Tire pressure and camber angle eccentric wear mileage test method, equipment, product and vehicle

Abstract

The invention provides a tire pressure and camber angle eccentric wear mileage test method, equipment, a product and a vehicle, and relates to the technical field of automobile tests.According to the method, a test route and a load condition highly matched with a real working condition are scientifically constructed by using big data such as a running track, a load, tire pressure and a driving behavior of an actual vehicle; and the dynamic positioning characteristics of the vehicle under different loads are accurately reflected by establishing a relation curve of the camber angle changing along with the loads, and the reality representativeness of the test is enhanced. The full-factor test design is adopted, the influence of the tire pressure, the camber angle and the interaction of the tire pressure and the camber angle on tire eccentric wear is comprehensively investigated, then an eccentric wear mileage mathematical model with predictive capacity is constructed, and quantitative evaluation and optimization of the tire life under different parameter combinations are achieved. And finally, the optimal tire pressure and camber angle combination meeting the durability target can be quickly screened out through model prediction, and a data-driven decision basis is provided for prolonging the service life of the tire and adjusting the parameters of the whole vehicle chassis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile testing, in particular to a tire pressure and camber angle eccentric wear mileage test method, device, product and vehicle. BACKGROUND

[0002] Optimization of tire pressure and camber angle is an important research direction in the field of automobile engineering, which directly affects the ride comfort, power performance, handling stability and tire life of the vehicle. Current research methods mainly include theoretical modeling and simulation, bench test and real vehicle road test, but these methods have certain limitations: the multi-body dynamics simulation results deviate from the actual road conditions; the bench test cannot completely restore the complex conditions of the real road, such as road roughness, steering inertia and temperature change; and the real vehicle road test often uses a fixed route for tire wear test, mainly aiming at a single working condition and a single factor, and cannot fully identify the optimal solution under different tire pressure and camber angle combinations. SUMMARY

[0003] The purpose of the present application is to provide a tire pressure and camber angle eccentric wear mileage test method, device, product and vehicle to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0004] The solution to the technical problem of the present application is: the present application provides a tire pressure and camber angle eccentric wear mileage test method, comprising the following steps: Collecting vehicle big data information, including the driving trajectory, load data, tire pressure data and driving behavior data of the tire eccentric wear vehicle; Based on the load data, the relationship curve between camber angle and vehicle load is established by comparing the camber angle values of the vehicle wheel under different load conditions to determine the test load condition; Based on the driving trajectory, the road surface proportion of urban roads, general highways and expressways is analyzed to develop a test route matching the actual driving conditions of the user; Using a full-factor test design scheme, taking tire pressure and camber angle as variables, performing real vehicle road test under the test route and test load condition to obtain eccentric wear mileage data corresponding to different variable combinations; Analyzing the eccentric wear mileage data to determine the influence of tire pressure and camber angle on eccentric wear mileage and the interaction between them, and constructing an eccentric wear mileage prediction model through mathematical model fitting; Inputting the target tire pressure and target camber angle into the eccentric wear mileage prediction model to obtain the corresponding wheel eccentric wear mileage prediction result, and determining the optimal combination of tire pressure and camber angle in combination with the preset eccentric wear mileage target.

[0005] Further, the driving behavior data is used to screen regular driving and exclude the influence of intense driving on tire eccentric wear, specifically including: based on longitudinal acceleration, lateral acceleration and vehicle speed data, confirming that the driving behavior of the tire eccentric wear vehicle belongs to regular driving.

[0006] Further, the load data includes the number of passengers, passenger position distribution and trunk load weight under different use scenarios, and the camber value under different load conditions is compared based on the load data to establish a camber-vehicle load relationship curve to determine the test load condition, including the following steps: Based on the load data, the typical load range and load distribution characteristics of the tire eccentric wear vehicle in actual use are counted; In a four-wheel positioning experiment environment, different load conditions in the typical load range are simulated, and the camber values corresponding to each load are measured by a four-wheel positioning device; Among them, the load conditions at least include empty load, half load and full load, wherein the full load condition corresponds to the maximum allowable total weight of the vehicle design; The camber values under different load conditions are linearly fitted to establish a camber-vehicle load relationship curve, and the correlation coefficient of the relationship curve is not less than a preset coefficient threshold, which is used to represent the linear correlation between vehicle load and camber; Based on the load distribution characteristics and the relationship curve, the load value with the highest frequency is selected as the test load condition, which needs to cover the main load scenarios in the user's daily use and ensure that the camber under the test load is in the linear interval of the relationship curve.

[0007] Further, the driving trajectory includes driving time and average vehicle speed, steering frequency, slope change and traffic signal distribution characteristics in the corresponding period; Based on the driving trajectory, the road surface proportions of urban roads, general highways and expressways are analyzed to develop a test route matching the actual driving conditions of the user, including the following steps: Based on the average vehicle speed, the driving trajectory is divided into three types of road surfaces: urban roads, general highways and expressways; The mileage proportions of the three types of roads are calculated by weighted average to construct a test route model containing the three types of roads; Among them, the weighted average takes the annual mileage of different vehicles as the weight to ensure that the statistical results reflect the typical driving characteristics of the user group; The consistency of the test route model and the actual driving conditions of the user is verified by real car test driving, specifically including: calculating the matching degree of the test route and the steering frequency, slope change and traffic signal distribution characteristics of the driving trajectory, and if the matching degree is not less than a preset matching degree threshold, the test route is determined as the final execution route.

[0008] Further, the full-factor experimental design scheme includes a two-factor two-level experimental design scheme; The full-factor experimental design scheme is used to perform real vehicle road tests under the test route and test load conditions with tire pressure and camber angle as variables, to obtain the wear-out mileage data corresponding to different variable combinations, including the following steps: Tire pressure and camber angle are used as test factors, each test factor is set to a high level and a low level, a two-factor two-level experimental design scheme is used, and four groups of parameter combinations are formed; The test vehicle is loaded according to the test load conditions, real vehicle driving tests are performed on the test route, and tire pressure stability is monitored in real time during the test to ensure that the tire pressure fluctuation range does not exceed the preset tire pressure fluctuation threshold, and the camber angle is calibrated daily by four-wheel positioning equipment; The test route takes a preset number of kilometers as a complete cycle unit, and the test vehicle of each group of parameter combinations completes at least one cycle; The exposed length of the inner side cord of the tire is used as the wear-out judgment index, and when the exposed length of the inner side cord of the left rear wheel reaches the preset exposed length threshold, the cumulative driving mileage at this time is recorded as the wear-out mileage corresponding to the parameter combination; If the inner side cord of the left rear wheel is not exposed after the test cycle, then the wear-out mileage is taken as the lower limit value of the wear-out mileage; The wear-out mileage data of the four groups of parameter combinations are arranged into a three-dimensional data set of tire pressure level, camber angle level and wear-out mileage.

[0009] Further, the wear-out mileage data is analyzed to determine the influence of tire pressure and camber angle on wear-out mileage and the interaction between the two, a wear-out mileage prediction model is constructed by mathematical model fitting, including the following steps: The wear-out mileage data is preprocessed to eliminate invalid samples caused by abnormal tire damage, tire pressure fluctuation exceeding the preset range or camber angle calibration deviation exceeding the preset range; the wear-out mileage data is classified according to the tire pressure level and camber angle level dimensions to obtain multiple groups of effective data under different parameter combinations; The main effect and interaction strength of tire pressure and camber angle are determined by comparing the mean value differences of wear-out mileage under different parameter combinations, wherein the main effect is calculated by the mean value difference between high and low level groups, and the interaction is determined by a three-dimensional response surface graph; A regression model containing interaction terms is constructed as the wear-out mileage prediction model with tire pressure and camber angle as independent variables and wear-out mileage as dependent variable; The regression model includes main effect terms, interaction terms and constant terms, and the model parameters are fitted by statistical methods to ensure that the model has good goodness of fit.

