Comprehensive evaluation method and system for air permeability of a jacket

Abstract

The present application relates to the technical field of garment comfort evaluation, and specifically discloses a comprehensive evaluation method and system for the air permeability of a jacket, which comprises a dynamic test process including a static period, a motion period and a recovery period, synchronously collects multi-dimensional subjective and objective data, and combines normality test, parameter and non-parameter statistical method selection, effect size calculation and fabric physical property correlation analysis to realize scientific and comprehensive evaluation of the air permeability of the jacket, has the advantages of being able to integrate multi-dimensional indexes, support dynamic process monitoring, apply advanced statistical analysis process and strictly regulate experimental conditions, so as to scientifically and objectively measure the air permeability and comfort performance of the jacket in a real use environment.

Description

Technical Field

[0001] This application relates to the field of clothing comfort evaluation technology, and more specifically, to a comprehensive evaluation method and system for the breathability performance of outdoor jackets. Background Technology

[0002] As protective clothing for outdoor sports, the breathability of a waterproof jacket directly affects the wearer's thermal and moisture comfort. While various functional waterproof jackets on the market claim excellent breathability and moisture permeability, a systematic and comprehensive evaluation system is currently lacking to scientifically verify their actual wearing comfort.

[0003] Traditional methods for evaluating the performance of outdoor jackets often rely on single-index tests (such as static laboratory tests for moisture permeability and air permeability), which do not adequately consider changes in the skin microclimate and subjective wearing experience during actual exercise. This makes it difficult to objectively reflect the overall comfort performance of outdoor jackets in real-world usage scenarios.

[0004] Specifically, the existing technology has the following drawbacks: (1) Lack of multi-dimensional comprehensive evaluation: Existing methods focus on single physical indicators and fail to systematically integrate multi-dimensional indicators such as clothing microclimate (temperature, humidity), perspiration, and subjective feelings.

[0005] (2) Insufficient dynamic monitoring capability: Traditional tests are mostly static laboratory tests, which cannot reflect the dynamic changes in clothing performance under motion.

[0006] (3) Limitations of statistical analysis methods: Existing studies mostly use simple mean comparisons, lacking the testing of data distribution characteristics and the reasonable application of nonparametric statistical methods.

[0007] (4) Insufficient sample size and control of individual differences: Existing studies generally have small sample sizes and lack effective control over the status of test subjects.

[0008] There is currently no effective technical solution to the above problems. Summary of the Invention

[0009] The purpose of this application is to provide a comprehensive evaluation method and system for the breathability performance of outdoor jackets, aiming to solve the technical problems mentioned in the background art.

[0010] To solve the above-mentioned technical problems, the solution proposed in this application is as follows: As one aspect of this application, a comprehensive evaluation method for the breathability of a waterproof jacket is provided. This method compares the physiological data and subjective feelings of test subjects while wearing the waterproof jacket, and processes the data through a statistical analysis process to generate a comprehensive quantitative evaluation of the waterproof performance of the jacket. The method includes: Step S1: In a constant temperature and humidity test environment, the test subjects are asked to wear the windproof jacket sample and perform a test procedure including a rest period, a specified intensity exercise period, and a post-exercise recovery period; and the subjective and objective test data of the test subjects are collected simultaneously; wherein, the subjective and objective test data include objective physiological index data and subjective sensory evaluation data, and the objective physiological index data includes at least temperature data and humidity data; Step S2: Process and analyze the collected subjective and objective test data, and execute the following sub-steps in sequence to output evaluation conclusions: Step S21: Perform a normality test on the subjective and objective test data to obtain the test results, and select a parametric analysis method or a non-parametric analysis method to calculate the first probability index based on the obtained test results. When the test subject wears the test jacket sample, determine whether there is a statistical difference between the data groups in the subjective and objective test data based on the first probability index to obtain the first analysis result. Step S22: If there is a statistical difference, use the Cohen d-value algorithm to calculate the effect size between the data groups in the subjective and objective test data. Based on the effect size, identify the degree of variation between the data groups in the subjective and objective test data. Based on the degree of variation, confirm that the statistical difference is caused by the test subjects wearing different windproof jackets. If there is a statistical difference, calculate the median and coefficient of variation of the data groups in the subjective and objective test data. Based on the median and coefficient of variation of the data groups in the subjective and objective test data, identify the difference in the wearing experience of different windproof jackets and the degree of wearing stability, and obtain the second analysis result. Step S23: If there is a statistical difference, obtain the physical performance parameters of the fabric of different rain jackets, and perform correlation analysis on the physical performance parameters of the fabric of the rain jacket to be tested based on subjective and objective test data, and obtain the correlation analysis coefficient; identify the physiological indicators that are sensitive to the thermal and moisture properties of the fabric based on the obtained correlation analysis coefficient, and obtain the third analysis result, wherein the physical performance parameters of the fabric include at least one of thermal resistance, moisture resistance, air permeability and moisture permeability. Step S24: Based on the first analysis results, second analysis results, and third analysis results obtained in steps S21 to S23, generate a comprehensive evaluation report.

