Automobile working condition analysis method and device, automobile and electronic equipment

Abstract

The invention provides an automobile working condition analysis method and device, an automobile, electronic equipment and a storage medium, and the method comprises the steps: obtaining the user operation data of the automobile; performing short-journey fragment division on the user operation data to obtain a first short-journey fragment set; performing principal component analysis on the first short-stroke fragment set to obtain a second short-stroke fragment set; clustering short-travel segments corresponding to typical road conditions in the second short-travel segment set to obtain a third short-travel segment set; and performing damage correlation analysis on each short-travel segment in the third short-travel segment set to obtain an analysis result. By implementing the application, the reliability and the accuracy of automobile working condition analysis can be improved, damage calculation and energy consumption calculation are accurately performed by associating with parts and durability life of the automobile, the method is suitable for various types of road conditions and automobile types, and the cost can be reduced to the greatest extent.

Description

Technical Field

[0001] The present application relates to the technical field of automobile operating conditions, and in particular to an automobile operating condition analysis method, device, automobile, electronic equipment and storage medium. Background Art

[0002] The cyclic operating condition analysis of automobiles is mainly used for emission testing of automobiles to evaluate the economy of automobiles. When constructing the cyclic operating conditions, the characteristic parameters of the failure load dominated by torque are lacking, resulting in weak overall load strength of the automobile cyclic operating conditions and lack of damage calculation. It is not suitable for reliability verification of mechanical components of electric drive systems.

[0003] In order to solve the above problems, the existing technology uses speed acceleration as the main characteristic parameter evaluation, which is mainly used for energy consumption analysis. It lacks the construction of load characteristic parameters and is not suitable for reliability analysis and verification; or it lacks the damage calculation of the constructed driving conditions and is not associated with the durability target, which is not suitable for reliability analysis and verification; the driving condition curve obtained is used for energy consumption analysis of traditional vehicles, which is not suitable for reliability verification of electric drive systems. Summary of the invention

[0004] The purpose of this application is to provide a vehicle operating condition analysis method, device, electronic device and storage medium, which can improve the reliability and accuracy of vehicle operating condition analysis, correlate with the vehicle's components and durability life, accurately calculate damage and energy consumption, be applicable to various types of road conditions and vehicle models, and minimize costs.

[0005] In a first aspect, an embodiment of the present application provides a method for analyzing an automobile operating condition, the method comprising:

[0006] Acquiring user operation data of the vehicle;

[0007] Dividing the user operation data into short-trip segments to obtain a first short-trip segment set;

[0008] Performing principal component analysis on the first short-trip segment set to obtain a second short-trip segment set;

[0009] Clustering the short trip segments corresponding to the typical road conditions in the second short trip segment set to obtain a third short trip segment set;

[0010] A damage association analysis is performed on each short-stroke segment in the third short-stroke segment set to obtain an analysis result.

[0011] In the above implementation process, by dividing the user's operating data into short-trip segments, and then analyzing and clustering the short-trip segments to obtain analysis results, the reliability and accuracy of the vehicle operating condition analysis can be improved, and it can be associated with the vehicle's components and durability life, and damage calculations and energy consumption calculations can be accurately performed. It is suitable for various types of road conditions and vehicle models, and can minimize costs.

[0012] Furthermore, the step of dividing the user operation data into short-trip segments to obtain a first short-trip segment set includes:

[0013] Eliminating abnormal data from the user operation data to obtain the user operation data after elimination;

[0014] Sequentially performing null value filling and noise reduction processing on the user operation data after elimination to obtain the user operation data to be analyzed;

[0015] The user operation data to be analyzed is divided into short-stroke segments according to the high-voltage power-on state to obtain the first short-stroke segment set.

[0016] In the above implementation process, the abnormal data in the user operation data is eliminated, filled and denoised before being divided into short-trip segments, which can maximize the availability and practicality of the user operation data and reduce errors in the calculation process.

[0017] Furthermore, the step of performing principal component analysis on the first short-trip segment set to obtain a second short-trip segment set includes:

[0018] Extracting characteristic parameters of short-trip segments in the first short-trip segment set;

[0019] A principal component analysis is performed on the characteristic parameters to obtain the second short-trip segment set.

[0020] In the above implementation process, characteristic parameters of the short-stroke segments in the first short-stroke segment set are extracted, and principal component analysis is performed based on the characteristic parameters, so that the rearranged second short-stroke segment set can highlight the characteristic parameters, which is convenient for subsequent damage analysis.

