Fabric evaluation and screening method based on multi-level performance indicators

Abstract

The application discloses a fabric evaluation and screening method based on a multi-level performance index, relates to the technical field of functional sportswear, and comprises the following steps: constructing a multi-level evaluation index system comprising a drag reduction performance, a light weight performance, a tensile performance and a compression performance; determining objective weights of each evaluation index based on an entropy weight method of wind tunnel aerodynamic test data and fabric multi-physical performance test data; and determining subjective weights of each evaluation index based on an analytic hierarchy process method of constructing a judgment matrix based on expert cognitive evaluation; constructing a fabric comprehensive quantitative evaluation model, and realizing performance evaluation, screening and partition application of candidate fabrics based on the evaluation model, so that the optimal configuration of fabric performance in different sports areas is realized, the overall aerodynamic drag reduction performance, compression performance and sports fitness of competitive clothing are effectively improved, and the problems of insufficient engineering basis in the fabric selection process of clothing, difficult unified quantitative representation of performance indexes and insufficient multi-performance collaborative optimization capability are solved.

Description

Technical Field

[0001] This invention relates to the field of functional sportswear technology, and in particular to a fabric evaluation and screening method based on multi-level performance indicators. Background Technology

[0002] Current designs for competitive compression sportswear largely rely on experience-based selection or single physical test results, making it difficult to achieve systematic and scientific drag reduction design tailored to individual athlete differences, speed ranges, and posture variations. Existing technologies mostly lack comprehensive fabric selection evaluation systems, multi-dimensional integrated evaluation models, and systematic design methods. This makes it difficult to fully utilize aerodynamic drag reduction efficiency in actual design, while also failing to effectively balance the garment's overall performance in terms of lightweighting, stretchability, compression characteristics, and wearing comfort. Consequently, problems such as poor drag reduction, mismatch between compression and muscle support functions, and poor fit may occur during actual exercise. Furthermore, existing design methods rely heavily on experience or simple experimental data, lacking systematic, multi-dimensional evaluation and optimization mechanisms, making it difficult to meet the personalized performance requirements of different sports, speed ranges, postures, and individual sizes. Summary of the Invention

[0003] Based on the above analysis, the present invention aims to provide a fabric evaluation and screening method based on multi-level performance indicators, which solves at least one of the following problems in the design of racing-type tight-fitting sportswear: poor drag reduction effect, inability to meet muscle support requirements in compression performance, poor fit, and difficulty in meeting the personalized needs of clothing for multiple working conditions and individual differences.

[0004] The objective of this invention is mainly achieved through the following technical solutions: A fabric evaluation and screening method based on multi-level performance indicators includes: S1: Based on the functional requirements of racing-type tight-fitting sportswear, a multi-level performance evaluation index system for fabrics is constructed. The evaluation index system includes at least primary and secondary indicators. The primary indicators are drag reduction performance, lightweight performance, tensile performance, and compression performance. The secondary indicators are quantitative evaluation indicators of the primary indicators. S2: The objective weights of quantitative evaluation indicators of relevant performance are determined by the entropy weight method based on wind tunnel aerodynamic test data and fabric multi-physical property test data; S3: The analytic hierarchy process (AHP) is used to construct a judgment matrix based on expert cognitive evaluation to determine the subjective weights of quantitative evaluation indicators of relevant performance. S4: Based on the objective weights and subjective weights, the evaluation indicators are weighted and integrated to construct a comprehensive quantitative evaluation model for fabrics; based on the comprehensive quantitative evaluation model for fabrics, the candidate fabrics are evaluated and screened for performance, and used to guide the zoning configuration of fabrics.

[0005] Preferably, the quantitative evaluation indicators for drag reduction performance include: friction reduction rate. f r Flow separation retardation rate Δ i r and local pressure difference reduction rate p r ; Among them, the flow separation retardation rate Δ i r satisfy: ; i : Flow separation angle under actual conditions; i 0: Flow separation angle under baseline conditions; Local pressure difference reduction rate p r satisfy: ; F pressure,0 This represents the local pressure differential resistance corresponding to the cylindrical model without fabric. F pressure,s The local pressure differential resistance of the fabric cylindrical model; Friction reduction rate f r satisfy: ; F fric,0 Frictional resistance under reference conditions F fric,0 = Total drag measured in wind tunnel using a cylindrical model F total,0 and F pressure,0 The difference; F fric,s For local frictional resistance, F fric,s = Total resistance of laying fabric cylindrical model F total,s and F pressure,s The difference.

[0006] Preferably, the quantitative evaluation index for lightweight performance is a standardized index based on the unit area weight (g / m²) of each fabric. m i ’ As a benchmark, it must satisfy: ; in, m i Let be the weight per unit area of ​​the i-th type of fabric, and max(m) and min(m) be the maximum and minimum weight per unit area in the test scheme, respectively.

[0007] Preferably, the quantitative evaluation index of tensile properties is the elongation in each direction. R l As a benchmark, it must satisfy: ; in, L 0 represents the initial gauge length of the fabric sample when it is not under load; L This is the gauge length of the fabric sample under standard tensile load.

[0008] Preferably, the quantitative evaluation index of compression performance is the contact pressure. P i and tensile stiffness K i As a benchmark, based on a preset target contact pressure P ref and preset target tensile stiffness K ref For actual contact pressure P i and tensile stiffness K i Target deviations are calculated separately, and a contact pressure matching index is constructed. M P,i Matching index with tensile stiffness M K,i It is used to characterize the compression performance level of the fabric; Contact pressure matching index M P,i satisfy: ; P i Let be the contact pressure measured in the i-th region of the fabric under the attached state; P ref The target contact pressure is preset according to the needs of the sport; This refers to the allowable deviation range of the contact pressure. Tensile stiffness matching index M K,i satisfy: ; in, K i Let be the tensile stiffness of the fabric in region i within a preset strain range; Kref The target tensile stiffness is preset according to the requirements of the sport; This represents the allowable deviation range for tensile stiffness.

