Tire pressure sensing system for monitoring surface temperature of tire

Abstract

The invention relates to the technical field of automobile safety monitoring, and discloses a tire pressure sensing system for monitoring the surface temperature of a tire. A temperature monitoring assembly of the system is arranged on the surface of a tire, collects temperature distribution information and forms a temperature space-time sequence; the tire pressure monitoring assembly is integrated in a tire and used for monitoring pressure changes and generating a pressure data sequence. The feature fusion component receives the temperature space-time sequence and the pressure data sequence, performs data fusion and then extracts coupling features of the temperature and the pressure; the grid construction assembly generates a multi-resolution thermal grid model according to monitoring resolution requirements of different areas on the surface of the tire; the thermal load analysis assembly analyzes thermal load change based on the time sequence of the multi-resolution thermal grid model to obtain a thermal load spectrum; and the stress calculation assembly calculates the thermal stress distribution of the key area of the tire through thermal stress simulation by using the thermal load spectrum to obtain stress state data. According to the system, the tire safety monitoring accuracy and early warning capability are improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle tire safety monitoring technology, specifically a tire pressure sensing system for monitoring tire surface temperature. Background Technology

[0002] Current tire safety monitoring primarily employs independent tire pressure sensors and surface temperature point measurements. Existing technologies rely heavily on single-parameter measurements for tire condition monitoring, with tire pressure and temperature data acquisition being independent. Temperature monitoring points are sparsely distributed, making it difficult to capture the complete temperature distribution on the tire surface. Data fusion methods are simplistic, and the coupling relationship between temperature and pressure has not been thoroughly analyzed. Mesh models are fixed, and the use of uniform mesh division cannot adapt to the monitoring accuracy requirements of different regions. Thermal load analysis is linearized, failing to consider the nonlinear characteristics of spatiotemporal temperature field variations. Stress calculation models are simplified, neglecting the influence of material anisotropy and contact boundary conditions. Existing methods need to address key technical challenges such as multi-parameter collaborative monitoring, refined temperature field modeling, dynamic thermal load analysis, and accurate stress calculation.

[0003] Traditional tire monitoring systems suffer from significant shortcomings in data integration and model accuracy. Fixed sensor deployment locations make it difficult to capture temperature gradient changes in key areas. Low data acquisition frequency fails to track rapid temperature changes during tire operation. Feature extraction is limited in scope, and the interaction mechanism between temperature and pressure is not quantified. Rigid mesh generation algorithms cannot adaptively adjust resolution based on tire wear conditions. A lack of thermal load spectrum analysis methods prevents effective extraction of time-frequency domain features. Stress simulation boundary conditions are idealized, deviating from actual operating conditions. Material parameter settings are empirical, failing to consider the impact of rubber aging on thermodynamic properties. Fixed condition assessment thresholds cannot adapt to differences in vehicle models and driving conditions. Existing technologies necessitate the development of a high-precision monitoring solution covering the entire process from data acquisition to condition assessment. Summary of the Invention

[0004] The purpose of this invention is to provide a tire pressure sensing system for monitoring tire surface temperature, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a tire pressure sensing system for monitoring tire surface temperature, the system comprising: Temperature monitoring component, tire pressure monitoring component, feature fusion component, mesh building component, thermal load analysis component, and stress calculation component; The temperature monitoring component is arranged on the tire surface to collect temperature distribution information and form a temperature spatiotemporal sequence. The tire pressure monitoring component is integrated inside the tire, monitors pressure changes and generates pressure data sequences; the feature fusion component receives temperature spatiotemporal sequences and pressure data sequences, performs data fusion and extracts the coupling features of temperature and pressure. The mesh construction component generates a multi-resolution thermal mesh model based on the monitoring resolution requirements of different areas of the tire surface; the thermal load analysis component analyzes the thermal load changes based on the time series analysis of the multi-resolution thermal mesh model to obtain the thermal load spectrum. The stress calculation component utilizes the thermal load spectrum to calculate the thermal stress distribution in key areas of the tire through thermal stress simulation, thereby obtaining stress state data.

