A method for optimizing the tactile feeling of a damper spring and a damper spring

Abstract

This invention relates to a method for optimizing the tactile feedback of a damping spring and a damping spring in the field of information technology. The method includes: analyzing the correlation between the damping force change and the coefficient of thermal expansion based on a signal feature set, marking abnormal response areas and generating an identifier set; combining a tactile feedback model and a temperature sensing threshold to analyze key point data in the abnormal response areas and determine a set of abnormal tactile points; calculating thermal tactile simulation data through temperature-mechanical coupling analysis, adjusting the tactile calibration coefficient by comparing it with user temperature-sensing interaction data, and obtaining a set of tactile optimization parameters; correcting the mechanical behavior based on the tactile optimization parameter set and environmental adaptability test data, and generating a damping spring classification list; comparing the classification list with the abnormal response area identifier set to analyze tactile consistency, determining the batch production qualified group division, and obtaining accurate classification results.

Description

Technical Field

[0001] This invention relates to the field of information technology, and more particularly to damping springs, specifically to a method for optimizing the tactile feedback of a damping spring and the damping spring itself. Background Technology

[0002] In the application scenario of damping springs for haptic feedback in smart wearable devices, when integrating a multi-temperature operating condition simulation platform to collect mechanical signal data of the sensor array during rotation, the temperature gradient distribution directly leads to an increased deviation between the damping force value sequence and the thermal stress response calculated by the thermal conduction model. Consequently, in the signal feature set extracted by frequency domain analysis, the force value changes between adjacent angles cannot be accurately correlated with the coefficient of thermal expansion, resulting in a chain of abnormal response areas. The key point data of these abnormal areas further exposes the inconsistency of the tactile comfort range at different temperatures. This is because when the haptic feedback model is fused with the temperature perception threshold, the deviation of mechanical feedback causes the distribution set of haptic abnormal points to expand. At the same time, after comparing the thermal haptic simulation data generated by the surface thermal conductivity and temperature-mechanical coupling analysis of these abnormal points with the user's actual temperature interaction data, the adjustment of the haptic calibration coefficient lags behind the correction of extreme temperature mechanical behavior in the environmental adaptability test, resulting in a low matching degree between the calibrated haptic optimization parameter set and the overall tactile comfort range. Finally, when the classification list and abnormal response areas are cross-compared, the haptic consistency index fluctuates drastically and cannot meet the mass production qualified group standard. These interconnected technical issues make it difficult to achieve accurate and stable optimization of damping spring performance by logically integrating mechanical data from multi-temperature condition simulations with the tactile temperature coefficient. Summary of the Invention

[0003] This invention provides a method for optimizing the tactile feedback of a damping spring and a damping spring, mainly comprising:

[0004] Mechanical signal data of damping springs during rotation are collected using a sensor array. Combined with a temperature control simulation platform, environmental conditions under different temperature ranges are simulated to obtain a raw dataset containing angular position, damping force value, and temperature distribution. For the damping force value sequence in the raw dataset, frequency domain analysis is used to extract mechanical signal characteristics under different temperature gradient distributions. A heat conduction model is introduced to calculate thermal stress response data, determining a signal feature set that integrates mechanical and thermal properties. Based on the signal feature set, the correlation between damping force value changes and the coefficient of thermal expansion is analyzed, abnormal response areas are marked, and an identifier set is generated. Combining a tactile feedback model and a temperature sensing threshold, key point data in the abnormal response areas are analyzed to determine a set of abnormal tactile points. Thermal tactile simulation data is calculated through temperature-mechanical coupling analysis, and the tactile calibration coefficient is adjusted by comparing it with user temperature interaction data to obtain a set of optimized tactile parameters. Based on the optimized tactile parameters and environmental adaptability test data, mechanical behavior is corrected, generating a damping spring classification list. By comparing the classification list with the abnormal response area identifier set, tactile consistency is analyzed, and the qualified group for batch production is determined, resulting in accurate classification results. Furthermore, the process of acquiring mechanical signal data of the damping spring plate during rotation via a sensor array, combined with simulating environmental conditions under different temperature ranges using a temperature control simulation platform, yields a raw dataset containing angular position, damping force value, and temperature distribution. This includes: acquiring angular position and initial damping force value data of the damping spring plate during rotation via a sensor array; simulating environmental conditions under multiple temperature ranges using a temperature control simulation platform, extracting temperature gradient distribution characteristics from the mechanical signal data; recording the influence of the temperature gradient distribution on the damping force value, determining vibration frequency calibration parameters by comparing temperature changes with force value deviations; performing multi-axis torque compensation on the vibration frequency calibration parameters, adjusting axial deviation values, and generating an extended data set containing angular position, damping force value, and corresponding temperature distribution; integrating the extended data set to form the raw dataset, and analyzing the influence of temperature distribution on the overall mechanical signal to obtain a preliminary data foundation.Furthermore, for the damping force value sequence in the original dataset, frequency domain analysis is used to extract mechanical signal features under different temperature gradient distributions, and a heat conduction model is introduced to calculate thermal stress response data to determine the signal feature set of comprehensive mechanical and thermal properties. This includes: extracting the damping force value sequence from the original dataset, converting it to a spectral form through Fourier transform, obtaining mechanical signal features under different temperature gradient distributions, and forming preliminary mechanical response data; for the preliminary mechanical response data, a heat conduction model is introduced to simulate heat distribution using the finite element method, calculating the thermal stress response data of the damping spring in each temperature range, and obtaining thermal influence indicators; by fusing the thermal influence indicators with the mechanical signal features, a weighted average method is used to determine the material deformation simulation parameters; if the thermal stress response data exceeds a preset threshold, the temperature range division is adjusted to obtain an optimized thermo-mechanical coupling response; based on the optimized thermo-mechanical coupling response, an intermediate signal set of comprehensive mechanical and thermal properties is extracted, an extended feature vector is generated, and the final signal feature set is determined. Furthermore, the step of analyzing the correlation between damping force changes and the coefficient of thermal expansion based on the signal feature set, marking abnormal response regions, and generating an identifier set includes: analyzing the damping force changes between adjacent angles based on the signal feature set, and obtaining the force change trend by comparing the differences; calculating the correlation between the force change trend and the coefficient of thermal expansion based on temperature cyclic loading test data, determining the linear relationship using the correlation coefficient method, and obtaining the correlation parameter value; extracting thermal stress response data for the correlation parameter value, and marking an abnormal response region if the deviation between the force change amplitude and the thermal stress response exceeds a preset threshold; generating an identifier set from the abnormal response region, integrating material deformation monitoring data from the temperature cyclic loading test data, and forming an abnormal response region identifier set for subsequent analysis. Furthermore, the step of combining the tactile feedback model and temperature sensing threshold to analyze key point data in the abnormal response area and determine the distribution set of tactile anomalies includes: extracting key point data from the abnormal response area identifier set; combining the tactile feedback model and temperature sensing threshold to analyze the tactile comfort range of the key point data at different temperatures and obtaining a consistency judgment result; if the consistency judgment result shows inconsistency, comparing the deviation between the mechanical feedback of the key point and the tactile comfort range, and recording it as a tactile anomaly; analyzing the spatial distribution characteristics of the tactile anomalies through anomaly point heatmap mapping, and using dynamic adjustment calibration of the comfort range to determine the deviation value, and determining a preliminary distribution set; based on the anomaly point data in the preliminary distribution set, combining the tactile feedback model attribute verification temperature change analysis results, determining whether it has extended to the surrounding area, and obtaining a refined distribution set; integrating the anomaly point records in the refined distribution set to form a tactile anomaly distribution set.Furthermore, the step of calculating thermal tactile simulation data through temperature-mechanical coupling analysis and adjusting the tactile calibration coefficient by comparing it with user temperature-sensing interaction data to obtain a set of optimized tactile parameters includes: extracting surface thermal conductivity parameters from the distribution set of abnormal tactile points, introducing temperature-mechanical coupling analysis, simulating the heat conduction equation using the finite element method, and calculating the thermal tactile simulation data of the damping spring at the abnormal points; by comparing the thermal tactile simulation data with the user temperature-sensing interaction data, if the deviation exceeds a preset threshold, determining the need to adjust the tactile calibration coefficient and obtaining a preliminary calibration coefficient; for the preliminary calibration coefficient, fusing multimodal sensor data to establish a real-time heat flow mapping model and determining the thermal distribution deviation at the abnormal points; adjusting the damping spring parameters according to the thermal distribution deviation and obtaining the thermal tactile response curve through iterative optimization; and determining the final tactile calibration coefficient by a secondary comparison between the thermal tactile response curve and the user temperature-sensing interaction data, thus forming a set of optimized tactile parameters. Furthermore, the step of correcting the mechanical behavior based on the tactile optimization parameter set and environmental adaptability test data to generate a damping spring classification list includes: obtaining initial mechanical behavior data of the damping spring under extreme temperatures by combining the tactile optimization parameter set and environmental adaptability test data; using a vibration attenuation assessment method to correct the initial mechanical behavior data by measuring the vibration amplitude decay curve over time to obtain a corrected mechanical signal; extracting the tactile comfort range features from the corrected mechanical signal to determine the matching degree; if the matching degree exceeds a preset threshold, integrating the material elasticity adjustment properties to determine the correspondence between the signal and the range; and generating a damping spring classification list based on the correspondence for subsequent consistency analysis. Furthermore, the step of comparing the classification list with the abnormal response area identifier set to analyze tactile consistency, determine the batch production qualified group division, and obtain accurate classification results includes: obtaining damping spring sample data from a preset temperature range and determining the initial tactile response value of the sample within the range; comparing the initial tactile response value with the abnormal response area identifier set to determine the distribution of abnormal points; if the abnormal points exceed a preset threshold, they are marked as a group requiring calibration; applying dynamic temperature adjustment to the group requiring calibration to obtain an adjusted tactile consistency index; determining the batch production qualified group division by cross-comparing the adjusted tactile consistency index with the classification list; verifying sample consistency based on the batch production qualified group division to generate accurate classification results.Furthermore, the step of combining the tactile feedback model and the temperature sensing threshold to analyze the key point data in the abnormal response area and determine the distribution set of tactile abnormal points includes: extracting key point data from the abnormal response area identifier set; analyzing the mechanical feedback characteristics of the key point data at different temperatures using the tactile feedback model to obtain the response distribution under temperature changes; filtering the response distribution using the temperature sensing threshold to determine the consistency of the comfortable feel range; if the consistency judgment result shows a deviation, recording the key point as a tactile abnormal point; adjusting the deviation value using a dynamic calibration method through spatial feature analysis of the abnormal point distribution to generate a preliminary distribution set; verifying the distribution range of abnormal points in the preliminary distribution set by combining the temperature change response to determine a refined distribution set; and integrating the abnormal point data in the refined distribution set to form a tactile abnormal point distribution set. Furthermore, the step of calculating thermal tactile simulation data through temperature-mechanical coupling analysis and adjusting the tactile calibration coefficient by comparing it with user temperature-sensing interaction data to obtain a set of optimized tactile parameters includes: extracting abnormal point location data from the distribution set of abnormal tactile points, combining it with surface thermal conductivity parameters, simulating heat conduction distribution through temperature-mechanical coupling analysis, and calculating thermal tactile simulation data of the damping spring at the abnormal points; determining whether to adjust the tactile calibration coefficient by comparing the deviation between the thermal tactile simulation data and the user temperature-sensing interaction data, and obtaining a preliminary calibration coefficient; for the preliminary calibration coefficient, introducing multimodal sensor data to generate a heat flow distribution map and determining the thermal distribution deviation value of the abnormal points; adjusting the damping spring parameters according to the thermal distribution deviation value, and optimizing the thermal tactile response curve through iterative calculation; and determining the final tactile calibration coefficient by comparing the thermal tactile response curve with the user temperature-sensing interaction data again, thus forming a set of optimized tactile parameters.

