Vehicle part damage degree determination method and device, vehicle and storage medium

Abstract

The invention discloses a vehicle part damage degree determination method and device, a vehicle and a storage medium, and the method comprises the steps: determining a first driving route according to a user demand which comprises terrain information, a vehicle application scene and a vehicle driving area; determining a first damage coefficient according to a first damage matrix of the first driving route and a second damage matrix of the second driving route; determining a second damage coefficient according to the second damage matrix and a third damage matrix of the third driving route; determining the test condition of the vehicle according to the first damage coefficient, the second damage coefficient and the mileage of the first driving route; and controlling the vehicle to test based on the test working condition, and continuously calculating a plurality of damage degrees of a plurality of parts of the vehicle. According to the invention, the technical problem of low test accuracy caused by the absence of systematicness in the durability test of the reinforced road in the test field in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of automation control technology, and more specifically, to a method, apparatus, vehicle, and storage medium for determining the degree of damage to vehicle parts. Background Technology

[0002] In the technical field of vehicle structural component durability testing, proving ground durability verification has always been a crucial step in ensuring the quality and performance of automotive products. However, existing technologies have significant shortcomings in this area, particularly in terms of the systematic nature and precision of the testing.

[0003] First, traditional methods for assessing vehicle durability requirements under different usage scenarios rely on statistical analysis of vehicle-to-everything (V2X) platforms or market research data to determine typical usage scenarios and lifespan targets. However, this load spectrum acquisition method is time-consuming and costly, especially when long-mileage load spectra are required, making it inefficient and unable to meet the needs of rapid development cycles. Furthermore, the evaluation of user road surface quality mainly uses qualitative classification, such as highways, national roads, and rural roads, lacking objective quantitative indicators. This can lead to ambiguous road surface classification results and fail to accurately reflect the impact of road surface unevenness on vehicle durability.

[0004] Secondly, while reinforced tracks can provide specific load conditions during durability testing at proving grounds, current methods for evaluating test release mileage employ a time-cut "one-size-fits-all" approach, ignoring the differences in damage among different vehicle assemblies and components. This approach fails to adequately consider the varying directional loads borne by different parts and the diversity of user scenarios, potentially leading to over- or under-testing of certain components. This affects the accuracy of test results and fails to effectively assess the differentiated damage to components, thus limiting the precision and reliability of vehicle durability testing.

[0005] Finally, existing technologies lack the ability to perform real-time calculations and display of load spectrum data. This means that drivers cannot monitor the damage to critical vehicle components in real time, failing to meet the need for real-time vehicle status monitoring, reducing the efficiency of preventative inspections and maintenance, and impacting vehicle operational safety and economy.

[0006] There is currently no effective solution to the above problems. Summary of the Invention

[0007] This invention provides a method, apparatus, vehicle, and storage medium for determining the degree of damage to vehicle parts, in order to at least solve the technical problem in the prior art where the durability test of the test track is not systematic, resulting in low test accuracy.

[0008] According to one embodiment of the present invention, a method for determining the damage degree of vehicle parts is provided, comprising: determining a first driving route according to user requirements, wherein the user requirements include terrain information, vehicle application scenario, and vehicle driving area; determining a first damage coefficient according to a first damage matrix of the first driving route and a second damage matrix of the second driving route, wherein the length of the second driving route is less than the length of the first driving route; determining a second damage coefficient according to the second damage matrix and a third damage matrix of a third driving route, wherein the length of the third driving route is less than the length of the second driving route; determining a test condition of the vehicle according to the first damage coefficient, the second damage coefficient, and the mileage of the first driving route, wherein the test condition includes the total vehicle test mileage and test road condition information; controlling the vehicle to perform tests based on the test condition, and continuously calculating multiple damage degrees of multiple parts of the vehicle.

[0009] Optionally, the method for determining the damage degree of vehicle parts further includes: acquiring initial load spectrum data of a first driving route; performing data processing operations on the initial load spectrum data to obtain target load spectrum data; slicing the target load spectrum data according to the mileage data of the first driving route to obtain multiple mileage segments; calculating multiple axle head acceleration values ​​for multiple mileage segments; determining multiple road surface roughness levels corresponding to multiple mileage segments based on the multiple axle head acceleration values ​​and an objective function, wherein the objective function is used to characterize the relationship between the axle head acceleration values ​​and the road surface roughness levels, and each mileage segment corresponds to one road surface roughness level; and determining a second driving route based on the multiple road surface roughness levels.

[0010] Optionally, the method for determining the damage degree of vehicle parts further includes: determining an initial second driving route based on multiple road surface roughness levels; performing power spectrum analysis on the target load spectrum data to obtain multiple power spectrum frequency bands; obtaining the number and type of sensors deployed on the vehicle; determining multiple output channels based on the number and type of sensors; calculating multiple channel damage ratios for multiple output channels based on multiple power spectrum frequency bands, a first damage matrix, and the damage matrix of the initial second driving route, wherein each output channel corresponds to one channel damage ratio; comparing the multiple channel damage ratios with a first threshold and a second threshold respectively to obtain a first comparison result; and determining the initial second driving route as the second driving route in response to the first comparison result indicating that the multiple channel damage ratios are all greater than the first threshold and less than the second threshold.

[0011] Optionally, the method for determining the damage degree of vehicle parts further includes: obtaining the spectral density of the test route and the vehicle speed; determining the time domain spectrum of the test route based on the spectral density and the vehicle speed; constructing the axle head acceleration response function based on the time domain spectrum; and constructing an objective function based on the axle head acceleration response function and the vehicle speed.

[0012] Optionally, the method for determining the damage degree of vehicle parts further includes: acquiring the spatial frequency, preset spatial frequency, and preset spectral density of the test route; comparing the spatial frequency and the preset spatial frequency to obtain a second comparison result; in response to the second comparison result indicating that the spatial frequency is less than or equal to the preset spatial frequency, calculating the spectral density of the test route based on the spatial frequency, preset spatial frequency, preset spectral density, and a first spectral density index; in response to the second comparison result indicating that the spatial frequency is greater than the preset spatial frequency, calculating the spectral density of the test route based on the spatial frequency, preset spatial frequency, preset spectral density, and a second spectral density index, wherein the second spectral density index is less than the first spectral density index.

[0013] Optionally, the method for determining the damage degree of vehicle parts further includes: constructing a damage matrix equation based on a first damage matrix and a second damage matrix; calculating the damage retention percentage value of the vehicle based on multiple output channels and a damage function; and iterating the damage retention percentage value based on the damage matrix equation to obtain a first damage coefficient.

[0014] Optionally, the method for determining the damage degree of vehicle parts further includes: obtaining the vehicle's rated mileage and current mileage; determining a target mileage damage value based on the rated mileage, the mileage of a first driving route, and a first damage matrix; determining the current mileage damage value based on the current mileage and a preset mathematical model; and calculating multiple damage degrees for multiple parts based on the current mileage damage value and the target mileage damage value.

[0015] Optionally, the method for determining the damage degree of vehicle parts further includes: continuously monitoring multiple damage degrees of multiple parts; and in response to the detection that the damage degree of any one of the multiple parts exceeds a preset threshold, sending the part information of any one part to the user terminal device.

[0016] According to one embodiment of the present invention, a device for determining the damage degree of vehicle parts is also provided, comprising: a first determining module, configured to determine a first driving route according to user requirements, wherein the user requirements include terrain information, vehicle application scenario, and vehicle driving area; a second determining module, configured to determine a first damage coefficient based on a first damage matrix of the first driving route and a second damage matrix of the second driving route, wherein the length of the second driving route is less than the length of the first driving route; a third determining module, configured to determine a second damage coefficient based on the second damage matrix and a third damage matrix of the third driving route, wherein the length of the third driving route is less than the length of the second driving route; a fourth determining module, configured to determine a test condition of the vehicle based on the first damage coefficient, the second damage coefficient, and the mileage of the first driving route, wherein the test condition includes the total vehicle test mileage and test road condition information; and a testing module, configured to control the vehicle to perform tests based on the test condition and continuously calculate multiple damage degrees of multiple parts of the vehicle.

[0017] Optionally, the device for determining the damage level of vehicle parts further includes: a first acquisition module for acquiring initial load spectrum data of a first driving route; a data processing module for performing data processing operations on the initial load spectrum data to obtain target load spectrum data; a slicing module for slicing the target load spectrum data according to the mileage data of the first driving route to obtain multiple mileage segments; a calculation module for calculating multiple axle head acceleration values ​​for multiple mileage segments; a fifth determination module for determining multiple road surface smoothness levels corresponding to multiple mileage segments according to the multiple axle head acceleration values ​​and an objective function, wherein the objective function is used to characterize the relationship between the axle head acceleration values ​​and the road surface smoothness level, and each mileage segment corresponds to one road surface smoothness level; and a sixth determination module for determining a second driving route according to the multiple road surface smoothness levels.

[0018] Optionally, the sixth determining module includes: a first determining unit, used to determine an initial second driving route based on multiple road surface smoothness levels; an analysis unit, used to perform power spectrum analysis on the target load spectrum data to obtain multiple power spectrum frequency bands; a first acquiring unit, used to acquire the number and type of sensors deployed on the vehicle; a second determining unit, used to determine multiple output channels based on the number and type of sensors; a first calculation unit, used to calculate multiple channel damage ratios of the multiple output channels based on the multiple power spectrum frequency bands, a first damage matrix, and the damage matrix of the initial second driving route, wherein each output channel corresponds to one channel damage ratio; a first comparison unit, used to compare the multiple channel damage ratios with a first threshold and a second threshold respectively to obtain a first comparison result; and a third determining unit, used to determine the initial second driving route as the second driving route in response to the first comparison result indicating that the multiple channel damage ratios are all greater than the first threshold and less than the second threshold.

[0019] Optionally, the device for determining the damage level of vehicle parts further includes: a second acquisition module for acquiring the spectral density of the test route and the vehicle speed; a seventh determination module for determining the time domain spectrum of the test route based on the spectral density and the vehicle speed; a first construction module for constructing an axle head acceleration response function based on the time domain spectrum; and a second construction module for constructing an objective function based on the axle head acceleration response function and the vehicle speed.

[0020] Optionally, the second acquisition module includes: a second acquisition unit for acquiring the spatial frequency, preset spatial frequency, and preset spectral density of the test route; a second comparison unit for comparing the spatial frequency and the preset spatial frequency to obtain a second comparison result; a second calculation unit for calculating the spectral density of the test route based on the spatial frequency, preset spatial frequency, preset spectral density, and a first spectral density index in response to the second comparison result indicating that the spatial frequency is less than or equal to the preset spatial frequency; and a third calculation unit for calculating the spectral density of the test route based on the spatial frequency, preset spatial frequency, preset spectral density, and a second spectral density index in response to the second comparison result indicating that the spatial frequency is greater than the preset spatial frequency, wherein the second spectral density index is less than the first spectral density index.

[0021] Optionally, the second determining module includes: a construction unit for constructing a damage matrix equation based on a first damage matrix and a second damage matrix; a third calculation unit for calculating the damage retention percentage value of the vehicle based on multiple output channels and a damage function; and an iteration unit for iterating the damage retention percentage value based on the damage matrix equation to obtain a first damage coefficient.

[0022] Optionally, the testing module includes: a third acquisition unit for acquiring the vehicle's rated mileage and current mileage; a fourth determination unit for determining a target mileage damage value based on the rated mileage, the mileage of the first driving route, and the first damage matrix; a fifth determination unit for determining the current mileage damage value based on the current mileage and a preset mathematical model; and a fourth calculation unit for calculating multiple damage degrees of multiple parts based on the current mileage damage value and the target mileage damage value.

[0023] Optionally, the testing module further includes: a monitoring unit for continuously monitoring multiple damage levels of multiple parts; and a sending unit for sending part information of any part to a user terminal device in response to the detection that the damage level of any part among the multiple parts exceeds a preset threshold.

