A tire temperature gradient monitoring system under high speed rotation

Abstract

The present application relates to the technical field of tire temperature gradient monitoring, and particularly relates to a tire high-speed rotation temperature gradient monitoring system, which comprises a working condition simulation module, a temperature gradient acquisition module, a data transmission module, a data processing module and a display and warning module, and the modules are electrically connected in sequence; the working condition simulation module is used for simulating different road roughness, different outdoor environments and different load working conditions encountered in the high-speed rotation process of the tire; the temperature gradient acquisition module is used for collecting temperature data of different parts in the high-speed rotation process of the tire; the data transmission module adopts a combination of wireless transmission and wired transmission, and transmits the temperature data acquired by the temperature gradient acquisition module and the working condition parameters of the working condition simulation module to the data processing module in real time; the system realizes multi-working condition simulation monitoring, comprehensively captures the change rule of the tire temperature gradient under different working conditions, and provides more actual use scene data support for tire research and development and performance optimization.

Description

Technical Field

[0001] This invention relates to the technical field of tire temperature gradient monitoring, and in particular to a tire temperature gradient monitoring system under high-speed rotation. Background Technology

[0002] As the core component of a vehicle in contact with the ground, the tire's operating condition directly affects vehicle driving safety, handling stability, and tire lifespan. During high-speed rotation, the tire generates heat through friction with the ground and through friction between the molecules within the tire carcass material. Simultaneously, influenced by the external environment, road conditions, and load, significant temperature gradients form in different parts of the tire (tread, sidewall, tire carcass interior, and rim connection). Abnormal temperature gradients are a major contributing factor to tire blowouts, accelerated wear, and performance degradation.

[0003] Existing tire temperature monitoring technologies mostly focus on monitoring tire surface temperature under single operating conditions. For example, conventional TPMS systems can only collect the internal air temperature or surface temperature of the tire, failing to achieve accurate monitoring of temperature gradients and unable to simulate temperature change patterns under different usage scenarios. Specifically, existing technologies have the following shortcomings: First, they cannot simulate the impact of different road surface roughness (such as asphalt, gravel, and cement) on the high-speed rotation heat generation and temperature gradient distribution of the tire. Road surface roughness directly determines the friction coefficient between the tire and the ground, thus affecting heat generation efficiency and temperature gradient distribution. Second, they lack simulation of different outdoor environments (such as high and low temperatures, humidity, and sunlight). Temperature and humidity affect the rate at which tires dissipate heat, while sunlight exacerbates the temperature rise of the tire surface. Existing systems struggle to meet the monitoring needs of different environments. Third, they cannot simulate temperature gradient changes under different load conditions (full load, half load, no load). The load size alters the contact pressure between the tire and the ground, leading to significant differences in frictional heat generation intensity and internal tire temperature distribution. Fourth, existing monitoring systems mostly employ a single sensor layout, failing to achieve full-dimensional, multi-point temperature acquisition of the tire. This makes it difficult to accurately capture the temperature gradient change characteristics under different operating conditions, resulting in incomplete and inaccurate monitoring data that cannot provide more reliable data support for tire research and development, safety warnings, and service life assessment. Summary of the Invention

[0004] To address the aforementioned technical issues, this invention provides a tire temperature gradient monitoring system under high-speed rotation that enables multi-condition simulation monitoring, comprehensively captures the changing patterns of tire temperature gradients under different conditions, and provides data support for tire research and development and performance optimization that is more closely aligned with actual usage scenarios.

[0005] The present invention provides a temperature gradient monitoring system for tires under high-speed rotation, comprising a working condition simulation module, a temperature gradient acquisition module, a data transmission module, a data processing module, and a display and early warning module, wherein each module is electrically connected in sequence. Operating condition simulation module: used to simulate different road surface roughness, different outdoor environments, and different loads encountered by the tire during high-speed rotation; Temperature gradient acquisition module: used to acquire temperature data of different parts of the tire during high-speed rotation, so as to achieve accurate capture of temperature gradient; Data transmission module: It adopts a combination of wireless and wired transmission to transmit the temperature data acquired by the temperature gradient acquisition module and the operating parameters of the operating condition simulation module to the data processing module in real time. Data processing module: electrically connected to the data transmission module and the display and warning module, including a microcontroller, a data storage unit and a data analysis unit; Microcontroller: Used to receive temperature data and operating parameters transmitted by the data transmission module, control the coordinated operation of various modules, set the monitoring frequency, and realize real-time data processing and command issuance; Data storage unit: used to store temperature data, operating parameters, and analysis results; Data Analysis Unit: Employing a multiple linear regression algorithm, this unit analyzes temperature data under varying road surface roughness, outdoor environments, and load conditions. It calculates the temperature difference between different parts of the tire, generates a temperature gradient curve, compares it with a preset safe temperature gradient threshold, determines whether the temperature gradient is abnormal, and establishes a correlation model between operating parameters and temperature gradient to provide data support for tire performance optimization. Display and early warning module: used to display monitoring data and analysis results in real time, and issue early warnings when the temperature gradient is abnormal.

[0006] Preferably, the working condition simulation module includes a road surface roughness simulation submodule, an outdoor environment simulation submodule, and a load simulation submodule. The three submodules work together to achieve accurate simulation of multiple working conditions. The road surface roughness simulation submodule includes a simulation test bench, a replaceable road surface simulation plate, and a drive unit; The simulation test bench is used to fix the tire. The drive unit drives the tire to rotate at high speed and fits it against the road surface simulation plate. The replaceable road surface simulation plate can be replaced with different roughness grades as needed (such as smooth asphalt road surface, rough gravel road surface, and ordinary cement road surface). By controlling the surface friction coefficient of the road surface simulation plate, the friction state between the tire and the ground under different actual road conditions can be simulated. The friction coefficient range can be adjusted from 0.3 to 0.8. The bonding pressure is consistent with the tire ground pressure when the vehicle is driving. Outdoor environment simulation submodule: includes an environment simulation box, which encloses the simulation test bench and tires, and integrates temperature control unit, humidity control unit and light control unit inside; The temperature control unit simulates extreme cold and high temperature environments, with a temperature control range of -40℃ to 80℃. The humidity control unit simulates different humidity conditions, with a humidity control range of 10%RH-90%RH; The illumination adjustment unit simulates sunny and cloudy lighting environments to simulate the heat dissipation and heating characteristics of tires under different outdoor conditions; Load simulation submodule: includes loading unit and pressure sensor; The loading unit is installed axially above the tire and simulates different load conditions of the vehicle, such as no load, half load, and full load, by applying different vertical loads to the tire. Pressure sensor: Collects the applied pressure in real time and feeds the pressure data back to the data processing module.

