Load spectrum determination method and device, electronic equipment, storage medium and vehicle

Abstract

The invention relates to a load spectrum determination method and device, electronic equipment, a storage medium and a vehicle. The method comprises the steps that motion characteristic parameters of each historical driving cycle in at least one historical driving cycle of each vehicle in multiple vehicles of the same vehicle type are acquired; based on the motion characteristic parameters of each historical driving cycle of each alternative vehicle, determining at least one kinematics fragment belonging to different motion types from a plurality of kinematics fragments to obtain a plurality of typical kinematics fragments, the plurality of kinematics fragments being kinematics fragments corresponding to each historical driving cycle of each alternative vehicle, each kinematic fragment is used for indicating a speed-time curve of a corresponding historical driving cycle; splicing the plurality of typical kinematics fragments to obtain a road spectrum of a target time length; and inputting the road spectrum of the target duration into a whole vehicle dynamics simulation model of the target vehicle model, and performing simulation to obtain a load spectrum of a target rotating part of the target vehicle model.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a method, device, electronic device, storage medium and vehicle for determining a load spectrum. Background Art

[0002] The external characteristics of electric vehicles (the power or torque measured at full load, which varies with the speed) are quite different from those of traditional fuel vehicles (the power or torque measured at full load, which varies with the speed). In addition, since there are no buffering devices such as clutches and shock absorbers, electric vehicles have more stringent requirements for reducers.

[0003] As the basis for the design of rotating parts such as reducers or motors, high-precision load spectra are of great significance to the driving safety of electric vehicles. The existing methods for constructing reducer load spectra or motor load spectra mainly collect driving data of various typical roads (i.e., obtain driving data of different sports types) through a test vehicle according to a preset collection ratio, and then use the collected data to construct the corresponding load spectrum.

[0004] However, the settings of various typical roads, preset collection ratios and other parameters often rely on empirical settings, which do not conform to actual driving conditions, resulting in the constructed load spectrum being unable to reflect actual driving conditions and having poor accuracy. Summary of the invention

[0005] The present application provides a method, device, electronic device, storage medium and vehicle for determining a load spectrum. The constructed load spectrum can reflect actual driving conditions with high accuracy.

[0006] In a first aspect, an embodiment of the present application provides a method for determining a load spectrum, comprising: obtaining actual driving data corresponding to multiple vehicles of the same vehicle model, the actual driving data including motion characteristic parameters of each historical driving cycle of at least one historical driving cycle of each vehicle; based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, determining at least one kinematic segment belonging to a different motion type from multiple kinematic segments to obtain multiple typical kinematic segments, the multiple kinematic segments being respective kinematic segments corresponding to each historical driving cycle of each candidate vehicle, each kinematic segment being used to indicate a speed-time curve of a corresponding historical driving cycle, and each candidate vehicle being one of the multiple vehicles; splicing the multiple typical kinematic segments to obtain a road spectrum of a target duration; inputting the road spectrum of the target duration into a whole vehicle dynamics simulation model of a target vehicle model, simulating to obtain a target load spectrum of the target vehicle model, the target load spectrum being a load spectrum of a target rotating component, and the whole vehicle dynamics simulation model including a module corresponding to the target rotating component.

[0007] In some embodiments of the present application, the actual driving data further includes damage parameters of the target rotating component of each historical driving cycle of each vehicle; before determining at least one kinematic segment belonging to a different motion type from a plurality of kinematic segments based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle and obtaining a plurality of typical kinematic segments, the method further includes:

[0008] Determining a damage standard value of the target rotating component of each vehicle based on a damage standard calculation formula and the damage parameter of each historical driving cycle of each vehicle;

[0009] Based on the damage standard value of each vehicle, a plurality of candidate vehicles are determined from the plurality of vehicles, and the damage standard value of each candidate vehicle is greater than the damage standard value of other vehicles in the plurality of vehicles except the plurality of candidate vehicles.

[0010] In some embodiments of the present application, the target rotating component is a reducer, the damage parameter includes torque and number of revolutions, the damage standard value is a damage value per unit mileage, and the damage standard calculation formula includes:

[0011]

[0012] Where D1 is the damage value per unit mileage, T i is the torque in the i-th period of constant torque in each historical driving cycle of the vehicle, N i The torque is T i is the total number of revolutions at , a is the power exponent of the torque, and S is the total mileage of each historical driving cycle of the vehicle.

[0013] In some embodiments of the present application, the target rotating component is a motor, the damage parameter includes the rotation speed, the damage standard value includes the proportion of high speed duration, and the damage standard calculation formula includes:

[0014]

[0015] Among them, D2 is the proportion of high speed time, t j is the duration of the motion process in which the jth speed is greater than or equal to the speed threshold in each historical driving cycle of the vehicle, t k is the total duration of the kth historical driving cycle of the vehicle.

[0016] In some embodiments of the present application, the determining of a plurality of candidate vehicles from the plurality of vehicles based on the damage standard value of each vehicle includes: determining a vehicle among the plurality of vehicles whose damage standard value is greater than or equal to a damage threshold as the plurality of candidate vehicles;

[0017] Alternatively, the multiple vehicles are sorted from large to small according to the damage standard value; and some of the top-ranked vehicles are determined as the multiple candidate vehicles.

[0018] In some embodiments of the present application, based on the motion characteristic parameters of each historical driving cycle of each alternative vehicle, at least one kinematic segment belonging to a different motion type is determined from multiple kinematic segments to obtain multiple typical kinematic segments, including: based on the motion characteristic parameters of each historical driving cycle of each alternative vehicle, the multiple kinematic segments are clustered to obtain multiple kinematic segment sets, each kinematic segment set includes multiple kinematic segments belonging to the same motion type; and some kinematic segments in each kinematic segment set are determined as the multiple typical kinematic segments.

[0019] In some embodiments of the present application, some of the kinematic segments in each kinematic segment set are determined as the multiple typical kinematic segments, including: determining the similarity between the kinematic segments in each kinematic segment set and the corresponding cluster centers based on a similarity formula; determining the kinematic segments in each kinematic segment set whose corresponding similarity is greater than or equal to a similarity threshold as the multiple typical kinematic segments, or determining the kinematic segments in each kinematic segment set that are ranked in the top M in descending order of corresponding similarity as the multiple typical kinematic segments.

[0020] In some embodiments of the present application, the target duration is a preset duration or a total duration of the multiple typical kinematic segments; wherein the preset duration is less than or equal to the total duration of the multiple typical kinematic segments.