[0006] Preferably, when generating a multi-resolution thermal mesh model, the mesh building component divides the tire surface into multiple curved sub-regions based on the curvature characteristics of the tire surface and the differences in data resolution. An independent computation thread is assigned to each surface sub-region; the grid cell size, number of grids, and grid coverage density are determined based on the data point density of each surface sub-region. Control each computation thread to generate mesh surface data for the curved sub-region according to the corresponding mesh cell size, mesh number and mesh coverage density; Construct a 3D mesh based on mesh surface data and obtain mesh information; assign unique identifiers to the vertices and volume elements of the mesh information; By synchronizing the mesh information of adjacent surface sub-regions through inter-thread communication, a continuous multi-resolution thermal mesh model is formed.

[0007] Preferably, when the feature fusion component extracts coupled features, it first standardizes the temperature spatiotemporal sequence and pressure data sequence to eliminate differences in data dimensions; it then uses a pre-configured feature extraction structure to extract temperature and pressure features from the standardized data to form an initial feature set; when the dimension of the initial feature set exceeds a preset threshold, it performs a dimensionality reduction operation to obtain a simplified feature set; it analyzes the time series of the simplified feature set to enhance the dynamic correlation characteristics of temperature and pressure interaction and outputs coupled features.

[0008] Preferably, the temperature monitoring component consists of multiple miniature temperature sensors arranged in an array on the inner wall of the tire, with each sensor collecting local temperature and generating a time-series signal; the tire pressure monitoring component is installed at the tire valve and records the pressure value in real time; the temperature monitoring component and the tire pressure monitoring component transmit the data to the feature fusion component wirelessly.

[0009] Preferably, when analyzing the thermal load spectrum, the thermal load analysis component establishes a geometric model of the tire, including the contact interface between the tire and the rim; applies thermal flow boundary conditions based on the thermal load spectrum; performs finite element thermal analysis to calculate the temperature distribution and thermal stress changes at key points of the tire; analyzes the time history and spatial gradient of the stress distribution to generate a dynamic thermal stress distribution map as stress state data.

[0010] Preferably, the system further includes: The life assessment component identifies thermal fatigue cycles based on stress state data and calculates the remaining life of the tire by combining the material's thermal fatigue curve.

[0011] Preferably, the life assessment component stores a thermal fatigue curve database of tire materials; based on stress state data, it identifies stress cycle patterns at key points and counts the number of fatigue cycles; it interpolates and calculates the cumulative damage at each key point based on the thermal fatigue curve database to deduce the remaining life; and it integrates the remaining life of all key points to assess the overall life status of the tire.

[0012] Preferably, when optimizing mesh connections, the mesh building component compares the mesh cell identifiers of adjacent surface sub-regions and adjusts the vertex coordinates to ensure mesh continuity; a smoothing algorithm is applied to optimize the mesh interface.

[0013] Preferably, the feature extraction structure includes multi-layer convolution and pooling operations for extracting spatial and temporal features from temperature images and pressure waveforms.

[0014] Preferably, the data from the temperature monitoring component and the tire pressure monitoring component are aligned through a time synchronization mechanism, and multi-sensor data fusion is performed in the feature fusion component to compensate for environmental interference.

[0015] Compared with the prior art, the beneficial effects of the present invention are: The feature fusion component receives and fuses spatiotemporal temperature and pressure data sequences to extract the coupling features between temperature and pressure. The spatiotemporal temperature sequence is acquired using an infrared thermometer array or fiber optic grating sensor, recording the change in tire surface temperature over time. The pressure data sequence is acquired using a piezoelectric or capacitive pressure sensor, reflecting the dynamic fluctuations in internal tire pressure. Data fusion employs Kalman filtering or particle filtering algorithms to eliminate measurement noise and timing discrepancies. Coupling feature extraction quantifies the interaction strength between temperature and pressure using cross-correlation analysis or Granger causality tests. Feature dimensions include key indicators such as phase difference, amplitude ratio, and energy transfer coefficient. Feature calculation considers the effects of tire rotation cycle and load variations to improve characterization accuracy. The coupling features reveal the tire's thermodynamic coupling mechanism, providing a comprehensive criterion for condition assessment.

