A method for optimizing heat dissipation performance of jacket

Abstract

The present invention discloses a method for optimizing the heat dissipation performance of jackets, which relates to the technical field of heat dissipation performance optimization. The method comprises: analyzing the heat dissipation requirements of several ventilation areas of a target jacket and establishing a size mapping ratio; dividing the vent size adjustment threshold into multiple first size intervals according to a first size interval to generate multiple first size distributions; producing multiple first jacket samples according to the multiple first size distributions and evaluating them to obtain multiple first heat dissipation adaptabilities; continuing to iteratively test the ventilation sizes, setting the size distribution corresponding to the maximum heat dissipation adaptability as the optimal heat dissipation design parameter, and producing them in batches. The present invention solves the technical problem of the prior art in that jacket vent design lacks specificity and cannot effectively dissipate heat according to different scenarios and human needs. It achieves the technical effect of optimizing the jacket vent size design, significantly improving the heat dissipation performance of the target jacket, and making it more suitable for actual usage scenarios and human heat dissipation needs.

Description

Technical Field

[0001] The present invention relates to the technical field of heat dissipation performance optimization, and in particular to a method for optimizing the heat dissipation performance of a jacket. Background Art

[0002] With the improvement of people's living standards and the increasing demand for outdoor sports and daily wear experience, jackets, as a common clothing category, are increasingly attracting attention for their heat dissipation performance. The human body's heat dissipation needs vary significantly in various sports scenarios and under different environmental temperature and humidity conditions. However, jackets currently on the market have many shortcomings in the design of ventilation holes. The size and layout of ventilation holes in some jackets are fixed, and the differences in heat dissipation in different wearing scenarios and groups of people are not taken into account. As a result, the wearer is prone to feeling stuffy in high temperatures, high humidity or when exercising a lot. In some jackets, when designing ventilation holes, there is a lack of in-depth research on the heat dissipation characteristics of various parts of the human body, resulting in poor ventilation effect, affecting wearing comfort and convenience of movement.

[0003] The existing technology has the technical problem that the design of jacket ventilation holes lacks specificity and cannot effectively dissipate heat according to different scenarios and human needs. Summary of the Invention

[0004] The present application provides a method for optimizing the heat dissipation performance of a jacket, which is used to solve the technical problem in the prior art that the ventilation hole design of the jacket lacks specificity and cannot effectively dissipate heat according to different scenarios and human needs.

[0005] In view of the above problems, the present application provides a method for optimizing the heat dissipation performance of a jacket, the method comprising:

[0006] A heat dissipation demand analysis is performed on several ventilation areas of a target jacket, and a size mapping ratio is established according to several heat dissipation demand coefficients; the ventilation hole size adjustment threshold is divided into multiple first size intervals according to a first size interval, and multiple first size distributions are generated in combination with the size mapping ratio; multiple first jacket samples are produced according to the multiple first size distributions, and multiple heat dissipation tests are performed on the multiple first jacket samples in a predetermined test platform to obtain multiple test data, and multiple first heat dissipation fitnesses are evaluated; iterative testing and evaluation of the ventilation size are continued until convergence, and the size distribution corresponding to the maximum heat dissipation fitness is set as the optimal heat dissipation design parameter, and batch production of the target jackets is carried out according to the optimal heat dissipation design parameter.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The heat dissipation requirements of several ventilation areas of the target jacket were analyzed separately, and a size mapping ratio was established. The ventilation hole size adjustment threshold was divided into multiple first size intervals according to the first size interval, generating multiple first size distributions. Multiple first jacket samples were produced according to these multiple first size distributions, and multiple heat dissipation tests were conducted to obtain multiple test data, and multiple first heat dissipation adaptability values ​​were evaluated. Iterative testing and evaluation of ventilation sizes were continued, and the size distribution corresponding to the maximum heat dissipation adaptability was set as the optimal heat dissipation design parameter. The target jackets were then mass-produced. This achieved the technical effect of optimizing the jacket vent size design, significantly improving the heat dissipation performance of the target jacket, and making it more suitable for actual usage scenarios and human heat dissipation needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a method for optimizing the heat dissipation performance of a jacket provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of the process of performing heat dissipation demand analysis in a jacket heat dissipation performance optimization method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] This application provides a method for optimizing the heat dissipation performance of a jacket, which is used to solve the technical problem in the prior art that the design of jacket ventilation holes lacks specificity and cannot effectively dissipate heat according to different scenarios and human needs.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0014] Examples, such as Figure 1 As shown, the present application provides a method for optimizing the heat dissipation performance of a jacket, the method comprising:

[0015] Step S100: performing heat dissipation demand analysis on a plurality of ventilation areas of the target jacket, and establishing a size mapping ratio according to a plurality of heat dissipation demand coefficients.

