 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 environmental characteristic data of a fishing site of a user of the fishing shoes; wherein the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature and ground biological coverage; The comprehensive design drawing generation module is used to generate the design drawings of the fishing shoe outsole adapted to different fishing environments and user characteristics according to the optimized first design parameter, second design parameter, third design parameter and fourth design parameter.

[0009] Based on the embodiment provided by the present application, the first design parameter of the sole body of the fishing shoe outsole is determined according to the three-dimensional shape and health parameters of the foot of the fishing shoe user; the second design parameter of the soft pin and the third design parameter of the hard spike are determined according to the gait characteristics, rhythm characteristics and pressure distribution data of the fishing shoe user when moving; wherein the soft pin and the hard spike are both arranged on the sole body; the fourth design parameter of the fine groove on the surface of the soft pin is determined according to the second design parameter of the soft pin, the use frequency and wear mode of the reference fishing shoe of the fishing shoe user; the first design parameter, the second design parameter, the third design parameter and the fourth design parameter are optimized according to the environmental characteristic data of the fishing site of the fishing shoe user; wherein the environmental characteristic data include ground humidity, ground material, ground stability, ground temperature and ground biological coverage; according to the optimized first design parameter, the second design parameter, the third design parameter and the fourth design parameter, 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.

[0010] 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 foot, personalized sole design can be achieved to improve wearing comfort and adaptability; the second and third design parameters of the soft pins and hard spikes are determined according to gait characteristics, rhythm characteristics and pressure distribution data, which can enhance the anti-slip and wear resistance of the sole and adapt to different fishing environments and user behavior habits; the frequency of use and wear pattern are taken into consideration when designing the soft pins and hard spikes, so that the sole is more durable in high-wear areas and the service life of the product is extended. By determining the fourth design parameter of the fine groove according to the second design parameter of the soft pin, the detailed design of the sole can be optimized; all design parameters are optimized according to the environmental characteristic data of the fishing site to ensure that the sole design can adapt to different ground humidity, material, stability, temperature and biological coverage; the design drawings generated after comprehensive consideration of all design parameters can manufacture fishing shoe outsoles that adapt to different fishing environments and user characteristics, thereby improving the user's wearing experience and satisfaction; through scientific design methods and precise parameter optimization, the market competitiveness of the product is improved to meet consumers' demand for high-performance fishing shoes; the optimized sole design can better adapt to the changing outdoor environment and provide users with safety and comfort experience in various terrains. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1is a flow chart of an optional intelligent design method for the outsole of a fishing shoe according to an embodiment of the present application; Figure 2 This is a structural diagram of an optional intelligent design system for the outsole of a fishing shoe according to an embodiment of the present application.

[0012] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0014] Alternatively, if Figure 1 As shown, the present application provides an intelligent design method for the outsole of a fishing shoe, comprising: S101, determining first design parameters of a sole body of an outsole of the fishing shoe according to a three-dimensional shape of a foot of a user of the fishing shoe and health parameters; S102, determining a second design parameter of the soft pin and a third design parameter of the hard spike according to the gait characteristics, rhythm characteristics and pressure distribution data of the user of the fishing shoes when moving; wherein the soft pin and the hard spike are both arranged on the sole body; S103, determining fourth design parameters of the fine grooves on the surface of the soft pin according to the second design parameters of the soft pin, the usage frequency and wear pattern of the reference fishing shoes of the fishing shoe user; S104, 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 of the user of the fishing shoe; wherein the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature, and ground biological coverage; S105, generating design drawings of the outer sole of the fishing shoe adapted to different fishing environments and user characteristics according to the optimized first design parameter, the second design parameter, the third design parameter and the fourth design parameter.

[0015] Based on the embodiment provided by the present application, the first design parameter of the sole body of the fishing shoe outsole is determined according to the three-dimensional shape and health parameters of the foot of the fishing shoe user; the second design parameter of the soft pin and the third design parameter of the hard spike are determined according to the gait characteristics, rhythm characteristics and pressure distribution data of the fishing shoe user when moving; wherein the soft pin and the hard spike are both arranged on the sole body; the fourth design parameter of the fine groove on the surface of the soft pin is determined according to the second design parameter of the soft pin, the use frequency and wear mode of the reference fishing shoe of the fishing shoe user; the first design parameter, the second design parameter, the third design parameter and the fourth design parameter are optimized according to the environmental characteristic data of the fishing site of the fishing shoe user; wherein the environmental characteristic data include ground humidity, ground material, ground stability, ground temperature and ground biological coverage; according to the optimized first design parameter, the second design parameter, the third design parameter and the fourth design parameter, 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.

