Vehicle operation data analysis method, device, equipment, medium and product

Abstract

The invention provides a vehicle operation data analysis method and device, equipment, a medium and a product, and relates to the technical field of vehicle data management. The method comprises the following steps: acquiring a data analysis demand text, and identifying a data analysis scheme corresponding to at least one target vehicle from the data analysis demand text; determining a corresponding to-be-analyzed data identifier and a target data analysis interface according to the target analysis dimension; according to the target vehicle identifier, the to-be-analyzed data identifier of the target analysis dimension and the analysis time range of the target analysis dimension, extracting corresponding to-be-analyzed data of the target vehicle on the target analysis dimension from pre-collected vehicle operation data; and inputting the to-be-analyzed data of the target analysis dimension into a target data analysis interface of the target analysis dimension for analysis to obtain a data analysis result of the target analysis dimension. According to the method, the vehicle operation data of the vehicle on the target analysis dimension is analyzed in a customized manner according to the actual demand of the user, and the data analysis efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle data management, and particularly to a method, device, equipment, medium and product for analyzing vehicle operation data. Background Art

[0002] With the popularization and rapid development of commercial vehicle Internet of Vehicles technology, vehicle manufacturers can comprehensively optimize the performance of the whole vehicle by acquiring and analyzing a large amount of real-time vehicle operation data.

[0003] In the prior art, the energy consumption level and power battery status of a vehicle are usually obtained by analyzing the data transmitted back by the Internet of Vehicles of new energy commercial vehicles, including vehicle basic information and power battery system data, so as to judge whether the energy consumption of the vehicle is abnormal and ensure the safety of the power battery.

[0004] However, the existing vehicle operation data analysis methods have the problem of low analysis efficiency because they cannot customize the analysis of the vehicle operation status according to the actual needs of users. Summary of the Invention

[0005] The present application provides a method, device, equipment, medium and product for analyzing vehicle operation data, which is used to customize the analysis of vehicle operation data on a target analysis dimension according to the actual needs of users, so as to improve the analysis efficiency, judge the vehicle operation status from the target analysis dimension, timely discover and solve potential problems, improve the reliability and safety of the vehicle, and optimize the vehicle operation strategy to enhance the power performance of the whole vehicle.

[0006] In a first aspect, the present application provides a method for analyzing vehicle operation data, the method comprising:

[0007] Obtain a data analysis requirement text, and identify at least one data analysis scheme corresponding to each target vehicle from the data analysis requirement text, each data analysis scheme including a target vehicle identifier, at least one target analysis dimension, and an analysis time range corresponding to each target analysis dimension, the target analysis dimension being one or more of all analyzable dimensions of all vehicle models;

[0008] Determine a corresponding data identifier to be analyzed and a target data analysis interface according to the target analysis dimension;

[0009] Extract the data to be analyzed corresponding to the target vehicle on the target analysis dimension from the pre-collected vehicle operation data according to the target vehicle identifier, the data identifier to be analyzed of the target analysis dimension, and the analysis time range of the target analysis dimension, the pre-collected vehicle operation data being data collected for all Controller Area Network (CAN) signals on all vehicle models;

[0010] Input the data to be analyzed in the target analysis dimension into the target data analysis interface of the target analysis dimension for analysis, obtain the data analysis result of the target analysis dimension, and perform corresponding storage according to the data analysis scheme.

[0011] In a possible design, determining the corresponding data identifier to be analyzed and the target data analysis interface according to the target analysis dimension includes:

[0012] Obtain the data text of the target analysis dimension according to the dimension identifier of the target analysis dimension, and extract the data identifier to be analyzed in the target analysis dimension from the data text;

[0013] Concatenate the dimension identifier of the target analysis dimension with the preset interface identifier to form the target interface identifier, and match the target interface identifier with the interface identifiers of multiple target data analysis interfaces respectively to determine the target data analysis interface corresponding to the target analysis dimension.

[0014] In a possible design, the acquisition process of the pre-acquired vehicle operation data includes:

[0015] When receiving the first configuration operation, generate a data acquisition scheme for the Controller Area Network (CAN) signal according to the first configuration operation, and each CAN signal corresponds to one or more data acquisition schemes;

[0016] When receiving the second configuration operation for the target vehicle, determine the target data acquisition scheme of the target vehicle from at least one data acquisition scheme according to the second configuration operation, and send the target data acquisition scheme to the target vehicle;

[0017] Receive the vehicle operation data collected by the target vehicle according to the target data acquisition scheme.

[0018] In a possible design, after performing corresponding storage according to the data analysis scheme, it further includes:

[0019] Determine the vehicle model of the target vehicle, and display the data analysis results obtained multiple times grouped by the vehicle model of the target vehicle and / or the target analysis dimension.

[0020] In a possible design, inputting the data to be analyzed in the target analysis dimension into the target data analysis interface of the target analysis dimension for analysis, obtaining the data analysis result of the target analysis dimension, includes:

[0021] For each target analysis dimension, determine the total data volume of the data to be analyzed in the target analysis dimension;

[0022] For each target analysis dimension, when the total data volume of the target analysis dimension is greater than or equal to the preset data volume threshold, split the data to be analyzed of the target analysis dimension into multiple groups of sub-data to be analyzed according to the target vehicle and / or the analysis time range, allocate computing resources and an entity of the corresponding target data analysis interface for each group of sub-data to be analyzed of the target analysis dimension, and call each computing resource and each entity of the target data analysis interface to generate a group of data analysis results of the target analysis dimension for a group of sub-data to be analyzed of the target analysis dimension;

[0023] When the total data volume is less than the preset data volume threshold, allocate computing resources and an entity of a target data analysis interface for the target analysis dimension, and call the computing resources and the entity to generate data analysis results for the data to be analyzed of the target analysis dimension. The computing processes of each entity are executed in parallel.

[0024] In a possible design, the target analysis dimension includes at least one of the following dimensions: basic operation characteristics, energy consumption characteristics, gear characteristics, motor characteristics, axle characteristics, driving behavior, vehicle operation dimension, or battery dimension;

[0025] The basic operation characteristics correspond to at least one of the following data analysis results: vehicle operation duration, vehicle operation mileage, average vehicle speed, average moving vehicle speed, power change amount, air conditioner operation duration, air conditioner operation duration ratio, heater operation duration, or heater operation duration ratio;

[0026] The energy consumption characteristics correspond to at least one of the following data analysis results: battery power consumption, battery recovered power, average battery power consumption per 100 kilometers, motor power consumption, motor recovered power, motor mechanical energy output, motor recovered power ratio, accessory power consumption, or accessory power consumption ratio;

[0027] The gear characteristics correspond to at least one of the following data analysis results: ratio of operation time of each gear, or ratio of recovered power of each gear;

[0028] The axle characteristics correspond to at least one of the following data analysis results: axle vehicle speed torque change situation, axle power distribution situation, or axle efficiency distribution situation;

[0029] The driving behavior corresponds to at least one of the following data analysis results: accelerator pedal distribution situation, brake pedal distribution situation, accelerator pedal vehicle speed distribution relationship, or brake pedal vehicle speed distribution relationship;

[0030] The vehicle operation dimension corresponds to at least one of the following data analysis results: vehicle speed distribution situation, vehicle speed torque change relationship, or vehicle speed battery power change relationship;

[0031] The battery dimension corresponds to at least one of the following data analysis results: battery state, or cumulative power consumption change.