[0007] Preferably, the temperature gradient acquisition module includes a temperature sensor, a sensor fixing assembly, and a signal conditioning unit; Temperature sensor: It adopts a combination of infrared temperature sensor and probe thermocouple. The infrared temperature sensor is used to collect the temperature of the tire tread and sidewall, and the probe thermocouple is used to collect the temperature of the tire carcass, inner carcass, and rim connection. The sensor accuracy is ≤±1.5℃, the response time is ≤1s, and the operating temperature range is -40℃~150℃, which can adapt to the monitoring needs of high-speed tire rotation and extreme environments. Sensor mounting components: The system combines embedded packaging with flexible mounting. The infrared temperature sensor is fixed to a pre-set mounting point on the tire surface with high-temperature resistant epoxy adhesive. The probe-type thermocouple is embedded into the tire at different depths through a vulcanization process. The sensor leads are made of wear-resistant and high-temperature resistant flexible wires to prevent breakage when the tire rotates at high speed. At the same time, it adopts IP68-level protection packaging to resist mud and water corrosion and mechanical impact. Signal conditioning unit: used to filter, amplify and convert the temperature signal collected by the sensor into an analog-to-digital signal, eliminate electromagnetic interference and vibration interference generated by high-speed rotation, and convert analog signals into digital signals.

[0008] Preferably, the data transmission module includes a wireless transmission unit and a wired transmission unit; Wireless transmission unit: Utilizing radio frequency (RF) transmission technology, it integrates a low-power transmission module, is mounted on the tire rim, and is electrically connected to the temperature gradient acquisition module. It is used to transmit real-time temperature data during tire rotation; the transmission distance is ≥50m, and the transmission rate is ≥1Mbps, ensuring the stability of data transmission under high-speed rotation and avoiding signal interruption. Wired transmission unit: Used to transmit road surface roughness level, ambient temperature, humidity, light intensity, load size and other operating parameters from the operating condition simulation module, as well as temperature data received by the wireless transmission unit; shielded cables are used to reduce external electromagnetic interference and ensure the accuracy of data transmission.

[0009] Preferably, the data analysis unit uses a multiple linear regression algorithm to determine whether the temperature gradient is abnormal. The specific method is as follows: 1. Determine the variables for regression analysis: Using road surface roughness, outdoor ambient temperature, outdoor ambient humidity, vehicle load, and driving speed as independent variables, and the real-time monitored temperatures of tire tread, tire shoulder, tire sidewall, and tire bead as dependent variables, a regression analysis sample set was constructed. 2. Data preprocessing: The sample data is processed by outlier removal, missing value interpolation, and normalization to eliminate dimensional differences and interference data, thereby improving the accuracy of regression fitting. 3. Multiple linear regression fitting: The regression coefficients are solved using the least squares method to establish a multiple linear regression model for operating parameters and temperatures of various parts of the tire. The formula is as follows: T=β0+β1X1+β2X2+β3X3+β4X4+β5X5 In the formula: T is the predicted temperature of the tire part, β0 is the intercept, β1-β5 are the regression coefficients, and X1-X5 are the road surface roughness, ambient temperature, ambient humidity, load, and vehicle speed, respectively. 4. Calculate the temperature difference between different parts of the tire: Based on the temperature of each part output by the regression model, calculate the radial temperature difference of the tread and bead, the circumferential temperature difference of adjacent measuring points on the same circumference, and the axial temperature difference of the inner and outer sides of the tire. 5. Generate temperature gradient curve: The temperature gradient is calculated using the formula G=ΔT / ΔL (ΔT is the temperature difference, ΔL is the physical distance between measuring points); a continuous temperature gradient curve is generated by linear interpolation with time, vehicle speed, and road surface roughness as the horizontal axis and temperature gradient as the vertical axis. 6. Temperature gradient anomaly detection: The real-time calculated temperature gradient is compared point by point with the preset safety threshold: Gradient ≤ threshold: judged as normal; Gradient > threshold: Determined as abnormal, and an abnormality flag is output to the early warning module.

[0010] Preferably, the correlation model between the operating parameters and the temperature gradient is obtained through the following steps: S1, Training Sample Construction Multiple sets of historical monitoring data of tires under different road surface roughness, different outdoor environments, different loads, and different vehicle speeds were collected to construct a training sample set: S1.1 Independent variables (operating condition parameters): X1 road surface roughness, X2 outdoor ambient temperature, X3 outdoor ambient humidity, X4 vehicle load, X5 vehicle speed; S1.2 Dependent variable (temperature gradient): Yr radial temperature gradient, Yc circumferential temperature gradient, Yz axial temperature gradient; Each sample group includes synchronously collected operating parameters and corresponding calculated temperature gradients, with a sample size of ≥1000 groups to ensure fitting accuracy. S2, Data Preprocessing Standardize the sample data to eliminate differences in units and values: S2.1. Use the 3σ criterion to remove abnormal samples caused by centrifugal interference and transmission errors; S2.2, Use linear interpolation to complete the missing working condition and gradient data; S2.3. Use minimum-maximum normalization to map all parameters to the interval [0,1], formula: X′=(X-Xmin) / (Xmax-Xmin); S3, Association Model Construction For radial, circumferential, and axial temperature gradients, multiple linear regression correlation models are established respectively. The general formula is as follows: Y=β0+β1X1+β2X2+β3X3+β4X4+β5X5 In the formula: Y: Predicted temperature gradient (Yr / Yc / Yz); β0: Model intercept term; β1~β5: Regression coefficients of operating condition parameters (characterizing the influence weights); X1~X5: Normalized operating condition variables; S4. Model Training (Coefficient Calculation) The least squares method is used as the training criterion to minimize the sum of squared errors between the predicted gradient and the actual gradient. ; The optimal regression coefficients β0~β5 are solved by matrix operations to complete the model fitting. S5. Model Validation and Optimization Select 20% of the sample as the test set, and calculate the coefficient of determination R² and mean squared error MSE: R² ≥ 0.9: The model fits satisfactorily; R2 < 0.9: Expand the sample size and retrain until the accuracy requirement is met; S6, Model Solidification The regression coefficients and model parameters after training are saved to obtain the final operating condition parameters and temperature gradient correlation model. Operating condition parameters can be input in real time, and the corresponding temperature gradient prediction values ​​can be output, providing quantitative data support for tire performance optimization.

