Vehicle performance evaluation method, electronic equipment, medium and computer program product

Abstract

The invention provides a vehicle performance evaluation method, electronic equipment, a medium and a computer program product, and relates to the technical field of data processing. The method comprises the following steps: acquiring vehicle data of a plurality of vehicles; the plurality of vehicles comprise to-be-evaluated modules of the same model; the vehicle data comprises a failure identifier and vehicle mileage data; determining a failed vehicle and a non-failed vehicle based on the failure identifier; the failed vehicle is a vehicle which cannot be normally used due to the fault of the to-be-evaluated module; and calculating a reliability index of the to-be-evaluated module by using the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle, so as to determine the performance of the vehicle by using the reliability index. According to the method, the vehicle performance is evaluated by combining the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle, so that the evaluation accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a vehicle performance evaluation method, electronic equipment, medium and computer program product. Background Art

[0002] As global climate change and environmental problems intensify, more and more countries and regions are beginning to promote environmental protection technologies and green energy to reduce environmental pollution and dependence on fossil fuels. By developing new energy vehicles, we can reduce dependence on oil and improve energy security.

[0003] With the development of the new energy vehicle industry and the continuous increase in market share, the reliability of new energy vehicle models has gradually become a hot topic in industry research. Therefore, how to improve the accuracy of vehicle performance evaluation is an urgent problem to be solved. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a vehicle performance evaluation method, electronic device, medium and computer program product for evaluating the performance of a vehicle by combining the vehicle mileage data of a failed vehicle and the vehicle mileage data of a non-failed vehicle to improve the accuracy of the evaluation.

[0005] In a first aspect, an embodiment of the present application provides a vehicle performance evaluation method, the method comprising: acquiring vehicle data of multiple vehicles; the multiple vehicles include a module to be evaluated of the same model; the vehicle data includes a failure mark and vehicle mileage data; determining failed vehicles and non-failed vehicles based on the failure mark; a failed vehicle is a vehicle that cannot be used normally due to a failure in the module to be evaluated; and calculating a reliability index of the module to be evaluated using the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle, so as to determine the performance of the vehicle using the reliability index.

[0006] The embodiment of the present application calculates the reliability index of the module to be evaluated by combining the vehicle mileage data of failed vehicles and the vehicle mileage data of non-failed vehicles. Since more comprehensive data is taken into account, the errors that may be caused by a single data source are avoided, thereby improving the accuracy of the evaluation.

[0007] In some embodiments, the reliability index of the module to be evaluated is calculated using the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle, including: calculating the shape parameter and scale parameter of the Weibull distribution based on the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle; calculating the reliability index of the module to be evaluated based on the shape parameter and the scale parameter.

[0008] The embodiment of the present application calculates the reliability of the module to be evaluated by calculating the shape parameters and scale parameters of the Weibull distribution. Since the Weibull distribution has good adaptability and flexibility, it can accurately reflect the failure characteristics of products at different stages, so as to facilitate the evaluation of the reliability of the module to be evaluated.

[0009] In some embodiments, calculating the reliability index of the module to be evaluated according to the shape parameter and the scale parameter includes: using an index calculation formula to calculate the reliability index of the module to be evaluated according to the shape parameter and the scale parameter; the index calculation formula is:

[0010]

[0011] Where R(t) represents the probability that the module to be evaluated has no failure when the random variable is t, t represents the random variable, β represents the shape parameter, and α represents the scale parameter.

[0012] The embodiment of the present application uses a formula to analyze and interpret the reliability index of the module to be evaluated, so as to obtain the result in a scientific way, thereby improving the accuracy of the evaluation.

[0013] In some embodiments, the module to be evaluated includes multiple components to be evaluated; after determining failed vehicles and non-failed vehicles based on failure identification, the method also includes: classifying failed vehicles according to the components to be evaluated to obtain multiple categories of failed vehicles; accordingly, using the vehicle mileage data of failed vehicles and the vehicle mileage data of non-failed vehicles to calculate the reliability index of the module to be evaluated, including: using the vehicle mileage data of each category of failed vehicles and the vehicle mileage data of non-failed vehicles to calculate the reliability index of the corresponding category of components to be evaluated to obtain multiple first index values; calculating the reliability index of the module to be evaluated based on the multiple first index values.