[0024] According to one embodiment of the present invention, a vehicle is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method for determining the degree of damage to vehicle parts as described above.

[0025] According to one embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method for determining the degree of damage to vehicle parts as described above.

[0026] According to one embodiment of the present invention, a non-volatile storage medium is also provided, wherein a computer program is stored in the non-volatile storage medium, wherein the computer program is configured to execute the method for determining the degree of damage of vehicle parts as described above when running.

[0027] According to one embodiment of the present invention, a computer program product is also provided, which stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for determining the degree of damage to vehicle parts as described above.

[0028] In this embodiment of the invention, a first driving route is determined based on user requirements, including terrain information, vehicle application scenarios, and vehicle driving areas. A first damage coefficient is determined based on a first damage matrix of the first driving route and a second damage matrix of the second driving route. A second damage coefficient is determined based on the second damage matrix and a third damage matrix of the third driving route. This achieves the goal of determining the vehicle's test conditions based on the first damage coefficient, the second damage coefficient, and the mileage of the first driving route. This achieves the technical effect of controlling the vehicle to be tested based on the test conditions and continuously calculating multiple damage degrees of multiple parts of the vehicle. Furthermore, it can solve the technical problem in the prior art where the durability test on the test track is not systematic, resulting in low test accuracy. Attached Figure Description

[0029] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0030] Figure 1 This is a flowchart of a method for determining the degree of damage to vehicle parts according to one embodiment of the present invention;

[0031] Figure 2 This is a structural block diagram of a device for determining the degree of damage to vehicle parts according to one embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] According to an embodiment of the present invention, an embodiment of a method for determining the degree of damage to vehicle parts is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system containing at least a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] This method embodiment can also be executed in an electronic device, similar control device, or vehicle-mounted terminal that includes a memory and a processor. Taking a vehicle-mounted terminal as an example, the vehicle-mounted terminal may include one or more processors and a memory for storing data. Optionally, the vehicle-mounted terminal may also include a communication device for communication functions and a display device. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the vehicle-mounted terminal. For example, the vehicle-mounted terminal may include more or fewer components than those described above, or have a different configuration than those described above.

[0036] A processor may include one or more processing units. For example, a processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microprocessor, a field-programmable gate array (FPGA), a neural network processing unit (NPU), a tensor processing unit (TPU), or an artificial intelligence (AI) type processor. Different processing units may be independent components or integrated into one or more processors. In some instances, electronic devices may also include one or more processors.

[0037] The memory can be used to store computer programs, such as the computer program corresponding to the method for determining the damage level of vehicle parts in this embodiment of the invention. The processor implements the aforementioned method for determining the damage level of vehicle parts by running the computer program stored in the memory. The memory may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to electronic devices via a grid. Examples of such grids include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0038] The communication device is used to receive or transmit data via a grid. Specific examples of the aforementioned grid may include a wireless grid provided by the mobile terminal's communication provider. In one example, the communication device includes a network interface controller (NIC), which can connect to other grid devices via a base station to communicate with the Internet. In another example, the communication device may be a radio frequency (RF) module used for wireless communication with the Internet. In some embodiments of this solution, the communication device is used to connect to mobile devices such as mobile phones and tablets, enabling the mobile device to send commands to the vehicle-mounted terminal.

[0039] The display device can be a touchscreen liquid crystal display (LCD) or a touch display (also referred to as a "touchscreen" or "touch display screen"). This LCD allows the user to interact with the user interface of the in-vehicle terminal. In some embodiments, the in-vehicle terminal has a graphical user interface (GUI), allowing the user to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. The human-machine interaction function may include a vehicle gear shifting function, and executable instructions for performing these functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0040] Figure 1 This is a flowchart of a method for determining the degree of damage to vehicle parts according to one embodiment of the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0041] Step S102: Determine the first driving route based on user needs, where user needs include terrain information, vehicle application scenarios, and vehicle driving areas.

[0042] Optionally, the execution subject in this embodiment is the vehicle control system. It should be noted that other electronic devices and processors can also be used as the execution subject, and no further limitations are made here.

[0043] In the technical solution provided in step S102 of the present invention, the usage characteristics of the tested vehicle and typical user routes across the country are statistically analyzed based on the vehicle network big data analysis platform, including but not limited to: usage scenarios, operating areas, terrain, load range, average vehicle speed, popular routes, etc. For example, for the tested vehicle in the express delivery scenario, typical user routes can be statistically analyzed through driving trajectory heatmap.

[0044] Furthermore, vehicle driving trajectory heatmaps can be obtained from the vehicle-to-everything (V2X) big data analysis platform, or popular driving routes can be determined based on market research results, in order to ultimately determine the typical user routes of the tested vehicle model in the express delivery and freight usage scenario nationwide, i.e., the primary driving route.

[0045] Specifically, the vehicle-to-everything (V2X) big data analysis platform is an analysis system that integrates vehicle status information, driving data, and user behavior data. It can analyze vehicle usage through real-time or historical data, providing data support for vehicle management and product improvement.

[0046] As an optional implementation method, vehicle usage information of the target user group can be collected through questionnaires, user interviews, or market data analysis. This information includes, but is not limited to, the vehicle's operating area, the terrain features it travels on, and the main application scenarios. This information provides an initial perspective on user needs for route selection. Based on real-time data from vehicle-mounted GPS and vehicle-to-everything (V2X) modules, heatmaps of vehicle routes are analyzed to identify the routes most frequently traveled by users in different application scenarios. This process typically considers parameters such as average vehicle speed, travel time, and route type to statistically analyze the mileage distribution of vehicles across various terrain types (such as highways, national roads, and rural roads).

[0047] Furthermore, based on the collected terrain information and application scenarios, user routes are categorized, and vehicle load conditions under each type of route are evaluated. For example, road surfaces are classified according to their unevenness level, or the type and intensity of loads on the vehicle are assessed based on the terrain characteristics of the route (such as mountainous areas, plains, or urban areas). Taking into account the characteristics of the vehicle's application scenarios and the driving area, one or more routes that can represent the target user's usage conditions are selected or constructed as the primary driving route. This route should not only reflect the terrain types most frequently traveled by the user but also include various load conditions to ensure that the test can comprehensively evaluate the vehicle's durability in actual use.

[0048] It is worth noting that, through the above implementation method, the primary driving route representing the user's actual usage scenario can be accurately determined based on the user's actual needs and usage conditions. This technical action makes the durability testing at the test track more closely resemble the user's real-world environment, effectively reducing unnecessary test mileage while ensuring the accuracy and reliability of test results. This significantly shortens the load spectrum acquisition cycle and reduces costs, thereby improving product development efficiency. Furthermore, by precisely matching test track conditions with user usage conditions, it is possible to avoid excessive or insufficient verification of vehicle components during testing, ensuring the accuracy of test track durability testing and providing strong support for vehicle durability design and verification.

[0049] Step S104: Determine the first damage coefficient based on the first damage matrix of the first driving route and the second damage matrix of the second driving route, wherein the length of the second driving route is less than the length of the first driving route.

[0050] In the technical solution provided in step S104 of the present invention, load spectrum data is collected on the first driving route, and a first damage matrix is ​​obtained through frequency domain analysis (e.g., power spectral density PSD analysis). This matrix contains the damage values ​​of different measurement points in various frequency bands. Similarly, for the shorter second driving route, load spectrum acquisition and damage matrix calculation are also performed to obtain a second damage matrix.

[0051] Furthermore, damage correlation analysis methods (such as the RDS method) are used to compare and analyze the first and second damage matrices to find the damage equivalence relationship between them. The specific analysis process may include data preprocessing, frequency band division, damage value calculation, and matrix solving. Based on the damage correlation analysis, a first damage coefficient is determined, which describes the relationship between the mileage traveled on the second driving route and the damage-equivalent mileage on the first driving route. The calculation of the damage coefficient typically considers the percentage of total damage retention, i.e., the proportion of damage values ​​in the second damage matrix relative to the first damage matrix.

[0052] The specific implementation method is as follows: First, sensors are installed on the vehicle. An acceleration sensor is installed above the front axle hub of the vehicle under test, and the vehicle speed is recorded via CAN or GPS signals. Based on the vehicle's six-degree-of-freedom load input, corresponding sensors are installed on components such as wheels, axles, suspension, frame, body, and suspension, including but not limited to: six-component force sensors, strain sensors, acceleration sensors, displacement sensors, and torsional angle sensors, for subsequent damage correlation analysis.

[0053] Then, the aforementioned sensors are correctly connected to the data acquisition device, and the sensor parameters are set and dynamically / statically adjusted using the data acquisition software to determine whether the sensors and data acquisition device are working properly. Furthermore, according to the actual load loaded by the user, load spectrum data is collected on the aforementioned determined typical user routes nationwide (i.e., the first driving route) based on the user's actual driving speed and driving habits.

[0054] Furthermore, the load spectrum of the collected typical user routes (i.e., the first driving route) across the country is processed to obtain clean load spectrum data, which facilitates subsequent frequency domain impairment correlation calculations. This mainly includes: glitch detection, drift correction, low-pass filtering, and operating condition interception.

[0055] Optionally, for enterprises that have already deployed vehicle cloud platforms, the configured data processing flow can be distributed to the data cleaning module and edge computing module of the vehicle terminal to perform real-time verification and processing of load spectrum data, so as to directly obtain clean load spectrum data.

[0056] Furthermore, the system also needs to perform mileage data slicing and user road surface unevenness statistics. First, the vehicle speed signal is integrated to calculate the driving mileage data. Then, the load spectrum of the first driving route is sliced ​​according to equal mileage. Depending on the length of the collected mileage and the test accuracy requirements, mileage slices can be performed according to arbitrary values, and the root mean square value of the axle head acceleration under each mileage segment is calculated.

[0057] Then, a dedicated load spectrum acquisition route (i.e., the second driving route) is constructed based on the road surface roughness classification results. For example, based on the road surface roughness classification results of typical user routes nationwide (i.e., the first driving route), five closed-loop user routes with a total length of 4950km are constructed in the target area, of which expressways account for 55% and national highways account for 45%.

[0058] Specifically, damage correlation analysis is then performed on typical user routes nationwide and dedicated routes for load spectrum acquisition, using the RDS method for correlation analysis. PSD analysis is then conducted on the clean load spectrum data processed in the previous steps. During PSD analysis, all sensor measurement points located on various vehicle assemblies and components are analyzed, focusing on sensor type and load direction.

[0059] For example, in the express delivery scenario, the load spectrum of the entire vehicle's various assemblies and components mainly comes from contributions from six frequency bands:

[0060] Frequency band 1: Driving operation frequency band, 0~0.8Hz;

[0061] Frequency band two: Spring runout frequency band, 0.8~4Hz;

[0062] Frequency band 3: Intermediate transition frequency band, 4-8Hz;

[0063] Frequency band four: unsprung vibration frequency band, 8-14Hz;

[0064] Frequency band 5: Vibration frequency band of suspended components, 14~22Hz;

[0065] Frequency band six: Noise and transmission system vibration frequency band, 22~50Hz;

[0066] Furthermore, the RDS is calculated according to the six frequency bands determined above, and then the damage ratio relationship between the typical user route nationwide (i.e., the first driving route) and the five closed-loop load spectrum acquisition dedicated routes (i.e., the second driving route) is solved according to the correlation model.

[0067] The damage correlation model based on RDS is as follows:

[0068]

[0069] The matrix form of the above model can be expressed as:

[0070]

[0071] Where A is the damage matrix of the second driving route, B is the damage matrix of the first driving route, i is the number of sensor channels, j is the number of frequency bands, k is the number of routes included in the second driving route, and x is the first damage coefficient.

[0072] As an optional implementation, a damage correlation model is constructed based on the damage matrices of the first and second driving routes. The model is represented in matrix form and includes the relationships between measurement points, frequency bands, and the number of routes. Furthermore, the damage correlation model is solved using mathematical optimization methods to find the parameter combination that makes the two damage matrices equivalent, i.e., the first damage coefficient.

