Method for adjusting hook-and-loop fastener process to reduce opening and closing noise

Abstract

This application relates to the field of process optimization technology, and particularly to a method for adjusting the opening and closing noise of hook and loop fasteners. The method includes: obtaining initial region parameters for multiple hook and loop fasteners; obtaining opening and closing test data for each hook and loop fastener; determining target region parameters based on the opening and closing test data; determining noise regions under different opening and closing conditions based on the target region parameters; determining the noise level of each noise region; and adjusting the target region parameters based on the noise level of the region. Therefore, the hook and loop fastener process adjustment method for reducing opening and closing noise provided by this application can make targeted local improvements to the parameters of the hook and loop fastener, thereby effectively reducing opening and closing noise while ensuring a certain level of adhesive performance.

Description

Technical Field

[0001] This application belongs to the field of process optimization technology, and in particular relates to a method for adjusting the hook and loop fastener process to reduce opening and closing noise. Background Technology

[0002] Hook and loop fasteners (Velcro) are fasteners that use hooks and loops to quickly secure and separate items. Their key advantages are ease of use, reusability, and the absence of additional tools. As a result, they are widely used in the design of infant and toddler products, such as diaper or clothing fasteners. They are mainly used to secure items, prevent shifting, or adjust the fit to ensure comfort and safety for infants and toddlers.

[0003] However, when hook and loop fasteners are used in infant and toddler products, the noise from their opening and closing is particularly prominent in the daily activities of infants and toddlers. When infants and toddlers move, roll over, or when parents frequently adjust the position of the products, the repeated friction and separation of the hook and loop surfaces generates a harsh noise, interfering with the infant's rest or play experience, and may even startle the infant due to sudden noise. Existing technologies typically reduce the opening and closing noise of hook and loop fasteners by improving the materials used. However, different areas of infant and toddler products experience different forces and usage frequencies. For example, the tear-off areas on diaper or clothing fasteners require frequent opening and closing. If only the overall hook and loop fastener is improved, it is difficult to accurately grasp the underlying mechanism of the opening and closing noise, and the noise reduction effect may be offset by localized high-frequency noise. Furthermore, hook and loop fasteners on infant and toddler products are often opened and closed at different angles and with different forces. If the noise changes of the hook and loop fastener under different usage conditions are not fully considered, the noise reduction effect can easily become unstable in actual use. Therefore, existing hook and loop fastener processes cannot make targeted localized improvements to the parameters of the fastener, making it difficult to effectively reduce opening and closing noise while ensuring a certain level of adhesive performance. Summary of the Invention

[0004] This application provides a method for adjusting the hook and loop fastener process to reduce opening and closing noise. It can make targeted local improvements to the parameters of the hook and loop fastener, thereby effectively reducing opening and closing noise while ensuring a certain level of adhesive performance.

[0005] In a first aspect, embodiments of this application provide a method for adjusting the hook and loop fastener process to reduce opening and closing noise, including: Obtain initial region parameters for multiple hook and loop fasteners; wherein the hook and loop fasteners include multiple regions in the loop portion and corresponding multiple regions in the hook portion, and at least one corresponding region between any two hook and loop fasteners has a different density and / or shape, the initial region parameters include the density and shape of each region of the hook and loop fasteners, the density of the hook and loop fasteners includes the fiber density of the loop portion and the arrangement density of the hook portion, and the shape of the hook and loop fasteners includes the fiber length of the loop portion and the hook tip shape of the hook portion; Obtain opening and closing test data for each of the hook and loop fasteners; wherein, the opening and closing test data is obtained by performing opening and closing tests on each of the hook and loop fasteners under the same opening and closing conditions, the opening and closing conditions include opening and closing force and opening and closing angle, and the opening and closing test data includes opening and closing speed, as well as the magnitude and frequency of friction sound; The target area parameters are determined based on the opening and closing test data; wherein, the target area parameters include the target density and target shape of the hook and loop fastener, and the target area parameters are used to balance the noise reduction requirements and adhesive performance of the hook and loop fastener; The noise region is determined based on the target region parameters under different opening and closing conditions; wherein, the noise region refers to the local area in the hook and loop fastener corresponding to the target region parameters that has the greatest impact on the noise level; The regional noise level is determined based on each of the noise regions; wherein the regional noise level is used to reflect the noise distribution of the hook and loop fastener corresponding to the target region parameters when used randomly under different opening and closing conditions; The target region parameters are adjusted based on the noise level of the region. The technical solution described above in this application embodiment has at least the following technical effects: The hook and loop fastener process adjustment method for reducing opening and closing noise provided in this application embodiment obtains initial region parameters of multiple hook and loop fasteners; obtains opening and closing test data of each hook and loop fastener; determines target region parameters based on the opening and closing test data; determines noise regions under different opening and closing conditions based on the target region parameters; determines the noise level of each noise region; and adjusts the target region parameters based on the noise level of the region. Therefore, the hook and loop fastener process adjustment method for reducing opening and closing noise provided in this application embodiment, by analyzing parameters such as the density and shape of the hook and loop fastener, combined with dynamic conditions such as opening and closing force and angle, accurately locates the local region with the greatest impact on noise, and dynamically optimizes the target region parameters based on the opening and closing test data. Through feedback of regional noise levels, a closed-loop adjustment mechanism is formed, achieving precise control from local parameters to the overall noise level, which is beneficial to improving the noise reduction effect and the balanced optimization capability of the hook and loop fastener performance.

[0006] In one possible implementation of the first aspect, determining the target region parameters based on the opening and closing test data includes: Based on the opening and closing speed in the opening and closing test data, the range of parameters for the area where the hook and loop fastener has a preset adhesive force is obtained. The target region parameters within the range of the region parameters are determined based on the opening and closing test data.

[0007] In one possible implementation of the first aspect, determining the regional noise level based on each of the noise regions includes: Determine the noise region parameters corresponding to each noise region based on the target region parameters; A first noise level for each noise region is obtained based on the parameters of each noise region; wherein, the first noise level is used to reflect the noise level of the noise region; The noise level of the region is obtained based on each of the first noise quantities.

[0008] In one possible implementation of the first aspect, adjusting the target region parameters according to the region noise level includes: Determine the correlation between the noise level of the area and the parameters of the target area; wherein, the noise level of the area includes the magnitude and frequency of the friction sound; When it is determined that the parameters of the high-adhesion zone in the target area need to be adjusted based on the noise level of the area, a first adjustment value of the parameters of the high-adhesion zone in the target area is determined based on the correlation and the preset noise range. When it is determined that the parameters of the low-adhesion zone in the target area parameters need to be adjusted based on the noise level of the area, a second adjustment value of the parameters of the low-adhesion zone in the target area parameters is determined based on the correlation and the preset noise range. The target region parameters are adjusted according to the first adjustment value and / or the second adjustment value.

[0009] In one possible implementation of the first aspect, the correlation includes a first correlation between the magnitude of the friction sound in the regional noise level and the parameters of the high-adhesion zone, and a second correlation between the frequency of the friction sound in the regional noise level and the parameters of the high-adhesion zone. The preset noise range includes a first preset range corresponding to the magnitude of the friction sound and a second preset range corresponding to the frequency of the friction sound. Determining a first adjustment value for the parameters of the high-adhesion zone in the target region parameters based on the correlation and the preset noise range includes: When the magnitude of the friction sound is not within the first preset range, a first parameter range is determined based on the first correlation to determine the parameters of the high adhesion zone. When the frequency of the friction sound is not within the second preset range, a second parameter range is determined based on the second correlation to determine the parameters of the high adhesion zone. Based on the first parameter range and the second parameter range, a first adjustment value for the parameters of the high adhesion zone is determined.