[0009] Preferably, step S4 includes: S401: A linear combination method is used to integrate subjective and objective weights to obtain the comprehensive weight vector matrix of each secondary indicator. w ,satisfy; ; in, α ∈[0,1] represents the weighted composite coefficient; S402: Yes w Normalization is performed to satisfy the requirements; =1; Overall performance score of each scheme S i satisfy: ; in, w j This is the comprehensive weight vector. For each scheme j The standardized values ​​of each indicator.

[0010] Preferably, step S2 includes: S201: Simplify the geometry of each part of the human body, construct a simplified geometric model of the human body, and apply fabric to the cylindrical surface of the simplified geometric model to conduct compression performance tests and drag reduction performance tests under different wind speeds. S202: Conduct quality tests and tensile property tests on the fabric; S203: Normalize the data obtained in steps S201 and S202 and convert them into dimensionless values; S204: Calculate the entropy value of the dimensionless value in step S203 based on the entropy method, and further determine the objective weight based on the entropy value.

[0011] Preferably, step S3 includes: S301: Organize an expert group composed of textile fabric experts, sports equipment design experts, and aerodynamic performance analysis experts to conduct pairwise importance comparisons between primary indicators and between secondary indicators using the Saaty nine-level scale method, and classify each comparison to establish a judgment matrix. S302: According to the AHP (Analytical Hierarchy Process), the judgment matrix A is first normalized by column, and the mean of each row is obtained to get the weight vector corresponding to each secondary indicator. A×w=λ max ×w, in,w For the weight vector, l max To determine the largest eigenvalue of a matrix; S303: Use consistency indicators and consistency ratios to perform consistency checks and select appropriate subjective weight vectors for secondary indicators. ; Consistency Indicators CI satisfy: ; Consistency ratio CR satisfy: ; Where n is the number of evaluation indicators; RI is the random consistency index; If CR < 0.1, the matrix weight result is considered valid. If CR ≥ 0.1, the result of the judgment matrix weight is invalid, and the judgment matrix should be reconstructed until the consistency test is passed.

[0012] A zoning design scheme for speed skating competition apparel fabric, obtained using the aforementioned fabric evaluation and screening method based on multi-level performance indicators, includes: The torso area, including the chest and abdomen, the midline of the back, and the waist and hips, is made of polyurethane-coated fabric with a coating thickness of 15µm to 25µm. When worn, the fabric has a stretch rate of 17% to 25% in the circumferential direction of the corresponding area. The limb areas, including the upper arm, thigh, forearm, and lower leg, are made of honeycomb quilted warp-knitted fabric. When worn, the fabric has an elongation of 25% to 38% in the circumferential direction of the corresponding area, a quilt depth of 0.4mm to 1.2mm, and a quilt spacing of 4mm to 6mm. The joint and splicing transition areas, including the armpit, knee and crotch areas, are made of four-way elastic fabric with a spandex content of 10% to 15%. When worn, the fabric has a stretch rate of 30% to 43% in the main deformation direction of the corresponding area.

[0013] Preferably, the upper arm and forearm areas use a honeycomb-patterned warp-knitted fabric with a pit depth of 0.6 mm and a spacing of 5 mm; the thigh and calf areas use a honeycomb-patterned warp-knitted fabric with a pit depth of 0.4 mm and a spacing of 6 mm.

[0014] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: (1) This invention realizes the transformation from experience-driven to data-driven by constructing a multi-level fabric quantitative evaluation and decision-making method. It changes the shortcomings of existing design methods that rely on experience or simple experimental data, lack systematic and multi-dimensional evaluation and optimization mechanisms, and cannot take into account multiple indicators such as drag reduction performance, comfort and weight. At the same time, it effectively improves the problems of poor drag reduction effect, difficulty in meeting muscle support requirements and poor fit in the design of racing tight sportswear in traditional methods, and can meet the personalized needs under multiple working conditions and individual differences.

[0015] (2) The racing suit of the present invention optimizes the configuration of the fabric of each part based on the method described above. Its average drag coefficient (Cd) is reduced by about 5.8% compared with the racing suit without wearing it, and by about 2.6% compared with the traditional single fabric, showing a significant drag reduction effect in the medium and high speed gliding range.

[0016] (3) This invention optimizes the fabric design scheme by constructing a multi-level performance index system and performing weight fusion, overcoming the problems of poor drag reduction effect, insufficient compression performance to meet muscle support requirements, and poor fit that exist in the traditional method of designing racing tight-fitting sportswear. It can meet the personalized needs under multiple working conditions and individual differences. While achieving high drag reduction effect, it is superior to conventional schemes in terms of gliding posture maintenance and movement continuity, which helps to improve power transmission efficiency and gliding stability. The core stability index for calculating center of mass offset and posture continuity is improved by 0.07 compared with the existing technology, and the proportion is improved by 8%.

[0017] Other features and advantages of the invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained from the embodiments described and the accompanying drawings. Attached Figure Description

[0018] Figure 1 This is a flowchart of the evaluation and screening method in the embodiments of the present invention. Detailed Implementation

[0019] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which constitute a part of the present invention and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the present invention.