[0014] The embodiment of the present application classifies the modules to be evaluated based on the components to be evaluated, thereby calculating the reliability index of the corresponding module to be evaluated based on the reliability index of each category of the components to be evaluated, thereby improving the accuracy of performance evaluation.

[0015] In some embodiments, determining the reliability index of the module to be evaluated based on multiple first index values ​​includes: taking the first index value with the smallest index value among the multiple first index values ​​as the reliability index of the module to be evaluated.

[0016] In the embodiment of the present application, the smallest first indicator value among the first indicator values ​​is used as the reliability indicator of the module to be evaluated, so that it can be clearly known that improving the performance problem of the component to be evaluated can better solve the performance problem of the module to be evaluated.

[0017] In some embodiments, each component to be evaluated corresponds to a first weight value; determining the reliability index of the module to be evaluated based on multiple first index values ​​includes: calculating the reliability index of the module to be evaluated based on each first index value and the corresponding first weight value.

[0018] The embodiment of the present application can avoid the disadvantage of neglecting other important indicators due to the prominence of a single indicator by comprehensively considering the performance of each component to be evaluated, thereby obtaining a more comprehensive evaluation result.

[0019] In some embodiments, the component to be evaluated includes multiple failure types; each failure type corresponds to a second weight value; after obtaining multiple categories of failed vehicles, the method also includes: for each category of failed vehicles, reclassifying based on the failure type to obtain multiple types of failed vehicles; accordingly, using the vehicle mileage data of each category of failed vehicles and the vehicle mileage data of non-failed vehicles to calculate the reliability index of the corresponding category of the component to be evaluated, including: calculating the reliability index of the corresponding type based on the vehicle mileage data of each type of failed vehicles and the vehicle mileage data of non-failed vehicles to obtain multiple second index values; calculating the reliability index of the component to be evaluated according to each second index value and the corresponding second weight value.

[0020] The embodiment of the present application further subdivides the components to be evaluated based on the failure type, so that the analysis granularity is higher, thereby improving the accuracy of the evaluation.

[0021] In some embodiments, obtaining vehicle data of multiple vehicles includes: obtaining original vehicle data of the multiple vehicles; and identifying and removing abnormal values ​​in the original vehicle data to obtain vehicle data.

[0022] The embodiment of the present application improves the accuracy of the data by removing outliers in the original vehicle data, thereby improving the accuracy of the evaluation.

[0023] In a second aspect, an embodiment of the present application provides a vehicle performance evaluation device, which includes: an acquisition module for acquiring vehicle data of multiple vehicles; the multiple vehicles include a module to be evaluated of the same model; the vehicle data includes a failure mark and vehicle mileage data; a determination module for determining a failed vehicle and a non-failed vehicle based on the failure mark; a failed vehicle is a vehicle that cannot be used normally due to a fault in the module to be evaluated; a calculation module for calculating a reliability index of the module to be evaluated using the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle, so as to determine the performance of the vehicle using the reliability index.

[0024] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, a storage medium and a bus, wherein the processor and the memory communicate with each other through the bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method steps of the first aspect.

[0025] In a fourth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium, comprising: the computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method steps of the first aspect.

[0026] In a fifth aspect, an embodiment of the present application provides a computer program product, including: a computer program, which executes the method steps of the first aspect when executed by a processor.

[0027] Other features and advantages of the present application will be described in the subsequent description, and in part will become apparent from the description, or will be understood by implementing the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 A schematic diagram of a process flow of a vehicle performance evaluation method provided in an embodiment of the present application;

[0030] Figure 2 A schematic diagram of the structure of a vehicle performance evaluation device provided in an embodiment of the present application;

[0031] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0033] It should be noted that all technical and scientific terms used in this document have the same meanings as those generally understood by technicians in the technical field to which this application belongs; the terms used in this document are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0034] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0035] Figure 1 A flow chart of a vehicle performance evaluation method provided in an embodiment of the present application. It can be understood that the method can be applied to a terminal device (also referred to as an electronic device) and a server; wherein the terminal device can specifically be a smart phone, a tablet computer, a computer, a personal digital assistant (PDA), etc.; the server can specifically be an application server or a Web server.