[0016] Specifically, a heat dissipation demand analysis was conducted for each ventilation zone of the target jacket. Common ventilation zones for the target jacket include the back, underarms, chest, waist, shoulders, upper arms, and neck. The ventilation design for these zones required targeted adjustments based on the analysis of different exercise types and physiological data. First, the target jacket's intended wear scenario was determined, including temperature and humidity ranges, as well as information about the intended wearer, such as gender and age. A sport type search was performed based on temperature and humidity ranges to identify the high-frequency exercise types commonly performed in these environments. A physiological data search was then performed, using gender and age ranges as constraints, to identify the high-frequency physiological data for the corresponding population. Using thermal imaging technology and human heat dissipation simulation software, heat dissipation was simulated for various combinations of high-frequency exercise types and high-frequency physiological data for several ventilation zones, including the back, underarms, and chest. The software calculated the heat dissipation demand intensity for each ventilation zone under various scenarios. These values ​​were then statistically processed to determine the mean heat dissipation demand intensity for each ventilation zone, which was then set as the heat dissipation demand coefficient.

[0017] Subsequently, these heat dissipation demand coefficients are arranged in order from large to small to construct a sequence, and the ratios of other coefficients to the smallest heat dissipation demand coefficient are calculated based on the smallest heat dissipation demand coefficient. Based on these ratios, the size mapping ratios of the ventilation holes in each ventilation area are established. For example, in this process, due to the combined influence of movement and physical signs, the heat dissipation demand coefficients of the back and armpits are usually higher. Therefore, according to the size mapping ratio, these areas with higher heat dissipation demand will correspond to larger ventilation holes to better meet the heat dissipation needs; while the heat dissipation demand coefficients of the shoulders and waist are relatively low. Under the effect of the size mapping ratio, these areas with lower heat dissipation demand have smaller ventilation holes, which can meet the basic heat dissipation needs while ensuring that the overall structure and appearance of the jacket are not overly affected.

[0018] Step S200: dividing the ventilation hole size adjustment threshold into a plurality of first size intervals according to a first size interval, and generating a plurality of first size distributions in combination with the size mapping ratio.

[0019] Specifically, the vent size adjustment threshold is first divided according to a pre-set first size interval. The vent size adjustment threshold represents the range of vent size variation allowed when optimizing the jacket's heat dissipation performance. This threshold is divided into multiple continuous first size intervals using the first size interval. For example, if the vent size adjustment threshold is 5-20 mm and the first size interval is set to 3 mm, the first size intervals would be 5-8 mm, 8-11 mm, 11-14 mm, 14-17 mm, and 17-20 mm. Next, the middle value of each first size interval is selected as the first reference size, resulting in multiple first reference sizes. Subsequently, a mapping calculation is performed on these multiple first reference sizes, based on the established size mapping ratio. Because different ventilation zones have different heat dissipation requirements, the size mapping ratio reflects these differences. Therefore, the first reference sizes are assigned to each ventilation zone based on this ratio. For example, if a ventilation zone has a size mapping ratio of 1.5 and a corresponding first reference size of 10 mm, after the mapping calculation, the first size of this ventilation zone is 15 mm. This calculation is performed for all ventilation zones to determine multiple first size sets. Finally, based on the location information of the ventilation areas on the jacket, the first sizes in the multiple first size sets are appropriately mapped and distributed to the corresponding areas, thereby generating multiple first size distributions. These first size distributions will be used to subsequently produce different jacket samples to test the effects of different vent size combinations on the jacket's heat dissipation performance.

[0020] Step S300: producing a plurality of first jacket samples according to the plurality of first size distributions, and performing a plurality of heat dissipation tests on the plurality of first jacket samples in a predetermined test platform to obtain a plurality of test data, and evaluating and obtaining a plurality of first heat dissipation adaptabilities.

[0021] Specifically, multiple first jacket samples are produced based on the generated multiple first size distributions. A pre-determined test platform is pre-established, comprising a temperature control device, a humidity control device, a thermal simulation manikin, and a data acquisition system. The data acquisition system includes multiple temperature and humidity sensors located in multiple ventilation zones of the thermal simulation manikin. The temperature and humidity of the test platform are controlled based on the midpoint of the expected temperature and humidity ranges for the wear scenario, creating a test environment close to reality. A motion test scheme is constructed using high-frequency motion patterns, and multiple heat dissipation tests are conducted on the multiple first jacket samples within the pre-determined test platform according to this scheme. Each test generates test data containing heat dissipation efficiencies of multiple ventilation zones, calculated based on the temperature and humidity during the test. The target heat dissipation efficiencies for each ventilation zone are determined, and a heat dissipation fitness evaluation function is constructed based on this. The test heat dissipation efficiencies in the multiple test data sets are averaged to obtain multiple test heat dissipation efficiency means. These means are then evaluated using the heat dissipation fitness evaluation function to obtain multiple first heat dissipation fitness values.

[0022] Step S400: Continue iterative testing and evaluation of ventilation size until convergence, set the size distribution corresponding to the maximum heat dissipation adaptability as the optimal heat dissipation design parameter, and batch produce the target jacket according to the optimal heat dissipation design parameter.

