Noise reduction earmuff performance evaluation method and system based on data analysis

Abstract

The invention discloses a noise reduction earmuff performance evaluation method and system based on data analysis, and belongs to the field of intelligent equipment performance evaluation.The method comprises the steps that a user use habit evaluation model is constructed, and the wearing duration ratio and the number of friction times between the earmuff and the ear circumference are imported into the use habit evaluation model to evaluate the use condition of the user for the earmuff; constructing an earmuff physical state evaluation model, importing the thickness of the earmuff sealing ring, the elastic modulus of the sealing material and the wearing region proportion into the physical state evaluation model to evaluate the current physical state of the earmuff, and constructing an ear circumference sealing performance evaluation model; the ear circumference geometric matching degree, the pressure distribution uniformity and the ear circumference wear rate are imported into a sealing performance evaluation model to evaluate the actual fitting effect of the earmuff on the ear circumference of the user, an earmuff future wear evaluation model is constructed, and the use habit of the user, the physical state of the earmuff and the ear circumference sealing performance are imported into the future wear evaluation model to evaluate the future wear condition of the earmuff. And quantitative evaluation of future wear of the earmuff is realized.

Description

Technical Field

[0001] This application belongs to the field of smart device performance evaluation, specifically a noise reduction earmuff performance evaluation method and system based on data analysis. Background Art

[0002] The safety and comfort of traditional noise-canceling earmuffs rely primarily on the user's subjective experience, lacking an objective, quantitative evaluation method. Existing technologies often evaluate earmuff performance based on simple material life tests or fixed acoustic indicators, failing to comprehensively analyze the user's actual usage behavior, physiological differences around the ear, and earmuff wear. Wear and aging of earmuffs can be difficult to detect in a timely manner, impacting noise reduction and wearing comfort. This is especially true for active noise-canceling earmuffs, where elastic degradation of the sealing ring or surface cracking and peeling can lead to reduced noise reduction effectiveness. User wearing behavior varies greatly, and standardized evaluation indicators fail to reflect individualized usage. Peri-ear sealing and wearing comfort cannot be quantified, and there is a lack of dynamic evaluation methods to predict future wear.

[0003] The present invention collects information about the wearer's usage habits, the material and structural status of the earmuffs, and the wear of the skin around the ears to reflect the aging, hardening, and loss of the earmuffs in real time, evaluate the user's wearing experience, and comprehensively consider the user's habits, the status of the earmuffs, and the conditions around the ears to predict the future wear of the earmuffs and help users replace the earmuffs in a timely manner. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, this application proposes a noise reduction earmuff performance evaluation method and system based on data analysis.

[0005] To achieve the above objectives, this application provides the following technical solutions: The noise reduction earmuff performance evaluation method based on data analysis includes the following specific steps: Build a user usage habit evaluation model, and import the wearing time ratio and the number of times the earmuffs rub against the ear area into the usage habit evaluation model to evaluate the user's usage of earmuffs; Construct an earmuff physical condition assessment model, import the thickness of the earmuff seal, the elastic modulus of the sealing material, and the proportion of the worn area into the physical condition assessment model to evaluate the current physical condition of the earmuff; Construct an ear seal evaluation model, incorporating ear geometry matching, pressure distribution uniformity, and ear wear rate into the model to assess the actual fit of the earmuffs around the user's ears. A future wear assessment model for earmuffs is constructed, and the user's usage habits, physical condition of the earmuffs, and the sealing performance around the ear are introduced into the future wear assessment model to evaluate the future wear of the earmuffs.