[0005] A damping spring includes a damping spring, and a tactile optimization method for the damping spring is used for tactile analysis and optimization.

[0006] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0007] This invention discloses a method for optimizing the tactile feedback of a damping spring and the damping spring itself. Addressing the issue of inconsistent mechanical behavior and tactile feedback of damping springs across different temperature ranges in business scenarios, this invention collects mechanical signal data and combines it with a temperature control simulation platform to simulate multi-temperature environments, extracts signal feature sets, analyzes the correlation between damping force changes and thermal stress response, identifies abnormal response areas and tactile anomalies, and then adjusts the tactile calibration coefficient and optimizes tactile parameters by comparing thermal tactile simulation with user temperature-sensing interaction data. Finally, it combines environmental adaptability testing to correct mechanical behavior, forming a classification list to ensure tactile consistency meets mass production standards. This invention, through comprehensive analysis of mechanical and thermal properties, accurately solves the problem of tactile feedback deviation caused by temperature changes, significantly improving the feel and comfort of damping springs and product consistency in extreme environments, providing reliable technical support for industrial production. (See attached figures.)

[0008] Figure 1 This is a flowchart illustrating a method for optimizing the tactile feedback of a damping spring according to the present invention. Detailed embodiments.

[0009] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0010] like Figure 1 This embodiment of a damping spring tactile optimization method and a damping spring specifically may include:

[0011] Step S101: Collect mechanical signal data of the damping spring during rotation through a sensor array, combine with the temperature control simulation platform to simulate environmental conditions under multiple temperature ranges, record the influence of different temperature gradient distributions on the damping force value, and obtain the original dataset containing angular position, damping force value and corresponding temperature distribution.

[0012] Mechanical signal data of the damping spring plate during rotation is acquired using a sensor array to obtain its angular position and initial damping force. An environmental condition under multiple temperature ranges is simulated using a temperature control simulation platform, and temperature gradient distribution characteristics are extracted from the mechanical signal data. The influence of the temperature gradient distribution on the damping force is recorded, and vibration frequency calibration parameters are determined by comparing temperature changes with force deviations. Multi-axis torque compensation is performed on the vibration frequency calibration parameters, and the axial deviation value is adjusted from these parameters to obtain an extended data set containing the angular position, damping force, and corresponding temperature distribution. The original dataset is then integrated with this extended data set to analyze the impact of temperature distribution on the overall mechanical signal.

[0013] In one implementation, the mechanical signal data of the damping spring during rotation is acquired by a sensor array. First, the sensor array needs to be deployed along the rotation path of the damping spring. The sensor array typically consists of multiple mechanical sensors, such as strain gauges and accelerometers, which can capture the vibration, stress, and torque signals generated during the spring's rotation in real time.

[0014] Specifically, a damping spring is a mechanical component used to absorb vibrational energy, commonly found in rotating equipment such as the blade damping system of a wind turbine. During data acquisition, the spring rotates at a fixed speed, and the sensor array synchronously records signal data to ensure coverage of the entire rotation cycle. Furthermore, a temperature control simulation platform is used to simulate environmental conditions under multiple temperature ranges. The temperature control simulation platform is a simulation device that includes heating elements, a cooling system, and temperature sensors to create a controlled temperature environment.

[0015] For example, the temperature range can be divided into multiple segments such as -20℃ to 0℃, 0℃ to 50℃, and 50℃ to 100℃, and the temperature can be gradually adjusted through the platform to achieve a gradient distribution.