[0032] Second aspect, the present application provides a vehicle operation data analysis device, which includes:

[0033] An acquisition module, configured to acquire a data analysis requirement text, and identify data analysis schemes corresponding to at least one target vehicle respectively from the data analysis requirement text. Each data analysis scheme includes a target vehicle identifier, at least one target analysis dimension, and an analysis time range corresponding to each target analysis dimension. The target analysis dimension is one or more of all analyzable dimensions of all vehicle models;

[0034] A determination module, configured to determine corresponding data identifiers to be analyzed and target data analysis interfaces according to the target analysis dimensions;

[0035] An extraction module, configured to extract data to be analyzed corresponding to the target vehicle in the target analysis dimension from the pre-collected vehicle operation data according to the target vehicle identifier, the data identifier to be analyzed of the target analysis dimension, and the analysis time range of the target analysis dimension. The pre-collected vehicle operation data is data collected for all Controller Area Network (CAN) signals on all vehicle models;

[0036] An analysis module, configured to input the data to be analyzed of the target analysis dimension into the target data analysis interface of the target analysis dimension for analysis, obtain the data analysis result of the target analysis dimension, and perform corresponding storage according to the data analysis scheme.

[0037] In a possible design, the determination module includes: a determination identifier module and a determination interface module;

[0038] The determination identifier module is configured to obtain a data text of the target analysis dimension according to the dimension identifier of the target analysis dimension, and extract the data identifier to be analyzed of the target analysis dimension from the data text;

[0039] The determination interface module is configured to splice the dimension identifier of the target analysis dimension with a preset interface identifier to form a target interface identifier, and respectively match the target interface identifier with the interface identifiers of multiple target data analysis interfaces to determine the target data analysis interface corresponding to the target analysis dimension.

[0040] In a possible design, the device further includes: a generation module, a determination scheme module, and a collection module;

[0041] The generation module is configured to generate a data collection scheme for Controller Area Network (CAN) signals according to a first configuration operation when receiving the first configuration operation. Each CAN signal corresponds to one or more data collection schemes;

[0042] A determination scheme module, which is used to determine a target data collection scheme for the target vehicle from at least one data collection scheme according to the second configuration operation when receiving the second configuration operation for the target vehicle, and send the target data collection scheme to the target vehicle;

[0043] A collection module, which is used to receive the vehicle operation data collected by the target vehicle according to the target data collection scheme.

[0044] In a possible design, the device further includes: a display module;

[0045] The display module is used to determine the vehicle model of the target vehicle and group and display the analysis results of the data obtained multiple times according to the vehicle model of the target vehicle and / or the target analysis dimension.

[0046] In a possible design, the analysis module includes: a data volume determination module, a splitting module, and an allocation module;

[0047] The data volume determination module is used to determine the total data volume of the data to be analyzed for each target analysis dimension.

[0048] The splitting module is used to, for each target analysis dimension, when the total data volume of the target analysis dimension is greater than or equal to the preset data volume threshold, split the data to be analyzed for the target analysis dimension into multiple groups of sub-data to be analyzed according to the target vehicle and / or the analysis time range, allocate computing resources and an entity of the corresponding target data analysis interface for each group of sub-data to be analyzed for the target analysis dimension, and call each computing resource and each entity of the target data analysis interface to generate a group of data analysis results for the target analysis dimension for a group of sub-data to be analyzed for the target analysis dimension;

[0049] The allocation module is used to, when the total data volume is less than the preset data volume threshold, allocate computing resources and an entity of a target data analysis interface for the target analysis dimension, and call the computing resources and the entity to generate data analysis results for the data to be analyzed for the target analysis dimension, and the computing processes of each entity are executed in parallel.

[0050] In a possible design, in this device, the target analysis dimension includes at least one of the following dimensions: basic operation characteristics, energy consumption characteristics, gear characteristics, motor characteristics, axle characteristics, driving behavior, vehicle operation dimension, or battery dimension;

[0051] The basic operation characteristics correspond to at least one of the following data analysis results: vehicle operation duration, vehicle operation mileage, average vehicle speed, average moving vehicle speed, power change amount, air conditioner operation duration, air conditioner operation duration ratio, heater operation duration, or heater operation duration ratio;

[0052] The energy consumption characteristics correspond to at least one of the following data analysis results: battery power consumption, battery recovered power, average battery power consumption per 100 kilometers, motor power consumption, motor recovered power, motor mechanical energy output, proportion of motor recovered power, accessory power consumption, or proportion of accessory power consumption;

[0053] The gear characteristics correspond to at least one of the following data analysis results: proportion of operation time of each gear, or proportion of recovered power of each gear;

[0054] The axle characteristics correspond to at least one of the following data analysis results: axle vehicle speed torque change situation, axle power distribution situation, or axle efficiency distribution situation;

[0055] The driving behavior corresponds to at least one of the following data analysis results: accelerator pedal distribution situation, brake pedal distribution situation, accelerator pedal vehicle speed distribution relationship, or brake pedal vehicle speed distribution relationship;

[0056] The vehicle operation dimension corresponds to at least one of the following data analysis results: vehicle speed distribution situation, vehicle speed torque change relationship, or vehicle speed battery power change relationship;

[0057] The battery dimension corresponds to at least one of the following data analysis results: battery state, or cumulative power consumption change.

[0058] Thirdly, the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor.

[0059] The memory stores computer-executable instructions.

[0060] The processor executes the computer-executable instructions stored in the memory to implement a vehicle operation data analysis method according to the invention content of the first aspect.

[0061] Fourthly, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement a vehicle operation data analysis method according to the invention content of the first aspect.

[0062] Fifthly, the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it is used to implement a vehicle operation data analysis method according to the invention content of the first aspect.

[0063] Based on the implementation manners provided in the above aspects of the present application, further combinations can be made to provide more implementation manners.