[0011] Furthermore, the procedure before step S1 includes: Step S0: Select healthy test subjects and control their lifestyle habits within the set experimental period. At the same time, wash and humidify the test samples of the rain jackets to be tested.

[0012] Furthermore, in step S0, the washing and conditioning treatment of the test jacket sample involves washing the test jacket sample according to the set washing method and then conditioning it for a preset time under standard atmospheric conditions with a temperature of 20±2℃ and a relative humidity of 65±5%.

[0013] Furthermore, step S1 specifically includes: Step S11: Have the test subject wear the test jacket sample and enter the constant temperature and humidity test environment for environmental adaptation. Step S12: While the test subject is at rest, continuously record the objective physiological indicators of the test subject and collect the test subject's first subjective sensory evaluation data. Step S13: While the test subject is in a state of exercise of a specified intensity, continuously record the objective physiological index data of the test subject, and collect the second subjective sensory evaluation data of the test subject. Step S14: After the test subject has stopped moving, record the objective physiological indicators of the test subject continuously in the resting state, and collect the third subjective functional evaluation data of the test subject.

[0014] Furthermore, in step S11, the constant temperature and humidity experimental environment is a temperature of 20±2℃, a relative humidity of 65±5%, and a wind speed of less than 0.1m / s.

[0015] Furthermore, in step S1, the objective physiological indicator data also includes the amount of sweat.

[0016] Furthermore, in step S21, the normality test is performed using the SW test method.

[0017] Furthermore, step S21 specifically includes: Step S211: Perform a normality test on the subjective and objective test data to obtain the test results; Step S212: When the subjective and objective test data conform to a normal distribution, use one-way ANOVA to perform significance testing on each data group in the subjective and objective test data, and confirm whether there is a statistical difference based on the significance test results. Step S213: When the subjective and objective test data do not conform to the normal distribution, the KS test method is used to detect the significance of each data group in the subjective and objective test data, and the statistical difference is confirmed based on the significance test results. Step S214: An existence analysis of the statistical differences between steps S212 and S213 is performed to obtain the first analysis result.

[0018] Furthermore, step S23 specifically includes: Step S231: When the subjective and objective test data conform to a normal distribution, the Pearson correlation analysis method is used to calculate the Pearson correlation coefficient between the subjective and objective test data and the physical performance parameters of the fabric. Step S232: When the subjective and objective test data do not conform to the normal distribution, Spearman correlation analysis is used to calculate the Spearman correlation coefficient between the subjective and objective test data and the physical performance parameters of the fabric. Step S233: Based on the Pearson correlation coefficient or Spearman correlation coefficient obtained in steps S231 and S232, identify the physiological indicators that are sensitive to the thermal and moisture properties of the fabric, and obtain the third analysis result.

[0019] As a second aspect of this application, a comprehensive evaluation system for the breathability of outdoor jackets is provided. This system compares physiological data and subjective feelings of test subjects while wearing the jacket, and processes this data through a statistical analysis process to generate a comprehensive quantitative evaluation of the jacket's breathability. The system includes: The data acquisition module is used to conduct a test procedure in a constant temperature and humidity experimental environment, where the test subjects wear the test jacket sample and perform a test process that includes a rest period, a specified intensity exercise period, and a post-exercise recovery period; and to simultaneously collect the subjective and objective test data of the test subjects; wherein the subjective and objective test data includes objective physiological index data and subjective sensory evaluation data, and the objective physiological index data includes at least temperature data and humidity data; The evaluation and analysis module is used to process and analyze the collected subjective and objective test data, and executes the following sub-steps in sequence to output evaluation conclusions; The evaluation and analysis module includes: The first analysis unit is used to perform a normality test on the subjective and objective test data to obtain the test result, and select a parametric analysis method or a non-parametric analysis method to calculate a first probability index based on the obtained test result. When the test subject wears the test jacket sample, the unit judges whether there is a statistical difference between the data groups in the subjective and objective test data based on the first probability index to obtain the first analysis result. The second analysis unit is used to calculate the effect size between data groups in the subjective and objective test data using the Cohen d-value algorithm when there is a statistical difference; identify the degree of variation between data groups in the subjective and objective test data based on the effect size; confirm that the statistical difference is caused by the test subjects wearing different rain jackets based on the degree of variation; and if there is a statistical difference, calculate the median and coefficient of variation of data groups in the subjective and objective test data; identify the difference in wearing experience and the degree of wearing stability of different rain jackets based on the median and coefficient of variation of data groups in the subjective and objective test data; and obtain the second analysis result. The third analysis unit is used to obtain the physical performance parameters of the fabric of different rain jackets when there are statistical differences, and to perform correlation analysis on the physical performance parameters of the fabric of the rain jacket to be tested based on subjective and objective test data, and obtain the correlation analysis coefficient; to identify the physiological indicators that are sensitive to the thermal and moisture properties of the fabric based on the obtained correlation analysis coefficient, and to obtain the third analysis result, wherein the physical performance parameters of the fabric include at least one of thermal resistance, moisture resistance, air permeability and moisture permeability. The evaluation report generation module is used to generate a comprehensive evaluation report based on the obtained first analysis results, second analysis results, and third analysis results.