[0073] It is worth noting that the main technical effect that can be achieved by the above-mentioned technical actions is that, through damage equivalence analysis, the test results of a shorter second driving route can represent the damage situation of a longer first driving route, thereby significantly shortening the load spectrum acquisition cycle and cost, while ensuring the accuracy of the durability test at the test track.

[0074] Step S106: Determine the second damage coefficient based on the second damage matrix and the third damage matrix of the third travel route, wherein the length of the third travel route is less than the length of the second travel route.

[0075] In the technical solution provided by step S106 of the present invention, firstly, load spectrum data on the second driving route is collected. This data includes the spectrum of various forces experienced by the vehicle under different road conditions and operating conditions. By processing and analyzing this data, a second damage matrix is ​​obtained, which contains the damage values ​​of the measuring points in different frequency bands.

[0076] Furthermore, based on the analysis results of the second damage matrix, a shorter third driving route is designed or selected. This route is significantly shorter than the second driving route, but it must be ensured that it is statistically representative or equivalent to the damage situation of the second driving route. Load spectrum is collected for the third driving route, and the third damage matrix is ​​calculated using the same frequency domain analysis method. This matrix can reflect the damage values ​​of the third driving route to each measuring point of the vehicle at different frequency bands.

[0077] Finally, by comparing the second and third damage matrices, a mathematical model or algorithm (such as the RDS method) is used to calculate the second damage coefficient, which is the ratio of the third driving route length to the damage equivalent mileage of the second driving route, and is used to quantify the equivalence of the damage between the third and second driving routes.

[0078] Specifically, based on the test track layout and site management requirements, load spectra were collected at the specified vehicle speed for various reinforced road surfaces (such as Belgian roads, cobblestone roads, fish-scale pothole roads, and dilapidated road sections) and various special operating conditions (such as figure-eight maneuvers, circular maneuvers, reversing into a parking space, and emergency braking). The collected load spectra were then cleaned and PSD analyzed.

[0079] The test track's different reinforced road surfaces and various specialized operating conditions (i.e., the third driving route) provide different road excitations, which can fully reflect the vehicle's six degrees of freedom load input. The load spectrum of each assembly and component of the vehicle also mainly comes from the contributions of six frequency bands:

[0080] Frequency band 1: Driving operation frequency band, 0-0.8Hz, such as figure-eight maneuvers and reversing into a parking space;

[0081] Frequency band two: Spring bounce frequency band, 0.8~4Hz, such as Belgian roads and uneven cement roads;

[0082] Frequency band three: intermediate transition frequency band and special road surface excitation frequency band, 4-8Hz, such as cobblestone road;

[0083] Frequency band four: unsprung movement frequency band, 8-15Hz, for example, Belgian road, large and small round protrusions;

[0084] Frequency band 5: Vibration frequency band of suspension components and excitation frequency band of special road surfaces, 15-26Hz, such as washboard roads;

[0085] Frequency band six: Noise and transmission system vibration frequency band, 26~50Hz;

[0086] Furthermore, the six main frequency bands determined by the load spectrum of the second driving route are combined with the six main frequency bands determined by the enhanced road and special operating conditions of the test track, and then the RDS is calculated.

[0087] The damage correlation model based on RDS is as follows:

[0088]

[0089] The matrix form of this model can be represented as:

[0090]

[0091] Where C is the damage matrix of the test track reinforced road and special operating conditions (i.e., the third driving route), A is the damage matrix of the second driving route, i is the number of sensor channels, j is the number of frequency bands, and k is the number of test track reinforced road surfaces and special operating conditions.

[0092] As an optional implementation, the relative damage spectrum (RDS) from damage equivalence theory is used to compare and analyze the second and third damage matrices to determine their damage equivalence relationship. Furthermore, the damage correlation model is solved through mathematical optimization to find the second damage coefficient, ensuring that the third driving route achieves a damage level equivalent to the second driving route within a shorter mileage.

[0093] It is worth noting that, based on the above technical steps, a shorter third driving route can be used instead of a longer second driving route for durability testing, significantly shortening the testing cycle and reducing costs. Furthermore, it ensures that the damage incurred on the third driving route over a shorter mileage is equivalent to the damage incurred on the second driving route over a longer mileage, improving the accuracy and reliability of the test results. In addition, it provides test track with more options for test route design, allowing for flexible adjustments to test routes based on actual conditions to optimize the use of test resources.

[0094] Step S108: Determine the test conditions of the vehicle based on the first damage coefficient, the second damage coefficient, and the mileage of the first driving route. The test conditions include the total vehicle test mileage and test road condition information.

[0095] In the technical solution provided by step S108 of the present invention, firstly, a first damage coefficient is calculated based on the damage matrix of the first driving route and the second driving route, and then a second damage coefficient is calculated based on the damage matrix of the second driving route and the third driving route. The two damage coefficients respectively reflect the characteristics of the second driving route and the third driving route in terms of damage equivalence.

[0096] Furthermore, the first damage coefficient, the second damage coefficient, and the actual mileage of the first driving route are combined for a comprehensive analysis. This analysis may involve mathematical calculations, statistical or predictive models to quantify the impact of different driving routes on vehicle damage.

[0097] Then, based on the above analysis results, the test conditions for the vehicle are determined, including the mileage to be driven during the whole vehicle test and the road conditions to be simulated during the test. The test road conditions may include different types of road conditions (such as highways, country roads, mountain roads, etc.) and specific road surface features (such as unevenness, slope, etc.).

[0098] Specifically, test conditions refer to the specific conditions and environments that a vehicle must endure during durability testing, including test mileage and simulated road condition information, which are the basic conditions for evaluating the structural durability of a vehicle.

[0099] The specific implementation method is as follows. In this embodiment, a total of 61 measurement points can be defined for damage correlation calculation of the test track and typical user routes nationwide. The formula for the total damage retention percentage is as follows:

[0100]

[0101] The formula for the damage ratio of sensor channel i is:

[0102]

[0103] Furthermore, the total damage retention percentage is in the range of 100±Δ%. Then, the damage correlation model of the frequency band is optimized under multiple working conditions to obtain the combination of different number of cycles for each enhanced road (i.e., the third driving route) and each special operation condition in the test track. The value range of Δ is generally [0,20]. In this application, when performing optimization, Δ can be constrained to the range of [0,1]. Finally, considering the test track layout, site management regulations, feasibility, test efficiency and test cycle, the durability driving specifications of the whole vehicle test track are finally determined through scheme review.

[0104] It is worth noting that this application also establishes a differentiated damage release test strategy for parts, that is, for all test point channels of the vehicle under test, damage correlation analysis is carried out separately for each part, and the following differentiated damage release strategy is formulated:

[0105] 1) If the damage ratio of the channel is in the range of 0.8 to 1.2 times, then the channel meets the assessment requirements, and the component release test mileage = the whole vehicle release test mileage;

[0106] 2) If the damage ratio of the channel is <0.8, it indicates that the durability test at the test track is insufficient. The release test mileage of the component = (the release test mileage of the whole vehicle / the damage ratio) × 0.8, that is, the test track verification mileage of the relevant components needs to be increased to achieve consistency with the user's target damage.

[0107] 3) If the damage ratio of the channel is >1.2, it indicates that there is over-verification in the durability test at the test track. The release test mileage of the component = (the release test mileage of the whole vehicle / the damage ratio) ×1.2. That is, the test track verification mileage of the relevant components needs to be reduced to achieve consistency with the user's target damage.

[0108] Optionally, for example, based on the verification accuracy requirements, the lower limit of the damage ratio in this embodiment is 0.8, which can be adjusted to any value such as 0.7 or 0.75, but cannot be lower than 0.5; the upper limit of the damage ratio is 1.2, which can be adjusted to any value such as 1.25 or 1.3, but cannot exceed 2.

[0109] Furthermore, based on the reliability series model theory, since failures are competitive, the weakest link has the highest failure risk. Therefore, differentiated release strategies are formulated for different placement positions of the same component and different stress directions of the same component.

[0110] 1) Damage calculations are performed for the same component at different locations, taking into account the load sensitivity of different locations. The final release mileage is determined by the Min (damage ratio) → Max (release mileage) calculation. Specifically, the Min (damage ratio) → Max (release mileage) calculation process is as follows: Damage ratio = Min (different locations of the same component), Release mileage = Max (release mileage corresponding to different locations of the same component). For example, for the lower reaction rod of the rear suspension, the damage ratio = Min (left front position, right front position, left rear position, right rear position), and the release mileage = Max (verification mileage of left front position, verification mileage of right front position, verification mileage of left rear position, verification mileage of right rear position).

[0111] 2) Damage calculations are performed on the same component under different force directions, taking into account the load sensitivity of different force directions. The final release mileage is determined by the Min (damage ratio) → Max (release mileage) calculation. Specifically, the Min (damage ratio) → Max (release mileage) calculation process is as follows: Damage ratio = Min (different force directions of the same component), Release mileage = Max (release mileage corresponding to different force directions of the same component). For example, for the rear suspension X-bar, the damage ratio = Min (Fx, Fy, Fz), and the release mileage = Max (verification mileage in the Fx direction, verification mileage in the Fy direction, verification mileage in the Fz direction).

[0112] As an optional implementation, a first damage coefficient and a second damage coefficient are applied to a mathematical model that considers the total mileage of the first driving route. The equivalent mileage required for the vehicle test is determined through calculation. Furthermore, based on the characteristics of the first damage coefficient, appropriate test road condition information is selected to ensure that the road conditions in the test can cover or be equivalent to the main road surface conditions and load characteristics in the first driving route.

[0113] As an alternative implementation, a training dataset is constructed based on historical damage data and test results. This dataset includes the correlation between damage coefficients, mileage, and test results. Furthermore, machine learning algorithms (such as cluster analysis and regression models) are used to train the model, enabling it to predict the optimal test conditions based on the damage coefficients and mileage. Then, by inputting a first damage coefficient, a second damage coefficient, and the mileage of a first driving route, the model outputs the total vehicle test mileage and test road condition information, thus optimizing the selection process for test conditions.

[0114] It is worth noting that the above technical steps allow for the determination of damage coefficients and actual vehicle usage conditions based on damage equivalence analysis results. This enables precise determination of test conditions, including the required mileage and road condition information for the entire vehicle, avoiding the non-specificity and resource waste of traditional "one-size-fits-all" testing methods. Furthermore, by using shorter second and third driving routes to estimate the vehicle's test mileage, the test preparation time is significantly shortened, reducing testing costs. In addition, it ensures that the test conditions more closely resemble the damage situations that vehicles may encounter in actual use, improving the reliability of test results and the confidence level of the experiment.

[0115] Step S110: Control the vehicle to perform tests based on test conditions and continuously calculate multiple damage degrees of multiple parts of the vehicle.

[0116] In the technical solution provided in step S110 of the present invention, the vehicle control system is programmed according to the determined test conditions, including the vehicle test mileage and test road condition information, and test parameters, such as vehicle speed, steering, and braking behavior, are set to ensure that the vehicle can simulate driving conditions under real-world usage scenarios. Corresponding sensors, including but not limited to acceleration sensors, strain sensors, and displacement sensors, are installed on key parts and components of the vehicle to collect load data of the vehicle in real time during the test.

[0117] Furthermore, the vehicle is controlled to undergo testing under predetermined test conditions, while the onboard data acquisition system continuously records the load response data of multiple vehicle components. This data can be used for subsequent damage calculation. Based on the collected load data, a damage calculation model (such as rainflow counting) is used to calculate the damage degree of multiple vehicle components in real time or periodically. Optionally, the damage degree reflects the extent to which the loads borne by the components during the test affect their service life.