[0023] Specifically, find the first size interval corresponding to the maximum first heat dissipation fitness, and determine it as the first optimal size interval. Further divide the first optimal size interval into multiple second size intervals according to smaller second size intervals, and generate multiple second size distributions in combination with the size mapping ratio. Then, continue to iteratively test and evaluate the ventilation size based on these second size distributions to obtain multiple second heat dissipation fitnesses. Repeat this iterative process until the test results converge. If convergence has not been achieved after reaching the predetermined number of iterations, stop the test, output all size distributions and their corresponding heat dissipation fitnesses, and determine the size distribution corresponding to the maximum heat dissipation fitness as the optimal heat dissipation design parameters. Finally, mass production of the target jacket is carried out according to this optimal heat dissipation design parameter to improve the heat dissipation performance of the jacket and enhance user wearing comfort.

[0024] In one possible implementation, Figure 2 As shown, step S100 also includes:

[0025] Step S110: obtaining the expected wearing scenario and the expected wearer group of the target jacket, wherein the expected wearing scenario includes a temperature range and a humidity range, and the expected wearer group includes a gender and age range.

[0026] Step S120: performing a motion type search based on the temperature range and humidity range as constraints to determine a plurality of high-frequency motion types.

[0027] Step S130: performing a physical sign search based on the gender and age range as constraints to determine a plurality of high-frequency physical sign data.

[0028] Step S140: Based on the multiple high-frequency motion types and the multiple high-frequency vital sign data, heat dissipation demand analysis is performed on the multiple ventilation areas respectively, and multiple heat dissipation demand intensity averages are determined and set as multiple heat dissipation demand coefficients.

[0029] Specifically, it is necessary to fully understand the expected use of the target jacket and obtain key information. On the one hand, the expected wearing scenario of the target jacket should be clarified, including the temperature range and humidity range. Different temperature and humidity environments will have a significant impact on the body's heat dissipation needs. For example, in high temperature and high humidity environments, it is more difficult for the human body to dissipate heat, and higher heat dissipation functions are required of the jacket. On the other hand, the expected wearer population should be determined, including gender and age range, because people of different genders and age groups have different body metabolic levels and heat tolerance, which in turn affects the demand for the jacket's heat dissipation performance.

[0030] Using the obtained temperature and humidity ranges as search constraints, we query databases or data related to exercise types to identify the types of exercise that are frequently performed within that specific temperature and humidity range. This identifies multiple high-frequency exercise types. For example, in hot and humid summer weather, people tend to engage in more relaxing indoor exercises like yoga and Pilates, while in cool and dry environments, they tend to participate more in outdoor activities like cycling and jogging. These high-frequency exercise types will serve as an important basis for subsequent analysis.

[0031] Vital sign retrieval is performed using gender and age range as constraints. Leveraging resources such as human physiological characteristic databases, multiple high-frequency vital sign data corresponding to that gender and age range is identified. For example, young and elderly people have different basal metabolic rates, and male and female body surface temperatures also differ. These vital signs can influence how and how quickly the body dissipates heat.

[0032] A multi-layer perceptron (MLP) neural network model is used to determine the heat dissipation demand coefficient for ventilation zones. Multiple high-frequency exercise types and high-frequency vital sign data are used as input features. The exercise types are one-hot encoded and converted into numerical values ​​that the neural network can process, such as [1, 0, 0] for running and [0, 1, 0] for cycling. Vital sign data such as basal metabolic rate, body surface temperature, and sweat volume are normalized and combined with the exercise type encoding data to form the input vector. An MLP neural network model is constructed, consisting of an input layer, multiple hidden layers, and an output layer. The number of neurons in the input layer is equal to the number of input features. The number of neurons in the hidden layer is determined based on experience and optimization. Feature transformation and extraction are performed using nonlinear activation functions (such as ReLU functions) to mine complex relationships in the data. The output layer has a single neuron, which is used to predict the heat dissipation demand intensity of ventilation zones. The model is trained using a large amount of labeled historical data (including the actual heat dissipation demand intensity of each ventilation zone under different exercise types and vital sign data combinations). An optimization algorithm, such as stochastic gradient descent, is used to minimize the loss (e.g., mean squared error loss function) between the predicted and true values. After training is complete, the trained model is fed with the current high-frequency motion type and high-frequency vital sign data for each ventilation zone. The model outputs a predicted value, representing the cooling demand intensity for that ventilation zone under the corresponding conditions. The average of these predicted values ​​is then calculated as the cooling demand coefficient for that ventilation zone. This coefficient reflects the relative cooling demand intensity of that zone under different motion scenarios, vital sign states, and environmental conditions, providing a strong basis for optimizing subsequent jacket ventilation design.

[0033] In one possible implementation, step S100 further includes:

[0034] Step S150: Arrange the plurality of heat dissipation requirement coefficients from large to small to construct a heat dissipation requirement coefficient sequence.

[0035] Step S160: taking the minimum heat dissipation requirement coefficient in the heat dissipation requirement coefficient sequence as a reference, respectively calculating the ratios of other heat dissipation requirement coefficients to the minimum heat dissipation requirement coefficient to obtain a plurality of coefficient ratios.

