Method and device for testing motility and wear resistance of socks

Abstract

The invention discloses a method and a device for testing motility and wear resistance of socks, and relates to the technical field of wear resistance detection of socks. The method comprises the following steps: S1, collecting user parameters and gait parameters of different motion types, constructing a gait model, calculating a pressure distribution evaluation value, and carrying out data preprocessing; s2, new material socks are installed, the stress path and the friction environment of the socks under the real movement condition are controlled and reproduced through the rhythms of different movement modes, the friction force, the sliding track and the local shearing condition are synchronously recorded, and a wear track is formed; s3, obtaining a sock sole image, performing visual identification and wear region extraction, and outputting a quantitative wear index; and S4, evaluating the wear resistance of the new material socks, carrying out suitability matching analysis, taking optimization measures, and outputting an evaluation report. The problem that due to the fact that different movement types have completely different gait cycles and movement postures, wear of the socks is difficult to test is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of sock wear resistance detection, and specifically provides a method and device for testing the athletic wear resistance of socks. Background Art

[0002] As a high-frequency used clothing item that is close to the skin, the wear resistance of socks is directly related to wearing comfort, service life, and the safety of athletic performance. Currently, there are widely used standard test methods for evaluating the wear resistance of fabrics on the market. These methods mainly judge the durability of fabrics by simulating repeated friction between the fabric and a standard grinding surface. Although these methods have certain value in the general evaluation of clothing materials, they have obvious deficiencies for functional textiles such as socks that are dynamically fitted and affected by multi-directional loads and complex stress fields.

[0003] These methods cannot reproduce the human gait rhythm, regional force changes, and shear slip process under different types of exercise, resulting in a disconnection between the test results and the actual usage scenarios. At the same time, traditional testing methods are difficult to capture the matching relationship between the worn area and the stressed area, unable to judge whether the sock structure design is reasonable, and prone to missing potential functional area failure risks. In addition, the current test indicators are generally single, lacking integrated analysis of dynamic parameters such as step frequency, contact area, and pressure intensity, and it is difficult to achieve systematic modeling and evaluation of the material properties, structural characteristics, and wear behavior.

[0004] Therefore, aiming at the above problems, there is an urgent need for a method and device for testing the athletic wear resistance of socks. Summary of the Invention

[0005] Technical Problems to be Solved Aiming at the deficiencies of the prior art, the present invention provides a method and device for testing the athletic wear resistance of socks, which solves the problem that it is difficult to test the wear of socks due to the completely different gait cycles and exercise postures of different types of exercise.

[0006] Technical solution: To achieve the above objectives, the present invention is realized through the following technical solutions: A method and device for testing the athletic performance and abrasion resistance of socks, including the following steps: S1, collect user parameters and gait parameters of different exercise types, construct a gait model, calculate the pressure distribution evaluation value according to the user parameters and gait parameters, and perform data preprocessing; S2, install new material socks, use the rhythm control of different exercise modes to reproduce the force path and friction environment of the socks under real exercise conditions, synchronously record the friction force, slip trajectory and local shear conditions through multiple sensors, and form a wear trajectory; S3, obtain the image of the sock bottom, perform visual recognition and extraction of the worn area, and output a quantitative wear index; S4, fuse the test data and wear results to evaluate the abrasion resistance of the new material socks, perform adaptability matching analysis, and take optimization measures according to the adaptability evaluation value, and output an evaluation report.

[0007] Further, collect the user parameters of the wearer. The user parameters include: height, weight, gender, foot type structure and plantar partition size. Extract the standard gait parameters according to different exercise types. The gait parameters include: step frequency data, single-step duration, leading landing part and proportion of force application time. Normalize the user parameters and gait parameters; use the controller to convert the gait parameters into control signals. The foot model realizes the landing and pushing actions through the front and rear drive actuators. The ankle angle adjustment machine adjusts the landing angle. Inertial measurement unit sensor measurement modules and displacement encoders are arranged at the bottom of the foot model and the ankle joint part respectively to realize a highly simulated gait dynamic simulation cycle.

[0008] Further, the specific process of calculating the pressure distribution evaluation value according to the user parameters and gait parameters and performing data preprocessing is as follows: Obtain the weight of the wearer, monitor the periodic oscillation through the inertial measurement unit sensor of the foot model to obtain the step frequency data, obtain the regional relative phase according to the leading landing part of the standard gait parameters, obtain the force area of the i-th plantar region through the actual measurement of the contact area by the flexible pressure sensing pad, and normalize the force area; Multiply the weight of the wearer by the gravity conversion pressure constant to obtain the total pressure value. Multiply the step frequency data, twice the pi and the current time to obtain the angle value in the current gait cycle. Add the relative phase of the i-th plantar region to the angle value to obtain the total phase angle value. Calculate the cosine function for the total phase angle value, multiply the calculation result of the cosine function by the landing impact weight factor and add the constant one, and then multiply the ratio of the added result to the force area of the i-th region and the total pressure value to obtain the pressure distribution evaluation value; Eliminate high-frequency noise for the pressure value, angle value and displacement data through low-pass filtering, perform standardization and normalization processing, and perform time-axis synchronization processing.

[0009] Furthermore, install new material socks and use the rhythm control of different exercise modes to reproduce the force path and friction environment of the socks under real exercise conditions. The specific process of forming the wear track by synchronously recording the friction force, slip trajectory, and local shear conditions through multiple sensors is as follows: Install the new material socks to be tested on the surface of the foot mold according to the standard process, use a tensiometer for tensioning, and record the initial length, width, and the positions of key force-bearing areas. Take multi-angle initial state images of the socks, including the front view, sole view, and local arch close-ups, and uniformly store them in the sample database; Call the gait trajectory according to the set exercise mode, make the foot mold perform periodic bionic movements driven by the control system to achieve dynamic simulation of different scenarios, and further establish the combined action of multi-directional loads of friction, compression, and tension between the bottom of the foot mold and the test table; During the test, use a flexible pressure sensing pad to collect the force data of each area of the sock bottom in real time, cooperate with the inertial measurement unit sensor module and the friction coefficient sensor to synchronously obtain the shear impact characteristics and the change of the foot trajectory, use the bottom friction tensiometer to monitor the change of the friction force, and use a laser displacement meter for slip trajectory reconstruction; Cache the force data collected during the test through a unified timestamp alignment mechanism, and perform filtering, normalization, and outlier removal steps.