[0010] In one possible implementation of the first aspect, the correlation further includes a third correlation between the magnitude of the frictional sound in the regional noise level and the parameters of the low-adhesion zone, and a fourth correlation between the frequency of the frictional sound in the regional noise level and the parameters of the low-adhesion zone. The step of determining a second adjustment value for the parameters of the low-adhesion zone in the target region parameters based on the correlation and the preset noise range includes: If the magnitude of the friction sound is not within the first preset range, a third parameter range for the parameters of the low adhesion zone is determined based on the third correlation. If the frequency of the friction sound is not within the second preset range, the fourth parameter range of the parameters of the low adhesion zone is determined based on the fourth correlation. Based on the third parameter range and the fourth parameter range, a second adjustment value for the parameters of the low adhesion zone is determined.

[0011] In one possible implementation of the first aspect, determining the noise region under different opening and closing conditions based on the target region parameters includes: Obtain the opening and closing time of the hook and loop fastener under each of the aforementioned opening and closing conditions; Based on the target region parameters and the opening and closing times, the noise region under each opening and closing condition is determined.

[0012] In one possible implementation of the first aspect, determining the target region parameters based on the opening and closing test data further includes: The noise level of the hook and loop fastener under each opening and closing condition is determined based on the opening and closing test data. The target noise level is obtained based on the preset hardness of the hook and loop fastener and the noise level of the hook and loop fastener under each of the opening and closing conditions. The target region parameters are determined based on the target noise levels.

[0013] In one possible implementation of the first aspect, obtaining the corresponding target noise level based on the preset stiffness of the hook and loop fastener and the noise level of the hook and loop fastener under each of the opening and closing conditions includes: The range of parameters for the region is determined based on the preset hardness of the hook and loop fastener. The target noise level under each opening and closing condition is obtained based on the range of the region parameters, the noise level of the hook and loop fastener under each opening and closing condition, and the corresponding initial region parameters.

[0014] In one possible implementation of the first aspect, determining the noise region parameters corresponding to each noise region based on the target region parameters includes: Each noise region is segmented according to the target region parameters to obtain the high adhesion region, the transition region, and the low adhesion region; Determine the noise region parameters corresponding to each of the high adhesion regions, the noise region parameters corresponding to each of the transition regions, and the noise region parameters corresponding to each of the low adhesion regions.

[0015] Secondly, embodiments of this application provide a hook and loop fastener process adjustment device for reducing opening and closing noise, comprising: An initial region parameter module is used to obtain initial region parameters for multiple hook and loop fasteners; wherein, the hook and loop fasteners include multiple regions in the loop portion and corresponding multiple regions in the hook portion, and at least one corresponding region between any two hook and loop fasteners has a different density and / or shape, the initial region parameters include the density and shape of each region of the hook and loop fasteners, the density of the hook and loop fasteners includes the fiber density of the loop portion and the arrangement density of the hook portion, and the shape of the hook and loop fasteners includes the fiber length of the loop portion and the hook tip shape of the hook portion; The opening and closing test data module is used to acquire opening and closing test data of each of the hook and loop fasteners; wherein, the opening and closing test data is obtained by performing opening and closing tests on each of the hook and loop fasteners under the same opening and closing conditions, the opening and closing conditions include opening and closing force and opening and closing angle, and the opening and closing test data includes opening and closing speed, as well as the magnitude and frequency of friction sound. The target area parameter module is used to determine the target area parameters based on the opening and closing test data; wherein, the target area parameters include the target density and target shape of the hook and loop fastener, and the target area parameters are used to balance the noise reduction requirements and adhesive performance of the hook and loop fastener; The noise region module is used to determine the noise region under different opening and closing conditions based on the target region parameters; wherein, the noise region refers to the local area in the hook and loop fastener corresponding to the target region parameters that has the greatest impact on the noise level; A zone noise level module is used to determine the zone noise level based on each of the noise zones; wherein the zone noise level is used to reflect the noise distribution of the hook and loop fastener corresponding to the target zone parameters when used randomly under different opening and closing conditions; An adjustment module is used to adjust the parameters of the target area according to the noise level of the area.

[0016] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of the first aspects above.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the first aspects above.

[0018] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to perform the method described in any one of the first aspects above.

[0019] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for adjusting the hook and loop fastener process to reduce opening and closing noise, provided in an embodiment of this application. Figure 2 This is a schematic diagram illustrating the implementation process of steps S300, S340, S400, S500, and S510 in the hook and loop fastener process adjustment method for reducing opening and closing noise provided in an embodiment of this application. Figure 3 This is a schematic diagram of the implementation process of steps S600, S620 and S630 in the hook and loop fastener process adjustment method for reducing opening and closing noise provided in an embodiment of this application. Figure 4 This is a schematic diagram of the opening and closing test in the hook and loop fastener process adjustment method for reducing opening and closing noise provided in an embodiment of this application; Figure 5 This is a schematic diagram of the noise levels in multiple areas of the hook and loop fastener in a hook and loop fastener process adjustment method for reducing opening and closing noise provided in an embodiment of this application; Figure 6 This is a schematic diagram of the hook and loop fastener process adjustment device for reducing opening and closing noise provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0023] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0024] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0026] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0028] In related technologies, reducing the opening and closing noise of hook and loop fasteners is usually achieved by improving the material of the fastener. However, different areas of infant and toddler products experience varying forces and usage frequencies. For example, the tear-off areas on diaper or clothing fasteners are frequently opened and closed. If only the overall hook and loop fastener is improved, it is difficult to accurately grasp the underlying mechanism of opening and closing noise at its root, and the noise reduction effect may be offset by local high-frequency noise. Furthermore, hook and loop fasteners on infant and toddler products are often opened and closed at different angles and with different forces. If the noise changes of the hook and loop fastener under different usage conditions are not fully considered, the noise reduction effect can easily become unstable in actual use. Therefore, existing hook and loop fastener processes cannot make targeted local improvements to the parameters of the hook and loop fastener, making it difficult to effectively reduce opening and closing noise while ensuring a certain level of adhesive performance.

[0029] To address the aforementioned issues, this application provides a method for adjusting the opening and closing noise of hook and loop fasteners. This method involves: acquiring initial region parameters for multiple hook and loop fasteners; acquiring opening and closing test data for each fastener; determining target region parameters based on the test data; determining noise regions under different opening and closing conditions based on the target region parameters; determining the noise level of each noise region; and adjusting the target region parameters based on the noise level. Therefore, the method provided in this application, by analyzing parameters such as the density and shape of the hook and loop fasteners and combining them with dynamic conditions such as opening and closing force and angle, accurately locates the local region with the greatest impact on noise levels. It then dynamically optimizes the target region parameters based on the opening and closing test data, forming a closed-loop adjustment mechanism through regional noise level feedback. This achieves precise control from local parameters to the overall noise level, which is beneficial for improving the noise reduction effect and the balanced optimization capability of the hook and loop fastener's adhesive performance.