[0021] In some embodiments of the present application, the target duration is the preset duration; the multiple typical kinematic segments are spliced ​​together to obtain a road spectrum of the target duration, including: the multiple typical kinematic segments are spliced ​​together according to the duration ratio of different motion types to obtain a road spectrum of the target duration, and the duration ratio is the ratio of the total motion duration of the kinematic segments under each motion type, by classifying the multiple kinematic segments according to different motion types.

[0022] In some embodiments of the present application, the multiple typical kinematic segments are spliced ​​according to the duration ratio of different motion types to obtain a road spectrum of the target duration, including: dividing the preset duration into multiple intervals according to the duration ratio, each interval corresponding to a motion type; executing the following step S1 for each interval to obtain the road spectrum of the preset duration; wherein S1 includes: determining at least one typical kinematic segment whose motion characteristic parameters best match the target motion type from the typical kinematic segments of the target motion type corresponding to the target interval; and splicing the at least one typical kinematic segment to the target interval, the target interval being any one of the multiple intervals.

[0023] In a second aspect, an embodiment of the present application provides a load spectrum determination device, comprising: an acquisition module, used to acquire actual driving data corresponding to multiple vehicles of the same model, the actual driving data including motion characteristic parameters of each historical driving cycle of at least one historical driving cycle of each vehicle; a determination module, used to determine at least one kinematic segment belonging to a different motion type from multiple kinematic segments based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, and obtain multiple typical kinematic segments, the multiple kinematic segments are respective kinematic segments corresponding to each historical driving cycle of each candidate vehicle, each kinematic segment is used to indicate a speed-time curve of a corresponding historical driving cycle, and each candidate vehicle is one of the multiple vehicles; a splicing module, used to splice the multiple typical kinematic segments to obtain a road spectrum of a target duration; a simulation module, used to input the road spectrum of the target duration into a whole vehicle dynamics simulation model of a target model, and simulate to obtain a target load spectrum of the target model, the target load spectrum being a load spectrum of a target rotating component, and the whole vehicle dynamics simulation model includes a module corresponding to the target rotating component.

[0024] In some embodiments of the present application, the actual driving data also includes damage parameters of the target rotating component of each historical driving cycle of each vehicle; the determination module is further used to determine at least one kinematic segment belonging to a different motion type from a plurality of kinematic segments based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, before obtaining a plurality of typical kinematic segments, and determine the damage standard value of the target rotating component of each vehicle based on a damage standard calculation formula and the damage parameters of each historical driving cycle of each vehicle; the determination module is further used to determine a plurality of candidate vehicles from the plurality of vehicles based on the damage standard value of each vehicle, the damage standard value of each candidate vehicle being greater than the damage standard value of other vehicles among the plurality of vehicles except the plurality of candidate vehicles.

[0025] In some embodiments of the present application, the target rotating component is a reducer, the damage parameter includes torque and number of revolutions, the damage standard value is a damage value per unit mileage, and the damage standard calculation formula includes:

[0026]

[0027] Where D1 is the damage value per unit mileage, T i is the torque in the i-th period of constant torque in each historical driving cycle of the vehicle, N i The torque is T i is the total number of revolutions at , a is the power exponent of the torque, and S is the total mileage of each historical driving cycle of the vehicle.

[0028] In some embodiments of the present application, the target rotating component is a motor, the damage parameter includes the rotation speed, the damage standard value includes the proportion of high speed duration, and the damage standard calculation formula includes:

[0029]

[0030] Among them, D2 is the proportion of high speed time, t j is the duration of the motion process in which the jth speed is greater than or equal to the speed threshold in each historical driving cycle of the vehicle, t k is the total duration of the kth historical driving cycle of the vehicle.

[0031] In some embodiments of the present application, the determination module is specifically used to determine the vehicles among the multiple vehicles whose damage standard values ​​are greater than or equal to the damage threshold as the multiple alternative vehicles; or, to sort the multiple vehicles from large to small according to the damage standard values; and to determine some of the top-ranked vehicles as the multiple alternative vehicles.

[0032] In some embodiments of the present application, the determination module is specifically used to cluster the multiple kinematic segments based on the motion characteristic parameters of each historical driving cycle of each alternative vehicle to obtain multiple kinematic segment sets, each kinematic segment set including multiple kinematic segments belonging to the same motion type; and determine some kinematic segments in each kinematic segment set as the multiple typical kinematic segments.

[0033] In some embodiments of the present application, the determination module is specifically used to determine the similarity between the kinematic segments in each kinematic segment set and the corresponding cluster center based on a similarity formula; determine the corresponding kinematic segments in each kinematic segment set whose similarity is greater than or equal to a similarity threshold as the multiple typical kinematic segments, or determine the corresponding kinematic segments in each kinematic segment set that are ranked in the top M in descending order of similarity as the multiple typical kinematic segments.

[0034] In some embodiments of the present application, the target duration is a preset duration or a total duration of the plurality of typical kinematic segments;

[0035] The preset duration is less than or equal to the total duration of the multiple typical kinematic segments.

[0036] In some embodiments of the present application, the target duration is the preset duration; the splicing module is specifically used to splice the multiple typical kinematic segments according to the duration ratio of different motion types to obtain a road spectrum of the target duration, and the duration ratio is the ratio of the total motion duration of the kinematic segments under each motion type, by classifying the multiple kinematic segments according to different motion types.

[0037] In some embodiments of the present application, the splicing module is specifically used to divide the preset duration into multiple intervals according to the proportion of the duration, each interval corresponding to a motion type; execute the following step S1 for each interval to obtain the road spectrum of the preset duration; wherein S1 includes: from the typical kinematic segments of the target motion type corresponding to the target interval, determine at least one typical kinematic segment whose motion feature parameters best match the target motion type; and splice the at least one typical kinematic segment to the target interval, the target interval being any one of the multiple intervals.

[0038] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, wherein the processor is used to execute a computer program stored in a memory, and when the computer program is executed by the processor, the steps of any load spectrum determination method provided in the first aspect are implemented.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any load spectrum determination method provided in the first aspect are implemented.

[0040] In a fifth aspect, an embodiment of the present application provides a vehicle, comprising: a load spectrum determination device as described in the second aspect, or an electronic device as described in the third aspect, or a computer-readable storage medium as described in the fourth aspect.

[0041] In a sixth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a computer program or instructions. When the computer program product runs on a processor, the processor executes the computer program or instructions to implement the steps of the method for determining the load spectrum as described in the first aspect.

[0042] In the seventh aspect, an embodiment of the present application provides a chip, which includes a processor, a memory and a communication interface, the communication interface is coupled to the processor, the memory is used to store programs or instructions that can be run on the processor, and the processor is used to execute the program or instructions to implement the steps of the load spectrum determination method as described in the first aspect.