[0020] On one hand, this invention discloses a fabric evaluation and screening method based on multi-level performance indicators, including: S1: Based on the functional requirements of racing-type tight-fitting sportswear, a multi-level performance evaluation index system for fabrics is constructed. The evaluation index system includes at least primary and secondary indicators. The primary indicators are drag reduction performance, lightweight performance, tensile performance, and compression performance. The secondary indicators are quantitative evaluation indicators of the primary indicators. S2: The objective weights of quantitative evaluation indicators of relevant performance are determined by the entropy weight method based on wind tunnel aerodynamic test data and fabric multi-physical property test data; S3: The analytic hierarchy process (AHP) is used to construct a judgment matrix based on expert cognitive evaluation to determine the subjective weights of quantitative evaluation indicators of relevant performance. S4: Based on the objective weights and subjective weights, the evaluation indicators are weighted and integrated to construct a comprehensive quantitative evaluation model for fabrics; based on the comprehensive quantitative evaluation model for fabrics, the candidate fabrics are evaluated and screened for performance, and used to guide the zoning configuration of fabrics.

[0021] In implementation, considering the fabric requirements of sportswear in this field, such as drag reduction, lightweighting, support performance, and comfort, the applicant decomposes the design-related performance of the fabric into four primary indicators: drag reduction, lightweighting, tensile performance, and compression performance. This achieves comprehensive quantification of the fabric's design-related performance, facilitating further evaluation. The analytic hierarchy process (AHP) is used to construct a judgment matrix through expert cognitive evaluation, introducing subjective weights for evaluation. Objective weights are introduced through the entropy weight method, enabling the evaluation of subjective performance such as comfort. This overcomes problems in traditional racing apparel design, such as poor drag reduction, mismatch between compression and muscle support functions, and poor fit. Simultaneously, the coexistence of subjective and objective evaluation indicators reduces data fluctuations and improves stability. It also maintains good stability and consistency for the personalized performance requirements of different sports, speed ranges, postures, and individual sizes, improving the overall stability and effectiveness of the design method.

[0022] Compared with existing technologies, this invention achieves a shift from "experience-driven to data-driven" by quantitatively evaluating the design performance of fabrics. This changes the shortcomings of existing design methods, which often rely on experience or simple experimental data and lack systematic, multi-dimensional evaluation and optimization mechanisms, and cannot take into account multiple indicators such as drag reduction performance, comfort, and weight. At the same time, it overcomes the problems of poor drag reduction effect, inadequate compression performance to meet muscle support requirements, and poor fit in the design of racing-type tight-fitting sportswear in traditional methods, and can meet the personalized needs of clothing for multiple working conditions and individual differences.

[0023] Compared with existing technologies, this invention constructs a multi-level performance evaluation system, which integrates evaluation criteria based on careful evaluation and experimental measurement into the evaluation and decision-making process. This optimizes fabric selection and overcomes the problems of poor drag reduction, insufficient compression performance to meet muscle support requirements, and poor fit that exist in traditional methods for designing racing-type tight-fitting sportswear. It can meet the personalized needs of clothing for multiple working conditions and individual differences. While achieving high drag reduction, it is superior to conventional solutions in terms of gliding posture maintenance and motion continuity, which helps to improve power transmission efficiency and gliding stability. The core stability index for calculating center of mass shift and posture continuity is improved by 0.07 compared with existing technologies, and the proportion is improved by 8%.

[0024] Specifically, the quantitative evaluation indicators for the primary indicator of drag reduction performance include the secondary indicator: friction reduction rate. f r Flow separation retardation rate Δ i r and local pressure difference reduction rate p r ; Among them, the flow separation retardation rate Δ i r satisfy: ; i : Flow separation angle under actual conditions; i 0: Flow separation angle under baseline conditions.

[0025] Local pressure difference reduction rate p r satisfy: ; F pressure,0 This represents the local pressure differential resistance corresponding to the cylindrical model without fabric. F pressure,s The local pressure differential resistance of the fabric cylindrical model.

[0026] Friction reduction rate f r satisfy: ; F fric,0 Frictional resistance under reference conditions F fric,0 = Total drag measured in wind tunnel using a cylindrical model F total,0 and F pressure,0 The difference; F fric,s For local frictional resistance, F fric,s = Total resistance of laying fabric cylindrical model F total,s and F pressure,s The difference.

[0027] Specifically, the quantitative evaluation index for the primary indicator of lightweight performance is the basis weight per unit area (g / m²) of each fabric. 2 Unit quality consistency index m i ’ As a standard and secondary indicator, the following must be satisfied: ; in, m i Let be the weight per unit area of ​​the i-th type of fabric, and max(m) and min(m) be the maximum and minimum weight per unit area in the test scheme, respectively.

[0028] Specifically, the quantitative evaluation index of the primary index, tensile performance, is based on the elongation rate in each direction. R l As a standard and secondary indicator, the following must be satisfied: ; in, L 0 represents the initial gauge length of the fabric sample when it is not under load; L This is the gauge length of the fabric sample under standard tensile load.

[0029] Specifically, the quantitative evaluation indicators for the primary indicator of compression performance include secondary indicators: contact pressure. P i and tensile stiffness K i ; Based on the preset target contact pressure P ref and preset target tensile stiffness K ref For actual contact pressure P i and tensile stiffness K i Target deviations are calculated separately, and a contact pressure matching index is constructed. M P,i Matching index with tensile stiffness M K,i It is used to characterize the compression performance level of the fabric; Contact pressure matching index M P,isatisfy: ; P i Let be the contact pressure measured on the fabric in region i under the attached state; P ref The target contact pressure is preset according to the needs of the sport; This refers to the allowable deviation range of the contact pressure. Tensile stiffness matching index M K,i satisfy: ; in, K i Let be the tensile stiffness of the fabric in region i within a preset strain range; K ref The target tensile stiffness is preset according to the requirements of the sport; This represents the allowable deviation range for tensile stiffness.

[0030] Specifically, step S2 includes: S201: Simplify the geometry of each part of the human body, construct a simplified geometric model of the human body, and apply fabric to the cylindrical surface of the simplified geometric model to conduct compression performance tests and drag reduction performance tests under different wind speeds. S202: Conduct quality tests and tensile property tests on the fabric; S203: Normalize the data obtained in steps S201 and S202 and convert them into dimensionless values; S204: Calculate the entropy value of the dimensionless value in step S203 based on the entropy method, and further determine the objective weight based on the entropy value.

