Vehicle part durability evaluation method and device, electronic equipment and storage medium

Abstract

The invention provides a vehicle part durability evaluation method and device, electronic equipment and a storage medium, and belongs to the technical field of vehicle part quality evaluation, and the method comprises the steps: obtaining part information of a target part in a vehicle cluster beyond a warranty period; the part information comprises a first number of first failure parts, running mileage data corresponding to the first failure parts and a second number of target parts in a vehicle cluster, the vehicle cluster comprises a plurality of vehicles, and each vehicle corresponds to one target part; selecting a to-be-fitted operation mileage from the operation mileage data based on the first number and the second number; the to-be-fitted operation mileage is subjected to fitting processing, the durability mileage of the target part is determined, the to-be-fitted operation mileage is the data set capable of reflecting the real service life of the target part, statistical quantification of the good to-be-fitted operation mileage is achieved, and the accuracy of durability evaluation of the vehicle part is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle component quality assessment technology, specifically to a method, apparatus, electronic device, and storage medium for assessing the durability of vehicle components. Background Technology

[0002] Vehicle component durability assessment is a core component in ensuring overall vehicle reliability and lifespan. Accurate durability assessment plays a crucial role in guiding design improvements, optimizing warranty strategies, and implementing predictive maintenance, effectively increasing vehicle uptime and reducing total lifecycle costs.

[0003] In related technologies, statistical analysis schemes based on market quality feedback are employed. For example, Chinese patent document CN202310935434.X discloses a scheme to improve maintenance efficiency by managing vehicle data packages and parts inventory. While this scheme optimizes the parts service process, it is essentially still a reactive response after a failure occurs, failing to provide a proactive assessment of component durability. Furthermore, such schemes heavily rely on data within the warranty period, lacking strong data support for assessing the durability of vehicle components. This results in significant data gaps in the assessment of component durability throughout its entire lifecycle, reducing the accuracy of durability assessments for vehicle components. Summary of the Invention

[0004] In view of this, it is necessary to provide a method, apparatus, electronic device and storage medium for evaluating the durability of vehicle parts, so as to solve the technical problem of low accuracy in the durability evaluation of vehicle parts in the prior art.

[0005] To address the aforementioned technical problems, in a first aspect, the present invention provides a method for evaluating the durability of vehicle components, comprising: Obtain component information of a target component in a vehicle cluster outside the warranty period. The component information includes a first number of first failed components, mileage data corresponding to the first failed components, and a second number of target components in the vehicle cluster. The vehicle cluster includes multiple vehicles, and each vehicle corresponds to one target component. Based on the first and second quantities, the running mileage to be fitted is selected from the running mileage data; The fitting process is performed on the running mileage to determine the durability mileage of the target component.

[0006] In one possible implementation, selecting the running mileage to be fitted from the running mileage data based on the first quantity and the second quantity includes: Calculate the ratio of the first quantity to the second quantity; When the ratio is less than a preset threshold, all the running mileage data will be used as the running mileage to be fitted. When the ratio is greater than or equal to the preset threshold, all the running mileage data are sorted by value, and the top k% of the running mileage data are selected as the running mileage to be fitted.

[0007] In one possible implementation, the fitting process for the running mileage to be fitted, to determine the durability mileage of the target component, includes: A statistical distribution model is used to fit the running mileage to be fitted, and the distribution function corresponding to the statistical distribution model is determined. The durability mileage is obtained by calculating the mileage corresponding to the B10 lifetime from the distribution function.

[0008] In one possible implementation, before obtaining the component information of the target component in a cluster of vehicles outside the warranty period, the following steps are also included: During the vehicle production phase, a cluster BOM is created for the target components of each vehicle in the vehicle cluster. The cluster BOM includes the chassis number of the vehicle and the unique identifier of all target components installed on the vehicle, and the warranty status of each target component is initialized to be within the warranty period. During the vehicle usage phase, the replacement event information of each component is recorded, and the replacement event information is synchronously updated to the corresponding vehicle's cluster BOM. When the replacement event occurs within the warranty period, the warranty status of the newly replaced component is within the warranty period; when the replacement occurs outside the warranty period, the warranty status of the newly replaced component in the cluster BOM is updated to outside the warranty period. The acquisition of component information for target components in a vehicle cluster outside the warranty period includes: Based on the warranty status and repair records recorded in the cluster BOM, the information of target components in the vehicle cluster that are out of warranty is statistically analyzed.