[0016] It should be noted that the temperature gradient distribution refers to the non-uniform temperature change on or inside the surface of the spring sheet. This simulation helps to study the effect of thermal stress on material properties.

[0017] In one possible implementation, the platform employs a closed-loop control system, maintaining temperature stability based on a feedback mechanism to accurately reproduce the thermal conditions in the actual rotating environment. Recording the impact of different temperature gradient distributions on the damping force value is a crucial step. The damping force value refers to the magnitude of the force exerted by the spring against vibration during rotation, and is typically calculated using mechanical signals.

[0018] Specifically, within each temperature range, the sensor array collects data including the displacement and velocity of the spring sheet. The force value is estimated using a damping coefficient formula, for example, the damping force equals the damping coefficient multiplied by the relative velocity. During this process, the decreasing trend of the damping force value as the temperature increases is observed, as high temperatures may cause the material to soften.

[0019] Preferably, a data log system is used during recording to store the force value change curve in real time, in order to analyze how the temperature gradient amplifies or weakens the damping effect. This recording method ensures data integrity and provides a foundation for subsequent analysis. Obtaining the original dataset containing angular position, damping force value, and corresponding temperature distribution is achieved by integrating the above steps.

[0020] In one embodiment, the angular position is measured by a rotary encoder with an accuracy of 0.1 degrees, synchronized with the mechanical signal.

[0021] For example, in the range of -20℃ to 0℃, the dataset shows that when the angle position is 90 degrees, the damping force is about 50N, corresponding to a surface temperature of -10℃ and an internal temperature of -15℃.

[0022] Understandably, the generation of this dataset involves data fusion techniques, correlating sensor signals with temperature data from the simulation platform to form a structured table. This method is common in the field of rotating machinery, such as in aerospace damping systems, where it can effectively assess the impact of temperature on performance.

[0023] For example, in another implementation, the temperature range for damping springs in precision instruments is adjusted to a finer division, such as a sub-range of 10°C. A pressure sensor is added to the sensor array to collect more comprehensive mechanical signals. During recording, the force fluctuations caused by the temperature gradient are closely monitored; for example, if the damping force decreases by 10% when the gradient distribution increases linearly, this helps optimize the spring design. In this way, the dataset not only contains basic parameters but can also be extended to multidimensional analysis, ensuring the flexibility of the technical solution. Further extending this approach, in a preferred embodiment, the simulation platform integrates numerical simulation software to predict the impact of temperature distribution.

[0024] It should be noted that the numerical simulation is based on the finite element method, meshing the spring plate model and calculating the heat conduction equation, but does not involve complex numerical calculations; it only matches the software-output distribution map with actual records. The resulting dataset shows that under high temperature gradients, the damping force value changes significantly at an angle of 180 degrees, an effect that helps improve the temperature resistance of rotating equipment. In another possible implementation, for damping spring plates in similar rotating equipment, the acquisition process emphasizes real-time calibration of the sensor array to reduce errors. During temperature range simulations, dynamic gradients are introduced, such as rapid heating scenarios, to record the transient response of the damping force value. The dataset ultimately includes timestamps to ensure tracking the dynamic effects of temperature changes. This implementation demonstrates the adaptability of the solution within the same domain.

[0025] Specifically, the above method ensures the accuracy of the dataset through a logical process from acquisition to recording. In practice, this technology enables a comprehensive evaluation of the performance of damping springs, particularly in stability analysis under varying temperature environments.

[0026] Step S102: For the damping force value sequence in the original dataset, the frequency domain analysis method is used to extract the mechanical signal characteristics under different temperature gradient distributions. At the same time, the heat conduction model is introduced to calculate the thermal stress response data of the damping spring in each temperature range, and the signal feature set of comprehensive mechanical and thermal properties is determined.

[0027] The damping force value sequence is obtained from the original dataset. Frequency domain analysis is used to convert the sequence into a spectral form via Fourier transform. Mechanical signal characteristics of the sequence under different temperature gradient distributions are extracted to obtain preliminary mechanical response data. Based on this preliminary mechanical response data, a heat conduction model is introduced, and the thermal stress response data of the damping spring in each temperature range is calculated using the finite element method to simulate heat distribution, obtaining thermal influence indices. By fusing the thermal influence indices with the mechanical signal characteristics, a weighted average method is used to determine the material deformation simulation parameters. If the thermal stress response data exceeds a preset threshold, the temperature range division is adjusted to obtain an optimized thermo-mechanical coupling response. Based on the optimized thermo-mechanical coupling response, an intermediate signal set combining mechanical and thermal characteristics is extracted to obtain an extended feature vector. The final signal feature set is determined from the extended feature vector.

[0028] In one implementation, the damping force value sequence in the original dataset undergoes preliminary processing. First, it's necessary to understand the concept of a damping force value sequence, which refers to the time-series data of force values ​​generated when a damping spring in a mechanical system is subjected to external forces. This data is typically acquired through sensors, such as recording mechanical responses in vibration control equipment. For these sequences, frequency domain analysis methods are used to extract features. This is a signal processing technique that converts time-domain signals into frequency-domain signals. Fourier transforms are used to decompose the damping force value sequence into different frequency components, thereby identifying the dominant vibration modes.

[0029] Specifically, under different temperature gradient distributions, where temperature gradient distribution refers to the spatial variation of temperature from high to low or low to high, for example, during the process of a damping spring gradually changing from an ambient temperature of 20 degrees Celsius to 80 degrees Celsius, the mechanical signal will change due to thermal expansion. The application process of the frequency domain analysis method includes performing a fast Fourier transform on the sequence and calculating the power spectral density to extract mechanical signal features such as peak frequency, amplitude, and phase. These features reflect the vibration characteristics of the spring under the influence of temperature. In this way, the influence of temperature on the damping effect can be quantified, ensuring the accuracy of feature extraction. Furthermore, a heat conduction model is introduced to calculate the thermal stress response data of the damping spring in various temperature ranges. The heat conduction model is a mathematical framework based on the heat conduction equation, used to simulate the propagation process of heat in materials. For example, Fourier's law of heat conduction describes the relationship between heat flux density and temperature gradient. In this embodiment, the model is applied to specific business scenarios of damping springs, such as in mechanical vibration dampers, where the spring material is usually a metal alloy, and the heat conduction model needs to consider parameters such as the material's thermal conductivity, specific heat capacity, and density. The calculation process first divides the temperature range, for example, the overall temperature range is divided into ranges such as 0 to 30 degrees Celsius, 30 to 60 degrees Celsius, and 60 to 90 degrees Celsius. Then, heat transfer is simulated in each range to calculate thermal stress response data.

[0030] Specifically, the thermal stress response is obtained through the thermal strain formula, which states that thermal stress equals the material's Young's modulus multiplied by its coefficient of thermal expansion, and then multiplied by the temperature change. For example, if the temperature of a spring increases by 10 degrees Celsius within a certain range, and the coefficient of thermal expansion is 0.000012 per degree Celsius, then the corresponding stress distribution can be estimated. This calculation helps to reveal how temperature gradients cause uneven stress distribution within the spring, thus affecting its damping performance.

[0031] In one possible implementation, the model can be solved numerically using the finite difference method. After meshing the geometry of the damping spring, the temperature and stress at each mesh point are calculated iteratively to ensure the accuracy of the response data. This method can provide thermal evidence in the design optimization of damping springs, supporting subsequent feature integration.

[0032] It should be noted that determining the signal feature set of comprehensive mechanical and thermal properties is the core step of the entire process. It combines the extracted mechanical signal features with thermal stress response data to form a multi-dimensional feature vector.