[0010] Further, any tire pressure and camber angle combination is substituted into the regression model to obtain a predicted value of the tire wear mileage, and the effectiveness of the regression model is verified, specifically including verifying that the predicted value needs to meet: when the camber angle is fixed as the optimal value, the tire pressure is raised from a low level to a high level, and the predicted value shows a monotonous increasing trend; when the tire pressure is between the high and low levels and the camber angle is the optimal value, the predicted value meets the superposition trend rule of the tire pressure and the camber angle acting alone.

[0011] In another aspect, the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the tire pressure and camber angle tire wear mileage test method described above when executing the computer program.

[0012] In another aspect, the present application provides a computer program product, which comprises a computer program, and the computer program implements the tire pressure and camber angle tire wear mileage test method described above when executed by a processor.

[0013] In another aspect, the present application provides a vehicle, and the tire wear mileage of the vehicle is obtained by the tire pressure and camber angle tire wear mileage test method described above.

[0014] The beneficial effects of the present application are: the present application provides a tire pressure and camber angle tire wear mileage test method, which integrates massive real vehicle running data and systematic test design, and improves the accuracy and efficiency of tire durability evaluation. The method uses big data such as the driving track, load, tire pressure and driving behavior of the actual vehicle to scientifically construct test routes and load conditions that are highly matched with real working conditions, and accurately reflects the dynamic positioning characteristics of the vehicle under different loads by establishing a relationship curve of the camber angle changing with the load, thereby enhancing the realistic representativeness of the test. The full-factor test design is adopted to comprehensively investigate the influence of tire pressure, camber angle and their interaction on tire wear, and then a tire wear mileage mathematical model with prediction ability is constructed to realize the quantitative evaluation and optimization of tire life under different parameter combinations. Finally, the optimal tire pressure and camber angle combination that meets the durability target can be quickly screened out through model prediction, which provides data-driven decision basis for tire life improvement and vehicle chassis parameter tuning, and effectively overcomes the defects of traditional test methods such as working condition distortion, long cycle, high cost and difficulty in quantifying the coupling effect of multiple factors. The present application also provides corresponding equipment, products and vehicles, and the beneficial effects of the equipment, products and vehicles are the same as those of the above-mentioned method, which will not be described here.

[0015] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0016] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0017] Figure 1 This is a flowchart of the tire pressure and camber angle wear mileage test method provided in this application; Figure 2 This is a structural diagram of the electronic device provided in this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] The present application will be further described below with reference to the accompanying drawings and specific embodiments. The described embodiments should not be considered as limitations on the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0020] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] Optimizing tire pressure and four-wheel alignment parameters (such as camber angle) is a crucial research area in automotive engineering, directly impacting vehicle ride comfort, power, handling, and tire life. Proper tire pressure ensures effective contact area between the tire and the road surface, improving grip and reducing rolling resistance, thereby enhancing fuel economy and handling stability. Camber angle, a key parameter in four-wheel alignment, directly affects tire contact with the ground and stress distribution; improper camber angle can lead to excessive wear on one side of the tire, significantly shortening its lifespan. Therefore, scientifically and rationally matching tire pressure and camber angle parameters not only helps improve overall vehicle performance but also effectively inhibits uneven tire wear, extends replacement intervals, and reduces user operating costs.

[0023] Currently, the research on tire uneven wear mainly relies on three technical paths: theoretical modeling and simulation, bench test and real vehicle road test. Among them, the multi-body dynamics simulation method (such as using ADAMS, CarSim, etc.) can analyze the stress characteristics and wear trend of the tire under different working conditions by establishing a vehicle system model, which has the advantages of low cost and strong repeatability. However, such simulation model is usually based on idealized assumptions, and it is difficult to accurately simulate the complex excitation factors in real road, such as random road roughness, dynamic load change, environmental temperature fluctuation and driving behavior difference, etc., which leads to a large deviation between the simulation results and the actual vehicle operation data, limiting its guiding value in engineering practice.

[0024] On the other hand, although the bench test can test the durability of the tire under specific load and positioning parameters in a controlled environment, its boundary conditions are far from the real driving environment, and it cannot reproduce the comprehensive mechanical response of the vehicle in dynamic processes such as turning, braking and bumping, etc. The test results lack real representativeness. Traditional real vehicle road test often uses fixed route and unified working condition to evaluate tire wear, mainly for single variable test, lacking of systematic research on the coupling effect of tire pressure and camber angle. At the same time, such test does not fully combine with the actual use data of users (such as real driving trajectory, load distribution, driving habit, etc.), leading to the test design deviating from the real scene, with problems of long cycle, high cost and low efficiency, which is difficult to quickly identify the optimal parameter combination, and cannot meet the needs of modern automobile research and development for high efficiency, precision and data-driven decision-making.

[0025] In view of the above problems, the present application proposes a tire pressure and camber angle uneven wear mileage test method, which is characterized by deeply integrating the real user vehicle operation data with scientific test design. First, by collecting and analyzing a large amount of driving trajectory, load, tire pressure and driving behavior data of tire uneven wear vehicles, the key influencing factors are identified and interference items such as aggressive driving are excluded to ensure the representativeness of the research object; secondly, based on the actual load data, a linear relationship curve of camber angle and total weight of the vehicle is established to determine the test load conditions that meet the user's daily use characteristics, and combined with the mileage proportion of urban roads, general highways and expressways in the driving trajectory, a test route highly matched with the real working condition is constructed to improve the authenticity of the test environment. On this basis, a two-factor two-level full-factor test design scheme is adopted to systematically investigate the influence of tire pressure and camber angle and their interaction on tire uneven wear mileage, and to obtain uneven wear data under multiple parameter combinations through real vehicle road test, and to construct a regression prediction model containing main effect and interaction term to realize the quantitative prediction of uneven wear mileage under any combination of tire pressure and camber angle, so as to quickly screen out the optimal parameter combination that meets the durability target, realize the change from "experience trial and error" to "data-driven optimization", and greatly improve the scientificity, efficiency and engineering application value of the test.

[0026] First, the tire pressure and camber angle of the bias wear mileage test method provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0027] Referring to Figure 1 The implementation process of the tire pressure and camber angle of the bias wear mileage test method provided by the embodiments of the present application includes but is not limited to the following steps.

[0028] Step S110, collect vehicle big data information.

[0029] Among them, the vehicle big data information includes the driving track of the tire bias wear vehicle, the load data, the tire pressure data and the driving behavior data.

[0030] In step S110, by systematically collecting the driving track, load data, tire pressure data and driving behavior data of the vehicle that has occurred tire bias wear, the key variables affecting tire wear can be extracted from the actual use scene. The driving track data records the position, speed and path information of the vehicle in different time periods, reflecting the user's travel habits and common road types; the load data contains the number of passengers, distribution and trunk load weight information of the vehicle during operation, reflecting the actual carrying state of the vehicle; the tire pressure data directly reflects the pressure level of the tire during use, which is an important parameter for evaluating its working condition; the driving behavior data includes vehicle speed, acceleration and other dynamic operation information, which helps to understand the running mode of the vehicle. These multi-dimensional big data jointly constitute the data basis for subsequent analysis and test design, ensuring that the research object comes from real problem scenes, and improving the real relevance and engineering application value of the entire test method.

[0031] Step S120, based on the load data, by comparing the camber angle values of the wheels under different load conditions, a relationship curve between the camber angle and the vehicle load is established to determine the test load condition.