[0016] The mesh construction component generates a multi-resolution thermal mesh model based on the monitoring resolution requirements of different regions on the tire surface. The monitoring resolution requirements are determined based on tire structural characteristics and historical fault data; a high-resolution mesh is used in the center of the tread, while a medium-resolution mesh is used in the shoulder area. Mesh generation employs an adaptive mesh refinement method, dynamically adjusting the mesh density according to the temperature gradient. The multi-resolution thermal mesh model includes coarse and fine mesh layers, with data transfer achieved through interconnection. Mesh node placement considers sensor locations and tire geometry to ensure continuous data acquisition. Model updates are adaptively adjusted based on tire wear, maintaining mesh consistency with the physical surface. Mesh quality is controlled using aspect ratio and skewness indices to ensure computational stability. The multi-resolution structure balances computational accuracy and efficiency to meet real-time monitoring requirements. The thermal load analysis component uses time-series analysis of the multi-resolution thermal mesh model to obtain the thermal load spectrum. Time-series analysis employs empirical mode decomposition or wavelet transform methods to extract the time-frequency characteristics of the thermal load. Thermal load calculation considers the combined effects of conduction, convection, and radiation heat transfer mechanisms. The load spectrum is generated using a power spectral density estimation method, reflecting the distribution characteristics of the thermal load across different frequency bands. The spectral peak positions correspond to the operating frequencies of the main heat sources, and the spectral width characterizes the degree of thermal load fluctuation. A sliding window mechanism is employed in the analysis process to track the time-varying characteristics of the thermal load. The load spectrum update frequency is synchronized with data acquisition to ensure real-time analysis. Through the synergistic effect of data fusion, coupled feature extraction, multi-resolution modeling, and thermal load analysis, accurate monitoring of the tire's thermal state is achieved. Multi-parameter fusion provides comprehensive state information, coupled features reveal the underlying mechanisms, multi-resolution grids optimize computational resources, and the thermal load spectrum quantifies dynamic characteristics. This integrated approach significantly improves the accuracy and reliability of tire safety monitoring. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the working principle of the tire pressure sensing system for monitoring tire surface temperature as described in this invention. Figure 2 A schematic diagram illustrating the working principle of generating a multi-resolution thermal mesh model for the mesh building components; Figure 3 The working principle of extracting coupled features for feature fusion components; Figure 4 A time history curve of thermal stress in critical areas of the tire. Figure 5 This is a time history curve of thermal stress in the critical area of ​​the tire. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 This invention provides a tire pressure sensing system for monitoring tire surface temperature. The system includes: a temperature monitoring component, a tire pressure monitoring component, a feature fusion component, a mesh construction component, a thermal load analysis component, and a stress calculation component. The temperature monitoring component is arranged on the tire surface to collect temperature distribution information and form a temperature spatiotemporal sequence. The tire pressure monitoring component is integrated inside the tire to monitor pressure changes and generate a pressure data sequence. The feature fusion component receives the temperature spatiotemporal sequence and the pressure data sequence, performs data fusion, and extracts the coupling features between temperature and pressure. The mesh construction component generates a multi-resolution thermal mesh model according to the monitoring resolution requirements of different areas of the tire surface. The thermal load analysis component analyzes the thermal load changes based on the time series of the multi-resolution thermal mesh model to obtain a thermal load spectrum. The stress calculation component uses the thermal load spectrum to calculate the thermal stress distribution in key areas of the tire through thermal stress simulation to obtain stress state data.

[0020] Example 1: See Figure 2In practical implementation, the mesh construction component divides the tire surface into multiple curved sub-regions based on the curvature characteristics and data resolution differences of the tire surface. The curvature characteristics are obtained by analyzing the curvature parameters of the 3D point cloud data of the tire surface. The data resolution differences come from the sensor deployment density of the temperature monitoring component in different areas of the tire. High curvature areas and sensor high-density areas are divided into independent curved sub-regions. An independent computing thread is allocated to each curved sub-region. The computing threads are executed in a parallel computing architecture. Each computing thread has independent storage space and computing resources. The mesh cell size, number of mesh divisions, and mesh coverage density are determined according to the data point density of each curved sub-region. Curved sub-regions with high data point density are configured with smaller mesh cell sizes and higher mesh coverage densities. Each computing thread is controlled to generate mesh surface data of the curved sub-region according to the corresponding mesh cell size, number of mesh divisions, and mesh coverage density. The mesh surface data includes vertex coordinates, patch connectivity, and normal vector information. A 3D mesh is constructed based on the mesh surface data, and mesh information is obtained. The 3D mesh is formed by stretching the 2D mesh surface data along the normal vector direction to form volume elements. The mesh information includes the topology and material properties of the volume elements. Unique identifiers are assigned to the vertices and volume elements of the mesh information. Vertex identifiers are generated using coordinate hash encoding, and volume element identifiers are generated based on their spatial position encoding. The mesh information of adjacent surface sub-regions is synchronized through inter-thread communication, which is implemented using a message passing interface. The synchronization process includes exchanging vertex identifiers and volume element identifiers of adjacent boundaries to form a continuous multi-resolution thermal mesh model.