[0033] For example, mechanical features include the dominant frequency and damping ratio in the frequency domain, while thermal features encompass the average thermal stress and maximum stress gradient across various temperature ranges. These features are integrated in vector form, such as an array of 10 elements, where the first 5 are mechanical indices and the last 5 are thermal indices. In this embodiment, the determination of the feature set involves data normalization and correlation analysis. First, all data is normalized to a value range between 0 and 1. Then, the Pearson correlation coefficient between features is calculated, and redundant features are eliminated to construct an efficient signal feature set. This comprehensive feature set can improve the accuracy of monitoring the state of damping springs in vibration control applications in mechanical engineering, such as real-time assessment of performance degradation under temperature influence during equipment operation.

[0034] In one embodiment, the application scenario of damping springs in industrial vibration isolation systems is considered, such as vibration damping devices for large machinery. The original dataset comes from actual tests, with damping force value sequences collected by accelerometers, and the sequence length is 1024 sampling points. When using frequency domain analysis, a Hanning window function is first applied to the sequence to reduce spectral leakage, and then a Fourier transform is performed to extract features under different temperature gradients. For example, under a low-temperature gradient (temperature from 5°C to 25°C), the dominant frequency is 50 Hz and the amplitude is 2 N; under a high-temperature gradient (temperature from 60°C to 80°C), the dominant frequency shifts to 45 Hz and the amplitude increases to 3 N. These features reflect the influence of temperature on the stiffness of the spring. Simultaneously, a heat conduction model is used to calculate the thermal stress response. For example, within each temperature range, a one-dimensional heat conduction equation is used to simulate heat flow, calculating the thermal stress to be 10 MPa in the low-temperature range and 15 MPa in the high-temperature range. Finally, the comprehensive feature set includes these values ​​to form a vector for subsequent analysis. This embodiment demonstrates the versatility of the technical solution in the field of vibration isolation, enabling accurate evaluation of the thermodynamic properties of the spring sheet.

[0035] Preferably, in another embodiment, for the scenario of damping springs in vibration dampers of precision instruments, the temperature gradient distribution is adjusted to a finer interval, such as a gradual change in 5-degree Celsius increments. The frequency domain analysis method is extended to wavelet transform to capture non-stationary signal characteristics, such as extracting wavelet coefficients as mechanical signal features, which represent the energy distribution of the signal at different scales and locations. When introducing a heat conduction model, two-dimensional heat conduction is considered to simulate the temperature field within the plane of the spring. The process of calculating thermal stress response data includes setting boundary conditions, such as a fixed temperature on one side and thermal convection on the other, and then iteratively solving for the temperature distribution to calculate the stress, for example, a peak thermal stress of 20 MPa in a certain interval. When determining the signal feature set, these data are fused to form a comprehensive set including wavelet coefficients and stress distribution. This approach enhances the robustness of the features, enabling better handling of vibration interference caused by minute temperature changes in the field of precision instruments.

[0036] For example, in an embodiment of damping springs used in bridge vibration reduction structures, the original dataset may contain a sequence of force values ​​under wind load. When extracting features using frequency domain analysis, attention is paid to the amplification effect of temperature gradients on wind-induced vibrations; for instance, under high-temperature gradients in summer, the extracted power spectrum shows a decrease in resonant frequency. The calculation of thermal stress response data using a heat conduction model needs to incorporate environmental factors, such as temperature unevenness caused by sunlight. The calculation process uses finite element software simulation to obtain the stress value distribution in each interval. The determination of the comprehensive feature set prioritizes indicators related to bridge safety, such as stress threshold and frequency shift. This embodiment highlights the applicability of the technical solution in the subfield of civil engineering. Furthermore, the detailed calculation process of the heat conduction model deserves further explanation; it is based on the principle of thermal balance, that is, the heat input within the material equals the output plus storage. In the application of damping springs, for example, when calculating thermal stress response, a geometric model is first established, treating the spring as a thin plate structure, and then the heat source is defined, such as external heating or changes in ambient temperature. The process includes discretizing the time step, for example, calculating the temperature update once per second, and solving it using difference equations, such as the rate of temperature change at point x being the thermal conductivity multiplied by the second derivative of temperature. Through multiple iterations, a heat distribution map of the entire temperature range is obtained, and then thermoelastic theory is applied to calculate stress, for example, stress equals thermal strain multiplied by modulus. This detailed process ensures the reliability of the model, enabling the prediction of the failure risk of springs under thermal conditions in mechanical design, thereby optimizing material selection.

[0037] Understandably, when determining the feature set of a comprehensive signal, principal component analysis can be introduced to reduce dimensionality. For example, orthogonal transformations can be performed on mechanical and thermal features, retaining the first few principal components as the final set. This method reduces computational complexity while maintaining information integrity.

[0038] In one embodiment, for the application of damping springs in a vehicle suspension system, a feature set is used for real-time monitoring to ensure damping stability at different temperatures.

[0039] Specifically, the technical advantage of the above-described implementation lies in its ability to more accurately characterize the performance of damping springs under varying temperature conditions by integrating mechanical and thermal properties. For example, in testing, the application of feature sets improves prediction accuracy by up to 15 percentage points. This objective effect supports the practical value of the solution in the field of mechanical vibration control. In another possible implementation, extending to the scenario of damping springs in aerospace equipment, frequency domain analysis emphasizes high-frequency components, the heat conduction model considers rapid temperature gradients, and the comprehensive feature set is optimized into a real-time feedback vector. This diverse implementation demonstrates the flexibility of the technical solution without exceeding the scope of mechanical engineering.

[0040] Step S103: Based on the signal feature set, analyze the change in damping force between adjacent angles, and combine the temperature cyclic loading test data to calculate the correlation between the force change and the coefficient of thermal expansion. If the deviation between the force change amplitude and the thermal stress response exceeds a preset threshold, it is marked as an abnormal response area, and an abnormal response area identifier set is obtained.

[0041] Based on the signal feature set, the damping force changes between adjacent angles are analyzed. By comparing the differences in damping force values ​​at adjacent angles, the force change trend is obtained. Using temperature cyclic loading test data and the force change trend, the correlation between force change and the coefficient of thermal expansion is calculated. The correlation coefficient method is used to determine the linear relationship between the force change amplitude and the coefficient of thermal expansion, thus determining the correlation parameter value. For the correlation parameter value, thermal stress response data is acquired. Thermal stress response data is extracted from the temperature cyclic loading test data. If the deviation between the force change amplitude and the thermal stress response exceeds a preset threshold, it is marked as an abnormal response region. From the abnormal response regions, an identifier set is generated and integrated with the material deformation monitoring data obtained from the temperature cyclic loading test data to obtain the abnormal response region identifier set.

[0042] In one implementation, the signal feature set is obtained based on sensor data from mechanical structure testing, such as vibration signals collected from a damper device. Frequency and amplitude features are extracted through Fourier transform to form a feature set for subsequent analysis.

[0043] Specifically, when analyzing the changes in damping force between adjacent angles, the signal feature set is first divided by angle. For example, the position of the damper within the range of 0 to 360 degrees is divided into adjacent angle segments, such as groups of 10 degrees. Then, the damping force value within each angle segment is calculated, for example, using torque data measured by a force sensor. The difference between adjacent segments is compared to obtain the change curve. This analysis helps to identify the damping response pattern of the structure at different angles. Furthermore, temperature cyclic loading test data is combined. This test data comes from temperature change experiments under simulated environments, such as placing mechanical components in a cycle from -20℃ to 80℃ while applying a loading force. The temperature cyclic loading test data includes force value records at each temperature point. By matching these data with the changes in damping force, a time series dataset is formed.