[0021] Furthermore, the step of clustering the short trip segments corresponding to the typical road conditions in the second short trip segment set to obtain a third short trip segment set includes:

[0022] Extracting a class label corresponding to each typical road condition in the second short trip segment set;

[0023] Obtain the distance data between each short trip segment corresponding to each class label and the cluster center of the short trip segments of various typical road conditions;

[0024] The distance data are sorted from small to large to obtain the third short-trip segment set.

[0025] In the above implementation process, the class labels of typical road conditions are extracted, and the distance data between each class label and the cluster center of short-trip segments of each typical road condition is obtained, which can more accurately cluster different types of short-trip segments and improve the clustering accuracy.

[0026] Furthermore, the step of performing damage association analysis on each short-trip segment in the third short-trip segment set to obtain an analysis result includes:

[0027] Obtaining total damage data of each short-stroke segment in the third short-stroke segment set;

[0028] Performing hypothesis testing on the total damage data to obtain a total damage score;

[0029] The total damage scores are concatenated to obtain a driving cycle condition;

[0030] The analysis results are obtained for the driving cycle conditions.

[0031] In the above implementation process, hypothesis testing is performed on the total damage data obtained, and then the total damage scores are spliced ​​to obtain a complete driving cycle condition, which can realize effective analysis of the vehicle condition.

[0032] Furthermore, the distance data between each short trip segment corresponding to each class label and the cluster center of the short trip segments of various typical road conditions is obtained by the following formula:

[0033]

[0034] Among them, x i is the variable value of the i-th characteristic parameter of the short-distance segment x, y i is the i-th characteristic parameter variable value of the short-trip segment cluster center y, and n is the number of short-trip segments.

[0035] In the above implementation process, the clustering accuracy and clustering precision can be improved by calculating the Euclidean distance, so that the label classification of short-trip segments is more accurate and can better reflect the parameter changes during the driving process of the car.

[0036] Furthermore, the total damage data of each short-stroke segment in the third short-stroke segment set is obtained by the following formula:

[0037]

[0038] Where m is the target damage analysis quantity, X iis the weight coefficient, D i is the pseudo damage data, and D is the total damage data.

[0039] In the above implementation process, the total damage data of the short-trip segment is obtained, which makes it easy to clearly obtain the damage to the parts caused by the car during driving, helps to analyze the working condition of the car and improve the efficiency of the working condition analysis.

[0040] In a second aspect, an embodiment of the present application further provides a vehicle operating condition analysis device, the device comprising:

[0041] An acquisition module, used for acquiring user operation data of the vehicle;

[0042] A division module, configured to divide the user operation data into short-trip segments to obtain a first short-trip segment set;

[0043] A principal component analysis module, configured to perform principal component analysis on the first short-trip segment set to obtain a second short-trip segment set;

[0044] a clustering module, configured to cluster the short trip segments corresponding to the typical road conditions in the second short trip segment set to obtain a third short trip segment set;

[0045] The damage analysis module is used to perform damage association analysis on each short-stroke segment in the third short-stroke segment set to obtain an analysis result.

[0046] In the above implementation process, by dividing the user's operating data into short-trip segments, and then analyzing and clustering the short-trip segments to obtain analysis results, the reliability and accuracy of the vehicle operating condition analysis can be improved, and it can be associated with the vehicle's components and durability life, and damage calculations and energy consumption calculations can be accurately performed. It is suitable for various types of road conditions and vehicle models, and can minimize costs.

[0047] In a third aspect, an automobile provided in an embodiment of the present application comprises the automobile operating condition analysis device of the second aspect.

[0048] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of the first aspects when executing the computer program.

[0049] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed on a computer, the computer executes the method described in any one of the first aspects.

[0050] In a sixth aspect, an embodiment of the present application provides a computer program product, which, when running on a computer, enables the computer to execute the method as described in any one of the first aspects.

[0051] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by implementing the above-mentioned technology of the present disclosure.

[0052] And it can be implemented according to the contents of the specification. The following is a detailed description of the preferred embodiments of the present application with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] 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 range values. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A schematic diagram of a process flow of a vehicle operating condition analysis method provided in an embodiment of the present application;

[0055] Figure 2 A schematic diagram of the structure of the vehicle operating condition analysis device provided in an embodiment of the present application;

[0056] Figure 3 A schematic diagram of the structural composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0058] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0059] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application, but are not used to limit the scope of the present application.