[0030] The hook and loop fastener process adjustment method for reducing opening and closing noise provided in this application embodiment can be applied to electronic devices. In this case, the electronic device is the executing subject of the hook and loop fastener process adjustment method for reducing opening and closing noise provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of electronic device.

[0031] For example, electronic devices can be industrial computers, programmable logic controllers, embedded control systems, distributed control systems, tablet computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, desktop computers, laptops, handheld computing devices, etc., but are not limited to these.

[0032] To better understand the hook and loop fastener process adjustment method for reducing opening and closing noise provided in the embodiments of this application, the specific implementation process of the hook and loop fastener process adjustment method for reducing opening and closing noise provided in the embodiments of this application will be described by way of example below.

[0033] Figure 1 This illustration shows a schematic flowchart of a hook and loop fastener process adjustment method for reducing opening and closing noise, provided in an embodiment of this application. The hook and loop fastener process adjustment method for reducing opening and closing noise includes: S100, Obtain initial region parameters for multiple hook and loop fasteners. Each hook and loop fastener includes multiple regions in its looped portion and corresponding regions in its hooked portion. At least one corresponding region between any two hook and loop fasteners has a different density and / or shape. The initial region parameters include the density and shape of each region of the hook and loop fastener. The density of the hook and loop fastener includes the fiber density of the looped portion and the arrangement density of the hooked portion. The shape of the hook and loop fastener includes the fiber length of the looped portion and the hook tip shape of the hooked portion.

[0034] It is understood that the areas on the loop side and the areas on the hook side of a hook and loop fastener correspond one-to-one. Multiple hook and loop fasteners refer to the same type of fasteners used in infant and toddler products, meaning that each fastener has the same area size and shape, and the corresponding adhesive force is within a preset range. Each hook and loop fastener is divided into multiple sections along the opening and closing direction based on its length (e.g., a 10cm long fastener is evenly divided into 5 sections), and the areas between each fastener are one-to-one. At least one corresponding area (which can be on the loop side or the hook side) of each fastener has a different density and / or shape. For example, if the difference in the density of the loop fibers in corresponding areas of a pair of hook and loop fasteners is ≥300 fibers / cm², the difference in the arrangement density of the hook side (i.e., the difference in the average distance between the hooks) is ≥0.2mm, the difference in fiber length is ≥0.5mm, or the difference in the hook tip shape (i.e., the difference in the radius of curvature of the hook tip) is ≥0.1mm, then it is determined that there are corresponding areas with different densities and / or shapes between the hook and loop fasteners.

[0035] For example, the initial area parameters can be obtained by calling the design data of each hook and loop fastener from the design documents of each hook and loop fastener (such as product design drawings).

[0036] Of course, a laser scanner can also be used to perform a 3D scan of the hook and loop fastener to obtain scan data. The scan data can be analyzed by point cloud processing and image analysis algorithms to extract the fiber density of the napped part (such as the number of fibers per square centimeter) and the arrangement density of the hook part (such as the average distance between hooks). The specific geometric parameters of the fiber length and hook tip shape (such as the radius of curvature of the hook tip) can be measured to obtain multiple sets of initial area parameters.

[0037] S200, acquire the opening and closing test data of each hook and loop fastener. The opening and closing test data is obtained by performing opening and closing tests on each hook and loop fastener under the same opening and closing conditions, including opening and closing force and opening and closing angle. The opening and closing test data includes opening and closing speed, as well as the magnitude and frequency of friction noise.

[0038] It is understandable that obtaining the opening and closing test data of each hook and loop fastener can refer to conducting opening and closing tests on each hook and loop fastener under preset common opening and closing forces and angles, such as... Figure 4 The diagram shows multiple hook and loop fasteners tested with the same opening and closing force and angle (e.g., opening and closing force: 10N, opening and closing angle: 30°). For example, hook and loop fastener A for Test 1 has a fiber density of 4000 fibers / cm², a fiber length of 2.2 mm, an average distance between hooks of 0.25 mm, and a hook tip radius of curvature of 0.15 mm; hook and loop fastener B for Test 2 has a fiber density of 3800 fibers / cm², a fiber length of 1.8 mm, an average distance between hooks of 0.23 mm, and a hook tip radius of curvature of 0.25 mm; hook and loop fastener C for Test 3 has a fiber density of 4500 fibers / cm², a fiber length of 2.2 mm, an average distance between hooks of 0.23 mm, and a hook tip radius of curvature of 0.25 mm. The average distance between hooks is 0.2 mm, and the radius of curvature of the hook tip is 0.08 mm. Test 4 corresponds to hook and loop fastener D: fiber density is 4000 fibers / cm², fiber length is 1.6 mm, average distance between hooks is 0.22 mm, and radius of curvature of the hook tip is 0.1 mm. Test 5 corresponds to hook and loop fastener E: fiber density is 3900 fibers / cm², fiber length is 2.2 mm, average distance between hooks is 0.45 mm, and radius of curvature of the hook tip is 0.13 mm. (The density and shape data of the above hook and loop fasteners are from tests conducted in the same corresponding area to obtain the opening and closing test data.)

[0039] For example, the opening and closing test device can be used to test the hook and loop fastener. The opening and closing test device can include a fixing mechanism, a clamping mechanism and a driving mechanism. The fixing mechanism is used to fix the rough side of the hook and loop fastener. The clamping mechanism is used to clamp the hook side of the hook and loop fastener. The power output end of the driving mechanism is connected to the clamping mechanism and is used to drive the clamping mechanism to move so that the clamping mechanism drives the hook side to move relative to the rough side, thereby simulating the tearing action of the hook side.

[0040] Optionally, the fixing mechanism can be a vacuum adsorption platform, a mechanical clamping mechanism, etc. The clamping mechanism can be an electric gripper, a pneumatic gripper, a spring clamp, etc. The drive mechanism includes a linear slide, a bracket, and a traction rope. The linear slide can be any existing type of lead screw slide, linear slide module, etc. The bracket is fixed to the slider of the linear slide. One end of the traction rope is detachably connected to the bracket, and the other end of the traction rope is connected to the clamping mechanism. By changing the connection position of the traction rope on the bracket along the height direction, the traction angle of the traction rope is changed, thereby adjusting the opening and closing angle. A force sensor (such as HBM S9M) can be installed between the clamping mechanism and the traction rope to monitor the opening and closing force in real time. Based on the deviation between the target force and the actual force, the motor output torque is adjusted to control the opening and closing force.

[0041] The opening and closing test device also includes acoustic sensors (such as MEMS microphones and acoustic analyzers) and speed measuring devices (such as laser displacement sensors). The acoustic sensors are fixed to the side of the fixing mechanism and their height is flush with the plane of the hook and loop fastener. The speed measuring device is fixed to the clamping mechanism. For example, the laser displacement sensor is fixed to the non-moving base of the clamping mechanism (such as the fixed housing of the electric gripper) by a rigid bracket. The laser emission port is aligned with the movement trajectory of the connection point between the clamping mechanism and the hook and loop fastener. The opening and closing test device tears open the pre-adhesive hook and loop fastener and records in real time the magnitude and frequency of friction sounds during the opening and closing process of multiple hook and loop fasteners under the same opening and closing conditions, as well as the opening and closing speed.