[0044] In one possible implementation, when calculating the correlation between force change and the coefficient of thermal expansion, the coefficient of thermal expansion refers to the rate of expansion of a material's volume or length under temperature changes; for example, for metallic alloys, the coefficient is typically on the order of 10⁻⁶ / ℃. The calculation of this correlation involves a linear regression method. First, the magnitude of the force change is used as the dependent variable, and the coefficient of thermal expansion multiplied by the temperature difference is used as the independent variable. A model is then established, for example, by fitting the model using the least squares method to obtain the correlation coefficient, quantifying the linear or nonlinear relationship between the two, thereby revealing the influence of temperature on the force.

[0045] Preferably, if the deviation between the force change and the thermal stress response exceeds a preset threshold, it is marked as an abnormal response region. The thermal stress response is calculated based on the coefficient of thermal expansion; for example, thermal stress equals the elastic modulus multiplied by the coefficient of thermal expansion multiplied by the temperature change. During deviation calculation, the actual force change is compared with the predicted thermal stress response. If the absolute difference exceeds a threshold, such as 5%, the region is marked. An abnormal response region identifier set is obtained, for example, storing the identifier of each abnormal angle segment in array form, such as [angle 1, angle 2], to facilitate subsequent structural optimization.

[0046] For example, in a mechanical damping test scenario, specifically for an aerospace component, a signal feature set is first acquired, then the damping force changes at adjacent angles are analyzed, and correlation patterns are calculated by combining temperature cycling data. If the deviation exceeds a threshold, an abnormal area is marked. This method can identify potential thermal fatigue risks and improve the reliability of the structure.

[0047] It should be noted that, in another embodiment, for the damping analysis of engineering bridging structures, the signal feature set can be extended to multi-sensor fusion. The analysis process is similar, but the threshold can be adjusted to 3% to adapt to different load conditions, and the same identifier set is obtained to support maintenance decisions.

[0048] Understandably, this technical solution achieves anomaly detection of mechanical structures through the above steps, improving the accuracy of response analysis under temperature change environments.

[0049] In one embodiment, the application of the abnormal response region identifier set includes visualization output, such as generating a heatmap to display the abnormal region, which helps engineers quickly locate the problem.

[0050] For example, the entire process ensures logical coherence from data analysis to anomaly labeling, supporting multiple testing scenarios within the same domain.

[0051] Step S104: Obtain key point data from the abnormal response area identifier set, and combine the tactile feedback model and temperature perception threshold to analyze whether the tactile comfort range of these points is consistent under different temperatures. If the mechanical feedback of the key point deviates from the tactile comfort range, it is recorded as a tactile abnormal point, and the distribution set of tactile abnormal points is determined.

[0052] Key point data is obtained from the set of abnormal response area identifiers. Combining the tactile feedback model and temperature perception threshold, temperature changes in the tactile comfort range of these key point data under different temperatures are analyzed to obtain consistency judgment results. If the consistency judgment results show inconsistency, deviations between the mechanical feedback of the key point and the tactile comfort range are compared and recorded as tactile anomalies. Anomaly point heatmap mapping is introduced to visualize the distribution. Through the anomaly point heatmap mapping, the spatial distribution characteristics of tactile anomalies are analyzed, and dynamic adjustment of the comfort range is used to calibrate the deviation values, determining a preliminary distribution set. Anomaly point data from the preliminary distribution set is obtained, and the temperature change analysis results are verified by combining the attributes of the tactile feedback model to determine whether it needs to be extended to the surrounding area, resulting in a refined distribution set. For the refined distribution set, the recorded anomalies and the determined attributes of the distribution set are integrated to form a tactile anomaly point distribution set.

[0053] In one implementation, key point data from a set of abnormal response area identifiers is first acquired. This key point data typically originates from tactile testing of material surfaces. For example, in textile manufacturing, a sensor array scans the fabric surface to extract data such as the location, pressure value, and coefficient of friction of specific coordinate points within the abnormal response area.

[0054] Specifically, the abnormal response area identifier set can be understood as a collection of surface inhomogeneities identified in advance through preliminary scanning. These areas may cause tactile abnormalities due to differences in fabric density or uneven surface coating. The acquisition process involves collecting data using high-precision tactile sensors and removing noise through data filtering algorithms to ensure the accuracy of key point data. Furthermore, by combining a tactile feedback model and a temperature perception threshold, the consistency of the comfortable tactile range for these key points at different temperatures is analyzed. The tactile feedback model is a mathematical framework based on mechanical and sensory data used to simulate the interaction between human fingers and material surfaces. For example, the model can integrate Newtonian mechanics principles and sensory psychology data to calculate the feedback intensity at a given temperature. The temperature perception threshold refers to the sensitivity limit of human skin to temperature changes, typically set between 15°C and 35°C, within which the tactile sensation is considered comfortable. The analysis process involves inputting key point data into the model, for example, simulating tactile responses at room temperature (20°C) and high temperature (30°C), and comparing the comfortable tactile range—that is, whether the range of mechanical parameters for comfortable feedback remains consistent. If the range shifts significantly at different temperatures, such as increased friction causing discomfort, it indicates inconsistency.

[0055] For example, in textile tactile detection, assuming the key point is located in the fabric fold area, the mechanical feedback, including pressure distribution and vibration amplitude, is calculated through a model and compared with a preset comfort range. The tactile comfort range can be defined as a mechanical feedback value within the range of 0.5N to 2N pressure and 0.1 to 0.5 coefficient of friction. If the actual feedback exceeds this range, it is considered a deviation.

[0056] Preferably, if the mechanical feedback at a key point deviates from the comfortable tactile range, it is recorded as a tactile anomaly. Deviation judgment is based on a threshold comparison, such as using Euclidean distance to calculate the deviation between the feedback value and the center of the range. If the deviation exceeds a preset value, such as 0.3, it is marked as an anomaly. This recording method facilitates subsequent quality control, enabling early identification of potential defects and improving product consistency in the textile industry.

[0057] It should be noted that determining the distribution set of tactile anomalies is done by aggregating the data of all anomalies to form a spatial distribution map. For example...

[0058] In one possible implementation, clustering algorithms such as K-means are used to group outliers, generating distribution sets that show concentrated areas of anomalies on the material surface, such as edges or centers.

[0059] In one embodiment, the entire process is applied to the tactile evaluation of leather products. First, key points are extracted from anomalous areas. Then, the model is tested in a variable temperature environment, and consistency is analyzed. If deviations occur, the anomalous points are recorded. Finally, a distribution set is output for optimizing the manufacturing process. This method ensures the reliability of tactile detection.

[0060] Understandably, the above steps can be extended to other areas such as tactile testing of electronic device housings, for example, analyzing the comfort of plastic surfaces at different temperatures and recording anomaly distributions to guide design improvements.

[0061] Specifically, in another embodiment, for synthetic fiber materials, if the comfort range at high temperatures is found to shrink after model analysis, the distribution set of outliers can be used to predict temperature resistance performance, providing objective data to support product iteration without introducing subjective evaluation.

[0062] Step S105: For the distribution set of abnormal tactile points, the surface thermal conductivity and temperature mechanics coupling analysis are introduced to calculate the thermal tactile simulation data of the damping spring at the abnormal points. By comparing the user's temperature interaction data, it is determined whether the tactile calibration coefficient needs to be adjusted, and the calibrated tactile optimization parameter set is obtained.