[0020] As can be seen from the above, the comprehensive evaluation method and system for the breathability performance of outdoor jackets provided in this application, through the design of a dynamic testing process including a rest period, an activity period, and a recovery period, simultaneously collects multi-dimensional subjective and objective data, and combines normality tests, selection of parametric and non-parametric statistical methods, effect size calculation, and correlation analysis of fabric physical properties, to achieve a scientific and comprehensive evaluation of the breathability performance of outdoor jackets. It has the advantages of being able to integrate multi-dimensional indicators, support dynamic process monitoring, apply advanced statistical analysis processes, and strictly standardize experimental conditions, thereby scientifically and objectively measuring the breathability and comfort performance of outdoor jackets in real-world usage environments. Attached Figure Description

[0021] Figure 1 A flowchart illustrating the comprehensive evaluation method for the breathability of a windproof jacket provided in this application embodiment; Figure 2 A flowchart of step S2 in the comprehensive evaluation method for the breathability of a windproof jacket provided in the embodiments of this application; Figure 3 A system structure block diagram of a comprehensive evaluation system for the breathability of outdoor jackets provided in this application embodiment; Figure reference numerals: 100, Comprehensive evaluation system; 101, Data acquisition module; 102, Evaluation analysis module; 1021, First analysis unit; 1022, Second analysis unit; 1023, Third analysis unit; 1024, Evaluation report generation module. Detailed Implementation

[0022] To better illustrate the present invention, the invention will now be described in further detail with reference to the accompanying drawings.

[0023] It should be understood that, in order to make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0024] The following description uses at least one specific embodiment as an example. In this embodiment: Firstly, such as Figure 1 and Figure 2 As shown, a comprehensive evaluation method for the breathability of outdoor jackets is provided. This method compares the physiological data and subjective feelings of test subjects while wearing the jacket, and processes the data through a statistical analysis process to generate a comprehensive quantitative evaluation of the breathability of the jacket. The method includes: Step S1: In a constant temperature and humidity test environment, the test subjects are asked to wear the windproof jacket sample and perform a test procedure including a rest period, a specified intensity exercise period, and a post-exercise recovery period; and the subjective and objective test data of the test subjects are collected simultaneously; wherein, the subjective and objective test data include objective physiological index data and subjective sensory evaluation data, and the objective physiological index data includes at least temperature data and humidity data; Step S2: Process and analyze the collected subjective and objective test data, and execute the following sub-steps in sequence to output evaluation conclusions: Step S21: Perform a normality test on the subjective and objective test data to obtain the test results, and select a parametric analysis method or a non-parametric analysis method to calculate the first probability index based on the obtained test results. When the test subject wears the test jacket sample, determine whether there is a statistical difference between the data groups in the subjective and objective test data based on the first probability index to obtain the first analysis result. Step S22: If there is a statistical difference, use the Cohen d-value algorithm to calculate the effect size between the data groups in the subjective and objective test data. Based on the effect size, identify the degree of variation between the data groups in the subjective and objective test data. Based on the degree of variation, confirm that the statistical difference is caused by the test subjects wearing different windproof jackets. If there is a statistical difference, calculate the median and coefficient of variation of the data groups in the subjective and objective test data. Based on the median and coefficient of variation of the data groups in the subjective and objective test data, identify the difference in the wearing experience of different windproof jackets and the degree of wearing stability, and obtain the second analysis result. Step S23: If there is a statistical difference, obtain the physical performance parameters of the fabric of different rain jackets, and perform correlation analysis on the physical performance parameters of the fabric of the rain jacket to be tested based on subjective and objective test data, and obtain the correlation analysis coefficient; identify the physiological indicators that are sensitive to the thermal and moisture properties of the fabric based on the obtained correlation analysis coefficient, and obtain the third analysis result, wherein the physical performance parameters of the fabric include at least one of thermal resistance, moisture resistance, air permeability and moisture permeability. Step S24: Based on the first analysis results, second analysis results, and third analysis results obtained in steps S21 to S23, generate a comprehensive evaluation report.

[0025] Specifically, the method includes the following steps: In step S1, the test subject needs to wear different waterproof jacket samples in a constant temperature and humidity experimental environment and perform a test procedure that includes a rest period, a specified intensity exercise period, and a post-exercise recovery period, while simultaneously collecting the subject's subjective and objective test data. This subjective and objective test data includes objective physiological index data and subjective sensory evaluation data. The objective physiological index data includes at least temperature and humidity data. For example, an environmental chamber can be set up, with the temperature controlled at 20°C and the relative humidity controlled at 65%. The test procedure can be designed to include an environmental adaptation phase, followed by a resting phase for the test subject, a phase of exercise at a preset intensity, and a post-exercise recovery phase. Throughout the process, skin temperature and skin humidity can be continuously recorded by sensors worn on the test subject's skin. Simultaneously, after each phase, the test subject's subjective feelings about the waterproof jacket's breathability, comfort, etc., are collected through a questionnaire to obtain subjective sensory evaluation data.