[0009] One possible implementation also includes: When it is detected that the replacement event information corresponding to a single target component in the maintenance record of the cluster BOM occurs within the warranty period and is determined to be a second failed component, the third quantity of the second failed component is counted. When the proportion of the target parts within the warranty period in the third quantity exceeds a preset ratio, a warning message is issued.

[0010] In one possible implementation, after obtaining the durability mileage of the target component, the method further includes: Obtain the durability mileage corresponding to each component in the vehicle; Statistical analysis was performed on multiple durability mileages to obtain the durability baseline mileage; Based on the absolute value of the difference between each durability mileage and the durability benchmark mileage, components to be optimized are selected from the vehicle's components.

[0011] In one possible implementation, after obtaining the durability mileage of the target component, the method further includes: Obtain the current operating mileage and corresponding durability mileage of the target component; Based on the current operating mileage and durability mileage, determine whether the current target component needs repair or replacement.

[0012] Secondly, the present invention also provides a vehicle component durability evaluation device, comprising: The acquisition unit is used to acquire component information of a target component in a vehicle cluster outside the warranty period. The component information includes a first number of first failed components, mileage data corresponding to the first failed components, and a second number of target components in the vehicle cluster. The vehicle cluster includes multiple vehicles, and each vehicle corresponds to one target component. The selection unit is used to select the running mileage to be fitted from the running mileage data based on the first quantity and the second quantity; The evaluation unit is used to perform fitting processing on the running mileage to be fitted, and to determine the durability mileage of the target component.

[0013] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the vehicle component durability assessment method described in any of the above implementations.

[0014] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instructions, which, when executed by a processor, can implement the steps in the vehicle component durability assessment method described in any of the above implementations.

[0015] The beneficial effects of this invention are: The vehicle component durability assessment method provided by this invention obtains component information of target components in a vehicle cluster outside the warranty period. This component information includes a first number of failed components, the corresponding mileage data of the failed components, and a second number of target components in the vehicle cluster. The vehicle cluster comprises multiple vehicles, each corresponding to one target component. This obtains a true lifespan sample reflecting the natural wear and tear of the components, providing robust mileage data for durability assessment. Furthermore, it facilitates subsequent calculation of the failure ratio and dynamically selects the optimal data fit based on the failure ratio, avoiding inaccurate assessments due to a lack of effective sample data. Based on the first and second numbers, the mileage data is selected... The mileage to be fitted can adaptively select different stages of data accumulation, providing robust conservative estimates during periods of data sparseness and accurate lifespan calculations during periods of abundant data. This effectively overcomes the shortcomings of traditional methods, which suffer from large evaluation biases when data is incomplete. It ensures that the mileage to be fitted is a dataset that reflects the true lifespan of the target part, thus improving the accuracy and reliability of the evaluation results. By fitting the mileage to be fitted, the durability mileage of the target component is determined. Since the mileage to be fitted is a dataset that reflects the true lifespan of the target part, the optimal mileage to be fitted is statistically quantified, improving the accuracy of the durability mileage and consequently improving the accuracy of the durability assessment of vehicle components. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic flowchart of an embodiment of the vehicle component durability assessment method provided by the present invention; Figure 2 For the present invention Figure 1 A schematic diagram of an embodiment of S102; Figure 3 For the present invention Figure 2 A schematic diagram of an embodiment of S103; Figure 4 A schematic flowchart of another embodiment of the vehicle component durability assessment method provided by the present invention; Figure 5 A flowchart illustrating the vehicle component durability assessment and optimization method provided by this invention; Figure 6A schematic diagram of the structure of the vehicle component durability evaluation device provided by the present invention; Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

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

[0019] In the description of the embodiments of the present invention, unless otherwise stated, "a plurality of" means two or more.

[0020] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] This invention provides a method, apparatus, electronic device, and storage medium for evaluating the durability of vehicle components, which will be described below.