[0118] The specific implementation method is as follows: by encapsulating strain sensors on key components and decoupling loads in all directions using a Wheatstone bridge, the components are sensorized, which is the foundation for real-time vehicle damage calculation. Appropriate sensors can be selected according to different needs, such as acceleration sensors, strain sensors, and pressure sensors. In this embodiment, three sets of strain sensors are encapsulated near the right axle head of the front axle to characterize the vertical, lateral, and longitudinal force loads on the front axle.

[0119] Furthermore, a load spectrum data processing flow is deployed on the cloud platform. The cloud platform receives the load spectrum signal uploaded from the vehicle terminal and sequentially performs null value removal, glitch elimination, drift correction, and low-pass filtering according to a preset process to obtain a clean load spectrum signal for subsequent damage calculation. In this embodiment, the road test telematics system, which interacts with the cloud platform, can display the processed clean load spectrum signal in real time.

[0120] Finally, a load spectrum analysis process is deployed on the cloud platform. The cleaned load spectrum signal is then subjected to vehicle speed integration and pseudo-damage calculation to obtain the cumulative value of pseudo-damage of the parts with mileage. This value is then compared with the pseudo-damage value of the target mileage of typical user routes across the country and fed back to the vehicle terminal in real time in the form of a percentage progress bar.

[0121] As an optional implementation, load data collected from the vehicle is uploaded to a cloud platform in real time via wireless communication, leveraging the powerful processing capabilities of cloud computing for data analysis. Specifically, the cloud platform is equipped with a damage calculation algorithm to process the uploaded data in real time, instantly calculating the damage level of multiple vehicle components. During testing, the cloud platform can provide real-time monitoring and reporting of component damage levels, facilitating timely adjustments to test parameters by testing engineers to ensure the effectiveness and safety of the test.

[0122] As an alternative implementation, edge computing devices can be integrated into the vehicle to handle initial data processing and local damage calculation, reducing reliance on cloud platforms. Specifically, local edge devices store load data during testing and send the data to a data center or cloud platform for detailed damage analysis after testing or periodically. Through subsequent centralized data analysis, the damage degree of parts is calculated based on the complete dataset, used to evaluate test results and subsequently improve vehicle design.

[0123] It is worth noting that the above technical steps ensure that durability testing is conducted under predetermined conditions, improving the repeatability and accuracy of the tests. Furthermore, continuous monitoring and calculation of component damage during testing helps to identify potential durability issues early, increasing the flexibility and responsiveness of the testing. In addition, real-time or post-test data analysis based on damage levels provides objective and quantitative data support for subsequent vehicle design improvements and quality assessments, promoting data-driven product optimization.

[0124] Steps S102 to S110 above show that, in this invention, a first driving route is determined based on user requirements, including terrain information, vehicle application scenarios, and vehicle driving areas. A first damage coefficient is determined based on a first damage matrix of the first driving route and a second damage matrix of the second driving route. A second damage coefficient is determined based on the second damage matrix and a third damage matrix of the third driving route. This achieves the goal of determining the vehicle's test conditions based on the first damage coefficient, the second damage coefficient, and the mileage of the first driving route. This achieves the technical effect of controlling the vehicle to be tested based on the test conditions and continuously calculating multiple damage degrees of multiple parts of the vehicle. Furthermore, it can solve the technical problem in the prior art where the durability test of the test track is not systematic, resulting in low test accuracy.

[0125] The method described in this embodiment will now be described in further detail.

[0126] Step S201: Obtain the initial load spectrum data of the first driving route;

[0127] Step S202: Perform data processing operations on the initial load spectrum data to obtain the target load spectrum data;

[0128] Step S203: Slice the target load spectrum data according to the mileage data of the first driving route to obtain multiple mileage segments;

[0129] Step S204: Calculate the acceleration values ​​of multiple shaft heads for multiple mileage segments;

[0130] Step S205: Determine multiple road surface roughness levels corresponding to multiple mileage segments based on multiple axle head acceleration values ​​and objective function. The objective function is used to characterize the relationship between axle head acceleration values ​​and road surface roughness levels, and each mileage segment corresponds to one road surface roughness level.

[0131] Step S206: Determine the second driving route based on multiple road surface smoothness levels.

[0132] In this embodiment, load spectrum data of the vehicle along a first driving route is collected by installing sensors on the vehicle, including the spectrum of various forces acting on the vehicle under different road conditions. Data cleaning operations are performed on the collected initial load spectrum data, including noise and outlier removal, filtering, and smoothing, to obtain clean target load spectrum data.

[0133] Furthermore, based on the mileage data of the first driving route, the target load spectrum data is divided into multiple mileage segments of equal length to facilitate subsequent analysis and processing. Within each mileage segment, the vertical acceleration value of the vehicle axle head is calculated as an indicator reflecting road surface roughness. Specifically, using an objective function, the calculated axle head acceleration value is mapped to the corresponding road surface roughness level. The objective function is a pre-established mathematical model that describes the relationship between the axle head acceleration value and the road surface roughness level.

[0134] Finally, based on the road surface smoothness levels of multiple mileage segments in the first driving route, a second driving route is designed or selected to ensure that it can represent or be equivalent to the road surface unevenness distribution and vehicle damage of the first driving route within a shorter mileage.

[0135] Optionally, load spectrum data reflects the spectrum of various forces acting on a vehicle during operation and is the basic data for evaluating the durability of vehicle structural components.

[0136] Optionally, the axle head acceleration value is the acceleration value of the vehicle axle head in the vertical direction, used to quantify the impact of road surface unevenness on vehicle vibration.

[0137] The specific implementation method is as follows: mileage data slicing and user road surface unevenness statistics are performed on the first driving route. First, the vehicle speed signal is integrated to calculate the driving mileage data. Then, the load spectrum of the user route is sliced ​​according to equal mileage. Depending on the length of the collected mileage and the test accuracy requirements, mileage slicing can be performed according to arbitrary values, and the root mean square value of the axle head acceleration under each mileage segment is calculated.

[0138] For example, this embodiment targets the express delivery scenario of tractor-trailers. The load spectrum of typical user routes nationwide is collected over a distance of 29,245 km. Through extensive statistical analysis of the root mean square values ​​of axle head acceleration under different mileage slices, it was found that the coefficient of variation of the data is smaller when the mileage slices are 2 to 10 km. Slices shorter than 2 km will affect the analysis efficiency due to the large amount of data, while slices longer than 10 km will affect the analysis accuracy due to the large mileage span. Therefore, this embodiment uses 5 km as an example to perform mileage slice and conduct statistical analysis of road surface grades, which can obtain a scatter plot of road surface roughness grades corresponding to the root mean square values ​​of average vehicle speed and axle head acceleration.

[0139] Furthermore, a dedicated load spectrum acquisition route (second driving route) was constructed based on the road surface roughness classification results. This embodiment uses the Changchun Nong'an test track as the center and, based on the road surface roughness classification results of typical user routes nationwide (first driving route), five closed-loop user routes were constructed in the target area, totaling 4950km, with expressways accounting for 55% and national highways accounting for 45%. Since the road surface roughness statistics of these five closed-loop user routes are almost identical to the road surface roughness classification results of typical user routes nationwide in the tractor-trailer express delivery scenario, they can represent the characteristics of typical user routes nationwide and serve as a dedicated load spectrum acquisition route for the tractor-trailer express delivery scenario, i.e., the second driving route.

[0140] As an optional implementation, the vehicle uploads load spectrum data and vehicle speed information to the data center in real time via a vehicle-to-everything (V2X) platform. The cloud platform processes the uploaded data, including data cleaning, frequency domain analysis, axle head acceleration calculation, and road surface smoothness level determination. Furthermore, using the processed data, the cloud platform automatically designs or recommends a second driving route that can equivalently reflect the road surface characteristics and vehicle damage of the first driving route with a shorter mileage.

[0141] It is worth noting that by combining the axle head acceleration value with the objective function, the road surface smoothness level on the first driving route can be automatically identified and quantified, improving the accuracy and efficiency of road quality assessment. Furthermore, based on the road surface smoothness level analysis of the first driving route, a second driving route can be designed or selected to ensure damage equivalence is achieved within a shorter distance, reducing the time and cost of load spectrum acquisition.

[0142] Step S2061: Determine the initial second driving route based on multiple road surface smoothness levels;

[0143] Step S2062: Perform power spectrum analysis on the target load spectrum data to obtain multiple power spectrum frequency bands;

[0144] Step S2063: Obtain the number and type of sensors deployed on the vehicle;

[0145] Step S2064: Determine multiple output channels based on the number and type of sensors;

[0146] Step S2065: Calculate the channel damage ratio of multiple output channels based on multiple power spectrum frequency bands, the first damage matrix and the damage matrix of the initial second driving route, wherein each output channel corresponds to one channel damage ratio.

[0147] Step S2066: Compare the multiple channel damage ratios with the first threshold and the second threshold respectively to obtain the first comparison result;

[0148] Step S2067: In response to the first comparison result indicating that the damage ratios of multiple channels are all greater than the first threshold and less than the second threshold, the initial second driving route is determined as the second driving route.

[0149] In this embodiment, based on the identification and analysis of the road surface smoothness levels of multiple mileage segments along the first driving route, an initial second driving route is selected or designed. This route covers and matches the key features of the first driving route in terms of length and road surface conditions. Then, power spectrum analysis is performed on the target load spectrum data to obtain multiple power spectrum frequency bands. These power frequency bands are correlated with the vehicle's dynamic response characteristics and are important parameters for assessing damage.

[0150] Furthermore, information on the number and type of sensors deployed on the vehicle is collected. These sensors are used to acquire various load data, including but not limited to acceleration, strain, and displacement. Multiple output channels are defined based on the sensor arrangement and type, with each channel corresponding to a specific sensor or a group of sensors, for subsequent damage ratio calculation. Then, using the power spectrum band, the first damage matrix (representing the damage situation of the first driving route), and the initial damage matrix of the second driving route, the channel damage ratio of multiple output channels is calculated through a mathematical model or algorithm. This ratio reflects the difference in damage equivalence between the second and first driving routes.

[0151] Finally, the calculated channel damage ratios are compared with the preset first and second thresholds. If all channel damage ratios fall between the two thresholds, the initial second driving route is determined as the final second driving route, meaning it is equivalent to the first driving route in terms of damage.

[0152] Specifically, road surface roughness level can be classified according to the road surface quality determined by the axle head acceleration value. It is a quantitative indicator for evaluating road surface roughness and vehicle vibration response.

[0153] Specifically, power spectrum analysis refers to analyzing a signal in the frequency domain, calculating the energy distribution of the signal at different frequencies, and using it for in-depth analysis of load spectrum data.

[0154] Specifically, the aforementioned output channels are logical channels representing sensor data streams or damage calculation results, used to process and analyze data from a single sensor or a group of sensors.

[0155] For example, if there are a total of 61 measurement points used for damage correlation calculation in this embodiment, then the total damage retention percentage can be defined as:

[0156]

[0157] The damage ratio of channel i can be expressed as:

[0158]

[0159] Optionally, for the damage ratio of channel i, a value in the range of 0.5 to 2 is considered excellent, and a value in the range of 0.2 to 5 is considered good.

[0160] Furthermore, rainflow curves were compared and verified for each measuring point to confirm the correlation effect. A high degree of overlap in the rainflow curves indicates a high correlation. In this embodiment, the rainflow curves of the vertical strain at the left and right leaf spring seats of the front axle were used as an example for correlation effect comparison, and the overlap was extremely high. Similar results were observed for other measuring points. Therefore, the five closed-loop user routes constructed above can be used as dedicated routes for load spectrum collection in the tractor-trailer express delivery scenario (i.e., the second driving route) to represent typical user routes nationwide in the tractor-trailer express delivery scenario (i.e., the first driving route). Therefore, in subsequent testing and development processes, it is not necessary to conduct load spectrum collection across the country, which significantly shortens the collection cycle and reduces costs.

[0161] As an optional implementation, a data analysis platform can be used to perform power spectrum analysis and damage matrix calculation on the target load spectrum data, automatically determining the output channel and calculating the channel damage ratio. Furthermore, the platform automatically compares the results with a threshold and intelligently determines a second driving route based on the results, reducing manual intervention and improving efficiency and accuracy.