[0031] Step S201 includes simplifying the geometry of each part of the human body into a cylindrical model, as shown in Table 1: Table 1. Cylindrical Model of Different Parts of the Human Body

[0032] Airflow boundary layer separation characteristic test: A low-speed wind tunnel was used to obtain the surface velocity gradient of each fabric model under different wind speeds. Combined with particle image velocimetry (PIV) technology, the circumferential angle corresponding to the flow separation point was accurately located. i and i 0.

[0033] Wind tunnel aerodynamic testing: A low-speed wind tunnel was used, and a three / six component force balance was used to measure the drag at different wind speeds to obtain the data for each fabric. F total,s , F total,0 , Fpressure,0 and F pressure,s .

[0034] F total,s , F total,0 It can be measured by a three / six component force balance. F pressure,0 and F pressure,s Local pressure difference resistance F pressure Calculated using the discrete integration method: ; Where, Δ θ Differential unit width =2~ 20° R Where is the radius of the cylinder. L Let n be the height of the cylinder, and n be the number of differential units. i For the differential unit index ∈ n , i i Let be the differential unit of the circumference angle. F pressure,0 This represents the local pressure differential resistance corresponding to the cylindrical model without fabric. F pressure,s The local pressure differential resistance of the fabric cylindrical model.

[0035] The compression performance test in step S201 includes contact pressure test and tensile stiffness test.

[0036] Specifically, obtaining contact pressure P i include: Each fabric was attached to a cylindrical model, and detection points were set every 2° to 20° around the circumference of the cylindrical model. The contact pressure at each detection point was measured using a thin-film pressure sensor or a flexible contact pressure sensor. P θ,i The average and standard deviation of the pressure at each measuring point were obtained through repeated experiments. Based on the regional division, the pressure at the corresponding measuring points was averaged to obtain the contact pressure of the i-th region. P i Then, based on the preset target contact pressure and allowable deviation range, the contact pressure matching index is calculated: ; P i Let be the contact pressure measured in the i-th region of the fabric under the attached state; P ref The target contact pressure is preset according to the needs of the sport; This refers to the allowable deviation range of the contact pressure. Specifically, to obtain tensile stiffness K i include: Standard rectangular specimens are cut from each fabric along the warp or weft direction. Uniaxial tensile tests are performed on a standard tensile testing machine. The stress-strain curves of the fabric within the target working strain range are recorded. The ratio of stress increment to strain increment within this range is calculated to obtain the tensile stiffness. K i : ; σ1 and σ2 are the tensile stresses corresponding to strains ε1 and ε2, respectively, and t i Let be the fabric thickness of region i; Subsequently, based on the preset target tensile stiffness and allowable deviation range, the tensile stiffness matching index is calculated. M K,i : ; in, K i Let be the tensile stiffness of the fabric in region i within a preset strain range; K ref The target tensile stiffness is preset according to the requirements of the sport; This represents the allowable deviation range for tensile stiffness.

[0037] In step S202, the mass test involves weighing the unit area of ​​each scheme using a 0.01% balance. m Thus, a unit quality consistency index is obtained. m i ’ ,include: According to GB / T4669-2008 or ASTM D3776 / D3776M, for the determination of unit area mass of fabrics, three square samples with sides of 10cm × 10cm were cut from each test fabric, weighed three times, and the average value was taken to calculate the unit mass value for each scheme. m (Unit: g / m) 2 The unit mass uniformity index of each test fabric is calculated according to the following formula. m i ’ ,satisfy: ; in, m i For the first i The weight of the fabric is given by max(m) and min(m), which are the maximum and minimum weight values ​​in the test scheme, respectively.

[0038] In step S202, the tensile properties test was conducted according to the international textile standard ISO 20932-1:2018. A universal fabric testing machine was used to measure the warp and weft tensile curves separately, and the warp elongation rate was calculated. R l1 , weft elongation R l2， It reflects the fabric's stretchability and resilience in both the warp and weft directions.

[0039] It should be noted that evaluating the contribution of each fabric to the local fit and rebound characteristics of speed skating competition clothing through this index can effectively guide clothing pattern design and fabric selection, reducing the impact of excessive tightness on athlete comfort or insufficient stretching leading to substandard support performance.

[0040] Specifically, step S203 includes: S2031. For m types of fabrics and n evaluation indicators, construct a data matrix X=(x ij ) m×n , ; Where i represents the i-th type of fabric, and j represents the j-th secondary index; S2032. Normalize the original data using the range standardization method: ; Standardized data y ij All values ​​are in the interval [0, 1].

[0041] Step S204 includes: S2041. Calculate the weighting of each indicator for each scheme: ; ;in, p ij represents the weight percentage of the i-th type of fabric on the j-th indicator; S2042. Obtain the entropy value of each secondary indicator based on the weight ratio of each indicator. e j : ; ; Where i represents the i-th type of fabric, and j represents the j-th secondary index. p ij represents the weight percentage of the i-th type of fabric on the j-th indicator; S2043, Calculate the degree of difference of each indicator. : ; S2044. Normalize the differences of all indicators to obtain the objective weight vector. : ; Where j represents the secondary indicator number. denoted as the index difference degree, and n is the number of evaluation indicators.