[0021] In some embodiments, when optimizing mesh connections, the mesh building component compares the mesh element identifiers of adjacent surface sub-regions. This comparison includes verifying the continuity and uniqueness of the identifiers. Vertex coordinates are adjusted to ensure mesh continuity. The adjustment of vertex coordinates is calculated based on the average position of the boundary vertices of adjacent surface sub-regions. A smoothing algorithm is applied to optimize the mesh interface. This smoothing algorithm uses a Laplace smoothing method to iteratively optimize the vertex positions at mesh connections. In a specific implementation, the determination of mesh element sizes follows the following formula: in: Indicates the size of the grid cell. This represents the data point density of the curved surface sub-region. This represents the size adjustment factor, which is related to the thermal conductivity of the tire material. The data point density is obtained by counting the number of temperature sensors per unit area. It can be understood that the number of mesh divisions is determined by the ratio of the surface area of ​​the curved sub-region to the mesh cell size, and the mesh coverage density is obtained by calculating the number of volume cells per unit volume.

[0022] It is understood that the unique identifier allocation process adopts a hierarchical encoding mechanism. The vertex identifier includes the surface sub-region number and the local vertex sequence number. The volume element identifier is generated based on the vertex identifier combined with the volume element type code. Inter-thread communication adopts a non-blocking communication mode, allowing computation threads to continue executing independent computation tasks while waiting for synchronization messages. Optionally, the number of iterations of the Laplacian smoothing method is dynamically adjusted according to the curvature difference between adjacent surface sub-regions, with higher iteration counts configured for regions with high curvature differences. In some embodiments, the continuity verification of the multi-resolution hot mesh model is completed by calculating the angle between the normal vectors at the boundary of adjacent surface sub-regions. When the angle between the normal vectors is less than a threshold, the mesh is determined to be continuous. Optionally, the division of surface sub-regions is based on a combination of curvature threshold and data density threshold, with the curvature threshold obtained through calibration using historical tire model data.

[0023] Example 2: See Figure 3 In specific implementation, when the feature fusion component extracts coupled features, it first standardizes the temperature spatiotemporal sequence and pressure data sequence. The standardization process uses the z-score standardization method to eliminate the dimensional differences between temperature and pressure data. A pre-configured feature extraction structure is used to extract temperature and pressure features from the standardized data. The feature extraction structure includes multi-layer convolution and pooling operations. The convolution operation extracts spatial features from the temperature image, and the pooling operation extracts temporal features from the pressure waveform to form an initial feature set. When the dimension of the initial feature set exceeds a preset threshold, a dimensionality reduction operation is performed to obtain a simplified feature set. The dimensionality reduction operation uses principal component analysis to retain the main feature components. The time series of the simplified feature set is analyzed to enhance the dynamic correlation characteristics of temperature and pressure interaction. The dynamic correlation characteristics are obtained by calculating the cross-correlation function of temperature and pressure features, and the coupled features are output.