[0042] S300 determines the target area parameters based on opening and closing test data. These parameters include the target density and shape of the hook and loop fastener, and are used to balance the noise reduction requirements and adhesive performance of the hook and loop fastener.

[0043] It is understandable that the target area parameters include the target density and target shape of each area of ​​the hook and loop fastener.

[0044] For example, based on opening and closing test data and parameters of each initial region, an RBF neural network (including an input layer with the number of nodes equal to the number of parameters in the high and low adhesive regions of the hook and loop fastener; a hidden layer using radial basis functions (RBF) as activation functions; and an output layer with multiple nodes corresponding to the sound pressure level of friction noise and the energy proportion of each frequency band) can be used to fit the relationship between the shape and density of the hook and loop fastener and the opening and closing speed and friction noise (including magnitude and frequency). A multi-objective optimization algorithm (such as NSGA-II) is then used to search for target region parameters that satisfy the optimal balance between the two objectives, taking the noise reduction requirement (minimizing the magnitude and frequency of friction noise) and the adhesive performance (minimizing the opening and closing speed) as two optimization objectives. For example, the objective function includes: f1(x) = w1 SPL+w2 DF, f2(x) = OS, where SPL is the frictional sound pressure level (dB), DF is the percentage of high-frequency noise energy (%), OS is the opening and closing speed (mm / s), and w1 and w2 are weighting coefficients (e.g., w1 = 0.7, w2 = 0.3). Constraints include: fiber density range (e.g., 4000 ≤ ρ ≤ 6000 fibers / cm). 2 The range of the hook tip curvature radius (e.g., 0.15≤r≤0.25mm). Under the condition of satisfying the constraints, the solution with the smallest f1(x) and the second best f2(x) is selected first to obtain the target region parameters.

[0045] For example, the specific composition of the training dataset for multiple sets of hook and loop fastener samples under different opening and closing conditions can be obtained through experimental testing: Input data: including the initial region parameters of the hook and loop fastener and opening and closing test data; Output data: corresponding to the magnitude and frequency of friction sound in the opening and closing test, and the adhesive force of the hook and loop fastener (which can be indirectly reflected by the opening and closing speed). The training process includes: normalizing the input and output data to eliminate the influence of dimensions and improve the training efficiency of the neural network; selecting a suitable RBF neural network structure, including the number of nodes in the input layer, hidden layer, and output layer, and the activation function of the hidden layer (such as a Gaussian function); randomly initializing the center vector, width parameter, and output layer weights of the RBF neural network; using the k-means clustering algorithm to determine the center vector of the hidden layer nodes, and using gradient descent or other optimization algorithms to adjust the width parameter and output layer weights to minimize the prediction error; dividing the training dataset into a training set and a validation set; using the training set to train the network, and using the validation set to evaluate the network performance and adjust the network parameters to obtain the RBF neural network.

[0046] S400 determines the noise region under different opening and closing conditions based on the target region parameters. The noise region refers to the local area within the hook and loop fastener that has the greatest impact on noise levels, corresponding to the target region parameters.

[0047] For example, under various opening and closing conditions, an opening and closing test database can be constructed by conducting opening and closing tests on multiple hook and loop fasteners (including collecting the magnitude and frequency of friction sound through a microphone, as well as physical parameters: synchronously acquiring or measuring the density and shape of each region of the hook and loop fastener, and matching the friction sound with the physical parameters by timestamp). For each time segment, the dynamic rate of change of the physical parameters is calculated, and a multiple linear regression model of friction sound and physical parameters is established. The contribution of physical parameters to noise is quantified by standardized regression coefficients. If the standardized regression coefficient of a local region corresponding to a certain time segment is greater than the mean, it is identified as a candidate noise region. For each set of opening and closing conditions, the frequency of occurrence of each candidate noise region within the hook and loop fastener is statistically analyzed, and a probability heatmap (such as Gaussian kernel density estimation) is generated. The frequency of occurrence of each candidate noise region within the hook and loop fastener is compared with a preset threshold to obtain the noise region.

[0048] Alternatively, a 3D model of the hook and loop fastener can be created using finite element analysis software (such as ANSYS Workbench) based on the parameters of the target area. The direction and velocity of displacement are determined according to the opening and closing conditions. The acoustic characteristics under different opening and closing conditions are simulated using the 3D model. Specifically, ANSYS Mechanical is used to solve the nodal vibration acceleration between the hook and loop surfaces during the opening and closing process. This vibration acceleration is used as the sound source input, and the sound pressure distribution is calculated using the ANSYS Acoustics module. Based on the sound pressure distribution, the local area with the greatest impact on noise levels (i.e., the noise region) is identified.

[0049] For example, for the three-dimensional model of the hook and loop fastener established using finite element analysis software, a constitutive model suitable for the hook and loop fastener material can be selected, such as a hyperelastic model (used to describe the large deformation behavior of rubber-like materials) or a viscoelastic model (used to describe the mechanical behavior of materials changing over time), and the parameters (such as shear modulus, bulk modulus, damping coefficient, etc.) can be determined. Define the contact type between the hook and loop surface (e.g., surface-to-surface contact), and set parameters such as contact stiffness and friction coefficient to simulate the contact behavior during the actual opening and closing process. Select an appropriate mesh type and size based on the model complexity and computational resources. Use finer meshes for critical areas to improve computational accuracy (e.g., hook tip area: mesh size refined to 0.08mm). Set acoustic boundary conditions to simulate sound wave propagation and reflection in the actual environment. For example, use the nodal vibration acceleration of the contact area between the hook and loop surfaces as the sound source; acoustic domain: hemispherical radiation boundary (radius 1m); impedance boundary conditions to simulate a semi-free field; frequency range: 20-5000Hz.

[0050] S500 determines the regional noise level based on each noise zone. The regional noise level reflects the noise distribution of the hook and loop fasteners corresponding to the target region parameters when used randomly under different opening and closing conditions.

[0051] For example, based on opening and closing test data and initial region parameters, an RBF neural network can be used to fit the relationship between the shape and density of the hook and loop fastener and the opening and closing speed. The corresponding opening and closing speed is determined based on the target region parameters. A mathematical model between the opening and closing speed and the adhesive force of the hook and loop fastener is established using historical experimental data (including adhesive forces corresponding to different opening and closing speeds in the opening and closing test data). This yields the adhesive force corresponding to the target region parameters, and the hook and loop fastener is divided into low-viscosity, transition, and high-viscosity regions. The noise weights of the low-viscosity, transition, and high-viscosity regions under different opening and closing conditions are obtained based on the area proportion of noise regions within these regions and the preset usage frequencies under different opening and closing conditions. The regional noise levels are then obtained by weighted summation based on the frictional sound (including magnitude and frequency) and corresponding noise weights within the low-viscosity, transition, and high-viscosity regions under different opening and closing conditions.

[0052] It is understood that the aforementioned low viscosity area is the low adhesion area in this specification, the aforementioned high viscosity area is the high adhesion area in this specification, and the aforementioned transition area is the transition area in this specification. The adhesive force of different areas can be determined based on the opening and closing speed of different areas of the hook and loop fastener, and the adhesive force can be compared with a preset range to determine the low adhesion area, high adhesion area, and transition area.