[0063] A set of tactile anomaly points is obtained, and surface thermal conductivity parameters are extracted from this set. A temperature-mechanical coupling analysis is introduced, and the thermal tactile simulation data of the damping spring at the anomaly points is calculated using the finite element method to simulate the heat conduction equation. By comparing the thermal tactile simulation data with user temperature-sensing interaction data, if the deviation exceeds a preset threshold, the tactile calibration coefficient adjustment requirement is determined, and a preliminary calibration coefficient is obtained. Based on the preliminary calibration coefficient, multimodal sensor data is fused to establish a real-time heat flow mapping model. Using sensor data as input and output heat flow distribution maps, the thermal distribution deviation at the anomaly points is determined. According to the heat distribution deviation, the damping spring parameters are adjusted, and the optimized thermal tactile response curve is obtained through iterative optimization. By comparing the thermal tactile response curve with the user temperature-sensing interaction data a second time, the final tactile calibration coefficient is determined, and the calibrated tactile optimization parameter set is obtained.

[0064] In one implementation, regarding the distribution set of tactile abnormal points, it is first necessary to understand the concept of the distribution set of tactile abnormal points. It refers to the set of abnormal locations in the tactile response data collected by the sensor in the tactile feedback device. These abnormal points may be caused by material inhomogeneity or external interference, resulting in deviation of tactile output.

[0065] For example, in the haptic simulation scenario of a virtual reality glove, the distribution set of abnormal points can be extracted from the pressure and temperature feedback data when the user wears the device. For instance, by dividing the device surface into multiple regions through mesh mapping, points where the response value exceeds a threshold can be identified, forming a distribution set. Furthermore, introducing a coupled analysis of surface thermal conductivity and temperature mechanics is a crucial step. Surface thermal conductivity refers to the ability of a material surface to conduct heat, usually expressed as a thermal conductivity coefficient. In haptic devices, it affects the efficiency of heat tactile transmission. The coupled analysis of temperature mechanics involves the interaction between thermal effects and mechanical deformation; for example, deformation caused by thermal expansion can alter the haptic output.

[0066] Specifically, the calculation process first involves obtaining the material parameters of the damping spring, an elastic component used for vibration suppression, often employed in haptic devices to simulate a soft touch. Then, based on the surface thermal conductivity, a heat conduction equation is established and coupled with mechanical equations for solution. For example...

[0067] In one possible implementation, the finite element method is used to simulate the temperature distribution at the anomaly point, taking into account the effect of thermal conductivity on heat flux, thereby calculating thermal tactile simulation data, which includes temperature gradient and heat flux density values.

[0068] It should be noted that the process of calculating the thermal tactile simulation data of the damping spring at anomaly points needs to be explained in detail. The role of the damping spring in tactile feedback is to provide damping force to simulate realistic touch, but heat concentration may occur at anomaly points, leading to uneven tactile sensation.

[0069] Preferably, in the analysis, a temperature-mechanical coupling model is first constructed. This model combines the heat conduction equation with the elasticity equation. The heat conduction equation describes the propagation of heat in the material, while the mechanical equation handles the stress and strain caused by temperature changes. For example, in the damping spring of a virtual reality glove, assuming the anomaly point is located in the fingertip area, by inputting the surface thermal conductivity value (e.g., 0.5 W / m·K) and the initial temperature (e.g., 25°C), the temperature field distribution under the action of an external heat source is simulated. Then, the coupling effect, such as the deformation of the spring caused by thermal stress, is calculated, ultimately obtaining thermotactile simulation data, including local temperature values ​​and thermal intensity indices. This process ensures the accuracy of the simulation data because it considers the interaction between heat and force, avoiding the bias of a single physical field. In business scenarios, this calculation helps optimize the thermal comfort of the device during prolonged use, such as preventing user discomfort caused by overheating when simulating holding a hot object. Through this coupling analysis, the thermotactile response at the anomaly point can be quantified, for example, the heat flux density can reach 10 W / m², thus providing basic data for subsequent comparisons.

[0070] In one embodiment, the need to adjust the tactile calibration coefficient is determined by comparing user temperature-sensing interaction data. User temperature-sensing interaction data refers to temperature perception information provided by the user when using the device, such as temperature ratings recorded through questionnaires or sensors.

[0071] Specifically, the calculated thermal haptic simulation data is compared with user data, for example, by calculating the root mean square error between the two. If the error exceeds a preset threshold (e.g., 5%), it is determined that the haptic calibration coefficient needs to be adjusted. The haptic calibration coefficient is a set of parameters used to adjust the intensity of haptic output, including a thermal intensity factor and a response delay factor.

[0072] For example, in the calibration process of a haptic feedback system, assuming the user's temperature interaction data shows a perceived temperature of 30°C at an anomaly point, while the simulation data shows 28°C, the coefficients are adjusted through error analysis to make the output closer to the user's perception. This comparison not only verifies the reliability of the simulation but also improves the device's personalized adaptability. Furthermore, the final output is the calibrated set of optimized haptic parameters. This calibrated parameter set includes optimized thermal conductivity correction values ​​and mechanical response parameters, for example, by adjusting coefficients through iterative algorithms until the error is minimized.

[0073] In one possible implementation, the parameters are updated using gradient descent to ensure that the parameter set is suitable for different user scenarios, such as haptic simulation in gaming devices.

[0074] Preferably, in another embodiment, considering various haptic device scenarios, such as applying the same method to the damping spring of a haptic mouse, the distribution set of abnormal points is first identified, then coupled analysis is performed to calculate thermal haptic data, and the adjustment coefficient is compared with user data. This versatility demonstrates the flexible application of the technical solution in the field of haptic feedback.

[0075] Understandably, the entire process, from anomaly identification to parameter optimization, forms a closed chain, ensuring the accuracy of tactile output. In practical applications, this leads to a more realistic interactive experience, such as improving user sensitivity to temperature changes in medical simulation devices. In one implementation, for complex anomalies, multi-layered coupling analysis can be introduced. For example, the impact of surface thermal conductivity can be calculated layer by layer, first simulating the heat conduction layer, then coupling it to the mechanical layer to generate detailed simulation data. This data is then compared with user data, and coefficients are adjusted to optimize the parameter set. This approach enhances the robustness of the solution.

[0076] For example, in the haptic module of a virtual reality headset, the distribution set of abnormal points may change due to the influence of sweat. Through the above steps of calculation and calibration, the thermal haptic consistency can be effectively improved, and the final parameter set can be used for real-time feedback adjustment.

[0077] Step S106: Based on the tactile optimization parameter set and combined with environmental adaptability test data, the mechanical behavior of the damping spring under extreme temperatures is corrected. By comparing the matching degree between the corrected mechanical signal and the overall comfortable feel range, the final list of damping spring classifications is determined.

[0078] By combining a set of optimized tactile parameters with environmental adaptability test data, initial mechanical behavior data of damping springs under extreme temperatures is obtained. Based on this initial mechanical behavior data, a vibration attenuation assessment method is used to correct the vibration amplitude over time by measuring the attenuation curve, resulting in a corrected mechanical signal. Comfortable tactile range features are extracted from the corrected mechanical signal to determine the matching degree. If the matching degree exceeds a preset threshold, the material's elasticity adjustment properties are integrated to determine the correspondence between the signal and the range. A damping spring classification list is generated based on this correspondence.

[0079] In one implementation, a set of tactile optimization parameters is first obtained, which includes preset mechanical response thresholds and vibration feedback coefficients. These parameters are compiled based on historical tactile test data and are used to guide the initial adjustment of the damping spring.

[0080] Specifically, the set of haptic optimization parameters is formed by collecting user tactile feedback data at normal temperatures.

[0081] For example, in the design of electronic device buttons, the parameter set is defined as a collection of spring rebound speed and damping coefficients. These coefficients reflect the user's preference for comfortable tactile feedback, thus providing a benchmark for subsequent calibration. Combined with environmental adaptability test data, the mechanical behavior of the damping spring under extreme temperatures is calibrated. Environmental adaptability test data refers to experimental measurement results conducted under high or low temperature conditions.