[0016] 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 foot, personalized sole design can be achieved to improve wearing comfort and adaptability; the second and third design parameters of the soft pins and hard spikes are determined according to gait characteristics, rhythm characteristics and pressure distribution data, which can enhance the anti-slip and wear resistance of the sole and adapt to different fishing environments and user behavior habits; the frequency of use and wear pattern are taken into consideration when designing the soft pins and hard spikes, so that the sole is more durable in high-wear areas and the service life of the product is extended. By determining the fourth design parameter of the fine groove according to the second design parameter of the soft pin, the detailed design of the sole can be optimized; all design parameters are optimized according to the environmental characteristic data of the fishing site to ensure that the sole design can adapt to different ground humidity, material, stability, temperature and biological coverage; the design drawings generated after comprehensive consideration of all design parameters can manufacture fishing shoe outsoles that adapt to different fishing environments and user characteristics, thereby improving the user's wearing experience and satisfaction; through scientific design methods and precise parameter optimization, the market competitiveness of the product is improved to meet consumers' demand for high-performance fishing shoes; the optimized sole design can better adapt to the changing outdoor environment and provide users with safety and comfort experience in various terrains.

[0017] Further, the second design parameters include the number, distribution and depth of the soft pins protruding from the surface of the sole body; the third design parameters include the number, distribution and depth of the hard spikes protruding from the surface of the sole body; according to the gait characteristics, rhythm characteristics and pressure distribution data of the user of the fishing shoes when moving, the second design parameters of the soft pins and the third design parameters of the hard spikes are determined and configured as follows: Capturing image frames of a walking posture of a user of the fishing shoes by a camera; The captured image frames of the walking posture are processed using an inter-frame difference method to obtain a target gait region; Based on the pressure change information when the sole of the fishing shoe user contacts the ground and the target gait area monitored by the flexible film pressure sensor array, a convolutional neural network is used to extract gait features to obtain gait features; wherein the gait features include muscle strength, tendons, bone length, bone density and center of gravity during movement; The gait rhythm of the fishing shoe user is analyzed by using a music rhythm analysis algorithm combined with gait characteristics to obtain rhythm characteristics; wherein the gait rhythm includes extracting beat position, intensity and duration; Using an integrated sensor design, the pressure distribution of the fishing shoe user's sole is measured through a sensor with a standardized DDR interface to obtain pressure distribution data; According to the stress concentration area and non-stress concentration area characterized by gait characteristics, rhythm characteristics and pressure distribution data, the second design parameters of the soft pin and the third design parameters of the hard spike are determined; wherein the pressure distribution data of the stress concentration area is greater than the preset pressure distribution data threshold.

[0018] It should be noted that stress concentration areas refer to areas with high plantar pressure. These areas are usually caused by factors such as sudden changes in geometric shape, material discontinuity or external loads, which lead to significant stress increases. The areas with low plantar pressure, that is, non-stress concentration areas, are characterized by relatively uniform stress distribution and no obvious stress increase. They are usually located in the middle area of ​​the foot or in areas where the geometric shape changes gently. In the division of plantar pressure areas, in addition to the forefoot area, midfoot area and rearfoot area, which are the main areas bearing pressure, areas with relatively low pressure can also be identified based on the actual plantar pressure distribution. These areas are of great significance for understanding the mechanical characteristics of the human body during movement, preventing sports injuries and designing suitable shoes. Soft pins are mainly distributed in areas with low plantar pressure to provide additional grip and cushioning, while hard spikes are arranged in stress concentration areas to increase the horizontal thrust of running shoes and increase the acceleration of athletes.

[0019] Further, the health parameter includes the weight and activity intensity of the fishing shoe user; the first design parameter includes the material, shape and thickness of the sole body; Soft pins are made of rubber or thermoplastic elastomer; hard spikes are made of metal materials.