[0060] Embodiment 1

[0061] Figure 1 is a flow chart of the automobile operating condition analysis method provided in the embodiment of the present application, such as Figure 1 As shown, the method includes:

[0062] S1, obtaining the user operation data of the car;

[0063] S2, dividing the user's running data into short-trip segments to obtain a first short-trip segment set;

[0064] S3, performing principal component analysis on the first short-trip segment set to obtain a second short-trip segment set;

[0065] S4, clustering the short trip segments corresponding to the typical road conditions in the second short trip segment set to obtain a third short trip segment set;

[0066] S5, performing damage association analysis on each short-trip segment in the third short-trip segment set to obtain an analysis result.

[0067] In the above implementation process, by dividing the user's operating data into short-trip segments, and then analyzing and clustering the short-trip segments to obtain analysis results, the reliability and accuracy of the vehicle operating condition analysis can be improved, and it can be associated with the vehicle's components and durability life, and damage calculations and energy consumption calculations can be accurately performed. It is suitable for various types of road conditions and vehicle models, and can minimize costs.

[0068] In S1, user operation data includes one month's operation data of active users (mileage or long operation time) in cities with determined regions, traffic, temperature and car ownership. The number of users in each city can be adjusted according to the planned car sales area, which can ensure that the extracted user operation data is representative and improve the coverage of constructed driving condition scenarios.

[0069] Furthermore, S2 includes:

[0070] Abnormal data is eliminated from the user operation data to obtain the user operation data after elimination;

[0071] Perform null value filling and noise reduction processing on the user operation data after elimination in turn to obtain the user operation data to be analyzed;

[0072] The user operation data to be analyzed is divided into short-stroke segments according to the high-voltage power-on state to obtain a first short-stroke segment set.

[0073] In the above implementation process, the abnormal data in the user operation data is eliminated, filled and denoised before being divided into short-trip segments, which can maximize the availability and practicality of the user operation data and reduce errors in the calculation process.

[0074] The acquired user operation data is preprocessed, including the removal of abnormal data such as vehicle speed, torque, acceleration, speed, filling of null values, and noise reduction, and then short-trip segments are divided according to the high-voltage power-on state. The trained machine learning model is used to classify short trips according to road conditions. A collection of multiple short-trip segments is obtained.

[0075] Furthermore, S3 includes:

[0076] Extracting characteristic parameters of the short-stroke segments in the first short-stroke segment set;

[0077] The principal component analysis is performed on the characteristic parameters to obtain the second short-trip segment set.

[0078] In the above implementation process, characteristic parameters of the short-stroke segments in the first short-stroke segment set are extracted, and principal component analysis is performed based on the characteristic parameters, so that the rearranged second short-stroke segment set can highlight the characteristic parameters, which is convenient for subsequent damage analysis.

[0079] Optionally, the characteristic parameters of this application are analyzed using the principal component analysis (PCA) method. The setting of characteristic parameters is mainly used for reliability analysis and verification of mechanical parts of the electric drive system. The characteristic parameters are mainly torque, acceleration, and speed parameters. The details are shown in the following table:

[0080]

[0081] Furthermore, S4 includes:

[0082] Extracting class labels corresponding to each typical road condition in the second short trip segment set;

[0083] Obtain the distance data between each short trip segment corresponding to each class label and the cluster center of the short trip segments of various typical road conditions;

[0084] The distance data are sorted from small to large to obtain a third short-trip segment set.

[0085] In the above implementation process, the class labels of typical road conditions are extracted, and the distance data between each class label and the cluster center of short-trip segments of each typical road condition is obtained, which can more accurately cluster different types of short-trip segments and improve the clustering accuracy.

[0086] The present application uses a k-means clustering algorithm to perform cluster analysis on the obtained short-trip segments of each typical road to obtain a clustered short-trip segment database (a third short-trip segment set).

[0087] The class labels of typical road conditions are extracted, and the distances between each kinematic segment and each k condensation point in the short-trip segment in each type of typical road condition are calculated, and the distances are sorted from small to large. In the embodiment of the present application, the Euclidean distance is selected as the distance between the short-trip segment and the cluster center (the cluster center of the short-trip segment of each type of typical road condition), and the calculation formula is as follows:

[0088]

[0089] Among them, x i is the variable value of the i-th characteristic parameter of the short-distance segment x, y i is the i-th characteristic parameter variable value of the short-trip segment cluster center y, and n is the number of short-trip segments.