[0010] Furthermore, the specific process of obtaining the sock bottom image and performing visual recognition and wear area extraction is as follows: After the motion simulation test is completed, remove the socks on the foot mold and flatten them in a standard manner. The flattened area is numbered according to the distribution of the sole area. Use a high-resolution platform to obtain the sock bottom image. The shooting methods include standard vertical orthographic projection images and oblique side light illumination images to enhance the recognition clarity of broken lines, holes, and color changes; After the image is obtained, automatically perform wear image recognition and analysis on the sock bottom image, perform image grayscale and background removal, use an edge detection algorithm to locate potential wear edges, and in the cleaned image, identify the key attributes of the wear area through region connectivity analysis and morphological feature extraction methods, including the total length of broken lines, hole diameter, and local color patch area index, and output a structured sock bottom wear map according to the initial state image taken.

[0011] Furthermore, the specific process of outputting the quantitative wear index is as follows: the wear area ratio is output by the ratio of the damaged area region to the total sock sole area, and the thread break density is obtained by the number of broken lines per unit area; for each region i divided from the sock sole, the wear area of the i-th region is identified by using the wear image and the wear area is normalized, the average pressure of the i-th region is obtained by integrating the average pressure distribution evaluation value over time, and the maximum average pressure among all regions is selected. The average pressure of the i-th region is divided by the maximum average pressure among all regions in the test to obtain the relative pressure intensity ratio of this region. By comparing the pressure distribution evaluation value of each region with the average pressure threshold, the high-pressure duration of this region is obtained by using the sensor data. The high-pressure exposure ratio of this region is obtained by the ratio of the high-pressure duration to the total test time. The pressure response weight factor is used to weight the relative pressure intensity ratio in exponential form to obtain the pressure intensity index term. The time exposure weight factor is used to weight the high-pressure exposure ratio in exponential form to obtain the high-pressure time index term. The pressure intensity index term and the high-pressure time index term are multiplied and then multiplied by the wear area and the regional structure weight factor of this region to obtain the fatigue matching contribution value of this region. The fatigue matching contribution values of all regions are summed up, and then divided by the sum of the products of all worn regions and the regional structure weight factor to obtain the fatigue concentration evaluation value; the fatigue concentration evaluation value is compared with the fatigue concentration threshold in real time. When the fatigue concentration evaluation value is greater than or equal to the fatigue concentration threshold, it is determined that the sock wear is reasonably concentrated, and it is automatically archived into the qualified sample library, recommended for use in the specified sports type, selected as a performance example, and appears on the final report summary page; when the fatigue concentration evaluation value is less than the fatigue concentration threshold, it is determined that the sock wear drifts, the region with significant wear but insufficient pressure loading is automatically located, the structure and force misalignment regions are identified, suggestions for local structure reconstruction are generated, the stress type is inferred according to the wear characteristics, a prompt to replace with high-wear-resistant and buffer materials is sent, the sock sample is automatically marked as to be verified for structural optimization, the test verification process is reloaded after adjustment, and the current test data is retained as the previous reference version.

[0012] Furthermore, the specific process of integrating test data and wear results to evaluate the abrasion resistance of new material socks is as follows: Integrate multi-dimensional data of user parameters, gait parameters, and the measured wear results to construct characteristic variables, including the wear rate under unit pressure and the gait peak-wear alignment index; Calculate the pressure distribution evaluation value of each area, and perform time integration operation to obtain the total cumulative force during the test period, and sum up the total cumulative forces of all areas; Identify the wear area of each area through wear image recognition and sum it up to obtain the total wear amount of the sock, and divide the total cumulative force of all areas by the total wear amount to obtain the unit wear evaluation value; Compare the unit wear evaluation value with the unit wear threshold in real time. When the unit wear evaluation value is less than the unit wear threshold, it is determined that the sock has a problem of insufficient material abrasion resistance, and it is recommended to replace it with high-strength fabric, and it is recommended to adopt partitioned differential reinforcement, and conduct linkage structure modeling analysis to check whether the material strength matches the stress area to avoid high-pressure aggregation in weak material areas; When the unit wear evaluation value is greater than or equal to the unit wear threshold, it is determined that the material performance is stable, and it is included in the qualified sample set of abrasion resistance and recommended for high-load or long-term exercise scenarios.

[0013] Furthermore, the specific process of performing adaptability matching analysis is as follows: Compare the test wear results of sock samples under different exercise modes and output the adaptability matching results; Obtain the unit wear evaluation value and fatigue concentration evaluation value of the sock sample, calculate the standard deviation and mean of the wear areas of all areas on the sock bottom according to wear image recognition, subtract the unit wear evaluation value from one and multiply by the wear resistance evaluation weight factor to obtain the wear resistance evaluation item, multiply the fatigue concentration evaluation value by the structural rationality weight factor to obtain the structural rationality evaluation item, subtract the ratio of the standard deviation to the mean of the wear area from one and multiply by the wear distribution weight factor to obtain the wear uniformity evaluation item, and sum up the wear resistance evaluation item, structural rationality evaluation item, and wear uniformity evaluation item to obtain the adaptability evaluation value.

[0014] Furthermore, optimization measures are taken according to the adaptability evaluation value, and the specific process of outputting the evaluation report is as follows: Compare the adaptability evaluation value with the adaptability threshold in real time. When the adaptability evaluation value is greater than or equal to the adaptability threshold, it is determined as "well-adapted type"; it is published in the product visualization report as a high-adaptability performance example; allowing continuous large-scale strength tests or multi-scenario expansion tests to be carried out; generating a complete test report for publicity and internal evaluation; when the adaptability evaluation value is less than the adaptability threshold, it is determined as "to-be-optimized type"; based on the components of the adaptability evaluation value, automatically identify the source of the performance bottleneck, and put forward targeted optimization suggestions from three aspects: material selection, structural layout, and test configuration; after the optimization plan is designed and adjusted, trigger a new round of simulated gait loading process, and re-conduct wear evaluation and performance scoring to achieve a closed-loop feedback control for product iteration and upgrade; automatically integrate the key parameters and evaluation results of the whole process, and output a structured and visualized standardized test report, including test parameters, sock images, wear images, and scoring suggestions, including outputting the most suitable population type and the most suitable sports scenario for this new material sock, and supporting the prediction of the expected lifespan of the sock under different exercise rhythms and populations.