[0024] In some embodiments, the feature extraction structure includes multi-layer convolution and pooling operations. The convolution operation uses a three-dimensional convolution kernel to extract spatial features from the temperature spatiotemporal sequence. The configuration of the three-dimensional convolution kernel is based on the multi-dimensional data structure of the temperature spatiotemporal sequence, which consists of a two-dimensional spatial distribution and a one-dimensional time series. Therefore, the size of the three-dimensional convolution kernel is designed to cover local spatial regions and a time sliding window. The convolution kernel uses a 3x3 or 5x5 kernel size in the spatial dimension to capture the temperature gradient changes on the tire surface. In the time dimension, the stride length is set to match the data sampling interval to ensure the coherence of temporal feature extraction. The pooling operation uses the max pooling method to extract temporal features from the pressure data sequence. Temperature features and pressure features are processed through independent convolution channels. The initial feature set is formed by concatenating temperature feature vectors and pressure feature vectors. In a specific implementation, the dimensionality reduction operation maps the high-dimensional initial feature set to a low-dimensional space through linear transformation. The dimensionality reduction process follows the following relationship: in: This represents the feature matrix after dimensionality reduction. The sample matrix represents the initial feature set. This represents the projection transformation matrix, which is calculated using the eigenvalue decomposition method.

[0025] It is understood that the enhanced dynamic correlation between temperature and pressure is achieved through time-series sliding window analysis. Sliding window analysis calculates the cross-correlation function between temperature and pressure features, and the peak position of the cross-correlation function reflects the phase relationship between temperature and pressure changes. Optionally, the number of convolution kernels in the feature extraction structure increases with the number of network layers, while the kernel size decreases with the number of network layers. In some embodiments, the pooling window size is related to the sampling frequency of the pressure data sequence; a larger pooling window is configured for higher sampling frequencies. It is understood that different standardization parameters are used for temperature and pressure data during the standardization process. The standardization parameters for temperature data include the temperature mean and temperature standard deviation, while the standardization parameters for pressure data include the pressure mean and pressure standard deviation.

[0026] Optionally, the dimensionality threshold of the initial feature set can be dynamically adjusted based on computing resource capacity, with a higher threshold set when computing resources are sufficient. In specific implementations, the output format of the coupled features is a multi-dimensional feature vector, which contains information from three dimensions: temperature spatial features, pressure-time features, and temperature-pressure interaction features. When performing data fusion, the feature fusion component employs a feature-level fusion strategy, which concatenates temperature and pressure features along their respective feature dimensions to form a unified feature representation for subsequent analysis and processing.

[0027] Example 3: In this implementation, the temperature monitoring component consists of multiple miniature temperature sensors arranged in an array on the inner wall of the tire. Each miniature temperature sensor collects local temperature data and generates a time-series signal. The tire pressure monitoring component is installed at the tire valve, recording pressure values ​​in real time and generating a pressure data sequence. The temperature monitoring component and the tire pressure monitoring component transmit data to the feature fusion component wirelessly using the Bluetooth Low Energy protocol. The data from the temperature monitoring component and the tire pressure monitoring component are aligned using a time synchronization mechanism. This mechanism uses a global clock source to assign a unified time stamp to both the temperature time-series signal and the pressure data sequence. Multi-sensor data fusion is performed in the feature fusion component, employing a weighted average method to compensate for environmental interference, including external temperature fluctuations and mechanical vibrations.

[0028] In some embodiments, the array arrangement of the miniature temperature sensors is optimized based on the geometry of the tire inner wall, and the sampling interval of the miniature temperature sensors is dynamically adjusted according to the vehicle's driving status. The pressure sensor of the tire pressure monitoring component is embedded inside the valve stem structure, and the acquisition of the pressure data sequence and the temperature time sequence signal use the same clock reference. It can be understood that the global clock source of the time synchronization mechanism comes from a Global Navigation Satellite System receiver, providing microsecond-level time accuracy. In specific implementations, environmental interference compensation is achieved by calculating the deviation between the measured value and the reference value; the deviation calculation follows the following formula: in: Indicates environmental interference deviation. Indicates the sensor measurement value, This represents the environmental reference value, which is provided by an auxiliary sensor mounted on the wheel hub.

[0029] It is understood that the multi-sensor data fusion process is executed in the embedded processor of the feature fusion component, which is configured with dual buffers to store temperature time-series signals and pressure data sequences. Data packets transmitted wirelessly contain timestamps and sensor identifiers; the timestamps are used to reconstruct the data timing sequence in the feature fusion component. Optionally, a time synchronization mechanism uses a linear interpolation method to estimate missing time points when data is lost. In some embodiments, the weighting coefficients for environmental interference compensation are dynamically adjusted based on sensor confidence levels, which are obtained through historical data consistency calculations. Optionally, the wireless transmission between the temperature monitoring component and the tire pressure monitoring component employs a time-division multiple access protocol to avoid signal collisions.