[0043] The technical solution provided by the embodiment of the present application has the following advantages compared with the prior art: In the embodiment of the present application, based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle in the actual driving data of multiple vehicles of the same model, multiple typical kinematic segments that can cover different motion types are determined from each kinematic segment of the multiple candidate vehicles, and then the multiple typical kinematic segments are spliced ​​according to the duration ratio of different motion types to obtain the road spectrum of the target duration, and then according to the road spectrum of the target duration, the load spectrum of the target rotating component of the target model is obtained by simulating the whole vehicle dynamics simulation model of the target model (which can be the same as or different from the models of the multiple vehicles). In this way, since the actual driving data of multiple vehicles of the same model can reflect the actual driving conditions, and the damage conditions of the target rotating components of multiple vehicles of the same model for the same motion type are basically the same, the load spectrum of the target rotating component of the target model is obtained by using the actual driving data of the vehicle of the same model. Compared with collecting driving data of various typical roads according to a preset collection ratio by a test vehicle of the target model, the embodiment of the present application does not rely on empirical settings, so that the load spectrum obtained by simulation can reflect the actual driving conditions with high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0046] Figure 1 A schematic diagram of a flow chart of a method for determining a load spectrum provided in this application;

[0047] Figure 2a A schematic flow chart of another method for determining a load spectrum provided in the present application;

[0048] Figure 2b A schematic flow chart of another method for determining a load spectrum provided in the present application;

[0049] Figure 3 A schematic flow chart of another method for determining a load spectrum provided in the present application;

[0050] Figure 4 A road map diagram provided for this application;

[0051] Figure 5 A schematic diagram of a load spectrum provided for this application;

[0052] Figure 6 A schematic diagram of the structure of a load spectrum determination device provided in the present application;

[0053] Figure 7 A schematic diagram of the hardware structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0054] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only part of the embodiments of the present application, rather than all of the embodiments.

[0056] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more.

[0057] First, some nouns or terms involved in the claims and description of the present invention are explained below.

[0058] The road spectrum specifically includes the speed spectrum and the speed-time curve.

[0059] The load spectrum is used to indicate the corresponding relationship between torque and speed.

[0060] The target rotating parts are reducers or motors of electric vehicles.

[0061] The existing solution is to collect driving data on various typical roads according to a preset collection ratio through a test vehicle, and then use the collected data to construct a load spectrum. However, since it is necessary to collect driving data on different roads on site, there are still problems such as high collection cost and long time period.

[0062] In the embodiment of the present application, based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle in the actual driving data of multiple vehicles of the same model, multiple typical kinematic segments that can cover different motion types are determined from each kinematic segment of the multiple candidate vehicles, and then the multiple typical kinematic segments are spliced ​​according to the duration ratio of different motion types to obtain the road spectrum of the target duration, and then according to the road spectrum of the target duration, the load spectrum of the target rotating component of the target model is obtained by simulating the whole vehicle dynamics simulation model of the target model (which can be the same as or different from the models of multiple vehicles). In this way, since the actual driving data of multiple vehicles of the same model can reflect the actual driving conditions, and the damage conditions of the target rotating components of multiple vehicles of the same model for the same motion type are basically the same, the load spectrum of the target rotating component of the target model is obtained by using the actual driving data of the vehicle of the same model. Compared with the driving data of various typical roads collected by the test vehicle of the target model according to the preset collection ratio, the embodiment of the present application does not rely on empirical settings, so that the load spectrum obtained by simulation can reflect the actual driving conditions and has high accuracy. Moreover, there is no need for the test vehicle to collect driving data on different roads on site. Instead, the actual driving data of vehicles previously counted can be used, which has a lower collection cost and a shorter time period.

[0063] The present application is applied to the load spectrum construction scenario of rotating parts. The electronic device in the embodiment of the present application can be a vehicle-mounted terminal, or a mobile phone, notebook, computer, etc. that communicates with the vehicle. The specific details can be determined based on actual conditions and are not limited here.

[0064] The technical solution of the present application is explained in detail below through several specific embodiments.

[0065] Figure 1 A flow chart of a method for determining a load spectrum provided in this application is shown in FIG. Figure 1 As shown, the method for determining the load spectrum may include the following steps 101 to 104 .

[0066] 101. Obtain actual driving data corresponding to multiple vehicles of the same model.

[0067] The actual driving data includes motion characteristic parameters of each historical driving cycle in at least one historical driving cycle of each vehicle.

[0068] The at least one historical driving cycle may be any historical driving cycle of the vehicle, or each historical driving cycle of the vehicle within a predetermined duration, or each historical driving cycle of each vehicle within a fixed time period. The specific number may be determined based on actual conditions and is not limited here.

[0069] The scheduled duration can be determined according to actual conditions and is not limited here. For example, the scheduled duration can be one month, two months, half a year, etc.

[0070] The fixed time period may also be determined according to actual conditions and is not limited here. For example, the fixed time period may be the time period from the time each vehicle leaves the factory to the current time.

[0071] The motion characteristic parameters are obtained based on each driving moment of each historical driving cycle of each vehicle and the speed parameters of each driving moment.

[0072] The actual driving data may also include other data parameters, which are not limited here.

[0073] Among them, a historical driving cycle is a driving process of a vehicle from starting to braking.

[0074] In the embodiment of the present application, the controller of each vehicle uploads the speed, acquisition time, damage parameters and other data collected by the sensor to the cloud. The cloud determines whether the vehicle is in a driving state based on the speed, divides the continuous driving data into the same historical driving cycle, records the start and end time of the historical driving cycle, and the total driving time, and then obtains the kinematic segments and motion characteristic parameters corresponding to the historical driving cycle based on the total driving time and vehicle speed.

[0075] 102. Based on the motion characteristic parameter of each historical driving cycle of each candidate vehicle, determine at least one kinematic segment belonging to a different motion type from a plurality of kinematic segments to obtain a plurality of typical kinematic segments.

[0076] The multiple kinematic segments are kinematic segments corresponding to each historical driving cycle of each candidate vehicle, each kinematic segment is used to indicate a speed-time curve of a corresponding historical driving cycle, and each candidate vehicle is one of the multiple vehicles.

[0077] Among them, one motion feature parameter corresponds to one kinematic segment, or it can be said that one kinematic segment is obtained according to the corresponding motion feature parameter.

[0078] Among them, all of the multiple vehicles can be alternative vehicles, or some of the multiple vehicles can be alternative vehicles. The specific details can be determined based on actual conditions and are not limited here.