[0026] In step S2, the collected subjective and objective test data need to be processed and analyzed to output evaluation conclusions. This step aims to transform the raw data into meaningful evaluation information.

[0027] Step S21 involves performing a normality test on the subjective and objective test data, obtaining the test results, and calculating a first probability index based on either parametric or nonparametric analysis methods. When the test subject wears the tested rain jacket sample, the first probability index is used to determine whether there are statistically significant differences between the data groups in the subjective and objective test data, thus obtaining the first analysis result. For example, the Shapiro-Wilk test can be used to perform a normality test on each data group. If the test results show that the data conforms to a normal distribution, a one-way ANOVA can be performed to compare the data differences between different rain jacket samples. If the data does not conform to a normal distribution, a nonparametric test method, such as the KW test, can be used to assess the differences between data groups. Through these tests, a p-value can be obtained as the first probability index. When the p-value is less than the preset significance level (e.g., 0.05), a statistically significant difference is considered to exist.

[0028] Step S22 involves calculating the effect size between data groups in the subjective and objective test data when a statistical difference exists, using the Cohen d-value algorithm. Based on the effect size, the degree of variation between data groups in the subjective and objective test data is identified. The degree of variation is then used to confirm that the statistical difference is caused by the test subjects wearing different rain jackets. If a statistical difference exists, the median and coefficient of variation of each data group in the subjective and objective test data are calculated. Based on the median and coefficient of variation of each data group in the subjective and objective test data, the differences in the wearing experience and the degree of wearing stability of different rain jackets are identified, and a second analysis result is obtained. For example, when step S21 confirms a statistical difference in skin temperature data among different rain jacket samples, the Cohen d-value can be further calculated to quantify the magnitude of this difference. A larger Cohen d-value indicates a significant difference in the impact of different rain jackets on skin temperature. Simultaneously, the median and coefficient of variation of skin temperature for each rain jacket sample at different test stages can be calculated. By comparing the median, we can intuitively see the differences in average wearing experience among different rain jackets; by comparing the coefficient of variation, we can assess the ability of different rain jackets to provide a stable wearing experience. The smaller the coefficient of variation, the more stable the wearing experience.

[0029] Step S23 involves obtaining the physical performance parameters of the fabrics of different rain jackets when statistical differences exist, and performing correlation analysis on the physical performance parameters of the fabrics of the rain jackets to be tested based on subjective and objective test data, obtaining the correlation coefficient. Based on the obtained correlation coefficient, physiological indicators sensitive to the thermal and moisture properties of the fabric are identified, and a third analysis result is obtained. The physical performance parameters of the fabric include at least one of thermal resistance, moisture resistance, air permeability, and moisture permeability. For example, the thermal resistance and moisture permeability data of each rain jacket fabric can be obtained from the rain jacket manufacturer or through laboratory testing. Subsequently, these physical performance parameters of the fabric are correlated with the objective physiological indicators such as skin temperature and skin humidity collected in step S1. If the data conforms to a normal distribution, Pearson correlation analysis can be used; if the data does not conform to a normal distribution, Spearman correlation analysis can be used.

[0030] Step S24 generates a comprehensive evaluation report based on the first, second, and third analytical results obtained from steps S21 to S23. This report integrates all analytical conclusions, providing a comprehensive quantitative evaluation of the breathability performance of the rain jacket. For example, the report may include statistical differences in various subjective and objective indicators among different rain jacket samples, effect size analysis, comparison of medians and coefficients of variation, and correlation analysis results between fabric physical performance parameters and physiological indicators. Finally, based on this comprehensive information, the report will give a rating or quantitative score for the breathability performance of each rain jacket and may propose improvement suggestions.

[0031] The following specific example will provide a more in-depth explanation of the above technical solution: Suppose a research institution needs to conduct a comprehensive evaluation of the breathability of three different brands of outdoor jackets (outdoor jacket A, outdoor jacket B, and outdoor jacket C).

[0032] First, before the experiment begins, researchers select 10 test subjects whose health conditions meet pre-defined standards and whose lifestyles are consistent. These test subjects are informed of the experimental procedures and precautions. Meanwhile, the three samples of the rain jackets are washed according to standard washing methods and conditioned under standard atmospheric conditions of 20°C and 65% relative humidity to ensure their initial consistency.