[0030] Example 4: In specific implementation, the thermal load analysis component establishes a geometric model of the tire when analyzing the thermal load spectrum. The geometric model includes the tire tread, sidewall, and bead structure, as well as the contact interface between the tire and the rim. The contact interface is generated by obtaining the rim surface contour data through 3D scanning technology and performing Boolean operations with the tire model. Based on the thermal load spectrum, a heat flux boundary condition is applied. The heat flux boundary condition is converted into a heat flux density distribution based on the spatiotemporal sequence data collected by the temperature monitoring component. The heat flux density distribution is calculated using Fourier's law. Finite element thermal analysis is performed. The finite element thermal analysis uses the transient heat conduction equation to solve the internal temperature field of the tire, calculating the temperature distribution and thermal stress changes at key points of the tire. Key points include the tread center, shoulder, and bead region. The thermal stress change is calculated based on the thermoelastic theory to calculate the stress response of the material due to the temperature gradient. The time history and spatial gradient of the stress distribution are analyzed. The time history is obtained by recording the stress value of the key points as a function of time. The spatial gradient is quantified by calculating the stress difference between adjacent grid points, generating a dynamic thermal stress distribution map. The dynamic thermal stress distribution map visualizes the stress magnitude and distribution in the form of a color cloud map, which is output as stress state data. In some embodiments, referring to Table 1, the geometric model construction parameters of the thermal load analysis component are organized and stored in the form of Table 1.

[0031] Table 1: Key Parameters for Thermal Load Analysis Parameter name Data source Numerical range unit Remark Tire elastic modulus Material test data 5-20 MPa With temperature change coefficient of thermal expansion Laboratory Measurement 1.5e-5-2.5e-5 1 / °C Rubber material properties thermal conductivity Standard Database 0.15-0.25 W / (m·K) Anisotropic parameters convective heat transfer coefficient Fluid simulation 10-50 W / (m²·K) Depends on vehicle speed Contact thermal resistance Interface Experiment 0.01-0.05 m²·K / W Wheel hub-tire interface It is understandable that the thermal stress calculation process follows the following formula: in: Indicates the thermal stress value. This indicates the elastic modulus of the tire material. Indicates the coefficient of thermal expansion. This represents the local temperature difference. The elastic modulus is obtained from a material database, the coefficient of thermal expansion is determined through thermodynamic testing, and the local temperature difference is derived from the finite element temperature field analysis results. Optionally, the contact interface of the geometric model is treated using a penalty function method, which allows for minute penetrations at the interface to simulate actual contact behavior. In some embodiments, the spatial gradient analysis uses the central difference method, which calculates the partial derivatives of the stress at the mesh points in the three coordinate directions.

[0032] It is understandable that the generation of the dynamic thermal stress distribution map employs a contour filling algorithm, which discretizes continuous stress values ​​into color levels, with the color mapping ranging from blue (low stress) to red (high stress). The time step of the finite element thermal analysis is adaptively adjusted according to the rate of change of the thermal load, with a smaller step size used during high-speed changes. Optionally, the application of thermal flux boundary conditions considers ambient temperature compensation, which is corrected in real time using data from external temperature sensors. In specific implementations, stress state data is stored in a time-series database format, recording the stress distribution matrix at each time point. The input of the thermal load spectrum supports multiple formats, including power spectral density and time-domain load sequences, ensuring compatibility with different data sources.

[0033] See Figure 4 This study presents the variation of thermal stress in three key areas of the tire—tread, shoulder, and bead—over time (hours). From a professional perspective, the tread stress (red curve) is at a relatively high level in the initial stage (approximately 0.00-0.75 hours), with a peak value approaching 8 MPa. This is closely related to the tread's direct contact with the ground and the concentration of thermal load. The shoulder stress (green curve) is generally located between the tread and bead, and its stress variation reflects the stress transfer and transition characteristics between the tread and bead regions. The bead stress (blue curve) has a relatively low initial value and subsequently fluctuates over time. From a temporal perspective, all three stresses show varying degrees of stress increase in the early stages, followed by a gradual decrease. This reflects the dynamic evolution of thermal stress in key areas of the tire under thermal load, which is of significant engineering importance for understanding tire thermal fatigue characteristics, optimizing tire structural design, and assessing remaining tire life.

