Data processing method, device, processor and electronic equipment of vehicle

Abstract

The application discloses a data processing method and device of a vehicle, a processor and electronic equipment. The method comprises the following steps: determining scene data of a driving scene in which the vehicle is located, and driving data of the vehicle in a driving process in the driving scene, wherein the scene data is used for representing a type to which the driving scene belongs and / or a state in which the driving scene is located, and the driving data is used for representing driving states of the vehicle in response to different driving operations; dividing the driving data based on the scene data to obtain a plurality of sub-driving data; determining a plurality of target sub-driving data from the plurality of sub-driving data based on different driving operations, wherein the plurality of target sub-driving data correspond to the different driving operations one by one; and splicing the plurality of target sub-driving data based on the scene data to obtain working condition data of the vehicle. The application solves the technical problem of low precision of the working condition data of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicles, and more specifically, to a data processing method, apparatus, processor, and electronic device for vehicles. Background Technology

[0002] Currently, in related technologies, the analysis of vehicle operating conditions mainly focuses on supporting energy consumption analysis or fuel consumption analysis. Therefore, the extracted features are all comprehensive features of short-stroke or kinematic segments, such as duration, average vehicle speed, maximum vehicle speed, and maximum acceleration.

[0003] Although the comprehensive features extracted above can reflect clustering features, they do not take into account the actual situation of the vehicle. This will result in limitations in the operating condition data obtained based on the above comprehensive features, leading to the technical problem of low accuracy of vehicle operating condition data.

[0004] There is currently no effective solution to the technical problem of low accuracy in the operating data of the aforementioned vehicles. Summary of the Invention

[0005] This application provides a vehicle data processing method, apparatus, processor, and electronic device to at least solve the technical problem of low accuracy of vehicle operating condition data.

[0006] According to one aspect of the embodiments of this application, a vehicle data processing method is provided. The method includes: determining scene data of a driving scenario in which the vehicle is located, and driving data of the vehicle in the driving process under the driving scenario, wherein the scene data is used to represent the type and / or state of the driving scenario, and the driving data is used to represent the driving state of the vehicle in response to different driving operations; dividing the driving data based on the scene data to obtain multiple sub-driving data; determining multiple target sub-driving data from the multiple sub-driving data based on different driving operations, wherein the multiple target sub-driving data correspond one-to-one with different driving operations; and concatenating the multiple target sub-driving data based on the scene data to obtain vehicle operating condition data.

[0007] Optionally, based on different driving operations, multiple target sub-driving data are determined from multiple sub-driving data, including: determining the driving characteristics of each of the multiple sub-driving data to obtain multiple driving characteristics; and determining multiple target sub-driving data based on the multiple driving characteristics, different driving operations, and multiple sub-driving data.

[0008] Optionally, multiple target sub-driving data are determined based on multiple driving features, different driving operations, and multiple sub-driving data, including: performing feature analysis on multiple driving features to obtain multiple feature analysis results; determining at least two target driving features from the multiple driving features according to the multiple feature analysis results, wherein the contribution of the target driving features to the sub-driving data is higher than the contribution of other driving features (excluding the target driving features) to the sub-driving data; and determining multiple target sub-driving data based on at least two target driving features, different driving operations, and multiple sub-driving data.

[0009] Optionally, based on at least two target driving features, different driving operations, and multiple sub-driving data, multiple target sub-driving data are determined, including: constructing a coordinate system with at least two target driving features as coordinate axes, wherein the coordinate system is divided into multiple grids according to a preset scale, and the multiple grids include multiple sub-driving data; mapping the multiple sub-driving data onto the coordinate system; and determining multiple target sub-driving data based on the multiple grids, different driving operations, and multiple mapped sub-driving data.

[0010] Optionally, based on multiple grids, different driving operations, and multiple mapped sub-driving data, multiple target sub-driving data are determined, including: numbering the multiple mapped sub-driving data according to the arrangement order of the multiple grids; determining the number of multiple numbered sub-driving data included in each grid, and the total number of multiple numbered sub-driving data; and determining multiple target sub-driving data from each grid according to the proportion of the number to the total number and different driving operations.

[0011] Optionally, the method further includes: acquiring raw driving data of the vehicle during the driving process; cleaning and / or supplementing the raw driving data to obtain driving data.

[0012] Optionally, based on the scene data, multiple target sub-driving data are spliced ​​together to obtain vehicle operating condition data, including: determining a splicing strategy that matches the scene data, wherein the splicing strategy is used to represent the way to splice multiple target sub-driving data according to the sequence relationship between different driving operations under the scene data; and splicing multiple target sub-driving data according to the splicing strategy to obtain operating condition data.

[0013] According to one aspect of the embodiments of this application, a vehicle data processing apparatus is provided. The apparatus may include: a first determining unit, configured to determine scene data of a driving scenario in which the vehicle is located, and driving data of the vehicle in the driving process within the driving scenario, wherein the scene data is used to represent the type and / or state of the driving scenario, and the driving data is used to represent the driving state of the vehicle in response to different driving operations; a dividing unit, configured to divide the driving data based on the scene data to obtain multiple sub-driving data; a second determining unit, configured to determine multiple target sub-driving data from the multiple sub-driving data based on different driving operations, wherein the multiple target sub-driving data correspond one-to-one with different driving operations; and a splicing unit, configured to splice the multiple target sub-driving data based on the scene data to obtain vehicle operating condition data.

[0014] According to another aspect of the embodiments of this application, a processor is also provided. The processor is used to run a program, wherein the program is executed by the processor to perform the methods described in the embodiments of this application.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0016] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided. This computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of the embodiments of this application.

[0017] According to another aspect of the embodiments of this application, a computer program product is also provided, the computer program product including a computer program, wherein the computer program implements the method in the embodiments of this application when executed by a processor.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the method in the embodiments of this application.

[0019] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods described in the embodiments of this application.