[0032] Step S120 reveals the influence of vehicle load changes on wheel geometric alignment parameters and scientifically sets the load conditions for the experiment accordingly. Under different loading conditions, the compression degree of the suspension system changes, leading to corresponding changes in the wheel camber angle. By utilizing actual collected load data, statistically analyzing typical load ranges in daily user use, and simulating these load conditions in an experimental environment, the camber angle values ​​of the wheels under corresponding working conditions can be measured, obtaining a set of measured load-camber angle data points. Mathematical fitting (such as linear regression) of these data establishes a quantitative relationship curve between camber angle and vehicle total weight. This not only visually demonstrates the changing trend between the two but can also be used to predict camber angle values ​​under specific loads. Based on this relationship curve and combined with usage characteristics such as load frequency, the most representative experimental load conditions can be reasonably selected, making the experimental process closer to real-world vehicle use, thereby improving the accuracy and applicability of the experimental results.

[0033] Step S130: Based on the driving trajectory, analyze the road surface ratio of urban roads, general roads and expressways to formulate a test route that matches the user's actual driving conditions.

[0034] In step S130, a highly representative test route is constructed based on the travel characteristics of real users to ensure that the real-vehicle test can be conducted under conditions close to actual use. By analyzing the collected vehicle trajectory data, the driving segments are divided into three typical road types: urban roads, general highways, and expressways, based on indicators such as average vehicle speed. The proportion of each type of road in the total driving mileage is further calculated to form a proportional model reflecting the user's daily driving structure. For example, if data analysis shows that users spend 30% of their time on urban roads, 25% on general highways, and 45% on expressways, the test route design will also follow this proportional distribution. Test routes developed in this way can comprehensively reflect the driving characteristics of different road types, avoiding deviations in test results due to a single route, thus ensuring that the data obtained from subsequent road tests can truly reflect the typical usage conditions of the target user group.

[0035] Step S140: Using a full factorial test design scheme, with tire pressure and camber angle as variables, conduct real vehicle road tests under test route and test load conditions to obtain mileage data corresponding to different combinations of variables.

[0036] In step S140, controlled real-vehicle tests are conducted to systematically acquire data on the actual impact of different combinations of tire pressure and camber angle on tire wear mileage. Employing a full-factor experimental design means treating tire pressure and camber angle as two independent variables, each with high and low levels, thus forming all possible combinations (four groups in total), ensuring the test covers key points in the variable space. The test is conducted under the real load conditions and representative test routes determined in the preceding steps, maximizing the replication of the vehicle's actual usage environment. During the test, other factors are kept constant, and only tire pressure and camber angle are adjusted. Through prolonged road driving, the tire wear development under each parameter combination is observed and recorded. When the tire reaches the preset wear endpoint (e.g., the exposed length of the inner cord of the left rear tire reaches a specified value), the cumulative mileage is recorded, which is the tire wear mileage under that combination. This process directly generates raw experimental data for subsequent modeling and analysis, and is a crucial link connecting theoretical design and actual performance verification.

[0037] Step S150: Analyze the wear mileage data, determine the degree of influence of tire pressure and camber angle on wear mileage and their interaction, and construct a wear mileage prediction model by fitting a mathematical model.

[0038] In step S150, in-depth statistical analysis is performed on the eccentricity mileage data obtained from the real-vehicle test to uncover the intrinsic relationships between variables and establish a quantifiable predictive tool. By comparing the eccentricity mileage data under four different parameter combinations, the main effects of tire pressure and camber angle can be calculated separately, i.e., the magnitude of the influence of changing a single parameter on eccentricity mileage. Simultaneously, the interaction between the two is analyzed, i.e., whether the influence of one parameter changes due to different values ​​of the other parameter. Based on this, a regression mathematical model containing main effect terms and interaction terms is constructed with tire pressure and camber angle as independent variables and eccentricity mileage as the dependent variable. By fitting the parameters of this model to better describe the variation patterns of the existing data, a predictive model for eccentricity mileage is ultimately formed. This model not only explains the experimental results but also provides a mathematical basis for subsequent evaluation of arbitrary parameter combinations.

[0039] Step S160: Input the target tire pressure and target camber angle into the wheel wear mileage prediction model to obtain the corresponding wheel wear mileage prediction results. Combine the preset wheel wear mileage target to determine the optimal combination of tire pressure and camber angle.

[0040] In step S160, the established predictive model is used for parameter optimization and decision support. Once the model is established, it can be applied to predict the performance of new parameter combinations. By inputting arbitrarily set target tire pressure and target camber angle values ​​into the model, the system can automatically output the corresponding predicted wheel wear mileage value, enabling rapid evaluation. Subsequently, the predicted result is compared with a pre-set wear mileage target (such as minimum durability requirements) to determine whether the parameter combination meets the design requirements. By traversing or optimizing multiple candidate combinations, the tire pressure and camber angle parameter combination that meets both performance requirements and maximizes tire life can be selected. This process realizes the transformation from "trial and error experimentation" to "predictive design," significantly improving product development efficiency and scientific rigor, and providing a direct basis for finally determining the optimal technical solution.

[0041] In some embodiments of this application, driving behavior data is used to filter out normal driving and exclude the influence of aggressive driving on tire wear. Specifically, this includes: based on longitudinal acceleration, lateral acceleration and vehicle speed data, confirming that the driving behavior of a vehicle with uneven tire wear belongs to normal driving.

[0042] Specifically, driving behavior data is used to filter out the effects of regular driving and aggressive driving on uneven tire wear. Its core function is to ensure that the analyzed uneven tire wear is primarily caused by structural parameters such as tire pressure and camber angle, rather than by extreme or abnormal driving operations. By collecting dynamic operating data such as longitudinal acceleration, lateral acceleration, and vehicle speed, a comprehensive characterization of the driver's operating habits and driving style can be achieved. Based on this data, reasonable threshold ranges are set, such as limiting longitudinal acceleration to no more than ±3 m / s², lateral acceleration to no more than ±4 m / s², and vehicle speed to within the normal road speed limit range, to identify whether there are frequent rapid accelerations, sudden braking, or high-speed cornering, or other aggressive driving behaviors. If a vehicle's driving behavior data consistently exceeds the preset thresholds within the statistical period, it is determined to belong to an atypical driving mode, and the vehicle's data will be removed or not included in subsequent analysis samples. This screening mechanism effectively eliminates interference from abnormal tire wear caused by aggressive driving behavior, thereby ensuring that the wear characteristics of the research subjects truly reflect the technical influence of tire pressure and camber angle, improving the purity of the test data and the scientific validity of the conclusions, and laying a reliable foundation for the subsequent establishment of an accurate wear mileage prediction model.

[0043] In some embodiments of this application, three signals—longitudinal acceleration, lateral acceleration, and vehicle speed—are analyzed. Through machine learning methods, these signals are compared with driver driving style factors to determine that there was no aggressive driving during vehicle use. Therefore, the influence of driving style is investigated, and the driver's driving style is identified. To ensure consistency between experimental verification and user verification, drivers with consistent driving styles are selected through signal analysis. At the same time, aggressive and conservative driving are avoided during the experiment.

[0044] In some embodiments of this application, load data includes the number of occupants, occupant position distribution, and trunk load weight under different usage scenarios. The load data comprehensively and realistically reflects the weight distribution differences caused by changes in personnel and cargo loading during actual vehicle use, providing a refined data foundation for subsequent load analysis and test condition setting. The number of occupants directly determines the basic load level of the vehicle, while the occupant position distribution (e.g., two people in the front row, three people in the rear row, or the driver alone) affects the load distribution between the front and rear axles, thus influencing the stress state of the suspension system and the changing trends of wheel alignment parameters. Furthermore, the trunk load weight, as another important variable, significantly increases the rear axle load, especially during long-distance travel or cargo-carrying scenarios, potentially leading to a significant shift in the rear wheel camber angle. By collecting this multi-dimensional load information, a complete load profile of the vehicle under different typical usage scenarios can be constructed. This not only improves the authenticity and representativeness of the data but also provides a reliable basis for accurately identifying the pattern of camber angle changes with the total vehicle weight and load distribution, ensuring that the load conditions on which the test design is based closely reflect real-world user habits.

