Service life detection method of vehicle fender, vehicle and readable storage medium

Abstract

The invention provides a service life detection method of a vehicle fender, a vehicle and a readable storage medium, the method is applied to the technical field of vehicle performance test.The method comprises the steps that in response to a service life detection instruction of the vehicle fender, working state data, historical maintenance records and manufacturing parameters of the vehicle fender are obtained, and the service life of the vehicle fender is detected; acquiring historical driving data of the vehicle; according to the working state data, the historical maintenance records, the manufacturing parameters and the historical driving data, at least one service life characteristic of the vehicle fender is extracted; obtaining a service life prediction strategy of the vehicle fender, and optimizing the configuration of the service life prediction strategy based on the historical driving data, the historical maintenance records and the manufacturing parameters; and generating the remaining service life of the vehicle fender according to the life prediction strategy and the at least one life characteristic. According to the method, the accuracy of the service life detection of the mudguard is improved, so that the sudden cracking and falling probability of the mudguard is reduced, the excessive maintenance and shutdown time is reduced, and the user security is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle performance testing technology, and more specifically, to a method for testing the lifespan of a vehicle mudguard, a vehicle, and a readable storage medium within the field of vehicle performance testing technology. Background Technology

[0002] As an important protective component under a vehicle, mudguards not only block mud, water, stones, and other debris kicked up by the wheels during driving, preventing them from impacting and corroding the vehicle body, but also help reduce secondary pollution to the road and surrounding environment, improving the overall cleanliness and environmental performance of the vehicle. However, because mudguards are exposed to complex and harsh working environments for a long time, such as high humidity, high dust, salt spray, ultraviolet radiation, and extreme temperature differences, coupled with the continuous mechanical vibration, impact loads, and gravel impacts during driving, their material properties are easily degraded, leading to aging, cracking, warping, deformation, and even complete detachment, seriously affecting their functional effectiveness and the integrity of the vehicle's appearance.

[0003] Currently, a systematic monitoring and early warning mechanism for the lifespan of automotive mudguards is still lacking. Existing maintenance methods mainly rely on manual visual inspection, which suffers from problems such as detection lag, strong subjectivity, and low efficiency, making it difficult to detect potential failure risks in a timely manner. In most cases, repairs or replacements are only carried out after the mudguards have been obviously damaged or lost their function. This not only increases vehicle maintenance costs and downtime, but also may cause secondary safety hazards due to sudden mudguard failure, such as traffic accidents caused by parts falling off and following vehicles being unable to avoid them, threatening road traffic safety. Summary of the Invention

[0004] This application provides a method for detecting the lifespan of vehicle mudguards, a vehicle, and a readable storage medium. This method improves the accuracy of mudguard lifespan detection, thereby reducing the probability of sudden cracking and detachment of mudguards, minimizing excessive repairs and downtime, and enhancing user safety.

[0005] In a first aspect, a method for detecting the lifespan of a vehicle mudguard is provided, comprising the following steps: in response to a lifespan detection command for the vehicle mudguard, acquiring working status data, historical maintenance records, and manufacturing parameters of the vehicle mudguard, as well as acquiring historical driving data of the vehicle; extracting at least one lifespan feature of the vehicle mudguard based on the working status data, historical maintenance records, manufacturing parameters, and historical driving data; acquiring a lifespan prediction strategy for the vehicle mudguard, and optimizing the configuration of the lifespan prediction strategy based on historical driving data, historical maintenance records, and manufacturing parameters; and generating the remaining service life of the vehicle mudguard based on the lifespan prediction strategy and at least one lifespan feature.

[0006] Based on the aforementioned lifespan testing method for vehicle mudguards, by comprehensively collecting working status data, historical maintenance records, manufacturing parameters, and historical vehicle mileage data, the actual usage and health status of the mudguards can be fully reflected. Working status data monitors multi-dimensional physical quantities such as temperature, humidity, vibration, and stress of the mudguards during driving, providing microscopic damage evidence for lifespan assessment. Historical maintenance records detail maintenance time and items, helping to understand damage patterns and repair effects. Manufacturing parameters cover key information such as mudguard material and installation torque, reflecting initial quality and design characteristics. Historical vehicle mileage data provides a macroscopic usage history, including cumulative mileage, speed distribution, and water wading frequency, providing a macroscopic usage context for lifespan prediction. Based on at least one lifespan feature extracted from these multi-source data, the health degradation trend of the mudguards can be accurately quantified, making the configuration of lifespan prediction strategies more scientific and reasonable. The optimized lifespan prediction strategy can dynamically adjust its configuration items, such as decision weights or relevant parameters of its built-in model, improving the accuracy of remaining lifespan prediction results. Therefore, it can provide early warning of potential mudguard failure risks, effectively reduce the probability of sudden cracking and detachment of mudguards, reduce excessive maintenance and downtime, and significantly improve users' sense of security and maintenance economy.

[0007] In conjunction with the first aspect, in some possible implementations, historical maintenance records include historical fender maintenance times. Based on historical driving data, historical maintenance records, and manufacturing parameters, the configuration of the lifespan prediction strategy is optimized, including: determining the previous fender maintenance time based on the historical fender maintenance time; extracting target historical driving data from the historical driving data based on the previous fender maintenance time; and optimizing the configuration of the lifespan prediction strategy based on the target historical driving data and manufacturing parameters.

[0008] By locating the last mudguard repair time and extracting all target historical driving data from then until the present, and using the actual usage intensity after repair as a benchmark, the decision path, integration weight, and incremental update parameters of the life prediction strategy are fine-tuned online. This ensures that the life prediction strategy is continuously synchronized with the latest vehicle operating conditions, thereby improving the timeliness and individual accuracy of remaining mileage prediction and reducing the occurrence of under-maintenance or over-maintenance caused by historical operating condition drift.

[0009] Combining the first aspect and the above implementation methods, in some possible implementation methods, the historical maintenance record includes the mudguard maintenance items corresponding to the last mudguard maintenance time. Based on the target historical driving data and manufacturing parameters, the configuration of the life prediction strategy is optimized, including: based on the target historical driving data, mudguard maintenance items and manufacturing parameters, the configuration of the life prediction strategy is optimized.