[0020] In this embodiment, scenario data of the driving scenario in which the vehicle is located and driving data of the vehicle during the driving process within the driving scenario are determined. Based on the scenario data, the driving data is divided into multiple sub-driving data. Based on different driving operations, multiple target sub-driving data are determined from the multiple sub-driving data. Based on the scenario data, the multiple target sub-driving data are spliced ​​together to obtain the vehicle's operating condition data. Since this embodiment, after determining the scenario data and driving data, divides the driving data based on the determined scenario data to obtain multiple sub-driving data, and then, based on different driving operations, determines multiple target sub-driving data from the divided multiple sub-driving data, and then splices the multiple target sub-driving data based on the aforementioned scenario data to obtain operating condition data, it achieves the goal of focusing on changes in driving behavior within short-distance travel or kinematic segments, thereby solving the technical problem of low accuracy in vehicle operating condition data and achieving the technical effect of improving the accuracy of vehicle operating condition data. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 This is a schematic diagram illustrating an application scenario of a vehicle data processing method according to an embodiment of this application;

[0023] Figure 2 This is a flowchart of a vehicle data processing method according to an embodiment of this application;

[0024] Figure 3 This is a flowchart of a method for constructing the driving conditions of a vehicle according to an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of the principal component analysis results of a typical accelerated driving behavior according to an embodiment of this application;

[0026] Figure 5 This is a schematic diagram of a grid representing a typical accelerated driving behavior according to an embodiment of this application;

[0027] Figure 6 This is a schematic diagram of a fitted road spectrum according to an embodiment of this application;

[0028] Figure 7 This is a schematic diagram of a vehicle data processing device according to an embodiment of this application;

[0029] Figure 8 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] According to an embodiment of this application, an embodiment of a vehicle data processing method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] As an optional implementation, the above-described vehicle data processing method can be applied, but is not limited to, to applications such as... Figure 1 The application scenarios shown. Figure 1 This is a schematic diagram illustrating an application scenario of a vehicle data processing method according to an embodiment of this application, such as... Figure 1 As shown, in the application scenario, terminal device 10 can communicate with server 13 via network 11, but is not limited to this. Server 13 can perform operations on the database, such as write or read data operations. Terminal device 10 may include, but is not limited to, a human-computer interaction screen, a processor, and a memory. The human-computer interaction screen can be used to display virtual machines on mobile terminal 10, but is not limited to this. Vehicle 12 can be used to respond to the aforementioned human-computer interaction operations, execute corresponding operations, or generate corresponding instructions and send the generated instructions to server 13.

[0034] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here. The vehicle data processing method of this application may include: step S102, determining scene data of the driving scenario in which the vehicle is located, and driving data of the vehicle during driving in the driving scenario; step S104, dividing the driving data based on the scene data to obtain multiple sub-driving data; step S106, determining multiple target sub-driving data from the multiple sub-driving data based on different driving operations; and step S108, concatenating the multiple target sub-driving data based on the scene data to obtain vehicle operating condition data.

[0035] It should be noted that all information and data involved in this application (including but not limited to scenario data, driving data, and operating condition data) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use, and processing of such data must comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0036] According to an embodiment of this application, a data processing method for a vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] Figure 2 This is a flowchart of a vehicle data processing method according to an embodiment of this application, such as... Figure 2 As shown, the method may include the following steps.

[0038] Step S201: Determine the scene data of the driving scenario in which the vehicle is located, and the driving data of the vehicle in the driving process under the driving scenario.

[0039] In the technical solution provided by step S201 of this application, the aforementioned scene data can be used to represent the type and / or state of the driving scene. Optionally, the aforementioned scene data can be referred to as driving scene data or driving scene data.

[0040] In this embodiment, the driving data can be used to represent the driving state of the vehicle in response to different driving operations. Optionally, the driving data is the same as the travel data, which may include: time in seconds, vehicle speed, powertrain torque, powertrain speed, gear information, and Global Positioning System (GPS) data, etc.

[0041] In this embodiment, the driving operation described above can be used to control the vehicle to produce driving behavior, which may include actions such as starting acceleration, normal acceleration, constant speed, deceleration, and stopping. For example, if the driving operation described above is a starting acceleration operation, it can control the vehicle to produce starting acceleration behavior. This is only an example and is not a specific limitation.

[0042] In this embodiment, scene data of the driving scenario in which the vehicle is located, and driving data of the vehicle during the driving process within the driving scenario are determined. Optionally, this embodiment identifies the driving scenario in which the vehicle is located to obtain scene data. Furthermore, driving data can be extracted from the vehicle's log files, thereby achieving the purpose of determining both scene data and driving data.

[0043] Optionally, raw driving data can be extracted from the vehicle's log files. Noise removal of the raw driving data yields the final driving data.

[0044] Step S202: Based on the scene data, the driving data is divided into multiple sub-driving data.

[0045] In the technical solution provided by step S202 of this application, the aforementioned multiple sub-driving data can be used to represent different segments of driving behavior.

[0046] In this embodiment, after determining the scene data of the driving scenario in which the vehicle is located, and the driving data of the vehicle during the driving process within that scenario, the driving data is divided based on the scene data to obtain multiple sub-driving data. Optionally, this embodiment, based on the determined scene data and driving data, classifies the scene data to obtain the scene category to which the scene data belongs. Then, according to the category to which the scene data belongs, the driving data is divided to obtain multiple sub-driving data, thereby achieving the purpose of identifying different driving behavior segments.

[0047] Optionally, classifying the weight information of the vehicle can obtain the weight classification to which the above weight information belongs, where the above weight information can be used to represent the weight (m) of the vehicle. For example, the above weight can also be referred to as mass, and the above weight classification can be marked as but not limited to: m < 4.5 tons (t), 4.5t ≤ m < 8.5t, 8.5t ≤ m < 12t, 12t < m. Dividing the driving data according to the above weight classification and the above scenario classification can obtain multiple sub-driving data.

[0048] Step S203, based on different driving operations, determine multiple target sub-driving data from the multiple sub-driving data.

[0049] In the technical solution provided in step S203 of the present application, the above multiple target sub-driving data correspond one-to-one to different driving operations, where the above multiple target sub-driving data can be used to represent the target driving behavior segments determined from different driving behavior segments.