[0011] Preferably, the operating condition simulation module further includes a speed adjustment unit; Speed ​​regulation unit: electrically connected to the drive unit, used to adjust the high-speed rotation speed of the tires to simulate the temperature gradient changes of the tires at different driving speeds; meeting the monitoring needs of different vehicle models.

[0012] Preferably, the data processing module further includes a data calibration unit; Data calibration unit: used to calibrate the data collected by the temperature sensor, eliminate sensor errors, and ensure the accuracy of temperature data. The calibration cycle is automatic every 24 hours.

[0013] Preferably, the display warning module includes a display unit and a warning unit; Display unit: Uses a touch screen to display the tire's high-speed rotation speed, temperature data of different parts, temperature gradient curve, current road surface roughness level, ambient temperature, humidity, light intensity, load size and other operating parameters in real time; Early warning unit: includes an audible and visual alarm and an alarm indicator light. When the data analysis unit determines that the temperature gradient exceeds the preset threshold, the audible and visual alarm will sound an alarm, the alarm indicator light will emit a red warning light, and the abnormal location and abnormal data will be displayed on the display unit.

[0014] Preferably, it also includes a power supply module, which is used to provide stable power to each module, including a built-in lithium battery, an external power interface, a power switching unit, and a power management unit; External power interface: The input end connects to an external DC power supply, and the output end connects to the power switching unit for connection to the laboratory regulated power supply, which directly powers the system and charges the built-in lithium battery; Built-in lithium battery: The output end connects to the power switching unit to provide offline portable power supply for the system, supporting continuous operation without external power supply; Power switching unit: The input end is connected to the external power interface and the built-in lithium battery respectively, and the output end is connected to the power management unit. It prioritizes the use of external power supply and automatically switches to the built-in lithium battery power supply when the external power supply is disconnected. Power Management Unit: The output terminals are electrically connected to the operating condition simulation module, temperature gradient acquisition module, data transmission module, data processing module, and display and early warning module, respectively, to complete voltage regulation, current limiting, overcharge and over-discharge protection, and output a constant voltage to power each module.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Enables multi-condition simulation monitoring. Through the three sub-modules of the condition simulation module, it can accurately simulate the high-speed rotation state of tires under different roughness road surfaces, different outdoor environments, and different load conditions. This solves the problem of the single monitoring scenario in the existing system and can comprehensively capture the changing patterns of tire temperature gradient under different conditions, providing more data support for tire research and development and performance optimization that is more in line with actual use scenarios. 2. Accurate and comprehensive temperature gradient monitoring: It adopts a combination of infrared temperature sensors and probe thermocouples, combined with multi-point and all-dimensional sensor layout, to realize synchronous temperature acquisition of different parts of the tire surface and interior. With the help of the signal conditioning unit to eliminate interference, it ensures the accuracy of temperature data. The data analysis unit generates temperature gradient curves to accurately capture the characteristics of temperature gradient changes, resulting in high monitoring accuracy and comprehensive data. 3. Highly practical and widely adaptable: It can adjust tire speed, load, road surface roughness, and environmental parameters to meet the tire monitoring needs of different vehicle types such as passenger cars and commercial vehicles. It can be used for tire research and development testing in the laboratory as well as for real-time monitoring of actual vehicles. It also has data storage, historical playback and abnormal warning functions. It is easy to operate and highly practical. 4. High stability and strong anti-interference capability: The sensor adopts high temperature and wear resistant packaging and fixing methods. Data transmission adopts a combination of shielded cable and low power wireless transmission, which effectively resists the vibration generated by high speed rotation, electromagnetic interference and the influence of extreme environment, ensuring long-term stable operation of the system and stable and reliable monitoring data transmission. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a structural diagram of the outdoor environment simulation submodule; Figure 3 This is a schematic diagram showing the connection between the power supply module and the operating condition simulation module, etc. Figure 4 This is a schematic diagram of the multiple linear regression algorithm. Detailed Implementation

[0017] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the present invention more comprehensive.

[0018] Example 1 like Figures 1 to 3 As shown, the present invention provides a temperature gradient monitoring system for tires under high-speed rotation, comprising a working condition simulation module, a temperature gradient acquisition module, a data transmission module, a data processing module, and a display and early warning module, wherein each module is electrically connected in sequence. Operating condition simulation module: used to simulate different road surface roughness, different outdoor environments, and different loads encountered by the tire during high-speed rotation; Temperature gradient acquisition module: used to acquire temperature data of different parts of the tire during high-speed rotation, so as to achieve accurate capture of temperature gradient; Data transmission module: It adopts a combination of wireless and wired transmission to transmit the temperature data acquired by the temperature gradient acquisition module and the operating parameters of the operating condition simulation module to the data processing module in real time. Data processing module: electrically connected to the data transmission module and the display and warning module, including a microcontroller, a data storage unit, a data analysis unit and a data calibration unit; The microcontroller, as the core control unit, receives temperature data and operating parameters from the data transmission module, controls the collaborative work of various modules, and sets the monitoring frequency to 5Hz. The data storage unit stores temperature data, operating parameters, and analysis results, supports cyclic storage, and retains 30 days of historical data. The data analysis unit uses a multiple linear regression algorithm to calculate the temperature difference between different parts of the tire, generates a temperature gradient curve, and presets a safe temperature gradient threshold (temperature difference between the tread and the inner layer of the tire ≤ 20℃, temperature difference between the sidewall and the rim connection ≤ 15℃) to determine whether the temperature gradient is abnormal. The data calibration unit automatically calibrates the temperature sensor every 24 hours to eliminate sensor errors.