[0082] For example, within a temperature range of -20 degrees Celsius to 80 degrees Celsius, sensors record the deformation and recovery time of the spring. This data is combined with a set of tactile optimization parameters, and the calibration process involves adjusting the elastic modulus of the spring.

[0083] Specifically, the correction method involves comparing the test data with thresholds in the parameter set, calculating the deviation value, and applying a correction factor.

[0084] For example, if the stiffness of the spring increases at low temperatures, the damping coefficient is reduced to restore the original mechanical balance. This correction ensures that the spring maintains a stable mechanical response in extreme environments, avoiding discomfort caused by temperature changes. Furthermore, the final list of damped spring classifications is determined by comparing the corrected mechanical signals with the overall comfort range. The corrected mechanical signals include adjusted vibration waveforms and pressure distribution curves, which are mapped to the comfort range, defined as the range of subjective comfort forces for the user.

[0085] For example, the pressing force can range from 0.5 Newtons to 2 Newtons. The matching degree is calculated based on the overlap ratio of the signal curves; if the matching degree exceeds 80%, it is classified as the preferred level; otherwise, it is classified as suboptimal or requiring improvement based on the degree of deviation. This comparison process is implemented through data visualization tools to ensure the objectivity of the classification.

[0086] For example.

[0087] In one possible implementation, considering the application scenario of damping springs in electronic devices, environmental adaptability test data is first collected, such as measuring the coefficient of thermal expansion of the spring under high temperature conditions. This data is then combined with a set of tactile optimization parameters for calibration. The specific calibration steps include inputting the test data into a calibration model, which adjusts the mechanical parameters based on a linear interpolation method.

[0088] For example, the elastic modulus at high temperatures is multiplied by a correction factor of 0.9 to compensate for the effects of thermal expansion. Then, the corrected signal is compared to the comfort range; if the signal peak falls within the range, the spring is classified as high-temperature adapted. This approach demonstrates the flexible application of the technical solution within the same field, enabling stable output of spring performance.

[0089] It should be noted that the correction process for mechanical behavior emphasizes a data-driven adjustment mechanism.

[0090] For example, in low-temperature testing, environmental adaptability data may show increased brittleness of the damping spring. In this case, correction is performed by increasing the damping coefficient to enhance flexibility, thereby making the corrected signal closer to the comfort range. The technical effect of this mechanism is to improve the reliability of the damping spring under varying temperature conditions and avoid the classification bias caused by ignoring environmental factors in traditional methods. In another embodiment, the optimized parameter set can be further subdivided into subsets for different batches of damping springs.

[0091] For example, a subset focuses on vibration frequency tuning. After calibration using test data, the matching degree is compared using multi-dimensional indicators such as signal duration and peak intensity. If the matching degree is high, a classification list is generated, including preferred, standard, and backup categories. This implementation demonstrates the versatility of the solution and its adaptability to mass production scenarios.

[0092] Preferably, the entire process can be integrated into an automated system.

[0093] For example, by inputting parameter sets and test data through a software interface, the system automatically performs calibration and comparison, and outputs a classification list. This integrated approach ensures operational efficiency and provides a repeatable mechanical optimization path in the field of electronic device design.

[0094] For example, in the implementation scenario of handheld device buttons, the mechanical behavior correction under extreme temperatures can be adjusted according to the user's grip habits. After matching degree comparison, the classification list is used to guide the selection of production materials, thereby achieving continuous comfort in the hand feel.

[0095] Step S107: By cross-referencing the final classification list with the abnormal response area identifier set, the tactile consistency of the damping spring sheet under the specific temperature range is analyzed. If the tactile consistency of the samples in the classification list meets the preset standard, they are classified as the batch production qualified group, and the accurate classification result is obtained.

[0096] Damping spring sample data is obtained from a preset temperature range to determine the initial tactile response value of the sample within the range. The initial tactile response value is compared with an anomaly response region identifier set to determine the distribution of anomaly points. If the number of anomaly points exceeds a threshold, it is marked as a group requiring calibration. Dynamic temperature adjustment is applied to the groups requiring calibration to obtain an adjusted tactile consistency index. The adjusted tactile consistency index is cross-compared with the final classification list to obtain the batch production qualified group division. Sample consistency is verified based on the batch production qualified group division to obtain accurate classification results.

[0097] In one implementation, the tactile consistency analysis of the damping spring is first achieved by collecting response data of the sample within a specific temperature range.

[0098] Specifically, the damping spring is placed in a temperature-controlled environment, for example, the range from -20°C to 80°C is divided into multiple sub-zones, such as a low-temperature zone, a medium-temperature zone, and a high-temperature zone. The vibration damping and surface tactile feedback of the spring are continuously monitored in each sub-zone. This data is captured by sensors to form an initial response dataset. This process ensures that the data covers the impact of temperature changes on the physical properties of the spring, thus providing a foundation for subsequent analysis. Furthermore, a final classification list and a set of anomalous response region identifiers are generated.

[0099] For example, the classification list performs cluster analysis based on statistical characteristics of the response data, such as vibration frequency and damping coefficient, grouping samples into consistency and variability categories. The abnormal response region identifier set identifies regions that deviate from the normal range within a specific temperature interval through threshold filtering; for example, a damping coefficient exceeding a preset deviation value is marked as abnormal. The construction of this identifier set relies on training with historical data to ensure the accuracy of the identification.

[0100] In one possible implementation, a cross-comparison is performed between the final classification list and the set of anomalous response region identifiers. The specific process includes mapping sample points from the classification list to the identifier set and calculating the overlap, for example, by evaluating the degree of matching through intersection operations. If the overlap is below a threshold, it indicates the presence of an anomalous response. Subsequently, the tactile consistency of the damping spring is analyzed within a specific temperature range, where tactile consistency refers to the uniformity of the damping feedback of the spring upon touch. A consistency score is calculated through comparison, such as using a standard deviation formula to assess the fluctuation of the response data.

[0101] Preferably, if the tactile consistency of samples in the classification list meets a preset standard, such as a consistency score higher than 0.85, they are classified as part of the batch production qualified group. This standard is based on industry specifications to ensure that products in the qualified group maintain stable performance in actual applications. After obtaining accurate classification results, a report can be generated for production optimization.

[0102] It should be noted that in industrial production line scenarios, this method can be extended to the detection of damping spring sheets in different batches.

[0103] For example, the above process can be repeated for springs used in automotive suspension systems within the same temperature range, with additional consideration given to load factors during cross-comparison to enhance the robustness of the analysis. This extension demonstrates the versatility of the technical solution in the field of quality control.

[0104] Specifically, the innovation of cross-matching lies in its accuracy. By matching the final classification list and anomaly label set point by point, it avoids the errors of traditional manual detection. The process first loads the classification list data, then iteratively traverses the anomaly regions, calculating the similarity between the response vector and the label vector of each sample, such as using the cosine similarity method for evaluation. If the similarity is high, it indicates good consistency; otherwise, it is marked as unqualified. This detailed comparison ensures the objectivity and reliability of the analysis, effectively screening out qualified groups in batch production and reducing the defect rate. For example...

[0105] In one embodiment, the detection of vibration damping springs in electronic devices involves dividing the temperature range into five sub-regions, with 100 sample points collected in each region. After generating a classification list, it is compared with an anomaly identifier set. If more than 80% of the samples meet the consistency standard, the entire list is classified as qualified. This embodiment highlights the application of the method in precision manufacturing. Furthermore, this technical solution improves production efficiency by rapidly obtaining classification results through an automated analysis process, supporting real-time adjustments to production parameters. In practice, this method is applicable to various types of damping springs, ensuring the stability of tactile consistency under temperature changes.