[0050] In this embodiment, after dividing the driving data based on the scenario data to obtain multiple sub-driving data, multiple target sub-driving data are determined from the multiple sub-driving data based on different driving operations. Optionally, based on the divided multiple sub-driving data, the multiple sub-driving data are respectively matched according to different driving operations, and the sub-driving data that matches the driving operation is determined as the target sub-driving data, thereby achieving the purpose of determining multiple target sub-driving data from the multiple sub-driving data.

[0051] [[ID=,12]]Optionally, determine the matching degree between each sub-driving data and different driving operations respectively to obtain multiple matching degrees. From the multiple matching degrees, determine the matching degree greater than the matching degree threshold as the target matching degree, and determine the sub-driving data with the target matching degree as the target sub-driving data.

[0052] Step S204, based on the scenario data, splice the multiple target sub-driving data to obtain the vehicle's operating condition data.

[0053] In the technical solution provided in step S204 of the present application, the above splicing can also be referred to as connection in the following text.

[0054] In this embodiment, the above operating condition data can be used to represent the driving condition of the vehicle.

[0055] In this embodiment, after determining multiple target sub-driving data from multiple sub-driving data based on different driving operations, the multiple target sub-driving data are concatenated based on scene data to obtain vehicle operating condition data. Optionally, this embodiment, based on the determined multiple target sub-driving data, concatenates the multiple target sub-driving data based on scene data to obtain concatenated data. This concatenated data can be used to represent a sequence of driving states related to the sequential relationships between different driving operations in a driving scenario. By determining the concatenated data as vehicle operating condition data, the purpose of determining the vehicle's driving operating condition is achieved.

[0056] In steps S201 to S204 of this application, scene data of the driving scenario in which the vehicle is located and driving data of the vehicle during the driving process in the driving scenario are determined; based on the scene data, the driving data is divided to obtain multiple sub-driving data; based on different driving operations, multiple target sub-driving data are determined from the multiple sub-driving data; based on the scene data, the multiple target sub-driving data are spliced ​​to obtain the vehicle's operating condition data. Since this embodiment of the application, after determining the scene data and driving data, divides the driving data based on the determined scene data to obtain multiple sub-driving data, and then, based on different driving operations, determines multiple target sub-driving data from the divided multiple sub-driving data, and then splices the multiple target sub-driving data based on the aforementioned scene data to obtain operating condition data, it achieves the purpose of focusing on changes in driving behavior within short-distance travel or kinematic segments, thereby solving the technical problem of low accuracy of vehicle operating condition data and achieving the technical effect of improving the accuracy of vehicle operating condition data.

[0057] The method described in this embodiment will be further described below.

[0058] As an optional embodiment, step S203 involves determining multiple target sub-driving data from multiple sub-driving data based on different driving operations, including: determining the driving characteristics of each of the multiple sub-driving data to obtain multiple driving characteristics; and determining multiple target sub-driving data based on the multiple driving characteristics, different driving operations, and multiple sub-driving data.

[0059] In this embodiment, the driving characteristics of each of the above-mentioned multiple sub-driving data are the feature parameters of different driving behavior segments.

[0060] In this embodiment, after dividing the driving data based on scene data to obtain multiple sub-driving data, the driving characteristics of each of the multiple sub-driving data are determined to obtain multiple driving characteristics. Then, based on the multiple driving characteristics, different driving operations and multiple sub-driving data, multiple target sub-driving data are determined.

[0061] Optionally, this embodiment, based on the division of multiple sub-driving data, determines the feature parameters of different driving behavior segments, thus obtaining multiple feature parameters. By combining multiple feature parameters, different driving operations, and different driving behavior segments, the target driving behavior segment can be determined. This achieves the goal of identifying the target driving behavior segment from different driving behavior segments, thereby realizing the technical effect of improving the effectiveness of the target driving behavior segment.

[0062] For example, performing principal component analysis on different driving behavior segments separately can yield multiple principal component analysis results. By combining these results with different driving operations, the target driving behavior segment can be identified from these segments.

[0063] The following section further describes the steps of determining multiple target sub-driving data based on multiple driving features, different driving operations, and multiple sub-driving data in this embodiment.

[0064] As an optional embodiment, determining multiple target sub-driving data based on multiple driving features, different driving operations, and multiple sub-driving data includes: performing feature analysis on the multiple driving features to obtain multiple feature analysis results; determining at least two target driving features from the multiple driving features according to the multiple feature analysis results, wherein the contribution of the target driving features to the sub-driving data is higher than the contribution of other driving features (excluding the target driving features) to the sub-driving data; and determining multiple target sub-driving data based on at least two target driving features, different driving operations, and multiple sub-driving data.

[0065] In this embodiment, the above feature analysis results are the principal component analysis results.

[0066] In this embodiment, the contribution of the target driving feature to the sub-driving data is higher than the contribution of other driving features (excluding the target driving feature) to the sub-driving data. For example, the at least two target driving features may include: initial velocity, duration, and average acceleration; these are merely illustrative examples and not specific limitations.

[0067] In this embodiment, after obtaining multiple driving features, feature analysis is performed on the multiple driving features to obtain multiple feature analysis results, and at least two target driving features are determined from the multiple driving features according to the multiple feature analysis results.

[0068] Optionally, this embodiment, based on obtaining multiple characteristic parameters, performs principal component analysis on these multiple characteristic parameters to obtain multiple principal component analysis results. Based on the multiple principal component analysis results, the initial velocity, duration, and average acceleration are determined as three characteristic parameters from among the multiple characteristic parameters.

[0069] For example, performing principal component analysis on a segment of driving behavior involving normal acceleration can yield the results and determine the proportion of each principal component. Then, based on the contribution of multiple feature parameters to the driving behavior segment, the three feature parameters with the highest contribution are selected.

[0070] In this embodiment, after determining at least two target driving features from multiple driving features according to the analysis results of multiple features, multiple target sub-driving data are determined based on the at least two target driving features, different driving operations, and multiple sub-driving data.