[0010] By incorporating the previous fender repair project as a key correction factor, the configuration items of the life prediction strategy can be dynamically adjusted based on the degree of structural integrity restoration achieved by different repair methods, enabling differentiated reset of the life baseline after repair. This mechanism ensures that the prediction results truly reflect the cumulative effect of repair quality and subsequent operating conditions, avoiding error drift caused by a uniform life prediction strategy, reducing the risk of premature replacement or delayed maintenance, improving user maintenance economy, vehicle safety, and after-sales satisfaction, while providing manufacturers with a traceable data loop for optimizing repair processes and material selection.

[0011] Combining the first aspect and the above implementation methods, in some possible implementation methods, the configuration of the life prediction strategy is optimized based on the target historical driving data and manufacturing parameters, including: generating an extraction time period based on the current time; in response to the fact that the last fender maintenance time is not within the extraction time period, extracting the first historical driving data from the target historical driving data based on the extraction time period; and optimizing the configuration of the life prediction strategy based on the first historical driving data and manufacturing parameters.

[0012] By introducing an extraction time period, when the last maintenance time is earlier than this time window, the life prediction strategy configuration is adjusted online using only the first historical driving data of the extraction time period. This ensures that the life prediction results are always synchronized with the latest vehicle usage intensity and environmental load, avoiding prediction lag or over-conservatism caused by outdated maintenance information. This significantly improves the timeliness and individual accuracy of the remaining mileage output, thereby achieving accurate replacement prompts, reducing the risk of over-maintenance, and optimizing user maintenance costs and overall vehicle safety.

[0013] Combining the first aspect and the above implementation methods, in some possible implementation methods, the historical maintenance record includes the historical mudguard maintenance time. Based on the working status data, historical maintenance record, manufacturing parameters, and historical driving data, at least one lifespan feature of the vehicle mudguard is extracted, including: determining the last mudguard maintenance time based on the historical mudguard maintenance time; extracting target historical driving data from the historical driving data based on the last mudguard maintenance time; fusing the working status data, manufacturing parameters, and target historical driving data to generate mudguard feature data; and performing feature extraction on the mudguard feature data to obtain at least one lifespan feature.

[0014] By using the most recent maintenance time as a benchmark, the system aligns and fuses the post-maintenance conditions, driving intensity, and manufacturing parameters along a timeline to obtain high-fidelity fender feature data. Based on this, life characteristics that more closely resemble the actual degradation process are extracted. This effectively reduces the possibility of feature drift, makes the life prediction strategy input more accurate, and makes the remaining life of the fender more reliable. Ultimately, this enables early warning, avoids sudden failures, reduces mis-maintenance and over-maintenance, and improves user maintenance economy, vehicle safety, and the closed-loop value of after-sales data.

[0015] Combining the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the working status data, manufacturing parameters, and target historical driving data are fused to generate mudguard feature data. This includes: obtaining the mudguard material and installation torque value from the manufacturing parameters, and generating vehicle mudguard feature data based on the mudguard material and installation torque value; extracting features from the working status data to generate mudguard working feature data; organizing the target historical driving data based on a preset timestamp to obtain driving feature data; and splicing the vehicle mudguard feature data, mudguard working feature data, and driving feature data to generate mudguard feature data.

[0016] By integrating static manufacturing differences, dynamic service loads, and actual post-repair usage intensity into unified fender characteristic data, prediction bias caused by omissions of material, torque, road conditions, or climate variables is reduced. The downstream life prediction strategy extracts more accurate life characteristics based on this, and the remaining mileage output is highly consistent with the actual attenuation, enabling early failure warning, avoiding sudden cracking and detachment, reducing mis-repair and over-maintenance, and improving user maintenance economy, vehicle safety, and the closed-loop value of brand after-sales data.

[0017] Combining the first aspect and the above implementation methods, in some possible implementation methods, feature extraction is performed on the mudguard feature data to obtain at least one lifespan feature, including: performing dimensionality reduction processing on the mudguard feature data to obtain target mudguard feature data; and performing feature extraction on the target mudguard feature data to obtain at least one lifespan feature.

[0018] By first reducing the dimensionality of high-dimensional mudguard feature data and eliminating redundant and collinear variables, a low-dimensional, high-information-density target feature set is obtained, significantly compressing the computational load and improving the signal-to-noise ratio. On this basis, segmented extraction is implemented to accurately extract life features such as maintenance-failure interval mileage, cumulative equivalent vibration damage value, and damp-heat coupling coefficient. This simplifies the input dimensions of the downstream life prediction strategy and clarifies its physical meaning, thereby further improving the accuracy and robustness of remaining life prediction, reducing the risk of false alarms and over-repair, and achieving multiple optimizations in maintenance costs, vehicle safety, and data processing efficiency.

[0019] In conjunction with the first aspect, in some possible implementations, the life detection method for vehicle mudguards further includes: generating maintenance suggestions for vehicle mudguards based on remaining lifespan and working status data, and displaying the remaining lifespan, working status data, and maintenance suggestions through an in-vehicle terminal and / or a mobile terminal; in response to the remaining lifespan being less than a preset lifespan threshold, generating corresponding warning information and displaying the warning information through a corresponding application on at least one of an in-vehicle central control display screen, an in-vehicle driving screen, and a mobile terminal, wherein the application includes a mudguard display interface for displaying one or more of the remaining lifespan, working status data, maintenance suggestions, and warning information.

[0020] By linking remaining service life with real-time operating status data, maintenance suggestions are automatically generated and simultaneously displayed on the vehicle and mobile terminals. When the remaining service life falls below a preset threshold, a warning message is immediately triggered and simultaneously pushed to the vehicle's central control display, driver's screen, and mobile terminal via the application. The application's mudguard display interface supports displaying one or more of the following on demand: remaining service life, operating status data, maintenance suggestions, and warning messages, achieving flexibility and interactivity in information display. This effectively improves the visibility and actionability of mudguard life detection results, reduces the risk of failure due to maintenance delays, minimizes excessive repairs and downtime, enhances users' control over vehicle health, and improves after-sales service efficiency and brand data loop value, demonstrating excellent comprehensive benefits in terms of safety, economy, and user experience.

[0021] Secondly, a vehicle is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned method for detecting the lifespan of a vehicle mudguard.