[0036] Step S170: establishing size mapping ratios of the ventilation holes of the plurality of ventilation areas according to the plurality of coefficient ratios.

[0037] Specifically, to facilitate subsequent analysis and establish a size mapping relationship, the previously obtained heat dissipation demand coefficients are sorted from largest to smallest. This sorting operation clearly demonstrates the relative magnitude of the heat dissipation demand of each ventilation zone, forming an ordered sequence of heat dissipation demand coefficients. For example, within the series of calculated heat dissipation demand coefficients, some areas may have larger coefficients, indicating a stronger heat dissipation demand; while others may have smaller coefficients, indicating a weaker heat dissipation demand. This sorting facilitates subsequent unified comparison and processing.

[0038] The minimum heat demand coefficient in the heat demand coefficient sequence is used as a benchmark reference. This minimum coefficient is chosen as the benchmark because it represents the lowest relative heat demand level among all ventilation zones. Based on this, the ratios of the remaining heat demand coefficients in the sequence to this minimum heat demand coefficient are calculated. In this way, the heat demand coefficient of each ventilation zone can be converted into a relative coefficient ratio by comparing it to the minimum coefficient. These coefficient ratios intuitively reflect the multiple relationship between the heat demand of each ventilation zone and the minimum demand. For example, if the heat demand coefficient of a ventilation zone is three times the minimum heat demand coefficient, then the coefficient ratio is 3, which clearly demonstrates the relative intensity of the heat demand in that zone.

[0039] Based on the multiple coefficient ratios obtained, size mapping ratios for the ventilation holes in several ventilation areas are established. Since the coefficient ratio reflects the relative size of the heat dissipation requirements of each ventilation area, and according to the principle that the higher the heat dissipation requirement, the larger the vents, the larger the coefficient ratio, the corresponding ventilation hole size. For example, areas with higher heat dissipation requirements, such as the back and underarms, often have larger coefficient ratios. Under the setting of the size mapping ratio, these areas will be equipped with larger ventilation holes. On the other hand, areas with lower heat dissipation requirements, such as the shoulders and waist, have relatively smaller coefficient ratios, and the corresponding ventilation hole sizes are also smaller. In this way, the heat dissipation requirements of each ventilation area are closely linked to the ventilation hole size, providing a scientific and reasonable basis for the subsequent precise design of the jacket vent size and layout, thereby effectively optimizing the jacket's heat dissipation performance.

[0040] In one possible implementation, step S200 further includes:

[0041] Step S210: Divide the ventilation hole size adjustment threshold into a plurality of first size intervals according to a first size interval, select the middle value of the first size interval as a first reference size, and obtain a plurality of first reference sizes.

[0042] Step S220: performing mapping calculations on the multiple first reference sizes according to the size mapping ratio to determine multiple first size sets, and mapping and distributing the first sizes in the multiple first size sets according to position information of a plurality of ventilation areas to generate the multiple first size distributions.

[0043] Specifically, the vent size adjustment threshold specifies the range within which the vent size can vary. This threshold is systematically divided according to a pre-set first size interval, thereby forming multiple continuous and non-overlapping first size intervals. For example, assuming the vent size adjustment threshold is 5-20 mm, if the first size interval is set to 3 mm, the first size intervals such as 5-8 mm, 8-11 mm, 11-14 mm, 14-17 mm, and 17-20 mm will be divided in sequence. In order to facilitate subsequent calculations and designs, the middle value of each first size interval is selected as the first reference size. For the 5-8 mm interval, the middle value is (5+8)÷2=6.5 mm, and so on. In this way, multiple first reference sizes are obtained, and these reference sizes will serve as an important basis for the subsequent calculation of the actual size of the vent.

[0044] Based on the dimension mapping ratio established earlier, a mapping calculation is performed on the multiple first reference dimensions obtained. Because different ventilation areas have different heat dissipation requirements, this is reflected in the dimension mapping ratio, meaning each ventilation area has its own corresponding ratio. For example, if a ventilation area has a dimension mapping ratio of 1.5 and a first reference dimension is 10 mm, then after the mapping calculation, the first dimension corresponding to this ventilation area is 10 × 1.5 = 15 mm. By performing this calculation for all first reference dimensions, multiple first dimension sets are determined. Then, taking into account the location information of several ventilation areas on the jacket, such as the specific distribution of ventilation areas on the back, underarms, and chest, the first dimensions in the multiple first dimension sets are mapped and distributed appropriately according to the location of the ventilation areas. Different ventilation areas correspond to different first dimensions, ultimately generating multiple first dimension distributions. These first dimension distributions provide clear dimensional parameters for subsequent production of different jacket prototypes, allowing for testing the impact of different vent size combinations on the jacket's heat dissipation performance, thereby providing data support for determining the optimal vent design.

[0045] In one possible implementation, step S300 further includes:

[0046] Step S310: Building a test platform, wherein the predetermined test platform includes a temperature control device, a humidity control device, a thermal simulation human body model and a data acquisition system, and the data acquisition system includes a plurality of temperature sensors and a plurality of humidity sensors arranged in a plurality of ventilation areas of the thermal simulation human body model.