[0020] Alternatively, if Figure 2 As shown, the present application provides an intelligent design system for the outsole of a fishing shoe, comprising: A sole body design module 201, for determining first design parameters of a sole body of the outsole of the fishing shoe according to the three-dimensional shape of the foot and health parameters of the fishing shoe user; The rubber pin design module 202 is used to determine the second design parameter of the soft pin and the third design parameter of the hard spike according to the gait characteristics, rhythm characteristics and pressure distribution data of the user of the fishing shoe when moving; wherein the soft pin and the hard spike are both arranged on the sole body; A fine groove design module 203, for determining fourth design parameters of the fine groove on the surface of the soft pin according to the second design parameters of the soft pin, the use frequency and wear pattern of the reference fishing shoes of the fishing shoe user; The design parameter optimization module 204 is used 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 of the fishing shoe user; wherein the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature and ground biological coverage; The comprehensive design drawing generation module 205 is used to generate design drawings of fishing shoe outsoles that are adapted to different fishing environments and user characteristics based on the optimized first design parameter, the second design parameter, the third design parameter and the fourth design parameter.

[0021] Further, the second design parameter includes the number, distribution and depth of the soft pins protruding from the surface of the sole body; the third design parameter includes the number, distribution and depth of the hard spikes protruding from the surface of the sole body; the rubber pin design module determines the second design parameter of the soft pin and the third design parameter of the hard spike according to the gait characteristics, rhythm characteristics and pressure distribution data of the fishing shoe user when moving, and is configured as follows: Capturing image frames of a walking posture of a user of the fishing shoes by a camera; The captured image frames of the walking posture are processed using an inter-frame difference method to obtain a target gait region; Based on the pressure change information when the sole of the fishing shoe user contacts the ground and the target gait area monitored by the flexible film pressure sensor array, a convolutional neural network is used to extract gait features to obtain gait features; wherein the gait features include muscle strength, tendons, bone length, bone density and center of gravity during movement; The gait rhythm of the fishing shoe user is analyzed by using a music rhythm analysis algorithm combined with gait characteristics to obtain rhythm characteristics; wherein the gait rhythm includes extracting beat position, intensity and duration; Using an integrated sensor design, the pressure distribution of the fishing shoe user's sole is measured through a sensor with a standardized DDR interface to obtain pressure distribution data; According to the stress concentration area and non-stress concentration area characterized by gait characteristics, rhythm characteristics and pressure distribution data, the second design parameters of the soft pin and the third design parameters of the hard spike are determined; wherein the pressure distribution data of the stress concentration area is greater than the preset pressure distribution data threshold.

[0022] Further, the fourth design parameters include the geometric shape, quantity, distribution and depth of the fine grooves; the fine groove design module determines the fourth design parameters of the fine grooves on the surface of the soft pin according to the second design parameters of the soft pin, the use frequency and wear pattern of the reference fishing shoes of the fishing shoe user, and is configured as: Determine the area of ​​the fine groove design according to the second design parameter of the soft pin; Based on the wear patterns and usage frequency of reference fishing shoes by fishing shoe users, high wear areas and low wear areas are identified from the areas of the thin groove design; 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.

[0023] 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; 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; 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.

[0024] 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; 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.

[0025] Among them, the optimization objective functions of both the macroscopic fine groove structure and the microscopic fine groove structure are It is expressed as:

[0026] 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; According to the optimal microscopic groove structure and the optimal macroscopic groove structure, the fourth design parameter of the groove on the surface of the soft pin is determined.

[0027] Furthermore, a computational fluid dynamics algorithm was used to simulate the effect of different groove distributions on the grip of the sole, which was configured as follows: Determine a mechanical model based on the physical phenomena of the sole contacting the ground, wherein the physical phenomena include the shape of the sole, material properties and ground conditions; A geometric model of the sole and the ground is established according to the mechanical model; wherein the geometric model includes the distribution and geometric shape of the fine grooves; Meshing the geometric model based on the structured grid type to discretize the continuous flow area; Define the initial conditions, wall conditions, inlet boundary conditions and outlet boundary conditions of the simulation; the inlet boundary conditions include velocity and pressure; the outlet boundary conditions include pressure and flow; the initial conditions include the state of the fluid at the beginning of the simulation; the wall conditions are the no-slip conditions or friction conditions set for the contact surface between the sole and the ground; Select a turbulence model for numerical simulation to obtain 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, the post-processor is used to analyze the velocity field, pressure field and turbulence characteristics, and visualize the analysis results; Compare simulation results for different slot distributions and analyze the effects of slot design on fluid velocity, pressure, and turbulence characteristics; identify how slot design affects fluid flow; A sensitivity analysis is performed to change the distribution and shape of the slots and to analyze the effects of the changes in the slots on the velocity field, pressure field and turbulence characteristics.