[0006] Preferably, the step of constructing a user usage habit evaluation model and importing the wearing time ratio and the number of times the earmuffs rub against the ear area into the usage habit evaluation model to evaluate the user's usage of the earmuffs comprises the following specific steps: S11. Obtaining a user's earmuff usage record through a built-in sensor in the earmuff or interaction with a mobile terminal, obtaining the duration of the user wearing the earmuff per unit time, recording the duration of each time the user wears the earmuff, and obtaining the number of times the user wears the earmuff per unit time; S12. Obtain the number of frictions between the earmuff and the skin around the ear per unit time based on the contact acceleration peak and friction acoustic characteristics; S13. Substitute the proportion of the time the user wears the earmuffs per unit time and the number of times the earmuffs rub against the skin around the ears into a user usage habit evaluation formula to evaluate the user's usage of the earmuffs. The user usage habit evaluation formula is: , where c is the number of times the earmuff rubs against the skin around the ear per unit time, is the upper limit of friction times per unit time, is the proportion of time spent wearing earmuffs per unit time, Use habit weights.

[0007] Preferably, the step of constructing an earmuff physical condition assessment model and importing the thickness of the earmuff sealing ring, the elastic modulus of the sealing material, and the wear area ratio into the physical condition assessment model to assess the current physical condition of the earmuff comprises the following specific steps: S21. Obtain the factory thickness of the earmuff sealing ring, measure the current actual thickness of the earmuff sealing ring, obtain the elastic modulus of the earmuff sealing material, and obtain the proportion of cracks, peeling, and wear areas on the sealing ring surface through earmuff surface image detection; S22. Substitute the thickness of the earmuff seal ring, the elastic modulus of the earmuff seal material, and the proportion of cracks, peeling, and wear areas on the seal ring surface into the earmuff physical condition evaluation formula to evaluate the physical condition of the earmuff. The earmuff physical condition evaluation formula is: ,in, is the physical state weight of the earmuff, h is the current sealing ring thickness, The thickness of the sealing ring when leaving the factory. is the elastic modulus of the current earmuff, is the elastic modulus of the earmuffs when leaving the factory, It is the proportion of cracks, peeling and wear areas on the sealing ring surface.

[0008] Preferably, the step of constructing a peri-ear sealing evaluation model and introducing peri-ear geometric matching, pressure distribution uniformity, and peri-ear wear rate into the sealing evaluation model to evaluate the actual fit of the earmuff around the user's ear comprises the following specific steps: S31. Obtain a point cloud dataset of the user's ear area through 3D scanning. Generate a 3D mesh model of the ear area through point cloud filtering and surface reconstruction. Simultaneously, obtain the earmuff seal ring shape model from the manufacturer and convert it into a point cloud format. Align the 3D mesh model of the ear area with the earmuff seal ring shape model using the ICP algorithm. Calculate the local gap between the ear area and the earmuff seal ring, which is the shortest distance from a point on the ear area surface to the seal ring surface. Substitute the local gap and contact area between the ear area and the earmuff seal ring into the ear area geometric matching evaluation formula to evaluate the fit between the earmuff and the ear area. The ear area geometric matching evaluation formula is: , where A is the area of ​​the contact area between the earmuff and the ear, S is the actual surface of the contact area around the ear, and S is the integration domain. The integration is performed over the entire contact area around the ear. It is the local gap between the ear and the earmuff sealing ring. is the maximum acceptable tolerance gap, dA is the surface area element, when the gaps of all contact points are less than or equal to the maximum acceptable tolerance gap, the matching degree is close to 1, indicating excellent geometric fit. When there are obvious gaps or large area gaps, the integral value increases and the matching degree decreases; S32. Using a distributed pressure sensor array, collect the pressure distribution of the earmuff in the contact area around the ear, calculate the average pressure and the pressure standard deviation, and substitute the pressure standard deviation into the pressure distribution uniformity formula to evaluate the pressure distribution. The pressure distribution uniformity formula is: ,in, is the pressure standard deviation, is the preset allowable pressure fluctuation tolerance; S33. Identify redness, indentations, and damage to the skin around the ears through image recognition and user self-reporting. Determine the degree of redness by comparing the red intensity difference between the target area and the user's normal skin. Substitute the redness and the wear area ratio into the periauricular wear rate calculation formula to assess the wear of the periauricular skin. The periauricular wear rate calculation formula is: , where a is the degree of wear redness and b is the proportion of wear area; S34. Substitute the ear geometry matching degree, pressure distribution uniformity, and ear wear rate into the ear sealing evaluation formula to evaluate the actual fit of the earmuff under the user's ear conditions. The ear sealing evaluation formula is: ,in, 、 and is the weight.