[0071] Optionally, based on determining at least two feature parameters, this embodiment combines at least two feature parameters with different driving operations to determine multiple target driving behavior segments from different driving behavior segments. This achieves the goal of determining target driving behavior segments from different driving behavior segments, thereby realizing the technical effect of improving the effectiveness of target driving behavior segments.

[0072] The steps for determining multiple target sub-driving data based on at least two target driving features, different driving operations, and multiple sub-driving data in this embodiment will be further described below.

[0073] As an optional embodiment, determining multiple target sub-driving data based on at least two target driving features, different driving operations, and multiple sub-driving data includes: constructing a coordinate system with at least two target driving features as coordinate axes, wherein the coordinate system is divided into multiple grids according to a preset scale, and the multiple grids include multiple sub-driving data; mapping the multiple sub-driving data onto the coordinate system; and determining multiple target sub-driving data based on the multiple grids, different driving operations, and multiple mapped sub-driving data.

[0074] In this embodiment, the coordinate system can be divided into multiple grids according to a preset scale. Optionally, the coordinate system can be a three-dimensional coordinate system or a two-dimensional coordinate system.

[0075] In this embodiment, the aforementioned multiple grids may include multiple sub-driving data. That is, the aforementioned multiple grids may include different segments of driving behavior.

[0076] In this embodiment, after determining at least two target driving features, a three-dimensional coordinate system is constructed using three feature parameters (e.g., initial velocity, duration, and average acceleration) as coordinate axes. Different driving behavior segments are mapped onto the three-dimensional coordinate system. By combining multiple grids and different driving operations, multiple target driving behavior segments can be determined from the multiple mapped driving behavior segments. This achieves the goal of determining target driving behavior segments from different driving behavior segments, thereby realizing the technical effect of improving the effectiveness of target driving behavior segments.

[0077] For example, a three-dimensional coordinate system is constructed with principal components 1, 2, and 3 (e.g., initial velocity, duration, and average acceleration) as the x, y, and z axes. Then, the three-dimensional coordinate system is divided into multiple grids according to a preset scale. Under the three-dimensional coordinate system, by combining multiple grids and different driving operations, multiple target driving behavior segments can be determined from multiple driving behavior segments.

[0078] The following section further describes the steps of determining multiple target sub-driving data based on multiple grids, different driving operations, and multiple mapped sub-driving data in this embodiment.

[0079] As an optional implementation method, multiple target sub-driving data are determined based on multiple grids, different driving operations, and multiple mapped sub-driving data, including: numbering the multiple mapped sub-driving data according to the arrangement order of the multiple grids; determining the number of multiple numbered sub-driving data included in each grid, and the total number of multiple numbered sub-driving data; and determining multiple target sub-driving data from each grid according to the proportion of the number to the total number and different driving operations.

[0080] In this embodiment, multiple mapped driving behavior segments are numbered according to the arrangement order of multiple grids. Then, the number of multiple numbered driving behavior segments included in each grid and the total number of multiple numbered driving behavior segments are determined. Based on the proportion of the number to the total number and different driving operations, multiple target driving behavior segments are determined from each grid. This achieves the goal of determining target driving behavior segments from different driving behavior segments, thereby realizing the technical effect of improving the effectiveness of target driving behavior segments.

[0081] For example, each driving behavior segment is numbered according to the grid arrangement order. The numbering method can be as follows: Assume that the x-axis of principal component 1 is divided into segments x1~x2, x2~x3, ..., xn-1~xn; the y-axis of principal component 2 is divided into segments y1~y2, y2~y3, ..., yn1~yn; and the z-axis of principal component 3 is divided into segments z1~z2, z2~z3, ..., zn-1~zn. Starting from grid x1~x2, y1~y2, and z1~z2, the driving behavior segments within it are numbered sequentially as 1, 2, 3, ..., nx1; then the driving behavior segments within grid x2~x3, y1~y2, and z1~z2 are continued to be numbered as nx1+1, nx1+2, ..., nx1+nx2; until the numbering of grid xn-1~xn, y1~y2, and z1~z2 is completed. Then continue numbering from grid x1~x2 and y2~y3 and z1~z2 to grid x2~x3 and y2~y3 and z1~z2, all the way to grid xn-1~xn and y2~y3 and z1~z2; continue numbering in this order until grid xn-1~xn and yn-1~yn and z1~z2; then repeat the above steps until grid xn-1~xn and yn-1~yn and zn-1~zn.

[0082] For another example, by counting the number of driving behavior segments within each grid, and then calculating the proportion of segments in each grid to the total number, we can obtain the number of target driving behavior segments to be extracted within each grid. For instance, a normal random distribution can be used to generate target driving behavior segments for the road spectrum to be fitted.

[0083] The data processing method for the vehicle described in this embodiment will be further described below.

[0084] As an optional embodiment, the method further includes: acquiring raw driving data of the vehicle during driving; cleaning and / or supplementing the raw driving data to obtain driving data.

[0085] In this embodiment, the aforementioned original driving data is the original driving data.

[0086] In this embodiment, after acquiring the original driving data of the vehicle during the driving process, the original driving data is cleaned and / or supplemented to obtain driving data.

[0087] Optionally, raw driving data can be extracted from the vehicle's log file, cleaned, and supplemented to obtain driving data. This achieves the goal of determining driving data and thus improves the accuracy of driving data.

[0088] Optionally, after extracting the raw driving data from the vehicle's log file, the raw driving data can be cleaned, and the cleaned raw driving data can be used as driving data. Alternatively, the raw driving data can be supplemented, and the supplemented raw driving data can be used as driving data.

[0089] The following section further describes the steps of stitching together multiple target sub-driving data based on scene data to obtain vehicle operating condition data in this embodiment.

[0090] As an optional embodiment, step S202 involves stitching together multiple target sub-driving data based on scene data to obtain vehicle operating condition data, including: determining a stitching strategy that matches the scene data, wherein the stitching strategy represents the method of stitching together multiple target sub-driving data according to the sequence relationship between different driving operations under the scene data; and stitching together multiple target sub-driving data according to the stitching strategy to obtain operating condition data.