[0090] Furthermore, S5 includes:

[0091] obtaining total damage data of each short-stroke segment in the third short-stroke segment set;

[0092] Conduct hypothesis testing on the total injury data to obtain the total injury score;

[0093] The total damage scores are spliced ​​to obtain the driving cycle conditions;

[0094] The analysis results are obtained for the driving cycle.

[0095] In the above implementation process, hypothesis testing is performed on the total damage data obtained, and then the total damage scores are spliced ​​to obtain a complete driving cycle condition, which can realize effective analysis of the vehicle condition.

[0096] For each short-stroke segment in the third short-stroke segment set sorted by cluster center, pseudo-damage calculation is performed according to the reliability model of typical components, and the weight coefficient X of each component is set. i , the total damage calculation formula is as follows:

[0097]

[0098] Where m is the target damage analysis quantity, X i is the weight coefficient, D i is the pseudo damage data, and D is the total damage data, that is, the weighted pseudo damage data.

[0099] The hypothesis test is performed on the weighted total damage of the components (total damage data), and the short-distance segments with total damage above the 90th percentile and closest to the cluster center are selected as the driving cycle splicing segments. The splicing process is shown in the following table:

[0100]

[0101]

[0102] For each road type, each type of short-trip segments selected in the previous step are spliced ​​to form urban, highway, and mountainous road driving cycle conditions. Taking urban road conditions as an example, short-trip segments are grouped into 3 categories, and each type of short-trip segments selected are spliced ​​to form an urban road driving cycle condition.

[0103] The number of cycles of each road type driving cycle is the total damage data target of the road type divided by the pseudo damage data of a single cycle. Taking the urban condition as an example, the sum of the total damage after weighting of the three types of short trips is the total damage Dc of the urban cycle condition. Assuming that the target damage of the urban condition over the entire life cycle is Dm, the number of cycles kc of the urban driving cycle condition is Dm / Dc. Similarly, the number of cycles kg and ks of the high-speed and mountainous driving cycle conditions are obtained. At this point, all road type driving cycle conditions are multiplied by their respective number of cycles to form the reliability cycle condition of the electric drive system representing the entire life cycle.

[0104] This application extracts the operating data of typical users in typical areas, divides the short-trip segments, and uses a trained machine learning model to classify the short-trip segments to form a typical road short-trip segment database. It sets key parameters that can characterize driving characteristics, such as torque, speed, acceleration, etc. that lead to load failure, and uses an unsupervised clustering analysis method to conduct damage correlation analysis on each type of short-trip segment data. After aggregation, a typical road driving cycle condition is formed. Finally, the number of cycles is determined based on the durability target. This application is suitable for reliability analysis and verification of electric drive systems.

[0105] This application can fully consider road characteristics, save the cost and cycle of traditional road data collection, shorten the verification time to achieve accelerated verification, and meet the reliability cycle times of the entire life cycle.

[0106] Embodiment 2

[0107] In order to execute the method corresponding to the above embodiment 1 to achieve the corresponding functions and technical effects, a vehicle operating condition analysis device is provided below, such as Figure 2 As shown, the device comprises:

[0108] Acquisition module 1, used to acquire user operation data of the car;

[0109] A division module 2 is used to divide the user operation data into short-trip segments to obtain a first short-trip segment set;

[0110] A principal component analysis module 3 is used to perform principal component analysis on the first short-trip segment set to obtain a second short-trip segment set;

[0111] A clustering module 4, configured to cluster the short trip segments corresponding to the typical road conditions in the second short trip segment set to obtain a third short trip segment set;

[0112] The damage analysis module 5 is used to perform damage correlation analysis on each short-trip segment in the third short-trip segment set to obtain an analysis result.

[0113] In the above implementation process, by dividing the user's operating data into short-trip segments, and then analyzing and clustering the short-trip segments to obtain analysis results, the reliability and accuracy of the vehicle operating condition analysis can be improved, and it can be associated with the vehicle's components and durability life, and damage calculations and energy consumption calculations can be accurately performed. It is suitable for various types of road conditions and vehicle models, and can minimize costs.

[0114] Furthermore, S2 includes:

[0115] Abnormal data is eliminated from the user operation data to obtain the user operation data after elimination;

[0116] Perform null value filling and noise reduction processing on the user operation data after elimination in turn to obtain the user operation data to be analyzed;

[0117] The user operation data to be analyzed is divided into short-stroke segments according to the high-voltage power-on state to obtain a first short-stroke segment set.

[0118] In the above implementation process, the abnormal data in the user operation data is eliminated, filled and denoised before being divided into short-trip segments, which can maximize the availability and practicality of the user operation data and reduce errors in the calculation process.