[0009] Preferably, the construction of the future wear assessment model for earmuffs, importing the user's usage habits, the physical condition of the earmuffs, and the tightness of the ear area into the future wear assessment model to assess the future wear of the earmuffs comprises the following specific steps: S41. Substitute the user's usage habits, the physical condition of the earmuff, and the tightness of the ear area into a formula for evaluating future earmuff wear to evaluate future earmuff wear. The formula for evaluating future earmuff wear is: ,in, User usage habit index, is the earmuff physical condition index, is the ear sealing index, t is the earmuff usage time, T is the future usage time, 、 and is the weight; S42. Compare the future wear of the earmuffs with a preset wear threshold. When the predicted wear is lower than the threshold, the earmuffs can continue to be used safely. When the predicted wear exceeds the threshold, the earmuffs can no longer guarantee the expected noise reduction and comfort performance, and a replacement reminder is automatically issued.

[0010] The noise reduction earmuff performance evaluation system based on data analysis is implemented based on the above-mentioned noise reduction earmuff performance evaluation method based on data analysis, and specifically includes: The user usage habit evaluation module is used to evaluate the user's usage of earmuffs based on the proportion of wearing time and the number of times the earmuffs rub against the ear area; An earmuff physical condition assessment module is used to assess the current physical condition of the earmuff based on the thickness of the earmuff seal, the elastic modulus of the seal material, and the proportion of the worn area; The ear seal evaluation module is used to evaluate the actual fit of the earmuffs around the user's ears through the ear geometry matching, pressure distribution uniformity and ear wear rate; The earmuff future wear assessment module is used to assess the future wear of earmuffs based on user usage habits, the physical condition of the earmuffs, and the sealing around the ear.

[0011] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned noise reduction earmuff performance evaluation method based on data analysis by calling the computer program stored in the memory.

[0012] A computer-readable storage medium is characterized in that it stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned noise reduction earmuff performance evaluation method based on data analysis.

[0013] Compared with the prior art, the present invention has the following advantages: The present application constructs a user usage habit evaluation model, which imports the wearing time ratio and the number of times the earmuffs rub against the ear area into the usage habit evaluation model to evaluate the user's usage of the earmuffs; constructs an earmuff physical state evaluation model, which imports the thickness of the earmuff sealing ring, the elastic modulus of the sealing material, and the wear area ratio into the physical state evaluation model to evaluate the current physical state of the earmuffs; constructs an earmuff sealing evaluation model, which imports the earmuff geometric matching degree, pressure distribution uniformity, and earmuff wear rate into the sealing evaluation model to evaluate the actual fit of the earmuffs around the user's ears; constructs an earmuff future wear evaluation model, which imports the user's usage habits, the earmuff physical state, and the earmuff sealing into the future wear evaluation model to evaluate the future wear of the earmuffs, thereby achieving a quantitative evaluation of the future wear of the earmuffs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a schematic diagram of the overall process of the noise reduction earmuff performance evaluation method based on data analysis in this application; Figure 2 A flow chart for evaluating future wear of earmuffs is provided for this application; Figure 3 This is a schematic diagram of the overall framework of the noise reduction earmuff performance evaluation system based on data analysis in this application. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0016] Example 1

[0017] See also Figure 1-Figure 2 The present application provides an embodiment of a noise reduction earmuff performance evaluation method based on data analysis, which includes the following specific steps: Build a user usage habit evaluation model, and import the wearing time ratio and the number of times the earmuffs rub against the ear area into the usage habit evaluation model to evaluate the user's usage of earmuffs; Construct an earmuff physical condition assessment model, import the thickness of the earmuff seal, the elastic modulus of the sealing material, and the proportion of the worn area into the physical condition assessment model to evaluate the current physical condition of the earmuff; Construct an ear seal evaluation model, incorporating ear geometry matching, pressure distribution uniformity, and ear wear rate into the model to assess the actual fit of the earmuffs around the user's ears. A future wear assessment model for earmuffs is constructed, and the user's usage habits, physical condition of the earmuffs, and the sealing performance around the ear are introduced into the future wear assessment model to evaluate the future wear of the earmuffs.