[0053] S600 adjusts target area parameters based on regional noise levels.

[0054] For example, the relationship between the shape and density of the hook and loop fastener and the friction noise (including magnitude and frequency) can be obtained using an RBF neural network. Then, an iterative optimization algorithm (such as gradient descent) can be used to continuously adjust the target area parameters based on feedback from the area noise level. In each iteration, the area noise level corresponding to the current parameter value is calculated, and the target area parameters are adjusted until the corresponding area noise level reaches a preset noise range.

[0055] In one possible implementation, please refer to Figure 2 S300, determines the target area parameters based on opening and closing test data, including: S310, based on the opening and closing speed in the opening and closing test data, obtains the range of area parameters that enable the hook and loop fastener to have a preset adhesive force.

[0056] For example, a mathematical model relating opening / closing speed and the adhesive force of the hook and loop fastener can be established using historical experimental data (including adhesive forces corresponding to different opening / closing speeds in opening / closing test data). For instance, polynomial features can be generated based on historical experimental data, which can then be divided into training and testing sets. The model can be trained using the training set and polynomial features, and evaluated using the mean squared error and coefficient of determination obtained from the testing set, resulting in a polynomial regression model. This model, i.e., the mathematical model relating opening / closing speed and the adhesive force of the hook and loop fastener, can be used to fit historical experimental data to determine the relationship between opening / closing speed and adhesive force. Using the established mathematical model, a corresponding opening / closing speed range can be obtained based on a preset adhesive force range. Based on the opening / closing test data and parameters of each initial region, an RBF neural network can be used to fit the relationship between the shape and density of the hook and loop fastener and the opening / closing speed. This further derives the range of regional parameters that enable the hook and loop fastener to possess a preset adhesive force within this opening / closing speed range, including the density of the napped fibers, the density of the hook arrangement, fiber length, and the shape of the hook tip.

[0057] S320 determines the target area parameters within the range of area parameters based on the opening and closing test data.

[0058] For example, within the range of regional parameters, based on the opening and closing test data and each initial regional parameter, the relationship between the shape and density of the hook and loop fastener and the opening and closing speed and friction noise (including magnitude and frequency) can be fitted using an RBF neural network. The optimal solution can be searched using the objective function set by the optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm, etc.) (such as minimizing noise, i.e. minimizing the magnitude and frequency of friction noise, maximizing adhesive force, i.e. minimizing opening and closing speed) to obtain the target regional parameters.

[0059] Through steps S310 to S320 above, a mathematical model is established, providing a scientific basis for adjusting the parameters of the hook and loop fastener, thus avoiding blind adjustments. This facilitates precise control of the hook and loop fastener's adhesive force: by preset the adhesive force range and derive the regional parameter range, the hook and loop fastener can achieve ideal adhesive effects in different application scenarios. Optimization algorithms quickly find the optimal parameter combination, improving adjustment efficiency. Comprehensive consideration of factors such as opening and closing speed, adhesive force, and noise contributes to an overall improvement in the performance of the hook and loop fastener.

[0060] In one possible implementation, please refer to Figure 2 S500 determines the regional noise level based on each noise zone, including: S510 determines the noise region parameters corresponding to each noise region based on the target region parameters.

[0061] For example, parameters can be mapped to each noise region based on the target region parameters to obtain the corresponding noise region parameters.

[0062] S520: The first noise level of each noise region is obtained based on the parameters of each noise region. The first noise level is used to reflect the noise intensity of the noise region.

[0063] For example, on the opening and closing test device, opening and closing tests can be performed on each noise region under corresponding opening and closing conditions according to the hook and loop fastener sample corresponding to the target region parameters. The magnitude and frequency of friction sound in each noise region can be recorded using an acoustic sensor, and the average friction sound (including magnitude and frequency) of each noise region can be calculated, i.e., the first noise quantity. Alternatively, the first noise quantity of each noise region can be obtained by inputting the parameters of each noise region, i.e., the physical parameters, based on a multiple linear regression model of friction sound and physical parameters.

[0064] S530, based on each first noise quantity, obtains the regional noise level.

[0065] For example, a weighting factor can be assigned to each noise region. The corresponding weighting factor can be determined based on the area of ​​the noise region and the preset usage frequency of the opening and closing conditions corresponding to that noise region. The region noise level is obtained based on the first noise level of each noise region within the hook and loop fastener and its corresponding weighting factor.

[0066] Through steps S510 to S530, by subdividing noise areas and adjusting parameters accordingly, noise reduction measures become more precise and effective. This improves the comfort and durability of the hook and loop fastener: optimized local parameters reduce unnecessary friction and wear, extending the lifespan of the fastener. Regional noise measurement and data analysis facilitate accurate quantification of noise levels in each area of ​​the fastener. By using a weighted average method to comprehensively consider the contribution of each noise area, the regional noise levels better reflect the actual noise performance of the fastener.

[0067] In one possible implementation, please refer to Figure 3 S600 adjusts target area parameters based on regional noise levels, including: S610, determine the correlation between the regional noise level and the target region parameters. The regional noise level includes the magnitude and frequency of friction noise.

[0068] For example, the corresponding hook and loop fastener can be divided into a high adhesion zone, a low adhesion zone, and a transition zone according to the parameters of each initial zone. Then, based on the opening and closing test data and the parameters of each initial zone, the shape and density of the high adhesion zone and the low adhesion zone in the hook and loop fastener can be obtained by fitting with an RBF neural network, and the relationship between them and the friction noise (including magnitude and frequency) can be determined to establish the correlation between the noise level of the zone and the parameters of the target zone.

[0069] S620, when it is determined that the parameters of the high-adhesion zone in the target area parameters need to be adjusted based on the regional noise level, the first adjustment value of the parameters of the high-adhesion zone in the target area parameters is determined based on the correlation and the preset noise range.

[0070] For example, if the noise level (magnitude or frequency of frictional sound) in the high-adhesion zone is determined to be greater than a preset threshold based on the regional noise level, the parameters of the high-adhesion zone that meet the preset noise range can be solved using the correlation relationship to obtain a first adjustment value. For example, the parameters can be iteratively adjusted using the gradient descent method so that the regional noise level predicted based on the correlation relationship is within the preset noise range, and the adjusted parameters are within the range of regional parameters.

[0071] S630, when it is determined that the parameters of the low-adhesion zone in the target area parameters need to be adjusted based on the regional noise level, a second adjustment value of the parameters of the low-adhesion zone in the target area parameters is determined based on the correlation and the preset noise range.

[0072] For example, if the noise level (magnitude or frequency of frictional sound) in the low-adhesion zone is determined to be greater than a preset threshold, the parameters of the low-adhesion zone that meet the preset noise range can be solved by using the correlation relationship to obtain the second adjustment value.

[0073] S640, adjust the target area parameters according to the first adjustment value and / or the second adjustment value.

[0074] For example, the parameters of the high-adhesion zone and / or the low-adhesion zone can be adjusted according to a first adjustment value for the high-adhesion zone and / or a second adjustment value for the low-adhesion zone. A third adjustment value for the transition zone is then determined based on the adjusted parameters of the high-adhesion zone and the low-adhesion zone, thereby adjusting the parameters of the target region. Distance weights can be defined based on the distances of each point in the transition zone to the high-adhesion zone and the low-adhesion zone. Points closer to the high-adhesion zone have a greater weight for their parameters; points closer to the low-adhesion zone have a greater weight for their parameters. For each parameter in the transition zone, a weighted average of the adjusted parameters of the high-adhesion zone and the low-adhesion zone is calculated based on the distance weights to obtain the third adjustment value for the transition zone.