[0162] It is worth noting that the above technical steps ensure that the initial second driving route can simulate the damage characteristics of the first driving route with a shorter mileage, improving the accuracy of durability test design. Furthermore, the data-driven approach provides quantitative evidence for the selection of the second driving route, reducing the uncertainty of subjective judgment. In addition, the process of determining the second driving route can be accelerated through automation or decision-making assistance, saving preparation time and costs for test design.

[0163] Step S301: Obtain the spectral density of the test route and the vehicle speed;

[0164] Step S302: Determine the time domain spectrum of the test route based on the spectral density and vehicle speed;

[0165] Step S303: Construct the shaft head acceleration response function based on the time domain spectrum;

[0166] Step S304: Construct the objective function based on the axle head acceleration response function and the vehicle speed.

[0167] In this embodiment, firstly, the spatial irregularity spectral density (i.e., the spectral characteristics of road surface irregularities) and a preset spectral density (a spectral density value set for a standard or target) of the test route are collected, while the vehicle's speed on the test route is recorded. Then, based on the spectral density of the test route and the vehicle's speed, the spatial spectral density is converted into a temporal spectrum. This conversion utilizes the relationship between vehicle speed and frequency; that is, the faster the vehicle speed, the higher the perceived temporal frequency of irregularities at the same spatial frequency.

[0168] Furthermore, based on the determined time-domain spectrum, an axle head acceleration response function is constructed. This function describes the acceleration response of the vehicle axle head at different frequencies and is crucial for evaluating the impact of vehicle vibration and road surface roughness.

[0169] Finally, by combining the axle head acceleration response function and the vehicle speed, an objective function is constructed. This function is used to quantify the relationship between axle head acceleration and road surface roughness level, thereby enabling the identification and evaluation of different road surface roughness levels.

[0170] Specifically, spectral density is a function that describes the distribution of signal energy in the frequency domain. For vehicle durability testing, spectral density reflects the intensity of road surface roughness at different frequencies.

[0171] Specifically, the axle head acceleration response function is a mathematical function that describes the vehicle axle head's response to acceleration at different frequencies, and it is key to assessing vehicle dynamic loads and damage.

[0172] Specifically, the objective function is a mathematical model used to quantify the relationship between axle head acceleration and road surface smoothness grade, and it is the core function for realizing road surface smoothness grade identification and evaluation.

[0173] The specific implementation method is as follows: First, road surface roughness is classified using power spectral density (PSD), and the road surface grade is determined by the spatial elevation spectral density of the road surface.

[0174]

[0175]

[0176] in: Spatial frequency ( ); The preset fundamental spatial frequency; The spectral density at the fundamental spatial frequency; is the spectral density index.

[0177] Furthermore, the aforementioned spatial road surface spectrum can be determined by vehicle speed. Transformed into a time-frequency spectrum:

[0178]

[0179] Furthermore, based on the vehicle quarter-model, the unsprung mass of the front axle can be approximated as a single-degree-of-freedom vibration system, where the root-mean-square acceleration response equation (axle head acceleration response function) of the single-degree-of-freedom system under white noise excitation is:

[0180]

[0181] in: This represents the root mean square value of the system response acceleration. It is the acceleration due to gravity; This is the natural frequency of the front axle spring; The system damping ratio; The acceleration power spectrum of the system input at the system's natural frequency ( ).

[0182] Optionally, although a vehicle is a complex dynamic system, for some typical dynamic responses of a vehicle, its response spectrum is similar under different user road excitations. The area of ​​the response spectrum envelope is the root mean square value of the acceleration. At the system's natural frequency, the root mean square value of the system is the largest.

[0183] Furthermore, by combining the acceleration response function, the root mean square response equation (i.e., the objective function) for the shaft head acceleration can be derived:

[0184]

[0185] For example, this embodiment uses a 6×4 air suspension tractor as the test vehicle. Other types of vehicles under test can have their corresponding model parameters determined according to the characteristics of the vehicle system.

[0186] Among them, the natural frequency of the vertical vibration of the front axle spring under full load is... Take 12Hz, system damping coefficient Take 2.5, gravitational acceleration Take 9.8, spectral density index Take 2.25, the fundamental space frequency. Pick The root mean square response equation for the front axle head acceleration can then be simplified to:

[0187]

[0188] in, The values ​​range from [2, 8, 32, 128, 512, 2048, 8196, 32768], each corresponding to a different road surface grade.

[0189] As an optional implementation, data processing software is used to import the collected test route spectral density, preset spectral density, and vehicle speed data. The software automatically performs a spatial-to-time domain spectral transformation and constructs an axle head acceleration response function based on the transformation result. Furthermore, the software can automatically generate an objective function based on the constructed acceleration response function and vehicle speed data for subsequent road surface smoothness assessment.

[0190] It is worth noting that the above technical steps enable quantitative analysis of the unevenness characteristics of the test route, providing a quantitative basis for damage assessment through time-domain spectrum and acceleration response function. Furthermore, based on vehicle speed, the adaptability and damage potential of the test route at different speeds can be evaluated, which helps optimize test conditions. In addition, by constructing an objective function to identify and evaluate different road surface smoothness levels, the accuracy and reliability of the assessment are enhanced.

[0191] Step S3011: Obtain the spatial frequency, preset spatial frequency, and preset spectral density of the test route;

[0192] Step S3012: Compare the spatial frequency with the preset spatial frequency to obtain a second comparison result;

[0193] Step S3013: In response to the second comparison result indicating that the spatial frequency is less than or equal to the preset spatial frequency, calculate the spectral density of the test route based on the spatial frequency, the preset spatial frequency, the preset spectral density and the first spectral density index.

[0194] Step S3014: In response to the second comparison result indicating that the spatial frequency is greater than the preset spatial frequency, calculate the spectral density of the test route based on the spatial frequency, the preset spatial frequency, the preset spectral density, and the second spectral density index, wherein the second spectral density index is less than the first spectral density index.

[0195] In this embodiment, firstly, the unevenness information of the vehicle axle head response on the test route is acquired and converted into a spatial frequency, that is, the frequency of amplitude change per unit length. Simultaneously, a preset spatial frequency is determined as a comparison benchmark, which is typically set based on ISO standards or other industry standards.

[0196] Furthermore, the acquired spatial frequency is compared with a preset spatial frequency to obtain a second comparison result. This result determines which spectral density index to use for subsequent calculations. Based on the second comparison result, if the spatial frequency is less than or equal to the preset spatial frequency, the first spectral density index is used for spectral density calculation; if the spatial frequency is greater than the preset spatial frequency, the second spectral density index is used, and the second spectral density index is less than the first spectral density index to accommodate different unevenness characteristics.

[0197] Specifically, the preset spatial frequency is a reference frequency defined by standards or engineering practice, used to compare with the actual measured spatial frequency and determine the spectral density index used in spectral density calculation.

[0198] Optionally, the first spectral density index and the second spectral density index are used to calculate the spectral density in different spatial frequency bands. Generally, the first spectral density index is suitable for lower spatial frequencies, and the second spectral density index is suitable for higher spatial frequencies, reflecting the characteristics of unevenness in different frequency bands.

[0199] The specific implementation method is as follows: First, road surface roughness is classified using power spectral density (PSD), and the road surface grade is determined by the spatial elevation spectral density of the road surface.

[0200]

[0201]

[0202] in: Spatial frequency ( ); The preset fundamental spatial frequency; The spectral density at the fundamental spatial frequency; is the spectral density index.

[0203] As an optional implementation, preset thresholds for spatial frequency and spectral density index can be determined in the signal processing software. The software then automatically reads and analyzes the axle head acceleration signal of the first travel route, calculates the spatial frequency, and compares it with the preset spatial frequency. Furthermore, the software automatically selects either the first or second spectral density index for spectral density calculation based on the comparison result.

[0204] It is worth noting that, through the above technical steps, the unevenness of the first driving route was quantitatively analyzed, and its spectral characteristics were obtained through spectral density calculation, providing a quantitative basis for damage assessment and test design. Furthermore, based on the comparison results of spatial frequencies with preset spatial frequencies, an appropriate spectral density index was selected for calculation, enhancing the adaptability and accuracy of spectral density analysis, especially when dealing with complex road surface characteristics. In addition, the independent spectral density calculation results provide data support for engineering decisions, helping to design test conditions that better reflect actual road conditions.

[0205] Step S1041: Construct a damage matrix equation based on the first damage matrix and the second damage matrix;

[0206] Step S1042: Calculate the damage retention percentage of the vehicle based on multiple output channels and the damage function;

[0207] Step S1043: Iterate the damage retention percentage value according to the damage matrix equation to obtain the first damage coefficient.

[0208] In this embodiment, a damage matrix equation is constructed using linear algebra principles, based on a first damage matrix (representing damage under actual use conditions) and a second damage matrix (representing expected damage under test conditions). The equation reflects the degree of damage matching between the two matrices, with the aim of finding an optimal coefficient that makes the damage under test conditions as equivalent as possible to the damage under actual use conditions.

[0209] Furthermore, by analyzing multiple output channels (i.e., load data measured by sensors at different locations or in different directions) and damage functions (mathematical expressions describing the damage accumulation process), the percentage of damage retained by the vehicle under test conditions can be calculated. This allows us to determine the degree to which the damage status of each part of the vehicle is maintained relative to the actual usage scenario under test conditions.

[0210] Finally, the percentage of damage retained is used as input, and iterative calculations are performed based on the damage matrix equation. This iterative process aims to gradually approach the optimal damage coefficient, making the damage under experimental conditions more accurately match the actual use scenario.

[0211] Specifically, the damage matrix contains information about the cumulative damage to various parts of a vehicle under specific road conditions, and is key data for assessing vehicle durability.

[0212] Specifically, the percentage of damage retained refers to the proportion of damage to various parts of a vehicle under test conditions relative to the damage under actual use scenarios, and is used to assess the consistency between test conditions and actual use scenarios.

[0213] The specific implementation method is as follows: a damage correlation model based on RDS is established as follows:

[0214]

[0215] The matrix form of the above model can be expressed as:

[0216]

[0217] Where A is the damage matrix of the second driving route, B is the damage matrix of the first driving route, i is the number of sensor channels, j is the number of frequency bands, k is the number of routes included in the second driving route, and x is the first damage coefficient.

[0218] Furthermore, in this embodiment, a total of 61 measurement points are defined for damage correlation calculation, and the formula for the total damage retention percentage is as follows:

[0219]

[0220] Furthermore, the total damage retention percentage is in the range of 100±Δ%. Then, the damage correlation model of the frequency band is optimized by multi-route constraint. Different combinations of the number of cycles for multiple routes in the constructed second driving route can be obtained. The value range of Δ is generally [0,20]. In this embodiment, the number of cycles for multiple routes in the second driving route is equal, which can make Δ→0. After multiple iterations, the optimal first damage coefficient is obtained.

[0221] As an optional implementation, a first damage matrix and a second damage matrix are input into the simulation software, and a target range for the damage retention percentage is set. Further, the software automatically constructs the damage matrix equation and iteratively calculates based on the damage retention percentage until a first damage coefficient that meets the conditions is found.

[0222] It is worth noting that the above technical steps enable a quantitative assessment of the equivalence between damage under test conditions and damage in actual use scenarios, providing precise guidance for test design. Furthermore, by iteratively calculating the damage coefficient, test conditions can be gradually adjusted to more accurately simulate vehicle damage in real-world use scenarios, improving the accuracy and efficiency of the tests. In addition, making decisions based on the percentage of damage retention and iterative calculation results reduces reliance on subjective experience, making durability test design more scientific and objective.