[0018] In this embodiment, it should be specifically explained that constructing a user usage habit evaluation model and importing the wearing time ratio and the number of times the earmuffs rub against the ear area into the usage habit evaluation model to evaluate the user's usage of the earmuffs includes the following specific steps: S11. Obtaining a user's earmuff usage record through a built-in sensor in the earmuff or interaction with a mobile terminal, obtaining the duration of the user wearing the earmuff per unit time, recording the duration of each time the user wears the earmuff, and obtaining the number of times the user wears the earmuff per unit time; S12. Obtain the number of frictions between the earmuff and the skin around the ear per unit time based on the contact acceleration peak and friction acoustic characteristics; In this embodiment, it should be specifically explained that the process of obtaining the number of friction events includes the following steps: by arranging an inertial sensor and a near-field microphone in the earmuff structure, the acceleration, angular velocity, and friction sound signals of the earmuff during wearing are synchronously collected; the inertial sensor obtains the vibration characteristics when the earmuff and the skin or hair around the user's ear slip or impact occur instantaneously; the microphone records the medium- and high-frequency broadband noise generated by friction; the collected signal is preprocessed, including filtering out low-frequency interference and removing environmental noise; the continuous signal is divided into short segments, and feature values ​​are extracted in each segment; the acceleration feature is used to identify whether there is obvious impact or slip; the acoustic feature is used to identify the spectral energy distribution and spectral flatness of the friction noise; event recognition is performed according to preset judgment rules; when the acceleration feature and the acoustic feature simultaneously exceed the threshold and the duration is not less than the minimum set value, it is determined to be a valid friction event; for adjacent trigger segments with a short interval, they are merged into a complete event, and a minimum interval time is set to avoid repeated counting; the total number of all independent events within the set detection time window is counted to obtain the number of friction events; S13. Substitute the proportion of the time the user wears the earmuffs per unit time and the number of times the earmuffs rub against the skin around the ears into a user usage habit evaluation formula to evaluate the user's usage of the earmuffs. The user usage habit evaluation formula is: , where c is the number of times the earmuff rubs against the skin around the ear per unit time, is the upper limit of friction times per unit time, is the proportion of time spent wearing earmuffs per unit time, To weight the usage habits, the user's usage habits are comprehensively evaluated through wearing time and friction times. The friction times can reflect the user's movement state when wearing headphones.

[0019] In this embodiment, it should be specifically explained that constructing an earmuff physical condition evaluation model and importing the thickness of the earmuff sealing ring, the elastic modulus of the sealing material, and the wear area ratio into the physical condition evaluation model to evaluate the current physical condition of the earmuff includes the following specific steps: S21. Obtain the factory thickness of the earmuff sealing ring, measure the current actual thickness of the earmuff sealing ring, obtain the elastic modulus of the earmuff sealing material, and obtain the proportion of cracks, peeling, and wear areas on the sealing ring surface through earmuff surface image detection; In this embodiment, it should be specifically explained that the elastic modulus of the earmuff sealing material is obtained by embedding an ultrathin deformation sensor or a flexible strain gauge inside the earmuff sealing ring. The sensor collects the compression and rebound process of the sealing ring in real time during the wearing and removing process. When the user wears the earmuff, the sealing ring is subjected to a certain degree of compression deformation by the soft tissue of the face. The sensor records the thickness difference or local strain value before and after compression. When the earmuff is removed or the external force is released, the sealing ring material begins to rebound. The sensor continuously monitors its recovery speed and final rebound amount. By comparing the proportional relationship between the compression deformation and the rebound amount and the time constant of the rebound process, the equivalent stiffness and viscoelastic properties of the material are obtained. The current elastic modulus is converted from the real-time data measured by the sensor through the calibration relationship between the rebound curve characteristics detected at the time of product delivery and the material elastic modulus. S22. Substitute the thickness of the earmuff seal ring, the elastic modulus of the earmuff seal material, and the proportion of cracks, peeling, and wear areas on the seal ring surface into the earmuff physical condition evaluation formula to evaluate the physical condition of the earmuff. The earmuff physical condition evaluation formula is: ,in, is the physical state weight of the earmuff, h is the current sealing ring thickness, The thickness of the sealing ring when leaving the factory. is the elastic modulus of the current earmuff, is the elastic modulus of the earmuffs when leaving the factory, It is the proportion of cracks, peeling and wear areas on the sealing ring surface.