[0022] According to the vehicle embodiment of this application, when the processor executes the computer program, the above-mentioned vehicle mudguard life detection method can be implemented. Based on the above-mentioned vehicle mudguard life detection method, the accuracy of mudguard life detection is improved, thereby reducing the probability of sudden cracking and falling off of the mudguard, reducing excessive maintenance and downtime, and improving user safety.

[0023] Thirdly, a computer-readable storage medium is provided having a computer program stored thereon, which is executed by a processor to implement the aforementioned method for detecting the lifespan of vehicle mudguards.

[0024] According to the embodiments of this application, a computer-readable storage medium storing a computer program thereon implements the above-described vehicle mudguard life detection method when executed by a processor. Based on the vehicle mudguard life detection method, the accuracy of mudguard life detection is improved, thereby reducing the probability of sudden cracking and detachment of mudguards, reducing excessive maintenance and downtime, and enhancing user safety. Attached Figure Description

[0025] Figure 1 This is a flowchart of a method for detecting the lifespan of vehicle mudguards according to some embodiments of this application; Figure 2 A flowchart illustrating a method for detecting the lifespan of a vehicle mudguard according to a specific embodiment of this application; Figure 3 This is a block diagram of a vehicle according to some embodiments of this application. Detailed Implementation

[0026] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0027] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0028] The method for detecting the lifespan of vehicle mudguards, the vehicle, and the readable storage medium disclosed in this application aim to solve the following key technical problems: 1. Challenges in real-time monitoring: Traditional methods rely on manual visual inspection or offline sampling, which results in long inspection cycles and sparse sampling points. This makes it impossible to continuously, synchronously, and with high precision collect data on the temperature, humidity, stress, and vibration of the mudguards during operation, leading to delayed acquisition of health status and passive delays in maintenance.

[0029] 2. Lack of early warning mechanism: The existing system lacks quantitative threshold standards for the aging characteristics of plastics / composite materials. On-board diagnostics only trigger alarms for electrical faults, and there are no codes to report the performance degradation stage. As a result, mudguards cannot be promptly reminded when they are approaching or reaching their service life, and maintenance has shifted from prevention to post-event remediation.

[0030] 3. Data integration and analysis: Multi-source heterogeneous data (sensor time series, driving conditions, maintenance records, manufacturing parameters, environmental climate) are stored in a scattered manner and have different sampling benchmarks, resulting in missing, drift and dimensional redundancy. There is an urgent need for unified alignment, cleaning and dimensionality reduction to establish a high signal-to-noise ratio dataset and support accurate prediction of remaining life.

[0031] 4. User interaction experience: Complex physical quantities (such as damp heat equivalent) are highly specialized, and traditional text lists are prone to misreading or information overload. They need to be transformed into intuitive and customizable warning content through standardization, graphics, and hierarchical methods to achieve synchronous display and real-time interaction between the vehicle terminal and the user's mobile client application.

[0032] Therefore, a life detection method for vehicle mudguards is needed to achieve timely perception of the health status of mudguards and accurate assessment of their remaining lifespan, thereby solving the problems of sparse sampling, delayed response, and passive maintenance associated with traditional methods.

[0033] Before explaining the life testing method for vehicle mudguards provided in the embodiments of this application, the hardware and software environment architecture involved in the embodiments of this application will be described first.

[0034] The hardware and software environment architecture involved in the embodiments of this application includes integrated sensors, wireless communication, big data platform and artificial intelligence algorithm.

[0035] Among them, the integrated sensor is used to collect real-time operating status signals of multiple physical quantities such as temperature, humidity, stress, and vibration in key areas of the mudguard (such as the suspension at the wheel position), providing the raw data basis for subsequent evaluation.

[0036] Wireless communication (such as Bluetooth, Wi-Fi, or 4G / 5G) is used to securely transmit encrypted high-frequency data output from sensors to in-vehicle terminals or cloud servers, enabling a seamless data link between the vehicle and the cloud.

[0037] The big data platform is used to uniformly store, clean, align, and fuse heterogeneous data from vehicles, maintenance, manufacturing, and the environment, constructing a high signal-to-noise ratio data lake to support efficient analysis. Specifically, the big data platform uses vehicles as the primary key, aligning driving conditions, working status data, maintenance records, and manufacturing parameters by timestamp at the second level, and generating standardized data tables after removing drifting, missing, and abnormal samples. Noise is reduced through deduplication, imputation, and normalization, retaining core variables strongly correlated with lifespan, forming low-noise, high-information-density training and validation sets for lifespan prediction strategy configuration optimization and online incremental updates, achieving high-precision prediction and dynamic assessment of mudguard remaining lifespan.

[0038] Artificial intelligence algorithms are used to establish mudguard life prediction strategies, outputting the remaining lifespan of mudguards in real time, and simultaneously generating maintenance suggestions and multi-level early warnings to achieve intelligent decision-making for health management. For example, an XGBoost-L2 (eXtremeGradient Boosting with L2 regularization) ensemble regression algorithm can be used to build a life prediction model. The life prediction strategy can then use this model to quickly output the remaining lifespan and make maintenance decisions.

[0039] Figure 1 This is a flowchart of a method for testing the lifespan of a vehicle mudguard according to some embodiments of this application. The method for testing the lifespan of a vehicle mudguard may include: S1, in response to the life detection command of the vehicle mudguard, acquires the working status data, historical maintenance records and manufacturing parameters of the vehicle mudguard, as well as the historical driving data of the vehicle.

[0040] Among them, the life detection command refers to the control signal issued by the vehicle system, mobile client application or cloud platform when the preset trigger conditions are met (such as vehicle start-up, mileage reaching a certain threshold, user active request or vehicle system periodic self-check), which is used to start the mudguard life detection process.

[0041] Working status data refers to a set of multi-dimensional physical quantities that are collected in real time by a high-precision sensor array deployed on and around the mudguard body, reflecting its working environment and structural response. These include, but are not limited to, temperature, humidity, vibration acceleration, stress change, impact amplitude and cumulative action time, and are used to quantify the mechanical and climatic loads that the mudguard bears during driving.

[0042] Historical maintenance records refer to the mudguard repair and replacement logs that can be stored on the automaker's after-sales cloud platform or the owner's mobile application. These records cover various damage patterns (e.g., cracking, deformation, detachment), repair type (i.e., repair or replacement), repair time, and repair service location information. Historical maintenance records are used to establish a mapping between failure events and operating conditions.