[0047] Step S320: Using the middle values ​​of the temperature range and humidity range in the expected wearing scenario as temperature parameters and humidity parameters, the temperature and humidity of the test platform are controlled to construct the predetermined test platform.

[0048] Specifically, the test platform was constructed. This platform primarily consists of a temperature control device, a humidity control device, a thermal simulation manikin, and a data acquisition system. The temperature control device precisely regulates the temperature of the test environment, ensuring it remains stable within a set range; the humidity control device regulates the ambient humidity, simulating various humidity conditions. The thermal simulation manikin is the core component of the entire test platform, simulating the heat production of the human body under different conditions. Its shape and dimensions are similar to those of a real human body, and it has a heat-generating function, allowing the heating location and intensity to be adjusted according to actual needs. The data acquisition system plays a key role in data collection. It consists of several temperature and humidity sensors strategically placed in various ventilated areas of the thermal simulation manikin, such as the back, underarms, and chest. Each sensor monitors temperature and humidity changes in its area in real time and transmits the data to subsequent analysis equipment.

[0049] To ensure the test environment closely resembles the target jacket's intended wearing scenario, the midpoints of the temperature and humidity ranges in the intended wearing scenario are used as the temperature and humidity parameters. For example, if the expected wearing scenario's temperature range is 15-25°C and the humidity range is 40%-60%, the temperature parameter is set to (15 + 25) ÷ 2 = 20°C, and the humidity parameter is set to (40% + 60%) ÷ 2 = 50%. The temperature and humidity control devices adjust the test platform's temperature and humidity according to the set temperature and humidity parameters, stabilizing them within this simulated environment. Once the temperature and humidity reach their set values ​​and remain stable, the test platform is complete. This test platform provides a stable environment that closely resembles actual wearing scenarios for subsequent thermal performance testing of jacket samples, ensuring the accuracy and reliability of the test data and providing a strong basis for evaluating the jacket's thermal performance.

[0050] In one possible implementation, step S300 further includes:

[0051] Step S330: constructing a motion test plan based on the multiple high-frequency motion types.

[0052] Step S340: According to the motion test scheme, multiple heat dissipation tests are performed on the multiple first jacket samples in a predetermined test platform to obtain multiple test data, wherein each test data includes multiple test heat dissipation efficiencies of multiple ventilation areas, and the test heat dissipation efficiency is calculated based on the test temperature and test humidity.

[0053] Specifically, a number of high-frequency sports types have been identified through analysis of the expected wearing scenarios of the target jacket. These high-frequency sports types are sports that people often perform in specific temperature and humidity ranges and are highly representative. Based on these high-frequency sports types, a complete set of sports test plans are carefully constructed by comprehensively considering factors such as the intensity, duration, and frequency of each sport. For example, if the high-frequency sports types include running, cycling, and yoga, then the specific duration, speed, or intensity standard of each sport will be clearly specified in the sports test plan. For example, running at a speed of 8 kilometers per hour for 30 minutes, cycling at a frequency of 120 revolutions per minute for 45 minutes, and each yoga movement maintained for a certain time and completing a specific sequence of movements. Such a sports test plan can simulate the sports scenes of the target jacket during actual use as realistically as possible.

[0054] Information such as exercise type, intensity, and duration from the exercise test plan is encoded and converted into numerical features. Furthermore, data such as the test temperature and humidity, as well as vent size and location for each ventilation zone within the predetermined test platform, are integrated as input features for the random forest regression model. The random forest regression model is trained using historical data from a large number of similar tests, which includes the aforementioned input features and their corresponding known heat dissipation efficiencies. During the training process, the model automatically learns the complex relationship between input features and heat dissipation efficiency. After training, for each test of each first jacket sample, the real-time collected exercise-related information, test temperature, and test humidity data are input into the trained random forest regression model. The model output is the heat dissipation efficiency of that ventilation zone for that test. Through multiple such tests, multiple test data sets are obtained for each first jacket sample under different test conditions. Each test data set contains the heat dissipation efficiency of each ventilation zone, providing rich data support for subsequent evaluation of the jacket's heat dissipation performance.

[0055] In one possible implementation, step S300 further includes:

[0056] Step S350: obtaining a plurality of target heat dissipation efficiencies of a plurality of ventilation areas, and constructing a heat dissipation fitness evaluation function based on the plurality of target heat dissipation efficiencies.

[0057] Step S360: performing mean calculation on a plurality of test heat dissipation efficiency sets in the plurality of test data to determine a plurality of test heat dissipation efficiency means.

[0058] Step S370: using the heat dissipation fitness evaluation function to perform fitness evaluation on the plurality of test heat dissipation efficiency averages to obtain the plurality of first heat dissipation fitnesses.