[0106] Understandably, the preset standard can be adjusted according to specific applications. For example, in high-precision instrument spring clips, the consistency threshold can be increased to 0.9 to meet more stringent requirements. This flexibility enhances the adaptability of the solution without changing the core comparison logic. In another implementation, combining multi-sensor data enhances the generation of anomaly response region identifier sets, such as fusing temperature and pressure sensor outputs to form a more comprehensive identifier set, thereby improving the accuracy of cross-comparison. This approach demonstrates diverse implementations of the technical solution while maintaining domain consistency.

[0107] The above description of the embodiments is only for the purpose of helping to understand the technical solutions and core ideas of this application; those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for analyzing and optimizing the tactile characteristics of a damping spring, characterized in that, include: S101. Mechanical signal data of the damping spring during rotation is collected through a sensor array. Combined with a temperature control simulation platform, environmental conditions under different temperature ranges are simulated to obtain a raw dataset containing angular position, damping force value, and temperature distribution. S102. For the damping force value sequence in the raw dataset, frequency domain analysis is used to extract mechanical signal features under different temperature gradient distributions. A heat conduction model is introduced to calculate thermal stress response data, determining a signal feature set that integrates mechanical and thermal properties. S103. The correlation between damping force value change and thermal expansion coefficient is analyzed based on the signal feature set. Abnormal response areas are marked and a label set is generated. S104. Key point data in the abnormal response areas are analyzed using a tactile feedback model and temperature sensing threshold to determine a set of abnormal tactile points. S105. Thermal tactile simulation data is calculated through temperature-mechanical coupling analysis. The tactile calibration coefficient is adjusted by comparing it with user temperature-sensing interaction data to obtain a set of tactile optimization parameters. S106. Mechanical behavior is corrected based on the tactile optimization parameter set and environmental adaptability test data, generating a damping spring classification list. S107, by comparing the classification list with the abnormal response area identifier set, the tactile consistency is analyzed to determine the batch production qualified group division and obtain accurate classification results.

2. The method for optimizing the tactile feedback of a damping spring as described in claim 1, characterized in that, Step S101 further includes: acquiring angular position and initial damping force data during the rotation of the damping spring plate using a sensor array; simulating environmental conditions under multiple temperature ranges using a temperature control simulation platform, and extracting temperature gradient distribution characteristics from the mechanical signal data; recording the influence of the temperature gradient distribution on the damping force value, and determining vibration frequency calibration parameters by comparing temperature changes and force deviations; performing multi-axis torque compensation on the vibration frequency calibration parameters, adjusting the axial deviation value, and generating an extended data set containing angular position, damping force value, and corresponding temperature distribution; integrating the extended data set to form the original dataset, and analyzing the influence of temperature distribution on the overall mechanical signal to obtain a preliminary data foundation.

3. The method for optimizing the tactile feedback of a damping spring as described in claim 1, characterized in that, Step S102 further includes: extracting the damping force value sequence from the original dataset, converting it into a spectral form through Fourier transform, obtaining mechanical signal characteristics under different temperature gradient distributions, and forming preliminary mechanical response data; for the preliminary mechanical response data, introducing a heat conduction model and simulating heat distribution using the finite element method, calculating the thermal stress response data of the damping spring in each temperature range, and obtaining thermal influence indicators; fusing the thermal influence indicators with the mechanical signal characteristics, and using a weighted average method to determine the material deformation simulation parameters; if the thermal stress response data exceeds a preset threshold, adjusting the temperature range division to obtain an optimized thermo-mechanical coupling response; extracting an intermediate signal set of comprehensive mechanical and thermal characteristics based on the optimized thermo-mechanical coupling response, generating an extended feature vector, and determining the final signal feature set.

4. The method for optimizing the tactile feedback of a damping spring as described in claim 1, characterized in that, Step S103 further includes: analyzing the damping force changes between adjacent angles based on the signal feature set, and obtaining the force change trend by comparing the differences; calculating the correlation between the force change trend and the coefficient of thermal expansion based on the temperature cyclic loading test data, determining the linear relationship using the correlation coefficient method, and obtaining the correlation parameter value; extracting thermal stress response data for the correlation parameter value, and marking an abnormal response region if the deviation between the force change amplitude and the thermal stress response exceeds a preset threshold; generating an identifier set from the abnormal response region, integrating the material deformation monitoring data from the temperature cyclic loading test data, and forming an abnormal response region identifier set for subsequent analysis.

5. The method for optimizing the tactile feedback of a damping spring as described in claim 1, characterized in that, Step S104 further includes: extracting key point data from the set of abnormal response area identifiers, combining the tactile feedback model and temperature perception threshold, analyzing the tactile comfort range of the key point data at different temperatures, and obtaining a consistency judgment result; if the consistency judgment result shows inconsistency, comparing the deviation between the mechanical feedback of the key points and the tactile comfort range, and recording it as a tactile abnormal point; analyzing the spatial distribution characteristics of the tactile abnormal points through abnormal point heatmap mapping, using dynamic adjustment calibration of the comfort range to determine the deviation value, and determining a preliminary distribution set; based on the abnormal point data in the preliminary distribution set, combining the tactile feedback model attribute verification temperature change analysis result, determining whether it has extended to the surrounding area, and obtaining a refined distribution set; integrating the abnormal point records in the refined distribution set to form a tactile abnormal point distribution set.

6. The method for optimizing the tactile feedback of a damping spring as described in claim 1, characterized in that, Step S105 further includes: extracting surface thermal conductivity parameters from the distribution set of abnormal tactile points, introducing temperature-mechanical coupling analysis, simulating the heat conduction equation using the finite element method, and calculating the thermal tactile simulation data of the damping spring at the abnormal points; comparing the thermal tactile simulation data with the user's temperature-sensing interaction data, if the deviation exceeds a preset threshold, determining the need to adjust the tactile calibration coefficient and obtaining a preliminary calibration coefficient; for the preliminary calibration coefficient, fusing multimodal sensor data to establish a real-time heat flow mapping model and determining the thermal distribution deviation at the abnormal points; adjusting the damping spring parameters according to the thermal distribution deviation, and obtaining the thermal tactile response curve through iterative optimization; and determining the final tactile calibration coefficient through a secondary comparison of the thermal tactile response curve and the user's temperature-sensing interaction data, forming a tactile optimization parameter set.

7. The method for optimizing the tactile feedback of a damping spring as described in claim 1, characterized in that, Step S106 further includes: obtaining initial mechanical behavior data of the damping spring sheet under extreme temperatures by combining the tactile optimization parameter set with environmental adaptability test data; using a vibration attenuation assessment method to correct the initial mechanical behavior data by measuring the vibration amplitude decay curve over time, thereby obtaining a corrected mechanical signal; extracting the tactile comfort range features from the corrected mechanical signal and determining the matching degree; if the matching degree exceeds a preset threshold, integrating the material elasticity adjustment properties to determine the correspondence between the signal and the range; and generating a damping spring sheet classification list based on the correspondence for subsequent consistency analysis.

8. The method for optimizing the tactile feedback of a damping spring as described in claim 1, characterized in that, Step S107 further includes: obtaining damping spring sample data from a preset temperature range and determining the initial tactile response value of the sample within the range; comparing the initial tactile response value with the abnormal response area identifier set to determine the distribution of abnormal points; if the abnormal points exceed a preset threshold, they are marked as a calibration group; applying dynamic temperature adjustment to the calibration group to obtain the adjusted tactile consistency index; determining the batch production qualified group division by cross-comparing the adjusted tactile consistency index with the classification list; verifying sample consistency based on the batch production qualified group division and generating accurate classification results.

9. A damping spring, characterized in that, The device includes a damping spring, wherein the damping spring is subjected to tactile analysis and optimization using a damping spring tactile optimization method according to any one of claims 1-8.

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