[0020] In this embodiment, it should be specifically explained that constructing an ear seal evaluation model and introducing the ear seal geometric matching, pressure distribution uniformity, and ear seal wear rate into the seal evaluation model to evaluate the actual fit of the earmuff around the user's ear includes the following specific steps: S31. Obtain a point cloud dataset of the user's ear area through 3D scanning. Generate a 3D mesh model of the ear area through point cloud filtering and surface reconstruction. Simultaneously, obtain the earmuff seal ring shape model from the manufacturer and convert it into a point cloud format. Align the 3D mesh model of the ear area with the earmuff seal ring shape model using the ICP algorithm. Calculate the local gap between the ear area and the earmuff seal ring. The local gap is the closest distance from a point on the ear area surface to the seal ring surface (the minimum Euclidean distance between corresponding points on the auricle). Substitute the local gap and contact area between the ear area and the earmuff seal ring into the ear area geometric matching evaluation formula to evaluate the fit between the earmuff and the ear area. The ear area geometric matching evaluation formula is: , where A is the area of ​​the contact area between the earmuff and the ear, S is the actual surface of the contact area around the ear, and S is the integration domain. The integration is performed over the entire contact area around the ear. It is the local gap between the ear and the earmuff sealing ring. is the maximum acceptable tolerance gap, dA is the surface area element, when the gaps of all contact points are less than or equal to the maximum acceptable tolerance gap, the matching degree is close to 1, indicating excellent geometric fit. When there are obvious gaps or large area gaps, the integral value increases and the matching degree decreases; In this embodiment, it should be specifically explained that three-dimensional data of the user's peri-ear area is obtained, and the auricle and its surrounding areas are collected from multiple angles. The collected point cloud data is preprocessed to remove discrete noise points and downsampled to reduce the data volume to obtain a peri-ear point cloud. The point cloud is converted into a three-dimensional mesh model through a surface reconstruction method. The three-dimensional model of the earmuff seal can be directly provided by the manufacturer as a design file and converted into a three-dimensional mesh. Preliminary alignment is performed using obvious geometric feature points, such as the position of the earlobe, the edge of the auricle, and the center of the earmuff. An iterative registration algorithm is used to finely match the models, gradually reducing the average deviation between the two until convergence to a set accuracy range, and the actual gap distribution between the earmuff and the peri-ear area is calculated; S32. Using a distributed pressure sensor array, collect the pressure distribution of the earmuff in the contact area around the ear, calculate the average pressure and the pressure standard deviation, and substitute the pressure standard deviation into the pressure distribution uniformity formula to evaluate the pressure distribution. The pressure distribution uniformity formula is: ,in, is the pressure standard deviation, is the preset allowable pressure fluctuation tolerance; S33. Identify redness, indentations, and damage to the skin around the ears through image recognition and user self-reporting. Determine the degree of redness by comparing the red intensity difference between the target area and the user's normal skin. Substitute the redness and the wear area ratio into the periauricular wear rate calculation formula to assess the wear of the periauricular skin. The periauricular wear rate calculation formula is: , where a is the degree of wear redness and b is the proportion of wear area; S34. Substitute the ear geometry matching degree, pressure distribution uniformity, and ear wear rate into the ear sealing evaluation formula to evaluate the actual fit of the earmuff under the user's ear conditions. The ear sealing evaluation formula is: ,in, 、 and is the weight.