[0036] To facilitate understanding of the technical solution provided by the embodiment of the present application, the application scenario of the vehicle performance evaluation method provided by the embodiment of the present application is introduced below by taking the server as the execution subject as an example. Figure 1 As shown, the method includes:

[0037] Step S101, the server obtains vehicle data of multiple vehicles; the multiple vehicles include a module to be evaluated of the same model; the vehicle data includes a failure mark and vehicle mileage data.

[0038] In step S101, the operation and maintenance personnel sort out the after-sales data after the user purchases the vehicle to obtain the vehicle data of multiple vehicles including the same model of the module to be evaluated, and then input the sorted vehicle data of the multiple vehicles to enable the server to obtain the vehicle data of the multiple vehicles. The after-sales data is stored in the data server.

[0039] However, due to errors in data transmission or data truncation during the process of collecting vehicle data by the data server, the acquired vehicle data may contain format errors or obviously erroneous values, which will affect subsequent data statistics and analysis. Therefore, it is necessary to eliminate the outliers in the acquired vehicle data.

[0040] Therefore, obtaining vehicle data of multiple vehicles includes: obtaining original vehicle data of multiple vehicles; identifying and removing abnormal values ​​in the original vehicle data to obtain vehicle data. The original vehicle data is vehicle data that has not been preprocessed.

[0041] The module to be evaluated refers to a specific component or system in the vehicle whose reliability needs to be analyzed. For example, the module to be evaluated is at least one of the reducer structure or the motor structure of the vehicle.

[0042] The failure mark is used to indicate whether the module to be evaluated of the vehicle has failed. For example, "1" indicates that the module to be evaluated of the vehicle has failed, and "0" indicates that the module to be evaluated of the vehicle has not failed. In addition to using numerical form for marking, character form can also be used for marking, for example, "A" indicates that the module to be evaluated of the vehicle has failed, and "B" indicates that the module to be evaluated of the vehicle has not failed.

[0043] The vehicle mileage data indicates the mileage of the vehicle.

[0044] In addition to the expiration mark and vehicle mileage data, vehicle data may also include the following data:

[0045] Vehicle identification information: Vehicle identification number (VIN), license plate number, make, model, year, etc.

[0046] Engine performance data: engine speed (RPM), torque, power output, fuel consumption rate, ignition timing, etc.

[0047] Driving data: speed, acceleration, driving distance (mileage), average speed, maximum speed, etc.

[0048] Fuel system data: fuel level, fuel pressure, fuel flow, fuel efficiency, etc.

[0049] Emission data: exhaust emission composition, emission volume, compliance with emission standards, etc.

[0050] Safety system data: airbag deployment status, anti-lock braking system (ABS), electronic stability control (ESC), etc.

[0051] Environmental data: external temperature, humidity, air pressure, light intensity, etc.

[0052] Location and navigation data: latitude and longitude coordinates, direction, altitude, speed (GPS data), route information, etc.

[0053] On-Board Diagnostics (OBD) data: fault codes, sensor status, system warnings, etc.

[0054] Vehicle usage: trip records, parking data, driving habits (such as frequency of sudden acceleration and deceleration), etc.

[0055] In-vehicle infotainment system data: audio, video, communication and navigation usage, etc.

[0056] Tire and suspension data: tire pressure, temperature, wear status, suspension load, etc.

[0057] Electrical system data: battery voltage, current, electrical load, state of charge, etc.

[0058] Air conditioning system data: air conditioning settings, temperature difference between inside and outside, air flow, etc.

[0059] Repair and maintenance data: service history, replaced parts, maintenance intervals, etc.

[0060] Driver assistance system data: status of automated driving assistance features, sensor inputs, system responses, etc.

[0061] Vehicle communication data: communication records between vehicles and other vehicles or infrastructure, such as Internet of Vehicles data.