[0075] Existing technologies do not consider the coupling effect of parameters in the high / low adhesion zones. Through steps S610 to S640, this method achieves precise parameter adjustment via data-driven modeling and optimization algorithms. Through multi-objective optimization and collaborative adjustment, core performance is ensured while reducing noise.

[0076] Optionally, please refer to Figure 3The correlation includes a first correlation between the magnitude of the frictional sound in the regional noise level and the parameters of the high-adhesion zone, and a second correlation between the frequency of the frictional sound in the regional noise level and the parameters of the high-adhesion zone. The preset noise range includes a first preset range corresponding to the magnitude of the frictional sound and a second preset range corresponding to the frequency of the frictional sound. In S620, the first adjustment value of the parameters of the high-adhesion zone in the target region parameters is determined according to the correlation and the preset noise range, including: S621, when the magnitude of the frictional sound is not within a first preset range, a first parameter range of the parameters of the high-adhesion zone is determined based on a first correlation.

[0077] For example, if the magnitude of the frictional sound is not within a first preset range, the parameters of the high-adhesion zone can be calculated based on a first correlation relationship to make the magnitude of the frictional sound within the first preset range.

[0078] S622, when the frequency of the friction sound is not within the second preset range, the second parameter range of the parameters of the high adhesion zone is determined based on the second correlation.

[0079] For example, an acceptable frequency range, i.e., a second preset range, can be set according to the sensitivity of the human ear. When the frequency of the friction sound is not within the second preset range, the second parameter range of the parameters of the high-adhesion area is calculated based on the second correlation, so that the frequency of the friction sound is within the second preset range.

[0080] S623, based on the first parameter range and the second parameter range, determines the first adjustment value of the parameters of the high adhesion zone.

[0081] For example, based on the first parameter range and the second parameter range, the first adjustment value of the parameters of the high adhesion zone can be obtained by weighted averaging of both the frequency and magnitude of the friction sound or by multi-objective optimization coordination (such as particle swarm optimization algorithm).

[0082] Traditional methods optimize only one metric, the magnitude or frequency of friction noise, leading to parameter conflicts (e.g., reducing magnitude but increasing frequency). This method addresses noise magnitude and frequency separately through steps S621 and S622, and coordinates the conflict through step S623, achieving global optimization. Data-driven models and weight allocation ensure a more scientifically sound preset range. Controlling parameters such as the frequency and magnitude of friction noise requires a weighted objective function and multiple iterations to achieve a balance between multiple objectives.

[0083] Optionally, please refer to Figure 3The correlation also includes a third correlation between the magnitude of frictional sound in the regional noise level and the parameters of the low-adhesion zone, and a fourth correlation between the frequency of frictional sound in the regional noise level and the parameters of the low-adhesion zone. In S630, a second adjustment value for the parameters of the low-adhesion zone in the target region parameters is determined based on the correlation and the preset noise range, including: S631, when the magnitude of the frictional sound is not within the first preset range, the third parameter range of the parameters of the low adhesion zone is determined based on the third correlation.

[0084] For example, if the magnitude of the friction noise is not within a first preset range, a third parameter range within the range of regional parameters can be determined based on a third correlation, so that the magnitude of the friction noise is within the first preset range.

[0085] S632, when the frequency of the friction sound is not within the second preset range, the fourth parameter range of the parameters of the low adhesion zone is determined based on the fourth correlation.

[0086] For example, if the frequency of the friction sound is not within the second preset range, the fourth parameter range of the parameters of the low adhesion zone can be determined based on the fourth correlation, so that the frequency of the friction sound is within the second preset range.

[0087] S633, based on the third parameter range and the fourth parameter range, determines the second adjustment value of the parameters of the low adhesion zone.

[0088] For example, if the ranges of the third and fourth parameters conflict, a second adjustment value for the parameters of the low-adhesion zone can be obtained by weighted averaging of both the frequency and magnitude of the friction sound or by multi-objective optimization coordination (such as a genetic algorithm).

[0089] Through steps S631 to S633 above, the quantitative relationship between parameters and noise in the low-adhesion zone is clarified through the third and fourth correlation relationships, making the adjustment more scientific. The magnitude and frequency of frictional sound must simultaneously meet the preset range. By processing and coordinating parameters step by step, a balance of multiple indicators is achieved.

[0090] In one possible implementation, please refer to Figure 2 S400 determines the noise region under different opening and closing conditions based on the target region parameters, including: S410, obtain the opening and closing time of the hook and loop fastener under each opening and closing condition.

[0091] For example, force-displacement data during the opening and closing process can be collected in real time on the opening and closing test device using force and displacement sensors based on the hook and loop fastener sample corresponding to the target area parameters. The time interval from the start of contact (force > 0.1 N) to complete separation (force < 0.1 N) can be calculated based on the force-displacement data. Alternatively, a threshold detection algorithm (e.g., force change > 0.5 N / ms) can be used to automatically identify the start and end points of opening and closing, calculate the time difference, and obtain the opening and closing time under each condition. Each opening and closing condition includes the opening and closing force and the opening and closing angle. For example, the opening and closing force is 10 N, 12 N, 14 N, 13 N, 12.5 N; the corresponding opening and closing angle is 30°, 45°, 60°, 50°, 40°.

[0092] S420 determines the noise region under each opening and closing condition based on the target region parameters and the opening and closing times.

[0093] For example, the sound pressure level (SPL) of each region during the opening and closing process can be synchronously recorded using a sound level meter (A-weighted, accuracy ±1dB) on the opening and closing test device based on the hook and loop tape sample corresponding to the target region parameters. Figure 5 As shown, the opening and closing regions corresponding to the time periods and frequency bands where the sound pressure level exceeds the background noise (e.g., more than 5 dB higher than the background noise) are defined as noise regions. When there is no opening or closing action, the background noise (e.g., 35 dB) is recorded.

[0094] Through the above steps S410 to S420, noise is analyzed synchronously based on the opening and closing time. Through time window and frequency domain analysis, it is beneficial to accurately locate the noise source.

[0095] In one possible implementation, please refer to Figure 2 S300, which determines the target area parameters based on opening and closing test data, also includes: S330 determines the noise level of the hook and loop fastener under each opening and closing condition based on the opening and closing test data.

[0096] For example, the noise level of the hook and loop fastener under each opening and closing condition can be obtained from the magnitude and frequency of the friction sound in the opening and closing test data.

[0097] S340, based on the preset softness and hardness of the hook and loop fastener and the noise level of the hook and loop fastener under various opening and closing conditions, obtains the corresponding target noise level.

[0098] It is understandable that the preset hardness or softness is represented by the elastic modulus.