[0119] Furthermore, S3 includes:

[0120] Extracting characteristic parameters of the short-stroke segments in the first short-stroke segment set;

[0121] The principal component analysis is performed on the characteristic parameters to obtain the second short-trip segment set.

[0122] In the above implementation process, characteristic parameters of the short-stroke segments in the first short-stroke segment set are extracted, and principal component analysis is performed based on the characteristic parameters, so that the rearranged second short-stroke segment set can highlight the characteristic parameters, which is convenient for subsequent damage analysis.

[0123] Furthermore, S4 includes:

[0124] Extracting class labels corresponding to each typical road condition in the second short trip segment set;

[0125] Obtain the distance data between each short trip segment corresponding to each class label and the cluster center of the short trip segments of various typical road conditions;

[0126] The distance data are sorted from small to large to obtain a third short-trip segment set.

[0127] In the above implementation process, the class labels of typical road conditions are extracted, and the distance data between each class label and the cluster center of short-trip segments of each typical road condition is obtained, which can more accurately cluster different types of short-trip segments and improve the clustering accuracy.

[0128] Furthermore, S5 includes:

[0129] obtaining total damage data of each short-stroke segment in the third short-stroke segment set;

[0130] Conduct hypothesis testing on the total injury data to obtain the total injury score;

[0131] The total damage scores are spliced ​​to obtain the driving cycle conditions;

[0132] The analysis results are obtained for the driving cycle.

[0133] In the above implementation process, hypothesis testing is performed on the total damage data obtained, and then the total damage scores are spliced ​​to obtain a complete driving cycle condition, which can realize effective analysis of the vehicle condition.

[0134] Furthermore, the distance data between each short trip segment corresponding to each class label and the cluster center of the short trip segments of various typical road conditions is obtained by the following formula:

[0135]

[0136] Among them, x i is the variable value of the i-th characteristic parameter of the short-distance segment x, y i is the i-th characteristic parameter variable value of the short-trip segment cluster center y, and n is the number of short-trip segments.

[0137] In the above implementation process, the clustering accuracy and clustering precision can be improved by calculating the Euclidean distance, so that the label classification of short-trip segments is more accurate and can better reflect the parameter changes during the driving process of the car.

[0138] Furthermore, the total damage data of each short-stroke segment in the third short-stroke segment set is obtained by the following formula:

[0139]

[0140] Where m is the target damage analysis quantity, X i is the weight coefficient, D i is the pseudo damage data, and D is the total damage data.

[0141] In the above implementation process, the total damage data of the short-trip segment is obtained, which makes it easy to clearly obtain the damage to the parts caused by the car during driving, helps to analyze the working condition of the car and improve the efficiency of the working condition analysis.

[0142] The above-mentioned automobile operating condition analysis device can implement the method of the above-mentioned embodiment 1. The options in the above-mentioned embodiment 1 are also applicable to this embodiment and will not be described in detail here.

[0143] The rest of the contents of the embodiments of the present application can refer to the contents of the above-mentioned embodiment 1, and will not be repeated in this embodiment.

[0144] Embodiment 3

[0145] An embodiment of the present application provides a car, including the car operating condition analysis device of embodiment 2.

[0146] Embodiment 4

[0147] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the automobile operating condition analysis method of embodiment 1.

[0148] Optionally, the above-mentioned electronic device may be a server.

[0149] See also Figure 3 , Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include a processor 31, a communication interface 32, a memory 33 and at least one communication bus 34. The communication bus 34 is used to realize direct connection and communication between these components. The communication interface 32 of the device in the embodiment of the present application is used to communicate signaling or data with other node devices. The processor 31 may be an integrated circuit chip with signal processing capabilities.

[0150] The processor 31 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. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor 31 can also be any conventional processor, etc.

[0151] The memory 33 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc. The memory 33 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 31, the device can execute the above-mentioned Figure 1 The method embodiment involves various steps.

[0152] Optionally, the electronic device may further include a storage controller and an input / output unit. The memory 33, the storage controller, the processor 31, the peripheral interface, and the input / output unit are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components may be electrically connected to each other via a communication bus 34. The processor 31 is used to execute executable modules stored in the memory 33, such as software function modules or computer programs included in the device.

[0153] The input and output unit is used to provide users with the task creation and to create an optional time period or preset execution time for the task to enable interaction between the user and the server. The input and output unit can be, but is not limited to, a mouse and a keyboard.