[0045] Furthermore, in step S120, based on the load data, by comparing the camber angle values ​​under different load conditions, a relationship curve between the camber angle and the vehicle load is established to determine the implementation process of the test load conditions, including but not limited to the following steps.

[0046] Step S210: Based on load data, statistically analyze the typical load range and load distribution characteristics of vehicles with uneven tire wear in actual use.

[0047] In step S210, representative load usage patterns are extracted from massive amounts of real-world operational data, providing clear input boundaries for subsequent experimental simulations. Statistical analysis of the collected load data identifies common total weight ranges in daily vehicle use, such as empty (driver only), half-load (2-4 people plus a small amount of luggage), and fully loaded (5 people plus trunk load), forming a "typical load range." Simultaneously, the frequency and duration of different load states, along with their corresponding driving scenarios (such as commuting, short trips, and long-distance travel), are further analyzed to obtain load distribution characteristics. These statistical results not only reveal the most frequently used load patterns but also provide data support for selecting the most representative experimental loads, ensuring that the simulated load conditions cover the actual usage of the vast majority of users, thus enhancing the universality and engineering application value of the experimental results.

[0048] Step S220: In a four-wheel alignment test environment, simulate different load conditions within a typical load range, and measure the camber angle values ​​corresponding to each load using a four-wheel alignment device.

[0049] The load conditions include at least no load, half load and full load, with the full load condition corresponding to the maximum permissible gross weight of the vehicle design.

[0050] In step S220, controlled experimental methods are used to obtain actual changes in the camber angle of the vehicle under different load conditions, establishing a measured correlation between load and alignment parameters. In a four-wheel alignment experimental environment, according to the typical load range determined in S210, different load conditions such as no load, half load, and full load are precisely applied using counterweights or simulated occupant mass to ensure that the full load condition reaches the maximum allowable gross weight of the vehicle, covering the entire usage range. After each loading, a high-precision four-wheel alignment device is used to perform alignment testing on the vehicle, focusing on collecting the camber angle values ​​of each wheel (especially the rear wheels prone to uneven wear). This process ensures the consistency of the measurement environment and the accuracy of the data, avoiding the influence of road interference factors. The measured data obtained in this way truly reflects the geometric change characteristics of the suspension system under different compression states, providing key experimental evidence for constructing a quantitative relationship between camber angle and load.

[0051] Step S230: Perform linear fitting on the camber angle values ​​under different load conditions to establish a relationship curve between the camber angle and the vehicle load. The correlation coefficient of the relationship curve shall not be lower than the preset coefficient threshold.

[0052] The correlation coefficient of the relationship curve is used to characterize the linear correlation between vehicle load and camber angle.

[0053] In step S230, the discrete data obtained from the experiment are transformed into a continuous relationship model with a mathematical expression, facilitating subsequent analysis and application. By performing linear regression fitting on the load-camber angle data points obtained in S220, a straight line equation describing the trend of camber angle variation with the total vehicle weight is obtained, namely the "camber angle-load relationship curve". The slope of this curve reflects the change in camber angle caused by a unit change in load, and has clear engineering physical significance. Setting the correlation coefficient (e.g., R²) to be no less than a preset threshold (e.g., 0.98) ensures that the fitting results have a high degree of linear correlation and statistical reliability. Only when the data points are closely distributed around the fitted straight line are a stable linear relationship between the camber angle and the load considered, thus ensuring that the model can be used to predict the camber angle value under unknown loads. If the correlation coefficient does not meet the standard, the experimental data needs to be re-examined or nonlinear modeling needs to be considered to ensure the scientific validity and applicability of the model.

[0054] Step S240: Based on the load distribution characteristics and relationship curve, select the load value with the highest frequency as the test load condition. The test load condition must cover the main load scenarios in daily use and ensure that the outward tilt angle under the test load is within the linear range of the relationship curve.

[0055] In step S240, the specific load parameters for the actual vehicle road test are finally determined to ensure that the test conditions closely resemble actual user use while also meeting the prerequisites for model application. The load distribution characteristics obtained in S210 show the usage frequency of different load states. Selecting the load value with the highest frequency as the test load makes the test results more representative and reflects the typical driving experience of most users. Simultaneously, based on the relationship curve established in S230, it is necessary to verify whether the camber angle corresponding to the selected load is within the linear range of the curve, i.e., the suspension system has not yet entered the nonlinear deformation zone (such as limit block contact or spring saturation). Only within the range where the camber angle change remains linear can the relationship curve have predictive power, and only then can the test data be interpretable and repeatable. Therefore, this step scientifically locks in the optimal test load conditions through dual constraints—usage frequency and physical linearity—providing a crucial guarantee for the accuracy and effectiveness of subsequent actual vehicle tests.

[0056] In some embodiments of this application, the driving trajectory includes driving time and the corresponding average vehicle speed, turning frequency, gradient change, and traffic light distribution characteristics. Driving time and the corresponding average vehicle speed together reflect the vehicle's operating speed characteristics under different road types, serving as fundamental indicators for distinguishing road levels. Turning frequency reflects the frequency of directional changes during driving; urban roads typically have higher turning frequencies due to dense intersections and complex road conditions, while highways have fewer turns. This parameter directly affects the lateral wear pattern of tires. Gradient change reflects the undulation of the road, affecting the dynamic transfer of vehicle load and tire ground pressure distribution. Traffic light distribution characteristics characterize the number of starts and stops and driving continuity, especially significantly impacting longitudinal tire wear and temperature accumulation under urban conditions. By collecting these refined trajectory features, the comprehensive road environment encountered by users in daily driving can be fully reconstructed, ensuring that subsequent test routes not only match the mileage ratio but also possess high representativeness in dynamic driving behavior and road incentive characteristics.

[0057] Furthermore, in step S130, the process of analyzing the road surface ratio of urban roads, general highways, and expressways based on the driving trajectory to formulate a test route that matches the user's actual driving conditions includes, but is not limited to, the following steps.

[0058] Step S310: Based on the average vehicle speed, the driving trajectory is divided into three road surface types: urban roads, general roads, and expressways.

[0059] In step S310, an objective and quantifiable road classification standard is established, transforming continuous driving trajectory data into structured road type information, laying the foundation for subsequent statistical analysis. By setting reasonable average speed threshold ranges (e.g., 0–40 km / h corresponds to urban roads, 40–80 km / h to general roads, and above 80 km / h to expressways), the entire driving trajectory can be segmented and classified. This classification method is simple, efficient, and highly consistent with the actual road traffic characteristics: urban roads have lower average speeds due to factors such as traffic lights, congestion, and speed limits; general roads connect urban and rural areas, have better road conditions but have intersections, and moderate speeds; expressways allow continuous high-speed driving. Through this step, the original spatiotemporal trajectory data is transformed into discrete data segments divided by road type, facilitating subsequent calculation of the cumulative mileage and proportion of each type of road surface, thus providing a clear quantitative basis for the structural design of the experimental route.

[0060] Step S320: Calculate the mileage percentage of the three types of road surfaces using a weighted average, and construct a test route model that includes the three types of road surfaces.

[0061] The weighted average uses the annual mileage of different vehicles as the weight to ensure that the statistical results reflect the typical driving characteristics of the user group.