[0033] In step S1, the experiment was conducted in a constant temperature and humidity environment, which was precisely controlled at a temperature of 20°C, a relative humidity of 65%, and a wind speed of less than 0.1 m / s. Each test subject wore a waterproof jacket A, a waterproof jacket B, and a waterproof jacket C on different experimental days. The testing process can be designed to include an environmental adaptation phase, such as the subject entering the experimental environment in a waterproof jacket for 30 minutes to adapt; followed by a static phase, such as the subject sitting still for 30 minutes, during which objective physiological indicators such as skin temperature and skin humidity are continuously recorded, and the first subjective sensory evaluation data (e.g., rating "stuffy feeling" on a scale of 1-7) is collected at the end of the static phase; next is a phase where the subject engages in exercise at a preset intensity, such as exercising on a treadmill at a speed of 6 km / h for 30 minutes, during which objective physiological indicators are continuously recorded, and the second subjective sensory evaluation data is collected at the end of the exercise phase; and a recovery phase, such as the subject stopping exercise and sitting still for 30 minutes, continuing to record objective physiological indicators, and the third subjective sensory evaluation data is collected at the end of the recovery phase. In this way, a large amount of subjective and objective test data is collected simultaneously.

[0034] In step S2, all collected subjective and objective test data are processed and analyzed.

[0035] Specifically, in step S21, researchers first conducted normality tests on data such as skin temperature, skin humidity, and subjective evaluation scores for each rain jacket at different testing stages. For example, the SW test was used. If the test results showed that a certain data point (e.g., skin temperature) conformed to a normal distribution, one-way ANOVA was used to compare the differences in skin temperature among rain jackets A, B, and C, calculating the first probability index (p-value). If the test results showed that a certain data point (e.g., subjective stuffiness score) did not conform to a normal distribution, the KW test was used for comparison, also obtaining the first probability index. Through these analyses, researchers confirmed that there were statistically significant differences among the different rain jacket samples in terms of skin temperature, skin humidity, and subjective stuffiness score, thus obtaining the first analytical results.

[0036] In step S22, due to the statistical differences, the researchers further used the Cohen d-value algorithm to calculate the effect sizes of skin temperature, skin humidity, and subjective stuffiness scores among different rain jackets. For example, they found that the Cohen d-value for skin temperature between rain jacket A and rain jacket B was 0.8, indicating a significant difference. Simultaneously, they calculated the median and coefficient of variation of skin temperature for each rain jacket during exercise. For example, the median skin temperature of rain jacket A was 35.5℃ with a coefficient of variation of 2%; the median skin temperature of rain jacket B was 36.2℃ with a coefficient of variation of 5%. This identified rain jacket A as better at maintaining stable skin temperature during exercise, with a smaller difference in wearing experience, leading to the second analysis result.

[0037] In step S23, researchers obtained physical performance parameters such as thermal resistance, moisture resistance, air permeability, and moisture permeability of three types of outdoor jacket fabrics. Subsequently, based on the skin temperature and skin humidity data collected in step S1, correlation analysis was performed between these physiological indicators and the fabric physical performance parameters. For example, since the skin temperature and humidity data follow a normal distribution, Pearson correlation analysis was used. The analysis results showed a high negative correlation between skin humidity and the fabric's moisture permeability (correlation coefficient -0.75), while a high positive correlation between skin temperature and the fabric's thermal resistance (correlation coefficient 0.68). This identified skin humidity and skin temperature as physiological indicators sensitive to the thermal and moisture properties of fabrics, leading to the third analytical result.

[0038] Finally, in step S24, the researchers generated a comprehensive evaluation report based on the results of the first, second, and third analyses. This report details the performance of jackets A, B, and C across various subjective and objective indicators, including statistical differences, effect sizes, medians, coefficients of variation, and correlations with fabric physical properties. The report concludes that jacket A performs excellently in breathability and wearing comfort; its fabric's low moisture resistance and high moisture permeability effectively reduce skin humidity and maintain a suitable thermal-humidity balance. While jacket B meets basic breathability requirements, its skin temperature fluctuates significantly during activity, and the stability of the wearing experience needs improvement. Jacket C's breathability does not meet requirements, resulting in higher skin temperature and humidity, and a higher subjective score for stuffiness. Through this comprehensive evaluation method, the research institution can provide consumers with scientific and quantitative purchasing advice and offer directions for product improvement to jacket manufacturers.

[0039] Furthermore, the procedure before step S1 includes: Step S0: Select healthy test subjects and control their lifestyle habits within the set experimental period. At the same time, wash and humidify the test samples of the rain jackets to be tested.

[0040] Specifically, screening healthy test subjects and controlling their lifestyle habits ensured the uniformity and stability of their physiological states, allowing the collected physiological and subjective data to more accurately reflect the breathability of the jackets. Simultaneously, the jacket samples underwent washing and conditioning to bring them to a standardized physical state, avoiding the influence of initial condition differences on the test results. These pretreatments resulted in a more objective comprehensive quantitative evaluation of the jacket's breathability performance.

[0041] Preferably, in step S0, the washing and conditioning of the test jacket sample involves washing the test jacket sample according to a set washing method and then conditioning it for a preset time under standard atmospheric conditions with a temperature of 20±2℃ and a relative humidity of 65±5%.