[0042] Specifically, step S3 includes: S301: The importance of each pair of primary indicators and secondary indicators is compared using the Saaty nine-level scale method, and a judgment matrix is ​​established by classifying each comparison. S302: According to the AHP (Analytical Hierarchy Process), the judgment matrix A is first normalized by column, and the mean of each row is obtained to get the weight vector corresponding to each secondary indicator. A×w=λ max ×w, in, w For the weight vector, l max To determine the largest eigenvalue of a matrix; S303: Use consistency indicators and consistency ratios to perform consistency checks and select appropriate subjective weight vectors for secondary indicators. ; Consistency Indicators CI satisfy: ; Consistency ratio CR satisfy: ; Where n is the number of evaluation indicators; RI It is a random consistency indicator; If CR < 0.1, the matrix weight result is considered valid. If CR ≥ 0.1, the result of the judgment matrix weight is invalid, and the judgment matrix should be reconstructed until the consistency test is passed.

[0043] Specifically, the 9-level scaling method can select the Saaty 9-level scale, including: 1) Principle of Importance Comparison When evaluating the weight of indicators, first determine the comparison objects at each level (e.g., between primary indicators or between secondary performance indicators under the same primary indicator), and then judge the relative importance of any two indicators i and j according to the pairwise comparison principle.

[0044] The criteria for judgment include: experimental results, expert experience, design objectives priority, and motion performance requirements.

[0045] The result of the judgment is expressed as a ratio α. ijThis indicates the degree of importance of indicator i relative to indicator j.

[0046] (2) Definition of the 9-level scale method The 1–9 scale system proposed by Saaty is adopted, and the specific classification is as follows:

[0047] This scale is used to form a matrix of pairwise comparison results.

[0048] (3) Hierarchical and matrix construction methods For each group of indicators at the same level, perform all pairwise comparisons, record the expert or calculation results, and form an n×n judgment matrix A=[α ij ]: ; Where A=[α ij ] n×n Represents the judgment matrix; α ij Indicates the importance level of indicator i relative to indicator j (value 1-9); α ji =1 / α ij .

[0049] Specifically, step S4 includes: S401: A linear combination method is used to integrate subjective and objective weights to obtain the comprehensive weight vector matrix of each secondary indicator. w ,satisfy; ; in, α ∈[0,1] represents the weighted composite coefficient; S402: Yes w Normalization is performed to satisfy the requirements; =1; Overall performance score of each scheme S i satisfy: ; in, w j This is the comprehensive weight vector. For each scheme j The standardized values ​​of each indicator indicate that the higher the score, the better the overall performance of the solution.

[0050] The comprehensive score is used to evaluate and rank the relative performance of candidate fabrics, reflecting the overall performance characteristics and applicability of different fabrics across various performance indicators, rather than directly determining the absolute superiority or inferiority of a single fabric. Therefore, the final selection of garment fabric is based on a differentiated configuration that matches the functional requirements and performance of each part.

[0051] On the other hand, the present invention also discloses a partitioned design scheme for speed skating competition uniform fabric, including: The torso area, including the chest and abdomen, the midline of the back, and the waist and hips, is made of polyurethane-coated fabric with a coating thickness of 15µm to 25µm. When worn, the fabric has a stretch rate of 17% to 25% in the circumferential direction of the corresponding area. The limb areas, including the upper arm, thigh, forearm, and lower leg, are made of honeycomb quilted warp-knitted fabric. When worn, the fabric has an elongation of 25% to 38% in the circumferential direction of the corresponding area, a quilt depth of 0.4mm to 1.2mm, and a quilt spacing of 4mm to 6mm. The joint and splicing transition areas, including the armpit, knee and crotch areas, are made of four-way elastic fabric with a spandex content of 10% to 15%. When worn, the fabric has a stretch rate of 30% to 43% in the main deformation direction of the corresponding area.

[0052] Preferably, the upper arm and forearm areas use a honeycomb-patterned warp-knitted fabric with a pit depth of 0.6 mm and a spacing of 5 mm; the thigh and calf areas use a honeycomb-patterned warp-knitted fabric with a pit depth of 0.4 mm and a spacing of 6 mm.

[0053] To further illustrate the technical solution of the present invention, the following embodiments and comparative examples are provided: Example 1 This embodiment discloses a fabric evaluation and screening method based on multi-level performance indicators, such as... Figure 1 As shown, it includes: S1: Determine the design-related performance of the fabric and its quantitative evaluation indicators based on the fabric requirements of sportswear; The alternative fabrics are shown in Table 2 below: Table 2 Fabric Parameters

[0054] S2: The objective weights of quantitative evaluation indicators of relevant performance are determined by the entropy weight method based on wind tunnel aerodynamic test data and fabric multi-physical property test data; S201: Simplify the geometry of each part of the human body, construct a simplified geometric model of the human body, and apply fabric to the cylindrical surface of the simplified geometric model to conduct compression performance tests and drag reduction performance tests under different wind speeds. Step S201 includes simplifying the geometry of each part of the human body into a cylindrical model, as shown in Table 3: Table 3. Cylindrical Model of Different Parts of the Human Body

[0055] Airflow boundary layer separation characteristic test: A low-speed wind tunnel was used to obtain the surface velocity gradient of each fabric model under different wind speeds. Combined with particle image velocimetry (PIV) technology, the circumferential angle corresponding to the flow separation point was accurately located. i and i 0. Taking the torso model as an example, the results are shown in Table 4: Table 4. Flow separation delay rate of each fabric in the torso model

[0056] Wind tunnel aerodynamic testing: A low-speed wind tunnel was used, and a three / six component force balance was used to measure the drag at different wind speeds to obtain the data for each fabric. F total,s , F total,0 , F pressure,0 and F pressure,s .