[0059] Specifically, by analyzing the heat dissipation efficiency required for comfortable heat dissipation in various parts of the human body under specific exercise intensities and environments, the target heat dissipation efficiency corresponding to several ventilation areas, such as the back, underarms, and chest, was determined. After obtaining these target heat dissipation efficiencies, a heat dissipation fitness evaluation function was constructed based on this. This function comprehensively considers multiple factors, such as the degree of compliance with heat dissipation performance standards and the consistency of heat dissipation in various ventilation areas. Its purpose is to more comprehensively and scientifically evaluate the heat dissipation performance of the jacket. The expression of the heat dissipation fitness evaluation function is: ,in, For heat dissipation adaptability, is the heat dissipation performance weight, is the heat dissipation consistency weight, M is the number of ventilation areas, Characterizes the heat dissipation demand coefficient of the mth ventilation zone, is the average heat dissipation efficiency of the mth ventilation area, is the target heat dissipation efficiency of the mth ventilation zone, is the variance of the mean values ​​of the M test heat dissipation efficiencies.

[0060] Based on the test data obtained from multiple heat dissipation tests, the tested heat dissipation efficiency of each ventilation zone is organized. All the tested heat dissipation efficiency data for the same ventilation zone is collected to form a test heat dissipation efficiency set. The mean of each set is calculated, and several test heat dissipation efficiency averages are determined through methods such as arithmetic averaging. These averages can more representatively reflect the average heat dissipation performance of each ventilation zone under different test conditions, reducing the randomness and error of individual test data and providing a more reliable data foundation for subsequent accurate evaluation.

[0061] Using the constructed heat dissipation fitness evaluation function, the fitness of several obtained test heat dissipation efficiency averages is evaluated. The test heat dissipation efficiency average of each ventilation area is substituted into the heat dissipation fitness evaluation function. The function will quantitatively evaluate each test heat dissipation efficiency average based on the set calculation rules, taking into account factors such as heat dissipation performance weight and heat dissipation consistency weight. Through such calculations, multiple first heat dissipation fitness levels are obtained. These first heat dissipation fitness levels can intuitively reflect the advantages and disadvantages of each first jacket sample in terms of heat dissipation performance, providing a key evaluation basis for the subsequent screening of the jacket design with the best heat dissipation performance, helping to further optimize the jacket's ventilation design and improve its overall heat dissipation performance.

[0062] In one possible implementation, step S350 further includes:

[0063] The expression of the heat dissipation fitness evaluation function is:

[0064] ;

[0065] in, For heat dissipation adaptability, is the heat dissipation performance weight, is the heat dissipation consistency weight, M is the number of ventilation areas, Characterizes the heat dissipation demand coefficient of the mth ventilation zone, is the average heat dissipation efficiency of the mth ventilation area, is the target heat dissipation efficiency of the mth ventilation zone, is the variance of the mean values ​​of the M test heat dissipation efficiencies.

[0066] Specifically, the heat dissipation fitness evaluation function is a key quantitative tool for evaluating the heat dissipation performance of jackets. Represents heat dissipation adaptability, which is used to comprehensively measure the heat dissipation performance of the jacket's ventilation area. The higher the value, the better the heat dissipation performance. is the heat dissipation performance weight, =(\frac{\partial_weight}} represents the heat dissipation consistency weight, reflecting the degree of heat dissipation compliance and the importance of heat dissipation consistency across ventilation zones in the overall evaluation. M represents the number of ventilation zones and limits the calculation range. For the target jacket, the sum of the number of ventilation zones (back, underarms, chest, etc.) is the value of M. Represents the relative heat dissipation demand of the mth ventilation zone under different sports scenarios, physical conditions, and environmental conditions. This is calculated based on an analysis of factors such as the target jacket's intended wearing scenario and demographics. It is the average value of the heat dissipation efficiency of the mth ventilation area after multiple heat dissipation tests. During the multiple heat dissipation tests on multiple first jacket samples, each ventilation area will get a test heat dissipation efficiency value each time. The average of these values ​​is obtained. , it can more stably reflect the actual heat dissipation efficiency level of the ventilation area. This is the target cooling efficiency value that should be achieved by the mth ventilation zone, based on ideal cooling conditions or actual usage requirements. For example, by studying the cooling efficiency required for comfortable cooling in different parts of the human body during specific movements and environments, a specific target cooling efficiency value was determined for the underarm ventilation zone, which serves as a standard for measuring actual cooling effectiveness. : Measures the dispersion of the mean heat dissipation efficiency values ​​across M ventilation zones. A larger variance indicates greater disparity between the mean heat dissipation efficiencies across the zones, indicating poorer heat dissipation consistency across the zones. Conversely, a smaller variance indicates better heat dissipation consistency across the zones. This function comprehensively evaluates the heat dissipation performance of the jacket's ventilation zones from the perspectives of both heat dissipation compliance and heat dissipation consistency, providing a basis for determining the optimal ventilation design.

[0067] In one possible implementation, step S400 further includes:

[0068] Step S410: selecting a first size interval corresponding to the maximum first heat dissipation adaptability as a first optimal size interval.