[0062] Step S102, the server determines a failed vehicle and a non-failed vehicle based on the failure identifier; a failed vehicle is a vehicle that cannot be used normally due to a failure of the module to be evaluated.

[0063] In step S102, the server determines the invalid vehicles and the non-invalid vehicles among the multiple vehicles by identifying the invalid identification. Table 1 is a vehicle data table of multiple vehicles provided in an embodiment of the present application. As shown in Table 1, the invalid vehicles and the non-invalid vehicles among the multiple vehicles, as well as the vehicle mileage data of the invalid vehicles and the vehicle mileage data of the non-invalid vehicles can be determined based on the invalid identification.

[0064] The data shown in Table 1 are part of the real data after being blurred. They are only used as examples for explanation and have no practical significance.

[0065] Table 1

[0066]

[0067]

[0068] The failed vehicle is a vehicle that cannot be used normally due to a failure of the module to be evaluated. The vehicle mileage data of the failed vehicle refers to the mileage that the vehicle has traveled when the module to be evaluated fails.

[0069] For example, after a vehicle has traveled 400,000 kilometers, the module to be evaluated of the vehicle fails. At this time, the vehicle is a failed vehicle, and its vehicle mileage data is 400,000 kilometers.

[0070] By obtaining the vehicle mileage data of the failed vehicle, it is helpful to analyze the specific cause of the failure of the module to be evaluated, thereby improving the design of the module to be evaluated, improving the manufacturing quality, and optimizing and maintaining the module to be evaluated.

[0071] A non-failed vehicle refers to a vehicle whose module to be evaluated has not failed. The vehicle mileage data of a non-failed vehicle refers to the mileage of the vehicle corresponding to the statistical cut-off time. The "statistical cut-off time" refers to the data used for recording or statistics ending at a specific time point to ensure that the collection of all vehicle data ends at this time point. The statistical cut-off time is set manually.

[0072] Since the vehicle mileage data of the non-failed vehicles provides the operating mileage information of the vehicle's module to be evaluated under normal use conditions, the durability of the vehicle's module to be evaluated can be understood by analyzing these data.

[0073] In step S103, the server calculates the reliability index of the module to be evaluated by using the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle, so as to determine the performance of the vehicle by using the reliability index.

[0074] In step S103, the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle are imported into the statistical analysis software so that the server calculates the reliability index of the module to be evaluated using the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle.

[0075] For example, the vehicle mileage data of failed vehicles and the vehicle mileage data of non-failed vehicles can be imported into Excel, R language, MATLAB or other professional software for analysis.

[0076] Reliability indicators may refer to the life of the module to be evaluated, or the failure rate under warranty vehicle mileage, etc.

[0077] When the server uses the vehicle mileage data of failed vehicles and the vehicle mileage data of non-failed vehicles to calculate the reliability index of the module to be evaluated, the vehicle mileage data of the failed vehicles and the vehicle mileage data of non-failed vehicles can be fitted by Weibull distribution or lognormal distribution and the distribution parameters can be estimated, and then the reliability index of the module to be evaluated can be calculated using the distribution parameters.

[0078] It should be noted that although the reliability index of the module to be evaluated is calculated, since the module to be evaluated belongs to the vehicle, the performance of the vehicle can be evaluated by using the reliability index of the module to be evaluated.

[0079] The embodiment of the present application calculates the reliability index of the module to be evaluated by combining the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle, thereby evaluating the performance of the vehicle based on the reliability index, thereby improving the evaluation accuracy.

[0080] In some embodiments, the server uses the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle to calculate the reliability index of the module to be evaluated, including: calculating the shape parameter and scale parameter of the Weibull distribution based on the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle; calculating the reliability index of the module to be evaluated according to the shape parameter and the scale parameter.

[0081] In the specific implementation process, the process of estimating the shape parameter and scale parameter of the Weibull distribution from the vehicle mileage data of the failed vehicles and the vehicle mileage data of the non-failed vehicles by maximum likelihood estimation is as follows:

[0082] The probability density function of the Weibull distribution is Among them, t represents a random variable, β represents a shape parameter, and α represents a scale parameter.