[0091] In this embodiment, the above-mentioned splicing strategy can be used to represent the way to splice multiple target sub-driving data according to the sequential relationship between different driving operations in scene data. For example, the splicing strategy corresponds to a way to connect driving behavior segments that satisfy the sequential relationship of driving behaviors and the feature parameter regularity of preceding and following driving behavior segments.

[0092] In this embodiment, after determining multiple target sub-driving data, a splicing strategy matching the scene data is determined. Then, according to the determined splicing strategy, multiple target driving behavior segments are spliced ​​together to obtain spliced ​​driving behavior segments. The spliced ​​driving behavior segments can be used as the vehicle's operating condition data, thereby achieving the goal of determining the vehicle's operating condition data and thus realizing the technical effect of improving the accuracy of the vehicle's operating condition data.

[0093] For example, by using a global traversal optimization method, we can obtain a connection method for driving behavior segments that satisfies the sequential relationship of driving behaviors and the characteristic parameter regularity of consecutive driving behavior segments. Following this connection method, multiple target driving behavior segments are spliced ​​together to form a fitted road spectrum.

[0094] In this embodiment, scenario data of the driving scenario in which the vehicle is located and driving data of the vehicle during the driving process within the driving scenario are determined. Based on the scenario data, the driving data is divided into multiple sub-driving data. Based on different driving operations, multiple target sub-driving data are determined from the multiple sub-driving data. Based on the scenario data, the multiple target sub-driving data are spliced ​​together to obtain the vehicle's operating condition data. Since this embodiment, after determining the scenario data and driving data, divides the driving data based on the determined scenario data to obtain multiple sub-driving data, and then, based on different driving operations, determines multiple target sub-driving data from the divided multiple sub-driving data, and then splices the multiple target sub-driving data based on the aforementioned scenario data to obtain operating condition data, it achieves the goal of focusing on changes in driving behavior within short-distance travel or kinematic segments, thereby solving the technical problem of low accuracy in vehicle operating condition data and achieving the technical effect of improving the accuracy of vehicle operating condition data.

[0095] The technical solutions of the embodiments of this application will be illustrated below with reference to preferred embodiments.

[0096] Currently, in related technologies, the analysis of vehicle operating conditions mainly focuses on supporting energy consumption analysis or fuel consumption analysis. Therefore, the extracted features are all comprehensive features of short-stroke or kinematic segments, such as duration, average vehicle speed, maximum vehicle speed, and maximum acceleration.

[0097] Although the comprehensive features extracted above can reflect clustering features, they do not take into account the actual situation of the vehicle. This will result in limitations in the operating condition data obtained based on the above comprehensive features, leading to the technical problem of low accuracy of vehicle operating condition data.

[0098] To address the aforementioned technical problems, this application proposes a vehicle data processing method. After determining scene data and driving data, the driving data is divided based on the determined scene data to obtain multiple sub-driving data. Then, based on different driving operations, multiple target sub-driving data can be determined from the divided sub-driving data. Subsequently, based on the aforementioned scene data, the multiple target sub-driving data are spliced ​​together to obtain operating condition data. This achieves the goal of focusing on changes in driving behavior within short-distance travel or kinematic segments, thereby solving the technical problem of low accuracy in vehicle operating condition data and ultimately achieving the technical effect of improving the accuracy of vehicle operating condition data.

[0099] In this embodiment, by executing the method for constructing vehicle driving conditions, vehicle driving condition data can be constructed to reflect the vehicle's driving conditions. For example, Figure 3It is a flowchart of a method for constructing a driving condition of a vehicle according to an embodiment of the present application. As Figure 3 shown, the method may include the following steps.

[0100] Step S301, obtain the background information and driving data of the vehicle.

[0101] In the technical solution provided in step S301 of the present application above, the above background information may include: operation scenarios and / or vehicle identification number (abbreviated as VIN). The above driving data may include: time in seconds, vehicle speed, powertrain torque, powertrain speed, gear information, and Global Positioning System (abbreviated as GPS) data, etc.

[0102] After obtaining the background information and driving data of the vehicle, execute step S302 to clean and preprocess the driving data.

[0103] In the technical solution provided in step S302 of the present application above, delete invalid data from the driving data, and calculate the acceleration of each instant through the vehicle speed.

[0104] In this embodiment, delete the driving data corresponding to short trips that meet the following conditions from the driving data: Condition ①, vmax < v0 (for example, v0 = 5 km / h) and the duration < t0 (for example, t0 = 30 s). Condition ②, the duration < t1 (for example, t1 = 15 s). Condition ③, shift into reverse gear, or for a vehicle without a reverse gear, the short trip with a negative motor rotation direction.

[0105] After cleaning and preprocessing the driving data, execute step S303 to classify and label the data according to the analysis purpose.

[0106] In the technical solution provided in step S303 of the present application above, calculate the total vehicle weight. Through GPS data, the geographical information or road information of the vehicle can be obtained. Through VIN or operation scenarios, the scenario information of the user can be obtained. Through time, the season information or user operation information can be obtained.

[0107] In this embodiment, label the real-time driving data.

[0108] For example, the labeled cases can be as follows: Label 1, the gross vehicle weight is labeled into four categories: m < 4.5t, 4.5t ≤ m < 8.5t, 8.5t ≤ m < 12t, and 12t < m. Label 2, the geographical information is labeled as specific cities or provinces. Label 3, the road information is labeled as cities, suburbs, national highways, expressways, mountainous areas, and non-road areas, etc. Label 4, the scenario information is labeled as scenarios such as express delivery, shopping malls, cold chain, and green channel. Label 5, the season information is labeled as spring, summer, autumn, and winter. Label 6, the user operation information is labeled as within 3 years of operation time, 3 - 5 years, more than 5 years, etc. The above classification methods are only examples. In actuality, classification work should be completed specifically according to the specific analysis purpose or the target market of vehicle development.