[0064] A method, device, equipment, medium and product for analyzing vehicle operation data provided by this application. The method includes: First, obtain a text of data analysis requirements, and identify data analysis schemes corresponding to at least one target vehicle from the text of data analysis requirements. Each data analysis scheme includes a target vehicle identifier, at least one target analysis dimension, and an analysis time range corresponding to each target analysis dimension. The target analysis dimension is one or more of all analyzable dimensions of all vehicle models. Then, determine corresponding data identifiers to be analyzed and target data analysis interfaces according to the target analysis dimensions. Then, according to the target vehicle identifier, the data identifiers to be analyzed of the target analysis dimension, and the analysis time range of the target analysis dimension, extract the data to be analyzed corresponding to the target vehicle in the target analysis dimension from the pre-collected vehicle operation data. The pre-collected vehicle operation data is data collected for all Controller Area Network (CAN) signals on all vehicle models. Finally, input the data to be analyzed of the target analysis dimension into the target data analysis interface of the target analysis dimension for analysis, obtain the data analysis result of the target analysis dimension, and store it correspondingly according to the data analysis scheme. The following technical effects are achieved: By obtaining the text of analysis requirements input by the user, and customizing the analysis of vehicle operation data of the vehicle in the target analysis dimension according to the text of analysis requirements, only processing the data related to the target analysis dimension reduces the data calculation amount. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0066] The drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0067] Figure 1 It is a schematic diagram of an application scenario of a method for analyzing vehicle operation data provided by an embodiment of this application;

[0068] Figure 2 It is a flow diagram of a method for analyzing vehicle operation data provided by an embodiment of this application Figure 1 ;

[0069] Figure 3 It is a flow diagram of a method for analyzing vehicle operation data provided by an embodiment of this application Figure 2 ;

[0070] Figure 4Schematic structural diagram of a vehicle operation data analysis device provided by an embodiment of the present application;

[0071] Figure 5 Schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0072] Reference numerals:

[0073] 110 - Data analysis server; 120 - On-vehicle terminal; 130 - Vehicle;

[0074] 410 - Acquisition module; 420 - Determination module; 430 - Extraction module; 440 - Analysis module;

[0075] 510 - Processor; 520 - Memory; 530 - Communication component; 540 - Bus. Detailed implementation manners

[0076] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0077] In the embodiments of the present application, the same or similar items with basically the same functions and effects are distinguished by using words such as "first" and "second". Those skilled in the art can understand that the words such as "first" and "second" do not limit the quantity and execution order, and the words such as "first" and "second" do not necessarily limit being different. It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific manner. In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more.

[0078] It should be noted that "when... " in the embodiments of the present application can be at the instant when a certain situation occurs or within a period of time after a certain situation occurs. The embodiments of the present application do not make specific limitations on this. In addition, a vehicle operation data analysis method provided by the embodiments of the present application is only an example, and a vehicle operation data analysis method may further include more or less content.

[0079] To facilitate a clear description of the technical solutions of the embodiments of the present application, the following briefly introduces some terms and technologies involved in the embodiments of the present application:

[0080] Controller Area Network (CAN): A communication protocol used in vehicle and industrial automation systems, which can be used to achieve efficient and reliable data communication between various electronic control units (ECUs) in a vehicle.

[0081] With the increasing popularity and rapid development of commercial vehicle Internet of Vehicles technology, vehicle manufacturers can achieve all-round optimization of vehicle performance by obtaining and analyzing a large amount of real-time vehicle operation data. This data-driven approach not only greatly enriches the manufacturer's understanding of the actual operating state of the vehicle but also makes it possible to optimize vehicle performance based on in-depth data analysis.

[0082] The existing technology usually focuses on the analysis of vehicle basic information and power battery system data in the Internet of Vehicles backhaul data of new energy commercial vehicles to obtain the vehicle's energy consumption level and the working state of the power battery, which not only helps to identify abnormal energy consumption situations but also provides an important guarantee for ensuring the safety of the power battery, ensuring normal vehicle energy consumption and power battery safety.

[0083] However, the existing data analysis methods fail to customize the analysis of the vehicle's operating state according to the actual needs of users. Therefore, there is a problem of low analysis efficiency. How to customize the analysis of vehicle operation data according to the actual needs of users, judge the vehicle's operating state for the target analysis dimension, and thus formulate an optimized operation strategy to improve the dynamic performance and economy of the whole vehicle is still an urgent problem to be solved.

[0084] Based on this, the embodiments of the present application propose a vehicle operation data analysis method, device, equipment, medium and product, which can be used in the technical field of vehicle data management, aiming to solve the above technical problems of the existing technology. By obtaining the actual analysis needs of users, customizing the analysis of vehicle operation data in the target analysis dimension according to the actual analysis needs, thereby improving the data analysis efficiency, judging the vehicle's operating state from the target analysis dimension, accurately identifying the vehicle's performance, not only can potential problems be discovered in time, improving the reliability and safety of the vehicle, but also the vehicle's operation strategy can be optimized based on the data analysis results, optimizing the vehicle's energy efficiency, reducing the operation cost, and ultimately achieving the purpose of improving the dynamic performance and economy of the whole vehicle, thus providing a solid foundation for the maintenance and optimization of commercial vehicles and even the research and development of the next generation of intelligent connected vehicles.

[0085] To facilitate the understanding of the technical solution of this application, first, the application scenario of the vehicle operation data analysis method provided in the embodiments of this application will be introduced.

[0086] Figure 1 It is a schematic diagram of the application scenario of a vehicle operation data analysis method provided in the embodiments of this application. It should be noted that Figure 1 The examples shown are only examples of the application scenarios to which the embodiments of this application can be applied, to help those skilled in the art understand the technical content of this application, but it does not mean that the embodiments of this application cannot be used in other devices, systems, environments or scenarios.

[0087] As Figure 1 shown, this application scenario includes: a data analysis server 110, a vehicle 130, and an in-vehicle terminal 120.

[0088] Among them, the in-vehicle terminal 120 can be a telematics box (TBox) respectively equipped on multiple vehicles 130, or various existing vehicle network modules, and no specific limitation is made here.

[0089] The data analysis server 110 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a background remote server such as a cloud server, and no specific limitation is made here either.

[0090] There is a wireless communication connection between the in-vehicle terminal 120 and the data analysis server 110, and the vehicle 130 can establish a stable data connection with the data analysis server 110 through the in-vehicle terminal 120.

[0091] The data analysis server 110 can send a vehicle operation data collection plan to the vehicle 130 through the in-vehicle terminal 120 to guide the vehicle 130 to collect data, and after the data collection is completed, receive the vehicle operation data sent by the vehicle 130 through the in-vehicle terminal 120. Then, according to the actual needs of the user, customize and analyze the vehicle operation data of the vehicle 130 in the target analysis dimension, so as to improve the analysis efficiency, and judge the running state of the vehicle 130 from the target analysis dimension, discover and solve potential problems in a timely manner, so as to improve the reliability and safety of the vehicle 130, optimize the vehicle operation strategy, and enhance the power performance of the whole vehicle.

[0092] Next, specific embodiments will be used to elaborate in detail on the technical solution of this application and how the technical solution of this application solves the above technical problems. These several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. Next, the embodiments of this application will be described with reference to the accompanying drawings.

[0093] Figure 2Flow schematic of a vehicle operation data analysis method provided by an embodiment of this application Figure 1 As Figure 2 shown, the method includes:

[0094] S201. Obtain a data analysis requirement text, and identify data analysis schemes corresponding to at least one target vehicle from the data analysis requirement text.