[0083] Likelihood function construction: According to the maximum likelihood function estimation principle, construct the likelihood function L(β,α), which is expressed as the product of the probability density of the data points corresponding to all random variables:

[0084]

[0085] By taking partial derivatives of the likelihood function with respect to β and α respectively and setting the derivatives equal to 0, we can obtain a system of equations with respect to β and α.

[0086] Solve the equations using methods such as Newton's method, gradient descent, or related functions in optimization software and algorithm packages to obtain estimates of β and α, and thus the shape and scale parameters of the Weibull distribution. For example, the fminsearch function in MATLAB can be used to find the parameter value that maximizes the log-likelihood function.

[0087] The reliability index of the module to be evaluated is calculated based on the shape parameter and scale parameter using the index calculation formula; the index calculation formula is:

[0088]

[0089] Where R(t) represents the probability that the module to be evaluated has no failure when the random variable is t, t represents the random variable, β represents the shape parameter, and α represents the scale parameter.

[0090] For example, the shape parameter β of the Weibull distribution is estimated to be 1.38 and the scale parameter α is 2140000 from the vehicle mileage data of the failed vehicles and the vehicle mileage data of the non-failed vehicles by maximum likelihood estimation. 90% of the vehicles among the multiple vehicles are not failed, that is, R(t)=0.9. Substituting R(t)=0.9, β=1.38, α=2140000 into the indicator calculation formula, t is approximately equal to 403,000 kilometers. From this result, it can be known that the modules to be evaluated of 10% of the vehicles fail after the vehicles have traveled 403,000 kilometers.

[0091] In some embodiments, the module to be evaluated includes multiple components to be evaluated; after determining failed vehicles and non-failed vehicles based on failure identification, the method also includes: classifying failed vehicles according to the components to be evaluated to obtain multiple categories of failed vehicles; accordingly, using the vehicle mileage data of failed vehicles and the vehicle mileage data of non-failed vehicles to calculate the reliability index of the module to be evaluated, including: using the vehicle mileage data of each category of failed vehicles and the vehicle mileage data of non-failed vehicles to calculate the reliability index of the corresponding category of components to be evaluated to obtain multiple first index values; calculating the reliability index of the module to be evaluated based on the multiple first index values.

[0092] It can be seen from the above embodiments that the module to be evaluated is a specific component or system in the vehicle. For example, the module to be evaluated can be a reducer structure. The reducer structure is composed of multiple components. For example, the reducer structure includes gears, bearings, input shafts, output shafts, housings and other components.

[0093] Therefore, the module to be evaluated includes multiple components to be evaluated, and failure of any component to be evaluated may cause failure of the corresponding module to be evaluated, thereby causing vehicle failure.

[0094] Furthermore, different components to be evaluated have different positions and functions in the module to be evaluated, which may result in differences in the timing of failure of the components to be evaluated.

[0095] For example, bearing failure can cause the reducer to have difficulty in rotating, position deviation, temperature rise, etc. Gear failure can cause the transmission efficiency of the reducer to decrease, noise to increase, vibration to intensify, etc.

[0096] Therefore, the failed vehicles are classified based on the components to be evaluated to obtain multiple categories of failed vehicles. For each category of failed vehicles, the reliability index of the corresponding category of the components to be evaluated is calculated using the vehicle mileage data of each category of failed vehicles and the vehicle mileage data of non-failed vehicles to obtain a first index value. Thus, the reliability index of the module to be evaluated is calculated based on multiple first index values.

[0097] As shown in Table 1, the failure category can be used to determine which category of failure the module to be evaluated belongs to.

[0098] For example, a failed vehicle due to a gear fault is classified as a failure category 2, and a failed vehicle due to a bearing fault is classified as a failure category 3.

[0099] For the failure 2 category, through the maximum likelihood estimation of the above embodiment, based on the vehicle mileage data of the corresponding failed vehicles and the vehicle mileage data of the non-failed vehicles, it is estimated that the shape parameter of the Weibull distribution is 1.42 and the scale parameter is 2262400.