[0042] In this embodiment, specifically, step S1 includes: Step S11: Have the test subject wear the test jacket sample and enter the constant temperature and humidity test environment for environmental adaptation. Step S12: While the test subject is at rest, continuously record the objective physiological indicators of the test subject and collect the test subject's first subjective sensory evaluation data. Step S13: While the test subject is in a state of exercise of a specified intensity, continuously record the objective physiological index data of the test subject, and collect the second subjective sensory evaluation data of the test subject. Step S14: After the test subject has stopped moving, record the objective physiological indicators of the test subject continuously in the resting state, and collect the third subjective functional evaluation data of the test subject.

[0043] Let's illustrate this with a specific example. In evaluating the breathability of a waterproof jacket, firstly, the test subject, wearing the jacket sample, enters a constant temperature and humidity environment (20℃, 65% relative humidity) for 30 minutes to acclimatize. During this time, the subject can sit still or perform light stretching to ensure physiological stability. Next, while the subject remains still, temperature and humidity data are continuously recorded on key areas such as the armpits, chest, and back using temperature and humidity sensors worn on the skin. Heart rate data is also recorded simultaneously using a heart rate monitor for 15 minutes. At the end of this resting period, a subjective questionnaire is given to the subject, collecting their subjective ratings of the jacket's stuffiness, dampness, and comfort, serving as the first subjective sensory evaluation data. Subsequently, the subject is required to perform 30 minutes of exercise at a prescribed intensity on a treadmill at a speed of 6 km / h and a 2% incline. During this time, the aforementioned objective physiological indicators are continuously recorded. Immediately after exercise, a subjective questionnaire was administered to the test subjects again, collecting their subjective ratings of the jacket's sweat-wicking effect, stickiness, and breathability, serving as the second subjective sensory evaluation data. Finally, after the test subjects stopped exercising, they were allowed to return to a resting state and continue sitting quietly in a constant temperature and humidity experimental environment for 30 minutes, during which objective physiological indicators were continuously recorded. At the end of the recovery period, a subjective questionnaire was administered again, collecting their subjective ratings of the jacket's dryness, warmth, and overall comfort, serving as the third subjective sensory evaluation data. This method allows for the acquisition of comprehensive test data covering different physiological states.

[0044] The above technical solution refines the test process S1 for the breathability of rain jackets into four distinct stages: environmental adaptation, static state, exercise at a specified intensity, and recovery to static state after exercise. Subjective and objective test data are collected for each stage, ensuring a comprehensive and detailed evaluation of the breathability of rain jackets under different physiological loads.

[0045] Preferably, in step S11, the constant temperature and humidity experimental environment is a temperature of 20±2℃, a relative humidity of 65±5%, and a wind speed of less than 0.1m / s.

[0046] Furthermore, in step S1, the objective physiological indicator data also includes the amount of sweat.

[0047] In step S1 of this application, in addition to collecting temperature and humidity data, perspiration is further included in the synchronous collection of objective physiological index data. In a constant temperature and humidity experimental environment, test subjects wearing the waterproof jacket sample sequentially perform the testing process of a rest period, a specified intensity exercise period, and a post-exercise recovery period. During this process, real-time or phased measurement of perspiration can directly quantify the subject's perspiration status. For example, during the specified intensity exercise period, the body's heat production increases significantly, and perspiration is the main heat dissipation method. At this time, the breathability and moisture permeability of the waterproof jacket will directly affect the evaporation efficiency of sweat and the dryness of the skin. By collecting perspiration data, the ability of the waterproof jacket to maintain the body's thermal and moisture balance under different activity intensities can be more accurately assessed. This perspiration data, along with temperature data, humidity data, and subjective sensory evaluation data, is then processed and analyzed in step S2. In the subsequent statistical analysis process, perspiration data will serve as an important input variable, participating in normality tests, effect size calculations, median and coefficient of variation analysis, and correlation analysis with fabric physical performance parameters. In this way, perspiration data can be corroborated and supplemented by other physiological indicators and subjective feelings, thus making the overall evaluation of the breathability of the jacket more comprehensive and avoiding the evaluation bias that may be caused by relying solely on temperature and humidity data.