[0015] Furthermore, it includes a data acquisition and preprocessing module, a dynamic loading and wear simulation module, a wear detection and intelligent evaluation module, and a loss modeling and sports scenario adaptability analysis module: Among them, the data acquisition and preprocessing module is used to collect user parameters and gait parameters of different sports types, construct a gait model, calculate the pressure distribution evaluation value according to the user parameters and gait parameters, and perform data preprocessing; the dynamic loading and wear simulation module is used to install the new material sock, use the rhythm control of different sports modes to reproduce the force path and friction environment of the sock under real sports conditions, synchronously record the friction force, slip trajectory and local shear conditions through multiple sensors to form a wear trajectory; the wear detection and intelligent evaluation module is used to obtain the sock bottom image, perform visual recognition and wear area extraction, and output a quantitative wear index; the loss modeling and sports scenario adaptability analysis module is used to fuse the simulation parameters and wear results to evaluate the wear resistance of the new material sock, perform adaptability matching analysis, and take optimization measures according to the adaptability evaluation value, and output an evaluation report.

[0016] Beneficial effects The present invention has the following beneficial effects: (1). In the present invention, by combining user parameters with sports types, a parameter-driven simulation gait control model is constructed, and dynamic loading is carried out using a foot mold to achieve a highly restored gait dynamic cycle. It breaks through the limitation of the traditional homogeneous load test model that cannot distinguish individual differences and sports characteristics, and can more realistically reproduce the friction and shear paths that the sock bears in the actual sports scenario.

[0017] (2) The present invention establishes pressure distribution evaluation values ​​for different areas of the sock sole, combines the features of broken lines, holes, spots, etc. in the wear image, calculates the wear area and force matching of each area, and thus obtains fatigue concentration evaluation values, which are used to measure the consistency and structural rationality of the wear area and the force concentration area of ​​the sock. This mechanism can quantify whether the wear is concentrated in the high-pressure area, and realize the refined diagnosis of whether the structural rationality and regional functional configuration match, which is an innovative fatigue-wear coupling analysis method.

[0018] (3) The present invention, through the proposed adaptability evaluation value, comprehensively reflects the material bearing capacity, structural partition rationality and wear distribution balance, and can effectively judge the matching degree of new material socks for specific sports scenes and crowd types. This scoring mechanism breaks through the traditional single "total wear" or "number of cycles" evaluation model, and has significant data interpretation and engineering guidance significance.

[0019] (4) The present invention, based on fatigue concentration value, unit wear value and adaptability evaluation value, can automatically identify the wear drift area and the risk of insufficient material wear resistance, generate local structure optimization suggestions and material replacement prompts, and trigger a new round of loading test, thus realizing the closed-loop feedback process of product testing-analysis-adjustment-retesting. This mechanism greatly improves the adaptability efficiency and development accuracy of new material socks in multiple scenarios, and promotes the intelligent evaluation and adaptive optimization of socks into a new stage.

[0020] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flow chart of a method for testing the sports wear resistance of socks; Figure 2 A module connection diagram of a socks sports wear resistance test device Figure 3 A bar graph of the fitness evaluation values ​​of a method and device for testing the sports wear resistance of socks; DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figures 1-3, an embodiment of the present invention provides a technical solution: a method and device for testing the athletic performance and wear resistance of socks, including the following steps: S1, collect user parameters and gait parameters of different exercise types, construct a gait model, calculate the pressure distribution evaluation value according to the user parameters and gait parameters, and perform data preprocessing; S2, install new material socks, use the rhythm control of different exercise modes to reproduce the force path and friction environment of the socks under real exercise conditions, synchronously record the friction force, slip trajectory and local shear conditions through multiple sensors to form a wear trajectory; S3, obtain the sock bottom image, perform visual recognition and wear area extraction, and output a quantitative wear index; S4, fuse the test data and wear results to evaluate the wear resistance of the new material socks, perform adaptability matching analysis, and take optimization measures according to the adaptability evaluation value, and output an evaluation report.

[0024] Specifically, the specific process of collecting user parameters and gait parameters of different exercise types and constructing a gait model is as follows: collect the user parameters of the wearer, and the user parameters include: height, weight, gender, foot type structure and sole partition size, extract the standard gait parameters according to different exercise types, and the gait parameters include: step frequency data, single-step duration, leading landing part and proportion of force application time, perform normalization processing on the user parameters and gait parameters, and this process can achieve accurate modeling of individual differences and exercise characteristics by the test system, and enhance the adaptability and restoration degree of the exercise scenario simulation; use the controller to convert the gait parameters into control signals, and the foot mold realizes the landing and pushing actions through the front and rear drive actuators, and the ankle angle adjustment mechanism adjusts the landing angle to effectively restore the movement states of foot types such as varus, valgus and flat feet, and improve the authenticity of gait simulation; inertial measurement unit sensor measurement modules and displacement encoders are respectively arranged at the bottom of the foot mold and the ankle joint part, which can achieve high-precision synchronous acquisition of the foot trajectory and attitude changes, ensure that the simulation actions are controllable and traceable, so as to realize a highly simulated gait dynamic simulation cycle, and provide a physical basis guarantee for subsequent dynamic load loading and wear behavior tracking.

[0025] In this implementation scheme, by collecting user individual parameters and gait characteristics of exercise types, a highly personalized gait control model is constructed, enabling the system to accurately adapt to the foot type structures and exercise behaviors of different populations, and significantly improving the simulation accuracy and application scope of the test scenario; using drive actuators and angle adjustment devices to truly restore dynamic postures such as varus, valgus and flat feet, improving the physical fit of bionic simulation; at the same time, the inertial measurement unit and displacement encoder achieve high-precision synchronous acquisition of the foot trajectory and attitude changes, ensuring the controllability and traceability of the test process, providing stable and reliable original data support for dynamic load control and wear cause analysis, thereby enhancing the system's real evaluation ability of the wear resistance of socks under complex exercise conditions.

[0026] Specifically, the specific process of calculating the pressure distribution evaluation value according to the user parameters and gait parameters and performing data preprocessing is as follows: Obtain the weight of the wearer, monitor the periodic oscillation through the inertial measurement unit sensor of the foot mold to obtain the step frequency data, obtain the regional relative phase according to the leading landing part of the standard gait parameters, obtain the force area of the i-th area of the sole through the actual measurement of the contact area by the flexible pressure sensing pad, and perform normalization processing on the force area; Multiply the weight of the wearer by the gravity conversion pressure constant to obtain the total pressure value, multiply the step frequency data, twice the pi, and the current time to obtain the angle value in the current gait cycle, add the relative phase of the i-th sole area to the angle value to obtain the total phase angle value, calculate the cosine function for the total phase angle value, multiply the calculation result of the cosine function by the landing impact weight factor and then add the constant one, and then multiply the ratio of the added result to the force area of the i-th area and the total pressure value to obtain the pressure distribution evaluation value; Eliminate high-frequency noise for the pressure value, angle value, and displacement data through low-pass filtering, perform standardization and normalization processing, and perform time-axis synchronization processing.