[0099] For example, historical data on the region parameters of the hook and loop fastener under different stiffness (elastic modulus) can be obtained. This historical data is divided into training data and test data for model training, parameter tuning, and performance evaluation. The training data is input into the model, and the predicted output is calculated layer by layer. The error between the predicted output and the true output is calculated using a loss function (such as mean squared error). The weight gradient is calculated layer by layer from the output layer to the input layer using the chain rule. Gradient descent or its variants (such as Adam) are used to adjust the weights and biases according to the gradient direction to minimize the loss function. The above steps are repeated to obtain a feedforward neural network model based on the test data. The feedforward neural network model is used to determine the range of region parameters based on the preset stiffness of the hook and loop fastener. The initial region parameters that need adjustment are determined based on the noise level of the hook and loop fastener under various opening and closing conditions. The initial region parameters that need adjustment are adjusted according to the range of region parameters to obtain the corresponding target noise level.

[0100] S350 determines the target area parameters based on the noise levels of each target.

[0101] For example, the target noise level can be used as a constraint, and the relationship between the shape and density of the hook and loop fastener and the friction sound (including magnitude and frequency) can be fitted using an RBF neural network based on the opening and closing test data and the parameters of each initial region to obtain the corresponding target region parameters.

[0102] Through steps S330 to S350, the noise level is quantified using sound pressure level and bandwidth, providing a more comprehensive reflection of noise characteristics. A multi-objective optimization algorithm is used to collaboratively adjust parameters and achieve global optimization.

[0103] Optionally, please refer to Figure 2 S340, based on the preset stiffness of the hook and loop fastener and the noise level of the hook and loop fastener under various opening and closing conditions, the corresponding target noise level is obtained, including: S341, determine the range of area parameters based on the preset softness and hardness of the hook and loop fastener.

[0104] For example, a correlation model between the area parameters and the hardness of the hook and loop fastener can be established, and the range of area parameters can be determined based on the preset hardness of the hook and loop fastener.

[0105] S342, based on the range of regional parameters, the noise level of the hook and loop fastener under each opening and closing condition, and the corresponding initial regional parameters, the target noise level under each opening and closing condition is obtained.

[0106] For example, the initial region parameters corresponding to noise levels greater than a preset threshold can be determined based on the noise level of the hook and loop fastener under each opening and closing condition. Multiple sets of initial parameter combinations can be randomly generated within the range of region parameters, and multi-objective optimization can be performed (minimizing noise level, minimizing cost, i.e. minimizing the change in region parameters) to obtain the target noise level that the initial region parameters corresponding to noise levels greater than the preset threshold need to be adjusted to.

[0107] Through steps S341 to S342 above, by using soft and hardness quantization and parameter constraints, the parameters are adjusted within the allowable performance range. Co-optimization of initial region parameters helps to achieve the lowest global noise. Co-optimization of multiple parameters helps to control costs while reducing noise.

[0108] Optionally, please refer to Figure 2 S510, determine the noise region parameters corresponding to each noise region based on the target region parameters, including: S511 divides each noise region according to the target region parameters to obtain a high-adhesion region, a transition region, and a low-adhesion region.

[0109] For example, the relationship between the shape and density of the hook and loop fastener and the opening and closing speed can be fitted using an RBF neural network based on the opening and closing test data and the parameters of each initial region. The corresponding opening and closing speed can be determined based on the parameters of the target region. A mathematical model between the opening and closing speed and the adhesive force of the hook and loop fastener can be established using historical experimental data (including the adhesive force corresponding to different opening and closing speeds in the opening and closing test data). The adhesive force corresponding to the parameters of the target region can be obtained. Each noise region can be segmented by thresholding to obtain a high adhesive region, a transition region, and a low adhesive region.

[0110] S512, determine the noise region parameters corresponding to each high adhesion zone, each transition zone, and each low adhesion zone.

[0111] For example, the target region parameters can be mapped to the noise region parameters corresponding to each high adhesion region, the noise region parameters corresponding to each transition region, and the noise region parameters corresponding to each low adhesion region.

[0112] Through steps S511 to S512 above, the noise region is divided into three categories, the functional positioning of each region is clarified, and parameters are derived in reverse based on the region type. Parameter mapping and collaborative verification facilitate the achievement of a dynamic balance between adhesive strength and noise.

[0113] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0114] Corresponding to the hook and loop fastener process adjustment method for reducing opening and closing noise described in the above embodiments, this application also provides a hook and loop fastener process adjustment device for reducing opening and closing noise. Each module of the device can realize each step of the hook and loop fastener process adjustment method for reducing opening and closing noise. Figure 6The diagram shows a structural block diagram of a hook and loop fastener process adjustment device for reducing opening and closing noise, provided in an embodiment of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0115] Reference Figure 6 The device includes: An initial region parameter module is used to obtain initial region parameters for multiple hook and loop fasteners; wherein, the hook and loop fasteners include multiple regions in the loop portion and corresponding multiple regions in the hook portion, and at least one corresponding region between any two hook and loop fasteners has a different density and / or shape, the initial region parameters include the density and shape of each region of the hook and loop fasteners, the density of the hook and loop fasteners includes the fiber density of the loop portion and the arrangement density of the hook portion, and the shape of the hook and loop fasteners includes the fiber length of the loop portion and the hook tip shape of the hook portion; The opening and closing test data module is used to acquire opening and closing test data of each of the hook and loop fasteners; wherein, the opening and closing test data is obtained by performing opening and closing tests on each of the hook and loop fasteners under the same opening and closing conditions, the opening and closing conditions include opening and closing force and opening and closing angle, and the opening and closing test data includes opening and closing speed, as well as the magnitude and frequency of friction sound. The target area parameter module is used to determine the target area parameters based on the opening and closing test data; wherein, the target area parameters include the target density and target shape of the hook and loop fastener, and the target area parameters are used to balance the noise reduction requirements and adhesive performance of the hook and loop fastener; The noise region module is used to determine the noise region under different opening and closing conditions based on the target region parameters; wherein, the noise region refers to the local area in the hook and loop fastener corresponding to the target region parameters that has the greatest impact on the noise level; A zone noise level module is used to determine the zone noise level based on each of the noise zones; wherein the zone noise level is used to reflect the noise distribution of the hook and loop fastener corresponding to the target zone parameters when used randomly under different opening and closing conditions; An adjustment module is used to adjust the parameters of the target area according to the noise level of the area.

[0116] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0118] This application also provides an electronic device. Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 7 As shown, the electronic device 7 of this embodiment includes: at least one processor 70 ( Figure 7 Only one is shown in the image), at least one memory 71 ( Figure 7 (Only one is shown in the image) and a computer program 72 stored in the at least one memory 71 and executable on the at least one processor 70. When the processor 70 executes the computer program 72, it causes the electronic device 7 to perform the steps in any of the above embodiments of the hook and loop fastener process adjustment method for reducing opening and closing noise, or causes the electronic device 7 to perform the functions of each module / unit in the above embodiments of the apparatus.

[0119] For example, the computer program 72 may be divided into one or more modules / units, which are stored in the memory 71 and executed by the processor 70 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 72 in the electronic device 7.

[0120] The electronic device 7 can be a computing device such as an industrial computer, a programmable logic controller, a desktop computer, a laptop, a handheld computer, or a cloud server. This electronic device may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will understand that... Figure 7The example shown is merely an illustration of electronic device 7 and does not constitute a limitation on electronic device 7. It may include more or fewer components than shown, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, buses, etc.