[0095] In an embodiment of this application, the execution subject of a vehicle operation data analysis method is a data analysis server.

[0096] Specifically, a user can input a data analysis requirement text to the data analysis server, and the data analysis requirement text may include data analysis schemes corresponding to at least one target vehicle. Each data analysis scheme includes a target vehicle identifier, at least one target analysis dimension, and an analysis time range corresponding to each target analysis dimension. The target analysis dimension is one or more of all analyzable dimensions of all vehicle models. For example, the target vehicle identifier may be ABC123; the target analysis dimension may be energy consumption characteristics and battery dimension; the analysis time range corresponding to the energy consumption characteristics may be: January 1, 2025 to April 1, 2025, and the analysis time range corresponding to the battery dimension may also be: January 1, 2025 to April 1, 2025.

[0097] The data analysis server can obtain the data analysis requirement text input by the user, and determine the data analysis scheme of the target vehicle from the data analysis requirement text. The specific number of target vehicles may be one or more, that is, the data analysis server can provide vehicle operation data analysis services for multiple vehicles at the same time.

[0098] S202. Determine corresponding data identifiers to be analyzed and target data analysis interfaces according to the target analysis dimension.

[0099] Specifically, the data analysis server can map to the corresponding data identifier to be analyzed according to the target analysis dimension through the pre-stored mapping relationship between multiple analysis dimensions and data identifiers to be analyzed; and map to the corresponding target data analysis interface according to the pre-stored mapping relationship between multiple analysis dimensions and data analysis interfaces according to the target analysis dimension.

[0100] S203. Extract the data to be analyzed corresponding to the target vehicle in the target analysis dimension from the pre-collected vehicle operation data according to the target vehicle identifier, the data identifier to be analyzed of the target analysis dimension, and the analysis time range of the target analysis dimension.

[0101] In an embodiment of this application, the pre-collected vehicle operation data is data collected for all controller area network (CAN) signals on all vehicle models.

[0102] Specifically, the pre-collected vehicle operation data is usually stored in a database. This database can be integrated with the data analysis server or set on other dedicated database servers. The data analysis server can extract the vehicle operation data from these database servers for analysis. By enabling the database server to focus on the storage and management of vehicle operation data while the data analysis server focuses on calculation and analysis tasks, resources can be better allocated.

[0103] The data analysis server can, according to the target vehicle identifier (ABC123), first screen out the data corresponding to the target vehicle from the database; then, according to the identifier of the data to be analyzed for the target analysis dimension, screen out the data corresponding to the target vehicle in the target analysis dimension; and then, according to the analysis time range corresponding to the target analysis dimension, extract the data to be analyzed corresponding to the target vehicle in the target analysis dimension within the analysis time range.

[0104] S204. Input the data to be analyzed for the target analysis dimension into the target data analysis interface of the target analysis dimension for analysis, obtain the data analysis result of the target analysis dimension, and perform corresponding storage according to the data analysis scheme.

[0105] Specifically, after the data analysis server determines the target data analysis interface and extracts the data to be analyzed corresponding to the target vehicle in the target analysis dimension from the database, it can input the proposed data to be analyzed into the corresponding target data analysis interface for analysis, thereby obtaining the data analysis result. For example, results such as the battery power consumption, battery recovered power, or accessory power consumption corresponding to the energy consumption characteristics; results such as the battery state or the change in cumulative power consumption corresponding to the battery dimension.

[0106] A vehicle operation data analysis method provided in this embodiment first obtains a data analysis requirement text and identifies at least one data analysis scheme corresponding to each target vehicle from the data analysis requirement text. Each data analysis scheme includes a target vehicle identifier, at least one target analysis dimension, and the analysis time range corresponding to each target analysis dimension. The target analysis dimension is one or more of all analyzable dimensions of all vehicle models; then, determine the corresponding identifier of the data to be analyzed and the target data analysis interface according to the target analysis dimension; then, according to the target vehicle identifier, the identifier of the data to be analyzed for the target analysis dimension, and the analysis time range of the target analysis dimension, extract the data to be analyzed corresponding to the target vehicle in the target analysis dimension from the pre-collected vehicle operation data. The pre-collected vehicle operation data is data collected from all controller area network (CAN) signals on all vehicle models; finally, input the data to be analyzed for the target analysis dimension into the target data analysis interface of the target analysis dimension for analysis, obtain the data analysis result of the target analysis dimension, and perform corresponding storage according to the data analysis scheme.

[0107] The following technical effects are achieved: By obtaining the analysis requirement text input by the user and customizing the analysis of the vehicle operation data of the vehicle in the target analysis dimension according to the analysis requirement text, only the data related to the target analysis dimension is processed, reducing the data calculation amount.

[0108] Figure 3 Schematic flow of a vehicle operation data analysis method provided by an embodiment of the present application Figure 2 In a possible example, as Figure 3 shown, on the basis of the Figure 2 embodiment, this embodiment details how the data analysis server determines the corresponding data identifier to be analyzed and the target data analysis interface, and how to perform data analysis. As Figure 3 shown, the method includes:

[0109] S301. When receiving a first configuration operation, generate a data acquisition scheme for the controller area network (CAN) signal according to the first configuration operation.

[0110] Specifically, this embodiment first details the data acquisition process of the pre-collected vehicle operation data. First, when the data analysis server receives a first configuration operation triggered by the user, it can generate a data acquisition scheme for different CAN signals according to the first configuration operation and the user requirements, and can further form a reusable data acquisition scheme library. Specifically, the data analysis server can define independent data acquisition schemes for each CAN signal of the vehicle (such as motor speed, vehicle speed, fuel quantity, brake state, etc.). Further, each CAN signal can correspond to one or more data acquisition schemes.

[0111] The data acquisition scheme can include the acquisition frequency, acquisition duration, trigger condition, data type, data format, and data storage strategy of the CAN signal, etc.

[0112] The acquisition frequency refers to how many times data is acquired per unit time, which can be specifically set according to the importance and real-time requirements of the CAN signal (for example, for high-frequency signals, such as vehicle speed, it can be acquired once per second; for low-frequency signals, such as microcontroller temperature, it can be acquired once every 10 seconds).

[0113] The acquisition duration refers to the duration of each acquisition.

[0114] The trigger condition, that is, the acquisition condition, refers to the condition for triggering data acquisition (such as triggering acquisition when the vehicle speed exceeds a preset threshold).

[0115] The data type refers to the type of data acquired, such as vehicle speed, motor speed, transmission gear position, etc.

[0116] The data format refers to the conversion of binary data into readable physical values (such as temperature, voltage, etc.) by parsing CAN messages.

[0117] The data storage strategy refers to the priority of storing vehicle operation data (such as real-time data is stored in hot storage and historical data is stored in cold storage).

[0118] Specifically, the data analysis server can generate corresponding data acquisition plans according to the parameter settings in the first configuration operation. For example, the user can select to collect vehicle speed and motor speed at a frequency of 10 times per second for a duration of 10 minutes.