[0034] Example 5: In specific implementation, the life assessment component identifies thermal fatigue cycles based on the stress state data output by the stress calculation component. The stress state data includes the thermal stress values ​​and distribution of the key areas of the tire at different time points. Thermal fatigue cycles are identified by analyzing the waveform characteristics of stress values ​​changing over time. The remaining life of the tire is calculated by combining the material thermal fatigue curve. The material thermal fatigue curve database stores fatigue performance data of different rubber formulations and cord materials under cyclic thermal loads. The life assessment component stores a database of thermal fatigue curves for tire materials. This database obtains fatigue life data of materials at different temperature amplitudes and stress levels through accelerated aging tests in the laboratory. Based on the stress state data, it identifies stress cycling patterns at key points, including stress amplitude, average stress, and cycle count characteristics. It then counts the number of fatigue cycles, obtained by processing the stress time history using the rainflow counting method. Based on the thermal fatigue curve database, it interpolates and calculates the cumulative damage at each key point. The interpolation calculation uses a linear interpolation method to estimate the life value corresponding to the current stress level between known fatigue data points. Finally, it derives the remaining life, calculated as the ratio of cumulative damage to the theoretical total life. Finally, it integrates the remaining life of all key points, using a weighted average criterion to assess the overall tire life status.

[0035] In some embodiments, the thermal fatigue curve database's data structure includes material number, temperature range, stress ratio, and cycle life fields. The database supports bilinear interpolation queries based on stress range and average stress. Stress cycle pattern identification employs a peak detection algorithm, which locates peaks and troughs in the stress-time curve. It can be understood that the calculation of cumulative damage is based on Miner's linear cumulative damage theory, which posits that damage from each stress cycle is additive. In specific implementations, the derivation of remaining life follows the following formula: in: Indicates remaining lifespan. Indicates the theoretical total lifespan. This represents the cumulative damage. The theoretical total life is obtained from the material's standard fatigue curve, and the cumulative damage is calculated by summing the ratios of actual stress cycles to standard fatigue strength.

[0036] Understandably, the selection of key points is based on stress concentration factors and historical failure statistics, with high stress concentration areas designated as key points for focused monitoring. The output of the life assessment component includes the remaining life percentage and confidence intervals, obtained through Monte Carlo simulation considering material property dispersion. Optionally, the thermal fatigue curve database update mechanism allows the addition of new material data, maintaining database timeliness by periodically importing the latest test data. In some embodiments, the statistical analysis of stress cycling patterns considers load sequence effects, which are corrected for the impact of high-low load order on damage accumulation. Optionally, the overall tire life status assessment results are presented in a visual format, including a life contour plot and a remaining life progress bar. The life assessment component communicates with the vehicle diagnostic system, which receives the life assessment results and triggers maintenance reminders.

[0037] See Figure 5 This demonstrates the variation of thermal stress in key tire regions over time (0-100s). The stress calculation component uses a thermal load spectrum to obtain this stress state data through thermal stress simulation. The curves show that the stress exhibits periodic fluctuations, which are closely related to the cyclical changes in thermal load during tire operation. This stress time history data forms the basis for the life assessment component to identify thermal fatigue cycles. The life assessment component can analyze the waveform characteristics of such stress values ​​changing over time, combine this with a material thermal fatigue curve database, use the rainflow counting method to count the number of fatigue cycles, calculate the cumulative damage based on Miner's linear cumulative damage theory, and then deduce the remaining tire life.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A tire pressure sensing system for monitoring tire surface temperature, characterized in that, The system includes a temperature monitoring component, a tire pressure monitoring component, a feature fusion component, a mesh building component, a thermal load analysis component, and a stress calculation component; The temperature monitoring component is arranged on the tire surface to collect temperature distribution information and form a temperature spatiotemporal sequence. The tire pressure monitoring component is integrated inside the tire to monitor pressure changes and generate pressure data sequences; The feature fusion component receives temperature spatiotemporal sequences and pressure data sequences, performs data fusion, and extracts the coupled features of temperature and pressure. The mesh building component generates a multi-resolution thermal mesh model based on the monitoring resolution requirements of different areas of the tire surface; The thermal load analysis component analyzes the thermal load changes based on the time series analysis of a multi-resolution thermal grid model to obtain the thermal load spectrum. The stress calculation component utilizes the thermal load spectrum to calculate the thermal stress distribution in key areas of the tire through thermal stress simulation, thereby obtaining stress state data.