[0100] If 90% of the vehicles in the failure 2 category have not failed, that is, R(t) = 0.9, substitute R(t) = 0.9, β = 1.42, α = 2262400 into the index calculation formula, and t is approximately equal to 464,000 kilometers. From this result, it can be known that in the failure 2 category, 10% of the components to be evaluated of the vehicles fail after the vehicles have traveled 464,000 kilometers. Thus, the corresponding first index value is 464,000 kilometers.

[0101] For failure category 3, through the maximum likelihood estimation of the above embodiment, based on the vehicle mileage data of the corresponding failed vehicles and the vehicle mileage data of the non-failed vehicles, it is estimated that the shape parameter of the Weibull distribution is 1.20 and the scale parameter is 9596600.

[0102] If 90% of the vehicles in the failure category 3 are not failed, that is, R(t) = 0.9, substitute R(t) = 0.9, β = 1.20, α = 9596600 into the index calculation formula, and t is approximately equal to 1.466 million kilometers. From this result, it can be known that under the failure category 3, 10% of the components to be evaluated of the vehicles fail after the vehicles have traveled 464,000 kilometers. Thus, the corresponding first index value is 1.466 million kilometers.

[0103] Furthermore, because each first indicator value reflects the performance of the corresponding component to be evaluated, it is necessary to determine the reliability indicator of the module to be evaluated based on multiple first indicator values.

[0104] Optionally, the server determines the reliability index of the module to be evaluated based on multiple first index values, including: the server uses the first index value with the smallest index value among the multiple first index values ​​as the reliability index of the module to be evaluated.

[0105] Based on the above embodiments, it can be seen that under Failure 2 category, 10% of the components to be evaluated of the vehicles fail after the vehicles have traveled 464,000 kilometers; under Failure 3 category, 10% of the components to be evaluated of the vehicles fail after the vehicles have traveled 1,466,000 kilometers. Therefore, improving the problems of the components to be evaluated corresponding to Failure 2 category can better solve the performance problems of the reducer.

[0106] Based on this, by taking the smallest first index value among the first index values ​​as the reliability index of the module to be evaluated, it can be clearly known that improving the performance problem of the component to be evaluated can better solve the performance problem of the module to be evaluated.

[0107] Since the component to be evaluated belongs to the module to be evaluated, in addition to considering selecting one of the first index values ​​to evaluate the reliability of the module to be evaluated, multiple first index values ​​may also be considered comprehensively.

[0108] Optionally, each component to be evaluated corresponds to a first weight value; determining the reliability index of the module to be evaluated based on multiple first index values ​​includes: calculating the reliability index of the module to be evaluated based on each first index value and the corresponding first weight value.

[0109] The first weight value is set in advance based on the importance or vulnerability of the component to be evaluated in the module to be evaluated. Methods for setting the weight value include but are not limited to hierarchical analysis method, entropy method, factor analysis method or priority diagram method.

[0110] For example, the first weight value set for the gear is 0.6, and the corresponding first index value is 46.4; the first weight value set for the bearing is 0.4, and the corresponding first index value is 146.6.

[0111] The reliability index of the module to be evaluated is calculated by weighted summation: 0.6*46.4+0.4*146.6=86.48. That is, the modules to be evaluated of 10% of the vehicles fail when the vehicles have traveled 864,800 kilometers.

[0112] The embodiment of the present application can avoid the disadvantage of neglecting other important indicators due to the prominence of a single indicator by comprehensively considering the performance of each component to be evaluated, thereby obtaining a more comprehensive evaluation result.

[0113] In some embodiments, the component to be evaluated includes multiple failure types; each failure type corresponds to a second weight value; after obtaining multiple categories of failed vehicles, the method also includes: for each category of failed vehicles, reclassifying based on the failure type to obtain multiple types of failed vehicles; accordingly, using the vehicle mileage data of each category of failed vehicles and the vehicle mileage data of non-failed vehicles to calculate the reliability index of the corresponding category of the component to be evaluated, including: calculating the reliability index of the corresponding type based on the vehicle mileage data of each type of failed vehicles and the vehicle mileage data of non-failed vehicles to obtain multiple second index values; calculating the reliability index of the component to be evaluated according to each second index value and the corresponding second weight value.