[0019] The display and early warning module includes a display unit and an early warning unit: it is used to display monitoring data and analysis results in real time, and to issue an early warning when the temperature gradient is abnormal; In the display and warning module, the display unit uses a touch screen to display tire speed, temperature data of various parts, temperature gradient curve and current operating parameters (road surface roughness level, ambient temperature, humidity, light intensity, load size) in real time. It supports data query, curve magnification and historical data playback. The warning unit includes an audible and visual warning device and an LED alarm light. When the temperature gradient exceeds the preset threshold, the audible and visual warning device will sound an alarm, the LED alarm light will light up, and the display unit will display the abnormal parts and abnormal data. The working condition simulation module includes a road surface roughness simulation submodule, an outdoor environment simulation submodule, and a load simulation submodule. The three submodules work together to achieve accurate simulation of multiple working conditions. The road surface roughness simulation submodule includes a simulation test bench, a replaceable road surface simulation plate, and a drive unit; The simulation test bench is used to fix the tire. The drive unit drives the tire to rotate at high speed and fits it against the road surface simulation plate. The replaceable road surface simulation plate can be replaced with different roughness levels as needed. By controlling the surface friction coefficient of the road surface simulation plate, the friction state between the tire and the ground under different actual road conditions is simulated. The bonding pressure is consistent with the tire ground pressure when the vehicle is driving. Specifically, in the road surface roughness simulation submodule, the simulation test bench adopts a steel structure frame to fix the tire to be monitored. The drive unit adopts a variable frequency motor with a power of 5.5kW, which can drive the tire to rotate at high speed. The speed is adjusted by the speed adjustment unit, with a range of 0-300km / h. The road surface simulation plate adopts a replaceable design and prepares three simulation plates with different roughness levels, corresponding to smooth asphalt road surface (friction coefficient 0.3-0.4), ordinary cement road surface (friction coefficient 0.5-0.6), and rough gravel road surface (friction coefficient 0.7-0.8), respectively. They are fixed to the simulation test bench with bolts and can be quickly replaced according to the test requirements. The outdoor environment simulation submodule includes an environment simulation box, which encloses the simulation test bench and tires. The box integrates a temperature control unit, a humidity control unit, and a light control unit. The temperature control unit simulates extreme environments of severe cold and high temperature, the humidity control unit simulates different humidity conditions, and the light control unit simulates sunny and cloudy lighting environments, thus simulating the heat dissipation and temperature rise characteristics of tires under different outdoor environments. Specifically: In the outdoor environment simulation submodule, the environment simulation chamber adopts an insulated and sealed structure to completely enclose the simulation test bench and tires. The temperature control unit uses a combination of electric heating tube and refrigeration compressor, with a temperature control range of -40℃ to 80℃. The humidity control unit uses a combination of ultrasonic humidifier and dehumidifier, with a humidity control range of 10%RH to 90%RH. The lighting control unit uses LED supplementary lighting, with a light intensity adjustment range of 500 and 10000 lux, which can simulate different lighting environments. Load simulation submodule: includes loading unit and pressure sensor; The loading unit adopts a hydraulic loading device, which can achieve continuous adjustment within the range of 0-50000N to simulate the vehicle's no-load, half-load, and full-load conditions. The pressure sensor adopts a strain gauge pressure sensor to collect the loading pressure in real time and feed it back to the data processing module to achieve precise control of the load. Specifically: Users input the target load value (such as 5000N for no load, 25000N for half load, and 50000N for full load) through the display warning module or host computer, and the load control unit receives and locks the target value; The load control unit drives the hydraulic servo loading device to move, the servo cylinder extends, pushes the loading head downward to contact the tire, and begins to apply a vertical load; The pressure sensor collects the current actual load pressure in real time and transmits it back to the load control unit in analog or digital form. The sampling frequency is synchronized with the temperature acquisition. The load control unit compares the actual pressure value with the set target pressure value in real time: If the actual pressure is less than the target pressure, the control loading device continues to apply pressure. If the actual pressure is greater than the target pressure, the loading device will slightly reduce the pressure. If the actual pressure is approximately equal to the target pressure (within the allowable range of ±1%), maintain the current oil pressure and lock the load. Throughout the entire process of high-speed tire rotation, changes in road surface roughness, and changes in ambient temperature, the feedback loop works continuously to automatically compensate for load fluctuations caused by tire deformation, vibration, and slippage, ensuring that the actual load remains stable near the set value and guaranteeing the accuracy and reliability of temperature gradient monitoring data. The load data collected by the pressure sensor is synchronized with the timestamps of road surface roughness, ambient temperature and humidity, tire speed, and temperature data, and stored together in the data storage unit to provide standard load input for subsequent data analysis units to perform multiple linear regression modeling, temperature gradient analysis, and working condition correlation model training. After the test is completed, the load control unit issues an unloading command, the servo cylinder resets, the loading head disengages from the tire, and the safe unloading is completed. In this embodiment, the three sub-modules of road surface roughness, outdoor environment, and load work together to fully reproduce the real operating conditions of tires rotating at high speed. Three types of road surface roughness simulation plates can be quickly replaced to adapt to different road scenarios. The environmental simulation chamber can simulate a wide temperature range of -40℃ to 80℃, a wide humidity range of 10%RH to 90%RH, and full illumination conditions, covering extreme outdoor environments. The load simulation sub-module adopts hydraulic servo closed-loop control, with a continuously adjustable load of 0~50000N and a control error of ≤±1%. It automatically stabilizes the load and compensates for fluctuations throughout the process, providing a standardized, high-precision, and repeatable experimental basis for temperature gradient monitoring. The system employs a combination of infrared temperature sensors and probe-type thermocouples, covering the circumferential / radial surfaces of the tire, the thickness direction inside the tire body, and the rim connection area to achieve full-range temperature data acquisition. The sensors are fixed with high-temperature resistant adhesives and vulcanization, and are equipped with IP68 waterproof and dustproof encapsulation and flexible leads, which can withstand high-speed rotation centrifugal force and the influence of harsh environments. The signal conditioning unit accurately processes the acquired signals to ensure reliable temperature data acquisition and achieve accurate capture of temperature gradients. With a microcontroller as the core, the system achieves coordinated control of all modules. A fixed 5Hz monitoring frequency meets the requirements for tracking dynamic temperature changes under high-speed rotation. The data storage unit supports 30-day cyclic storage, which can completely retain historical data. The data calibration unit automatically calibrates every 24 hours, effectively eliminating sensor drift errors. The data analysis unit uses a multiple linear regression algorithm to accurately calculate temperature differences, generate gradient curves, and complete anomaly detection. At the same time, it constructs a correlation model between operating conditions and temperature gradients, providing quantitative data support for tire performance optimization. The system can automate the entire process of setting, simulating, collecting, processing and issuing warnings without human intervention. It is fully adaptable to various scenarios such as high-speed tire performance testing, safety monitoring, structural optimization and material research and development in the laboratory, and greatly improves the efficiency and accuracy of tire temperature gradient monitoring.