[0062] Step S320 aims to improve the representativeness and fairness of data statistics, avoiding distortion of the experimental route due to sample selection bias. In actual data collection, there are significant differences in the total annual mileage of different vehicles. If a simple average (i.e., equal weight for each vehicle) is used, the usage characteristics of high-mileage vehicles may be diluted by low-mileage vehicles, causing the statistical results to deviate from the overall usage patterns of the real user group. By introducing annual mileage as a weight for weighted averaging, the driving trajectory of high-mileage vehicles occupies a larger proportion in the total percentage calculation, thus more accurately reflecting the typical working conditions of high-frequency users. For example, the data influence of a user who drives 30,000 kilometers per year should be much greater than that of a user who drives 5,000 kilometers per year. The mileage percentages of urban areas, general roads, and highways calculated based on this weighted method can truly reflect the overall travel structure of the target user group. The experimental route model constructed based on this is more statistically representative and has greater engineering guidance significance in terms of road type allocation.

[0063] Step S330 verifies the consistency between the test route model and the user's actual driving conditions through a real vehicle test drive. Specifically, this includes: calculating the matching degree of the test route and the driving trajectory in terms of turning frequency, slope change and traffic light distribution characteristics. If the matching degree is not lower than the preset matching degree threshold, the test route is determined as the final execution route.

[0064] In step S330, the test route model is verified and calibrated at the dynamic feature level to ensure that it not only matches the mileage ratio but also highly replicates real-world driving behavior and road incentives. Even if the test route's mileage ratio in urban areas, general roads, and highways matches the statistical data, there may still be dynamic feature deviations. For example, the test route may be long but have few turns, or have a gentle slope and no traffic lights, failing to realistically simulate urban congestion. Therefore, it is necessary to collect dynamic indicators such as turning frequency (number of turns per unit mileage), slope change (standard deviation or cumulative change of road longitudinal slope), and traffic light distribution (number of starts and stops or red light waiting time) of the test route through real-vehicle test drives, and compare them with similar features of the original user's driving trajectory to calculate the matching degree (such as correlation coefficient or relative error). Only when the matching degree of each dynamic feature is not lower than a preset threshold (such as 90%) is the test route considered to have sufficient realism. This verification mechanism effectively ensures the comprehensive representativeness of the test environment and provides key support for the credibility of subsequent real-vehicle road test results.

[0065] In some embodiments of this application, the full-factor experimental design scheme includes a two-factor, two-level experimental design scheme. This scheme clarifies the basic framework of the experimental design and employs scientific and systematic experimental methods to comprehensively examine the influence of two key factors, tire pressure and camber angle, on tire wear, and identifies their main effects and interactions. The two-factor, two-level design means treating tire pressure and camber angle as independent variables, with each variable having two engineering-representative levels (e.g., low tire pressure / high tire pressure, large camber angle / small camber angle). Through full-factor combinations, 2×2=4 parameter configurations can be formed. This design method can cover all possible variable combinations, avoiding the omission of potential optimal solutions or interaction effects, and is more efficient and complete than single-factor rotation experiments. Simultaneously, this scheme has a simple structure and moderate data volume, making it suitable for implementation in real-vehicle road tests. It ensures the effectiveness of statistical analysis while considering experimental costs and timelines, providing a clear and complete data foundation for subsequent predictive model construction.

[0066] Furthermore, in step S140, a full factorial test design scheme is adopted, with tire pressure and camber angle as variables, and a real vehicle road test is conducted under test route and test load conditions. The process of obtaining the wear mileage data corresponding to different combinations of variables includes, but is not limited to, the following steps.

[0067] Step S410: Using tire pressure and camber angle as test factors, each test factor is set to a high level and a low level. A two-factor, two-level test design is adopted to form four sets of parameter combinations.

[0068] In step S410, the experimental variables and their value ranges are systematically planned to ensure that the experiment covers typical operating conditions in the key parameter space. By determining tire pressure and camber angle as experimental factors, and reasonably setting their high and low levels based on actual user data or design boundaries (for example, low tire pressure corresponds to the commonly low values ​​in market feedback, high tire pressure corresponds to the recommended standard value; large camber angle corresponds to the measured value under full load, and small camber angle corresponds to the optimized target value), four sets of parameter combinations with comparative significance can be constructed: low tire pressure + large camber angle, low tire pressure + small camber angle, high tire pressure + large camber angle, and high tire pressure + small camber angle. Each combination represents a specific technical state and is used to evaluate tire wear performance under different parameter combinations. This step is the premise for the entire experiment, determining the dimension and structure of data collection, and providing a complete input matrix for subsequent analysis of main effects, interactions, and the establishment of regression models.

[0069] Step S420: Load the test vehicle according to the test load conditions, conduct a real vehicle driving test on the test route, and monitor the tire pressure stability in real time during the test to ensure that the tire pressure fluctuation range does not exceed the preset tire pressure fluctuation threshold. The camber angle is calibrated daily by a four-wheel alignment device.

[0070] The test route is divided into a preset number of kilometers as a complete cycle unit, and the test vehicle of each parameter combination must complete at least one cycle.

[0071] In step S420, real-vehicle verification is conducted under highly controlled conditions that realistically replicate user operating conditions to ensure the reliability and comparability of the test results. The test vehicle is loaded strictly according to the test load conditions determined in S120 to maintain consistency in suspension condition and initial camber angle values. The test is conducted on a representative test route defined in S130 to ensure the authenticity of the driving environment. Tire pressure is continuously monitored during the test; if fluctuations exceed a preset threshold (e.g., ±10 kPa), intervention and adjustment are implemented to prevent data distortion due to tire pressure drift. Simultaneously, the camber angle is calibrated daily using a four-wheel alignment device to compensate for alignment deviations caused by temperature changes, mechanical loosening, or tire deformation, ensuring that the camber angle parameter remains stable at the set level throughout the entire test cycle. The test route is divided into fixed-mileage cyclic units (e.g., 5000 km), and each parameter combination completes at least one full cycle to accumulate sufficient driving data and observe wear development trends, ensuring the sufficiency and effectiveness of the test.

[0072] Step S430: Using the exposed length of the inner cord of the tire as the indicator for determining uneven wear, when the exposed length of the inner cord of the left rear wheel reaches the preset exposed length threshold, the cumulative mileage at this time is recorded as the uneven wear mileage corresponding to this parameter combination. If the inner cord of the left rear wheel is not exposed after the test cycle, the mileage of one cycle is used as the lower limit of the uneven wear mileage.

[0073] In step S430, an objective, quantifiable endpoint determination standard directly related to tire life is established to accurately measure tire durability performance under different parameter combinations. The exposed length of the inner cord on the left rear wheel is chosen as the wear index because, in actual user feedback, inner wear on the left rear wheel is the most common failure mode, making it typical and representative. When the tire wears to the point where the cord layer (i.e., steel wire or cord) is exposed, it has reached the safety limit requiring replacement. Therefore, defining the exposed cord length reaching a preset threshold (e.g., 2mm) as the test endpoint has clear engineering significance. The cumulative mileage from the start of the test to the occurrence of this state is recorded as the "wear mileage" under this parameter combination; a larger value indicates a longer tire life. For combinations that do not reach the wear endpoint within the specified cycles, the cycle mileage is used as the lower limit of the wear mileage. This ensures the test can be terminated and provides conservative estimation data for subsequent modeling, avoiding misjudgment.

[0074] Step S440: Organize the wear mileage data of the four sets of parameters into a three-dimensional dataset of tire pressure level, camber angle level and wear mileage.