[0079] Among them, if some of the multiple vehicles are alternative vehicles, then any part of the multiple vehicles can be alternative vehicles, or some of the multiple vehicles that meet the target conditions can be alternative vehicles. The specific details can be determined based on actual conditions and are not limited here.

[0080] The target condition can be determined according to actual conditions and is not limited here. For example, the target condition is that the damage standard value of the target damaged part of the vehicle is greater than or equal to a certain threshold value.

[0081] In some embodiments of the present application, multiple typical kinematic segments belonging to different motion types can be manually determined from multiple kinematic segments, or the device can automatically determine multiple typical kinematic segments belonging to different motion types from multiple kinematic segments. The specific determination can be made based on actual conditions and is not limited here.

[0082] In some embodiments of the present application, Figure 1 ,like Figure 2a As shown, the above step 102 can be specifically implemented through the following steps 102a and 102b.

[0083] 102a. Based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, cluster the multiple kinematic segments to obtain multiple kinematic segment sets.

[0084] Each kinematic segment set includes multiple kinematic segments belonging to the same motion type.

[0085] The clustering process may be a clustering process based on a distance clustering algorithm, wherein the distance-based clustering algorithm includes but is not limited to: a K-means algorithm, a K-means++ algorithm, a bi-kmeans algorithm, an Agglomerative algorithm, and other clustering algorithms.

[0086] In the embodiment of the present application, clustering processing can be performed on the motion characteristic parameters of each historical driving cycle of each candidate vehicle to achieve clustering processing on multiple kinematic segments. Specifically, the motion characteristic parameters of each historical driving cycle of each candidate vehicle can be clustered, so that the motion characteristic parameters of each historical driving cycle of each vehicle are divided into multiple motion types, and the motion characteristic parameters belonging to the same motion type are grouped into a group to obtain multiple motion characteristic parameter sets, and correspondingly, the kinematic segments belonging to the same motion type are grouped into a group to obtain multiple kinematic segment sets.

[0087] 102b. Determine some kinematic segments in each kinematic segment set as the plurality of typical kinematic segments.

[0088] In some embodiments of the present application, any part of the kinematic segments in each kinematic segment set can be determined as the multiple typical kinematic segments, or some kinematic segments in each kinematic segment set that meet certain conditions can be determined as the multiple typical kinematic segments, which can be determined according to actual conditions and is not limited here.

[0089] In the embodiment of the present application, a clustering algorithm can be used to quickly determine the multiple typical kinematic segments belonging to different motion types from the multiple kinematic segments, thereby improving the accuracy and feasibility of the load spectrum of the target rotating component of the target vehicle model.

[0090] In some embodiments of the present application, the above step 102b can be specifically implemented through the following steps 102b1 and 102b2.

[0091] 102b1. Based on a similarity formula, determine the similarity between the kinematic segments in each kinematic segment set and the corresponding cluster center.

[0092] Determining the similarity between the kinematic segments in each kinematic segment set and the corresponding cluster center is actually determining the similarity between the motion feature parameters in each motion feature parameter set and the corresponding cluster center. The similarity between a kinematic segment and the corresponding cluster center is the similarity between the corresponding motion feature parameters and the corresponding cluster center.

[0093] 102b2. The corresponding kinematic segments in each kinematic segment set whose similarities are greater than or equal to the similarity threshold are determined as the multiple typical kinematic segments, or the corresponding kinematic segments in each kinematic segment set that are ranked in the top M in descending order of similarity are determined as the multiple typical kinematic segments.

[0094] The similarity formulas include but are not limited to the Euclidean distance formula, the Manhattan distance formula, the Minkowski distance formula, and the like.

[0095] For example, the similarity formula is the Euclidean distance formula (i.e., the similarity measurement indicator is the Euclidean distance). If the Euclidean distance between a kinematic segment and the corresponding cluster center is larger, the similarity is smaller. If the Euclidean distance between a kinematic segment and the corresponding cluster center is smaller, the similarity is greater. If the similarity between a kinematic segment and the corresponding cluster center is greater than or equal to the similarity threshold, it can be understood that the Euclidean distance between a kinematic segment and the corresponding cluster center is less than or equal to the distance threshold. The corresponding kinematic segments in each kinematic segment set that are ranked in the top M in terms of similarity from large to small can be ranked in the top M in terms of Euclidean distance from small to large in each kinematic segment set.

[0096] In an embodiment of the present application, the corresponding kinematic segments in each kinematic segment set whose similarity is greater than or equal to the similarity threshold are determined as the multiple typical kinematic segments through a similarity formula, or the corresponding kinematic segments in each kinematic segment set that are ranked in the top M in descending order of similarity are determined as the multiple typical kinematic segments, so that the multiple typical kinematic segments belonging to different motion types can be quickly determined from the multiple kinematic segments, thereby improving the accuracy and feasibility of the load spectrum of the target rotating part of the target vehicle model.

[0097] Exemplarily, the motion characteristic parameters of each historical driving cycle of the alternative vehicle can be calculated (as a cluster sample) based on data parameters such as each driving moment of each historical driving cycle of the alternative vehicle and the vehicle speed at each driving moment. Exemplarily, the motion characteristic parameters can include acceleration time ratio, maximum acceleration, average speed, etc. The motion characteristic parameters can also include at least one of acceleration standard deviation, speed standard deviation, driving distance, etc., which are not limited here.

[0098] The motion characteristic parameters of each historical driving cycle of each candidate vehicle are taken as multiple samples and clustered using the K-means clustering algorithm. The motion characteristic parameters of each historical driving cycle of each vehicle are divided into multiple motion types, and then the motion characteristic parameters belonging to the same motion type are grouped together. Correspondingly, the kinematic segments belonging to the same motion type are grouped together to obtain multiple kinematic segment sets. The samples close to the cluster center in each motion type are taken as typical motion characteristic parameters of the motion type, and then the kinematic segments corresponding to the typical motion characteristic parameters are taken as typical kinematic segments, which are used to construct a typical driving cycle kinematic segment library.

[0099] First, let the hyperparameter K in the K-means algorithm take values ​​from 1 to n, and calculate the SSE (sum of the squared errors) when K takes different values. When K is less than the actual number of clusters, the increase in K will greatly increase the degree of aggregation of each cluster, so the decrease in SSE will be large. When K reaches the actual number of clusters, the return of the degree of aggregation obtained by increasing K will rapidly decrease, so the decrease in SSE will drop sharply, and then tend to be flat as the K value continues to increase. The point that first tends to be flat is selected as the K value of the clustering algorithm.

[0100]

[0101] Among them, K represents the number of clusters, p represents the sample, and m q Represents the cluster center of the qth class.