[0121] The processor 70 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0122] In some embodiments, the memory 71 may be an internal storage unit of the electronic device 7, such as a hard disk or memory of the electronic device 7. In other embodiments, the memory 71 may be an external storage device of the electronic device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 7. Furthermore, the memory 71 may include both internal and external storage units of the electronic device 7. The memory 71 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 71 can also be used to temporarily store data that has been output or will be output.

[0123] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0124] This application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the steps in any of the above method embodiments.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0126] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0127] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0128] In the embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications 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 application, and should all be included within the protection scope of this application.

Claims

1. A method for adjusting the hook and loop fastener process to reduce opening and closing noise, characterized in that, include: Obtain initial region parameters for multiple hook and loop fasteners; wherein the hook and loop fasteners include multiple regions in the loop portion and corresponding multiple regions in the hook portion, and at least one corresponding region between any two hook and loop fasteners has a different density and / or shape, the initial region parameters include the density and shape of each region of the hook and loop fasteners, the density of the hook and loop fasteners includes the fiber density of the loop portion and the arrangement density of the hook portion, and the shape of the hook and loop fasteners includes the fiber length of the loop portion and the hook tip shape of the hook portion; Obtain opening and closing test data for each of the hook and loop fasteners; wherein, the opening and closing test data is obtained by performing opening and closing tests on each of the hook and loop fasteners under the same opening and closing conditions, the opening and closing conditions include opening and closing force and opening and closing angle, and the opening and closing test data includes opening and closing speed, as well as the magnitude and frequency of friction sound; The target area parameters are determined based on the opening and closing test data; wherein, the target area parameters include the target density and target shape of the hook and loop fastener, and the target area parameters are used to balance the noise reduction requirements and adhesive performance of the hook and loop fastener; The noise region is determined based on the target region parameters under different opening and closing conditions; wherein, the noise region refers to the local area in the hook and loop fastener corresponding to the target region parameters that has the greatest impact on the noise level; The regional noise level is determined based on each of the noise regions; wherein the regional noise level is used to reflect the noise distribution of the hook and loop fastener corresponding to the target region parameters when used randomly under different opening and closing conditions; The parameters of the target area are adjusted according to the noise level of the area.

2. The hook and loop fastener process adjustment method for reducing opening and closing noise as described in claim 1, characterized in that, Determining the target area parameters based on the opening and closing test data includes: Based on the opening and closing speed in the opening and closing test data, the range of parameters for the area where the hook and loop fastener has a preset adhesive force is obtained. The target region parameters within the range of the region parameters are determined based on the opening and closing test data.

3. The hook and loop fastener process adjustment method for reducing opening and closing noise as described in claim 1, characterized in that, The step of determining the regional noise level based on each of the noise regions includes: Determine the noise region parameters corresponding to each noise region based on the target region parameters; A first noise level for each noise region is obtained based on the parameters of each noise region; wherein, the first noise level is used to reflect the noise level of the noise region; The noise level of the region is obtained based on each of the first noise quantities.

4. The hook and loop fastener process adjustment method for reducing opening and closing noise as described in claim 3, characterized in that, The step of adjusting the target area parameters according to the area noise level includes: Determine the correlation between the noise level of the area and the parameters of the target area; wherein, the noise level of the area includes the magnitude and frequency of the friction sound; When it is determined that the parameters of the high-adhesion zone in the target area need to be adjusted based on the noise level of the area, a first adjustment value of the parameters of the high-adhesion zone in the target area is determined based on the correlation and the preset noise range. When it is determined that the parameters of the low-adhesion zone in the target area parameters need to be adjusted based on the noise level of the area, a second adjustment value of the parameters of the low-adhesion zone in the target area parameters is determined based on the correlation and the preset noise range. The target region parameters are adjusted according to the first adjustment value and / or the second adjustment value.

5. The hook and loop fastener process adjustment method for reducing opening and closing noise as described in claim 4, characterized in that, The correlation includes a first correlation between the magnitude of the friction sound in the regional noise level and the parameters of the high-adhesion zone, and a second correlation between the frequency of the friction sound in the regional noise level and the parameters of the high-adhesion zone. The preset noise range includes a first preset range corresponding to the magnitude of the friction sound and a second preset range corresponding to the frequency of the friction sound. Determining a first adjustment value for the parameters of the high-adhesion zone in the target region parameters based on the correlation and the preset noise range includes: When the magnitude of the friction sound is not within the first preset range, a first parameter range is determined based on the first correlation to determine the parameters of the high adhesion zone. When the frequency of the friction sound is not within the second preset range, a second parameter range is determined based on the second correlation to determine the parameters of the high adhesion zone. Based on the first parameter range and the second parameter range, a first adjustment value for the parameters of the high adhesion zone is determined.

6. The hook and loop fastener process adjustment method for reducing opening and closing noise as described in claim 5, characterized in that, The correlation also includes a third correlation between the magnitude of the friction sound in the regional noise level and the parameters of the low-adhesion zone, and a fourth correlation between the frequency of the friction sound in the regional noise level and the parameters of the low-adhesion zone. The step of determining a second adjustment value for the parameters of the low-adhesion zone in the target region parameters based on the correlation and the preset noise range includes: If the magnitude of the friction sound is not within the first preset range, a third parameter range for the parameters of the low adhesion zone is determined based on the third correlation. If the frequency of the friction sound is not within the second preset range, the fourth parameter range of the parameters of the low adhesion zone is determined based on the fourth correlation. Based on the third parameter range and the fourth parameter range, a second adjustment value for the parameters of the low adhesion zone is determined.

7. The hook and loop fastener process adjustment method for reducing opening and closing noise as described in claim 1, characterized in that, The step of determining the noise region under different opening and closing conditions based on the target region parameters includes: Obtain the opening and closing time of the hook and loop fastener under each of the aforementioned opening and closing conditions; Based on the target region parameters and the opening and closing times, the noise region under each opening and closing condition is determined.

8. The hook and loop fastener process adjustment method for reducing opening and closing noise as described in claim 2, characterized in that, The step of determining the target area parameters based on the opening and closing test data further includes: The noise level of the hook and loop fastener under each opening and closing condition is determined based on the opening and closing test data. The target noise level is obtained based on the preset hardness of the hook and loop fastener and the noise level of the hook and loop fastener under each of the opening and closing conditions. The target region parameters are determined based on the target noise levels.

9. The hook and loop fastener process adjustment method for reducing opening and closing noise as described in claim 8, characterized in that, The method of obtaining the corresponding target noise level based on the preset stiffness of the hook and loop fastener and the noise level of the hook and loop fastener under each of the opening and closing conditions includes: The range of parameters for the region is determined based on the preset hardness of the hook and loop fastener. The target noise level under each opening and closing condition is obtained based on the range of the region parameters, the noise level of the hook and loop fastener under each opening and closing condition, and the corresponding initial region parameters.

10. The hook and loop fastener process adjustment method for reducing opening and closing noise as described in claim 4, characterized in that, The step of determining the noise region parameters corresponding to each noise region based on the target region parameters includes: Each noise region is segmented according to the target region parameters to obtain the high adhesion region, the transition region, and the low adhesion region; Determine the noise region parameters corresponding to each of the high adhesion regions, the noise region parameters corresponding to each of the transition regions, and the noise region parameters corresponding to each of the low adhesion regions.

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