[0109] After classifying and labeling the data according to the analysis purpose, step S304 is executed to divide the driving behavior segments.

[0110] In the technical solution provided in step S304 of the present application, the driving data can be divided into multiple driving behavior segments according to a preset division rule, that is, divided into five driving behavior segments: starting acceleration, normal acceleration, constant speed, deceleration, and stopping.

[0111] After dividing the driving behavior segments, step S305 is executed to record the characteristics of different driving behavior segments.

[0112] In the technical solution provided in step S305 of the present application, the parameter list of the characteristics of the above different driving behavior segments can be as shown in Table 1 below.

[0113] For example, taking a 4.5 - 8.5t light truck vehicle, shopping mall scenario, and urban working conditions as an example, collecting the driving data of the light truck vehicle, dividing the driving data, different driving behavior segments can be obtained, classifying and statistically analyzing the characteristics of different driving behavior segments, and the driving behavior type list as shown in Table 2 below can be obtained.

[0114] Taking another example, if the fitting duration of the target road spectrum is 1800s, then through geometric ratio calculation, the target quantity and target duration of each driving behavior in the fitting road spectrum as shown in Table 3 below can be obtained. Taking the normal acceleration driving behavior as an example, the statistical table of the normal acceleration driving behavior can be as shown in Table 4 below. Performing principal component analysis on the driving behavior segments of the normal acceleration driving behavior, the principal component analysis result as shown in Table 5 below can be obtained. Among them, the proportion situation of the principal component analysis can be as shown in Table 6 below. It can be seen from Table 6 that the cumulative contribution of the first three principal components reaches 78.24%. Selecting the first three principal components as the analysis dimensions, the driving behavior points of the normal acceleration driving behavior as shown Figure 4 can be obtained. Figure 4 is a schematic diagram of the principal component analysis result of a normal acceleration driving behavior according to an embodiment of the present application. From Figure 4 As can be seen from this, the driving behavior points of ordinary acceleration driving behavior exhibit a high degree of feature clustering characteristics.

[0115] After recording the characteristics of different driving behavior segments, step S306 is performed to divide all driving behavior segments into grids and number them.

[0116] In the technical solution provided by step S306 of this application, the coordinate system is divided into multiple grids with principal components 1, 2, and 3 as x, y, and z axes, and a certain scale is used. Each grid may include multiple driving behavior segments.

[0117] For example, the above grid can be as follows: Figure 5 As shown, Figure 5 This is a schematic diagram of a grid representing a typical accelerated driving behavior according to an embodiment of this application. Figure 5 As can be seen, multiple grids are arranged in a certain order in the coordinate system, and multiple driving behavior segments can be distributed in different grids.

[0118] For another example, each driving behavior segment is numbered according to the grid arrangement order. The numbering method can be as follows: Assume that the x-axis of principal component 1 is divided into segments x1~x2, x2~x3, ..., xn-1~xn; the y-axis of principal component 2 is divided into segments y1~y2, y2~y3, ..., yn1~yn; and the z-axis of principal component 3 is divided into segments z1~z2, z2~z3, ..., zn-1~zn. Starting from grid x1~x2, y1~y2, and z1~z2, the driving behavior segments within it are numbered sequentially as 1, 2, 3, ..., nx1; then the driving behavior segments within grid x2~x3, y1~y2, and z1~z2 are continued to be numbered as nx1+1, nx1+2, ..., nx1+nx2; until the numbering of grid xn-1~xn, y1~y2, and z1~z2 is completed. Then continue numbering from grid x1~x2 and y2~y3 and z1~z2 to grid x2~x3 and y2~y3 and z1~z2, all the way to grid xn-1~xn and y2~y3 and z1~z2; continue numbering in this order until grid xn-1~xn and yn-1~yn and z1~z2; then repeat the above steps until grid xn-1~xn and yn-1~yn and zn-1~zn.

[0119] The number of driving behavior segments within each grid is counted, as shown in Table 7 below.

[0120] After dividing all driving behavior segments into grids and numbering them, step S307 is executed to extract the target driving behavior segment.

[0121] In the technical solution provided in step S307 of this application, the number of target driving behavior segments to be extracted in each grid can be obtained according to the proportion of the number of segments in each grid to the total number. For example, the number of target driving behavior segments to be extracted in each grid can be shown in Table 8 below. Target driving behavior segments to be fitted to the road spectrum are generated using a normal random distribution, and the results are shown in Table 9 below. For ordinary acceleration driving behavior, the final generated target driving behavior segments are shown in Table 10 below. Similarly, all target driving behavior segments to be fitted for other driving behaviors can be generated.

[0122] Table 1 List of Feature Parameters

[0123]

[0124] Table 2 List of Driving Behaviors

[0125]

[0126] Table 3. Target Quantity and Target Duration for Each Driving Behavior

[0127]

[0128] Table 4. Statistics on Normal Acceleration Driving Behavior

[0129]

[0130] Table 5. Principal Component Analysis Results

[0131]

[0132] Table 6 Principal Component Proportion Analysis Table

[0133]

[0134] Table 7. Number of driving behavior segments within each grid.

[0135]

[0136] Table 8. Number of Target Driving Behavior Segments to be Extracted

[0137]

[0138] Table 9 Target Driving Behavior Segments

[0139]

[0140] Table 10 Target Driving Behavior Segments for Ordinary Driving Behavior

[0141]

[0142] After extracting the target driving behavior segment, step S308 is executed to splice the target driving behavior segment.

[0143] In the technical solution provided by step S308 of this application, the target driving behavior segments are sequentially connected based on the sequential relationship of driving behaviors and the characteristic parameter patterns of preceding and following driving behavior segments.

[0144] For example, taking 4.5~8.5t light trucks, supermarket scenarios, and urban working conditions as examples, the sequential relationship of driving behavior in big data is statistically analyzed. That is, the probability of occurrence of a specific combination of three consecutive driving behavior segments is statistically analyzed, and classified according to the initial velocity. The classification results are shown in Table 11 below.