[0102] Secondly, the motion feature parameters are normalized.

[0103] Finally, the K value selected in the previous step is used as the number of clusters of the K-means algorithm, the cluster centers are randomly initialized, the normalized motion feature parameters are clustered, and K motion feature parameter sets are obtained, and the similarity between each sample in each motion feature parameter set and its cluster center is calculated, and then the corresponding motion feature parameters in each motion feature parameter set whose similarity is greater than or equal to the similarity threshold are determined as the multiple typical motion feature parameters, and then the kinematic segments corresponding to the multiple typical motion feature parameters are determined as the multiple typical kinematic segments, or the corresponding motion feature parameters in each motion feature parameter set whose similarities are arranged from large to small in the top M are determined as the multiple typical motion feature parameters, and then the kinematic segments corresponding to the multiple typical motion feature parameters are determined as the multiple typical kinematic segments.

[0104] 103. The multiple typical kinematic segments are spliced ​​together to obtain a road spectrum of target duration.

[0105] In the embodiment of the present application, all or part of the kinematic segments in the multiple typical kinematic segments may be spliced ​​together to obtain a path spectrum of a target duration.

[0106] In some embodiments of the present application, the target duration is a preset duration or a total duration of the plurality of typical kinematic segments;

[0107] The preset duration is less than or equal to the total duration of the plurality of typical kinematic segments. The preset duration can be set according to actual conditions and is not limited here.

[0108] It can be understood that when all the kinematic segments in the multiple typical kinematic segments are spliced, the target duration is the total duration of the multiple typical kinematic segments; when some of the kinematic segments in the multiple typical kinematic segments are spliced, the target duration is the preset duration. In this way, the embodiments of the present application provide different splicing processing schemes, which can be determined according to actual conditions and are not limited here.

[0109] In some embodiments of the present application, the target duration is the preset duration; Figure 2a ,like Figure 2b As shown, the above step 103 can be specifically implemented through the following step 103a.

[0110] 103a. The multiple typical kinematic segments are spliced ​​according to the duration ratios of different motion types to obtain a road spectrum of the target duration.

[0111] The duration ratio is obtained by classifying the multiple kinematic segments according to different motion types, and the ratio of the total motion duration of the kinematic segments under each motion type.

[0112] Exemplarily, assuming that there are three types of exercise, the duration ratio is the ratio of the sum of the durations of each typical kinematic segment under each of the three exercise types, that is, the total exercise duration of exercise type 1: the total exercise duration of exercise type 2: the total exercise duration of exercise type 3.

[0113] In the embodiment of the present application, the multiple typical kinematic segments are spliced ​​according to the duration ratio of different motion types, and the resulting road spectrum is closer to the actual driving behavior and can better reflect the actual driving conditions.

[0114] The above step 103a can be specifically implemented through the following steps 103a1 and 103a2.

[0115] 103a1. Divide the preset duration into multiple intervals according to the duration ratio, and each interval corresponds to a sport type.

[0116] 103a2. Execute the following step S1 for each section to obtain the road spectrum of the preset duration.

[0117] Among them, S1 includes: determining at least one typical kinematic segment whose motion characteristic parameters best match the target motion type from the typical kinematic segments of the target motion type corresponding to the target interval; splicing the at least one typical kinematic segment to the target interval, and the target interval is any one of the multiple intervals.

[0118] Exemplarily, the typical kinematic segment whose motion characteristic parameters best match the target motion type can be at least one kinematic segment whose characteristics such as speed, acceleration, acceleration duration ratio, maximum acceleration, and average speed best match (are most similar to) the target motion type, or it can be at least one kinematic segment with the greatest similarity to the cluster center of the set of kinematic segments corresponding to the target motion type. The specific details can be determined based on actual conditions.

[0119] In an embodiment of the present application, from the typical kinematic segments corresponding to each motion type, at least one typical kinematic segment whose motion characteristic parameters best match the target motion type is determined, and then the at least one typical kinematic segment is spliced ​​into the corresponding duration interval, which can further make the obtained road spectrum closer to the actual driving behavior and better reflect the actual driving conditions.

[0120] 104. Input the road spectrum of the target duration into a vehicle dynamics simulation model of a target vehicle model, and simulate to obtain a target load spectrum of the target vehicle model.

[0121] Among them, the target load spectrum is the load spectrum of the target rotating component, and the whole vehicle dynamics simulation model includes a module corresponding to the target rotating component.

[0122] The target vehicle model may be the same as or different from the vehicle models of the multiple vehicles in the above step 101, which is not limited here.

[0123] The dynamics of different vehicle models often vary greatly. When the road spectrum constructed based on the operating big data of a certain vehicle model is applied to other vehicle models, it is necessary to use the vehicle dynamics simulation model to simulate the load spectrum of the target rotating parts based on the road spectrum. First, an initial vehicle dynamics simulation model is established. The initial vehicle dynamics simulation model includes but is not limited to battery modules, motor modules (such as front motor modules, rear motor modules), reducer modules, and vehicle parameter modules. Secondly, according to the configuration parameters of the target vehicle model (such as wind resistance, dynamics, frontal area, etc.), the relevant parameters of each module in the vehicle dynamics simulation model are adjusted to obtain the vehicle dynamics simulation model of the target vehicle model. For details, please refer to the existing relevant technologies, which will not be repeated here.

[0124] In the embodiment of the present application, based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle in the actual driving data of multiple vehicles of the same model, multiple typical kinematic segments that can cover different motion types are determined from each kinematic segment of the multiple candidate vehicles, and then the multiple typical kinematic segments are spliced ​​according to the duration ratio of different motion types to obtain the road spectrum of the target duration, and then according to the road spectrum of the target duration, the load spectrum of the target rotating component of the target model is obtained by simulating the whole vehicle dynamics simulation model of the target model (which can be the same as or different from the models of multiple vehicles). In this way, since the actual driving data of multiple vehicles of the same model can reflect the actual driving conditions, and the damage conditions of the target rotating components of multiple vehicles of the same model for the same motion type are basically the same, the load spectrum of the target rotating component of the target model is obtained by using the actual driving data of the vehicle of the same model. Compared with the driving data of various typical roads collected by the test vehicle of the target model according to the preset collection ratio, the embodiment of the present application does not rely on empirical settings, so that the load spectrum obtained by simulation can reflect the actual driving conditions and has high accuracy.