[0027] Among them, the specific calculation formula for the pressure distribution evaluation value is:

[0028] In the formula, is the pressure distribution evaluation value of the i-th area of the sole at the current time t, which is used to calculate the pressure per unit area of the i-th area of the sole at the moment t to reflect the dynamic change of the sole force during a gait cycle; is the gravity conversion pressure constant, which is a fixed value of 9.8 / 1000; is the weight of the wearer, and the weight of the wearer will affect the total force intensity of the sole; is the step frequency data, which is automatically identified by monitoring the periodic oscillation through the inertial measurement unit sensor and is used to distinguish the movement rhythm characteristics such as jogging, running fast, and jumping; is the regional relative phase, which is used to describe when different areas are stressed during a gait cycle. For example: the forefoot leads and the heel lags, and is usually set to , ; is the force area of the i-th area of the sole; is the landing impact weight factor, which is collected by the inertial measurement unit module for the vertical acceleration, estimated through the acceleration peak value, and then normalized to obtain the relative impact intensity, and the range is between 0 and 0.5.

[0029] In this implementation scheme, by fusing multi-dimensional parameters such as user body weight, step frequency, landing rhythm, and plantar structure, a regional pressure distribution evaluation model with time dynamics is constructed, which can accurately simulate the real load changes in different gait cycles of each region of the sole; the step frequency is extracted by using an inertial measurement unit, combined with the relative phase of the region and the actually measured area of the flexible pressure sensing pad, and a cosine function model with phase regulation is established to achieve fine modeling of the pressure fluctuations in different regions; at the same time, data preprocessing mechanisms such as low-pass filtering, normalization, and time-axis synchronization are introduced to effectively improve the data quality and the stability of the system response.

[0030] Specifically, install new material socks, and use the rhythm control of different exercise modes to reproduce the force path and friction environment of the socks under real exercise conditions. The specific process of forming the wear track by synchronously recording the friction force, slip trajectory, and local shear conditions through multiple sensors is as follows: install the new material socks to be tested on the surface of the foot mold according to the standard process, use a tensile tester for tensioning treatment, and record the initial length, width, and the positions of key force-bearing areas. Take multi-angle initial state images of the socks, including front views, sole views, and local close-ups of the arch, and store them uniformly in the sample database. This process helps to construct a standardized before-and-after state comparison system, improving the subsequent wear recognition accuracy and sample traceability; call the gait trajectory according to the set exercise mode, and make the foot mold perform periodic bionic motion under the drive of the control system to achieve dynamic simulation of different scenarios. This gait loading method can truly restore the sole force rhythm and slip behavior under various types of exercises, enhancing the environmental credibility of the wear load path; further establish a multi-directional load joint action of friction, compression, and tension between the bottom of the foot mold and the test table, significantly improving the mechanical multi-dimensionality of the simulation environment and making the wear test results more practical and reliable; during the test process, the force data of each area of the sock bottom are collected in real time through a flexible pressure sensing pad, and the shear impact characteristics and foot trajectory changes are obtained synchronously with the inertial measurement unit sensor module and the friction coefficient sensor. The bottom friction tensiometer is used to monitor the friction force change, and the laser displacement meter is used for slip trajectory reconstruction to achieve full-process, multi-channel, and high-frequency data synchronization acquisition of wear load behavior, providing data support for establishing the mapping model between the wear mechanism and the load trajectory; the force data collected during the test are cached through a unified time stamp alignment mechanism, and after steps such as filtering, normalization, and outlier removal, the data quality is ensured to be stable and the structure is complete, facilitating subsequent analysis, modeling, and decision-making.

[0031] In this implementation scheme, the accuracy of wear identification and the traceability of samples are improved through standardized installation and initial state recording; based on the control of multiple types of movement rhythms, the reproduction of real plantar forces and slip paths is achieved, enhancing the restoration degree of the test environment; a combined action of friction-compression-tension is constructed to improve the multi-dimensional adaptability of the test load; multiple sensors are integrated to synchronously collect forces, friction, and trajectories, providing high-quality data support for wear mechanism modeling; unified timestamps and data preprocessing processes ensure the stability and reliability of the data structure, supporting subsequent analysis and optimization.

[0032] Specifically, the specific process of obtaining the sock sole image and performing visual recognition and wear area extraction is as follows: After the motion simulation test is completed, remove the sock from the foot mold and flatten it in a standard manner. The flattened area is numbered according to the distribution of the plantar area. Use a high-resolution platform to obtain the sock sole image. The shooting methods include standard vertical orthographic projection images and oblique side light illumination images, enhancing the recognition clarity of line breaks, holes, and color changes. This image acquisition method can improve the image's ability to distinguish fine wear features and enhance the accuracy and stability of subsequent algorithm recognition. After the image is obtained, automatically perform wear image recognition and analysis on the sock sole image, perform image grayscale conversion and background removal, and use an edge detection algorithm to locate potential wear edges, which can effectively remove background interference information and extract the true boundary shape of the wear area. In the cleaned image, through region connectivity analysis and morphological feature extraction methods, key attributes of the wear area are identified, including the total length of broken lines, hole diameters, and local color patch area indicators, realizing multi-dimensional quantitative characterization of the wear degree and enhancing the evaluation dimension. And according to the initial state image taken, a structured sock sole wear atlas is output, which can be used as an intuitive basis for subsequent fatigue evaluation and material structure optimization, improving the result interpretability and data reuse efficiency.

[0033] In this implementation scheme, through high-resolution image acquisition and standard flattening operations, the recognition ability of fine wear features on the sock sole such as line breaks, holes, and color changes is improved, enhancing the image clarity and the accuracy and stability of algorithm recognition; combined with grayscale conversion, background removal, and edge detection algorithms, the true boundary of the wear area can be accurately extracted, reducing background interference; through region connectivity analysis and morphological feature extraction, multi-dimensional quantitative expression of the wear degree is realized, effectively supporting the fine evaluation of sock sole wear behavior; the finally output structured wear atlas strengthens the decision-making ability of the system in wear mechanism analysis and material structure optimization.