[0048] Secondly, such as Figure 3 As shown, a comprehensive evaluation system 100 for the breathability of outdoor jackets is provided. This system is used to generate a comprehensive quantitative evaluation of the breathability of outdoor jackets by comparing the physiological data and subjective feeling data of test subjects when wearing outdoor jackets, and processing them through a statistical analysis process. The system includes: The data acquisition module 101 is used to, in a constant temperature and humidity experimental environment, have the test subjects wear the windproof jacket sample and perform a test process including a rest period, a specified intensity exercise period, and a post-exercise recovery period; and to simultaneously collect the subjective and objective test data of the test subjects; wherein, the subjective and objective test data includes objective physiological index data and subjective sensory evaluation data, and the objective physiological index data includes at least temperature data and humidity data; Evaluation and analysis module 102 is used to process and analyze the collected subjective and objective test data, and execute the following sub-steps in sequence to output evaluation conclusions; The evaluation and analysis module includes: The first analysis unit 1021 is used to perform a normality test on the subjective and objective test data to obtain the test result, and select a parametric analysis method or a non-parametric analysis method to calculate a first probability index based on the obtained test result. When the test subject wears the test sample of the windbreaker, the first probability index is used to determine whether there is a statistical difference between the data groups in the subjective and objective test data, and the first analysis result is obtained. The second analysis unit 1022 is used to calculate the effect size between data groups in the subjective and objective test data using the Cohen d-value algorithm when there is a statistical difference, identify the degree of variation between data groups in the subjective and objective test data based on the effect size, confirm that the statistical difference is caused by the test subjects wearing different windproof jackets based on the degree of variation; and if there is a statistical difference, calculate the median and coefficient of variation of data groups in the subjective and objective test data, identify the difference in wearing experience and the degree of wearing stability of different windproof jackets based on the median and coefficient of variation of data groups in the subjective and objective test data, and obtain the second analysis result. The third analysis unit 1023 is used to obtain the physical performance parameters of the fabric of different rain jackets when there are statistical differences, and to perform correlation analysis on the physical performance parameters of the fabric of the rain jacket to be tested based on subjective and objective test data, and obtain the correlation analysis coefficient; to identify the physiological indicators that are sensitive to the thermal and moisture properties of the fabric based on the obtained correlation analysis coefficient, and obtain the third analysis result, wherein the physical performance parameters of the fabric include at least one of thermal resistance, moisture resistance, air permeability and moisture permeability. The evaluation report generation module 1024 is used to generate a comprehensive evaluation report based on the obtained first analysis results, second analysis results, and third analysis results.

[0049] In summary, this embodiment provides a comprehensive evaluation method and system for the breathability performance of outdoor jackets. By designing a dynamic testing process that includes rest, activity, and recovery periods, it simultaneously collects multi-dimensional subjective and objective data. Combined with normality tests, selection of parametric and non-parametric statistical methods, effect size calculation, and correlation analysis of fabric physical properties, it achieves a scientific and comprehensive evaluation of the breathability performance of outdoor jackets. It has the advantages of integrating multi-dimensional indicators, supporting dynamic process monitoring, applying advanced statistical analysis processes, and strictly standardizing experimental conditions, thereby scientifically and objectively measuring the breathability and comfort performance of outdoor jackets in real-world usage environments.

[0050] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit them. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure.

Claims

1. A comprehensive evaluation method for the breathability of outdoor jackets, comprising comparing physiological data and subjective feeling data of test subjects while wearing outdoor jackets, and processing the data through a statistical analysis process to generate a comprehensive quantitative evaluation of the breathability of outdoor jackets, characterized in that... The method includes: Step S1: In a constant temperature and humidity test environment, the test subjects are asked to wear the windproof jacket sample and perform a test procedure including a rest period, a specified intensity exercise period, and a post-exercise recovery period; and the subjective and objective test data of the test subjects are collected simultaneously; wherein, the subjective and objective test data include objective physiological index data and subjective sensory evaluation data, and the objective physiological index data includes at least temperature data and humidity data; Step S2: Process and analyze the collected subjective and objective test data, and execute the following sub-steps in sequence to output evaluation conclusions: Step S21: Perform a normality test on the subjective and objective test data to obtain the test results, and select a parametric analysis method or a non-parametric analysis method to calculate the first probability index based on the obtained test results. When the test subject wears the test jacket sample, determine whether there is a statistical difference between the data groups in the subjective and objective test data based on the first probability index to obtain the first analysis result. Step S22: If there is a statistical difference, use the Cohen d-value algorithm to calculate the effect size between the data groups in the subjective and objective test data. Based on the effect size, identify the degree of variation between the data groups in the subjective and objective test data. Based on the degree of variation, confirm that the statistical difference is caused by the test subjects wearing different windproof jackets. If there is a statistical difference, calculate the median and coefficient of variation of the data groups in the subjective and objective test data. Based on the median and coefficient of variation of the data groups in the subjective and objective test data, identify the difference in the wearing experience of different windproof jackets and the degree of wearing stability, and obtain the second analysis result. Step S23: If there is a statistical difference, obtain the physical performance parameters of the fabric of different rain jackets, and perform correlation analysis on the physical performance parameters of the fabric of the rain jacket to be tested based on subjective and objective test data, and obtain the correlation analysis coefficient; identify the physiological indicators that are sensitive to the thermal and moisture properties of the fabric based on the obtained correlation analysis coefficient, and obtain the third analysis result, wherein the physical performance parameters of the fabric include at least one of thermal resistance, moisture resistance, air permeability and moisture permeability. Step S24: Based on the first analysis results, second analysis results, and third analysis results obtained in steps S21 to S23, generate a comprehensive evaluation report.

2. The comprehensive evaluation method according to claim 1, characterized in that, The procedure preceding step S1 also includes: Step S0: Select healthy test subjects and control their lifestyle habits within the set experimental period. At the same time, wash and humidify the test samples of the rain jackets to be tested.

3. The comprehensive evaluation method according to claim 2, characterized in that, In step S0, the washing and conditioning of the test jacket sample involves washing the test jacket sample according to the set washing method and then conditioning it for a preset time under standard atmospheric conditions with a temperature of 20±2℃ and a relative humidity of 65±5%.