[0057] Specifically, F total,s , F total,0 It can be measured by a three / six component force balance. F pressure,0 and F pressure,s Local pressure difference resistance F pressure Calculated using the discrete integration method: ; Where, Δ θ Differential unit width =2~ 20° R Where is the radius of the cylinder. L Let n be the height of the cylinder, and n be the number of differential units. i For the differential unit index ∈ n , i i Let be the differential unit of the circumference angle. F pressure,0 This represents the local pressure differential resistance corresponding to the cylindrical model without fabric. F pressure,s To assess the local pressure differential resistance of the fabric-laid cylindrical model, taking the torso model as an example, the results are shown in Table 5: Table 5. Pressure differential reduction rate and coefficient of variation for each scheme of the trunk model.

[0058] The compression performance test in step S201 includes contact pressure test and tensile stiffness test.

[0059] Specifically, obtaining contact pressure P i include: Each fabric was attached to a cylindrical model, and detection points were set every 2° to 20° around the circumference of the cylindrical model. The contact pressure at each detection point was measured using a thin-film pressure sensor or a flexible contact pressure sensor. P θ,i The average and standard deviation of the pressure at each measuring point were obtained through repeated experiments. Based on the regional division, the pressure at the corresponding measuring points was averaged to obtain the contact pressure of the i-th region. P i Then, based on the preset target contact pressure and allowable deviation range, the contact pressure matching index is calculated: ; P i Let be the contact pressure measured in the i-th region of the fabric under the attached state; P ref The target contact pressure is preset according to the needs of the sport; The allowable deviation range for contact pressure is shown in Table 6, taking the torso model as an example. Table 6 Contact Pressure Matching Index for Different Schemes of the Torso Model

[0060] Specifically, to obtain tensile stiffness K i include: Standard rectangular specimens are cut from each fabric along the warp or weft direction. Uniaxial tensile tests are performed on a standard tensile testing machine. The stress-strain curves of the fabric within the target working strain range are recorded. The ratio of stress increment to strain increment within this range is calculated to obtain the tensile stiffness. K i : ; σ1 and σ2 are the tensile stresses corresponding to strains ε1 and ε2, respectively, and t i Let be the fabric thickness of region i; Subsequently, based on the preset target tensile stiffness and allowable deviation range, the tensile stiffness matching index is calculated. M K,i : ; in, K i Let be the tensile stiffness of the fabric in region i within a preset strain range; Kref The target tensile stiffness is preset according to the requirements of the sport; The allowable deviation range of tensile stiffness is shown in Table 7, taking the torso model as an example. Table 7 Tensile Stiffness Matching Index of Various Schemes for the Torso Model

[0061] S202: Conduct quality tests and tensile property tests on the fabric; Specifically, in step S202, the mass test involves weighing the unit area mass of each scheme using a 0.01% balance. m Thus, a unit quality consistency index is obtained. m i ’ ,include: According to GB / T4669-2008 or ASTM D3776 / D3776M, for the determination of unit area mass of fabrics, three square samples with sides of 10cm × 10cm were cut from each test fabric, weighed three times, and the average value was taken to calculate the unit mass value for each scheme. m (Unit: g / m²), calculate the uniformity index per unit mass of each test fabric according to the following formula. m i ’ ,satisfy: ; in, m i For the first i The weight of the fabric is given by max(m) and min(m), which represent the maximum and minimum weights in the test scheme, respectively. The results are shown in Table 8. Table 8. Weight of each fabric

[0062] Specifically, in step S202, the tensile performance test is conducted according to the international textile standard ISO 20932-1:2018. A universal fabric testing machine is used to measure the warp and weft tensile curves separately, and the warp elongation rate is calculated. R l1 , weft elongation R l2， The results, reflecting the fabric's stretchability and resilience in both warp and weft directions, are shown in Table 9. Table 9. Range of warp and weft elongation rates for each fabric under specified loads.

[0063] S203: Normalize the data obtained in steps S201 and S202 and convert them into dimensionless values; S2031. For m types of fabrics and n evaluation indicators, construct a data matrix X=(x ij ) m×n , ; Where i represents the i-th type of fabric, and j represents the j-th secondary index; S2032. Normalize the original data using the range standardization method: ; Standardized data y ij All values ​​are in the interval [0, 1].

[0064] S204: Calculate the entropy value of the dimensionless value in step S203 based on the entropy method, and further determine the objective weight based on the entropy value; Specifically, step S204 includes: S2041. Calculate the weighting of each indicator for each scheme: , ;in, p ij represents the weight percentage of the i-th type of fabric on the j-th indicator; S2042. Obtain the entropy value of each secondary indicator based on the weight ratio of each indicator. e j : ; ; Where i represents the i-th type of fabric, and j represents the j-th secondary index. p ij represents the weight percentage of the i-th type of fabric on the j-th indicator; S2043, Calculate the degree of difference of each indicator. : ; S2044. Normalize the differences of all indicators to obtain the objective weight vector. : ; Where j represents the j-th secondary indicator, denoted as the index difference degree, and n is the number of evaluation indicators.

[0065] Taking the torso model as an example, the weighting results of the objective evaluation indicators are shown in Table 10: Table 10 Weights of Objective Evaluation Indicators for Trunk Model

[0066] S3: The analytic hierarchy process (AHP) is used to construct a judgment matrix based on expert cognitive evaluation to determine the subjective weights of quantitative evaluation indicators of relevant performance. S301: An expert group composed of textile fabric experts, sports equipment design experts, and aerodynamic performance analysis experts was organized to conduct pairwise importance comparisons between primary indicators and between secondary indicators using the Saaty nine-level scale method, and to classify each comparison and establish a judgment matrix; the results of the judgment matrix are shown in Table 11. Table 11 Saaty Level 9 Scale

[0067] S302: According to the AHP (Analytical Hierarchy Process), the judgment matrix A is first normalized by column, and the mean of each row is obtained to get the weight vector corresponding to each secondary indicator. A×w=λ max ×w, in, w For the weight vector, l max To determine the largest eigenvalue of a matrix; S303: Use consistency indicators and consistency ratios to perform consistency checks and select appropriate subjective weight vectors for secondary indicators. ; Consistency Indicators CI satisfy: ; Consistency ratio CR satisfy: ; Where n is the number of evaluation indicators; If CR < 0.1, the matrix weight result is considered valid. If CR ≥ 0.1, the result of the judgment matrix weight is invalid, and the judgment matrix should be reconstructed until the consistency test is passed.