[0034] Specifically, the specific process of outputting the quantitative wear index is as follows: the wear area ratio is output by the ratio of the damaged area region to the total sock sole region, and the thread break density is obtained by the number of broken lines per unit area; for each region i divided from the sock sole, the wear area of the i-th region is identified by using the wear image and the wear area is normalized, the average pressure of the i-th region is obtained by integrating the average value of the average pressure distribution evaluation value over time, and the maximum average pressure among all regions is screened out. The average pressure of the i-th region is divided by the maximum average pressure among all regions in the test to obtain the relative pressure intensity ratio of this region. By comparing the pressure distribution evaluation value of each region with the average pressure threshold, the high-pressure duration of this region is obtained by using the sensor data. The high-pressure exposure ratio of this region is obtained by the ratio of the high-pressure duration to the total test time. The pressure response weight factor is used to weight the relative pressure intensity ratio in an exponential form to obtain the pressure intensity index term. The time exposure weight factor is used to weight the high-pressure exposure ratio in an exponential form to obtain the high-pressure time index term. The pressure intensity index term and the high-pressure time index term are multiplied and then multiplied by the wear area and the regional structure weight factor of this region to obtain the fatigue matching contribution value of this region. The fatigue matching contribution values of all regions are summed up and divided by the total sum of the product of all worn regions and the regional structure weight factor to obtain the fatigue concentration evaluation value; the fatigue concentration evaluation value is compared with the fatigue concentration threshold in real time. When the fatigue concentration evaluation value is greater than or equal to the fatigue concentration threshold, it is determined that the sock wear is reasonably concentrated, and it is automatically archived into the qualified sample library, recommended for use in the specified sports type, selected as a performance example, and appears on the final report summary page; when the fatigue concentration evaluation value is less than the fatigue concentration threshold, it is determined that the sock wear drifts, the region with significant wear but insufficient pressure loading is automatically located, the structure and force mismatch region is identified, a local structure reconstruction suggestion is generated, the stress type is inferred according to the wear characteristics, a prompt to replace the high-wear-resistant and buffer materials is sent, the sock sample is automatically marked as to be verified for structure optimization, and after adjustment, the reloading test verification process is carried out, and the current test data is retained as the previous reference version.

[0035] Among them, the specific calculation formula for the fatigue concentration evaluation value is:

[0036] In the formula, is the fatigue concentration evaluation value, a quantitative index to measure whether the actual wear distribution of the sock matches its force mode; is the wear area of the i-th region of the sole identified by the wear image, quantitatively reflecting the wear intensity of the sock in this region; is the average pressure of the i-th region, indicating the force level of this region of the sock sole; is the maximum average pressure among all regions, used to construct the relative pressure ratio; is the high-pressure duration of the i-th region in the high-pressure state, reflecting the cumulative duration of this region in the high-pressure state; is the total test time, which is a reference value for standardizing the high - pressure time to make the comparison between different test durations possible; is the regional structure weight factor, which is obtained by normalizing the proportion of the cumulative pressure of the region in the total load by extracting the landing frequency of each region in a complete cycle from the gait parameters, and the range is between 0.05 and 0.6; is the pressure response weight factor, which is obtained by non - linear fitting based on multiple sets of data of average pressure and wear area, and the range is between 0.8 and 1.5; is the time exposure weight factor, which is obtained by bivariate regression fitting by comparing the high - pressure duration and wear area of multiple regions, and the range is between 0.5 and 1.2.

[0037] In this implementation plan, through image recognition and multi - region partition analysis, the accurate quantification of multi - dimensional indexes such as the wear area, broken - wire density, pressure intensity ratio, and high - pressure exposure ratio of the sock bottom is realized, and then a fatigue concentration evaluation value is constructed to effectively reflect the consistency between the wear area and the force - bearing mode; this process not only improves the coupling modeling ability between wear behavior and mechanical response, but also can realize the automatic discrimination of wear rationality and structural adaptability; based on the evaluation results, the abnormal wear area can be accurately located, suggestions for replacing with high - wear - resistant materials and structural reconstruction can be put forward, and a feedback loop can be formed to drive a new round of verification tests, so as to realize the accurate screening, performance grading, and design iteration optimization of sock products.

[0038] Specifically, the specific process of integrating test data and wear results to evaluate the wear resistance of new - material socks is as follows: integrate multi - dimensional data of user parameters, gait parameters, and the tested wear results to construct characteristic variables, including the wear rate under unit pressure and the gait peak - to - wear alignment index; calculate the pressure distribution evaluation value of each region, and perform time - integral operation to obtain the total cumulative force during the test cycle, and sum up the total cumulative force of all regions; obtain the wear area of each region through wear image recognition and sum it up to get the total wear amount of the sock, and divide the total cumulative force of all regions by the total wear amount to obtain the unit wear evaluation value; compare the unit wear evaluation value with the unit wear threshold in real time. When the unit wear evaluation value is less than the unit wear threshold, it is determined that the sock has a problem of insufficient material wear resistance, and it is recommended to replace it with high - strength fabric, and it is recommended to adopt partition - differentiated reinforcement, and link the structural modeling to analyze whether the material strength matches the force - bearing region to avoid high - pressure aggregation in the weak - material area; when the unit wear evaluation value is greater than or equal to the unit wear threshold, it is determined that the material shows stability, and it is classified into the qualified sample set of wear resistance and recommended for high - load or long - time exercise scenarios.

[0039] Among them, the specific calculation formula of the unit wear evaluation value is:

[0040] In the formula, is the unit wear evaluation value, which measures the cumulative load borne by the whole sock under unit wear; is the total number of standard areas divided on the sole, such as the forefoot, arch, heel, etc., for regional statistics; is the pressure distribution evaluation value of the i-th area on the sole at the current time ; is the total cumulative force of area i within the test period , which is used to represent the total strength of the load. Among them, represents the upper limit of the integration time interval to ensure the time consistency of the force data in each area; is the wear area of the i-th area on the sole obtained by wear image recognition.

[0041] In this implementation scheme, by integrating user parameters, gait data, and wear results, a unit wear evaluation value is constructed to accurately quantify the wear resistance of sock materials under moving loads; based on threshold comparison, insufficient material strength can be automatically identified, and suggestions for replacing with high-strength fabrics and regional reinforcement can be put forward to avoid mismatch in high-pressure areas; when the material performance is stable, it is classified into the qualified sample library for high-load movement recommendations, realizing the closed-loop control of wear resistance screening and material optimization.