[0223] Step S1101: Obtain the vehicle's rated mileage and current mileage;

[0224] Step S1102: Determine the target mileage damage value based on the rated mileage, the mileage of the first driving route, and the first damage matrix;

[0225] Step S1103: Determine the current mileage damage value based on the current mileage and the preset mathematical model;

[0226] Step S1104: Calculate multiple damage degrees for multiple parts based on the current mileage damage value and the target mileage damage value.

[0227] In this embodiment, the vehicle's rated mileage (i.e., the expected mileage during its designed service life) and the vehicle's actual current mileage are collected to provide basic information for subsequent damage calculation. Based on the rated mileage, the mileage of a specific first driving route, and a first damage matrix (representing the expected damage distribution of different parts under that driving route), the degree of accumulated damage to each part when the vehicle reaches its rated service life is calculated using mathematical methods, serving as the target benchmark for damage monitoring.

[0228] Furthermore, using a pre-set mathematical model, the degree of damage to each component under the current mileage is calculated. Optionally, the pre-set mathematical model may be based on factors such as vehicle load and driving habits, reflecting the cumulative damage to components under current actual usage conditions. Then, the current mileage damage value is compared with the target mileage damage value to calculate the degree of damage to each component relative to the design life end, i.e., the proportion of current damage to the expected total damage, used to visually display the damage status of the components.

[0229] Specifically, the rated mileage is the ideal distance traveled during the vehicle's design life, and is usually associated with the B10 life target.

[0230] Specifically, the damage level of a part is used to represent the degree of accumulated damage to a vehicle part relative to the end of its design life, and is an important indicator for measuring the health status of a part.

[0231] The specific implementation method is as follows: a component damage ratio database is constructed. A load spectrum analysis process is deployed on a cloud platform to sequentially perform vehicle speed integration and pseudo-damage calculation on the cleaned load spectrum signal to obtain the cumulative value of component pseudo-damage with mileage. This value is then compared with the preset pseudo-damage value of the target mileage of typical user routes nationwide, and the feedback is sent to the vehicle terminal in real time in the form of a percentage progress bar.

[0232]

[0233] Among them, the target mileage damage value of typical user routes nationwide is determined by conducting damage statistical analysis on typical user routes nationwide collected in the above steps.

[0234] As an optional implementation, key vehicle data (such as mileage and load spectrum) can be uploaded to the enterprise cloud platform in real time. Furthermore, the cloud platform automatically calculates the target mileage damage value and the current mileage damage value based on a preset algorithm. Simultaneously, the cloud platform continuously updates the component damage level and transmits the results to the in-vehicle terminal in real time for viewing by the driver or maintenance personnel.

[0235] As an alternative implementation, drivers or managers periodically record the vehicle's current mileage and collect data on component damage. Simultaneously, data analysts use specialized software to calculate the degree of component damage offline based on the rated mileage and the collected damage data, and generate periodic damage reports.

[0236] It is worth noting that the above technical steps provide quantitative indicators of component damage, making the damage status of components visible and facilitating timely understanding of the health status of critical vehicle components by drivers or maintenance personnel. Whether through real-time monitoring via a cloud platform or periodic manual data analysis, timely damage assessments can be provided, contributing to preventative maintenance and extending vehicle lifespan. Furthermore, damage data provides direct data support for vehicle repair strategies, spare parts replacement timing, and vehicle performance evaluation, reducing reliance on experience and guesswork.

[0237] Step S1105: Continuously monitor multiple damage levels of multiple parts;

[0238] Step S1106: In response to the detection that the damage degree of any one of the multiple parts exceeds a preset threshold, the part information of any one part is sent to the user terminal device.

[0239] In this embodiment, damage data of multiple vehicle parts is continuously collected and analyzed through real-time data streams from sensors, on-board diagnostic systems, or cloud platforms. This process may include data cleaning, damage model updates, and damage calculation.

[0240] Furthermore, a preset threshold is set to represent the safe upper limit of component damage. When the damage level of any component exceeds this threshold, an alarm mechanism is triggered, and immediate action is taken. Once the component damage level exceeds the threshold, the system will automatically or via a cloud platform send detailed information about the specific component, including the component name, damage level, and possible causes of damage, to the user's terminal device, such as a mobile phone, tablet, or in-vehicle display.

[0241] Optionally, the aforementioned user terminal equipment refers to a device that allows users to receive information about part damage, such as a mobile phone, tablet computer, or in-vehicle display system, used to receive and display alarm information to prompt users to take maintenance measures.

[0242] Specifically, based on the differentiated release relationship for damage to each assembly and component determined in the above steps, the equivalent proportion coefficient of damage to each component can be obtained, i.e., the verification mileage proportion relationship. By establishing a database of equivalent proportion coefficients of damage to each component, the sensorization of a single component can be extended to multiple components of the whole vehicle, all of which have sensorization characteristics. This enables the mutual calculation and conversion between the equivalent mileage of different components, and displays them in the form of percentage progress bars according to the preset classification principles. This achieves the comprehensiveness and coverage of load spectrum data analysis, and can guide drivers to carry out preventive inspections and maintenance.

[0243] In particular, this method can continuously accumulate a database of equivalent proportion coefficients of component damage under actual user operating conditions. If the user replaces a component, the component damage progress bar can be cleared. This method can provide technical support for enterprises to adjust after-sales service policies and carry out comprehensive repair services. At the same time, it provides an important basis for the residual value assessment of used cars, so as to give full play to the residual use value of vehicles.

[0244] Specifically, by monitoring the percentage of damage progress of components, drivers can access the road load spectrum and damage progress bar through menu options on the instrument panel or multimedia display screen. This allows drivers to understand in real time the load spectrum from the road surface that the vehicle's key components are subjected to, and to intuitively present the cumulative damage progress of components, thus achieving dynamic monitoring of component damage.

[0245] As an optional implementation, vehicle sensor data is uploaded to a cloud platform in real time, where the platform handles data processing and damage calculation. Furthermore, the cloud platform automatically compares the damage level with preset thresholds; if the threshold is exceeded, an alarm process is immediately triggered. After the alarm is triggered, the cloud platform automatically pushes information about the damaged parts to the user's registered mobile device or in-vehicle display system, ensuring the user receives timely notification.

[0246] As an alternative implementation, the vehicle is equipped with a built-in damage calculation system, which performs data processing and damage calculation directly within the vehicle's onboard system. Simultaneously, the onboard system continuously monitors component damage and incorporates a threshold detection mechanism. If a threshold is exceeded, the onboard alarm system is immediately activated. Alarm information can be displayed directly on the onboard screen or sent to the user's mobile phone via the vehicle's communication system, ensuring that the user receives critical information whether inside or outside the vehicle.

[0247] It is worth noting that the above technical steps enable continuous monitoring of vehicle component damage, eliminating the need for regular manual inspections and improving the timeliness and efficiency of monitoring. Furthermore, once component damage exceeds acceptable levels, the user is immediately notified, facilitating a rapid response and allowing for the scheduling of maintenance or replacement, thus preventing potential safety risks and performance degradation. In addition, the component damage information received by the user terminal provides direct data support for maintenance decisions, helping to develop maintenance plans based on actual conditions rather than experience or fixed schedules.

[0248] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or grid device, etc.) to execute the methods of the various embodiments of the present invention.

[0249] This embodiment also provides a device for determining the degree of damage to vehicle parts. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0250] Figure 2 This is a structural block diagram of a vehicle part damage determination device 200 according to one embodiment of the present invention, as shown below. Figure 2 As shown, the device includes: a first determining module 201, a second determining module 202, a third determining module 203, a fourth determining module 204, and a testing module 205.

[0251] The first determining module 201 is used to determine the first driving route according to user requirements, wherein the user requirements include terrain information, vehicle application scenario and vehicle driving area.

[0252] The second determining module 202 is used to determine a first damage coefficient based on a first damage matrix of a first driving route and a second damage matrix of a second driving route, wherein the length of the second driving route is less than the length of the first driving route.

[0253] The third determining module 203 is used to determine the second damage coefficient based on the second damage matrix and the third damage matrix of the third driving route, wherein the length of the third driving route is less than the length of the second driving route;

[0254] The fourth determining module 204 is used to determine the test conditions of the vehicle based on the first damage coefficient, the second damage coefficient and the mileage of the first driving route, wherein the test conditions include the total vehicle test mileage and test road condition information.

[0255] Test module 205 is used to control the vehicle to perform tests based on test conditions and continuously calculate multiple damage degrees of multiple parts of the vehicle.

[0256] Optionally, the vehicle part damage determination device 200 further includes: a first acquisition module for acquiring initial load spectrum data of a first driving route; a data processing module for performing data processing operations on the initial load spectrum data to obtain target load spectrum data; a slicing module for slicing the target load spectrum data according to the mileage data of the first driving route to obtain multiple mileage segments; a calculation module for calculating multiple axle head acceleration values ​​of multiple mileage segments; a fifth determination module for determining multiple road surface smoothness levels corresponding to multiple mileage segments according to the multiple axle head acceleration values ​​and an objective function, wherein the objective function is used to characterize the relationship between the axle head acceleration values ​​and the road surface smoothness level, and each mileage segment corresponds to one road surface smoothness level; and a sixth determination module for determining a second driving route according to the multiple road surface smoothness levels.

[0257] Optionally, the sixth determining module includes: a first determining unit, used to determine an initial second driving route based on multiple road surface smoothness levels; an analysis unit, used to perform power spectrum analysis on the target load spectrum data to obtain multiple power frequency bands; a first acquiring unit, used to acquire the number and type of sensors deployed on the vehicle; a second determining unit, used to determine multiple output channels based on the number and type of sensors; a first calculation unit, used to calculate multiple channel damage ratios of the multiple output channels based on the multiple power spectrum frequency bands, a first damage matrix, and the damage matrix of the initial second driving route, wherein each output channel corresponds to one channel damage ratio; a first comparison unit, used to compare the multiple channel damage ratios with a first threshold and a second threshold respectively to obtain a first comparison result; and a third determining unit, used to determine the initial second driving route as the second driving route in response to the first comparison result indicating that the multiple channel damage ratios are all greater than the first threshold and less than the second threshold.

[0258] Optionally, the vehicle part damage determination device 200 further includes: a second acquisition module for acquiring the spectral density of the test route and the vehicle speed; a seventh determination module for determining the time domain spectrum of the test route based on the spectral density and the vehicle speed; a first construction module for constructing an axle head acceleration response function based on the time domain spectrum; and a second construction module for constructing an objective function based on the axle head acceleration response function and the vehicle speed.

[0259] Optionally, the second acquisition module includes: a second acquisition unit for acquiring the spatial frequency, preset spatial frequency, and preset spectral density of the test route; a second comparison unit for comparing the spatial frequency and the preset spatial frequency to obtain a second comparison result; a second calculation unit for calculating the spectral density of the test route based on the spatial frequency, preset spatial frequency, preset spectral density, and a first spectral density index in response to the second comparison result indicating that the spatial frequency is less than or equal to the preset spatial frequency; and a third calculation unit for calculating the spectral density of the test route based on the spatial frequency, preset spatial frequency, preset spectral density, and a second spectral density index in response to the second comparison result indicating that the spatial frequency is greater than the preset spatial frequency, wherein the second spectral density index is less than the first spectral density index.

[0260] Optionally, the second determining module 202 includes: a construction unit for constructing a damage matrix equation based on a first damage matrix and a second damage matrix; a third calculation unit for calculating the damage retention percentage value of the vehicle based on multiple output channels and a damage function; and an iteration unit for iterating the damage retention percentage value based on the damage matrix equation to obtain a first damage coefficient.

[0261] Optionally, the test module 205 includes: a third acquisition unit for acquiring the vehicle's rated mileage and current mileage; a fourth determination unit for determining a target mileage damage value based on the rated mileage, the mileage of the first driving route, and the first damage matrix; a fifth determination unit for determining the current mileage damage value based on the current mileage and a preset mathematical model; and a fourth calculation unit for calculating multiple damage degrees of multiple parts based on the current mileage damage value and the target mileage damage value.