[0023] The execution subject of the vehicle component durability assessment method in this application embodiment can be the vehicle component durability assessment device provided in this application embodiment, or different types of electronic devices such as server equipment, physical host, or user equipment (UE) that integrate the vehicle component durability assessment device. The vehicle component durability assessment device can be implemented in hardware or software. The UE can specifically be a terminal device such as a smartphone, tablet computer, laptop computer, handheld computer, desktop computer, or personal digital assistant (PDA).

[0024] Figure 1 This is a schematic flowchart of an embodiment of the vehicle component durability assessment method provided by the present invention, as shown below. Figure 1 As shown, the methods for evaluating the durability of vehicle components include: S101. Obtain component information of a target component in a vehicle cluster outside the warranty period. The component information includes a first number of first failed components, mileage data corresponding to the first failed components, and a second number of target components in the vehicle cluster. The vehicle cluster includes multiple vehicles, and each vehicle corresponds to one target component.

[0025] Among these, vehicle parts can be engines, transmissions, etc.

[0026] "Outside the warranty period" refers to components that are beyond the warranty period. Components outside the warranty period contain the true life distribution of components under normal use conditions. Therefore, in this embodiment, selecting component information of target components in the vehicle cluster outside the warranty period can provide more reasonable and scientific data for durability analysis.

[0027] The target component refers to a component of a vehicle that needs to be evaluated for durability. This component can be an independent component or a series of components. In this embodiment, a series of components is selected as the target component to take advantage of the similar durability of the components in the series, thereby significantly increasing the statistical sample size and solving the data sparsity problem.

[0028] The vehicle cluster consists of multiple vehicles, each corresponding to a target component.

[0029] The first failed component refers to the target component that fails outside the warranty period; the first quantity is the total number of first failed components in the vehicle cluster. The second quantity is the total number of target components in the vehicle cluster, which is also the total number of vehicles in the cluster. Mileage data refers to the dataset of mileage for each first failed component.

[0030] The inventors discovered that in related technologies, statistical analysis schemes based on market quality feedback heavily rely on data within the warranty period and lack a large number of component failure cases after the warranty period, resulting in a serious data gap in the assessment of the durability of components throughout their entire life cycle.

[0031] Specifically, a Bill of Materials (BOM) for maintenance information of each target component in the vehicle cluster can be pre-built. Statistics can be collected on the target components that are out of warranty in the BOM to obtain the component information of the target components, including the first quantity of the first failed component, the mileage data corresponding to the first failed component, and the second quantity of the target components in the vehicle cluster.

[0032] Understandably, by obtaining the mileage data of target components that fail outside the warranty period, a true life sample reflecting the natural wear and tear of the components can be obtained, providing strong mileage data for durability assessment. By obtaining the number of target components that fail outside the warranty period and the total number of target components outside the warranty period, the failure ratio can be calculated subsequently, and the optimal data fit can be dynamically selected based on the failure ratio, avoiding the problem of inaccurate assessment caused by the lack of effective sample data.

[0033] S102. Based on the first quantity and the second quantity, select the running mileage to be fitted from the running mileage data.

[0034] Among them, the running mileage to be fitted refers to the data selected from the running mileage data that meets the requirements for durability assessment, and is used for subsequent fitting processing.

[0035] Specifically, the running mileage to be fitted can be dynamically selected from the running mileage data based on the ratio of the first quantity and the second quantity. It can adapt to different stages of data accumulation and select the running mileage to be fitted. It provides a robust conservative estimate during the data sparse period and an accurate life calculation during the data sufficient period. It effectively overcomes the defect of large evaluation bias in traditional methods when the data is incomplete. It ensures that the running mileage to be fitted is a dataset that can reflect the true life of the target part, which is conducive to improving the accuracy and reliability of the evaluation results.

[0036] S103. Perform fitting processing on the running mileage to be fitted to determine the durability mileage of the target component.

[0037] Specifically, a statistical distribution model can be used to fit the mileage to be fitted, and the durability mileage of the target component can be determined based on the fitting result. Understandably, since the mileage to be fitted is a dataset that reflects the true lifespan of the target component, fitting the mileage to be fitted achieves statistical quantification of the optimal mileage, improves the accuracy of the durability mileage, and thus improves the accuracy of the durability assessment of vehicle components.