[0028] Furthermore, the post-processor is used to analyze the velocity field, pressure field and turbulence characteristics and visualize the analysis results. It is configured as follows: The velocity field is analyzed using a post-processor to calculate the average velocity, maximum velocity, and unevenness of velocity distribution in the contact area between the sole and the ground; Analyze the change in direction and velocity of fluid flow based on the following formula; ; in, It is the velocity change caused by the fine groove; is the drag coefficient; is the fluid density, is the fluid velocity; is the area of ​​the groove; the drag coefficient refers to the ratio of the resistance encountered by the fluid during movement to the fluid density, velocity and resistance area. This is a dimensionless coefficient used to describe the degree of resistance encountered by the fluid when passing through a pipe, hole or other object. In the design of fishing shoes, the drag coefficient can help understand and predict the flow resistance of fluids (such as water, mud, etc.) when the sole contacts the ground, thereby optimizing the design of the sole to improve grip and reduce sliding.

[0029] 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 area; Analyze turbulence characteristics, including turbulence kinetic energy, turbulence stress, and turbulence frequency; evaluate the impact of turbulence on the grip of the sole; Use visualization tools of computational fluid dynamics software to display velocity fields, pressure fields, and turbulence characteristics in the form of graphics and animations; Produce streamlines, isosurfaces, and vector plots to demonstrate fluid flow and the interaction between the sole and the ground; Color coding is used to display the velocity, pressure and turbulence intensity in different areas.

[0030] Further, 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 site of the fishing shoe user, and is configured as follows: set up is 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; Define the objective function set of the multi-objective optimization problem and map the design variables to the objective function space; where the objective function set ; Represents performance indicators related to design parameters; performance indicators include grip, wear resistance, weight and cost; The NSGA-II algorithm is used to calculate the design variable set Optimize; Among them, the NSGA-II algorithm maintains the diversity of solutions through non-dominated sorting and crowding 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 in each non-dominated layer to maintain the diversity of the population; the crowding degree calculation formula is expressed as: ;in, Is an individual The degree of congestion; is the number of objective functions in the objective function set; j is the cumulative index; and Is an individual In the The function value of two adjacent individuals on the objective function; It is The maximum value of the objective function; It is The minimum value of the objective function; Select individuals according to the non-dominated hierarchy and crowding degree to form a new parent population; Perform crossover and mutation operations on the parent population to generate a new offspring population; Repeat the non-dominated sorting, crowding calculation, crossover and mutation operations until the number of iterations is met; The obtained Pareto optimal solution set is analyzed to optimize the first design parameter, the second design parameter, the third design parameter and the fourth design parameter.

[0031] Further, the health parameter includes 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 pin is made of butyl rubber; Soft pins are made of rubber or thermoplastic elastomer; hard spikes are made of metal materials.

[0032] It should be noted that in the present application, the embodiments implemented by the intelligent design system side of the fishing shoe outsole can be cross-referenced with the embodiments implemented by the intelligent design method side of the fishing shoe outsole, and the present application will not go into details one by one.

[0033] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also 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: include: Determining first design parameters of a sole body of an outsole of the fishing shoe according to a three-dimensional shape of a foot of a user of the fishing shoe and health parameters; Determine the second design parameter of the soft pin and the third design parameter of the hard spike according to the gait characteristics, rhythm characteristics and pressure distribution data of the user of the fishing shoes when moving; wherein the soft pin and the hard spike are both arranged on the sole body; Determining fourth design parameters of the fine grooves on the surface of the soft pin according to the second design parameters of the soft pin, the usage frequency and wear pattern of the reference fishing shoes of the fishing shoe user; The first design parameter, the second design parameter, the third design parameter and the fourth design parameter are optimized according to the environmental characteristic data of the fishing ground of the user of the fishing shoes; wherein the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature and ground biological coverage; According to the optimized first design parameter, second design parameter, third design parameter and fourth design parameter, a design drawing of the fishing shoe outer sole adapted to different fishing environments and user characteristics is generated.