[0262] Optionally, the test module 205 further includes: a monitoring unit for continuously monitoring multiple damage levels of multiple parts; and a sending unit for sending part information of any part to a user terminal device in response to the detection that the damage level of any part among the multiple parts exceeds a preset threshold.

[0263] Embodiments of the present invention also provide a vehicle, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the above-described method for determining the degree of damage to vehicle parts.

[0264] Optionally, in this embodiment, the vehicle may be configured to store a computer program for performing the following steps:

[0265] Step S102: Determine the first driving route based on user needs, where user needs include terrain information, vehicle application scenarios, and vehicle driving areas.

[0266] Step S104: Determine the first damage coefficient based on the first damage matrix of the first driving route and the second damage matrix of the second driving route, wherein the length of the second driving route is less than the length of the first driving route.

[0267] Step S106: Determine the second damage coefficient based on the second damage matrix and the third damage matrix of the third driving route, wherein the length of the third driving route is less than the length of the second driving route;

[0268] Step S108: Determine the test conditions of the vehicle based on the first damage coefficient, the second damage coefficient, and the mileage of the first driving route. The test conditions include the total vehicle test mileage and test road condition information.

[0269] Step S110: Control the vehicle to perform tests based on test conditions and continuously calculate multiple damage degrees of multiple parts of the vehicle.

[0270] Optionally, the processor, when executing the program, also implements the following steps: acquiring initial load spectrum data of the first driving route; performing data processing operations on the initial load spectrum data to obtain target load spectrum data; slicing the target load spectrum data according to the mileage data of the first driving route to obtain multiple mileage segments; calculating multiple axle head acceleration values ​​for the multiple mileage segments; determining multiple road surface smoothness levels corresponding to the multiple mileage segments based on the multiple axle head acceleration values ​​and the objective function, wherein the objective function is used to characterize the relationship between the axle head acceleration values ​​and the road surface smoothness level, and each mileage segment corresponds to one road surface smoothness level; and determining the second driving route based on the multiple road surface smoothness levels.

[0271] Optionally, the processor, when executing the program, also implements the following steps: determining an initial second driving route based on multiple road surface smoothness levels; performing power spectrum analysis on the target load spectrum data to obtain multiple power spectrum frequency bands; acquiring the number and type of sensors deployed on the vehicle; determining multiple output channels based on the number and type of sensors; calculating multiple channel damage ratios for the multiple output channels based on the multiple power spectrum frequency bands, the first damage matrix, and the damage matrix of the initial second driving route, wherein each output channel corresponds to one channel damage ratio; comparing the multiple channel damage ratios with a first threshold and a second threshold respectively to obtain a first comparison result; and determining the initial second driving route as the second driving route in response to the first comparison result indicating that the multiple channel damage ratios are all greater than the first threshold and less than the second threshold.

[0272] Optionally, the processor may also perform the following steps when executing the program: obtaining the spectral density of the test route and the vehicle speed; determining the time-domain spectrum of the test route based on the spectral density and the vehicle speed; constructing the axle head acceleration response function based on the time-domain spectrum; and constructing the objective function based on the axle head acceleration response function and the vehicle speed.

[0273] Optionally, the processor, when executing the program, further implements the following steps: acquiring the spatial frequency, preset spatial frequency, and preset spectral density of the test route; comparing the spatial frequency and the preset spatial frequency to obtain a second comparison result; in response to the second comparison result indicating that the spatial frequency is less than or equal to the preset spatial frequency, calculating the spectral density of the test route based on the spatial frequency, preset spatial frequency, preset spectral density, and a first spectral density index; in response to the second comparison result indicating that the spatial frequency is greater than the preset spatial frequency, calculating the spectral density of the test route based on the spatial frequency, preset spatial frequency, preset spectral density, and a second spectral density index, wherein the second spectral density index is less than the first spectral density index.

[0274] Optionally, the processor may also perform the following steps when executing the program: constructing a damage matrix equation based on the first damage matrix and the second damage matrix; calculating the damage retention percentage of the vehicle based on multiple output channels and damage functions; and iterating the damage retention percentage based on the damage matrix equation to obtain the first damage coefficient.

[0275] Optionally, when the processor executes the program, it also performs the following steps: obtaining the vehicle's rated mileage and current mileage; determining the target mileage damage value based on the rated mileage, the mileage of the first driving route, and the first damage matrix; determining the current mileage damage value based on the current mileage and a preset mathematical model; and calculating multiple damage degrees for multiple parts based on the current mileage damage value and the target mileage damage value.

[0276] Optionally, when the processor executes the program, it also performs the following steps: continuously monitoring multiple damage levels of multiple parts; in response to detecting that the damage level of any one of the multiple parts exceeds a preset threshold, sending the part information of any one part to the user terminal device.

[0277] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0278] Embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the above-described method for determining the degree of damage to vehicle parts.

[0279] Optionally, in this embodiment, the electronic device may be configured to store a computer program for performing the following steps:

[0280] Step S102: Determine the first driving route based on user needs, where user needs include terrain information, vehicle application scenarios, and vehicle driving areas.

[0281] Step S104: Determine the first damage coefficient based on the first damage matrix of the first driving route and the second damage matrix of the second driving route, wherein the length of the second driving route is less than the length of the first driving route.

[0282] Step S106: Determine the second damage coefficient based on the second damage matrix and the third damage matrix of the third driving route, wherein the length of the third driving route is less than the length of the second driving route;

[0283] Step S108: Determine the test conditions of the vehicle based on the first damage coefficient, the second damage coefficient, and the mileage of the first driving route. The test conditions include the total vehicle test mileage and test road condition information.

[0284] Step S110: Control the vehicle to perform tests based on test conditions and continuously calculate multiple damage degrees of multiple parts of the vehicle.

[0285] Optionally, the processor, when executing the program, also implements the following steps: acquiring initial load spectrum data of the first driving route; performing data processing operations on the initial load spectrum data to obtain target load spectrum data; slicing the target load spectrum data according to the mileage data of the first driving route to obtain multiple mileage segments; calculating multiple axle head acceleration values ​​for the multiple mileage segments; determining multiple road surface smoothness levels corresponding to the multiple mileage segments based on the multiple axle head acceleration values ​​and the objective function, wherein the objective function is used to characterize the relationship between the axle head acceleration values ​​and the road surface smoothness level, and each mileage segment corresponds to one road surface smoothness level; and determining the second driving route based on the multiple road surface smoothness levels.

[0286] Optionally, the processor, when executing the program, also implements the following steps: determining an initial second driving route based on multiple road surface smoothness levels; performing power spectrum analysis on the target load spectrum data to obtain multiple power spectrum frequency bands; acquiring the number and type of sensors deployed on the vehicle; determining multiple output channels based on the number and type of sensors; calculating multiple channel damage ratios for the multiple output channels based on the multiple power spectrum frequency bands, the first damage matrix, and the damage matrix of the initial second driving route, wherein each output channel corresponds to one channel damage ratio; comparing the multiple channel damage ratios with a first threshold and a second threshold respectively to obtain a first comparison result; and determining the initial second driving route as the second driving route in response to the first comparison result indicating that the multiple channel damage ratios are all greater than the first threshold and less than the second threshold.

[0287] Optionally, the processor may also perform the following steps when executing the program: obtaining the spectral density of the test route and the vehicle speed; determining the time-domain spectrum of the test route based on the spectral density and the vehicle speed; constructing the axle head acceleration response function based on the time-domain spectrum; and constructing the objective function based on the axle head acceleration response function and the vehicle speed.

[0288] Optionally, the processor, when executing the program, further implements the following steps: acquiring the spatial frequency, preset spatial frequency, and preset spectral density of the test route; comparing the spatial frequency and the preset spatial frequency to obtain a second comparison result; in response to the second comparison result indicating that the spatial frequency is less than or equal to the preset spatial frequency, calculating the spectral density of the test route based on the spatial frequency, preset spatial frequency, preset spectral density, and a first spectral density index; in response to the second comparison result indicating that the spatial frequency is greater than the preset spatial frequency, calculating the spectral density of the test route based on the spatial frequency, preset spatial frequency, preset spectral density, and a second spectral density index, wherein the second spectral density index is less than the first spectral density index.

[0289] Optionally, the processor may also perform the following steps when executing the program: constructing a damage matrix equation based on the first damage matrix and the second damage matrix; calculating the damage retention percentage of the vehicle based on multiple output channels and damage functions; and iterating the damage retention percentage based on the damage matrix equation to obtain the first damage coefficient.

[0290] Optionally, when the processor executes the program, it also performs the following steps: obtaining the vehicle's rated mileage and current mileage; determining the target mileage damage value based on the rated mileage, the mileage of the first driving route, and the first damage matrix; determining the current mileage damage value based on the current mileage and a preset mathematical model; and calculating multiple damage degrees for multiple parts based on the current mileage damage value and the target mileage damage value.

[0291] Optionally, when the processor executes the program, it also performs the following steps: continuously monitoring multiple damage levels of multiple parts; in response to detecting that the damage level of any one of the multiple parts exceeds a preset threshold, sending the part information of any one part to the user terminal device.

[0292] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0293] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program configured to execute the above-described method for determining the degree of damage to vehicle parts when run on a computer or processor.

[0294] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0295] Step S102: Determine the first driving route based on user needs, where user needs include terrain information, vehicle application scenarios, and vehicle driving areas.

[0296] Step S104: Determine the first damage coefficient based on the first damage matrix of the first driving route and the second damage matrix of the second driving route, wherein the length of the second driving route is less than the length of the first driving route.

[0297] Step S106: Determine the second damage coefficient based on the second damage matrix and the third damage matrix of the third driving route, wherein the length of the third driving route is less than the length of the second driving route;

[0298] Step S108: Determine the test conditions of the vehicle based on the first damage coefficient, the second damage coefficient, and the mileage of the first driving route. The test conditions include the total vehicle test mileage and test road condition information.

[0299] Step S110: Control the vehicle to perform tests based on test conditions and continuously calculate multiple damage degrees of multiple parts of the vehicle.

[0300] Optionally, the storage medium is configured to store program code for performing the following steps: acquiring initial load spectrum data of a first driving route; performing data processing operations on the initial load spectrum data to obtain target load spectrum data; slicing the target load spectrum data according to the mileage data of the first driving route to obtain multiple mileage segments; calculating multiple axle head acceleration values ​​for the multiple mileage segments; determining multiple road surface smoothness levels corresponding to the multiple mileage segments based on the multiple axle head acceleration values ​​and an objective function, wherein the objective function is used to characterize the relationship between the axle head acceleration values ​​and the road surface smoothness level, and each mileage segment corresponds to one road surface smoothness level; and determining a second driving route based on the multiple road surface smoothness levels.

[0301] Optionally, the storage medium is configured to store program code for performing the following steps: determining an initial second driving route based on multiple road surface smoothness levels; performing power spectrum analysis on the target load spectrum data to obtain multiple power spectrum frequency bands; obtaining the number and type of sensors deployed on the vehicle; determining multiple output channels based on the number and type of sensors; calculating multiple channel damage ratios for the multiple output channels based on the multiple power spectrum frequency bands, a first damage matrix, and the damage matrix of the initial second driving route, wherein each output channel corresponds to one channel damage ratio; comparing the multiple channel damage ratios with a first threshold and a second threshold respectively to obtain a first comparison result; and determining the initial second driving route as the second driving route in response to the first comparison result indicating that the multiple channel damage ratios are all greater than the first threshold and less than the second threshold.

[0302] Optionally, the storage medium is configured to store program code for performing the following steps: obtaining the spectral density of the test route and the vehicle speed; determining the time-domain spectrum of the test route based on the spectral density and the vehicle speed; constructing the axle head acceleration response function based on the time-domain spectrum; and constructing an objective function based on the axle head acceleration response function and the vehicle speed.