[0043] Manufacturing parameters refer to the batch-level static attributes of mudguards archived by the manufacturing execution system before they leave the factory. These parameters may include mudguard material batch, material formula, injection mold number, wall thickness and reinforcing rib layout, installation torque, heat treatment and coating process parameters, etc. Manufacturing parameters are used to trace the baseline differences in lifespan caused by manufacturing variability.

[0044] Historical vehicle driving data refers to fleet-level operational data continuously uploaded to the cloud via a T-Box (Telematics-Box, in-vehicle connected terminal) or vehicle controller. This data can include cumulative mileage, daily mileage distribution, average speed distribution, acceleration or braking intensity, road surface classification results, and may also include historical ambient temperature and humidity curves and water wading frequency. Historical vehicle driving data is used to reconstruct the true load spectrum encountered by the mudguards throughout their entire lifespan.

[0045] For example, when a user issues a fender life detection request in the client application, the mileage reaches a set mileage threshold, the cumulative running time after vehicle startup reaches a specified time limit, or a cloud-based periodic inspection command is issued, the onboard system or cloud platform sends a life detection command. Subsequently, a high-precision sensor array collects and transmits working status data such as temperature, humidity, triaxial vibration, strain, and impact amplitude in real time. At the same time, the system reads historical driving data stored in the ECU (Electronic Control Unit) through the onboard T-Box, including cumulative mileage, average vehicle speed, acceleration and deceleration intensity, wading frequency, and annual ambient temperature and humidity curves. It also obtains the vehicle's fender historical maintenance records (such as damage mode, replacement time, operating mileage, and maintenance nature) and manufacturer-archived manufacturing parameters (such as material batch, mold number, wall thickness, installation torque, and painting process) from the vehicle manufacturer's after-sales cloud platform.

[0046] S2, based on working status data, historical maintenance records, manufacturing parameters and historical driving data, extract at least one life characteristic of the vehicle mudguard.

[0047] Among them, life characteristics refer to key parameters or statistics extracted from the vehicle's mudguard's working status data, historical maintenance records, manufacturing parameters, and historical driving data, which can quantitatively characterize the trend of structural health degradation and are directly related to its remaining service life. Life characteristics may include maintenance-failure interval mileage, cumulative equivalent vibration damage value, etc.

[0048] Specifically, the acquired working status data, historical maintenance records, manufacturing parameters, and historical driving data are analyzed and fused in parallel. Quantitative indicators that characterize the health degradation trend of the mudguard structure are calculated using multi-dimensional algorithms. At least one life feature, including maintenance-failure interval mileage and cumulative equivalent vibration damage value, is extracted and used as input to downstream life prediction strategies to assess its remaining service life.

[0049] S3 acquires the life prediction strategy for vehicle mudguards and optimizes the configuration of the life prediction strategy based on historical driving data, historical maintenance records and manufacturing parameters.

[0050] The lifespan prediction strategy refers to a systematic, data-driven decision-making system used to assess and predict the remaining service life of vehicle mudguards. The configuration of the lifespan prediction strategy involves pre-setting and tuning a complete set of rules, thresholds, weights, correction factors, and mapping relationships necessary for the strategy's operation. This includes, but is not limited to, the extraction time period length, feature dimensionality reduction order, and tiered warning mileage thresholds. By customizing these configuration items, the lifespan prediction strategy can be optimized.

[0051] Furthermore, a lifespan prediction strategy can be implemented using a fender lifespan prediction model to assess its remaining service life. The fender lifespan prediction model is a machine learning regression model used to map lifespan characteristics to the remaining service life of a vehicle's fenders. The lifespan prediction strategy configuration can also be optimized by adjusting the model parameters of the fender lifespan prediction model. The model parameters of the fender lifespan prediction model are a set of values ​​that need to be optimized during the training phase and remain fixed during the inference phase. These parameters may include decision path parameters, ensemble weight parameters, and incremental update parameters, used to quantify the mapping relationship between lifespan characteristics and remaining mileage. These model parameters can be obtained by adjusting historical driving data, maintenance records, and manufacturing parameters. This fender lifespan prediction model can be constructed by technical personnel in related fields according to actual conditions, without specific restrictions. The remaining service life of a vehicle's fender can be expressed as the remaining mileage that the fender can withstand under current operating conditions until it first reaches the failure criterion.

[0052] Specifically, by comprehensively considering multiple factors, such as historical driving data, historical maintenance records, and manufacturing parameters, the configuration of the life prediction strategy is optimized, thereby improving the accuracy and reliability of the prediction.

[0053] S4, based on the life prediction strategy and at least one life characteristic, generate the remaining lifespan of the vehicle fender.

[0054] Specifically, based on the optimized life prediction strategy and at least one standardized life feature vector, the remaining mileage prediction value of the mudguard under the current operating conditions is generated as the remaining service life of the vehicle mudguard.

[0055] When the vehicle system, mobile client application, or cloud is triggered by preset conditions such as startup, mileage, or time, a life detection command is issued. The vehicle immediately collects operating condition data such as temperature, humidity, vibration, and strain through a high-precision sensor array deployed on the mudguard. At the same time, the T-Box reads historical driving data such as cumulative mileage, vehicle speed, and water wading frequency from the ECU, and retrieves the mudguard's historical maintenance records and manufacturing batch parameters. Subsequently, the multi-source data is analyzed and fused in parallel to extract life characteristics that can characterize structural health degradation, such as maintenance-failure interval mileage and cumulative equivalent vibration damage value. Combined with an optimized life prediction strategy, the remaining mileage that the mudguard can withstand before its first failure under the current operating conditions is generated, thus realizing life detection.

[0056] By collecting multi-dimensional data on operating conditions, maintenance, manufacturing, and driving, and through optimized life prediction strategies, health degradation is transformed into visible remaining mileage, providing early warnings to prevent sudden cracking and detachment, and avoiding driving safety hazards and road pollution. Based on the prediction results, precise replacement is achieved, avoiding excessive maintenance and vehicle downtime, and reducing users' time and cost costs. In addition, the massive amount of real-world data collected is fed back to the platform, which can continuously optimize material selection, mold parameters, and installation processes, forming a closed loop to improve product durability and achieving a triple technical effect of safety, economy, and environmental protection.