[0038] In summary, the vehicle component durability assessment method provided by this invention obtains component information of target components in a vehicle cluster outside the warranty period. This component information includes a first number of first failed components, the corresponding mileage data of the first failed components, and a second number of target components in the vehicle cluster. The vehicle cluster includes multiple vehicles, each corresponding to one target component. This obtains a true lifespan sample reflecting the natural wear and tear of the components, providing robust mileage data for durability assessment. Furthermore, it facilitates subsequent calculation of the failure ratio and dynamically selects the optimal data fit based on the failure ratio, avoiding inaccurate assessments due to a lack of effective sample data. Based on the first and second numbers, the mileage data is used to calculate the failure ratio. The data selection of the running mileage to be fitted can adapt to different stages of data accumulation. By selecting the running mileage to be fitted, it provides a robust and conservative estimate during periods of data sparseness and an accurate lifespan calculation during periods of sufficient data. This effectively overcomes the shortcomings of traditional methods, which suffer from large evaluation biases when data is incomplete. It ensures that the running mileage to be fitted is a dataset that can reflect the true lifespan of the target part, which is conducive to improving the accuracy and reliability of the evaluation results. The running mileage to be fitted is then fitted to determine the durability mileage of the target component. Since the running mileage to be fitted is a dataset that can reflect the true lifespan of the target part, it achieves statistical quantification of the optimal running mileage to be fitted, improving the accuracy of the durability mileage, and thus improving the accuracy of the durability assessment of vehicle components.

[0039] In some embodiments of the present invention, such as Figure 2 As shown, step S102 includes: S201. Calculate the ratio of the first quantity to the second quantity; S202. When the ratio is less than a preset threshold, all the running mileage data is taken as the running mileage to be fitted. S203. When the ratio is greater than or equal to the preset threshold, all the running mileage data are sorted by value, and the top k% of the running mileage data are selected as the running mileage to be fitted.

[0040] The preset threshold is a critical ratio used to measure whether the number of first failed parts is sufficient. This ratio is the proportion of the number of first failed parts to the total number of target parts. For example, the preset threshold can be 10%.

[0041] k can be 10, meaning that the top 10% of the running mileage data is selected as the running mileage to be fitted.

[0042] Specifically, when the ratio is less than a preset threshold, it indicates that the proportion of failed components is low. All running mileage data is used as the running mileage to be fitted in order to solve the problem of data sparsity.

[0043] When the ratio is greater than or equal to the preset threshold, it indicates that the proportion of failed parts is relatively high, that is, the sample data is sufficient. Therefore, sorting all the running mileage data by value and selecting the top k% of the running mileage data as the running mileage to be fitted can more accurately capture the tail features of the life distribution, which is conducive to improving the accuracy and stability of the subsequent evaluation results.

[0044] In some embodiments of the present invention, such as Figure 3 As shown, step S103 includes: S301. Use a statistical distribution model to fit the running mileage to be fitted, and determine the distribution function corresponding to the statistical distribution model; S302. Calculate the mileage corresponding to the B10 lifetime from the distribution function to obtain the durability mileage.

[0045] Among them, the statistical distribution models include the normal distribution model and / or the Weibull distribution model.

[0046] Specifically, the running mileage to be fitted can be input into the statistical distribution model to obtain the distribution function corresponding to the statistical distribution model. The running mileage corresponding to the B10 life can be calculated from the distribution function to obtain the durability mileage.

[0047] In one specific implementation, the statistical distribution model is taken as a normal distribution model for illustration. The expression of the normal distribution model is: Let the population standard deviation σ = 1, and the simplified model is as follows: After fitting, the mean μ and standard deviation σ are obtained. After obtaining the complete distribution parameters, the value of B10 lifetime is calculated from the determined distribution function using the definition of B10 lifetime (i.e., the lifetime corresponding to a cumulative failure probability of 10%). For example, the durability mileage is B10 lifetime ≈ μ - 1.28σ, and for example, B10 lifetime ≈ 352,000 kilometers.