[0069] Step S420: Divide the first optimal size interval into multiple second size intervals according to a second size interval, and generate multiple second size distributions in combination with the size mapping ratio, wherein the second size interval is smaller than the first size interval.

[0070] Step S430: performing iterative testing and evaluation of ventilation sizes according to the plurality of second size distributions, and outputting a plurality of second heat dissipation adaptabilities.

[0071] Step S440: Continue iterative testing and evaluation of ventilation dimensions until convergence, and set the dimension distribution corresponding to the maximum heat dissipation adaptability as the optimal heat dissipation design parameter.

[0072] Specifically, after conducting multiple heat dissipation tests on multiple first jacket samples and obtaining multiple first heat dissipation adaptability values, the first size range corresponding to the maximum first heat dissipation adaptability value was selected from these results and determined as the first optimal size range. This range is considered to be the range of vent sizes most likely to achieve optimal heat dissipation performance for the jacket within the current testing range.

[0073] To further accurately identify the optimal size, the first optimal size interval is further divided into second size intervals smaller than the first, resulting in multiple second size intervals. Because the second size intervals are smaller, the resulting intervals are more refined, allowing for a more accurate exploration of the impact of vent size on heat dissipation performance. Combining these second size intervals with the previously established size mapping ratio, multiple second size distributions are generated. These second size distributions provide a variety of vent size combinations for subsequent, more precise testing.

[0074] Based on the multiple generated second size distributions, we conducted further iterative testing and evaluation of ventilation dimensions. During the testing process, we simulated various real-world usage scenarios within the pre-defined test platform, and subjected jacket samples with different second size distributions to heat dissipation tests. By collecting and analyzing the test data, we calculated and output multiple second heat dissipation fitness levels. These second heat dissipation fitness levels reflect the jacket's heat dissipation performance under different second size distributions.

[0075] Finally, this iterative testing and evaluation process continues. Each iteration builds on the previous results, narrowing the search scope until the test results converge. Convergence means that further iterations will not significantly improve heat dissipation performance. At this point, the size distribution corresponding to the maximum heat dissipation adaptability achieved throughout the entire iterative process is determined as the optimal heat dissipation design parameter. This parameter represents the vent size design that achieves the best heat dissipation performance for the jacket after comprehensively considering various factors. It provides precise guidance for subsequent mass production, helping to improve the jacket's heat dissipation performance and user comfort.

[0076] In one possible implementation, step S440 further includes:

[0077] Step S441: Continue iterative testing and evaluation of ventilation size until a predetermined number of iterations is reached, then stop the test, output multiple size distributions and corresponding multiple heat dissipation fitnesses, and set the size distribution corresponding to the maximum heat dissipation fitness as the optimal heat dissipation design parameter.

[0078] Specifically, the iterative testing and evaluation of ventilation dimensions continues, with continuous repetition. Each iteration follows the same methodology: new size ranges are created based on the previous round's results, generating a new size distribution. Heat dissipation testing is then performed on the corresponding jacket samples on a predetermined test platform to determine the new thermal adaptability. This iterative process continues until a predetermined number of iterations is reached. Once this number is reached, all testing is terminated. At this point, the multiple size distributions generated throughout the testing process, along with their corresponding thermal adaptability values, are displayed. From these outputs, the size distribution with the highest thermal adaptability is selected and determined as the optimal thermal design parameter. This parameter, derived through extensive testing and comparison, maximizes the jacket's thermal performance and provides a key basis for subsequent mass production of target jackets, ensuring that the jackets produced meet user thermal requirements and enhance wearing comfort.

[0079] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0080] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0081] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for optimizing the heat dissipation performance of a jacket, characterized in that: include: Conduct heat dissipation demand analysis on several ventilation areas of the target jacket and establish size mapping ratios based on several heat dissipation demand coefficients; Dividing the ventilation hole size adjustment threshold into a plurality of first size intervals according to the first size interval, and generating a plurality of first size distributions in combination with the size mapping ratio; Producing a plurality of first jacket samples according to the plurality of first size distributions, and performing a plurality of heat dissipation tests on the plurality of first jacket samples in a predetermined test platform to obtain a plurality of test data, and evaluating and obtaining a plurality of first heat dissipation adaptabilities; Continue iterative testing and evaluation of ventilation dimensions until convergence, set the dimension distribution corresponding to the maximum heat dissipation adaptability as the optimal heat dissipation design parameters, and batch-produce target jackets according to the optimal heat dissipation design parameters; Continue iterative testing and evaluation of ventilation dimensions until convergence, and set the dimension distribution corresponding to the maximum heat dissipation adaptability as the optimal heat dissipation design parameters, including: Selecting a first size interval corresponding to the maximum first heat dissipation adaptability as a first optimal size interval; Dividing the first optimal size interval into a plurality of second size intervals according to a second size interval, and generating a plurality of second size distributions in combination with the size mapping ratio, wherein the second size interval is smaller than the first size interval; Performing iterative testing and evaluation of ventilation sizes according to the plurality of second size distributions, and outputting a plurality of second heat dissipation adaptability; Continue iterative testing and evaluation of ventilation size until convergence, and set the size distribution corresponding to the maximum heat dissipation fitness as the optimal heat dissipation design parameter.