2. The tire pressure sensing system for monitoring tire surface temperature as described in claim 1, characterized in that, When generating a multi-resolution thermal mesh model, the mesh building component divides the tire surface into multiple curved sub-regions based on the curvature characteristics of the tire surface and the differences in data resolution. An independent computation thread is assigned to each surface sub-region; the grid cell size, number of grids, and grid coverage density are determined based on the data point density of each surface sub-region. Control each computation thread to generate mesh surface data for the curved sub-region according to the corresponding mesh cell size, mesh number and mesh coverage density; Construct a 3D mesh based on mesh surface data and obtain mesh information; Assign unique identifiers to the vertices and volume elements of the mesh; By synchronizing the mesh information of adjacent surface sub-regions through inter-thread communication, a continuous multi-resolution thermal mesh model is formed.

3. The tire pressure sensing system for monitoring tire surface temperature as described in claim 1, characterized in that, When the feature fusion component extracts coupled features, it first standardizes the temperature spatiotemporal sequence and pressure data sequence to eliminate differences in data dimensions; then, it uses a pre-configured feature extraction structure to extract temperature and pressure features from the standardized data to form an initial feature set. When the dimension of the initial feature set exceeds a preset threshold, a dimensionality reduction operation is performed to obtain a simplified feature set; the time series of the simplified feature set is analyzed to enhance the dynamic correlation characteristics of temperature and pressure interaction, and coupled features are output.

4. The tire pressure sensing system for monitoring tire surface temperature as described in claim 1, characterized in that, The temperature monitoring component consists of multiple miniature temperature sensors arranged in an array on the inner wall of the tire. Each sensor collects local temperature data and generates a time-series signal. The tire pressure monitoring component is installed at the tire valve and records the pressure value in real time. The temperature monitoring component and the tire pressure monitoring component transmit data to the feature fusion component wirelessly.

5. A tire pressure sensing system for monitoring tire surface temperature as described in claim 1, characterized in that, When analyzing the thermal load spectrum, the thermal load analysis component establishes a geometric model of the tire, including the contact interface between the tire and the wheel hub. Apply heat flux boundary conditions based on the thermal load spectrum; Perform finite element thermal analysis to calculate the temperature distribution and thermal stress changes at key points of the tire; The time history and spatial gradient of stress distribution are analyzed to generate a dynamic thermal stress distribution map, which serves as stress state data.

6. The tire pressure sensing system for monitoring tire surface temperature as described in claim 1, characterized in that, The system also includes: The life assessment component identifies thermal fatigue cycles based on stress state data and calculates the remaining life of the tire by combining the material's thermal fatigue curve.

7. A tire pressure sensing system for monitoring tire surface temperature as described in claim 6, characterized in that, The life assessment component stores a database of thermal fatigue curves for tire materials. Based on stress state data, identify stress cycle patterns at key points and count the number of fatigue cycles; calculate the cumulative damage at each key point by interpolation based on the thermal fatigue curve database, and deduce the remaining life; integrate the remaining life of all key points to assess the overall life status of the tire.

8. A tire pressure sensing system for monitoring tire surface temperature as described in claim 2, characterized in that, When optimizing mesh connections, the mesh building component compares the mesh cell identifiers of adjacent surface sub-regions and adjusts the vertex coordinates to ensure mesh continuity; a smoothing algorithm is applied to optimize the mesh interface.

9. A tire pressure sensing system for monitoring tire surface temperature as described in claim 3, characterized in that, The feature extraction structure includes multi-layer convolution and pooling operations to extract spatial and temporal features from temperature images and pressure waveforms.

10. A tire pressure sensing system for monitoring tire surface temperature as described in claim 4, characterized in that, The data from the temperature monitoring component and the tire pressure monitoring component are aligned through a time synchronization mechanism, and multi-sensor data fusion is performed in the feature fusion component to compensate for environmental interference.

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