[0145] Table 11 Classification Results

[0146]

[0147] For example, taking 4.5~8.5t light trucks, shopping mall scenarios, and urban driving conditions as examples, we can statistically analyze the characteristic parameter patterns of preceding and following driving behavior segments. Specifically, we can statistically analyze the probability of specific combinations of acceleration / duration / final velocity occurring between two consecutive driving behavior segments of a particular type. For instance, in a combination of acceleration followed by deceleration, the acceleration of the preceding segment is relatively small (e.g., acceleration <0.15m / s²). 2 When the acceleration of the previous acceleration segment is small (<0.15 m / s²), combined with the distribution of acceleration in the next acceleration segment, it can be statistically determined that if the acceleration of the previous segment is small (<0.15 m / s²) and the acceleration of the next acceleration segment is also small (e.g., the acceleration of the next segment is <0.15 m / s²), then... 2 The duration distribution at that time. For example, the above duration distribution can be shown in Table 12 below.

[0148] Table 12 Duration Distribution Table

[0149]

[0150] Based on the above patterns, a global traversal optimization method can be used to obtain connection methods for driving behavior segments that satisfy the sequential relationship of driving behaviors and the characteristic parameter patterns of preceding and following driving behavior segments. The connection methods for driving behavior segments are shown in Table 13 below.

[0151] Table 13. Connection Methods of Driving Behavior Segments

[0152]

[0153] After splicing together the target driving behavior segments, step S309 is executed to form a fitted road spectrum.

[0154] In the technical solution provided by step S309 of this application, the aforementioned fitted road spectrum can be as follows: Figure 6 As shown, for example, Figure 6 This is a schematic diagram of a fitted road spectrum according to an embodiment of this application. Figure 6 It can be seen that, taking 4.5~8.5t light trucks, supermarket scenarios, and urban working conditions as examples, the fitted road spectrum shows that the speed of light trucks fluctuates over time.

[0155] In this embodiment, after determining the scene data and driving data, the driving data is divided based on the determined scene data to obtain multiple sub-driving data. Then, based on different driving operations, multiple target sub-driving data can be determined from the multiple sub-driving data obtained. Subsequently, based on the aforementioned scene data, the multiple target sub-driving data are spliced ​​together to obtain the operating condition data. This achieves the goal of focusing on changes in driving behavior within short-distance travel or kinematic segments, thereby solving the technical problem of low accuracy of vehicle operating condition data and achieving the technical effect of improving the accuracy of vehicle operating condition data.

[0156] According to an embodiment of this application, a vehicle data processing apparatus is also provided. It should be noted that this vehicle data processing apparatus can be used to execute a vehicle data processing method according to one of the embodiments.

[0157] Figure 7 This is a schematic diagram of a vehicle data processing device according to an embodiment of this application. Figure 7 As shown, the data processing device 700 for the vehicle may include: a first determining unit 701, a dividing unit 702, a second determining unit 703, and a splicing unit 704.

[0158] The first determining unit 701 is used to determine the scene data of the driving scenario in which the vehicle is located, and the driving data of the vehicle in the driving process in the driving scenario. The scene data is used to represent the type and / or state of the driving scenario, and the driving data is used to represent the driving state of the vehicle in response to different driving operations.

[0159] The segmentation unit 702 is used to segment driving data based on scene data to obtain multiple sub-driving data.

[0160] The second determining unit 703 is used to determine multiple target sub-driving data from multiple sub-driving data based on different driving operations, wherein the multiple target sub-driving data correspond one-to-one with different driving operations.

[0161] The stitching unit 704 is used to stitch together multiple target sub-driving data based on scene data to obtain vehicle operating condition data.

[0162] Optionally, the second determining unit 703 may include: a first determining module, used to determine the driving characteristics of each of the multiple sub-driving data to obtain multiple driving characteristics; and a second determining module, used to determine multiple target sub-driving data based on the multiple driving characteristics, different driving operations, and multiple sub-driving data.

[0163] Optionally, the second determining module may include: an analysis submodule, used to perform feature analysis on multiple driving features respectively to obtain multiple feature analysis results; a first determining submodule, used to determine at least two target driving features from the multiple driving features according to the multiple feature analysis results, wherein the contribution of the target driving features to the sub-driving data is higher than the contribution of other driving features (excluding the target driving features) to the sub-driving data; and a second determining submodule, used to determine multiple target sub-driving data based on at least two target driving features, different driving operations, and multiple sub-driving data.

[0164] Optionally, the second determining submodule can determine multiple target sub-driving data based on at least two target driving features, different driving operations, and multiple sub-driving data by performing the following steps: constructing a coordinate system with at least two target driving features as coordinate axes, wherein the coordinate system is divided into multiple grids according to a preset scale, and the multiple grids include: multiple sub-driving data; mapping the multiple sub-driving data to the coordinate system; and determining multiple target sub-driving data based on the multiple grids, different driving operations, and multiple mapped sub-driving data.

[0165] Optionally, the second determining submodule can determine multiple target sub-driving data based on multiple grids, different driving operations, and multiple mapped sub-driving data by performing the following steps: numbering the multiple mapped sub-driving data according to the arrangement order of the multiple grids; determining the number of multiple numbered sub-driving data included in each grid, and the total number of multiple numbered sub-driving data; determining multiple target sub-driving data from each grid according to the proportion of the number to the total number, and different driving operations.

[0166] Optionally, the vehicle data processing device 700 may include: an acquisition unit for acquiring raw driving data of the vehicle during driving; and a processing unit for cleaning and / or supplementing the raw driving data to obtain driving data.

[0167] Optionally, the splicing unit 704 may include: a third determining module, used to determine a splicing strategy that matches the scene data, wherein the splicing strategy is used to represent the way to splice multiple target sub-driving data according to the sequence relationship between different driving operations under the scene data; and a splicing module, used to splice multiple target sub-driving data according to the splicing strategy to obtain working condition data.