[0125] With the development of intelligent connected vehicles and the requirements of national regulations, most of the existing electric vehicles are required to be able to collect and upload real-time driving data such as speed and torque related to the damage of rotating parts (such as reducers or motors). Using real driving data, combined with the vehicle dynamics simulation model, the load spectrum of rotating parts can be constructed quickly and accurately. This solution only needs to collect the existing sensor signals of the vehicle, does not need to increase the hardware cost of the single vehicle, and does not affect other control functions of the current controller; at the same time, this solution does not require additional test vehicles and testers, which can reduce costs and shorten the development cycle; and this solution uses real vehicle driving data to efficiently and low-costly construct the load spectrum of different models, which is closer to real driving behavior and can better reflect the real driving conditions.

[0126] In some embodiments of the present application, the actual driving data also includes damage parameters of the target rotating parts of each historical driving cycle of each vehicle; Figure 2b ,like Figure 3 As shown, before the above step 102, the method for determining the load spectrum provided in the embodiment of the present application may further include the following steps 105 and 106.

[0127] 105. Determine a damage standard value of the target rotating component of each vehicle based on a damage standard calculation formula and the damage parameter of each historical driving cycle of each vehicle.

[0128] In some embodiments of the present application, if the target rotating component is a reducer, the damage standard calculation formula may be a formula related to torque and / or rotational speed, which may be specifically determined based on actual conditions and is not limited here.

[0129] Exemplarily, the target rotating component is a reducer, the damage parameter includes torque and number of revolutions, the damage standard value is a damage value per unit mileage, and the damage standard calculation formula includes:

[0130]

[0131] Where D1 is the damage value per unit mileage, T i is the torque in the i-th period of constant torque in each historical driving cycle of the vehicle, N i The torque is T i is the total number of revolutions at , a is the power exponent of the torque, and S is the total mileage of each historical driving cycle of the vehicle.

[0132] Among them, a can be determined according to actual conditions and is not limited here.

[0133] In an embodiment of the present application, when the target rotating component is a reducer, the damage parameters include torque and number of revolutions, and the damage standard value is a damage value per unit mileage, the damage standard value can be calculated more accurately through the above-mentioned damage standard calculation formula, thereby obtaining alternative vehicles that cover most of the vehicle damage conditions.

[0134] Exemplarily, the target rotating component is a reducer, and the damage standard calculation formula may also be the ratio of the duration of time when the torque is greater than the torque threshold in the total operating time in each driving cycle of the vehicle.

[0135] In some embodiments of the present application, if the target rotating component is a motor, the damage standard calculation formula may be a formula related to the rotational speed, which may be specifically determined based on actual conditions and is not limited here.

[0136] Exemplarily, the target rotating component is a motor, the damage parameter includes the rotation speed, the damage standard value is the proportion of high speed duration, and the damage standard calculation formula includes:

[0137]

[0138] Among them, D2 is the proportion of high speed time, t j is the duration of the motion process in which the jth speed is greater than or equal to the speed threshold in each historical driving cycle of the vehicle, t k is the total duration of the kth historical driving cycle of the vehicle.

[0139] In the embodiment of the present application, the target rotating component is a motor, the damage parameter includes the rotational speed, and the damage standard value is the proportion of high speed time. Through the above damage standard calculation formula, the damage standard value can be calculated more accurately, thereby obtaining alternative vehicles that cover most vehicle damage conditions.

[0140] 106. Determine a plurality of candidate vehicles from the plurality of vehicles based on the damage standard value of each vehicle.

[0141] Among them, the damage standard value of each alternative vehicle is greater than the damage standard value of other vehicles among the multiple vehicles except the multiple alternative vehicles.

[0142] In some embodiments of the present application, the above step 106 can be specifically implemented through the following step 106a or through the following steps 106b and 106c.

[0143] 106a. Determine the vehicles among the multiple vehicles whose damage standard values ​​are greater than or equal to the damage threshold as the multiple candidate vehicles.

[0144] The damage threshold can be determined according to actual conditions and is not limited here.

[0145] In an embodiment of the present application, based on the relationship between the damage standard value and the damage threshold of each vehicle, an alternative vehicle that can cover most of the vehicle damage conditions can be quickly determined from multiple vehicles, thereby improving the accuracy and feasibility of the load spectrum of the target rotating parts of the target vehicle model.

[0146] 106b. Sort the multiple vehicles according to the damage standard values ​​from large to small.

[0147] 106c. Determine some of the top-ranked vehicles as the multiple candidate vehicles.

[0148] The preset number may be set according to actual conditions and is not limited here. For example, the preset number may be 20%, 30%, 40%, 50%, etc. of the total number of the multiple vehicles.

[0149] In an embodiment of the present application, the multiple vehicles are sorted from large to small according to the damage standard value, and some of the top-ranked vehicles (i.e., some of the vehicles with the damage standard values ​​in the front) are determined as the multiple alternative vehicles. In this way, the alternative vehicles that can cover most of the automobile damage conditions can be quickly determined from the multiple vehicles, thereby improving the accuracy and feasibility of the load spectrum of the target rotating parts of the target vehicle model.

[0150] For example, Figure 4 As shown, it is a road spectrum of preset duration obtained according to the above steps 101 to 103 and steps 105 and 106 of the embodiment of the present application, as shown in FIG. Figure 5 As shown, according to Figure 4 The load spectrum obtained by road spectrum simulation is shown in Figure 1, where: Figure 5 The solid line in represents the torque-time curve. Figure 5 The dashed line in represents the speed-time curve.

[0151] The present application also provides a load spectrum determination device, Figure 6 A schematic diagram of a load spectrum determination device provided in this application is shown in FIG. Figure 6 As shown, the load spectrum determination device includes:

[0152] An acquisition module 601 is used to acquire actual driving data corresponding to multiple vehicles of the same vehicle model, wherein the actual driving data includes motion characteristic parameters of each historical driving cycle of at least one historical driving cycle of each vehicle; a determination module 602 is used to determine at least one kinematic segment belonging to a different motion type from multiple kinematic segments based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, and obtain multiple typical kinematic segments, wherein the multiple kinematic segments are respective kinematic segments corresponding to each historical driving cycle of each candidate vehicle, each kinematic segment is used to indicate a speed-time curve of a corresponding historical driving cycle, and each candidate vehicle is one of the multiple vehicles; a splicing module 603 is used to splice the multiple typical kinematic segments to obtain a road spectrum of a target duration; a simulation module 604 is used to input the road spectrum of the target duration into a whole vehicle dynamics simulation model of a target vehicle model, and simulate to obtain a target load spectrum of the target vehicle model, wherein the target load spectrum is a load spectrum of a target rotating component, and the whole vehicle dynamics simulation model includes a module corresponding to the target rotating component.