[0048] In some embodiments of the present invention, before step S101, the method further includes: during the vehicle production stage, creating a cluster BOM for the target components of each vehicle in the vehicle cluster, wherein the cluster BOM includes the chassis number of the vehicle and the unique identifier of all target components installed on the vehicle, and initializing the warranty status of each target component to be within the warranty period; during the vehicle use stage, recording the replacement event information of each component, and synchronously updating the replacement event information to the cluster BOM of the corresponding vehicle, wherein when the replacement event information occurs within the warranty period, the warranty status of the newly replaced component is within the warranty period; when the replacement occurs outside the warranty period, the warranty status of the newly replaced component in the cluster BOM is updated to be outside the warranty period; the step of obtaining the component information of the target components in the vehicle cluster outside the warranty period includes: based on the warranty status and maintenance records recorded in the cluster BOM, statistically analyzing the target component information in the vehicle cluster that is outside the warranty period.

[0049] The inventors discovered that in related technologies, statistical analysis schemes based on market quality feedback heavily rely on data within the warranty period. They lack an effective collection mechanism for a large number of component failure cases after the warranty period, resulting in serious data gaps in the assessment of component durability throughout its entire life cycle. To address this, this embodiment establishes a clustered BOM, which records all component repairs both within and outside the warranty period, enabling the effective collection of a large number of component failure cases after the warranty period.

[0050] Specifically, during the vehicle production phase, a cluster BOM is created for the target components of each vehicle in the vehicle cluster. The cluster BOM includes the vehicle's chassis number and unique identifiers for all target components installed on the vehicle, and the warranty status of each target component is initialized to within the warranty period. During the vehicle usage phase, replacement event information for each of the aforementioned components is recorded, and the replacement event information is synchronously updated to the corresponding vehicle's cluster BOM. When the replacement event occurs within the warranty period, the warranty status of the newly replaced component is within the warranty period; when the replacement occurs outside the warranty period, the warranty status of the newly replaced component in the cluster BOM is updated to outside the warranty period, providing a reliable data foundation for the full lifecycle management and durability assessment of vehicle components.

[0051] Based on the warranty status and repair records recorded in the cluster BOM, the component information of the target components in the vehicle cluster that are out of warranty can be obtained by statistically analyzing the target component information in the vehicle cluster.

[0052] In one specific implementation, components can be managed in the following manner: First, a new naming rule for commercial vehicle parts is defined. Taking the chassis system as an example, the naming convention is 28XXXXX-XXXXX. The first two digits represent the chassis system, the third digit represents the vehicle type, such as tractor-trailer, the fourth digit represents the target market segment, such as long-haul logistics or medium- and long-haul logistics, the fifth digit represents the drive type, such as 6×4 or 4×2, the sixth and seventh digits represent longitudinal beams, reinforcing beams, etc., in sequence, and the last five digits represent the specific vehicle model to distinguish personalized configuration differences. If the first seven digits are the same, they are determined to be part of the same series, thus increasing the statistical sample size.

[0053] Secondly, when a new car rolls off the production line, the system starts working, records the chassis number of the car, associates and records all the part numbers on the car, forms the whole vehicle BOM library, and defines the current mileage as 0.

[0054] Finally, a three-tiered parts transfer warehouse is established. Based on parts failure risk and maintenance cycles, these three tiers are differentiated. When a vehicle experiences parts failure, it enters the service station for repair and diagnosis. Mileage is confirmed, and the warranty period is determined. If within the warranty period, the issue is identified as a parts quality problem and included in reliability failure statistics. If the number of failures of this part series within the warranty period exceeds a certain threshold, the quality department intervenes to investigate the cause of the failure, implements improvements, and closes the system. The service station inputs chassis number and other information into the transfer warehouse to request the parts. After replacement, the mileage of this part in the vehicle's BOM is updated to 0. If outside the warranty period, the actual vehicle mileage is recorded, accumulating the number of failures of this part series outside the warranty period. By establishing a parts transfer warehouse, the time required for parts transfer is reduced, and the inventory pressure on service stations is alleviated. Simultaneously, all parts transfers and replacements for both in-warranty and out-of-warranty repairs can be recorded, effectively preventing data loss.

[0055] In some embodiments of the invention, the method further includes: when it is detected that the replacement event information corresponding to a single target component in the maintenance record of the cluster BOM occurs within the warranty period and is determined to be a second failed component, then a third quantity of the second failed component is counted; when the proportion of the third quantity to the number of target components within the warranty period is greater than a preset ratio, an early warning message is issued.

[0056] The second failed component refers to the target component that fails within the warranty period; the third quantity is the total number of second failed components in the vehicle cluster.