[0119] S302. When receiving the second configuration operation for the target vehicle, determine the target data acquisition plan for the target vehicle from at least one data acquisition plan according to the second configuration operation, and send the target data acquisition plan to the target vehicle.

[0120] Specifically, when the data analysis server receives the second configuration operation triggered by the user, it can determine the target data acquisition plan for the target vehicle from at least one data acquisition plan according to the second configuration operation, and send the plan to the target vehicle.

[0121] The second configuration operation usually contains information about the target vehicle, such as the target vehicle identifier.

[0122] The data analysis server can select one or more plans from the generated data acquisition plans as the target data acquisition plan according to the parameter settings in the second configuration operation. For example, the user can select the acquisition plan for the vehicle speed, battery power, and motor speed of the target vehicle. Then, send the target data acquisition plan to the target vehicle.

[0123] S303. Receive the vehicle operation data collected by the target vehicle according to the target data acquisition plan.

[0124] Specifically, after receiving the target data acquisition plan sent by the data analysis server, the target vehicle can update the local data acquisition rules (such as modifying the sampling frequency, trigger conditions, etc.). And collect the target vehicle operation data according to the target data acquisition plan. That is, collect the vehicle operation data according to the specific plan such as the specified acquisition frequency, acquisition duration, and trigger conditions. And after the collection is completed, send the vehicle operation data collected according to the target data acquisition plan to the data analysis server.

[0125] After receiving the vehicle operation data sent by the target vehicle, the data analysis server can store these vehicle operation data in the local database, or further store them in the database on the database server, so as to reduce the storage burden of the data analysis server and enable it to focus on computing and analysis tasks.

[0126] S304. Obtain the data analysis requirement text, and identify at least one data analysis scheme corresponding to each target vehicle from the data analysis requirement text.

[0127] S304 is similar to S201, and will not be elaborated in this embodiment.

[0128] S305. Obtain the data text of the target analysis dimension according to the dimension identifier of the target analysis dimension, and extract the data identifier to be analyzed of the target analysis dimension from the data text.

[0129] Specifically, the dimension identifier of the target analysis dimension is used to uniquely identify a specific vehicle performance dimension. A configuration file can be correspondingly set for each dimension identifier, and the data text of the target analysis dimension is stored in the configuration file. Alternatively, the data analysis server can obtain the data text related to the dimension from the metadata preset for each dimension identifier.

[0130] Then, the data analysis server can further extract the data identifier to be analyzed of the target analysis dimension, and the splicing rule of the dimension identifier and the preset interface identifier from the data text.

[0131] Furthermore, the target analysis dimension includes at least one of the following dimensions: basic operation characteristics, energy consumption characteristics, gear characteristics, motor characteristics, axle characteristics, driving behavior, vehicle operation dimension, or battery dimension.

[0132] The basic operation characteristics correspond to at least one of the following data analysis results: vehicle operation duration, vehicle operation mileage, average vehicle speed, average moving vehicle speed, power change amount, air conditioner operation duration, air conditioner operation duration ratio, heater operation duration, or heater operation duration ratio. Specifically, the average vehicle speed refers to the total driving distance of the vehicle divided by the total time during the entire driving process, including all stop times; the average moving vehicle speed refers to the total driving distance of the vehicle divided by the moving time within the actual moving time, excluding stop times; the power change amount refers to the change amount of the battery power during the entire discharge process of the battery.

[0133] The energy consumption characteristics correspond to at least one of the following data analysis results: battery power consumption, battery recovered power, average battery power consumption per 100 kilometers, motor power consumption, motor recovered power, motor mechanical energy output, proportion of motor recovered power, accessory power consumption, or proportion of accessory power consumption. Specifically, the battery power consumption refers to the sum of the power consumed by the battery in the discharging state; the battery recovered power refers to the sum of the battery charging amounts when the battery is in a non-charging state; the average battery power consumption per 100 kilometers refers to the power consumed when the vehicle travels 100 kilometers; the motor power consumption refers to the power consumed by the drive motor in the driving state; the motor recovered power refers to the power recovered by the drive motor in the recovery state; the motor mechanical energy output refers to the cumulative value of the mechanical energy output by the drive motor within a certain period; the proportion of motor recovered power refers to the proportion of the electric energy recovered by the drive motor through the regenerative braking system in the total energy consumption during vehicle driving; the accessory power consumption refers to the power consumed by vehicle accessories such as the cab air conditioner, battery air conditioner, steering oil pump, and brake air pump; the proportion of accessory power consumption refers to the proportion of the electric energy consumed by vehicle accessories in the total energy consumption during vehicle operation.

[0134] The gear characteristics correspond to at least one of the following data analysis results: proportion of operation time of each gear, or proportion of recovered power of each gear. Specifically, the proportion of recovered power of each gear refers to the ratio of the recovered power of each gear to the sum of the recovered powers of all gears.

[0135] The axle characteristics correspond to at least one of the following data analysis results: axle vehicle speed torque change situation, axle power distribution situation, or axle efficiency distribution situation. Specifically, the axle vehicle speed torque change situation refers to the change situation of the output speed and torque of the drive axle; the axle power distribution situation refers to the dynamic proportional relationship in which the torque and power output by the power system in the vehicle drive axle are distributed to the left and right drive wheels through the differential; the axle efficiency distribution situation refers to the distribution characteristics of the mechanical transmission efficiency of each component in the power transmission path (such as the main reducer, differential, half shaft, etc.) in different working conditions in the vehicle drive axle, and the influence of the efficiency difference between the left and right drive wheels on the vehicle power loss, energy utilization rate, and driving performance.

[0136] The driving behavior corresponds to at least one of the following data analysis results: accelerator pedal distribution situation, brake pedal distribution situation, accelerator pedal vehicle speed distribution relationship, or brake pedal vehicle speed distribution relationship. Specifically, the accelerator pedal distribution situation refers to the distribution situation of the accelerator pedal at different positions or different opening states during vehicle driving; the brake pedal distribution situation refers to the distribution situation of the brake pedal at different positions or different opening states during vehicle driving; the accelerator pedal vehicle speed distribution relationship refers to the distribution proportion of the accelerator pedal in each opening interval at each vehicle speed interval (such as 0 - 10 km / h); the brake pedal vehicle speed distribution relationship refers to the distribution proportion of the brake pedal in each opening interval at each vehicle speed interval.

[0137] The vehicle operation dimension corresponds to at least one of the following data analysis results: vehicle speed distribution, the relationship between vehicle speed and torque change, or the relationship between vehicle speed and battery power change. Specifically, the vehicle speed distribution refers to the duration of each vehicle speed interval after dividing the vehicle speed into multiple vehicle speed intervals; the relationship between vehicle speed and torque change refers to the relationship between vehicle speed and torque change; the relationship between vehicle speed and battery power change refers to the relationship between vehicle speed and battery power change.