2. The method for optimizing heat dissipation performance of a jacket according to claim 1, characterized in that: The cooling requirements of several ventilation areas of the target jacket were analyzed separately, including: Obtaining an expected wearing scenario and an expected wearer group of the target jacket, wherein the expected wearing scenario includes a temperature range and a humidity range, and the expected wearer group includes a gender and an age range; Performing a motion type search based on the temperature range and humidity range as constraints to determine a plurality of high-frequency motion types; Performing a physical sign search based on the gender and age range as constraints to determine a plurality of high-frequency physical sign data; Based on the multiple high-frequency motion types and the multiple high-frequency vital sign data, heat dissipation demand analysis is performed on each of the multiple ventilation areas to determine multiple heat dissipation demand intensity averages, which are set as multiple heat dissipation demand coefficients.

3. The method for optimizing heat dissipation performance of a jacket according to claim 2, characterized in that: A size mapping ratio is established based on several cooling requirement factors, including: Arranging the plurality of heat dissipation demand coefficients from large to small to construct a heat dissipation demand coefficient sequence; Taking the minimum heat dissipation demand coefficient in the heat dissipation demand coefficient sequence as a benchmark, respectively calculating the ratios of other heat dissipation demand coefficients to the minimum heat dissipation demand coefficient to obtain a plurality of coefficient ratios; A size mapping ratio of the ventilation holes of the plurality of ventilation areas is established according to the plurality of coefficient ratios.

4. The method for optimizing heat dissipation performance of a jacket according to claim 3, characterized in that: Dividing the ventilation hole size adjustment threshold into a plurality of first size intervals according to the first size interval, and generating a plurality of first size distributions in combination with the size mapping ratio, including: Dividing the ventilation hole size adjustment threshold into a plurality of first size intervals according to a first size interval, selecting a middle value of the first size interval as a first reference size, and obtaining a plurality of first reference sizes; Mapping calculations are performed on the multiple first reference sizes according to the size mapping ratio to determine multiple first size sets, and first sizes in the multiple first size sets are mapped and distributed according to position information of a plurality of ventilation areas to generate the multiple first size distributions.

5. The method for optimizing heat dissipation performance of a jacket according to claim 2, characterized in that: Build a predefined test platform, including: Building a test platform, wherein the predetermined test platform includes a temperature control device, a humidity control device, a thermal simulation manikin, and a data acquisition system, wherein the data acquisition system includes a plurality of temperature sensors and a plurality of humidity sensors disposed in a plurality of ventilation areas of the thermal simulation manikin; The middle values ​​of the temperature range and humidity range in the expected wearing scenario are used as temperature parameters and humidity parameters, and the temperature and humidity of the test platform are controlled to construct the predetermined test platform.

6. The method for optimizing heat dissipation performance of a jacket according to claim 5, characterized in that: Perform multiple heat dissipation tests on the multiple first jacket samples in a predetermined test platform to obtain multiple test data, including: constructing a motion test plan based on the multiple high-frequency motion types; According to the motion test scheme, multiple heat dissipation tests are performed on the multiple first jacket samples in a predetermined test platform to obtain multiple test data, wherein each test data includes multiple test heat dissipation efficiencies of multiple ventilation areas, and the test heat dissipation efficiency is calculated based on the test temperature and test humidity.

7. The method for optimizing heat dissipation performance of a jacket according to claim 6, characterized in that: The evaluation results in multiple first-level heat dissipation adaptability, including: Obtaining a plurality of target heat dissipation efficiencies of a plurality of ventilation areas, and constructing a heat dissipation fitness evaluation function based on the plurality of target heat dissipation efficiencies; performing mean calculation on a plurality of test heat dissipation efficiency sets in the plurality of test data to determine a plurality of test heat dissipation efficiency means; The heat dissipation fitness evaluation function is used to perform fitness evaluation on the plurality of test heat dissipation efficiency averages to obtain the plurality of first heat dissipation fitnesses.

8. The method for optimizing heat dissipation performance of a jacket according to claim 7, characterized in that: The expression of the heat dissipation fitness evaluation function is: ； in, For heat dissipation adaptability, is the heat dissipation performance weight, is the heat dissipation consistency weight, M is the number of ventilation areas, Characterizes the heat dissipation demand coefficient of the mth ventilation zone, is the average heat dissipation efficiency of the mth ventilation area, is the target heat dissipation efficiency of the mth ventilation zone, is the variance of the mean values ​​of the M test heat dissipation efficiencies.

9. The method for optimizing heat dissipation performance of a jacket according to claim 1, characterized in that: Continue iterative testing and evaluation of ventilation size until the predetermined number of iterations is reached, then stop the test, output multiple size distributions and corresponding multiple heat dissipation fitness, and set the size distribution corresponding to the maximum heat dissipation fitness as the optimal heat dissipation design parameter.

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