[0057] Specifically, when the proportion of the target parts within the warranty period exceeds the preset ratio, it indicates that there are significant quality problems with the parts within the warranty period. Therefore, an early warning message is issued to prompt the relevant department to analyze the cause and optimize and improve the corresponding target parts.

[0058] In some embodiments of the present invention, such as Figure 4 As shown, after step S103, the following steps are also included: S401. Obtain the durability mileage of each component in the vehicle. S402. Statistical analysis of multiple durability mileages is performed to obtain the durability benchmark mileage; S403. Based on the absolute value of the difference between each durability mileage and the durability benchmark mileage, select the components to be optimized from the vehicle's components.

[0059] Specifically, multiple durability mileages are statistically analyzed. This can be done by selecting the median or calculating the average of multiple durability mileages as the durability benchmark mileage. The absolute value of the difference between each durability mileage and the durability benchmark mileage is calculated. When the absolute value of the difference is greater than a preset difference threshold, it indicates that the corresponding component's durability mileage deviates significantly from the durability benchmark mileage. Therefore, components with large deviations are selected as components to be optimized. When the durability mileage of a component to be optimized is less than the durability benchmark mileage, the component is strengthened. When the durability mileage of a component to be optimized is greater than the durability benchmark mileage, the component is weakened. This process aims to bring the durability of all components in the vehicle to an average level, thereby maximizing benefits.

[0060] In some embodiments of the present invention, after step S103, the method further includes: obtaining the current operating mileage and corresponding durability mileage of the current target component; and detecting whether the current target component needs to be repaired or replaced based on the current operating mileage and durability mileage.

[0061] Specifically, based on the current operating mileage and durability mileage, it detects whether the target components need to be repaired or replaced, achieving the purpose of predictive maintenance and effectively reducing operational losses.

[0062] In one specific implementation, based on the current operating mileage and durability mileage, the driver is reminded in advance that the part is due for maintenance, so that it can be maintained or replaced in a timely manner, avoiding unexpected stops and repairs during operation, which would affect operational efficiency and achieve the purpose of predictive maintenance.

[0063] It is worth noting that a data-driven iterative update mechanism can also be established. As the vehicle cluster operates, new component information is continuously acquired, and based on the updated first and second quantities, the running mileage to be fitted is reselected and fitted to output the updated durability mileage.

[0064] Understandably, the embodiments of this application obtain the average durability of each component through a systematic and continuous statistical study of the durability of various components of commercial vehicles; this feedback guides OEMs to strengthen the design of components with weak durability and optimize the design of redundant components, so that the durability of each component of the vehicle reaches the average level, thereby maximizing the OEM's profits; it also provides feedback to drivers to anticipate and replace parts that are about to reach their durability cycle, scheduling maintenance after the return trip or during downtime, avoiding vehicle breakdowns during operation, resulting in cargo delay penalties, driver idling costs, and high field rescue costs. Furthermore, as a high-value asset, the residual value of commercial vehicles is directly linked to their condition. Anticipatory maintenance can prevent irreversible damage to key components (such as engines and transmissions) due to sudden failures, extending the overall service life of the vehicle. Good vehicle condition also allows the vehicle to obtain a higher residual value in the second-hand market.

[0065] In one specific implementation, such as Figure 5 The diagram shown is a flowchart of a method for evaluating and optimizing the durability of vehicle components. The specific process is as follows: Steps for establishing a parts coding system: Establish systematic parts coding rules and assign a unique identifier to each part or part series.

[0066] Vehicle BOM construction steps: Based on the aforementioned component coding system, construct the product structure list of the whole vehicle, associate the vehicle chassis number with the identifiers of all components, and form the initial vehicle BOM data.

[0067] Part failure handling steps: When a part fails during vehicle operation, perform maintenance operations, update the vehicle BOM data accordingly, and record maintenance event information.

[0068] Warranty status determination steps: Determine whether the repair event occurred within the warranty period.

[0069] Quality monitoring sub-process during warranty period: If a repair incident occurs within the warranty period, the quality monitoring sub-process during warranty period will be executed: further determine whether the number of failure cases of this series of parts exceeds the preset threshold; if it exceeds the threshold, it will be determined as a quality problem, and the quality department will intervene to conduct root cause analysis and implement improvement measures to close the problem.