[0021] In this embodiment, it should be specifically explained that constructing an earmuff future wear assessment model and importing the user's usage habits, the physical condition of the earmuff, and the tightness of the ear area into the future wear assessment model to assess the future wear of the earmuff includes the following specific steps: S41. Substitute the user's usage habits, the physical condition of the earmuff, and the tightness of the ear area into a formula for evaluating future earmuff wear to evaluate future earmuff wear. The formula for evaluating future earmuff wear is: ,in, User usage habit index, is the earmuff physical condition index, is the ear sealing index, t is the earmuff usage time, T is the future usage time, 、 and is the weight; S42. Compare the future wear of the earmuffs with a preset wear threshold. When the predicted wear is lower than the threshold, the earmuffs can continue to be used safely. When the predicted wear exceeds the threshold, the earmuffs can no longer guarantee the expected noise reduction and comfort performance, and a replacement reminder is automatically issued.

[0022] It should be noted that the values ​​of the various setting parameters in this embodiment are determined as follows: representative earmuff wear data is obtained, and at the same time, user feedback on the effect of earmuff use is obtained, and experts are hired to manually judge whether the earmuff wear meets the requirements. At the same time, the obtained historical data is substituted into the calculation results and judgment results of each step in this embodiment into the fitting software, and the values ​​of the various setting parameters that meet the highest judgment accuracy are output.

[0023] The advantages of this embodiment over the prior art are: The present application constructs a user usage habit evaluation model, which imports the wearing time ratio and the number of times the earmuffs rub against the ear area into the usage habit evaluation model to evaluate the user's usage of the earmuffs; constructs an earmuff physical state evaluation model, which imports the thickness of the earmuff sealing ring, the elastic modulus of the sealing material, and the wear area ratio into the physical state evaluation model to evaluate the current physical state of the earmuffs; constructs an earmuff sealing evaluation model, which imports the earmuff geometric matching degree, pressure distribution uniformity, and earmuff wear rate into the sealing evaluation model to evaluate the actual fit of the earmuffs around the user's ears; constructs an earmuff future wear evaluation model, which imports the user's usage habits, the earmuff physical state, and the earmuff sealing into the future wear evaluation model to evaluate the future wear of the earmuffs, thereby achieving a quantitative evaluation of the future wear of the earmuffs.

[0024] Example 2

[0025] like Figure 3As shown, a noise reduction earmuff performance evaluation system based on data analysis is implemented based on the above-mentioned noise reduction earmuff performance evaluation method based on data analysis, which specifically includes a user usage habit evaluation module, an earmuff physical state evaluation module, an earmuff sealing evaluation module and an earmuff future wear evaluation module. The user usage habit evaluation module is used to evaluate the user's usage of the earmuffs through the wearing time ratio and the number of times the earmuffs rub against the earmuffs; the earmuff physical state evaluation module is used to evaluate the current physical state of the earmuffs through the thickness of the earmuff sealing ring, the elastic modulus of the sealing material and the proportion of the wear area; the earmuff sealing evaluation module is used to evaluate the actual fit of the earmuffs around the user's ears through the earmuff geometric matching degree, pressure distribution uniformity and earmuff wear rate; the earmuff future wear evaluation module is used to evaluate the future wear of the earmuffs through the user's usage habits, the earmuff physical state and the earmuff sealing.

[0026] Example 3

[0027] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned noise reduction earmuff performance evaluation method based on data analysis by calling the computer program stored in the memory.