[0168] In this embodiment, a vehicle data processing device is provided. The device may include: a first determining unit, used to determine scene data of the driving scenario in which the vehicle is located, and driving data of the vehicle in the driving process under the driving scenario, wherein the scene data is used to represent the type and / or state of the driving scenario, and the driving data is used to represent the driving state of the vehicle in response to different driving operations; a segmentation unit, used to segment the driving data based on the scene data to obtain multiple sub-driving data; a second determining unit, used to determine multiple target sub-driving data from the multiple sub-driving data based on different driving operations, wherein the multiple target sub-driving data correspond one-to-one with different driving operations; and a stitching unit, used to stitch the multiple target sub-driving data based on the scene data to obtain the vehicle's operating condition data. This achieves the goal of focusing on changes in driving behavior within short-distance travel or kinematic segments, thereby solving the technical problem of low accuracy of vehicle operating condition data and achieving the technical effect of improving the accuracy of vehicle operating condition data.

[0169] According to an embodiment of this application, a processor is also provided for running a program, wherein the program is executed by the processor to perform the methods described in the embodiment.

[0170] According to an embodiment of this application, an electronic device is also provided. Figure 8 This is a schematic diagram of an electronic device according to an embodiment of this application, such as... Figure 8 As shown, the electronic device 800 may include a memory 810 and a processor 820, wherein the memory 810 is used to store an executable program; and the processor 820 is used to run the program stored in the memory 810, wherein the program executes the methods in various embodiments of this application when it runs.

[0171] In this application, "multiple" refers to two or more.

[0172] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0173] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0174] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0175] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided. This computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method described in the embodiments.

[0176] Computer-readable storage media, also known as computer storage media, may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. These propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable storage media can transmit, propagate, or transfer programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0177] The program code contained in a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, or any suitable combination thereof.

[0178] According to an embodiment of this application, a computer program product is also provided, which includes a computer program, wherein the computer program, when executed by a processor, implements the method in the embodiment.

[0179] According to an embodiment of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the method described in the embodiment.

[0180] According to an embodiment of this application, a computer program is also provided, which, when executed by a processor, implements the method described in the embodiment.

[0181] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0182] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0183] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0184] The units described as separate components may or may not be physically separate. Similarly, the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0185] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0186] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0187] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A vehicle data processing method, characterized in that, include: Determine the scene data of the driving scenario in which the vehicle is located, and the driving data of the vehicle in the driving process under the driving scenario, wherein the scene data is used to represent the type and / or state of the driving scenario, and the driving data is used to represent the driving state of the vehicle in response to different driving operations; Based on the scenario data, the driving data is divided into multiple sub-driving data; Based on the different driving operations, multiple target sub-driving data are determined from the multiple sub-driving data, wherein each of the multiple target sub-driving data corresponds one-to-one with the different driving operations; Based on the scene data, multiple target sub-driving data are stitched together to obtain the vehicle's operating condition data.

2. The method according to claim 1, characterized in that, Based on the different driving operations, multiple target sub-driving data are determined from the multiple sub-driving data, including: The driving characteristics of each of the multiple sub-driving data are determined to obtain multiple driving characteristics; Based on the multiple driving characteristics, the different driving operations, and the multiple sub-driving data, multiple target sub-driving data are determined.

3. The method according to claim 2, characterized in that, Based on multiple driving characteristics, different driving operations, and multiple sub-driving data, multiple target sub-driving data are determined, including: Multiple driving features are analyzed separately to obtain multiple feature analysis results; Based on the analysis results of multiple features, at least two target driving features are determined from the multiple driving features, wherein the contribution of the target driving features to the sub-driving data is higher than the contribution of other driving features (excluding the target driving features) to the sub-driving data. Based on the at least two target driving features, the different driving operations, and the multiple sub-driving data, multiple target sub-driving data are determined.

4. The method according to claim 3, characterized in that, Based on the at least two target driving features, the different driving operations, and the multiple sub-driving data, multiple target sub-driving data are determined, including: A coordinate system is constructed using the at least two target driving features as coordinate axes, wherein the coordinate system is divided into multiple grids according to a preset scale, and the multiple grids include: multiple sub-driving data; Map the multiple sub-driving data onto the coordinate system; Based on the multiple grids, the different driving operations, and the multiple mapped sub-driving data, multiple target sub-driving data are determined.

5. The method according to claim 4, characterized in that, Based on multiple grids, different driving operations, and multiple mapped sub-driving data, multiple target sub-driving data are determined, including: The multiple mapped sub-driving data are numbered according to the arrangement order of the multiple grids; Determine the number of sub-driving data items with multiple numbers included in each grid, and the total number of sub-driving data items with multiple numbers; Based on the proportion of the stated quantity to the total quantity, and the different driving operations, multiple target sub-driving data are determined from each of the grids.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Obtain the raw driving data of the vehicle during the driving process; The original driving data is cleaned and / or supplemented to obtain the driving data.

7. The method according to any one of claims 1 to 5, characterized in that, Based on the scene data, multiple target sub-driving data are stitched together to obtain the vehicle's operating condition data, including: Determine a splicing strategy that matches the scenario data, wherein the splicing strategy is used to represent the way to splice multiple target sub-driving data according to the sequence relationship between the different driving operations under the scenario data; According to the stitching strategy, multiple target sub-driving data are stitched together to obtain the operating condition data.

8. A data processing device for a vehicle, characterized in that, include: The first determining unit is used to determine the scene data of the driving scenario in which the vehicle is located, and the driving data of the vehicle in the driving process under the driving scenario, wherein the scene data is used to represent the type and / or state of the driving scenario, and the driving data is used to represent the driving state of the vehicle in response to different driving operations. A segmentation unit is used to segment the driving data based on the scene data to obtain multiple sub-driving data. The second determining unit is used to determine multiple target sub-driving data from multiple sub-driving data based on the different driving operations, wherein the multiple target sub-driving data correspond one-to-one with the different driving operations; The stitching unit is used to stitch together multiple target sub-driving data based on the scene data to obtain the vehicle's operating condition data.

9. A processor, characterized in that, The processor is used to run a program, wherein the program is executed by the processor to perform the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 7.

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