[0070] Out-of-warranty data collection steps: If the repair occurs outside the warranty period, the out-of-warranty data processing procedure is executed: cumulatively count the number of out-of-warranty failure cases m of the component, and the total number of samples n of the component that have exceeded the warranty period; calculate the ratio x = m / n.

[0071] Data filtering and fitting sub-process: Based on the ratio x, different data fitting strategies are selected: If x < 1 / 10, the operating mileage data of all failure cases are input into the statistical distribution model for fitting to obtain the first durability reference value a; if x ≥ 1 / 10, the operating mileage of all failure cases is sorted by value, and the top 10% of the mileage data is input into the statistical distribution model for fitting to obtain the second durability reference value b.

[0072] Durability benchmark determination steps: Based on the step-fit ​​results, determine the durability benchmark for the components: The component's B10 lifespan is determined to be greater than the first durability reference value a; The B10 life of this component is determined to be the second durability reference value b; Through continuous data accumulation and iterative updates, the actual B10 lifespan value c of the component was eventually approximated.

[0073] Design optimization and maintenance decision-making steps: Based on durability benchmarks, perform the following operations: The durability benchmarks are fed back to the R&D department to strengthen the design of weak components in the vehicle system and optimize the design of redundant components, thereby maximizing benefits. Based on the vehicle's actual mileage, the system can issue early maintenance warnings to users, prompting them to replace parts that are about to reach the end of their lifespan, thus achieving predictive maintenance.

[0074] To better implement the vehicle component durability assessment method in this invention embodiment, based on the vehicle component durability assessment method, correspondingly, as follows: Figure 6 As shown, this embodiment of the invention also provides a vehicle component durability assessment device. The vehicle component durability assessment device 600 includes: The acquisition unit 601 is used to acquire component information of a target component in a vehicle cluster outside the warranty period. The component information includes a first number of first failed components, mileage data corresponding to the first failed components, and a second number of target components in the vehicle cluster. The vehicle cluster includes multiple vehicles, and each vehicle corresponds to one target component. The selection unit 602 is used to select the running mileage to be fitted from the running mileage data based on the first quantity and the second quantity. Evaluation unit 603 is used to perform fitting processing on the running mileage to be fitted, and to determine the durability mileage of the target component.

[0075] The vehicle component durability assessment device 200 provided in the above embodiments can realize the technical solutions described in the above vehicle component durability assessment method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above vehicle component durability assessment method embodiments, and will not be repeated here.

[0076] like Figure 7 As shown, the present invention also provides an electronic device 700. The electronic device 700 includes a processor 701, a memory 702, and a display 707. Figure 7 Only some components of the electronic device 700 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0077] In some embodiments, processor 701 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 702 or process data, such as the vehicle component durability assessment method of the present invention.

[0078] In some embodiments, processor 701 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 701 may be local or remote. In some embodiments, processor 701 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, intranet, multi-cloud, etc., or any combination thereof.

[0079] In some embodiments, memory 702 may be an internal storage unit of electronic device 700, such as a hard disk or memory of electronic device 700. In other embodiments, memory 702 may also be an external storage device of electronic device 700, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 700.

[0080] Furthermore, the memory 702 may include both internal storage units of the electronic device 700 and external storage devices. The memory 702 is used to store application software and various types of data installed on the electronic device 700.

[0081] In some embodiments, display 707 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 707 is used to display information from electronic device 700 and to display a visual user interface. Components 701-707 of electronic device 700 communicate with each other via a system bus.

[0082] In one embodiment, when processor 701 executes the vehicle component durability assessment program in memory 702, the following steps can be performed: Obtain component information of a target component in a vehicle cluster outside the warranty period. The component information includes a first number of first failed components, mileage data corresponding to the first failed components, and a second number of target components in the vehicle cluster. The vehicle cluster includes multiple vehicles, and each vehicle corresponds to one target component. Based on the first and second quantities, the running mileage to be fitted is selected from the running mileage data; The fitting process is performed on the running mileage to determine the durability mileage of the target component.

[0083] It should be understood that when the processor 701 executes the vehicle component durability assessment program in the memory 702, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0084] Furthermore, the embodiments of the present invention do not specifically limit the type of electronic device 700 mentioned. Electronic device 700 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, electronic device 700 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0085] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions in the vehicle component durability assessment methods provided in the above-described method embodiments.