[0068] The weighting results of the AHP subjective evaluation indicators are shown in Table 12: Table 12 Weights of AHP Subjective Evaluation Indicators

[0069] CR < 0.1 indicates that the weighting result is reasonable.

[0070] S4: Based on the objective weights and subjective weights, the evaluation indicators are weighted and integrated to construct a comprehensive quantitative evaluation model for fabrics; based on the comprehensive quantitative evaluation model for fabrics, the candidate fabrics are evaluated and screened for performance, and used to guide the zoning configuration of fabrics.

[0071] S401: A linear combination method is used to integrate subjective and objective weights to obtain the comprehensive weight vector matrix of each secondary indicator.w ,satisfy; ; in, α ∈[0,1] is the weighting coefficient, which is 0.5 in this embodiment; S402: Yes w Normalization is performed to satisfy the requirements; =1; Overall performance score of each scheme S i satisfy: ; in, w j This is the comprehensive weight vector. For each scheme j The standardized values ​​of each indicator indicate that the higher the score, the better the overall performance of the solution.

[0072] The comprehensive performance score of each torso model scheme can be obtained using the above method. S i As a result, the comprehensive performance scores of the upper arm and forearm models, and the thigh and lower leg models can be obtained using the same method. S i The results are shown in Table 13: Table 13 Overall performance scores of the three models and their respective schemes

[0073] The results of the fabric zoning design are shown in Table 14 below: Table 14 Results of Fabric Selection and Differentiated Zoning Design

[0074] splicing process The different fabric seams are arranged in the direction of airflow, with the seam angle not exceeding 15° from the mainstream direction. Transition seams are overlocked with a width of 6–8 mm and then heat-pressed at low temperature to reduce airflow disturbance and ensure overall fit. Key structural lines (shoulder cap, leg sides, and hip area) are optimized based on local flow field analysis and fabric performance matching results to maximize downstream flow disturbance control and local drag reduction.

[0075] Comparative Example This comparative example discloses a fabric evaluation and screening method based on multi-level performance indicators. The difference between this method and Example 1 is that both methods use a single-layer polyurethane coated fabric C1 for the torso, without differentiating the fabrics according to regional differences. The implementation results are shown in Table 15. Table 15. Implementation Results of Examples and Comparative Examples

[0076] The results show: 1. Air resistance test The average drag coefficient at speeds of 11–17 m / s was obtained through wind tunnel aerodynamic testing. The average drag coefficient (Cd) of the racing suit with the differentiated fabric configuration in this embodiment was reduced by about 5.8% compared with the suit without a racing suit, and by about 2.6% compared with the comparative scheme (using a single fabric for the torso in Example 1), showing a significant drag reduction effect in the medium and high speed gliding range.

[0077] 2. Objective evaluation of posture (multi-source motion capture) The three-dimensional posture of the torso and joints during gliding was captured using a motion capture system. The core stability index (0–1, dimensionless) was calculated based on the center of mass shift and posture continuity. This index indirectly reflects the rationality of pressure distribution in the core area and the adaptability of movement. The core stability index was 0.92±0.02, while the comparative scheme was 0.85±0.03. This indicates that the embodiment is superior to the conventional scheme in terms of maintaining gliding posture and movement continuity, contributing to improved power transmission efficiency and gliding stability, with an improvement of 0.07 compared to the comparative scheme and 8% compared to the comparative scheme.

[0078] 3. Athlete's subjective evaluation (out of 5 points) Several high-level athletes tried on and evaluated the competition uniforms, rating them on a scale of 1–5 for comfort, smoothness of movement, and overall fit. The results showed that comfort, smoothness of movement, and overall fit were rated at 4.6±0.2, 4.7±0.2, and 4.5±0.2, respectively, while the comparative version scored only 3.8±0.3, 3.9±0.3, and 3.7±0.3. The evaluation results indicate that this embodiment significantly improves fabric softness, fit, and gliding feel, representing a 20% improvement over the comparative version.

[0079] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A fabric evaluation and screening method based on multi-level performance indicators, characterized in that, include: S1: Based on the functional requirements of racing-type tight-fitting sportswear, a multi-level performance evaluation index system for fabrics is constructed. The evaluation index system includes at least primary and secondary indicators. The primary indicators are drag reduction performance, lightweight performance, tensile performance, and compression performance. The secondary indicators are quantitative evaluation indicators of the primary indicators. S2: The objective weights of quantitative evaluation indicators of relevant performance are determined by the entropy weight method based on wind tunnel aerodynamic test data and fabric multi-physical property test data; S3: The analytic hierarchy process (AHP) is used to construct a judgment matrix based on expert cognitive evaluation to determine the subjective weights of quantitative evaluation indicators of relevant performance. S4: Based on the objective weights and subjective weights, the evaluation indicators are weighted and integrated to construct a comprehensive quantitative evaluation model for fabrics; Based on the comprehensive quantitative evaluation model for fabrics, the performance of candidate fabrics is evaluated and screened, and the results are used to guide the zoning and configuration of fabrics.