[0057] In some embodiments, outlier removal processing can also be performed on the operating status data acquired by the high-precision sensor array. Specifically, extreme values ​​caused by sensor malfunction or human error are removed. For example, values ​​with a temperature >120°C or a vibration frequency >500Hz are removed. This improves data quality and ensures the accuracy of subsequent feature extraction and lifetime prediction.

[0058] In some embodiments of this application, historical maintenance records include historical fender maintenance times. Based on historical driving data, historical maintenance records, and manufacturing parameters, the configuration of the life prediction strategy is optimized, including: determining the last fender maintenance time based on the historical fender maintenance time; extracting target historical driving data from the historical driving data based on the last fender maintenance time; and optimizing the configuration of the life prediction strategy based on the target historical driving data and manufacturing parameters.

[0059] Specifically, historical maintenance records include historical mudguard maintenance dates. Historical mudguard maintenance dates refer to the specific dates and times when mudguards were repaired or replaced during the after-sales process, used to synchronize with mileage and operating condition data.

[0060] Among them, the target historical driving data refers to the operating condition data such as the vehicle's cumulative mileage, average speed, acceleration and deceleration intensity, water wading frequency, and environmental load generated during the period from the last mudguard maintenance time to the current time, which is used to align with the actual usage intensity after maintenance.

[0061] Specifically, the system reads the most recent fender maintenance time from the historical maintenance records and uses that time as a benchmark to extract all running segments from the complete historical driving data up to the current date as the target historical driving data. Then, it inputs the cumulative mileage, average speed, acceleration and deceleration intensity, water wading frequency, and other data of the target historical driving data, as well as the corresponding manufacturing parameters of the fender, into the online update module to adjust the relevant configuration of the life prediction strategy. This ensures that the life prediction strategy parameters are synchronized with the actual usage intensity of the vehicle, and that the remaining life output always reflects the latest operating conditions.

[0062] By locating the last mudguard repair time and capturing all target historical driving data from then until the present, and using the actual usage intensity after repair as a benchmark, the relevant configuration items of the life prediction strategy are fine-tuned online. This ensures that the life prediction strategy is continuously synchronized with the latest vehicle operating conditions, thereby improving the timeliness and individual accuracy of remaining mileage prediction and reducing the occurrence of under-maintenance or over-maintenance caused by historical operating condition drift.

[0063] In some embodiments of this application, the historical maintenance record includes the fender maintenance items corresponding to the last fender maintenance time. Based on the target historical driving data and manufacturing parameters, the configuration of the life prediction strategy is optimized, including: based on the target historical driving data, the fender maintenance items and manufacturing parameters, the configuration of the life prediction strategy is optimized.

[0064] Specifically, historical maintenance records include the mudguard maintenance items corresponding to the last mudguard maintenance time. Mudguard maintenance items refer to the specific operation types performed on the mudguard during maintenance work and their corresponding process information, including replacement, welding repair, adding reinforcing ribs, surface coating repair, installation position correction, and resetting tightening torque, etc., used to distinguish the degree of restoration of structural integrity and life baseline by different maintenance methods.

[0065] Specifically, based on the last fender repair time, target historical driving data is extracted, and repair items (such as replacement, welding repair, and reinforcement reinforcement) in the same repair record are read. Then, based on the repair items and combined with the material batch, wall thickness and installation torque in the manufacturing parameters, the relevant configuration of the life prediction strategy is adjusted differently. For example, a new life baseline is used for replacement items, a local fatigue reduction coefficient is introduced for welding repair items, and the weight of damp heat aging rate is reduced for paint repair items.

[0066] By incorporating the previous fender repair project as a key correction factor, the configuration items of the life prediction strategy can be dynamically adjusted based on the degree of structural integrity restoration achieved by different repair methods, enabling differentiated reset of the life baseline after repair. This mechanism ensures that the prediction results truly reflect the cumulative effect of repair quality and subsequent operating conditions, avoiding error drift caused by a uniform life prediction strategy, reducing the risk of premature replacement or delayed maintenance, improving user maintenance economy, vehicle safety, and after-sales satisfaction, while providing manufacturers with a traceable data loop for optimizing repair processes and material selection.

[0067] In some embodiments of this application, the configuration of the life prediction strategy is optimized based on target historical driving data and manufacturing parameters, including: generating an extraction time period based on the current time; in response to the previous mudguard maintenance time not being within the extraction time period, extracting first historical driving data from the target historical driving data based on the extraction time period; and optimizing the configuration of the life prediction strategy based on the first historical driving data and manufacturing parameters.

[0068] The extraction time period refers to a time window formed by looking back a predetermined fixed duration from the current moment. For example, the extraction time period could be from the current moment to six months prior. This is used to define the most recent driving range that has timely reference value for the lifespan prediction strategy and reflects recent usage intensity and climate load. The first historical driving data refers to data extracted from the target historical driving data corresponding to the extraction time period, including cumulative mileage, average speed, acceleration / deceleration intensity, water wading frequency, and environmental temperature and humidity load within that extraction time period, used to characterize the vehicle's recent actual usage intensity.

[0069] For example, the life prediction strategy generates an extraction time period with the current time as the endpoint and a fixed duration of six months in advance, and determines whether the last mudguard maintenance time falls within this time window; if the maintenance time is earlier than the start of the window, the cumulative mileage, average speed, acceleration and deceleration intensity, water wading frequency, and environmental temperature and humidity load data for the corresponding six-month interval are extracted from the target historical driving data as the first historical driving data. Combined with manufacturing parameters, such as material batch, wall thickness and torque information, the life prediction strategy is adjusted so that the life prediction strategy recalibrates the remaining life output based on the recent actual usage intensity.

[0070] By introducing an extraction time period, when the last maintenance time is earlier than this time window, the life prediction strategy configuration items are adjusted online using only the first historical driving data of the extraction time period. This ensures that the life prediction results are always synchronized with the latest vehicle usage intensity and environmental load, avoiding prediction lag or overly conservative results caused by outdated maintenance information. This improves the timeliness and individual accuracy of the remaining mileage output, thereby achieving accurate replacement prompts, reducing the risk of over-maintenance, and optimizing user maintenance costs and overall vehicle safety.