[0020] Example 2 Based on Example 1, the present invention provides a temperature gradient monitoring system for tires under high-speed rotation, wherein the temperature gradient acquisition module includes a temperature sensor, a sensor fixing component, and a signal conditioning unit. Temperature sensor: A combination of infrared temperature sensor and probe thermocouple is used. The infrared temperature sensor is used to collect the temperature of the tire tread and sidewall, and the probe thermocouple is used to collect the temperature of the tire carcass, inner carcass, and rim connection. The specific arrangement is as follows: Four sets of infrared temperature sensors are evenly arranged circumferentially on the tire surface, spaced at 90° intervals; two sets of infrared temperature sensors are arranged radially, located in the center of the tread and the center of the sidewall, respectively; three sets of probe thermocouples are evenly arranged inside the tire along the tire body thickness direction, located in the surface, middle, and inner layers of the tire body, respectively; and one set of probe thermocouples is arranged at the rim connection, for a total of nine sets of sensors, to achieve full-dimensional temperature acquisition; Sensor mounting components: The system combines embedded packaging with flexible mounting. The infrared temperature sensor is fixed to a pre-set mounting point on the tire surface with high-temperature resistant epoxy adhesive. The probe-type thermocouple is embedded inside through the tire vulcanization process. The lead wire is made of flexible polytetrafluoroethylene wire with a diameter of 0.5mm and a temperature resistance of 200℃. It is also encapsulated with IP68 waterproof and dustproof rating to ensure stability during high-speed rotation and in harsh environments. The signal conditioning unit uses an instrumentation amplifier to filter and amplify the sensor signal, and converts the analog signal into a digital signal through an ADC converter to output a stable and reliable digital temperature signal, ensuring the accuracy of temperature gradient calculation. Specifically: 1. Signal amplification: A high-precision instrument amplifier is used to amplify the weak voltage signal of the thermocouple and the analog signal of the infrared sensor with low noise, improve the signal-to-noise ratio, and suppress vibration interference and vehicle electromagnetic interference caused by high-speed rotation. 2. Filtering: Built-in active filter circuit to filter out high-frequency noise, power frequency interference and pseudo signals generated by rotational jitter, ensuring smooth and accurate temperature signal; 3. Analog-to-digital conversion (ADC): The analog temperature signal is converted into a digital signal through a high-precision ADC converter, which has high resolution and good linearity, ensuring that the temperature acquisition error is ≤ ±0.5℃; 4. Sampling frequency: The system sampling rate is set to 10Hz, which means that the synchronous acquisition of 9 sensors is completed every 0.1 seconds to ensure real-time tracking of temperature changes under high-speed rotation and to avoid losing transient characteristics of temperature gradient. The temperature gradient acquisition module achieves accurate capture of temperature gradients under high-speed rotation through the following methods: 1. Multi-point synchronous data acquisition: Nine sets of sensors are used to sample at the same time to obtain the temperature distribution on the surface and inside of the tire at the same time, ensuring the time consistency of gradient calculation; 2. Full-dimensional spatial coverage: 4 points in the circumferential direction, 2 points in the radial direction, 3 points in the thickness direction, and 1 connection point form a spatial temperature field sampling network, which can directly calculate the radial temperature gradient of the tread, sidewall, carcass, and rim, the circumferential temperature gradient of the temperature difference at different positions on the circumference, and the thickness direction temperature gradient of the carcass surface, middle layer, and inner layer. 3. High-speed and stable transmission: The sensor is fixed and reliable, with no signal attenuation or drift. It maintains continuous and stable data acquisition even when the tire is rotating at high speed, without data loss or jumps, ensuring that the temperature gradient curve is true and reliable. 4. Digital Standard Output: The conditioned and converted digital temperature signal is directly sent to the data processing module for temperature difference calculation, gradient solution, and curve generation, providing a high-precision data source for subsequent anomaly detection and performance optimization.