[0075] Step S440 involves structuring and standardizing the raw test results to provide a clear data format for subsequent statistical analysis and model building. By classifying and organizing the four sets of test results, a three-dimensional dataset is formed: the first dimension is tire pressure level (low / high), the second dimension is camber angle level (large / small), and the third dimension is the corresponding wear mileage value. This three-dimensional dataset not only intuitively displays the performance under different parameter combinations but also facilitates main effect calculations, interaction analysis, and regression modeling. For example, the main effect of tire pressure can be evaluated by comparing the difference in wear mileage under the same camber angle with different tire pressures, or the presence of synergistic or offsetting effects between two factors can be observed through response surface plots. This dataset serves as a crucial bridge connecting real-vehicle testing and mathematical models, ensuring seamless integration from physical testing to data analysis and enhancing the systematicity and operability of the overall methodology.

[0076] In some embodiments of this application, in step S150, the process of analyzing the wear mileage data, determining the degree of influence of tire pressure and camber angle on the wear mileage and their interaction, and constructing a wear mileage prediction model by fitting a mathematical model includes, but is not limited to, the following steps.

[0077] Step S510: Preprocess the uneven wear mileage data, remove invalid samples caused by abnormal tire damage, tire pressure fluctuations exceeding the preset range, or camber angle calibration deviations exceeding the preset range, and classify the uneven wear mileage data according to tire pressure level and camber angle level to obtain multiple sets of valid data under different parameter combinations.

[0078] Step S510 aims to ensure the accuracy, consistency, and reliability of the data used in subsequent analyses, serving as a prerequisite for building a high-quality predictive model. During real-vehicle road tests, despite stringent monitoring measures, individual samples may still experience abnormal tire damage due to external factors (such as punctures from road debris or collisions with road shoulders), or deviations in tire pressure from set values ​​or uncalibrated camber angles due to equipment malfunctions or human error. If these abnormal samples are not removed, they will introduce significant noise, interfering with the identification of the true effects of tire pressure and camber angle, and potentially leading to erroneous conclusions. Therefore, by setting clear judgment criteria—such as tire pressure fluctuations exceeding ±10 kPa, camber angle measurement deviations greater than ±5′, or the presence of concentrated exposure of cords outside the inner side—such invalid data can be effectively identified and excluded. Based on this, the remaining valid eccentric wear mileage data are categorized and organized according to two dimensions: tire pressure level (high / low) and camber angle level (large / small), forming four independent data groups, each corresponding to a specific parameter combination. This structured classification method not only facilitates subsequent statistical comparisons, but also provides a clear data framework for calculating main effects and interactions.

[0079] Step S520: By comparing the differences in the average wear mileage under different parameter combinations, the main effects and interaction strength of tire pressure and camber angle are determined. The main effects are calculated by the difference in the average values ​​of high and low level groups, and the interaction is determined by the three-dimensional response surface plot.

[0080] In step S520, the inherent relationship between variables and the response is explored from a statistical perspective, quantifying the influence of each factor and identifying their coupling characteristics. The main effect reflects the average impact of a single factor's change on tire wear mileage. For example, the main effect of tire pressure can be calculated by subtracting the average tire wear mileage of the low tire pressure group from the average tire wear mileage of the high tire pressure group. A positive value indicates that increasing tire pressure helps extend tire life, while a negative value has the opposite effect. Similarly, the main effect of camber angle can be obtained. This analysis helps determine which parameter has a more significant impact on tire life. More importantly, this step visually displays the performance change trend under the combined effect of two factors by plotting a three-dimensional response surface diagram (with tire pressure and camber angle as the horizontal and vertical axes, and tire wear mileage as the vertical axis). If the surface shows obvious distortion or inconsistent slope changes, it indicates the existence of an interaction, that is, the effect of one factor depends on the value level of the other factor. For example, increasing tire pressure at a small camber angle may significantly extend tire life, but the effect weakens or even reverses at a large camber angle. Determining the existence and direction of the interaction through graphical means provides a direct basis for constructing a mathematical model that includes interaction terms.

[0081] Step S530: Using tire pressure and camber angle as independent variables and mileage of uneven wear as dependent variable, construct a regression model containing interaction terms as a mileage of uneven wear prediction model.

[0082] The regression model includes main effect terms, interaction terms, and constant terms, and uses statistical methods to fit the model parameters to ensure that the model has a good fit.

[0083] In step S530, the experimental observation results are transformed into a mathematical tool with predictive capabilities, achieving a transformation from empirical data to a theoretical model. Based on the analytical conclusions of the previous steps, a multiple linear regression model is established to reflect the synergistic effect of the two factors. This model comprehensively covers the key mechanisms affecting tire wear eccentricity, avoiding prediction biases that may result from ignoring the interaction. Statistical methods such as least squares are used to fit the model parameters, ensuring that the model output closely approximates the measured data. Simultaneously, residual analysis and other indicators are used to evaluate the model's goodness of fit, ensuring sufficient explanatory power and stability. The resulting tire wear mileage prediction model not only reproduces known experimental results but, more importantly, possesses extrapolation capabilities, allowing it to predict tire wear mileage under any combination of tire pressure and camber angle, providing quantitative decision support for engineering optimization.

[0084] In some embodiments of this application, arbitrary combinations of tire pressure and camber angle are substituted into the regression model to obtain predicted values ​​for uneven wear mileage, and the effectiveness of the regression model is verified. This ensures that the constructed mathematical model not only has good data fitting ability but also conforms to the physical laws of tire wear and common engineering sense. Traditional regression models may only rely on statistical indicators such as R² to evaluate performance, but cannot guarantee that their prediction results are reasonable in an engineering sense. Therefore, this method verifies the logical consistency and extrapolation reliability of the model by setting verification conditions with clear physical meaning. By inputting parameter combinations not involved in the modeling and observing whether the predicted output conforms to the expected technical trend, it is possible to effectively identify whether the model has an abnormal response (such as increasing tire pressure shortening life), thereby preventing erroneous decisions due to sample bias or overfitting, and ensuring the credibility and guiding value of the model in practical applications.

[0085] Verifying the effectiveness of the regression model specifically includes verifying that the predicted values ​​must meet the following conditions: when the camber angle is fixed at its optimal value, the predicted values ​​show a monotonically increasing trend as tire pressure increases from a low level to a high level. When the tire pressure is between high and low levels and the camber angle is at its optimal value, the predicted values ​​conform to the superimposed trend of the individual effects of tire pressure and camber angle.

[0086] Specifically, firstly, when the camber angle is fixed at its optimal value (e.g., a smaller camber angle), as the tire pressure changes from low to high levels, the predicted mileage should show a monotonically increasing trend. This aligns with the basic engineering principle that "reasonably increasing tire pressure can reduce tire deformation and heat generation, extending tire life," ensuring the model's description of the main effect of tire pressure is correct. Secondly, when the tire pressure is at an intermediate value between high and low levels and the camber angle is at its optimal value, the prediction result should conform to the superposition law of the individual effects of tire pressure and camber angle. That is, the predicted value should fall between the experimental data for the corresponding operating conditions and show a reasonable interpolation trend, indicating that the model still has good extrapolation ability at non-experimental points, and the interaction terms are appropriately set, without causing non-physical fluctuations. These two verifications together ensure that the model is accurate not only at experimental points but also provides scientific and reliable predictive support in real-world usage scenarios.

[0087] In some embodiments of this application, taking real user vehicle operation data as an example, and combining systematic real vehicle tests and mathematical modeling, key factors affecting tire wear are identified, a wear mileage prediction model is constructed, and the optimal combination of tire pressure and camber angle is determined, thereby effectively extending tire life, including the following steps.

[0088] Step (1) Collect basic information about the vehicle with uneven tire wear, including vehicle production date, repair mileage, repair location, vehicle VIN, problem description, etc., as shown in Table 1 below.