[0042] Specifically, the specific process of performing adaptability matching analysis is as follows: comparing the test wear results of sock samples under different movement modes and outputting the adaptability matching results, which helps to reveal the performance stability and adaptability of socks in multiple scenarios; obtaining the unit wear evaluation value and fatigue concentration evaluation value of the sock sample, calculating the standard deviation and mean of the wear areas of all areas on the sock bottom according to wear image recognition, which can effectively reflect the uniformity and local concentration degree of wear distribution. Subtracting the unit wear evaluation value from the constant one and then multiplying by the wear resistance evaluation weight factor to obtain the wear resistance evaluation item, multiplying the fatigue concentration evaluation value by the structural rationality weight factor to obtain the structural rationality evaluation item, subtracting the ratio of the standard deviation to the mean of the wear area from the constant one and then multiplying by the wear distribution weight factor to obtain the wear uniformity evaluation item, and summing up the wear resistance evaluation item, the structural rationality evaluation item, and the wear uniformity evaluation item to obtain the adaptability evaluation value.

[0043] Among them, the specific calculation formula of the adaptability evaluation value is:

[0044] In the formula, is the adaptability evaluation value, which comprehensively measures wear resistance, fatigue concentration, and wear distribution uniformity, facilitating the judgment of whether the sock is suitable for a certain type of movement scenario. The higher the value, the more suitable the sock is for this type of movement scenario; is the unit wear evaluation value, which measures the average force accumulation corresponding to unit wear, The smaller the value, the more wear-resistant the material is; is the fatigue concentration evaluation value, which measures whether the stress concentration leads to wear concentration. The higher the value, the better the match, indicating good consistency between the structure and the stress path; is the standard deviation of the wear area of all regions, which measures the dispersion degree of wear between different regions. The larger the value, the more uneven the wear; is the mean value of the wear area of all regions; represents the distribution consistency of wear. The closer the value is to 1, the more uniform the distribution; is the wear resistance evaluation weight factor, which is obtained by linear regression fitting of the unit wear evaluation values of multiple groups of sock samples, and the range is between 0.3 and 0.5; is the structural rationality weight factor, which is obtained by linear regression fitting of the fatigue concentration evaluation values of several socks under different structural designs, and the range is between 0.3 and 0.5; is the wear distribution weight factor, which is obtained by linear regression fitting of the standard deviation and mean value of the wear area of multiple groups of socks, and the range is between 0.2 and 0.4.

[0045] Set the wear resistance evaluation weight factor to 0.4, the structural rationality weight factor to 0.35, and the wear distribution weight factor to 0.25, so that these three weight factors remain unchanged. Under the continuous change of the unit wear evaluation value, fatigue concentration evaluation value and regional wear area of the new material socks, calculate the suitability evaluation value of the socks. As shown in Table 1, the data table of the suitability evaluation value.

[0046] Table 1 Data table of suitability evaluation value

[0047] As Figure 3 shown, it is a column chart of the suitability evaluation value of a sock motility and wear resistance test method and device provided by an embodiment of the present application. As shown in Table 1 and Figure 3 It can be seen that when these three weight factors remain unchanged and the unit wear evaluation value, fatigue concentration evaluation value and regional wear area of the new material socks change continuously, different sock samples have different suitability evaluation values.

[0048] In this implementation scheme, by comparing the wear performance under different motion modes, the performance adaptability of socks in multiple scenarios is comprehensively revealed; at the same time, by integrating the three core indicators of unit wear, fatigue concentration and wear distribution, a suitability evaluation value is constructed, which can accurately reflect the comprehensive characteristics such as material durability, structural matching degree and wear uniformity; this method not only improves the interpretability and scientificity of the evaluation results, but also provides data support and quantitative basis for sock design optimization and application scenario matching.

[0049] Specifically, and based on the adaptability evaluation value, the specific process of taking optimization measures and outputting the evaluation report is as follows: Compare the adaptability evaluation value with the adaptability threshold in real time. When the adaptability evaluation value is greater than or equal to the adaptability threshold, it is determined as "well-adapted type"; it is published in the product visualization report as a high-adaptability performance example to enhance the credibility of product recommendation and market orientation; continue to carry out large-scale strength tests or multi-scenario expansion tests to further verify its broad-spectrum adaptability; generate a complete test report for publicity and internal evaluation; when the adaptability evaluation value is less than the adaptability threshold, it is determined as "to-be-optimized type"; based on the constituent factors of the adaptability evaluation value, automatically identify the source of performance bottlenecks to ensure the efficiency and accuracy of problem location improvement, and put forward targeted optimization suggestions from three aspects: material selection, structural layout, and test configuration; after the optimization plan is designed and adjusted, trigger a new round of simulated gait loading process, re-conduct wear evaluation and performance scoring, and achieve closed-loop feedback control for product iteration and upgrade; automatically integrate the key parameters and evaluation results of the whole process, and output a structured and visualized standardized test report, including test parameters, sock images, wear images, scoring suggestions, including outputting the most suitable population type and the most suitable sports scenario for this new material sock, and supporting the prediction of the expected lifespan of the sock under different exercise rhythms and populations.

[0050] In this implementation plan, by comparing the adaptability evaluation value with the threshold in real time, the system can automatically judge the performance level of the sock sample, and recommend to enter the large-scale test and market promotion process in the case of "well-adapted type" to improve the conversion efficiency of excellent samples; for the "to-be-optimized type" samples, the system can accurately identify performance bottlenecks, put forward targeted optimization suggestions for materials, structures, and test plans, and achieve rapid iteration through the closed-loop control mechanism; at the same time, the test report is output in a structured and visualized manner, covering core information such as applicable population, sports scenario, and lifespan prediction, significantly enhancing the application guidance and R & D decision-making value of the report.

[0051] Refer to Figure 2As shown in the figure, the second aspect of the present invention provides a testing device for the athletic wear resistance of socks, which is applied to the above-mentioned testing method for the athletic wear resistance of socks, and includes a data acquisition and preprocessing module, a dynamic loading and wear simulation module, a wear detection and intelligent evaluation module, and a loss modeling and motion scenario adaptation analysis module: Among them, the data acquisition and preprocessing module is used to collect user parameters and gait parameters of different sports types, construct a gait model, calculate the pressure distribution evaluation value according to the user parameters and gait parameters, and perform data preprocessing; the dynamic loading and wear simulation module is used to install new material socks, use the rhythm control of different motion modes to reproduce the force path and friction environment of the socks under real motion conditions, synchronously record the friction force, slip trajectory and local shear conditions through a variety of sensors, and form a wear trajectory; the wear detection and intelligent evaluation module is used to obtain the image of the sock bottom, perform visual recognition and wear area extraction, and output a quantitative wear index; the loss modeling and motion scenario adaptation analysis module is used to fuse the simulation parameters and wear results to evaluate the wear resistance of new material socks, perform adaptability matching analysis, and take optimization measures according to the adaptability evaluation value, and output an evaluation report.