[0021] The data transmission module includes a wireless transmission unit and a wired transmission unit; Wireless transmission unit: It adopts radio frequency (RF) transmission technology, integrates a low-power transmission module, is installed on the tire rim, and is electrically connected to the temperature gradient acquisition module to transmit real-time temperature data during tire rotation. Wired transmission unit: used to transmit road surface roughness level, ambient temperature, humidity, light intensity, load size and other operating parameters from the operating condition simulation module, as well as temperature data received by the wireless transmission unit; like Figure 4 As shown, the data analysis unit uses a multiple linear regression algorithm to determine whether the temperature gradient is abnormal. The specific method is as follows: 1. Determine the variables for regression analysis: Using road surface roughness, outdoor ambient temperature, outdoor ambient humidity, vehicle load, and driving speed as independent variables, and the real-time monitored temperatures of tire tread, tire shoulder, tire sidewall, and tire bead as dependent variables, a regression analysis sample set was constructed. 2. Data preprocessing: The sample data is processed by outlier removal, missing value interpolation, and normalization to eliminate dimensional differences and interference data, thereby improving the accuracy of regression fitting. 3. Multiple linear regression fitting: The regression coefficients are solved using the least squares method to establish a multiple linear regression model for operating parameters and temperatures of various parts of the tire. The formula is as follows: T=β0+β1X1+β2X2+β3X3+β4X4+β5X5 In the formula: T is the predicted temperature of the tire part, β0 is the intercept, β1-β5 are the regression coefficients, and X1-X5 are the road surface roughness, ambient temperature, ambient humidity, load, and vehicle speed, respectively. 4. Calculate the temperature difference between different parts of the tire: Based on the temperature of each part output by the regression model, calculate the radial temperature difference of the tread and bead, the circumferential temperature difference of adjacent measuring points on the same circumference, and the axial temperature difference of the inner and outer sides of the tire. 5. Generate temperature gradient curve: The temperature gradient is calculated using the formula G=ΔT / ΔL (ΔT is the temperature difference, ΔL is the physical distance between measuring points); a continuous temperature gradient curve is generated by linear interpolation with time, vehicle speed, and road surface roughness as the horizontal axis and temperature gradient as the vertical axis. 6. Temperature gradient anomaly detection: The real-time calculated temperature gradient is compared point by point with the preset safety threshold: Gradient ≤ threshold: judged as normal; Gradient > threshold: Determined as abnormal, an abnormality flag is output to the early warning module; The correlation model between the operating parameters and the temperature gradient is obtained through the following steps: S1, Training Sample Construction Multiple sets of historical monitoring data of tires under different road surface roughness, different outdoor environments, different loads, and different vehicle speeds were collected to construct a training sample set: S1.1 Independent variables (operating condition parameters): X1 road surface roughness, X2 outdoor ambient temperature, X3 outdoor ambient humidity, X4 vehicle load, X5 vehicle speed; S1.2 Dependent variable (temperature gradient): Yr radial temperature gradient, Yc circumferential temperature gradient, Yz axial temperature gradient; Each sample group includes synchronously collected operating parameters and corresponding calculated temperature gradients, with a sample size of ≥1000 groups to ensure fitting accuracy. S2, Data Preprocessing Standardize the sample data to eliminate differences in units and values: S2.1. Use the 3σ criterion to remove abnormal samples caused by centrifugal interference and transmission errors; S2.2, Use linear interpolation to complete the missing working condition and gradient data; S2.3. Use minimum-maximum normalization to map all parameters to the interval [0,1], formula: X′=(X-Xmin) / (Xmax-Xmin); S3, Association Model Construction For radial, circumferential, and axial temperature gradients, multiple linear regression correlation models are established respectively. The general formula is as follows: Y=β0+β1X1+β2X2+β3X3+β4X4+β5X5 In the formula: Y: Predicted temperature gradient (Yr / Yc / Yz); β0: Model intercept term; β1~β5: Regression coefficients of operating condition parameters (characterizing the influence weights); X1~X5: Normalized operating condition variables; S4. Model Training (Coefficient Calculation) The least squares method is used as the training criterion to minimize the sum of squared errors between the predicted gradient and the actual gradient. ; The optimal regression coefficients β0~β5 are solved by matrix operations to complete the model fitting. S5. Model Validation and Optimization Select 20% of the sample as the test set, and calculate the coefficient of determination R² and mean squared error MSE: R² ≥ 0.9: The model fits satisfactorily; R2 < 0.9: Expand the sample size and retrain until the accuracy requirement is met; S6, Model Solidification Save the regression coefficients and model parameters after training to obtain the final working condition parameters and temperature gradient correlation model. The working condition parameters can be input in real time and the corresponding temperature gradient prediction values ​​can be output, providing quantitative data support for tire performance optimization. The operating condition simulation module also includes a speed regulation unit; Speed ​​regulation unit: electrically connected to the drive unit, used to adjust the high-speed rotation speed of the tires to simulate the temperature gradient changes of the tires at different driving speeds; It also includes a power supply module, which provides a stable power supply to each module, including a built-in lithium battery, an external power interface, a power switching unit, and a power management unit; External power interface: The input end connects to an external DC power supply, and the output end connects to the power switching unit for connection to the laboratory regulated power supply, which directly powers the system and charges the built-in lithium battery; Built-in lithium battery: The output end connects to the power switching unit to provide offline portable power supply for the system, supporting continuous operation without external power supply; Power switching unit: The input end is connected to the external power interface and the built-in lithium battery respectively, and the output end is connected to the power management unit. It prioritizes the use of external power supply and automatically switches to the built-in lithium battery power supply when the external power supply is disconnected. Power Management Unit: The output terminals are electrically connected to the operating condition simulation module, temperature gradient acquisition module, data transmission module, data processing module, and display and early warning module, respectively, to complete voltage regulation, current limiting, overcharge and over-discharge protection, and output constant voltage to power each module; In this embodiment, the precise capture of temperature gradients under high-speed rotation is achieved through the deployment of nine sets of full-dimensional sensors and high-precision signal conditioning; wireless and wired dual-mode transmission is adopted to ensure stable data transmission under rotating conditions; accurate gradient calculation and anomaly detection are completed by relying on the multiple linear regression algorithm, and the self-trained working condition-temperature gradient correlation model can provide quantitative data support for tire performance optimization; the addition of a speed adjustment unit improves the working condition simulation dimension, and the automatic switching of dual power supplies improves the system's endurance and power supply stability. The overall system monitoring accuracy, working condition adaptability, data reliability, and intelligence level are significantly improved.