4. The comprehensive evaluation method according to claim 1, characterized in that, Step S1 specifically includes: Step S11: Have the test subject wear the test jacket sample and enter the constant temperature and humidity test environment for environmental adaptation. Step S12: While the test subject is at rest, continuously record the objective physiological indicators of the test subject and collect the test subject's first subjective sensory evaluation data. Step S13: While the test subject is in a state of exercise of a specified intensity, continuously record the objective physiological index data of the test subject, and collect the second subjective sensory evaluation data of the test subject. Step S14: After the test subject has stopped moving, record the objective physiological indicators of the test subject continuously in the resting state, and collect the third subjective functional evaluation data of the test subject.

5. The comprehensive evaluation method according to claim 4, characterized in that, In step S11, the constant temperature and humidity experimental environment is a temperature of 20±2℃, a relative humidity of 65±5%, and a wind speed of less than 0.1m / s.

6. The comprehensive evaluation method according to claim 1, characterized in that, In step S1, the objective physiological indicator data also includes the amount of sweat.

7. The comprehensive evaluation method according to claim 1, characterized in that, In step S21, the normality test is performed using the SW test method.

8. The comprehensive evaluation method according to claim 1, characterized in that, Step S21 specifically includes: Step S211: Perform a normality test on the subjective and objective test data to obtain the test results; Step S212: When the subjective and objective test data conform to a normal distribution, use one-way ANOVA to perform significance testing on each data group in the subjective and objective test data, and confirm whether there is a statistical difference based on the significance test results. Step S213: When the subjective and objective test data do not conform to the normal distribution, the KS test method is used to detect the significance of each data group in the subjective and objective test data, and the statistical difference is confirmed based on the significance test results. Step S214, combined with the existence analysis of statistical differences between steps S212 and S213, yields... First analysis results.

9. The comprehensive evaluation method according to claim 1, characterized in that, Step S23 specifically includes: Step S231: When the subjective and objective test data conform to a normal distribution, the Pearson correlation analysis method is used to calculate the Pearson correlation coefficient between the subjective and objective test data and the physical performance parameters of the fabric. Step S232: When the subjective and objective test data do not conform to the normal distribution, Spearman correlation analysis is used to calculate the Spearman correlation coefficient between the subjective and objective test data and the physical performance parameters of the fabric. Step S233: Based on the Pearson correlation coefficient or Spearman correlation coefficient obtained in steps S231 and S232, identify the physiological indicators that are sensitive to the thermal and moisture properties of the fabric, and obtain the third analysis result.

10. A comprehensive evaluation system for the breathability of outdoor jackets, the system being used to generate a comprehensive quantitative evaluation of the breathability of outdoor jackets by comparing physiological data and subjective feeling data of test subjects while wearing various styles of outdoor jackets, and processing this data through a statistical analysis process, characterized in that... The system includes: The data acquisition module is used to conduct a test procedure in a constant temperature and humidity experimental environment, where the test subjects wear the test jacket sample and perform a test process that includes a rest period, a specified intensity exercise period, and a post-exercise recovery period; and to simultaneously collect the subjective and objective test data of the test subjects; wherein the subjective and objective test data includes objective physiological index data and subjective sensory evaluation data, and the objective physiological index data includes at least temperature data and humidity data; The evaluation and analysis module is used to process and analyze the collected subjective and objective test data, and executes the following sub-steps in sequence to output evaluation conclusions; The evaluation and analysis module includes: The first analysis unit is used to perform a normality test on the subjective and objective test data to obtain the test result, and select a parametric analysis method or a non-parametric analysis method to calculate a first probability index based on the obtained test result. When the test subject wears the test jacket sample, the unit judges whether there is a statistical difference between the data groups in the subjective and objective test data based on the first probability index to obtain the first analysis result. The second analysis unit is used to calculate the effect size between data groups in the subjective and objective test data using the Cohen d-value algorithm when there is a statistical difference; identify the degree of variation between data groups in the subjective and objective test data based on the effect size; confirm that the statistical difference is caused by the test subjects wearing different rain jackets based on the degree of variation; and if there is a statistical difference, calculate the median and coefficient of variation of data groups in the subjective and objective test data; identify the difference in wearing experience and the degree of wearing stability of different rain jackets based on the median and coefficient of variation of data groups in the subjective and objective test data; and obtain the second analysis result. The third analysis unit is used to obtain the physical performance parameters of the fabric of different rain jackets when there are statistical differences, and to perform correlation analysis on the physical performance parameters of the fabric of the rain jacket to be tested based on subjective and objective test data, and obtain the correlation analysis coefficient; to identify the physiological indicators that are sensitive to the thermal and moisture properties of the fabric based on the obtained correlation analysis coefficient, and to obtain the third analysis result, wherein the physical performance parameters of the fabric include at least one of thermal resistance, moisture resistance, air permeability and moisture permeability. The evaluation report generation module is used to generate a comprehensive evaluation report based on the obtained first analysis results, second analysis results, and third analysis results.

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