2. The intelligent design method for the outsole of a fishing shoe according to claim 1, characterized in that: The second design parameters include the number, distribution and depth of the soft pins protruding from the surface of the sole body; the third design parameters include the number, distribution and depth of the hard spikes protruding from the surface of the sole body; the second design parameters of the soft pins and the third design parameters of the hard spikes are determined according to the gait characteristics, rhythm characteristics and pressure distribution data of the user of the fishing shoes when moving, and are configured as follows: Capturing an image frame of the walking posture of the user of the fishing shoes by a camera; The captured image frames of the walking posture are processed using an inter-frame difference method to obtain a target gait region; According to the pressure change information when the sole of the fishing shoe user contacts the ground and the target gait area monitored by the flexible 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; Analyzing the gait rhythm of the user of the fishing shoes by using a music rhythm analysis algorithm in combination with the gait characteristics to obtain the rhythm characteristics; wherein the gait rhythm includes extracting the beat position, intensity and duration; Using an integrated sensor chip design, the pressure distribution of the sole of the user of the fishing shoes is measured through a sensor chip with a standardized DDR interface to obtain the pressure distribution data; According to the gait characteristics, the rhythm characteristics and the stress concentration areas and non-stress concentration areas represented by the pressure distribution data, the second design parameters of the soft pin and the third design parameters of the hard spikes are determined; wherein the pressure distribution data of the stress concentration area is greater than a preset pressure distribution data threshold.

3. The intelligent design method for the outsole of a fishing shoe according to claim 1, characterized in that: The health parameters include the weight and activity intensity of the user of the fishing shoes; the first design parameters include the material, shape and thickness of the sole body; The soft pin is made of rubber or thermoplastic elastomer; the hard shoe spike is made of metal material.

4. An intelligent design system for fishing shoe outsoles, the system implementing the method according to claim 1, characterized in that: include: A sole body design module, used to determine first design parameters of a sole body of the outsole of the fishing shoe according to the three-dimensional shape of the foot and health parameters of the user of the fishing shoe; A rubber pin design module, used to determine a second design parameter of a soft pin and a third design parameter of a hard spike according to gait characteristics, rhythm characteristics and pressure distribution data of a user of the fishing shoes when moving; wherein the soft pin and the hard spike are both arranged on the sole body; a fine groove design module, for determining fourth design parameters of the fine groove on the surface of the soft pin according to the second design parameters of the soft pin, the use frequency and wear pattern of the reference fishing shoes 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 environmental characteristic data of a fishing site of a user of the fishing shoes; wherein the environmental characteristic data includes ground humidity, ground material, ground stability, ground temperature and ground biological coverage; The comprehensive design drawing generation module is used to generate the design drawings of the fishing shoe outsole adapted to different fishing environments and user characteristics according to the optimized first design parameter, second design parameter, third design parameter and fourth design parameter.

5. The intelligent design system for fishing shoe outsoles according to claim 4, characterized in that: in, The second design parameter includes the number, distribution and depth of the soft pins protruding from the surface of the sole body; the third design parameter includes the number, distribution and depth of the hard spikes protruding from the surface of the sole body; the rubber pin design module determines the second design parameter of the soft pins and the third design parameter of the hard spikes according to the gait characteristics, rhythm characteristics and pressure distribution data of the user of the fishing shoes when moving, and is configured as follows: Capturing an image frame of the walking posture of the user of the fishing shoes by a camera; The captured image frames of the walking posture are processed using an inter-frame difference method to obtain a target gait region; According to the pressure change information when the sole of the fishing shoe user contacts the ground and the target gait area monitored by the flexible 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; Analyzing the gait rhythm of the user of the fishing shoes by using a music rhythm analysis algorithm in combination with the gait characteristics to obtain the rhythm characteristics; wherein the gait rhythm includes extracting the beat position, intensity and duration; Using an integrated sensor chip design, the pressure distribution of the sole of the user of the fishing shoes is measured through a sensor chip with a standardized DDR interface to obtain the pressure distribution data; According to the gait characteristics, the rhythm characteristics and the stress concentration areas and non-stress concentration areas represented by the pressure distribution data, the second design parameters of the soft pin and the third design parameters of the hard spikes are determined; wherein the pressure distribution data of the stress concentration area is greater than a preset pressure distribution data threshold.

6. The intelligent design system for fishing shoe outsoles according to claim 5, characterized in that: The fourth design parameters include the geometric shape, quantity, distribution and depth of the fine grooves; the fine groove design module determines the fourth design parameters of the fine grooves on the surface of the soft pin according to the second design parameters of the soft pin, the usage frequency and wear pattern of the reference fishing shoes of the fishing shoe user, and is configured as follows: Determining a region for fine groove design according to a second design parameter of the soft pin; identifying high wear areas and low wear areas from the areas of the slot design based on the wear pattern and usage frequency of a reference fishing shoe of a user of the fishing shoe; 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; According to the grip impact simulation results, a cross-scale topology optimization algorithm based on coupled deep learning is used to generate an optimal microscopic fine groove structure and an optimal macroscopic fine groove structure under various boundary conditions; 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; The optimization objective functions of the macroscopic fine groove structure and the microscopic fine groove structure are: It is expressed as: ; 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; According to the optimal microscopic groove structure and the optimal macroscopic groove structure, the fourth design parameter of the groove on the surface of the soft pin is determined.