[0052] In this implementation plan, by constructing four major functional modules, a full-process and multi-dimensional intelligent evaluation of the athletic wear resistance of socks is realized. The data acquisition and preprocessing module ensures the personalization and accuracy of the gait model and force data, and improves the pertinence of the test simulation; the dynamic loading and wear simulation module highly restores the actual motion scenario and ensures the authenticity and reliability of the wear path; the wear detection and intelligent evaluation module realizes the high-precision recognition and index quantification of the wear image, and enhances the analysis depth; the loss modeling and adaptation analysis module fuses multi-source data, outputs a scientific adaptability evaluation result, and links optimization suggestions and feedback control to form an integrated closed-loop mechanism for product testing, evaluation and improvement, significantly improving the R & D efficiency of socks and the intelligent level of applicability judgment.

[0053] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0054] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A testing method for the athletic wear resistance of socks, characterized in that, It includes the following steps: S1. Collect user parameters and gait parameters of different exercise types, construct a gait model, calculate a pressure distribution evaluation value based on the user parameters and gait parameters, and perform data preprocessing; S2. Install new material socks, use the rhythm control of different exercise modes to reproduce the force path and friction environment of the socks under real exercise conditions, synchronously record the friction force, slip trajectory and local shear conditions through multiple sensors, and form a wear trajectory; S3. Obtain the sock bottom image, perform visual recognition and wear area extraction, and output a quantitative wear index; S4. Integrate the test data and wear results to evaluate the wear resistance of the new material socks, conduct adaptability matching analysis, and take optimization measures according to the adaptability evaluation value, and output an evaluation report.

2. The method for testing the athletic wear resistance of socks according to claim 1, wherein The specific process of collecting user parameters and gait parameters of different exercise types and constructing a gait model is as follows: Collect the user parameters of the wearer. The user parameters include: height, weight, gender, foot type structure and sole partition size. Extract the standard gait parameters according to different exercise types. The gait parameters include: step frequency data, single-step duration, leading landing part and proportion of force application time. Normalize the user parameters and gait parameters; use the controller to convert the gait parameters into control signals. The foot mold realizes the landing and pushing actions through the front and rear drive actuators. The ankle angle adjustment machine adjusts the landing angle. Inertial measurement unit sensor measurement modules and displacement encoders are arranged at the bottom of the foot mold and the ankle joint respectively to realize a highly simulated gait dynamic simulation cycle.

3. A method for testing the athletic wear resistance of socks according to claim 1, characterized in that, The specific process of calculating the pressure distribution evaluation value based on the user parameters and gait parameters and performing data preprocessing is as follows: Obtain the weight of the wearer, monitor the periodic oscillation through the inertial measurement unit sensor of the foot mold to obtain the step frequency data, obtain the regional relative phase according to the leading landing part of the standard gait parameters, obtain the force area of the i-th area of the sole through the actual measurement of the contact area by the flexible pressure sensing pad, and normalize the force area; multiply the weight of the wearer by the gravity conversion pressure constant to obtain the total pressure value, multiply the step frequency data, twice the pi and the current time to obtain the angle value in the current gait cycle, add the relative phase of the i-th sole area to the angle value to obtain the total phase angle value, calculate the cosine function for the total phase angle value, multiply the calculation result of the cosine function by the landing impact weight factor and add the constant one, and then multiply the ratio of the addition result to the force area of the i-th area and the total pressure value to obtain the pressure distribution evaluation value; Eliminate high-frequency noise from the pressure value, angle value and displacement data through low-pass filtering, perform standardization and normalization processing, and perform time-axis synchronization processing.

4. A method for testing the athletic wear resistance of socks according to claim 1, characterized in that, The specific process of installing new material socks, using the rhythm control of different exercise modes to reproduce the force path and friction environment of the socks under real exercise conditions, synchronously record the friction force, slip trajectory and local shear conditions through multiple sensors, and form a wear trajectory is as follows: Install the new material socks to be tested on the surface of the foot mold according to the standard process, perform tensioning treatment using a stretcher, and record the initial length, width, and the positions of key stress areas. Take multi-angle initial state images of the socks, including front views, sole views, and local close-ups of the arch, and uniformly store them in the sample database; Call the gait trajectory according to the set motion mode, make the foot mold perform periodic bionic motion under the drive of the control system, realize the dynamic simulation of different scenarios, and further establish the combined action of multi-directional loads of friction, compression, and tension between the bottom of the foot mold and the test table; During the test, the force data of each area of the sock bottom are collected in real time through a flexible pressure sensing pad, and the shear impact characteristics and foot trajectory changes are synchronously obtained by cooperating with the inertial measurement unit sensor module and the friction coefficient sensor. A bottom friction tensiometer is used to monitor the friction force change, and a laser displacement meter is used for slip trajectory reconstruction; Cache the force data collected during the test through a unified timestamp alignment mechanism, and go through steps of filtering, normalization, and outlier removal.