[0138] The battery dimension corresponds to at least one of the following data analysis results: battery state, or cumulative power consumption change. Specifically, the battery state refers to the real-time level of the battery power during the use of the vehicle; the cumulative power consumption change refers to the change of the consumed battery power with time, driving mileage, and vehicle operation conditions.

[0139] S306. Concatenate the dimension identifier of the target analysis dimension with the preset interface identifier to form a target interface identifier, and then match the target interface identifier with the interface identifiers of multiple target data analysis interfaces respectively to determine the target data analysis interface corresponding to the target analysis dimension.

[0140] Specifically, after obtaining the concatenation rule of the dimension identifier of the target analysis dimension and the preset interface identifier, the data analysis server can, according to this concatenation rule, concatenate the dimension identifier of the target analysis dimension with the preset interface identifier to form a target interface identifier. Then traverse the interface identifier list of multiple target data analysis interfaces, match the target interface identifier with each interface identifier in the interface list, and find the target data analysis interface corresponding to the target analysis dimension. Further, the interface identifier can be obtained by adding a prefix or a suffix to the dimension identifier.

[0141] S307. Extract the data to be analyzed corresponding to the target vehicle in the target analysis dimension from the pre-collected vehicle operation data according to the target vehicle identifier, the data identifier to be analyzed of the target analysis dimension, and the analysis time range of the target analysis dimension.

[0142] S307 is similar to S203, and will not be elaborated in this embodiment.

[0143] S308. For each target analysis dimension, determine the total data volume of the data to be analyzed of the target analysis dimension.

[0144] Specifically, after determining the target data analysis interface and the data to be analyzed, the data analysis server can further input the data to be analyzed into the target data analysis interface for analysis. In this process, for each target analysis dimension, the data analysis server can first obtain the number of data entries or the total amount of data (such as the number of bytes) by traversing or counting the data set of the target analysis dimension, so as to determine the total amount of data to be analyzed under this dimension. This is to facilitate judging whether to perform data splitting for parallel computing according to whether the total amount of data to be analyzed under the target analysis dimension is less than the preset data volume threshold, thereby improving the data analysis efficiency.

[0145] S309. For each target analysis dimension, when the total amount of data in the target analysis dimension is greater than or equal to the preset data volume threshold, split the data to be analyzed in the target analysis dimension into multiple groups of sub-data to be analyzed according to the target vehicle and / or the analysis time range, and allocate computing resources and an entity of the corresponding target data analysis interface to each group of sub-data to be analyzed in the target analysis dimension, and call each computing resource and each entity of the target data analysis interface to generate a group of data analysis results for a group of sub-data to be analyzed in the target analysis dimension.

[0146] Specifically, for each target analysis dimension, when the total amount of data in the target analysis dimension is greater than or equal to the preset data volume threshold, the data analysis server can split the data to be analyzed into multiple groups of sub-data to be analyzed according to the target vehicle and / or the analysis time range, and allocate computing resources and an entity of the target data analysis interface to each group of sub-data to be analyzed in the target analysis dimension. Then call each computing resource and each entity of the target data analysis interface to generate the corresponding data analysis results for each group of sub-data to be analyzed in the target analysis dimension.

[0147] In this process, the computing processes of each entity are executed in parallel. For each target analysis dimension, when the total amount of data in the target analysis dimension is greater than or equal to the preset data volume threshold, by splitting the data to be analyzed into multiple groups of sub-data to be analyzed, allocating computing resources and an entity of the target data analysis interface to each group of sub-data to be analyzed, and making the computing processes of each entity execute in parallel, the data analysis efficiency is improved.

[0148] Furthermore, the data analysis server can also integrate the data analysis results generated by each sub-data to be analyzed to form the final data analysis results of the target analysis dimension.

[0149] S310. When the total amount of data is less than the preset data volume threshold, allocate computing resources and an entity of a target data analysis interface to the target analysis dimension, and call the computing resources and the entity to generate data analysis results for the data to be analyzed in the target analysis dimension.

[0150] Specifically, for each target analysis dimension, when the total data volume is less than the preset data volume threshold, the data analysis server can directly allocate computing resources and an entity of a target data analysis interface for the target analysis dimension.

[0151] Similarly, the computing processes of the entities of each target analysis dimension are executed in parallel to further improve the data analysis efficiency.

[0152] Specifically, the data analysis server can use multi-threading, multi-process, or distributed computing frameworks, etc. to implement the parallel execution of each computing resource and entity.

[0153] S311. Perform corresponding storage according to the data analysis scheme.

[0154] S311 is similar to S204. For the same parts, they will not be elaborated in this embodiment.

[0155] S312. Determine the vehicle model of the target vehicle, and group and display the data analysis results obtained multiple times according to the vehicle model of the target vehicle and / or the target analysis dimension.

[0156] Specifically, after obtaining the data analysis results, the data analysis server can further determine the vehicle model of the target vehicle through the collected CAN signals or vehicle registration information, so as to group and display the data analysis results obtained multiple times according to the vehicle model and / or analysis dimension. For example, group and display according to the vehicle model of the target vehicle to compare the performance differences between different vehicle models; or group and display according to the vehicle model of the target vehicle and the target analysis dimension to display the analysis results of different analysis dimensions for different vehicle models respectively; or group and display according to the target analysis dimension to facilitate the display of the analysis results according to different analysis dimensions.

[0157] Furthermore, the data analysis server can display the grouped data analysis results through intuitive charts and an interactive interface.

[0158] A vehicle operation data analysis method provided by an embodiment of the present application improves the accuracy of the collected vehicle operation data by defining an independent data collection scheme for each CAN signal and enabling each CAN signal to correspond to one or more data collection schemes. After receiving the vehicle operation data sent by the target vehicle, the data analysis server stores the vehicle operation data in the database on the database server, reducing the storage burden on the data analysis server and enabling it to focus on calculation and analysis tasks. Whether to perform data splitting for parallel calculation is determined according to whether the total data volume of the data to be analyzed in the target analysis dimension is less than the preset data volume threshold, improving the data analysis efficiency. By splitting the data to be analyzed into multiple groups of sub-data to be analyzed, allocating computing resources and an entity of the target data analysis interface to each group of sub-data to be analyzed respectively, and enabling the computing processes of each entity to be executed in parallel, the data analysis efficiency is improved. The analysis result after grouping is presented through intuitive charts and an interactive interface, making the data analysis result more intuitive and conducive to analysis and use.

[0159] Embodiments of the present invention can divide functional modules for an electronic device or a main control device according to the above method examples. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional module. It should be noted that the division of modules in the embodiments of the present invention is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0160] Figure 4 It is a schematic structural diagram of a vehicle operation data analysis device provided by an embodiment of the present application. As Figure 4 shown, the device includes: an acquisition module 410; a determination module 420; an extraction module 430; an analysis module 440.