[0022] Example 3 Based on Example 1, the present invention provides a tire temperature gradient monitoring system under high-speed rotation. The road surface simulation plate is equipped with three types of road surfaces: corrugated road surface, icy road surface, and waterlogged road surface, with friction coefficients of 0.2-0.3, 0.1-0.2, and 0.25-0.35, respectively, which can simulate complex and harsh road conditions. The drive unit uses a servo motor instead of a frequency converter motor, improving the speed accuracy to ±0.5km / h, which is suitable for ultra-high-speed tire testing. The outdoor environment simulation submodule is a fully enclosed, insulated environment simulation box with an overall size of 2m×2m×1.5m. It completely encloses the simulation test bench and tires, with a sealing rating of IP65, to avoid interference from the external environment. Temperature control unit: Combination of electric heating element and variable frequency refrigeration compressor, heating rate ≥5℃ / min, cooling rate ≥3℃ / min, temperature range -40℃~80℃, control accuracy ±0.5℃, can simulate extreme working conditions such as polar cold and summer sun exposure; Humidity control unit: Combination of ultrasonic humidifier and condenser dehumidifier, humidity range 10%RH~90%RH, control accuracy ±2%RH, simulating dry, humid and high humidity environments; Lighting adjustment unit: Uses full-spectrum LED supplemental lighting with two light intensity levels: 500 lux (cloudy) and 10000 lux (sunny), to simulate the effect of different sunlight intensities on tire heat dissipation; The environmental simulation chamber is equipped with a rainfall simulation unit and a snowfall simulation unit. The rainfall adjustment range is 0-50mm / h, and the snowfall adjustment range is 0-30mm / h. It can simulate the tire temperature change characteristics under rain and snow. The temperature control unit uses a heat pump system instead of a refrigeration compressor to reduce energy consumption.

[0023] Example 4 Based on Example 2, a fiber optic grating temperature sensor array is used to replace the original combination of infrared temperature sensor and probe thermocouple; the fiber optic grating sensor is an all-fiber passive structure, completely resistant to electromagnetic interference, can withstand centrifugal acceleration of ≥8000g, and is suitable for ultra-high speed rotation conditions of 350km / h. The number of sensors has been expanded to 12 sets, with temperature measurement points set at three key stress areas: the tire bead root, the tire bead seal, and the tire shoulder transition area, further refining the density of tire temperature field acquisition. The sensor mounting assembly, based on high-temperature epoxy adhesive bonding and vulcanization pre-embedding, adds a high-temperature silicone buffer layer to reduce mechanical stress damage to the sensors caused by high-speed tire deformation. The lead wires use silver-plated polytetrafluoroethylene wires, significantly improving conductivity and aging resistance. The signal conditioning unit uses an integrated multi-channel temperature acquisition chip, which integrates signal amplification, active filtering, ADC conversion, and digital temperature compensation functions. With a sampling rate of 20Hz, it can accurately capture the transient change characteristics of temperature gradient and improve the signal anti-interference capability. Based on the multiple linear regression algorithm, a hybrid analysis model is constructed by integrating the ridge regression algorithm and the BP neural network algorithm to solve the multicollinearity problem of working parameters such as road surface, load, and vehicle speed. The working condition simulation module adds a rain and snow simulation unit and a salt spray simulation unit, which are integrated inside the environmental simulation chamber. The salt spray concentration is continuously adjustable to simulate the temperature gradient changes of tires under humid, snowy, and corrosive environments.

[0024] In this embodiment, by expanding the simulation monitoring capabilities for complex scenarios such as rain, snow, salt spray, and ultra-high speed, the system's intelligence, stability, and versatility are significantly enhanced, and it can simultaneously meet the dual needs of laboratory tire R&D testing and on-board real-vehicle online monitoring.

[0025] The main functions achieved by this invention are: Simulating multi-dimensional operating conditions of tires under high-speed rotation, including different road surfaces, outdoor environments, loads, and rotational speeds, the system collects temperature data at multiple points on the tire surface and inside the tire through a full-dimensional sensor array, accurately capturing temperature gradients. It employs both wireless and wired dual-mode transmission of temperature and operating condition data, and uses a multiple linear regression algorithm to calculate temperature gradients and identify anomalies. It automatically trains a correlation model between operating parameters and temperature gradients, providing data support for tire performance optimization. Simultaneously, it enables visualized display of monitoring data, audible and visual warnings for abnormal temperature gradients, and ensures stable power supply to the system through automatic switching between dual power sources.

[0026] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A temperature gradient monitoring system for tires under high-speed rotation, characterized in that, It includes a working condition simulation module, a temperature gradient acquisition module, a data transmission module, a data processing module, and a display and early warning module, and each module is electrically connected in sequence; Operating condition simulation module: used to simulate different road surface roughness, different outdoor environments, and different loads encountered by the tire during high-speed rotation; Temperature gradient acquisition module: used to acquire temperature data of different parts of the tire during high-speed rotation, so as to achieve accurate capture of temperature gradient; Data transmission module: It adopts a combination of wireless and wired transmission to transmit the temperature data acquired by the temperature gradient acquisition module and the operating parameters of the operating condition simulation module to the data processing module in real time. Data processing module: electrically connected to the data transmission module and the display and warning module, including a microcontroller, a data storage unit and a data analysis unit; Microcontroller: Used to receive temperature data and operating parameters transmitted by the data transmission module, control the coordinated operation of various modules, set the monitoring frequency, and realize real-time data processing and command issuance; Data storage unit: used to store temperature data, operating parameters, and analysis results; Data Analysis Unit: Employing a multiple linear regression algorithm, this unit analyzes temperature data under varying road surface roughness, outdoor environments, and load conditions. It calculates the temperature difference between different parts of the tire, generates a temperature gradient curve, compares it with a preset safe temperature gradient threshold, determines whether the temperature gradient is abnormal, and establishes a correlation model between operating parameters and temperature gradient to provide data support for tire performance optimization. Display and early warning module: used to display monitoring data and analysis results in real time, and issue early warnings when the temperature gradient is abnormal.

2. The temperature gradient monitoring system for tires under high-speed rotation as described in claim 1, characterized in that, The working condition simulation module includes a road surface roughness simulation submodule, an outdoor environment simulation submodule, and a load simulation submodule. The three submodules work together to achieve accurate simulation of multiple working conditions. The road surface roughness simulation submodule includes a simulation test bench, a replaceable road surface simulation plate, and a drive unit; The simulation test bench is used to fix the tire; The drive unit drives the tire to rotate at high speed and fits it against the road surface simulation board; The replaceable road surface simulation board can be replaced with different roughness levels as needed. By controlling the surface friction coefficient of the road surface simulation board, the friction state between the tire and the ground under different actual road conditions can be simulated. The bonding pressure is consistent with the tire ground pressure when the vehicle is driving. Outdoor environment simulation submodule: includes an environment simulation box, which encloses the simulation test bench and tires, and integrates temperature control unit, humidity control unit and light control unit inside; The temperature control unit simulates extreme environments of severe cold and high temperature; The humidity control unit simulates different humidity conditions; The illumination adjustment unit simulates sunny and cloudy lighting environments to simulate the heat dissipation and heating characteristics of tires under different outdoor conditions; Load simulation submodule: includes loading unit and pressure sensor; The loading unit is installed axially above the tire and simulates different load conditions of the vehicle, such as no load, half load, and full load, by applying different vertical loads to the tire. Pressure sensor: Collects the applied pressure in real time and feeds the pressure data back to the data processing module.