[0153] In some embodiments of the present application, the actual driving data also includes damage parameters of the target rotating component of each historical driving cycle of each vehicle; the determination module 602 is also used to determine at least one kinematic segment belonging to a different motion type from a plurality of kinematic segments based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, and before obtaining a plurality of typical kinematic segments, determine the damage standard value of the target rotating component of each vehicle based on a damage standard calculation formula and the damage parameters of each historical driving cycle of each vehicle; the determination module 602 is also used to determine a plurality of candidate vehicles from the plurality of vehicles based on the damage standard value of each vehicle, the damage standard value of each candidate vehicle being greater than the damage standard value of other vehicles among the plurality of vehicles except the plurality of candidate vehicles.

[0154] In some embodiments of the present application, the target rotating component is a reducer, the damage parameter includes torque and number of revolutions, the damage standard value is a damage value per unit mileage, and the damage standard calculation formula includes:

[0155]

[0156] Where D1 is the damage value per unit mileage, T i is the torque in the i-th period of constant torque in each historical driving cycle of the vehicle, N i The torque is T i is the total number of revolutions at , a is the power exponent of the torque, and S is the total mileage of each historical driving cycle of the vehicle.

[0157] In some embodiments of the present application, the target rotating component is a motor, the damage parameter includes the rotation speed, the damage standard value includes the proportion of high speed duration, and the damage standard calculation formula includes:

[0158]

[0159] Among them, D2 is the proportion of high speed time, t j is the duration of the motion process in which the jth speed is greater than or equal to the speed threshold in each historical driving cycle of the vehicle, t k is the total duration of the kth historical driving cycle of the vehicle.

[0160] In some embodiments of the present application, the determination module 602 is specifically used to determine the vehicles among the multiple vehicles whose damage standard values ​​are greater than or equal to the damage threshold as the multiple alternative vehicles; or, to sort the multiple vehicles from large to small according to the damage standard values; and to determine some of the top-ranked vehicles as the multiple alternative vehicles.

[0161] In some embodiments of the present application, the determination module 602 is specifically used to cluster the multiple kinematic segments based on the motion characteristic parameters of each historical driving cycle of each alternative vehicle to obtain multiple kinematic segment sets, each kinematic segment set including multiple kinematic segments belonging to the same motion type; and determine some kinematic segments in each kinematic segment set as the multiple typical kinematic segments.

[0162] In some embodiments of the present application, the determination module 602 is specifically used to determine the similarity between the kinematic segments in each kinematic segment set and the corresponding cluster center based on a similarity formula; determine the corresponding kinematic segments in each kinematic segment set whose similarity is greater than or equal to a similarity threshold as the multiple typical kinematic segments, or determine the corresponding kinematic segments in each kinematic segment set that are ranked in the top M in descending order of similarity as the multiple typical kinematic segments.

[0163] In some embodiments of the present application, the target duration is a preset duration or a total duration of the plurality of typical kinematic segments;

[0164] The preset duration is less than or equal to the total duration of the multiple typical kinematic segments.

[0165] In some embodiments of the present application, the target duration is the preset duration; the splicing module 603 is specifically used to splice the multiple typical kinematic segments according to the duration ratio of different motion types to obtain the road spectrum of the target duration, and the duration ratio is the ratio of the total motion duration of the kinematic segments under each motion type, when the multiple kinematic segments are classified according to different motion types.

[0166] In some embodiments of the present application, the splicing module 603 is specifically used to divide the preset duration into multiple intervals according to the proportion of the duration, each interval corresponding to a motion type; execute the following step S1 for each interval to obtain the road spectrum of the preset duration; wherein S1 includes: from the typical kinematic segments of the target motion type corresponding to the target interval, determine at least one typical kinematic segment whose motion feature parameters best match the target motion type; and splice the at least one typical kinematic segment to the target interval, the target interval being any one of the multiple intervals.

[0167] It should be noted that the above-mentioned load spectrum determination device can be the electronic device in the above-mentioned method embodiment of the present application, or it can be a functional module and / or functional entity in the electronic device that can realize the function of the device embodiment, and the embodiment of the present application is not limited.

[0168] In the embodiment of the present application, each module can implement the method for determining the load spectrum provided in the above method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0169] Figure 7 The structural schematic diagram of an electronic device provided in the embodiment of the present application is used to exemplarily illustrate the electronic device that implements the method for determining any load spectrum in the embodiment of the present application, and should not be understood as a specific limitation on the embodiment of the present application.

[0170] like Figure 7 As shown, the electronic device 700 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 are also stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0171] Typically, the following devices may be connected to the I / O interface 705: input devices 706 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 708 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 709. The communication device 709 may allow the electronic device 700 to communicate with other devices wirelessly or by wire to exchange data. Although the electronic device 700 having various devices is shown, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0172] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 709, or installed from the storage device 708, or installed from the ROM 702. When the computer program is executed by the processor 701, the functions defined in the method for determining any load spectrum provided in the embodiment of the present application can be executed.

[0173] It should be noted that the computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0174] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0175] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0176] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains actual driving data corresponding to multiple vehicles of the same model, the actual driving data includes motion characteristic parameters of each historical driving cycle in at least one historical driving cycle of each vehicle; based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, determines at least one kinematic segment belonging to a different motion type from multiple kinematic segments to obtain multiple typical kinematic segments, the multiple kinematic segments are respective kinematic segments corresponding to each historical driving cycle of each candidate vehicle, each kinematic segment is used to indicate a speed-time curve of a corresponding historical driving cycle, and each candidate vehicle is one of the multiple vehicles; splices the multiple typical kinematic segments to obtain a road spectrum of a target duration; inputs the road spectrum of the target duration into a whole vehicle dynamics simulation model of a target vehicle model, and simulates to obtain a target load spectrum of the target vehicle model, the target load spectrum being a load spectrum of the target rotating component.

[0177] In an embodiment of the present application, a computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, and the above-mentioned programming languages ​​include but are not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and also include conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed completely on the computer, partially on the computer, as an independent software package, partially on the computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0178] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0179] The units involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of a unit does not, in some cases, constitute a limitation on the unit itself.

[0180] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0181] In the context of the present application, a computer-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0182] An embodiment of the present application further provides a vehicle, comprising: the above-mentioned load spectrum determination method device, or the above-mentioned electronic device, or the above-mentioned computer-readable storage medium.