[0071] In some embodiments of this application, historical maintenance records include historical mudguard maintenance times. Based on working status data, historical maintenance records, manufacturing parameters, and historical driving data, at least one lifespan feature of the vehicle mudguard is extracted, including: determining the last mudguard maintenance time based on historical mudguard maintenance times; extracting target historical driving data from historical driving data based on the last mudguard maintenance time; fusing the working status data, manufacturing parameters, and target historical driving data to generate mudguard feature data; and performing feature extraction on the mudguard feature data to obtain at least one lifespan feature.

[0072] Among them, mudguard feature data refers to a comprehensive data set formed by aligning and integrating working status data, manufacturing parameters and target historical driving data according to a unified time axis. It covers multi-dimensional information such as temperature, humidity, vibration, stress, cumulative mileage, average vehicle speed, acceleration and deceleration intensity, water wading frequency, material batch, wall thickness, and installation torque.

[0073] Specifically, based on the historical fender maintenance time, all target historical driving data from the time of maintenance to the present is extracted and fused with the working status data and corresponding manufacturing parameters within the same time period using timestamp alignment and normalization to form fender characteristic data covering mechanical load, environmental stress, usage intensity and manufacturing dispersion. Subsequently, the fender characteristic data is segmented to generate at least one life feature such as maintenance-failure interval mileage, cumulative equivalent vibration damage value, and damp-heat coupling coefficient, which is used to input downstream life prediction strategies and complete the remaining life assessment.

[0074] By using the most recent maintenance time as a benchmark, and aligning and fusing the post-maintenance conditions, driving intensity, and manufacturing parameters along the timeline, high-fidelity mudguard feature data is obtained. Based on this, life characteristics that more closely resemble the actual degradation process are extracted. This effectively reduces the possibility of feature drift, provides a more accurate basis for life prediction strategies, makes the remaining lifespan of the mudguard more reliable, and ultimately achieves early warning, avoids sudden failures, reduces mis-repairs and over-maintenance, and improves user maintenance economy, vehicle safety, and the closed-loop value of after-sales data.

[0075] In some embodiments of this application, working status data, manufacturing parameters, and target historical driving data are fused to generate mudguard feature data, including: obtaining mudguard material and installation torque values ​​from manufacturing parameters, and generating vehicle mudguard feature data based on mudguard material and installation torque values; extracting features from working status data to generate mudguard working feature data; organizing target historical driving data based on preset timestamps to obtain driving feature data; and splicing vehicle mudguard feature data, mudguard working feature data, and driving feature data to generate mudguard feature data.

[0076] The mudguard material refers to the polymer formula or composite material grade and batch number used in the mudguard body, used to distinguish the fatigue limit, aging rate and strength baseline of different materials. The installation torque value refers to the measured value of the standard tightening torque set at the assembly station for the mudguard and body connecting fasteners, used to quantify the connection stiffness and potential stress concentration factor.

[0077] A preset timestamp refers to a unified time base consistent with the sampling period of the working status data, used for segmenting, aligning, and resampling the target historical driving data. Driving characteristic data refers to the cumulative mileage, average speed, acceleration / deceleration intensity, water wading frequency, and environmental temperature and humidity statistics obtained after processing according to the preset timestamp within that time period, used to characterize the actual usage intensity and climatic load of the vehicle after maintenance. The preset timestamp can be set by technicians according to actual conditions, and there are no specific restrictions.

[0078] Specifically, the process begins by reading the mudguard material and installation torque values ​​from the manufacturing parameters, and then generating characteristic data for the vehicle mudguard based on these parameters. Next, a sliding window statistical analysis is performed on the operating status data to extract features such as temperature, humidity, root mean square vibration, and peak impact frequency, generating mudguard operating characteristic data. Simultaneously, historical driving data is processed using preset timestamps (e.g., resampling, aggregation) to form driving characteristic data (e.g., average vehicle speed, acceleration / deceleration intensity, wading frequency, and average ambient temperature and humidity). Finally, the three types of features are concatenated column-wise to output mudguard characteristic data of a unified dimension, which is then used by downstream life prediction strategies to generate the remaining lifespan of the mudguard.

[0079] By integrating static manufacturing differences, dynamic service loads, and actual post-repair usage intensity into unified fender characteristic data, prediction bias caused by omissions of material, torque, road conditions, or climate variables is reduced. The downstream life prediction strategy extracts more accurate life characteristics based on this, and the remaining mileage output is highly consistent with the actual attenuation, enabling early failure warning, avoiding sudden cracking and detachment, reducing mis-repair and over-maintenance, and improving user maintenance economy, vehicle safety, and the closed-loop value of brand after-sales data.

[0080] In some embodiments of this application, feature extraction is performed on the mudguard feature data to obtain at least one lifespan feature, including: performing dimensionality reduction processing on the mudguard feature data to obtain target mudguard feature data; and performing feature extraction on the target mudguard feature data to obtain at least one lifespan feature.

[0081] Among them, the target mudguard feature data refers to the set of low-dimensional, high-information-density vectors obtained after dimensionality reduction processing of the original mudguard feature data, retaining key explanatory dimensions to eliminate multicollinearity.

[0082] Specifically, the original mudguard feature data is first subjected to dimensionality reduction processing, such as principal component analysis and linear discriminant analysis, to obtain low-dimensional, high-information-density target mudguard feature data. Then, the target mudguard feature data is segmented and truncated to generate at least one life feature, such as maintenance-failure interval mileage, cumulative equivalent vibration damage value, and damp-heat coupling coefficient, which is used to input downstream life prediction strategies and complete the remaining life assessment.

[0083] By first reducing the dimensionality of high-dimensional mudguard feature data and eliminating redundant and collinear variables, a low-dimensional, high-information-density target feature set is obtained, significantly compressing the computational load and improving the signal-to-noise ratio. On this basis, segmented extraction is implemented to accurately extract life features such as maintenance-failure interval mileage, cumulative equivalent vibration damage value, and damp-heat coupling coefficient. This simplifies the input dimensions of the downstream life prediction strategy and clarifies its physical meaning, thereby further improving the accuracy and robustness of remaining life prediction, reducing the risk of false alarms and over-repair, and achieving multiple optimizations in maintenance costs, vehicle safety, and data processing efficiency.