3. The temperature gradient monitoring system for tires under high-speed rotation as described in claim 1, characterized in that, The temperature gradient acquisition module includes a temperature sensor, a sensor mounting assembly, and a signal conditioning unit; Temperature sensor: A combination of infrared temperature sensor and probe thermocouple is used. The infrared temperature sensor is used to collect the temperature of the tire tread and sidewall, and the probe thermocouple is used to collect the temperature of the tire carcass, inner carcass, and rim connection. Sensor mounting components: The infrared temperature sensor is fixed to a preset mounting point on the tire surface with high-temperature resistant epoxy adhesive, and the probe thermocouple is embedded into the tire at different depths through a vulcanization process. Signal conditioning unit: used to filter, amplify and convert the temperature signal collected by the sensor into an analog-to-digital signal, eliminate electromagnetic interference and vibration interference generated by high-speed rotation, and convert analog signals into digital signals.

4. The temperature gradient monitoring system for tires under high-speed rotation as described in claim 1, characterized in that, The data transmission module includes a wireless transmission unit and a wired transmission unit; Wireless transmission unit: It adopts radio frequency transmission technology, integrates a low-power transmission module, is installed on the tire rim, and is electrically connected to the temperature gradient acquisition module to transmit real-time temperature data during tire rotation. Wired transmission unit: Used to transmit road surface roughness level, ambient temperature, humidity, light intensity, load size and other operating parameters from the operating condition simulation module, as well as temperature data received by the wireless transmission unit.

5. The temperature gradient monitoring system for tires under high-speed rotation as described in claim 1, characterized in that, The data analysis unit uses a multiple linear regression algorithm to determine whether the temperature gradient is abnormal. The specific method is as follows: Using road surface roughness, outdoor ambient temperature, outdoor ambient humidity, vehicle load, and driving speed as independent variables, and the real-time monitored temperatures of tire tread, tire shoulder, tire sidewall, and tire bead as dependent variables, a regression analysis sample set was constructed. The sample data is processed by outlier removal, missing value interpolation, and normalization to eliminate dimensional differences and interference data, thereby improving the accuracy of regression fitting. The regression coefficients were solved using the least squares method, and a multiple linear regression model of the temperature of various parts of the tire was established. Based on the temperature of each part output by the regression model, calculate the radial temperature difference of the tread and bead, the circumferential temperature difference of adjacent measuring points on the same circumference, and the axial temperature difference of the inner and outer sides of the tire. The temperature gradient is calculated using the formula G=ΔT / ΔL. A continuous temperature gradient curve is generated by linear interpolation with time, vehicle speed, and road surface roughness as the horizontal axis and temperature gradient as the vertical axis. The real-time calculated temperature gradient is compared point by point with the preset safety threshold: if the gradient is less than or equal to the threshold, it is considered normal; if the gradient is greater than or equal to the threshold, it is considered abnormal, and an abnormality indicator is output to the early warning module.

6. The temperature gradient monitoring system for a tire under high-speed rotation as described in claim 1, characterized in that, The correlation model between the operating parameters and the temperature gradient is obtained through the following steps: Construct a multi-condition historical data sample set, with road surface roughness, outdoor environmental parameters, load, and vehicle speed as independent variables and tire temperature gradient as dependent variable; After data preprocessing, the least squares method was used to fit the multiple linear regression model; The accuracy of the model is verified by testing the test set, and the optimal parameters are solidified to obtain the correlation model.

7. The temperature gradient monitoring system for tires under high-speed rotation as described in claim 1, characterized in that, The operating condition simulation module also includes a speed regulation unit; Speed ​​regulation unit: electrically connected to the drive unit, used to adjust the high-speed rotation speed of the tires to simulate the temperature gradient changes of the tires at different driving speeds.

8. The temperature gradient monitoring system for tires under high-speed rotation as described in claim 1, characterized in that, The data processing module also includes a data calibration unit; Data calibration unit: used to calibrate the data collected by the temperature sensor, eliminate sensor errors, and automatically calibrate once every 24 hours.

9. The temperature gradient monitoring system for tires under high-speed rotation as described in claim 1, characterized in that, The display and warning module includes a display unit and a warning unit; Display unit: Uses a touch screen to display the tire's high-speed rotation speed, temperature data of different parts, temperature gradient curve, current road surface roughness level, ambient temperature, humidity, light intensity, load size and other operating parameters in real time; Early warning unit: includes an audible and visual alarm and an alarm indicator light. When the data analysis unit determines that the temperature gradient exceeds the preset threshold, the audible and visual alarm will sound an alarm, the alarm indicator light will emit a red warning light, and the abnormal location and abnormal data will be displayed on the display unit.

10. The temperature gradient monitoring system for a tire under high-speed rotation as described in claim 1, characterized in that, It also includes a power supply module, which provides a stable power supply to each module, including a built-in lithium battery, an external power interface, a power switching unit, and a power management unit; External power interface: The input end connects to an external DC power supply, and the output end connects to the power switching unit for connection to the laboratory regulated power supply, which directly powers the system and charges the built-in lithium battery; Built-in lithium battery: The output end connects to the power switching unit to provide offline portable power supply for the system, supporting continuous operation without external power supply; Power switching unit: The input end is connected to the external power interface and the built-in lithium battery respectively, and the output end is connected to the power management unit. It prioritizes the use of external power supply and automatically switches to the built-in lithium battery power supply when the external power supply is disconnected. Power Management Unit: The output terminals are electrically connected to the operating condition simulation module, temperature gradient acquisition module, data transmission module, data processing module, and display and early warning module, respectively, to complete voltage regulation, current limiting, overcharge and over-discharge protection, and output a constant voltage to power each module.

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