[0183] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

[0184] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or to be performed in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0185] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for determining a load spectrum, characterized in that: The method comprises: Acquire actual driving data corresponding to a plurality of vehicles of the same vehicle model, wherein the actual driving data includes motion characteristic parameters of each historical driving cycle in at least one historical driving cycle of each vehicle; Based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, at least one kinematic segment belonging to a different motion type is determined from a plurality of kinematic segments to obtain a plurality of typical kinematic segments, wherein the plurality of kinematic segments are kinematic segments corresponding to each historical driving cycle of each candidate vehicle, each kinematic segment is used to indicate a speed-time curve of a corresponding historical driving cycle, and each candidate vehicle is one of the plurality of vehicles; Splicing the multiple typical kinematic segments to obtain a road spectrum of target duration; The road spectrum of the target duration is input into the whole vehicle dynamics simulation model of the target vehicle model, and the target load spectrum of the target vehicle model is simulated. The target load spectrum is the load spectrum of the target rotating component, and the whole vehicle dynamics simulation model includes a module corresponding to the target rotating component.

2. The method according to claim 1, characterized in that The actual driving data also includes damage parameters of the target rotating component of each historical driving cycle of each vehicle; before determining at least one kinematic segment belonging to different motion types from a plurality of kinematic segments based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle and obtaining a plurality of typical kinematic segments, the method further includes: Determining a damage standard value of the target rotating component of each vehicle based on a damage standard calculation formula and the damage parameter of each historical driving cycle of each vehicle; Based on the damage standard value of each vehicle, a plurality of candidate vehicles are determined from the plurality of vehicles, the damage standard value of each candidate vehicle being greater than the damage standard values ​​of other vehicles in the plurality of vehicles except the plurality of candidate vehicles.

3. The method according to claim 2, characterized in that The target rotating component is a reducer, the damage parameters include torque and number of revolutions, the damage standard value is a damage value per unit mileage, and the damage standard calculation formula includes: Where D1 is the damage value per unit mileage, T i is the torque in the i-th period of constant torque in each historical driving cycle of the vehicle, N i The torque is T i is the total number of revolutions at , a is the power exponent of the torque, and S is the total mileage of each historical driving cycle of the vehicle.

4. The method according to claim 2, characterized in that: The target rotating component is a motor, the damage parameter includes the rotation speed, the damage standard value is the proportion of high speed duration, and the damage standard calculation formula includes: Among them, D2 is the proportion of high speed time, t j is the duration of the motion process in which the jth speed is greater than or equal to the speed threshold in each historical driving cycle of the vehicle, t k is the total duration of the kth historical driving cycle of the vehicle.

5. The method according to claim 2, characterized in that: The step of determining a plurality of candidate vehicles from the plurality of vehicles based on the damage standard value of each vehicle comprises: Determine the vehicles whose damage standard values ​​are greater than or equal to the damage threshold value among the multiple vehicles as the multiple candidate vehicles; Alternatively, the plurality of vehicles are sorted from largest to smallest according to the damage standard values; Some of the vehicles with high rankings are determined as the plurality of candidate vehicles.

6. The method according to claim 1, characterized in that The method of determining at least one kinematic segment belonging to different motion types from a plurality of kinematic segments based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, and obtaining a plurality of typical kinematic segments, comprises: Based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, clustering the multiple kinematic segments to obtain multiple kinematic segment sets, each kinematic segment set including multiple kinematic segments belonging to the same motion type; Some kinematic segments in each kinematic segment set are determined as the multiple typical kinematic segments.

7. The method according to claim 6, characterized in that The step of determining some kinematic segments in each kinematic segment set as the plurality of typical kinematic segments comprises: Based on a similarity formula, determining the similarity between the kinematic segments in each kinematic segment set and the corresponding cluster center; The kinematic segments whose corresponding similarities in each kinematic segment set are greater than or equal to the similarity threshold are determined as the multiple typical kinematic segments, or the kinematic segments whose corresponding similarities in each kinematic segment set are arranged in descending order and ranked in the top M are determined as the multiple typical kinematic segments.

8. The method according to any one of claims 1 to 7, characterized in that The target duration is a preset duration or a total duration of the plurality of typical kinematic segments; The preset duration is less than or equal to the total duration of the multiple typical kinematic segments.

9. The method according to claim 8, characterized in that The target duration is the preset duration; the splicing of the multiple typical kinematic segments to obtain a road spectrum of the target duration includes: The multiple typical kinematic segments are spliced ​​according to the duration ratio of different motion types to obtain the road spectrum of the target duration. The duration ratio is the ratio of the total motion duration of the kinematic segments under each motion type after the multiple kinematic segments are classified according to different motion types.

10. The method according to claim 9, characterized in that The step of splicing the plurality of typical kinematic segments according to the duration ratios of different motion types to obtain the road spectrum of the target duration includes: According to the duration ratio, the preset duration is divided into a plurality of intervals, each interval corresponding to a sport type; For each of the intervals, the following step S1 is performed to obtain the road spectrum of the preset duration; Wherein, S1 comprises: determining at least one typical kinematic segment whose motion characteristic parameters best match the target motion type from typical kinematic segments of the target motion type corresponding to the target interval; The at least one typical kinematic segment is spliced ​​into the target interval, where the target interval is any one of the multiple intervals.

11. A device for determining a load spectrum, characterized in that: include: An acquisition module, configured to acquire actual driving data corresponding to a plurality of vehicles of the same vehicle model, wherein the actual driving data includes motion characteristic parameters of each historical driving cycle in at least one historical driving cycle of each vehicle; a determination module, configured to determine, based on the motion characteristic parameters of each historical driving cycle of each candidate vehicle, at least one kinematic segment belonging to a different motion type from a plurality of kinematic segments, to obtain a plurality of typical kinematic segments, wherein the plurality of kinematic segments are kinematic segments corresponding to each historical driving cycle of each candidate vehicle, each kinematic segment is used to indicate a speed-time curve of a corresponding historical driving cycle, and each candidate vehicle is one of the plurality of vehicles; A splicing module, used for splicing the multiple typical kinematic segments to obtain a road spectrum of target duration; A simulation module is used to input the road spectrum of the target duration into the vehicle dynamics simulation model of the target vehicle model, and simulate to obtain the target load spectrum of the target vehicle model. The target load spectrum is the load spectrum of the target rotating component. The vehicle dynamics simulation model includes a module corresponding to the target rotating component.

12. An electronic device, characterized in that: include: A processor, wherein the processor is used to execute a computer program stored in a memory, wherein the computer program, when executed by the processor, implements the steps of the method for determining a load spectrum according to any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for determining a load spectrum according to any one of claims 1 to 10 are implemented.

14. A vehicle, characterized in that: include: The load spectrum determination device according to claim 11, or the electronic device according to claim 12, or includes the computer-readable storage medium according to claim 13.

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