[0084] In some embodiments of this application, the method for detecting the lifespan of a vehicle mudguard further includes: generating maintenance suggestions for the vehicle mudguard based on remaining lifespan and working status data, and displaying the remaining lifespan, working status data, and maintenance suggestions through an in-vehicle terminal and / or a mobile terminal; in response to the remaining lifespan being less than a preset lifespan threshold, generating corresponding warning information and displaying the warning information through a corresponding application on at least one of an in-vehicle central control display screen, an in-vehicle driving screen, and a mobile terminal, wherein the application includes a mudguard display interface for displaying one or more of the remaining lifespan, working status data, maintenance suggestions, and warning information.

[0085] Specifically, after the life prediction strategy generates the remaining service life of the mudguards, it generates maintenance suggestions for the vehicle's mudguards based on the remaining service life and the vehicle's operating status data. For example, if the remaining service life of the mudguards is greater than 5,000 km and the vehicle's operating status data shows that vibration, temperature, and humidity are all within the light load range, it will prompt "Good condition, routine check next maintenance"; if the remaining service life of the mudguards is less than or equal to 5,000 km and the operating status data shows that vibration exceeds the threshold or water wading is frequent, it will prompt "Schedule a special inspection within two weeks"; if the remaining service life of the mudguards is less than or equal to 2,000 km or the peak impact reaches the limit, it will immediately prompt "About to fail, please schedule replacement immediately and avoid high-speed water wading".

[0086] Furthermore, the remaining service life, operating status data, and maintenance recommendations can be displayed through at least one of the following: the in-vehicle central control display screen, the in-vehicle driving screen, and the mobile terminal.

[0087] In some embodiments, the vehicle displays remaining service life, operating status data, and maintenance recommendations only through the in-vehicle central control display. Alternatively, the vehicle may simultaneously utilize both the in-vehicle central control display and the in-vehicle driver screen to display remaining service life, operating status data, and maintenance recommendations. Another example is that after the user leaves the vehicle, they receive remaining service life and maintenance recommendations only through a user-end mobile application. The display method can be configured according to the usage scenario and is not specifically limited. This allows users to intuitively obtain information and quickly complete maintenance operations in any scenario.

[0088] A preset lifespan threshold refers to a critical mileage or time limit set for the remaining service life of a vehicle's mudguards. This threshold can be uniformly issued by the OEM based on material fatigue limits, road durability test statistics, or regional operating condition calibrations, or it can be customized by the user through vehicle settings or personal preferences; there are no specific restrictions. The preset lifespan threshold is used to provide early warnings for inspection, maintenance, or replacement at the end of the lifespan, avoiding safety hazards and secondary damage caused by sudden cracking or detachment. The preset lifespan threshold can be set to 3000 km, 5000 km, or 10000 km, or other reasonable values, depending on the mileage.

[0089] The application includes a mudguard display interface that shows one or more of the following: remaining service life, operating status data, maintenance suggestions, and warning messages. It can automatically switch between displaying only a progress bar, overlaying warning icons, or expanding to a full card based on the remaining mileage. Users can also manually select the remaining service life, operating status data, maintenance suggestions, and warning messages to display in the settings, achieving a seamless switch from minimalist to detailed information, ensuring both driving safety and personalized viewing needs.

[0090] Specifically, if the predicted remaining service life is lower than a preset service life threshold (e.g., 3000 km), multi-level warning information is immediately generated. For example, a red warning box pops up simultaneously on the vehicle's central control display and the driver's screen, accompanied by a prompt sound. The warning card is automatically pinned to the top of the mudguard display interface on the user's mobile client application, scrolling to display the remaining mileage, real-time operating conditions, and maintenance suggestions.

[0091] By linking remaining service life with real-time operating status data, maintenance suggestions are automatically generated and simultaneously displayed on the vehicle and mobile terminals. When the remaining service life falls below a preset threshold, a warning message is immediately triggered and simultaneously pushed to the vehicle's central control display, driver's screen, and mobile terminal via the application. The application's mudguard display interface supports displaying one or more of the following on demand: remaining service life, operating status data, maintenance suggestions, and warning messages, achieving flexibility and interactivity in information display. This effectively improves the visibility and actionability of mudguard life detection results, reduces the risk of failure due to maintenance delays, minimizes excessive repairs and downtime, enhances users' control over vehicle health, and improves after-sales service efficiency and brand data loop value, demonstrating excellent comprehensive benefits in terms of safety, economy, and user experience.

[0092] As a specific embodiment of this application, refer to Figure 2 The life testing method for vehicle mudguards in this application may include: S201, Sensor Deployment: High-precision sensors are installed at key locations on the mudguard (such as the suspension at the wheel position) to monitor parameters such as temperature, humidity, stress changes, and vibration frequency, and to collect real-time data on the working status of the mudguard.

[0093] S202, Data Transmission and Processing: The sensor-collected operational status data is transmitted to the vehicle system or cloud server via wireless communication technologies (such as Bluetooth, Wi-Fi, or 4G / 5G) for initial data cleaning and preprocessing. Simultaneously, historical maintenance records, manufacturing parameters, and historical driving data are acquired.

[0094] S203, Lifespan Feature Extraction: Based on the collected working status data, combined with the acquired historical maintenance records, manufacturing parameters, and historical driving data, lifespan features are extracted.

[0095] S204, Lifetime Prediction Strategy Detection: Lifetime detection is performed by combining extracted lifetime features and lifetime prediction strategies.

[0096] S205, Analysis and Result Push: Determine the threshold for remaining lifespan, generate maintenance suggestions or early warning information, and send them to the vehicle terminal and mobile terminal.

[0097] First, high-precision sensors are deployed at key locations on the suspension to collect temperature, humidity, stress, and vibration data in real time. The data is transmitted to the vehicle or cloud via an encrypted wireless link and cleaning is completed simultaneously. After incorporating historical maintenance, manufacturing, and driving data, cumulative damage, aging coefficient, and other life characteristics are extracted. Then, a life prediction strategy is input to output the remaining lifespan. Finally, maintenance suggestions or multi-level warnings are generated based on preset life thresholds and simultaneously sent to the vehicle's infotainment system and mobile terminals, realizing a closed-loop management system of sensing, transmission, calculation, and push.

[0098] The life testing method for vehicle mudguards in this application has the following technical advantages: 1. Improve safety: Monitor the health status of mudguards in real time and actively trigger multi-level warnings at critical life stages, enabling drivers to take precautions in advance and eliminate driving safety hazards caused by sudden cracking or detachment of mudguards during high-speed driving, thereby improving the overall active safety level of the vehicle.