[0161] The acquisition module 410 is configured to acquire a data analysis requirement text and identify at least one data analysis scheme corresponding to each target vehicle from the data analysis requirement text. Each data analysis scheme includes a target vehicle identifier, at least one target analysis dimension, and an analysis time range corresponding to each target analysis dimension. The target analysis dimension is one or more of all analyzable dimensions of all vehicle models.

[0162] The determination module 420 is configured to determine a corresponding data identifier to be analyzed and a target data analysis interface according to the target analysis dimension.

[0163] An extraction module 430 is configured to extract the data to be analyzed corresponding to the target vehicle in the target analysis dimension from the pre-collected vehicle operation data according to the target vehicle identifier, the data identifier to be analyzed in the target analysis dimension, and the analysis time range of the target analysis dimension. The pre-collected vehicle operation data is the data collected for all Controller Area Network (CAN) signals on all vehicle models.

[0164] An analysis module 440 is configured to input the data to be analyzed in the target analysis dimension into the target data analysis interface of the target analysis dimension for analysis, obtain the data analysis result of the target analysis dimension, and perform corresponding storage according to the data analysis scheme.

[0165] In a possible design, the determination module 420 includes: an identification determination module and an interface determination module.

[0166] The identification determination module is configured to obtain the data text of the target analysis dimension according to the dimension identifier of the target analysis dimension, and extract the data identifier to be analyzed in the target analysis dimension from the data text.

[0167] The interface determination module is configured to splice the dimension identifier of the target analysis dimension with the preset interface identifier to form a target interface identifier, and respectively match the target interface identifier with the interface identifiers of multiple target data analysis interfaces to determine the target data analysis interface corresponding to the target analysis dimension.

[0168] In a possible design, the device further includes: a generation module, a scheme determination module, and a collection module.

[0169] The generation module is configured to generate a data collection scheme for Controller Area Network (CAN) signals according to the first configuration operation when receiving the first configuration operation. Each CAN signal corresponds to one or more data collection schemes.

[0170] The scheme determination module is configured to determine the target data collection scheme of the target vehicle from at least one data collection scheme according to the second configuration operation when receiving the second configuration operation for the target vehicle, and send the target data collection scheme to the target vehicle.

[0171] The collection module is configured to receive the vehicle operation data collected by the target vehicle according to the target data collection scheme.

[0172] In a possible design, the device further includes: a display module.

[0173] The display module is configured to determine the vehicle model of the target vehicle, and group and display the data analysis results obtained multiple times according to the vehicle model of the target vehicle and / or the target analysis dimension.

[0174] In a possible design, the analysis module 440 includes: a data volume determination module, a splitting module, and an allocation module.

[0175] The data volume determination module is used to determine the total data volume of the data to be analyzed for each target analysis dimension.

[0176] The splitting module is used to, for each target analysis dimension, when the total data volume of the target analysis dimension is greater than or equal to the preset data volume threshold, split the data to be analyzed for the target analysis dimension into multiple groups of sub-data to be analyzed according to the target vehicle and / or the analysis time range, allocate a computing resource and an entity of the corresponding target data analysis interface for each group of sub-data to be analyzed for the target analysis dimension, and call each computing resource and each entity of the target data analysis interface to generate a group of data analysis results for the target analysis dimension for a group of sub-data to be analyzed for the target analysis dimension.

[0177] The allocation module is used to, when the total data volume is less than the preset data volume threshold, allocate a computing resource and an entity of a target data analysis interface for the target analysis dimension, and call the computing resource and the entity to generate a data analysis result for the data to be analyzed for the target analysis dimension, and the computing processes of the entities are executed in parallel.

[0178] In a possible design, in this device, the target analysis dimension includes at least one of the following dimensions: basic operation characteristics, energy consumption characteristics, gear characteristics, motor characteristics, axle characteristics, driving behavior, vehicle operation dimension, or battery dimension.

[0179] The basic operation characteristics correspond to at least one of the following data analysis results: vehicle operation duration, vehicle operation mileage, average vehicle speed, average moving vehicle speed, power change amount, air conditioner operation duration, air conditioner operation duration ratio, heater operation duration, or heater operation duration ratio.

[0180] The energy consumption characteristics correspond to at least one of the following data analysis results: battery power consumption, battery recovered power, average battery power consumption per 100 kilometers, motor power consumption, motor recovered power, motor mechanical energy output, motor recovered power ratio, accessory power consumption, or accessory power consumption ratio.

[0181] The gear characteristics correspond to at least one of the following data analysis results: ratio of operation time of each gear, or ratio of recovered power of each gear.

[0182] The axle characteristics correspond to at least one of the following data analysis results: axle vehicle speed torque change situation, axle power distribution situation, or axle efficiency distribution situation.

[0183] The driving behavior corresponds to at least one of the following data analysis results: accelerator pedal distribution situation, brake pedal distribution situation, accelerator pedal vehicle speed distribution relationship, or brake pedal vehicle speed distribution relationship;

[0184] The vehicle operation dimension corresponds to at least one of the following data analysis results: vehicle speed distribution, relationship between vehicle speed and torque change, or relationship between vehicle speed and battery power change.

[0185] The battery dimension corresponds to at least one of the following data analysis results: battery state, or cumulative power consumption change.

[0186] A vehicle operation data analysis device provided in this embodiment can execute the vehicle operation data analysis method of the above embodiment. The implementation principle and technical effect are similar, and will not be elaborated here in this embodiment.

[0187] In the specific implementation of the foregoing vehicle operation data analysis device, each module can be implemented as a processor. The processor can execute computer execution instructions stored in the memory, so that the processor executes the above vehicle operation data analysis method.

[0188] Figure 5 It is a schematic structural diagram of an electronic device provided in an embodiment of the present application. As Figure 5 shown, the electronic device includes: at least one processor 510 and a memory 520. The electronic device further includes a communication component 530. Among them, the processor 510, the memory 520, and the communication component 530 are connected through a bus 540.

[0189] In the specific implementation process, at least one processor 510 executes the computer execution instructions stored in the memory 520, so that at least one processor 510 executes a vehicle operation data analysis method executed on the electronic device side as above.

[0190] The specific implementation process of the processor 510 can refer to the above method embodiment. The implementation principle and technical effect are similar, and will not be elaborated here in this embodiment.

[0191] In the above embodiment, it should be understood that the processor can be a central processing unit (English: Central Processing Unit, abbreviated: CPU), and can also be other general-purpose processors, digital signal processors (English: Digital Signal Processor, abbreviated: DSP), application specific integrated circuits (English: Application Specific Integrated Circuit, abbreviated: ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0192] The memory may include high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

[0193] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience in representation, the buses in the drawings of this application are not limited to only one bus or one type of bus.

[0194] The functions implemented for the electronic device and the master control device are introduced for the solution provided by the embodiments of the present invention. It can be understood that in order for the electronic device or the master control device to implement the above functions, it includes the corresponding hardware structures and / or software modules for executing each function. Combining the units and algorithm steps of each example described in the embodiments disclosed in the embodiments of the present invention, the embodiments of the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiments of the present invention.