[0089] Table 1 Example of vehicle information regarding uneven tire wear

[0090] Analysis of Table 1 shows that uneven tire wear primarily occurs on the rear tires, with the left rear tire showing more severe wear. Surveys of vehicle owners revealed that the vehicles are mainly used for business travel, often operating at 70% to 100% load. Further analysis of vehicle driving data, including tire pressure, GPS trajectory, vehicle speed, and longitudinal and lateral acceleration, revealed that vehicles experiencing uneven wear generally had lower tire pressures, ranging from 2.2 to 2.3 bar. Machine learning analysis of driving styles confirmed that these vehicles were driven using normal driving techniques, ruling out the influence of aggressive driving on uneven tire wear.

[0091] Step (2) In order to determine the test load conditions, the relationship between wheel camber angle and vehicle load was studied. On the four-wheel alignment machine, the tire pressure was set to the lowest value (220 kPa in the front / 220 kPa in the rear). Different passenger and luggage loads were simulated on a 7-seater MPV vehicle, and the change of the left rear wheel camber angle was measured. The data are shown in Table 2.

[0092] Table 2. Measured data on the variation of wheel camber angle under different occupant positions and total load.

[0093] Analysis of the total weight and left rear camber angle data revealed a highly linear relationship between wheel camber angle and vehicle load, with a correlation coefficient of 0.9876. Based on the characteristic of users' daily use being primarily under high loads, the test was conducted under full load conditions.

[0094] Step (3): Based on the driving trajectories and speed information of the four vehicles, analyze the actual driving conditions of the user. Road types are categorized according to average speed: urban roads (10-40 km / h), general roads (40-80 km / h), and expressways (>80 km / h). Through weighted average calculation, the mileage ratio of urban roads, general roads, and expressways in the test route is determined to be approximately 3:2.5:4.5. The test conditions are shown in Table 3.

[0095] Table 3 Examples of test conditions

[0096] Furthermore, with a test cycle of 5000 kilometers, the mileage distribution for each road surface is shown in Table 4.

[0097] Table 4 Examples of experimental sub-cycles with a distance of 5000 kilometers

[0098] Furthermore, a 2×2 full factorial test (without replication) was designed, with tire pressure and rear wheel camber angle as variables, forming four sets of parameter combinations, as shown in Table 5.

[0099] Table 5 2×2 Full Factorial Experiment Protocol

[0100] Step (4): Conduct a real-vehicle road test under the determined test route and full-load conditions. During the test, monitor tire pressure stability in real time to ensure that fluctuations are within the allowable range, and camber angle is calibrated daily. When the exposed length of the inner cord of the left rear wheel reaches 2mm, record the cumulative mileage as the wear mileage of this combination. The test results are shown in Table 6.

[0101] Table 6. Example of 2×2 full-factor (without replication) trial results

[0102] Furthermore, the experimental results data in Table 6 were coded, as shown in Table 7.

[0103] Table 7. Examples of coding for data results analysis

[0104] Furthermore, based on the measured data from the 2×2 full factorial test, the main effect of tire pressure (A) was obtained by calculating the difference between the mean wear mileage of the high-pressure combination (samples 03 and 04) and the low-pressure combination (samples 01 and 02), specifically as follows: This indicates that, under otherwise unchanged conditions, increasing tire pressure can significantly extend tire life; the main effect of camber angle (B) is calculated by comparing the difference in mean wear mileage between small camber angle combinations (samples 02 and 04) and large camber angle combinations (samples 01 and 03), i.e.: This indicates that reducing the camber angle has a positive effect on suppressing uneven wear and improving tire durability; the interaction (AB) satisfies the following calculation formula (1): (1); Substituting the data from Table 7 into formula (1) yields the interaction effect. The value is: This indicates a positive synergistic effect between tire pressure and camber angle. That is, when the tire pressure is high and the camber angle is small, the improvement in tire life is greater than the simple sum of the effects of the two alone, which further verifies the significant impact of the coupling effect of the two on tire wear.

[0105] Furthermore, by encoding the two factors of tire pressure (A) and camber angle (B) as The variables with varying values ​​were used to construct a regression model that includes both main effects and interaction effects: Among them, the intercept term The average of the four experimental results is calculated as follows: Regression coefficient , and The values ​​are half the values ​​of the main effect and the interaction effect, respectively (because the coding range is 2), that is... , , This model can be used to predict the mileage of uneven wear under any combination of tire pressure and camber angle, and has good fitting performance and engineering application value.

[0106] Step (5) predicts the tire wear mileage under the combination of tire pressure 230 / 270 kPa (mean 250 kPa) and camber angle -10′ (B=+1). The tire pressure code value A=-0.294 is calculated and substituted into the regression equation to obtain the predicted value Y ≈ 47117 km. The results are then verified: the predicted value is between sample 02 (30328 km) and sample 04 (77885 km), which conforms to the trend of "the higher the tire pressure and the smaller the camber angle, the longer the tire life". Since the camber angle is the optimal value, the predicted value is significantly higher than that of the low tire pressure combination, which verifies the rationality of the model. In summary, this embodiment successfully constructs a tire wear mileage prediction model by integrating user big data, scientific experimental design and statistical modeling, providing a reliable technical path for determining the optimal tire pressure and camber angle combination.

[0107] Secondly, refer to Figure 2 This application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned tire pressure and camber angle wear mileage test method.

[0108] Furthermore, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, implements the aforementioned method for testing tire pressure and camber angle wear mileage.

[0109] Furthermore, this application provides a vehicle in which the wheel wear mileage is obtained by the aforementioned tire pressure and camber angle wear mileage test method.

[0110] In summary, the tire pressure and camber angle wear mileage test method, equipment, product, and vehicle provided in this application have the following technical effects.

[0111] This application embodiment achieves a technological leap from "experience-driven" to "data-driven" by deeply integrating vehicle big data analysis and systematic test design. This method first accurately identifies key factors affecting tire wear based on real user vehicle driving trajectories, loads, tire pressures, and driving behavior data, eliminating interference from atypical conditions such as aggressive driving and ensuring the high representativeness of the research subjects. By establishing a highly correlated linear relationship curve between camber angle and vehicle load, and combining this with the proportion of mileage in urban areas, general roads, and highways during actual user driving, the test load and test route are scientifically set, significantly improving the realism and condition matching of the test environment. A two-factor, two-level full-factor experimental design is employed to conduct real-vehicle road tests under real loads and representative routes, comprehensively acquiring tire wear mileage data under different combinations of tire pressure and camber angle, effectively revealing the main effects and interactions of each factor. By constructing a regression prediction model including main effects and interaction terms, quantitative prediction of tire wear mileage under any combination of tire pressure and camber angle is achieved, providing a mathematical tool for parameter optimization. Finally, combined with preset durability targets, the optimal parameter combination that meets the requirements of smoothness, comfort, and handling stability can be quickly determined, significantly shortening the development cycle, reducing testing costs, and improving tire lifespan and overall vehicle quality, demonstrating significant engineering application value and promising prospects for widespread adoption.

[0112] It should be noted that in all specific embodiments of this application, all data processing activities related to user identity or personal characteristics, such as user information, user behavior data, historical data, and location information, will be conducted in accordance with the principles of legality, legitimacy, and necessity. All data collection, use, storage, and processing will be subject to compliance with applicable national and regional laws, regulations, and industry standards, and informed consent from users will be obtained in a clear and explicit manner before processing. For the processing of sensitive personal information, separate consent from users will be obtained through prominent means such as pop-up prompts and independent confirmation pages. If any processing conflicts with laws and regulations, the laws and regulations will prevail, and necessary data processing will only be carried out within the scope permitted by laws and regulations, ensuring that all data-based applications, analyses, and technical implementations are conducted within the scope permitted by laws and regulations.