[0099] 2. Reduced maintenance costs: With accurate prediction of remaining lifespan, users can schedule inspections and replacements in an orderly manner before performance degrades to its limit, avoiding emergency repairs after failure, resulting in vehicle body scratches and secondary downtime losses, significantly reducing maintenance costs throughout the vehicle's life cycle.

[0100] 3. Enhance user experience: Warning information is presented simultaneously on the vehicle's infotainment system and mobile terminals in the form of graphical progress bars, color-coded levels, and voice broadcasts. It supports one-click scheduling and personalized display, simplifies the operation process, and enhances users' sense of control and satisfaction with the vehicle's health status.

[0101] 4. Data-driven decision-making: The platform continuously gathers sensor timing data, operating load data, maintenance records, and manufacturing parameters to form large-scale, high-quality data assets. This provides OEMs with quantitative basis for optimizing material selection, structural design, and process control, and promotes iterative upgrades in product durability.

[0102] 5. Environmental Contribution: Timely replacement of aging mudguards can reduce the splashing of mud, water, and gravel onto the road surface and nearby vehicles during driving, reduce the risk of road pollutant spread, and contribute to green travel and sustainable development of the transportation environment.

[0103] This application also provides a vehicle, as shown in reference to Figure 3 The vehicle 300 includes: a memory 310, a processor 320, and a computer program stored in the memory 310 and executable on the processor 320. The processor 320 executes the program to implement the above-mentioned method for detecting the lifespan of the vehicle mudguards.

[0104] This application also provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the above-described method for detecting the lifespan of vehicle mudguards.

[0105] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the 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.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0107] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0108] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

[0109] Any process or method described in the flowchart or otherwise herein is to be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0110] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), 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). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0111] 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 instruction 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.

[0112] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0113] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0114] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

[0115] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0116] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for testing the lifespan of a vehicle mudguard, characterized in that, Includes the following steps: In response to a life testing command for a vehicle mudguard, the system acquires the vehicle mudguard's working status data, historical maintenance records, and manufacturing parameters, as well as the vehicle's historical driving data. Based on the working status data, the historical maintenance records, the manufacturing parameters, and the historical driving data, at least one lifespan characteristic of the vehicle mudguard is extracted; The life prediction strategy for the vehicle mudguard is obtained, and the configuration of the life prediction strategy is optimized based on the historical driving data, the historical maintenance records and the manufacturing parameters. The remaining service life of the vehicle fender is generated based on the life prediction strategy and the at least one life characteristic.

2. The method for testing the lifespan of vehicle mudguards according to claim 1, characterized in that, The historical maintenance records include historical fender maintenance times. The configuration of the lifespan prediction strategy, based on the historical driving data, the historical maintenance records, and the manufacturing parameters, includes: Based on the historical mudguard repair times, determine the last mudguard repair time; Based on the last mudguard repair time, extract the target historical driving data from the historical driving data; Based on the target historical driving data and the manufacturing parameters, the configuration of the life prediction strategy is optimized.

3. The method for detecting the lifespan of a vehicle mudguard according to claim 2, characterized in that, The historical maintenance record includes the fender maintenance items corresponding to the last fender maintenance time. The configuration of the life prediction strategy optimized based on the target historical driving data and the manufacturing parameters includes: Based on the target's historical driving data, the fender maintenance items, and the manufacturing parameters, the configuration of the life prediction strategy is optimized.

4. The method for testing the lifespan of vehicle mudguards according to claim 2, characterized in that, The step of optimizing the configuration of the life prediction strategy based on the target historical driving data and the manufacturing parameters includes: Generate the extraction time period based on the current time; In response to the fact that the last mudguard repair time was not within the extraction time period, first historical driving data is extracted from the target historical driving data according to the extraction time period; Based on the first historical driving data and the manufacturing parameters, the configuration of the life prediction strategy is optimized.

5. The method for testing the lifespan of a vehicle mudguard according to claim 1, characterized in that, The historical maintenance records include historical mudguard maintenance times. The step of extracting at least one lifespan characteristic of the vehicle mudguard based on the working status data, the historical maintenance records, the manufacturing parameters, and the historical driving data includes: Based on the historical mudguard repair times, determine the last mudguard repair time; Based on the last mudguard repair time, extract the target historical driving data from the historical driving data; The working status data, the manufacturing parameters, and the target historical driving data are fused together to generate mudguard feature data; Feature extraction is performed on the mudguard feature data to obtain at least one lifespan feature.

6. The method for testing the lifespan of a vehicle mudguard according to claim 5, characterized in that, The process of fusing the working status data, the manufacturing parameters, and the target historical driving data to generate mudguard feature data includes: The mudguard material and installation torque value are obtained from the manufacturing parameters, and the feature data of the vehicle mudguard are generated based on the mudguard material and the installation torque value. Feature extraction is performed on the working status data to generate mudguard working feature data; The target's historical driving data is processed based on a preset timestamp to obtain driving characteristic data; The feature data of the vehicle mudguard, the working feature data of the mudguard, and the driving feature data are spliced ​​together to generate the mudguard feature data.

7. The method for testing the lifespan of a vehicle mudguard according to claim 5, characterized in that, The step of extracting features from the mudguard feature data to obtain the at least one lifespan feature includes: The mudguard feature data is subjected to dimensionality reduction processing to obtain the target mudguard feature data; Feature extraction is performed on the target mudguard feature data to obtain at least one lifespan feature.

8. The method for testing the lifespan of a vehicle mudguard according to claim 1, characterized in that, Also includes: Based on the remaining service life and the working status data, maintenance suggestions for the vehicle mudguards are generated, and the remaining service life, the working status data, and the maintenance suggestions are displayed through the vehicle terminal and / or mobile terminal. In response to the remaining service life being less than a preset service life threshold, a corresponding warning message is generated and displayed through a corresponding application on at least one of the following: the vehicle central control display screen, the vehicle driving screen, and the mobile terminal. The application includes a mudguard display interface for displaying one or more of the remaining service life, the working status data, the maintenance suggestions, and the warning message.

9. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the life detection method for vehicle mudguards as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the life detection method for vehicle mudguards as described in any one of claims 1-8.

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