[0154] Understandably, Figure 3 The structure shown is for illustration only. The electronic device may also include Figure 3 More or fewer components as shown, or with Figure 3 Different configurations shown. Figure 3 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0155] In addition, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which implements the automobile operating condition analysis method of embodiment 1 when executed by a processor.

[0156] The embodiment of the present application also provides a computer program product, which, when running on a computer, enables the computer to execute the method described in the method embodiment.

[0157] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of boxes in the block diagram and / or the flowchart, can be implemented with a dedicated hardware-based device that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0158] In addition, 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.

[0159] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0160] 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 changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0161] The above is only a specific implementation of the present application, but the protection range of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical range disclosed in the present application, which should be included in the protection range of the present application. Therefore, the protection range of the present application should be based on the protection range of the claims.

[0162] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for analyzing automobile operating conditions, characterized in that: The method comprises: Acquiring user operation data of the vehicle; Dividing the user operation data into short-trip segments to obtain a first short-trip segment set; Performing principal component analysis on the first short-trip segment set to obtain a second short-trip segment set; Clustering the short trip segments corresponding to the typical road conditions in the second short trip segment set to obtain a third short trip segment set; A damage association analysis is performed on each short-stroke segment in the third short-stroke segment set to obtain an analysis result.

2. The automobile operating condition analysis method according to claim 1, characterized in that: The step of dividing the user operation data into short-trip segments to obtain a first short-trip segment set includes: Eliminating abnormal data from the user operation data to obtain the user operation data after elimination; Sequentially performing null value filling and noise reduction processing on the user operation data after elimination to obtain the user operation data to be analyzed; The user operation data to be analyzed is divided into short-stroke segments according to the high-voltage power-on state to obtain the first short-stroke segment set.

3. The automobile operating condition analysis method according to claim 1, characterized in that: The step of performing principal component analysis on the first short-trip segment set to obtain a second short-trip segment set includes: Extracting characteristic parameters of short-trip segments in the first short-trip segment set; A principal component analysis is performed on the characteristic parameters to obtain the second short-trip segment set.

4. The automobile operating condition analysis method according to claim 1, characterized in that: The step of clustering the short trip segments corresponding to the typical road conditions in the second short trip segment set to obtain a third short trip segment set includes: Extracting a class label corresponding to each typical road condition in the second short trip segment set; Obtain the distance data between each short trip segment corresponding to each class label and the cluster center of the short trip segments of various typical road conditions; The distance data are sorted from small to large to obtain the third short-trip segment set.

5. The automobile operating condition analysis method according to claim 1, characterized in that: The step of performing damage association analysis on each short-stroke segment in the third short-stroke segment set to obtain an analysis result comprises: Obtaining total damage data of each short-stroke segment in the third short-stroke segment set; Performing hypothesis testing on the total damage data to obtain a total damage score; The total damage scores are concatenated to obtain a driving cycle condition; The analysis results are obtained for the driving cycle conditions.

6. The automobile operating condition analysis method according to claim 1, characterized in that: The distance data between each short trip segment corresponding to each class label and the cluster center of the short trip segments of various typical road conditions is obtained by the following formula: Among them, x i is the variable value of the i-th characteristic parameter of the short-distance segment x, y i is the i-th characteristic parameter variable value of the short-trip segment cluster center y, and n is the number of short-trip segments.

7. The automobile operating condition analysis method according to claim 1, characterized in that: The total damage data of each short-stroke segment in the third short-stroke segment set is obtained by the following formula: Where m is the target damage analysis quantity, X i is the weight coefficient, D i is the pseudo damage data, and D is the total damage data.

8. An automobile operating condition analysis device, characterized in that: The device comprises: An acquisition module, used for acquiring user operation data of the vehicle; A division module, configured to divide the user operation data into short-trip segments to obtain a first short-trip segment set; A principal component analysis module, configured to perform principal component analysis on the first short-trip segment set to obtain a second short-trip segment set; a clustering module, configured to cluster the short trip segments corresponding to the typical road conditions in the second short trip segment set to obtain a third short trip segment set; The damage analysis module is used to perform damage association analysis on each short-stroke segment in the third short-stroke segment set to obtain an analysis result.

9. A car, characterized in that: The vehicle operating condition analysis device comprises the vehicle operating condition analysis device of claim 8.

10. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the automobile operating condition analysis method according to any one of claims 1 to 7.

11. A storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the automobile operating condition analysis method as claimed in any one of claims 1 to 7.

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