[0113] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0114] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of ordinary skill of an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary skill. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0115] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several programs to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0116] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0117] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Additionally, computer-readable media can even be paper or other suitable media on which programs can be printed, for example, by optically scanning the paper or other media, then editing, interpreting, or, if necessary, processing it in a suitable manner to obtain the program electronically, and then storing it in computer memory.

[0118] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0119] In the foregoing description of this specification, the reference to terms such as "one embodiment / implementation," "another embodiment / implementation," or "certain embodiments / implementations," etc., indicates that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in an embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0120] Although embodiments of the invention have been shown and described, those skilled in the art will understand 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 claims and their equivalents.

[0121] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method of testing for tire pressure and camber wear-out mileage, characterized by, The method comprises the following steps: Collecting vehicle big data information, including driving track, load data, tire pressure data and driving behavior data of tire uneven wear vehicle; Based on the load data, the relationship curve between camber and vehicle load is established by comparing the camber values under different load conditions to determine the test load condition; Based on the driving track, the pavement proportion of urban roads, general highways and expressways is analyzed to develop a test route matching the actual driving conditions of the user; A full-factorial test design scheme is adopted to take tire pressure and camber as variables to conduct real vehicle road test under the test route and test load condition to obtain uneven wear mileage data corresponding to different variable combinations; The influence of tire pressure and camber on uneven wear mileage and the interaction between them are determined by analyzing the uneven wear mileage data and fitting a mathematical model to construct an uneven wear mileage prediction model; The target tire pressure and target camber are input into the uneven wear mileage prediction model to obtain the corresponding wheel uneven wear mileage prediction result, and the optimal combination of tire pressure and camber is determined in combination with the preset uneven wear mileage target.

2. The tire pressure and camber wear mileage test method according to claim 1, characterized by, The driving behavior data is used to screen regular driving and exclude the influence of intense driving on tire uneven wear, specifically including: based on longitudinal acceleration, lateral acceleration and vehicle speed data, confirming that the driving behavior of the tire uneven wear vehicle belongs to regular driving.

3. The tire pressure and camber wear mileage test method according to claim 1, characterized by, The load data includes the number of passengers, the position distribution of passengers and the loading weight in the trunk under different use scenarios. Based on the load data, the camber values under different load conditions are compared to establish a relationship curve between camber and vehicle load to determine the test load condition, which comprises the following steps: Based on the load data, the typical load range and load distribution characteristics of the tire uneven wear vehicle in actual use are counted; In a four-wheel positioning test environment, different load conditions in the typical load range are simulated, and the camber values corresponding to each load are measured by a four-wheel positioning device; The load conditions at least include empty load, half load and full load, wherein the full load condition corresponds to the maximum allowable total weight of the vehicle design; The camber values under different load conditions are linearly fitted to establish a relationship curve between camber and vehicle load, and the correlation coefficient of the relationship curve is not less than a preset coefficient threshold, which is used to represent the linear correlation between vehicle load and camber; Based on the load distribution characteristics and the relationship curve, the load value with the highest frequency is selected as the test load condition, which needs to cover the main load scenarios in the user's daily use and ensure that the camber under the test load is within the linear interval of the relationship curve.

4. The tire pressure and camber wear mileage test method according to claim 1, characterized by, The driving track includes driving time and average vehicle speed, steering frequency, slope change and traffic signal distribution characteristics in the corresponding period; Based on the driving track, the pavement proportion of urban roads, general highways and expressways is analyzed to develop a test route matching the actual driving conditions of the user, which comprises the following steps: The driving track is divided into three types of pavement, i.e. urban roads, general highways and expressways based on the average vehicle speed; The mileage proportion of the three types of pavement is calculated by weighted average to construct a test route model containing the three types of pavement; The weighted average is weighted by the annual mileage of different vehicles, so as to ensure that the statistical result reflects the typical driving characteristics of the user group; The consistency of the test route model and the actual driving condition of the user is verified through real vehicle test driving, and specifically includes: calculating the matching degree of the test route and the steering frequency, slope change and traffic signal distribution characteristics of the driving track, and if the matching degree is not lower than a preset matching degree threshold, it is determined that the test route is the final execution route.

5. The tire pressure and camber wear mileage test method according to claim 1, characterized by, The full-factorial experimental design scheme includes a two-factor two-level experimental design scheme; The real vehicle road test is performed under the test route and test load conditions by using the full-factorial experimental design scheme with tire pressure and camber angle as variables, and the tire wear mileage data corresponding to different variable combinations are obtained, including the following steps: Tire pressure and camber angle are used as test factors, each test factor is set to high level and low level, a two-factor two-level experimental design scheme is used, and four groups of parameter combinations are formed; The test vehicle is loaded according to the test load condition, real vehicle driving test is performed on the test route, and tire pressure stability is monitored in real time during the test to ensure that the tire pressure fluctuation range does not exceed a preset tire pressure fluctuation threshold, and the camber angle is calibrated by four-wheel positioning equipment every day; The test route takes a preset kilometer as a complete cycle unit, and the test vehicle of each group of parameter combinations completes at least one cycle; The exposed length of the inner side cord of the tire is used as the tire wear judgment index, and when the exposed length of the inner side cord of the left rear wheel reaches a preset exposed length threshold, the cumulative driving mileage at this time is recorded as the tire wear mileage corresponding to the parameter combination; If the inner side cord of the left rear wheel is not exposed after the test cycle, the cycle mileage is used as the lower limit value of the tire wear mileage; The tire wear mileage data of the four groups of parameter combinations are arranged into a three-dimensional data set of tire pressure level, camber angle level and tire wear mileage.

6. The tire pressure and camber wear mileage test method according to claim 5, characterized by The tire wear mileage data is analyzed to determine the influence degree of tire pressure and camber angle on tire wear mileage and the interaction between them, and a tire wear mileage prediction model is constructed by mathematical model fitting, including the following steps: The tire wear mileage data is preprocessed to eliminate invalid samples caused by abnormal tire damage, tire pressure fluctuation exceeding the preset range or camber angle calibration deviation exceeding the preset range; the tire wear mileage data is classified according to the tire pressure level and camber angle level dimensions to obtain multiple groups of effective data under different parameter combinations; The main effect and interaction intensity of tire pressure and camber angle are determined by comparing the mean value difference of tire wear mileage under different parameter combinations, wherein the main effect is calculated by the mean value difference of high and low level groups, and the interaction is determined by a three-dimensional response surface graph; A regression model containing interaction terms is constructed as the tire wear mileage prediction model with tire pressure and camber angle as independent variables and tire wear mileage as dependent variable; The regression model contains main effect terms, interaction terms and constant terms, and the model parameters are fitted by statistical methods to ensure that the model has good fitting goodness.

7. The tire pressure and camber wear mileage test method according to claim 6, characterized by The regression model is verified by substituting any tire pressure and camber angle combination into the regression model to obtain a predicted value of the tire wear mileage, and the effectiveness of the regression model is verified, specifically including verifying that the predicted value satisfies: when the camber angle is fixed at an optimal value, the tire pressure increases from a low level to a high level, and the predicted value shows a monotonically increasing trend; when the tire pressure is between the high and low levels and the camber angle is the optimal value, the predicted value conforms to the superposition trend rule of the tire pressure and the camber angle acting alone.

8. An electronic device, comprising: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the tire pressure and camber angle tire wear mileage test method of any one of claims 1 to 7 when executing the computer program.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the tire pressure and camber angle tire wear mileage test method of any one of claims 1 to 7.

10. A vehicle characterized by comprising: The tire wear mileage of the wheels of the vehicle is obtained by the tire pressure and camber angle tire wear mileage test method of any one of claims 1 to 7.

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