5. A method for testing the athletic wear resistance of socks according to claim 1, characterized in that, The specific process of obtaining the sock bottom image, performing visual recognition, and extracting the worn area is as follows: After the motion simulation test is completed, remove the socks from the foot mold and flatten them in a standard manner. The flattened areas are numbered according to the distribution of the sole area. Use a high-resolution platform to obtain the sock bottom image. The shooting methods include standard vertical orthographic projection images and oblique side light illumination images to enhance the recognition clarity of broken lines, holes, and color changes; After the image is obtained, automatically perform wear image recognition and analysis on the sock bottom image, perform image grayscale processing and background removal, use an edge detection algorithm to locate potential wear edges. In the cleaned image, identify the key attributes of the worn area, including the total length of broken lines, hole diameter, and local color patch area index, through region connectivity analysis and morphological feature extraction methods, and output a structured sock bottom wear atlas according to the initial state image taken; 6. A method for testing the athletic wear resistance of socks according to claim 1, characterized in that The specific process of outputting the quantitative wear index is as follows: Output the wear area ratio by the ratio of the damaged area region to the total sock sole area, and obtain the broken line density by the number of broken lines per unit area; for each region i divided from the sock sole, use the wear image recognition to obtain the wear area of the i-th region and normalize the wear area, use the average pressure distribution evaluation value to perform time integral averaging to obtain the average pressure of the i-th region, and screen out the maximum average pressure among all regions. Divide the average pressure of the i-th region by the maximum average pressure among all regions in the test to obtain the relative pressure intensity ratio of this region. Compare the pressure distribution evaluation value of each region with the average pressure threshold, and use the sensor data to obtain the high-pressure duration of this region. Obtain the high-pressure exposure ratio of this region by the ratio of the high-pressure duration to the total test time. Weight the relative pressure intensity ratio by the pressure response weight factor in an exponential form to obtain the pressure intensity index term. Weight the high-pressure exposure ratio by the time exposure weight factor in an exponential form to obtain the high-pressure time index term. Multiply the pressure intensity index term and the high-pressure time index term, and multiply the result by the wear area and the regional structure weight factor of this region to obtain the fatigue matching contribution value of this region. Sum the fatigue matching contribution values of all regions, and divide by the sum of the products of all worn regions and the regional structure weight factor to obtain the fatigue concentration evaluation value; Compare the fatigue concentration evaluation value with the fatigue concentration threshold in real time. When the fatigue concentration evaluation value is greater than or equal to the fatigue concentration threshold, it is determined that the sock wear is reasonably concentrated, automatically archived into the qualified sample library, recommended for use in the specified sports type, selected as a performance example, and appears on the final report summary page; When the fatigue concentration evaluation value is less than the fatigue concentration threshold, it is determined that the sock wear drifts. Automatically locate the area with significant wear but insufficient pressure loading, identify the structural and stress mismatch areas, generate suggestions for local structural reconstruction, infer the stress type based on the wear characteristics, send a prompt to replace with high-wear-resistant and buffer materials, automatically mark this sock sample as pending verification of structural optimization, perform an adjusted reloading test verification process, and retain the current test data as the previous reference version.

7. A method for testing the athletic wear resistance of socks according to claim 1, characterized in that The specific process of evaluating the wear resistance of new material socks by integrating test data and wear results is as follows: Integrate multi-dimensional data of user parameters, gait parameters, and the measured wear results to construct characteristic variables, including the loss rate under unit pressure and the gait peak and wear alignment index; Calculate the pressure distribution evaluation value of each region, and perform time integral operation to obtain the total cumulative force during the test period, and sum the total cumulative forces of all regions; obtain the wear area of each region by wear image recognition and sum to obtain the total wear of the sock. Divide the total cumulative force of all regions by the total wear to obtain the unit wear evaluation value; Compare the unit wear evaluation value with the unit wear threshold in real time. When the unit wear evaluation value is less than the unit wear threshold, it is determined that the sock has a problem of insufficient material wear resistance. It is recommended to replace it with a high-strength fabric, and it is recommended to adopt partitioned differential reinforcement. Link the structural modeling to analyze whether the material strength matches the stress area, and avoid high-pressure aggregation in weak material areas; When the unit wear evaluation value is greater than or equal to the unit wear threshold, it is determined that the material shows stability, and it is classified into the qualified sample set of wear resistance and recommended for high-load or long-time movement scenarios.

8. A method for testing the athletic wear resistance of socks according to claim 1, characterized in that The specific process of performing the adaptability matching analysis is as follows: Compare the test wear results of the sock sample under different movement modes, and output the adaptability matching result; obtain the unit wear evaluation value and fatigue concentration evaluation value of the sock sample, calculate the standard deviation and mean of the wear area of all regions of the sock bottom according to the wear image recognition, subtract the unit wear evaluation value from one and multiply by the wear resistance evaluation weight factor to obtain the wear resistance evaluation item, multiply the fatigue concentration evaluation value by the structural rationality weight factor to obtain the structural rationality evaluation item, subtract the ratio of the standard deviation to the mean of the wear area from one and multiply by the wear distribution weight factor to obtain the wear uniformity evaluation item, and sum the wear resistance evaluation item, structural rationality evaluation item and wear uniformity evaluation item to obtain the adaptability evaluation value.

9. A method for testing the athletic wear resistance of socks according to claim 1, characterized in that The specific process of taking optimization measures according to the adaptability evaluation value and outputting the evaluation report is as follows: Compare the adaptability evaluation value with the adaptability threshold in real time. When the adaptability evaluation value is greater than or equal to the adaptability threshold, it is determined as "good adaptability type"; it is published in the product visualization report as an example of high adaptability performance; allowing continuous large-scale strength tests or multi-scenario expansion tests to be carried out; Generate a complete test report for publicity and internal evaluation; When the adaptability evaluation value is less than the adaptability threshold, it is determined as "to-be-optimized adaptability type"; based on the constituent factors of the adaptability evaluation value, automatically identify the source of the performance bottleneck, and put forward targeted optimization suggestions from three aspects: material selection, structural layout and test configuration; After the optimization plan is designed and adjusted, trigger a new round of simulated gait loading process, re-perform wear evaluation and performance scoring, and realize the closed-loop feedback control of product iteration and upgrade; Automatically integrate the key parameters and evaluation results of the whole process, and output a structured and visualized standardized test report, including test parameters, sock images, wear images, scoring suggestions, including outputting the most suitable population type and the most suitable movement scenario for this new material sock, and supporting the prediction of the expected lifespan of the sock under different movement rhythms and populations.

10. A testing device for the athletic wear resistance of socks, characterized in that, It includes a data acquisition and preprocessing module, a dynamic loading and wear simulation module, a wear detection and intelligent evaluation module, and a loss modeling and movement scenario adaptability analysis module: Among them, the data acquisition and preprocessing module is used to collect user parameters and gait parameters of different movement types, construct a gait model, calculate the pressure distribution evaluation value according to the user parameters and gait parameters, and perform data preprocessing; The dynamic loading and wear simulation module is used to install the new material sock, use the rhythm control of different movement modes to reproduce the force path and friction environment of the sock under real movement conditions, and synchronously record the friction force, slip trajectory and local shear conditions through multiple sensors to form a wear trajectory; The wear detection and intelligent evaluation module is used to obtain the sock bottom image, perform visual recognition and wear area extraction, and output a quantitative wear index; The loss modeling and motion scenario adaptation analysis module is used to evaluate the abrasion resistance of new material socks by integrating simulation parameters and wear results, conduct adaptability matching analysis, take optimization measures according to the adaptability evaluation value, and output an evaluation report.

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