[0028] This electronic device can vary significantly depending on its configuration or performance, and can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the noise-canceling earmuff performance evaluation method based on data analysis provided in the aforementioned embodiment of the noise-canceling earmuff performance evaluation method based on data analysis. The electronic device can also include other components for implementing device functions. For example, the electronic device can also include components such as a wired or wireless network interface and input / output interfaces for data input and output. This embodiment is not described in detail here.

[0029] Example 4

[0030] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; when the computer program is executed on a computer device, the computer device executes the above-mentioned noise reduction earmuff performance evaluation method based on data analysis.

[0031] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0032] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

Claims

1. A noise reduction earmuff performance evaluation method based on data analysis, characterized in that: It includes the following specific steps: Build a user usage habit evaluation model, and import the wearing time ratio and the number of times the earmuffs rub against the ear area into the usage habit evaluation model to evaluate the user's usage of earmuffs; Construct an earmuff physical condition assessment model, import the thickness of the earmuff seal, the elastic modulus of the sealing material, and the proportion of the worn area into the physical condition assessment model to evaluate the current physical condition of the earmuff; Construct an ear seal evaluation model, incorporating ear geometry matching, pressure distribution uniformity, and ear wear rate into the model to assess the actual fit of the earmuffs around the user's ears. A future wear assessment model for earmuffs is constructed, and the user's usage habits, physical condition of the earmuffs, and the sealing performance around the ear are introduced into the future wear assessment model to evaluate the future wear of the earmuffs.

2. The noise reduction earmuff performance evaluation method based on data analysis according to claim 1, characterized in that: The method of constructing a user usage habit evaluation model and importing the wearing time ratio and the number of times the earmuffs rub against the ear area into the usage habit evaluation model to evaluate the user's usage of the earmuffs includes the following specific steps: S11. Obtaining a user's earmuff usage record through a built-in sensor in the earmuff or interaction with a mobile terminal, obtaining the duration of the user wearing the earmuff per unit time, recording the duration of each time the user wears the earmuff, and obtaining the number of times the user wears the earmuff per unit time; S12. Obtain the number of frictions between the earmuff and the skin around the ear per unit time based on the contact acceleration peak and friction acoustic characteristics; S13. Substitute the proportion of the time the user wears the earmuffs per unit time and the number of times the earmuffs rub against the skin around the ears into a user usage habit evaluation formula to evaluate the user's usage of the earmuffs. The user usage habit evaluation formula is: , where c is the number of times the earmuff rubs against the skin around the ear per unit time, is the upper limit of friction times per unit time, is the proportion of time spent wearing earmuffs per unit time, Use habit weights.

3. The noise reduction earmuff performance evaluation method based on data analysis according to claim 2, characterized in that: The step of constructing an earmuff physical condition evaluation model and importing the thickness of the earmuff sealing ring, the elastic modulus of the sealing material, and the wear area ratio into the physical condition evaluation model to evaluate the current physical condition of the earmuff includes the following specific steps: S21. Obtain the factory thickness of the earmuff sealing ring, measure the current actual thickness of the earmuff sealing ring, obtain the elastic modulus of the earmuff sealing material, and obtain the proportion of cracks, peeling, and wear areas on the sealing ring surface through earmuff surface image detection; S22. Substitute the thickness of the earmuff seal ring, the elastic modulus of the earmuff seal material, and the proportion of cracks, peeling, and wear areas on the seal ring surface into the earmuff physical condition evaluation formula to evaluate the physical condition of the earmuff. The earmuff physical condition evaluation formula is: ,in, is the physical state weight of the earmuff, h is the current sealing ring thickness, The thickness of the sealing ring when leaving the factory. is the elastic modulus of the current earmuff, is the elastic modulus of the earmuffs when leaving the factory, It is the proportion of cracks, peeling and wear areas on the sealing ring surface.