[0303] Optionally, the storage medium is configured to store program code for performing the following steps: obtaining the spatial frequency, preset spatial frequency, and preset spectral density of the test route; comparing the spatial frequency and the preset spatial frequency to obtain a second comparison result; in response to the second comparison result indicating that the spatial frequency is less than or equal to the preset spatial frequency, calculating the spectral density of the test route based on the spatial frequency, the preset spatial frequency, the preset spectral density, and a first spectral density index; in response to the second comparison result indicating that the spatial frequency is greater than the preset spatial frequency, calculating the spectral density of the test route based on the spatial frequency, the preset spatial frequency, the preset spectral density, and a second spectral density index, wherein the second spectral density index is less than the first spectral density index.

[0304] Optionally, the storage medium is configured to store program code for performing the following steps: constructing a damage matrix equation based on a first damage matrix and a second damage matrix; calculating the damage retention percentage value of the vehicle based on multiple output channels and a damage function; and iterating the damage retention percentage value according to the damage matrix equation to obtain a first damage coefficient.

[0305] Optionally, the storage medium is configured to store program code for performing the following steps: obtaining the vehicle's rated mileage and current mileage; determining a target mileage damage value based on the rated mileage, the mileage of a first driving route, and a first damage matrix; determining the current mileage damage value based on the current mileage and a preset mathematical model; and calculating multiple damage degrees for multiple parts based on the current mileage damage value and the target mileage damage value.

[0306] Optionally, the storage medium is configured to store program code for performing the following steps: continuously monitoring multiple damage levels of multiple parts; in response to detecting that the damage level of any of the multiple parts exceeds a preset threshold, sending the part information of any part to a user terminal device.

[0307] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0308] Embodiments of the present invention also provide a computer program product, including a computer program, wherein the computer program, when executed by a processor, implements the steps of the above-described method for determining the degree of damage to vehicle parts.

[0309] Optionally, in this embodiment, the computer program product described above may be configured to store a computer program for performing the following steps:

[0310] Step S102: Determine the first driving route based on user needs, where user needs include terrain information, vehicle application scenarios, and vehicle driving areas.

[0311] Step S104: Determine the first damage coefficient based on the first damage matrix of the first driving route and the second damage matrix of the second driving route, wherein the length of the second driving route is less than the length of the first driving route.

[0312] Step S106: Determine the second damage coefficient based on the second damage matrix and the third damage matrix of the third driving route, wherein the length of the third driving route is less than the length of the second driving route;

[0313] Step S108: Determine the test conditions of the vehicle based on the first damage coefficient, the second damage coefficient, and the mileage of the first driving route. The test conditions include the total vehicle test mileage and test road condition information.

[0314] Step S110: Control the vehicle to perform tests based on test conditions and continuously calculate multiple damage degrees of multiple parts of the vehicle.

[0315] Optionally, the computer program may further implement the following steps when executing the program: acquiring initial load spectrum data of the first driving route; performing data processing operations on the initial load spectrum data to obtain target load spectrum data; slicing the target load spectrum data according to the mileage data of the first driving route to obtain multiple mileage segments; calculating multiple axle head acceleration values ​​for the multiple mileage segments; determining multiple road surface smoothness levels corresponding to the multiple mileage segments based on the multiple axle head acceleration values ​​and the objective function, wherein the objective function is used to characterize the relationship between the axle head acceleration values ​​and the road surface smoothness level, and each mileage segment corresponds to one road surface smoothness level; and determining the second driving route based on the multiple road surface smoothness levels.

[0316] Optionally, the computer program, when executing the program, also implements the following steps: determining an initial second driving route based on multiple road surface smoothness levels; performing power spectrum analysis on the target load spectrum data to obtain multiple power frequency bands; acquiring the number and type of sensors deployed on the vehicle; determining multiple output channels based on the number and type of sensors; calculating multiple channel damage ratios for the multiple output channels based on the multiple power spectrum frequency bands, the first damage matrix, and the damage matrix of the initial second driving route, wherein each output channel corresponds to one channel damage ratio; comparing the multiple channel damage ratios with a first threshold and a second threshold respectively to obtain a first comparison result; and determining the initial second driving route as the second driving route in response to the first comparison result indicating that the multiple channel damage ratios are all greater than the first threshold and less than the second threshold.

[0317] Optionally, the computer program may also perform the following steps when executing the program: obtaining the spectral density of the test route and the vehicle speed; determining the time domain spectrum of the test route based on the spectral density and the vehicle speed; constructing the axle head acceleration response function based on the time domain spectrum; and constructing the objective function based on the axle head acceleration response function and the vehicle speed.

[0318] Optionally, when the computer program executes the program, it further implements the following steps: obtaining the spatial frequency, preset spatial frequency, and preset spectral density of the test route; comparing the spatial frequency and the preset spatial frequency to obtain a second comparison result; in response to the second comparison result indicating that the spatial frequency is less than or equal to the preset spatial frequency, calculating the spectral density of the test route based on the spatial frequency, the preset spatial frequency, the preset spectral density, and a first spectral density index; in response to the second comparison result indicating that the spatial frequency is greater than the preset spatial frequency, calculating the spectral density of the test route based on the spatial frequency, the preset spatial frequency, the preset spectral density, and a second spectral density index, wherein the second spectral density index is less than the first spectral density index.

[0319] Optionally, when the computer program executes the program, it also performs the following steps: constructing a damage matrix equation based on the first damage matrix and the second damage matrix; calculating the damage retention percentage value of the vehicle based on multiple output channels and damage functions; and iterating the damage retention percentage value based on the damage matrix equation to obtain the first damage coefficient.

[0320] Optionally, when the computer program executes the program, it also performs the following steps: obtaining the vehicle's rated mileage and current mileage; determining the target mileage damage value based on the rated mileage, the mileage of the first driving route, and the first damage matrix; determining the current mileage damage value based on the current mileage and a preset mathematical model; and calculating multiple damage degrees for multiple parts based on the current mileage damage value and the target mileage damage value.

[0321] Optionally, when the computer program executes the program, it also performs the following steps: continuously monitoring multiple damage levels of multiple parts; in response to detecting that the damage level of any one of the multiple parts exceeds a preset threshold, sending the part information of any one part to the user terminal device.

[0322] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0323] In the embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0324] The units described as separate components may or may not be physically separate. Similarly, the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0325] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0326] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0327] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the degree of damage to vehicle parts, characterized in that, include: The first driving route is determined based on user needs, wherein the user needs include terrain information, vehicle application scenarios, and vehicle driving areas. A first damage coefficient is determined based on a first damage matrix of the first driving route and a second damage matrix of the second driving route, wherein the length of the second driving route is less than the length of the first driving route; A second damage coefficient is determined based on the second damage matrix and the third damage matrix of the third driving route, wherein the length of the third driving route is less than the length of the second driving route; The test conditions of the vehicle are determined based on the first damage coefficient, the second damage coefficient, and the mileage of the first driving route, wherein the test conditions include the total vehicle test mileage and test road condition information. The vehicle is controlled to perform tests based on the test conditions, and multiple damage degrees of multiple parts of the vehicle are continuously calculated.

2. The method for determining the degree of damage to vehicle parts according to claim 1, characterized in that, The method further includes: Obtain the initial load spectrum data of the first driving route; Perform data processing operations on the initial load spectrum data to obtain the target load spectrum data; The target load spectrum data is sliced ​​based on the mileage data of the first driving route to obtain multiple mileage segments; Calculate multiple shaft head acceleration values ​​for the multiple mileage segments; Multiple road surface smoothness grades corresponding to the multiple mileage segments are determined based on the multiple axle head acceleration values ​​and the objective function, wherein the objective function is used to characterize the relationship between the axle head acceleration values ​​and the road surface smoothness grades, and each mileage segment corresponds to one road surface smoothness grade; The second driving route is determined based on the multiple road surface smoothness grades.

3. The method for determining the degree of damage to vehicle parts according to claim 2, characterized in that, Determining the second driving route based on the multiple road surface smoothness grades includes: The initial second driving route is determined based on the multiple road surface smoothness levels; Power spectrum analysis was performed on the target load spectrum data to obtain multiple power spectrum frequency bands; Obtain the number and type of sensors deployed on the vehicle; Multiple output channels are determined based on the number and type of sensors; The multiple channel damage ratios of the multiple output channels are calculated based on the multiple power spectrum frequency bands, the first damage matrix, and the damage matrix of the initial second driving route, wherein each output channel corresponds to one channel damage ratio. The multiple channel damage ratios are compared with a first threshold and a second threshold respectively to obtain a first comparison result; In response to the first comparison result indicating that the damage ratios of the multiple channels are all greater than the first threshold and less than the second threshold, the initial second driving route is determined to be the second driving route.

4. The method for determining the degree of damage to vehicle parts according to claim 2, characterized in that, The method further includes: Obtain the spectral density of the test route and the vehicle speed; The time-domain spectrum of the test route is determined based on the spectral density and the vehicle speed. Construct the shaft head acceleration response function based on the time-domain spectrum; The objective function is constructed based on the axle head acceleration response function and the vehicle speed.

5. The method for determining the degree of damage to vehicle parts according to claim 4, characterized in that, Obtaining the spectral density of the test route includes: Obtain the spatial frequency, preset spatial frequency, and preset spectral density of the test route; The spatial frequency is compared with the preset spatial frequency to obtain a second comparison result; In response to the second comparison result indicating that the spatial frequency is less than or equal to the preset spatial frequency, the spectral density of the test route is calculated based on the spatial frequency, the preset spatial frequency, the preset spectral density, and the first spectral density index. In response to the second comparison result indicating that the spatial frequency is greater than the preset spatial frequency, the spectral density of the test route is calculated based on the spatial frequency, the preset spatial frequency, the preset spectral density, and the second spectral density index, wherein the second spectral density index is less than the first spectral density index.

6. The method for determining the degree of damage to vehicle parts according to claim 3, characterized in that, Determining the first damage coefficient based on the first damage matrix and the second damage matrix includes: Construct a damage matrix equation based on the first damage matrix and the second damage matrix; The damage retention percentage of the vehicle is calculated based on the multiple output channels and the damage function; The first damage coefficient is obtained by iterating the damage retention percentage value according to the damage matrix equation.

7. The method for determining the degree of damage to vehicle parts according to claim 1, characterized in that, Calculating multiple damage degrees for multiple parts of the vehicle includes: Obtain the vehicle's rated mileage and current mileage; The target mileage damage value is determined based on the rated driving mileage, the mileage of the first driving route, and the first damage matrix; The current mileage damage value is determined based on the current mileage and a preset mathematical model. The damage levels of the multiple parts are calculated based on the current mileage damage value and the target mileage damage value.

8. The method for determining the degree of damage to vehicle parts according to claim 7, characterized in that, The method further includes: Continuously monitor multiple damage levels of the multiple components; In response to the detection that the damage level of any of the plurality of parts exceeds a preset threshold, the part information of any of the parts is sent to the user terminal device.

9. A device for determining the degree of damage to vehicle parts, characterized in that, include: The first determining module is used to determine a first driving route based on user needs, wherein the user needs include terrain information, vehicle application scenarios, and vehicle driving areas. The second determining module is used to determine a first damage coefficient based on the first damage matrix of the first driving route and the second damage matrix of the second driving route, wherein the length of the second driving route is less than the length of the first driving route; The third determining module is used to determine the second damage coefficient based on the second damage matrix and the third damage matrix of the third driving route, wherein the length of the third driving route is less than the length of the second driving route; The fourth determining module is used to determine the test conditions of the vehicle based on the first damage coefficient, the second damage coefficient, and the mileage of the first driving route, wherein the test conditions include the total vehicle test mileage and test road condition information; The testing module is used to control the vehicle to perform tests based on the test conditions and to continuously calculate multiple damage degrees of multiple parts of the vehicle.

10. A vehicle comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method for determining the degree of damage to vehicle parts as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute, when run on a computer or processor, the method for determining the degree of damage to vehicle parts as described in any one of claims 1 to 8.

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