2. The fabric evaluation and screening method based on multi-level performance indicators according to claim 1, characterized in that, Quantitative evaluation indicators for drag reduction performance include: friction reduction rate. f r Flow separation retardation rate Δ θ r and local pressure difference reduction rate p r ; Among them, the flow separation retardation rate Δ θ r satisfy: ； θ : Flow separation angle under actual conditions; θ 0: Flow separation angle under baseline conditions; Local pressure difference reduction rate p r satisfy: ； F pressure,0 This represents the local pressure differential resistance corresponding to the cylindrical model without fabric. F pressure,s The local pressure differential resistance of the fabric cylindrical model; Friction reduction rate f r satisfy: ； F fric,0 Frictional resistance under reference conditions F fric,0 = Total drag measured in wind tunnel using a cylindrical model F total,0 and F pressure,0 The difference; F fric,s For local frictional resistance, F fric,s = Total resistance of laying fabric cylindrical model F total,s and F pressure,s The difference.

3. The fabric evaluation and screening method based on multi-level performance indicators according to claim 1, characterized in that, The quantitative evaluation index for lightweight performance is based on the uniform index of weight per unit area and mass per unit mass for each fabric. m i ’ As a benchmark, it must satisfy: ； in, m i Let be the weight of the i-th type of fabric, in g / m²; max(m) and min(m) are the maximum and minimum weights in the test scheme, respectively, in g / m².

4. The fabric evaluation and screening method based on multi-level performance indicators according to claim 1, characterized in that, The quantitative evaluation index of tensile properties is the elongation rate of each fabric under a preset load condition. R l As a benchmark, it must satisfy: ； in, L 0 represents the initial gauge length of the fabric sample when it is not under load; L This is the gauge length of the fabric sample under standard tensile load.

5. The fabric evaluation and screening method based on multi-level performance indicators according to claim 1, characterized in that, The quantitative evaluation indicators of compressibility include contact pressure. P i and tensile stiffness K i Based on the preset target contact pressure P ref and preset target tensile stiffness K ref For actual contact pressure P i and tensile stiffness K i Target deviations are calculated separately, and a contact pressure matching index is constructed. M P,i Matching index with tensile stiffness M K,i It is used to characterize the compression performance level of the fabric; Contact pressure matching index M P,i satisfy: ； P i Let be the contact pressure measured in the i-th region of the fabric under the attached state; P ref The target contact pressure is preset according to the needs of the sport; This refers to the allowable deviation range of the contact pressure. Tensile stiffness matching index M K,i satisfy: ； in, K i Let be the tensile stiffness of the fabric in region i within a preset strain range; K ref The target tensile stiffness is preset according to the requirements of the sport; This represents the allowable deviation range for tensile stiffness.

6. The fabric evaluation and screening method based on multi-level performance indicators according to any one of claims 1-5, characterized in that, Step S4 includes: S401: A linear combination method is used to combine subjective and objective weights to obtain the comprehensive weight vector matrix of each secondary indicator. w ,satisfy; ； in, α ∈[0,1] represents the weighted composite coefficient; S402: Yes w Normalization is performed to satisfy the requirements; =1； Overall performance score of each scheme S i satisfy: ； in, w j This is the comprehensive weight vector. For each scheme j The standardized values ​​of each indicator.

7. The fabric evaluation and screening method based on multi-level performance indicators according to claim 6, characterized in that, Step S2 includes: S201: Simplify the geometry of each part of the human body, construct a simplified geometric model of the human body, and apply fabric to the cylindrical surface of the simplified geometric model to conduct compression performance tests and drag reduction performance tests under different wind speeds. S202: Conduct quality tests and tensile property tests on the fabric; S203: Normalize the data obtained in steps S201 and S202 and convert them into dimensionless values; S204: Calculate the entropy value of the dimensionless value in step S203 based on the entropy method, and further determine the objective weight based on the entropy value.

8. The fabric evaluation and screening method based on multi-level performance indicators according to claim 7, characterized in that, Step S3 includes: S301: The importance of pairwise comparisons between primary indicators and between secondary indicators is performed using the Saaty nine-level scale method, and a judgment matrix is ​​established for each comparison. S302: According to the AHP (Analytical Hierarchy Process), the judgment matrix A is first normalized by column, and the mean of each row is obtained to get the weight vector corresponding to each secondary indicator. A×w=λ max ×w; in, w For the weight vector, λ max To determine the largest eigenvalue of a matrix; S303: Use consistency indicators and consistency ratios to perform consistency checks and select appropriate subjective weight vectors for secondary indicators. ; Consistency Indicators CI satisfy: ; Consistency ratio CR satisfy: ; Where n is the number of evaluation indicators; RI is the random consistency index; If CR < 0.1, the matrix weight result is considered valid. If CR ≥ 0.1, the result of the judgment matrix weight is invalid, and the judgment matrix should be reconstructed until the consistency test is passed.

9. A partitioned design scheme for speed skating competition uniform fabric, characterized in that, Obtained using the fabric evaluation and screening method based on multi-level performance indicators as described in any one of claims 1-6, including: The torso area, including the chest and abdomen, the midline of the back, and the waist and hips, is made of polyurethane-coated fabric with a coating thickness of 15µm to 25µm. When worn, the fabric has a stretch rate of 17% to 25% in the circumferential direction of the corresponding area. The limb areas, including the upper arm, thigh, forearm, and lower leg, are made of honeycomb quilted warp-knitted fabric. When worn, the fabric has an elongation of 25% to 38% in the circumferential direction of the corresponding area, a quilt depth of 0.4mm to 1.2mm, and a quilt spacing of 4mm to 6mm. The joint and splicing transition areas, including the armpit, knee and crotch areas, are made of four-way elastic fabric with a spandex content of 10% to 15%. When worn, the fabric has a stretch rate of 30% to 43% in the main deformation direction of the corresponding area.

10. The speed skating competition uniform fabric partitioning design scheme according to claim 9, characterized in that, The upper arm and forearm areas use a honeycomb knitted fabric with a pit depth of 0.6mm and a spacing of 5mm; the thigh and calf areas use a honeycomb knitted fabric with a pit depth of 0.4mm and a spacing of 6mm.

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