[0114] In the automotive field, there are many reasons for the failure of the components to be evaluated. For example, bearing failure may be caused by damage to rolling elements, cage breakage, excessive wear, etc.

[0115] Therefore, the component to be evaluated includes multiple failure types, and each category of failed vehicles is reclassified based on the failure type, thereby performing a finer-grained division to obtain failed vehicles of different failure types under the same component to be evaluated.

[0116] As for the reliability index of the component to be evaluated, the reliability index of the corresponding type is calculated based on the vehicle mileage data of each type of failed vehicles and the vehicle mileage data of non-failed vehicles, a plurality of second index values ​​are obtained, and then the reliability index of the component to be evaluated is calculated based on each second index value and the corresponding second weight value.

[0117] It should be noted that the method of setting the second weight value may refer to the method of setting the first weight value, and the process of calculating the reliability index of the component to be evaluated according to each second indicator value and the corresponding second weight value may refer to the above-mentioned embodiment of calculating the reliability index of the module to be evaluated according to each first indicator value and the corresponding first weight value, which will not be repeated here.

[0118] The embodiment of the present application further subdivides the components to be evaluated based on the failure type, so that the analysis granularity is higher, thereby improving the accuracy of the evaluation.

[0119] Figure 2 The schematic diagram of the structure of a vehicle performance evaluation device provided in an embodiment of the present application includes: an acquisition module 201, a determination module 202 and a calculation module 203; wherein,

[0120] The acquisition module 201 is used to acquire vehicle data of multiple vehicles; the multiple vehicles include a module to be evaluated of the same model; the vehicle data include a failure mark and vehicle mileage data; the determination module 202 is used to determine a failed vehicle and a non-failed vehicle based on the failure mark; a failed vehicle is a vehicle that cannot be used normally due to a failure in the module to be evaluated; the calculation module 203 is used to calculate the reliability index of the module to be evaluated using the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle, so as to determine the performance of the vehicle using the reliability index.

[0121] Based on the above embodiment, the calculation module 203 is specifically used to: calculate the shape parameter and scale parameter of the Weibull distribution based on the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle; and calculate the reliability index of the module to be evaluated according to the shape parameter and the scale parameter.

[0122] On the basis of the above embodiment, the calculation module 203 is specifically used to: calculate the reliability index of the module to be evaluated according to the shape parameter and the scale parameter using the index calculation formula; the index calculation formula is:

[0123]

[0124] Where R(t) represents the probability that the module to be evaluated has no failure when the random variable is t, t represents the random variable, β represents the shape parameter, and α represents the scale parameter.

[0125] On the basis of the above embodiment, the module to be evaluated includes multiple components to be evaluated; the device also includes a first classification module, which is used to classify failed vehicles according to the components to be evaluated to obtain multiple categories of failed vehicles; accordingly, the reliability index of the module to be evaluated is calculated using the vehicle mileage data of the failed vehicles and the vehicle mileage data of the non-failed vehicles, including: using the vehicle mileage data of each category of failed vehicles and the vehicle mileage data of the non-failed vehicles to calculate the reliability index of the components to be evaluated of the corresponding category to obtain multiple first index values; calculating the reliability index of the module to be evaluated based on the multiple first index values.

[0126] On the basis of the above embodiment, the first classification module is specifically used to: use the first indicator value with the smallest indicator value among the multiple first indicator values ​​as the reliability indicator of the module to be evaluated.

[0127] On the basis of the above embodiment, each component to be evaluated corresponds to a first weight value; the first classification module is specifically used to calculate the reliability index of the module to be evaluated based on each first index value and the corresponding first weight value.

[0128] On the basis of the above embodiment, the components to be evaluated include multiple failure types; each failure type corresponds to a second weight value; the first classification module is specifically used to: for each category of failed vehicles, re-classify based on the failure type to obtain multiple types of failed vehicles; accordingly, use the vehicle mileage data of each category of failed vehicles and the vehicle mileage data of non-failed vehicles to calculate the reliability index of the corresponding category of components to be evaluated, including: calculating the reliability index of the corresponding type based on the vehicle mileage data of each type of failed vehicles and the vehicle mileage data of non-failed vehicles to obtain multiple second index values; calculating the reliability index of the component to be evaluated according to each second index value and the corresponding second weight value.