[0086] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0087] The above provides a detailed description of the vehicle component durability assessment method, apparatus, electronic device, and storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for evaluating the durability of vehicle components, characterized in that, include: Obtain component information of a target component in a vehicle cluster outside the warranty period. The component information includes a first number of first failed components, mileage data corresponding to the first failed components, and a second number of target components in the vehicle cluster. The vehicle cluster includes multiple vehicles, and each vehicle corresponds to one target component. Based on the first and second quantities, the running mileage to be fitted is selected from the running mileage data; The fitting process is performed on the running mileage to determine the durability mileage of the target component.

2. The method for evaluating the durability of vehicle components according to claim 1, characterized in that, The step of selecting the running mileage to be fitted from the running mileage data based on the first quantity and the second quantity includes: Calculate the ratio of the first quantity to the second quantity; When the ratio is less than a preset threshold, all the running mileage data will be used as the running mileage to be fitted. When the ratio is greater than or equal to the preset threshold, all the running mileage data are sorted by value, and the top k% of the running mileage data are selected as the running mileage to be fitted.

3. The method for evaluating the durability of vehicle components according to claim 1, characterized in that, The fitting process for the running mileage to be fitted, to determine the durability mileage of the target component, includes: A statistical distribution model is used to fit the running mileage to be fitted, and the distribution function corresponding to the statistical distribution model is determined. The durability mileage is obtained by calculating the mileage corresponding to the B10 lifetime from the distribution function.

4. The method for evaluating the durability of vehicle components according to claim 1, characterized in that, Before obtaining component information for the target component in a cluster of vehicles outside the warranty period, the following steps are also included: During the vehicle production phase, a cluster BOM is created for the target components of each vehicle in the vehicle cluster. The cluster BOM includes the chassis number of the vehicle and the unique identifier of all target components installed on the vehicle, and the warranty status of each target component is initialized to be within the warranty period. During the vehicle usage phase, the replacement event information of each component is recorded, and the replacement event information is synchronously updated to the corresponding vehicle's cluster BOM. When the replacement event occurs within the warranty period, the warranty status of the newly replaced component is within the warranty period; when the replacement occurs outside the warranty period, the warranty status of the newly replaced component in the cluster BOM is updated to outside the warranty period. The acquisition of component information for target components in a vehicle cluster outside the warranty period includes: Based on the warranty status and repair records recorded in the cluster BOM, the information of target components in the vehicle cluster that are out of warranty is statistically analyzed.

5. The method for evaluating the durability of vehicle components according to claim 4, characterized in that, Also includes: When it is detected that the replacement event information corresponding to a single target component in the maintenance record of the cluster BOM occurs within the warranty period and is determined to be a second failed component, the third quantity of the second failed component is counted. When the proportion of the target parts within the warranty period in the third quantity exceeds a preset ratio, a warning message is issued.

6. The method for evaluating the durability of vehicle components according to claim 1, characterized in that, After obtaining the durability mileage of the target component, the process also includes: Obtain the durability mileage corresponding to each component in the vehicle; Statistical analysis was performed on multiple durability mileages to obtain the durability baseline mileage; Based on the absolute value of the difference between each durability mileage and the durability benchmark mileage, components to be optimized are selected from the vehicle's components.

7. The method for evaluating the durability of vehicle components according to claim 1, characterized in that, After obtaining the durability mileage of the target component, the process also includes: Obtain the current operating mileage and corresponding durability mileage of the target component; Based on the current operating mileage and durability mileage, determine whether the current target component needs repair or replacement.

8. A device for evaluating the durability of vehicle components, characterized in that, include: The acquisition unit is used to acquire component information of a target component in a vehicle cluster outside the warranty period. The component information includes a first number of first failed components, mileage data corresponding to the first failed components, and a second number of target components in the vehicle cluster. The vehicle cluster includes multiple vehicles, and each vehicle corresponds to one target component. The selection unit is used to select the running mileage to be fitted from the running mileage data based on the first quantity and the second quantity; The evaluation unit is used to perform fitting processing on the running mileage to be fitted, and to determine the durability mileage of the target component.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the vehicle component durability assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can perform the steps in the vehicle component durability assessment method according to any one of claims 1 to 7.

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