4. The noise reduction earmuff performance evaluation method based on data analysis according to claim 3, characterized in that: The method of constructing an ear seal evaluation model and introducing the ear geometry matching degree, pressure distribution uniformity, and ear wear rate into the seal evaluation model to evaluate the actual fit of the earmuff around the user's ear includes the following specific steps: S31. Obtain a point cloud dataset of the user's ear area through 3D scanning. Generate a 3D mesh model of the ear area through point cloud filtering and surface reconstruction. Simultaneously, obtain an outer shape model of the earmuff seal ring from the manufacturer and convert it into a point cloud format. Align the 3D mesh model of the ear area with the outer shape model of the earmuff seal ring, calculate the local gap between the ear area and the earmuff seal ring, and substitute the local gap and contact area between the ear area and the earmuff seal ring into the ear area geometric matching evaluation formula to evaluate the fit between the earmuff and the ear area. The ear area geometric matching evaluation formula is: , where A is the area of ​​the contact area between the earmuff and the ear, S is the actual surface of the contact area around the ear, It is the local gap between the ear and the earmuff sealing ring. is the maximum acceptable tolerance gap, dA is the surface area element; S32. Using a distributed pressure sensor array, collect the pressure distribution of the earmuff in the contact area around the ear, calculate the average pressure and the pressure standard deviation, and substitute the pressure standard deviation into the pressure distribution uniformity formula to evaluate the pressure distribution. The pressure distribution uniformity formula is: ,in, is the pressure standard deviation, is the preset allowable pressure fluctuation tolerance; S33. Identify redness, indentations, and damage to the skin around the ears through image recognition and user self-reporting. Determine the degree of redness by comparing the red intensity difference between the target area and the user's normal skin. Substitute the redness and the wear area ratio into the periauricular wear rate calculation formula to assess the wear of the periauricular skin. The periauricular wear rate calculation formula is: , where a is the degree of wear redness and b is the proportion of wear area; S34. Substitute the ear geometry matching degree, pressure distribution uniformity, and ear wear rate into the ear sealing evaluation formula to evaluate the actual fit of the earmuff under the user's ear conditions. The ear sealing evaluation formula is: ,in, 、 and is the weight.

5. The noise reduction earmuff performance evaluation method based on data analysis according to claim 4, characterized in that: The construction of the future wear assessment model for earmuffs, in which the user's usage habits, the physical condition of the earmuffs, and the tightness of the ear area are introduced into the future wear assessment model to assess the future wear of the earmuffs, comprises the following specific steps: S41. Substitute the user's usage habits, the physical condition of the earmuff, and the tightness of the ear area into a formula for evaluating future earmuff wear to evaluate future earmuff wear. The formula for evaluating future earmuff wear is: ,in, User usage habit index, is the earmuff physical condition index, is the ear sealing index, t is the earmuff usage time, T is the future usage time, 、 and is the weight; S42. Compare the future wear of the earmuffs with a preset wear threshold. When the predicted wear is lower than the threshold, the earmuffs can continue to be used safely. When the predicted wear exceeds the threshold, the earmuffs can no longer guarantee the expected noise reduction and comfort performance, and a replacement reminder is automatically issued.

6. A noise reduction earmuff performance evaluation system based on data analysis, which is implemented based on the noise reduction earmuff performance evaluation method based on data analysis according to any one of claims 1 to 5, characterized in that: Specifically include: The user usage habit evaluation module is used to evaluate the user's usage of earmuffs based on the proportion of wearing time and the number of times the earmuffs rub against the ear area; An earmuff physical condition assessment module is used to assess the current physical condition of the earmuff based on the thickness of the earmuff seal, the elastic modulus of the seal material, and the proportion of the worn area; The ear seal evaluation module is used to evaluate the actual fit of the earmuffs around the user's ears through the ear geometry matching, pressure distribution uniformity and ear wear rate; The earmuff future wear assessment module is used to assess the future wear of earmuffs based on user usage habits, the physical condition of the earmuffs, and the sealing around the ear.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the noise reduction earmuff performance evaluation method based on data analysis according to any one of claims 1 to 5 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the noise reduction earmuff performance evaluation method based on data analysis according to any one of claims 1 to 5.

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