7. The intelligent design system for fishing shoe outsoles according to claim 6, characterized in that: The computational fluid dynamics algorithm is used to simulate the influence of different groove distributions on the grip of the sole, and is configured as follows: Determine a mechanical model according to the physical phenomenon of the sole contacting the ground; wherein the physical phenomenon includes the shape of the sole, material properties and ground conditions; Establishing 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 fine grooves; Meshing the geometric model based on a structured grid type to discretize a continuous flow region; 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; the initial conditions include the state of the fluid at the beginning of the simulation; the wall conditions are no-slip conditions or friction conditions set for the contact surface between the sole and the ground; Select a turbulence model for numerical simulation to obtain fluid velocity, pressure and turbulence characteristics at each grid point; wherein the turbulence model includes at least one of the following: a Spalart-Allmaras model, a k-ε model, and a k-ω model; After the simulation is completed, the post-processor is used to analyze the velocity field, pressure field and turbulence characteristics, and visualize the analysis results; Compare simulation results for different slot distributions and analyze the effects of slot design on fluid velocity, pressure, and turbulence characteristics; identify how slot design affects fluid flow; A sensitivity analysis is performed to change the distribution and shape of the slots and to analyze the effects of the changes in the slots on the velocity field, pressure field and turbulence characteristics.

8. The intelligent design system for fishing shoe outsoles according to claim 7, characterized in that: The post-processor is used to analyze the velocity field, pressure field and turbulence characteristics and visualize the analysis results, and is configured as follows: Analyzing the velocity field using the post-processor to calculate the average velocity, maximum velocity and unevenness of velocity distribution in the contact area between the sole and the ground; Analyze the change in direction and velocity of fluid flow based on the following formula; ; in, It is the velocity change caused by the fine groove; is the drag coefficient; is the fluid density, is the fluid velocity; is the area of ​​the slot; 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 area; Analyze turbulence characteristics, including turbulence kinetic energy, turbulence stress and turbulence frequency; evaluate the effect of turbulence on the grip of the sole; Use visualization tools of computational fluid dynamics software to display velocity fields, pressure fields, and turbulence characteristics in the form of graphics and animations; Produce streamlines, isosurfaces, and vector plots to demonstrate fluid flow and the interaction between the sole and the ground; Color coding is used to display the velocity, pressure and turbulence intensity in different areas.

9. The intelligent design system for fishing shoe outsoles according to claim 8, 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 site of the user of the fishing shoes, and is configured as follows: set up is 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; Define the objective function set of the multi-objective optimization problem and map the design variables to the objective function space; wherein the objective function set ; indicating performance indicators related to design parameters; the performance indicators include grip, wear resistance, weight and cost; The NSGA-II algorithm is used to Optimize; wherein the NSGA-II algorithm maintains the diversity of solutions through non-dominated sorting and congestion calculation; the goal of the NSGA-II algorithm is to find the Pareto optimal solution set; Performing fast non-dominated sorting on the population to divide the population into a plurality of non-dominated levels; The crowding degree of individuals in each non-dominated layer is calculated to maintain the diversity of the population; the crowding degree calculation formula is expressed as: ;in, Is an individual The degree of congestion; is the number of objective functions in the objective function set; j is the cumulative index; and Is an individual In the The function value of two adjacent individuals on the objective function; It is The maximum value of the objective function; It is The minimum value of the objective function; Select individuals according to the non-dominated hierarchy and crowding degree to form a new parent population; Perform crossover and mutation operations on the parent population to generate a new offspring population; Repeat the non-dominated sorting, crowding calculation, crossover and mutation operations until the number of iterations is met; The obtained Pareto optimal solution set is analyzed to optimize the first design parameter, the second design parameter, the third design parameter and the fourth design parameter.

10. The intelligent design system for fishing shoe outsoles according to claim 8, characterized in that: The health parameters include the weight and activity intensity of the user of the fishing shoes; the first design parameters include the material, shape and thickness of the sole body; The soft pin is made of rubber or thermoplastic elastomer; the hard shoe spike is made of metal material.

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