[0195] This application also provides a computer-readable storage medium. Computer-executable instructions are stored in the computer-readable storage medium. When the processor executes the computer-executable instructions, it is used to implement a vehicle operation data analysis method as described above.

[0196] The above-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disc. The readable storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0197] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in an electronic device or a master device.

[0198] The present application also provides a computer program product, which includes a computer program stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and the execution of the computer program by at least one processor causes the electronic device to execute the solution provided in the above-mentioned embodiments.

[0199] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes various media such as ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0200] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing vehicle operation data, characterized in that, The method includes: Obtain a data analysis requirement text, and identify data analysis schemes corresponding to at least one target vehicle from the data analysis requirement text. Each data analysis scheme includes a target vehicle identifier, at least one target analysis dimension, and an analysis time range corresponding to each target analysis dimension. The target analysis dimension is one or more of all analyzable dimensions of all vehicle models; Determine corresponding data identifiers to be analyzed and target data analysis interfaces according to the target analysis dimensions; Extract the data to be analyzed corresponding to the target vehicle in the target analysis dimension from the pre-collected vehicle operation data according to the target vehicle identifier, the data identifier to be analyzed of the target analysis dimension, and the analysis time range of the target analysis dimension. The pre-collected vehicle operation data is data collected for all Controller Area Network (CAN) signals on all vehicle models; Input the data to be analyzed of the target analysis dimension into the target data analysis interface of the target analysis dimension for analysis, obtain the data analysis result of the target analysis dimension, and perform corresponding storage according to the data analysis scheme.

2. The method according to claim 1, wherein The determining corresponding data identifiers to be analyzed and target data analysis interfaces according to the target analysis dimensions includes: Obtain the data text of the target analysis dimension according to the dimension identifier of the target analysis dimension, and extract the data identifier to be analyzed of the target analysis dimension from the data text; Concatenate the dimension identifier of the target analysis dimension with a preset interface identifier to form a target interface identifier, and respectively match the target interface identifier with the interface identifiers of multiple target data analysis interfaces to determine the target data analysis interface corresponding to the target analysis dimension.

3. The method according to claim 1, characterized in that, The acquisition process of the pre-collected vehicle operation data includes: When receiving a first configuration operation, generate a data acquisition scheme for Controller Area Network (CAN) signals according to the first configuration operation. Each CAN signal corresponds to one or more of the data acquisition schemes; When receiving a second configuration operation for a target vehicle, determine the target data acquisition scheme of the target vehicle from at least one of the data acquisition schemes according to the second configuration operation, and send the target data acquisition scheme to the target vehicle; Receive the vehicle operation data collected by the target vehicle according to the target data acquisition scheme.

4. The method according to claim 1, characterized in that, After performing corresponding storage according to the data analysis scheme, it further includes: Determine the vehicle model of the target vehicle, and group and display the multiple obtained data analysis results according to the vehicle model of the target vehicle and / or the target analysis dimension.

5. The method according to claim 1, characterized in that, Inputting the data to be analyzed of the target analysis dimension into the target data analysis interface of the target analysis dimension for analysis to obtain the data analysis result of the target analysis dimension includes: For each target analysis dimension, determine the total data volume of the data to be analyzed of the target analysis dimension; For each of the target analysis dimensions, when the total data volume of the target analysis dimension is greater than or equal to the preset data volume threshold, split the data to be analyzed of the target analysis dimension into multiple groups of sub-data to be analyzed according to the target vehicle and / or the analysis time range, allocate computing resources and an entity of the corresponding target data analysis interface for each group of the sub-data to be analyzed of the target analysis dimension, and call each of the computing resources and each entity of the target data analysis interface to generate a group of data analysis results of the target analysis dimension for a group of the sub-data to be analyzed of the target analysis dimension; When the total data volume is less than the preset data volume threshold, allocate computing resources and an entity of one of the target data analysis interfaces for the target analysis dimension, and call the computing resources and the entity to generate data analysis results for the data to be analyzed of the target analysis dimension. The computing processes of the entities are executed in parallel.

6. The method according to claim 1, wherein The target analysis dimension includes at least one of the following dimensions: basic operation characteristics, energy consumption characteristics, gear characteristics, motor characteristics, axle characteristics, driving behavior, vehicle operation dimension, or battery dimension; The basic operation characteristics correspond to at least one of the following data analysis results: vehicle operation duration, vehicle operation mileage, average vehicle speed, average moving vehicle speed, power change amount, air conditioner operation duration, air conditioner operation duration ratio, warm air operation duration, or warm air operation duration ratio; The energy consumption characteristics correspond to at least one of the following data analysis results: battery power consumption, battery recovered power, average battery power consumption per 100 kilometers, motor power consumption, motor recovered power, motor mechanical energy output, motor recovered power ratio, accessory power consumption, or accessory power consumption ratio; The gear characteristics correspond to at least one of the following data analysis results: operation time ratio of each gear, or recovered power ratio of each gear; The axle characteristics correspond to at least one of the following data analysis results: axle vehicle speed torque change situation, axle power distribution situation, or axle efficiency distribution situation; The driving behavior corresponds to at least one of the following data analysis results: accelerator pedal distribution situation, brake pedal distribution situation, accelerator pedal vehicle speed distribution relationship, or brake pedal vehicle speed distribution relationship; The vehicle operation dimension corresponds to at least one of the following data analysis results: vehicle speed distribution situation, vehicle speed torque change relationship, or vehicle speed battery power change relationship; The battery dimension corresponds to at least one of the following data analysis results: battery state, or cumulative power consumption change.

7. A vehicle operation data analysis device, characterized in that, The device includes: An acquisition module, configured to acquire a data analysis requirement text, and identify a data analysis scheme corresponding to at least one target vehicle from the data analysis requirement text. Each data analysis scheme includes a target vehicle identifier, at least one target analysis dimension, and an analysis time range corresponding to each target analysis dimension. The target analysis dimension is one or more of all analyzable dimensions of all vehicle models; A determination module, configured to determine a corresponding data identifier to be analyzed and a target data analysis interface according to the target analysis dimension; An extraction module, configured to extract the data to be analyzed corresponding to the target vehicle in the target analysis dimension from the pre-collected vehicle operation data according to the target vehicle identifier, the data identifier to be analyzed in the target analysis dimension, and the analysis time range of the target analysis dimension, where the pre-collected vehicle operation data is data collected for all controller area network (CAN) signals on all vehicle models; An analysis module, configured to input the data to be analyzed in the target analysis dimension into the target data analysis interface of the target analysis dimension for analysis, obtain the data analysis result of the target analysis dimension, and perform corresponding storage according to the data analysis scheme.

8. An electronic device, characterized in that, Comprising: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the vehicle operation data analysis method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by the processor, they are used to implement the vehicle operation data analysis method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the vehicle operation data analysis method according to any one of claims 1 to 6.

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