[0129] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 3As shown, the electronic device includes a processor 301 (processor), a memory 302 (memory) and a bus 303; wherein the processor 301 and the memory 302 communicate with each other through the bus 303. The processor 301 is used to call the program instructions in the memory 302 to execute the methods provided by the above-mentioned method embodiments.

[0130] The processor 301 can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 301 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. It can implement or execute various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0131] The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0132] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0133] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable the computer to execute the methods provided by the above-mentioned method embodiments.

[0134] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0135] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0137] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A vehicle performance evaluation method, characterized in that: The method comprises: Acquire vehicle data of a plurality of vehicles; the plurality of vehicles include modules to be evaluated of the same model; the vehicle data include failure identification and vehicle mileage data; Determine a failed vehicle and a non-failed vehicle based on the failure identifier; the failed vehicle is a vehicle that cannot be used normally due to a failure of the module to be evaluated; The reliability index of the module to be evaluated is calculated using the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle, so as to determine the performance of the vehicle using the reliability index.

2. The method according to claim 1, characterized in that: The calculating the reliability index of the module to be evaluated by using the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle includes: Calculating a shape parameter and a scale parameter of a Weibull distribution based on the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle; The reliability index of the module to be evaluated is calculated according to the shape parameter and the scale parameter.

3. The method according to claim 2, characterized in that The calculating the reliability index of the module to be evaluated according to the shape parameter and the scale parameter includes: The reliability index of the module to be evaluated is calculated according to the shape parameter and the scale parameter using an index calculation formula; the index calculation formula is: Wherein, R(t) represents the probability that the module to be evaluated has no failure when the random variable is t, t represents the random variable, β represents the shape parameter, and α represents the scale parameter.

4. The method according to claim 1, characterized in that The module to be evaluated includes a plurality of components to be evaluated; after determining a failed vehicle and a non-failed vehicle based on the failure identifier, the method further includes: Classifying the failed vehicles according to the components to be evaluated to obtain multiple categories of failed vehicles; Correspondingly, the calculating of the reliability index of the module to be evaluated by using the vehicle mileage data of the failed vehicle and the vehicle mileage data of the non-failed vehicle includes: Calculating the reliability index of the components to be evaluated of the corresponding category by using the vehicle mileage data of each category of failed vehicles and the vehicle mileage data of the non-failed vehicles to obtain a plurality of first index values; The reliability index of the module to be evaluated is calculated based on the multiple first index values.

5. The method according to claim 4, characterized in that The step of determining the reliability index of the module to be evaluated based on the multiple first index values ​​includes: The first index value with the smallest index value among the multiple first index values ​​is used as the reliability index of the module to be evaluated.

6. The method according to claim 4, characterized in that Each of the components to be evaluated corresponds to a first weight value; and determining the reliability index of the module to be evaluated based on the multiple first index values ​​includes: The reliability index of the module to be evaluated is calculated based on each of the first index values ​​and the corresponding first weight value.

7. The method according to claim 4, characterized in that The component to be evaluated includes multiple failure types; Each of the failure types corresponds to a second weight value; after obtaining multiple types of failed vehicles, the method further includes: For each category of disabled vehicles, reclassify based on the failure type to obtain multiple types of disabled vehicles; Accordingly, the calculation of the reliability index of the component to be evaluated of the corresponding category by using the vehicle mileage data of each category of failed vehicles and the vehicle mileage data of the non-failed vehicles includes: Calculating a reliability index of a corresponding type based on the vehicle mileage data of each type of failed vehicles and the vehicle mileage data of the non-failed vehicles to obtain a plurality of second index values; The reliability index of the component to be evaluated is calculated according to each of the second index values ​​and the corresponding second weight value.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is performed.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is executed.

10. A computer program product, characterized in that include: A computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is executed.

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